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A Multiscale Ray-Shooting Model for Termination Detection of Tree-Like Structures in Biomedical Images

Digital reconstruction (tracing) of tree-like structures, such as neurons, retinal blood vessels, and bronchi, from volumetric images and 2D images is very important to biomedical research. Many existing reconstruction algorithms rely on a set of good seed points. The 2D or 3D terminations are good candidates for such seed points. In this paper, we propose an automatic method to detect terminations for tree-like structures based on a multiscale ray-shooting model and a termination visual prior. The multiscale ray-shooting model detects 2D terminations by extracting and analyzing the multiscale intensity distribution features around a termination candidate. The range of scale is adaptively determined according to the local neurite diameter estimated by the Rayburst sampling algorithm in combination with the gray-weighted distance transform. The termination visual prior is based on a key observation-when observing a 3D termination from three orthogonal directions without occlusion, we can recognize it in at least two views. Using this prior with the multiscale ray-shooting model, we can detect 3D terminations with high accuracies. Experiments on 3D neuron image stacks, 2D neuron images, 3D bronchus image stacks, and 2D retinal blood vessel images exhibit average precision and recall rates of 87.50% and 90.54%. The experimental results confirm that the proposed method outperforms other the state-of-the-art termination detection methods.

Adaptive Pulse Width Control and Sampling for Low Power Pulse Oximetry

Remote sensing of physiological parameters could be a cost effective approach to improving health care, and low-power sensors are essential for remote sensing because these sensors are often energy constrained. This paper presents a power optimized photoplethysmographic sensor interface to sense arterial oxygen saturation, a technique to dynamically trade off SNR for power during sensor operation, and a simple algorithm to choose when to acquire samples in photoplethysmography. A prototype of the proposed pulse oximeter built using commercial-off-the-shelf (COTS) components is tested on 10 adults. The dynamic adaptation techniques described reduce power consumption considerably compared to our reference implementation, and our approach is competitive to state-of-the-art implementations. The techniques presented in this paper may be applied to low-power sensor interface designs where acquiring samples is expensive in terms of power as epitomized by pulse oximetry.

Sublime Experience for Sustainable Underground Space: Integration of the Artists' Works in Chichu Art Museum

This paper investigates a vision of the underground environment associated with an aesthetic discipline. Its fundamental notion is sublimity, which was a phenomenon that involved a number of artworks engaged with changing the perception of the underground experience. This paper seeks to clarify how the idea of the living environment underground has changed by examining the works of writers, painters, and architects who have drawn inspiration from the concept of imaginary underworlds. Through a case study of the Chichu Art Museum, a representative underground space in terms of a sustainable relationship between architectural spaces and nature that could be experienced as sublime, this paper considers how to integrate visitors to distribute their awareness of artists' work. It also stimulates visitors' perceptions of a more sustainable future through sublime experiences, offering a way to understand underground integration with artworks. Therefore, this paper contributes to the knowledge of the relationship between architecture and artwork by increasing the aesthetic value of the underground space and considering how art intervenes in architecture to create a sustainable didactic.

FRODO: An In-Depth Analysis of a System to Reject Outlier Samples From a Trained Neural Network

An important limitation of state-of-the-art deep learning networks is that they do not recognize when their input is dissimilar to the data on which they were trained and proceed to produce outputs that will be unreliable or nonsensical. In this work, we describe FRODO (Free Rejection of Out-of-Distribution), a publicly available method that can be easily employed for any trained network to detect input data from a different distribution than is expected. FRODO uses the statistical distribution of intermediate layer outputs to define the expected in-distribution (ID) input image properties. New samples are judged based on the Mahalanobis distance (MD) of their layer outputs from the defined distribution. The method can be applied to any network, and we demonstrate the performance of FRODO in correctly rejecting OOD samples on three distinct architectures for classification, localization, and segmentation tasks in chest X-rays. A dataset of 21,576 X-ray images with 3,655 in-distribution samples is defined for testing. The remaining images are divided into four OOD categories of varying levels of difficulty, and performance at rejecting each type is evaluated using receiver operating characteristic (ROC) analysis. FRODO achieves areas under the ROC (AUC) of between 0.815 and 0.999 in distinguishing OOD samples of different types. This is shown to be comparable with the best-performing state-of-the-art method tested, with the substantial advantage that FRODO integrates seamlessly with any network and requires no extra model to be constructed and trained.

Encoding Time Series as Multi-Scale Signed Recurrence Plots for Classification Using Fully Convolutional Networks

Recent advances in time series classification (TSC) have exploited deep neural networks (DNN) to improve the performance. One promising approach encodes time series as recurrence plot (RP) images for the sake of leveraging the state-of-the-art DNN to achieve accuracy. Such an approach has been shown to achieve impressive results, raising the interest of the community in it. However, it remains unsolved how to handle not only the variability in the distinctive region scale and the length of sequences but also the tendency confusion problem. In this paper, we tackle the problem using Multi-scale Signed Recurrence Plots (MS-RP), an improvement of RP, and propose a novel method based on MS-RP images and Fully Convolutional Networks (FCN) for TSC. This method first introduces phase space dimension and time delay embedding of RP to produce multi-scale RP images; then, with the use of asymmetrical structure, constructed RP images can represent very long sequences (>700 points). Next, MS-RP images are obtained by multiplying designed sign masks in order to remove the tendency confusion. Finally, FCN is trained with MS-RP images to perform classification. Experimental results on 45 benchmark datasets demonstrate that our method improves the state-of-the-art in terms of classification accuracy and visualization evaluation.

[Transforming university hospitals while maintaining their triple mission of care, teaching and research]

University hospital have demonstrated their effectiveness since their creation in 1958. They have risen to the challenge of expertise in research, care and teaching, but also to the challenge of responsibility towards the health territories they serve. The context of the health crisis has prompted them to confirm this commitment while encouraging them to continue the evolution of a model that combines care, research and teaching.

Why is pet goods consumption imperceptible for economists? A scoping review

Nowadays, pets more frequently are becoming family members which deserve certain products and goods, as well as services. In this way, pets are becoming consumers even they do not have a possibility to make decisions (as opposed to human being) as we analyze taking into account human being. Recently pet-related topics are gaining more attention in the press and among researchers in the field of marketing and psychology. Numerous articles regarding pet-related business patterns, like pet insurance, day care and pet friendly hotels are published. No wonder, the popularity of pets among households has been growing for many years. In this article, a scoping review aimed at identifying available studies about expenditures on pet goods and owners' economic consumption choices has been conducted. A comprehensive search strategy was used across Scopus and EBSCO database. The results show that there is only a few studies concerning pet goods consumption through the lens of economic theories. As such this topic in not explored enough while the market of goods and services is growing.

Systematic review of drivers influencing building deconstructability: Towards a construct-based conceptual framework

Deconstruction is an innovative and sustainable option for building end-of-life. It can turn the negative impacts of demolition, including diverting valuable resources from the congested landfill into beneficial use through reuse and recycling. However, the feasibility of deconstruction has placed a massive limitation on the implementation of deconstruction. This research carried out a systematic literature review of 35 academic and 3 non-academic pieces of literature to develop a construct-based deconstructability framework. This framework - built around technical, economic, legal, operational, schedule and social construct - describes the condition under which deconstruction is likely to work and drivers influencing deconstructability. A total of 44 drivers influencing deconstructability were established and ranked from which design and building technology, cost including expense and revenues from the resale, supply and demand of the recovered component and material, the schedule for the deconstruction were identified as most influential. However, every identified driver should be considered during the deconstructability assessment of a building.

Bi-histogram modification method for non-uniform illumination and low-contrast images

Researchers face non-uniform illumination and low-contrast image challenges during the image-processing stage. A new contrast enhancement method is proposed in this paper to address these challenges. The proposed method first separates the dark and bright regions of an image. Then, these regions are enhanced using two new enhancers, namely, dark and bright. Modified clipped histogram equalization is then applied for contrast enhancement. Finally, the details of the image are added back into the illumination-corrected and contrast-enhanced image for the final output image. Visually, the proposed method successfully produces better images with more uniform illumination and better contrast than the state-of-the-art methods. This claim is supported by quantitative analysis that shows that the proposed method produces the best average measure of enhancement, natural image quality evaluator, and entropy values of 797 test images compared with other state-of-the-art methods.

Semantic Neighborhood-Aware Deep Facial Expression Recognition

Different from many other attributes, facial expression can change in a continuous way, and therefore, a slight semantic change of input should also lead to the output fluctuation limited in a small scale. This consistency is important. However, current Facial Expression Recognition (FER) datasets may have the extreme imbalance problem, as well as the lack of data and the excessive amounts of noise, hindering this consistency and leading to a performance decreasing when testing. In this paper, we not only consider the prediction accuracy on sample points, but also take the neighborhood smoothness of them into consideration, focusing on the stability of the output with respect to slight semantic perturbations of the input. A novel method is proposed to formulate semantic perturbation and select unreliable samples during training, reducing the bad effect of them. Experiments show the effectiveness of the proposed method and state-of-the-art results are reported, getting closer to an upper limit than the state-of-the-art methods by a factor of 30% in AffectNet, the largest in-the-wild FER database by now.

An Efficient K-Persistent Spread Estimator for Traffic Measurement in High-Speed Networks

Traffic measurement in high-speed networks has many important functions in improving network performance, assisting resource allocation, and detecting anomalies. In this paper, we study a generalized problem called k-persistent spread estimation, which measures the volume of persist traffic elements in each flow that appear during at least k out of t measurement periods, where k and t are two positive integers that can be arbitrarily set in user queries, with k <= t. Solutions to this problem have interesting applications in network attack detection, popular content identification, user access profiling, etc. There is very limited prior art for this problem, only addressing the special case of k = t under a flawed assumption. Removing this assumption, we propose an efficient and accurate estimator for generalized k-persistent traffic measurement, with k <= t. Our method relies on bitwise SUM, instead of bitwise AND in the prior art, to combine the information collected from different periods. This change has fundamental impact on the probabilistic analysis that derives the estimator, particular over space-saving virtual bitmaps. Based on real network traces, we demonstrate experimentally the effectiveness of our new method in estimating the k-persistent spreads of all network flows. Our estimator performs much better than the prior art on its case of k = t. We also incorporate a sampling module to the estimator for improved flexibility, and give a use study on how to detect and find DDoS attackers using the proposed estimator.

2.4-GHz Highly Selective IoT Receiver Front End With Power Optimized LNTA, Frequency Divider, and Baseband Analog FIR Filter

High selectivity becomes increasingly important with an increasing number of devices that compete in the congested 2.4-GHz industrial, scientific, and medical (ISM)-band. In addition, low power consumption is very important for Internet-of-Things (IoT) receivers. We propose a 2.4-GHz zero-intermediate frequency (IF) receiver front-end architecture that reduces power consumption by 2 x compared with state-of-the-art and improves selectivity by >20-dB without compromising on other receiver metrics. To achieve this, the entire receive chain is optimized. The low-noise transconductance amplifier (LNTA) is optimized to combine low noise with low power consumption. State-of-the-art sub-30-nm complementary metal-oxide-semiconductor (CMOS) processes have almost equal strength complementary field-effect transistors (FETs) that result in altered design tradeoffs. A Windmill 25%-duty cycle frequency divider architecture is proposed, which uses only a single NOR-gate buffer per phase to minimize power consumption and phase noise. The proposed divider requires half the power consumption and has 2 dB or more reduced phase noise when benchmarked against state-of-the-art designs. An analog finite impulse response (FIR) filter is implemented to provide very high receiver selectivity with ultralow power consumption. The receiver front end is fabricated in a 22-nm fully depleted silicon-on-insulator (FDSOI) technology and has an active area of 0.5 mm(2). It consumes 370 mu W from a 700-mV supply voltage. This low power consumption is combined with a 5.5-dB noise figure. The receiver front end has -7.5-dBm input-referred third-order-intercept point (IIP3) and 1-dB gain compression for a -22-dBm blocker, both at maximum gain of 61 dB. From three channels offset onward, the adjacent channel rejection (ACR) is >= 63 dB for Bluetooth Low-Energy (BLE), BT5.0, and IEEE802.15.4.

Optimal Joint Detection and Estimation That Maximizes ROC-Type Curves

Combined detection-estimation tasks are frequently encountered in medical imaging. Optimal methods for joint detection and estimation are of interest because they provide upper bounds on observer performance, and can potentially be utilized for imaging system optimization, evaluation of observer efficiency, and development of image formation algorithms. We present a unified Bayesian framework for decision rules that maximize receiver operating characteristic (ROC)-type summary curves, including ROC, localization ROC (LROC), estimation ROC (EROC), free-response ROC (FROC), alternative free-response ROC (AFROC), and exponentially-transformed FROC (EFROC) curves, succinctly summarizing previous results. The approach relies on an interpretation of ROC-type summary curves as plots of an expected utility versus an expected disutility (or penalty) for signal-present decisions. We propose a general utility structure that is flexible enough to encompass many ROC variants and yet sufficiently constrained to allow derivation of a linear expected utility equation that is similar to that for simple binary detection. We illustrate our theory with an example comparing decision strategies for joint detection-estimation of a known signal with unknown amplitude. In addition, building on insights from our utility framework, we propose new ROC-type summary curves and associated optimal decision rules for joint detection-estimation tasks with an unknown, potentially-multiple, number of signals in each observation.

Exploring the Factors of Cooperation between Artists and Technologists in Creating New Media Art Works: Based on AHP

The deeper the combination of art and technology, the more extensive the cooperation between artists and technologists. In many cases, the creation of New Media Art requires the cooperation of artists and technologists. However, since New Media Art is an emerging art form, the process of cocreating New Media Art between the two is at the exploration stage. Especially in areas with underdeveloped New Media Art and underdeveloped technology, there exist many problems in the cooperation between the two, such as a lack of complete understanding of the factors involved in the cooperation process and a lack of reasonable planning for the cooperation process. Therefore, this study analyzes the factors that affect the collaboration process between the two creating New Media Art. Common factors are collected from the literature and then added or deleted after expert opinions. Then, analytical hierarchy process (AHP) method is applied to get the weight of each factor and understand the influence degree of the factor. The research results show that there are relatively fixed factors influencing the collaboration process between artists and technologists in creating New Media Art, and various factors have different degrees of influence on cooperation. Therefore, in the process of cooperation between the two parties, more emphasis should be placed on the factors of cooperation, which makes the cooperation more scientific.

State of the art of thermal storage for demand-side management

Thermal energy storage (TES) is widely recognized as a means to integrate renewable energies into the electricity production mix on the generation side, but its applicability to the demand side is also possible. In recent decades, TES systems have demonstrated a capability to shift electrical loads from high-peak to off-peak hours, so they have the potential to become a powerful instrument in demand-side management programs (DSM). Thermal storage is a technology that ensures energy security, efficiency and environmental quality. Of particular interest are applications where TES systems help manage the mismatch between availability of renewable electricity and the demand for electricity in buildings where hot water, heating and cooling are delivered by heat pumps and air conditioning for example. Thus this paper demonstrates the state of the art of present applications of thermal storage for demand-side management. A particular focus of this work is the attention paid to the characteristics of DSM and their relationship to different thermal storage systems. If TES effectiveness for the abovementioned applications is demonstrated, TES devices have a small percentage of the potential market. Therefore challenges and guidelines for a development plan are suggested. (c) 2011 Elsevier Ltd. All rights reserved.

Pattern-Aided Regression Modeling and Prediction Model Analysis

This paper first introduces pattern aided regression (PXR) models, a new type of regression models designed to represent accurate and interpretable prediction models. This was motivated by two observations: (1) Regression modeling applications often involve complex diverse predictor-response relationships, which occur when the optimal regression models (of given regression model type) fitting two or more distinct logical groups of data are highly different. (2) State-of-the-art regression methods are often unable to adequately model such relationships. This paper defines PXR models using several patterns and local regression models, which respectively serve as logical and behavioral characterizations of distinct predictor-response relationships. The paper also introduces a contrast pattern aided regression (CPXR) method, to build accurate PXR models. In experiments, the PXR models built by CPXR are very accurate in general, often outperforming state-of-the-art regression methods by big margins. Usually using (a) around seven simple patterns and (b) linear local regression models, those PXR models are easy to interpret; in fact, their complexity is just a bit higher than that of (piecewise) linear regression models and is significantly lower than that of traditional ensemble based regression models. CPXR is especially effective for high-dimensional data. The paper also discusses how to use CPXR methodology for analyzing prediction models and correcting their prediction errors.

OPAQUE3, encoding a transmembrane bZIP transcription factor, regulates endosperm storage protein and starch biosynthesis in rice

Starch and storage proteins are the main components of rice (Oryza sativa L.) grains. Despite their importance, the molecular regulatory mechanisms of storage protein and starch biosynthesis remain largely elusive. Here, we identified a rice opaque endosperm mutant, opaque3 (o3), that overaccumulates 57-kDa proglutelins and has significantly lower protein and starch contents than the wild type. The o3 mutant also has abnormal protein body structures and compound starch grains in its endosperm cells. OPAQUE3 (O3) encodes a transmembrane basic leucine zipper (bZIP) transcription factor (OsbZIP60) and is localized in the endoplasmic reticulum (ER) and the nucleus, but it is localized mostly in the nucleus under ER stress. We demonstrated that O3 could activate the expression of several starch synthesis-related genes (GBSSI, AGPL2, SBEI, and ISA2) and storage protein synthesis-related genes (OsGluA2, Prol14, and Glb1). O3 also plays an important role in protein processing and export in the ER by directly binding to the promoters and activating the expression of OsBIP1 and PDIL1-1, two major chaperones that assist with folding of immature secretory proteins in the ER of rice endosperm cells. High-temperature conditions aggravate ER stress and result in more abnormal grain development in o3 mutants. We also revealed that OsbZIP50 can assist O3 in response to ER stress, especially under high-temperature conditions. We thus demonstrate that O3 plays a central role in rice grain development by participating simultaneously in the regulation of storage protein and starch biosynthesis and the maintenance of ER homeostasis in endosperm cells.

Use of muralism to promote awareness about aquatic ecosystems and wise water consumption in northwestern Ecuador

In this article, we present the results of the use of muralism as an artistic tool applied to environmental education with a focus on the promotion of awareness of aquatic ecosystems and rationed water consumption in the province of Esmeraldas in northwestern Ecuador. The research was conducted by an interdisciplinary team of professors and students from the Pontificia Universidad Catolica del Ecuador Sede Esmeraldas between 2017 and 2018 with the support of mural artists. The project involved a target audience in participating in the design and painting of four large-format murals with graphic representations of the ecosystems and people's relationship to water. The murals were developed in four different locations in the city where they could be viewed and appreciated by the community in general. One corner of each of these murals was deliberately left blank to serve as a slate where people could reflect and leave their comments. Pre- and post-test surveys were administered to the participants of this study and then evaluated and analyzed using McNemar statistics to measure the changes in participants' knowledge, awareness, and attitude. The reaction of the public to the murals was also part of the study. Their interaction was analyzed using a qualitative evaluation matrix specifically designed for this project. The results obtained in this study show that the use of mural art is an effective tool for environmental education programs. Murals represent a place for interaction; therefore, they become effective spaces for expressing and communicating messages. The impact of murals on awareness of aquatic ecosystems and use of water were more significant in children and teenagers because people are more receptive at a young age. The rest of the target groups participating in the study, although stimulated by the murals, presented minor changes in their responses. The public's reaction to the murals was extremely positive. The results showed that murals that depicted elements relating people's identity to the ecosystem helped reconnect them with nature and reminded them of their responsibility to conserve it. Thus, mural art can serve as a pedagogical tool for environmental education, helping communities connect with the environmental reality and encouraging them to commit to conservation through tangible work.

Behavior change, health, and health disparities 2022: Innovations in tobacco control and regulatory science to decrease cigarette smoking

This Special Issue of Preventive Medicine (PM) is the 9th in a series on behavior change, health, and health disparities. This topic is critically important to improving population health. Unhealthy lifestyles including substance misuse, unhealthy food choices, physical inactivity, and non-adherence with medical regimens are important preventable causes of chronic disease and premature death. This year we focus on cigarette smoking, which continues to have devastating health impacts including more than 8 million annual premature deaths globally and 480,000 in the U.S. where most of the research reported in this Special Issue was conducted. While the introduction of new tobacco products into the marketplace like electronic nicotine delivery systems (ENDS) demands attention, it is essential that we remain focused on the enormous challenges involved in eliminating cigarette smoking. This Special Issue examines innovations in tobacco control and regulatory science aimed towards reducing cigarette smoking. Discussion of new tobacco products is largely limited to their role in this overarching aim of reducing combusted cigarette use. We discuss important innovations in tobacco control (e.g., digital text-based interventions, ENDS-assisted cessation, financial incentives) and regulatory science (e.g., nicotine reduction in cigarettes, flavor bans). Throughout, attention is given to the important topic of disparities in terms of understanding the uneven adverse impacts of cigarette smoking and efforts to eliminate it, and the critical importance of researching vulnerable populations. Across these topics we have recruited contributions from accomplished investigators, clinicians, and policymakers to acquaint readers with recent advances while also noting knowledge gaps and unresolved challenges.

Spectral Image Processing for Museum Lighting Using CIE LED Illuminants

This work presents a spectral color-imaging procedure for the detailed colorimetric study of real artworks under arbitrary illuminants. The results demonstrate this approach to be a powerful tool for art and heritage professionals when deciding which illumination to use in museums, or which conservation or restoration techniques best maintain the color appearance of the original piece under any illuminant. Spectral imaging technology overcomes the limitations of common area-based point-measurement devices such as spectrophotometers, allowing a local study either pixelwise or by selected areas. To our knowledge, this is the first study available that uses the proposed CIE (Commission Internationale de l'Eclairage) light-emitting diode (LED) illuminants in the context of art and heritage science, comparing them with the three main CIE illuminants A, D50, and D65. For this, the corresponding colors under D65 have been calculated using a chromatic adaptation transform analogous to the one in CIECAM02. For the sample studied, the CIE LED illuminants with the lowest average CIEDE2000 color differences from the standard CIE illuminants are LED-V1 for A and LED-V2 for D50 and D65, with 1.23, 1.07, and 1.57 units, respectively. The work studied is a Moorish epigraphic frieze of plasterwork with a tiled skirting from the Nasrid period (12th-15th centuries) exhibited in the Museum of the Alhambra (Granada, Spain).

Evaluation and removal efficiencies of a rural WWTP for metals and anions in Lufkin, East Texas (USA)

The present study quantified element concentrations and evaluated the removal efficiencies of the Lufkin Wastewater Treatment Plant (LWWTP): a public municipal wastewater treatment plant in East Texas. Macroelements (Na, K, Mg, Ca, Al, Fe, Se, Zn, P, and S) and microelements (Ni, Pb, Mn, Cr, Mo, Cu, Co, V, As, B, Ba) were detected using ICP-OES and ICP-MS. In addition, the anion concentrations (Br-, NO3-, NO2-, PO43-, F-, Cl-, and SO42-) and their percent removal from the LWWTP were assessed by using ion chromatography. Whereas macroelements in the influent were above the maximum ceiling limits, the total metal concentrations in the effluent were found below the USEPA (below μg/L) guidelines. In general, the removal efficiencies for metals in LWWTP were ≥ 94%. The removal efficiencies of the anions were > 100% (Br-), 16.42% (Cl-), 78.89% (F-), 182.59% (NO3-), > 100% (NO2-), 51.81% (PO43-), and 67.01% (SO42-). In addition, Pierson correlation coefficients between the anions and cations, and implications for usage and suggested improvements of the treatment plants are proposed.

R-theta local neighborhood pattern for unconstrained facial image recognition and retrieval

In this paper R-Theta Local Neighborhood Pattern (RTLNP) is proposed for facial image retrieval. RTLNP exploits relationships amongst the pixels in local neighborhood of the reference pixel at different angular and radial widths. The proposed encoding scheme divides the local neighborhood into sectors of equal angular width. These sectors are again divided into subsectors of two radial widths. Average grayscales values of these two subsectors are encoded to generate the micropatterns. Performance of the proposed descriptor has been evaluated and results are compared with the state of the art descriptors e.g. LBP, CSLBP, CSLTP, LDP, LTrP, MBLBP, and SLBP. The most challenging facial constrained and unconstrained databases, namely; AT&T, CARIA-Face-V5-Cropped, LFW, and Color FERET have been used for showing the efficiency of the proposed descriptor. Proposed descriptor is also tested on near infrared (NIR) face databases; CASIA NIR-VIS 2.0 and PolyU-NIRFD to explore its potential with respect to NIR facial images. Better retrieval rates of RTLNP as compared to the existing state of the art descriptors show the effectiveness of the descriptor.

Decreased anti-Mullerian hormone concentration in follicular fluid of female smokers undergoing artificial reproductive techniques

Background: Several reports indicate that women who smoke have an increased risk of failure to conceive compared with their non-smoker counterparts. Here, we assessed the effect of smoking during the Assisted Reproduction Therapy (ART) on a potential marker of ovarian reserve, anti-mullerian hormone (AMH) in the follicular fluid (FF). Materials and Methods: This was a cohort prospective study to assess the association between cigarette smoking and AMH concentrations in FF in fifty-six women undergoing their first ART cycle. Self-reported smoking status over time was also collected through personal interview. The main outcome measured was the association between current smoking and AMH concentrations in FF. Smoking status was assessed by FF cotinine concentrations. Analysis of covariance was performed to test statistical interaction between the main outcome and confounders. Results: The mean concentration of AMH in follicular fluid was significantly decreased among smokers (1.02+/-0.14 vs. 1.74+/-0.15, P<0.05). No statistical interaction was found between this difference in AMH concentrations and confounders like age and BMI. Thus, our data support the idea that AMH is decreased in active smokers across the fertile age. Conclusions: The hypothesis of decreased AMH concentration in follicular fluid in female smokers was confirmed. The mechanisms through which cigarette smoking induces this fall in AMH are unknown and additional research is needed to improve our comprehension of the negative impact of smoking on ART outcomes. (C) 2012 Elsevier Ltd. All rights reserved.

Implementation and Analyses of an Eco-Driving Algorithm for Different Battery Electric Powertrain Topologies Based on a Split Loss Integration Approach

Eco-driving algorithms optimize the speed profile to reduce the energy consumption of a vehicle. This paper presents an eco-driving algorithm for battery electric powertrains that applies a split loss integration approach to incorporate the component losses. The algorithm consistently uses loss models to overcome the drawbacks of efficiency maps, which cannot represent no-load losses at zero torque. The use of loss models is crucial since the optimal solution includes gliding, during which there are no-load losses. An analysis shows, that state-of-the-art nonlinear programming algorithms cannot represent these no-load losses at zero torque with a small modeling error. To effectively compute the powertrain losses with only a small error in comparison to the measurement data, we introduce a tailored combination of nonlinear inequality constraints that interleave two polynomial fits. This approach can properly represent reality. We parameterize the algorithm and validate the vehicle model used with real-world measurement data. Furthermore, we investigate the influence of the proposed interleaved fits by comparing them to a single continuous high-order polynomial fit and to the state of the art. The algorithm is published open source.

Prognostic Factors in Acute Myeloid Leukemia with t(8;21)/ AML1-ETO: Strategies to Define High-Risk Patients

Acute myeloid leukemia (AML) with t(8;21)/AML1-ETO is considered to have favorable prognosis. However, outcome is not universally satisfactory. The aim of this study was to search for potential prognostic risk factors which can help individualized treatment in t(8;21) AML patients. All available clinical and laboratory indicators were analyzed retrospectively in 103 t (8;21) AML patients. All patients were followed up for median of 30 months (range 0.3-73 months). CD56 and IDH1 were found to be closely related to high recurrence (p = 0.002; p = 0.001) and incidence of cumulative recurrence (p = 0.001; p < 0.0001). C-KIT was associated with a high cumulative incidence of non-relapse mortality (p < 0.0001). Elevated galectin-3 (gal-3) had a significantly adverse effect on overall survival (OS) and disease-free survival (DFS) of patients receiving standard-dose cytarabine-based consolidation chemotherapy. In multivariable analysis, gal-3 (p = 0.01), CD56 (p = 0.002), IDH1 (p = 0.007) and C-KIT (p = 0.041) were the independent unfavorable factors for OS. CD56 (p = 0.019), IDH1 (p = 0.001) and consolidation chemotherapy regimen (p = 0.041) were the independent risk factors in terms of DFS. A scoring system incorporating gal-3, CD56, IDH1 and C-KIT proved to be helpful for predicting OS in t (8;21) AML patients. Our results revealed that those carrying four factors mentioned above should be considered to be high-risk patients.

Species-specific effects and the ecological role of programmed cell death in the microalgae Ankistrodesmus (Sphaeropleales, Selenastraceae)

Reports of programmed cell death (PCD) in phytoplankton raise questions about the ecological evolutionary role of cell death in these organisms. We induced PCD by nitrogen deprivation and unregulated cell death (non-PCD) in one strain of the green microalga Ankistrodesmus densus and investigated the effects of the cell death supernatants on phylogenetically related co-occurring organisms using growth rates and maximum biomass as proxies of fitness. PCD-released materials from A. densus CCMA-UFSCar-3 significantly increased growth rates of two conspecific strains compared to healthy culture (HC) supernatants and improved the maximum biomass of all A. densus strains compared to related species. Although growth rates of non-A. densus with PCD supernatants were not statistically different from HC treatment, biomass gain was significantly reduced. Thus, the organic substances released by PCD, possibly nitrogenous compounds, could promote conspecific growth. These results support the argument that PCD may differentiate species or subtypes and increases inclusive fitness in this model unicellular chlorophyte. Further research, however, is needed to identify the responsible molecules and how they interact with cells to provide the PCD benefits.

Long-term intake of aspartame-induced cardiovascular toxicity is reflected in altered histochemical parameters, evokes oxidative stress, and trigger P53-dependent apoptosis in a mouse model

Aspartame (ASP) is probably the best known artificial sugar substitute that is used widely in food. Many experimental studies have reported the toxicity of long-term administration of ASP in various organ tissues. However, there is little evidence available about the nature and mechanisms of the adverse effects of long-term consumption of ASP on the cardiovascular system. This study was conducted to evaluate the possible effects of ASP on heart tissue. For this study 36 mature male mice were divided into one control group and three groups which received respectively 40 mg/kg, 80 mg/kg and 160 mg/kg ASP orally, for 90 days. ASP at the doses of 80 and 160 mg/kg increased the serum content of malondialdehyde (MDA), but decreased serum nitric oxide (NO), creatine kinase (CK) and CK-MB, as well as blood superoxide dismutase (SOD) levels. Serum level of total anti-oxidant capacity (TAC) in blood was also reduced in serum at the dose of 80 mg/kg. Histochemical staining, including Periodic acid-Schiff, Masson's trichrome and Verhoeff-van Gieson staining, indicated that ASP at doses of 80 and 160 mg/kg reduced glycogen deposition and decreased the number of collagen and elastic fibres in the cardiac tissue. The cardiac expression of pro-apoptotic genes, including P53, Bax, Bcl-2 and Caspase-3, was modulated at the dose of 160 mg/kg. Moreover, transcription of Caspase-3 was up-regulated at the dose of 80 mg/kg. In conclusion, long-term consumption of ASP any higher than the acceptable daily intake (40 mg/kg) appears to act by promoting oxidative stress, has the potential to alter both histopathological and biochemical parameters, and induces P53-dependent apoptosis in cardiac tissue.

Convolutional Invasion and Expansion Networks for Tumor Growth Prediction

Tumor growth is associated with cell invasion and mass-effect, which are traditionally formulated by mathematical models, namely reaction-diffusion equations and biomechanics. Such models can be personalized based on clinical measurements to build the predictive models for tumor growth. In this paper, we investigate the possibility of using deep convolutional neural networks to directly represent and learn the cell invasion andmass-effect, and to predict the subsequent involvement regions of a tumor. The invasion network learns the cell invasion from information related to metabolic rate, cell density, and tumor boundary derived from multimodal imaging data. The expansion network models the mass-effect from the growing motion of tumor mass. We also study different architectures that fuse the invasion and expansion networks, in order to exploit the inherent correlations among them. Our network can easily be trained on population data and personalized to a target patient, unlike most previous mathematical modeling methods that fail to incorporate population data. Quantitative experiments on a pancreatic tumor data set show that the proposed method substantially outperforms a state-of-the-art mathematical model-based approach in both accuracy and efficiency, and that the information captured by each of the two subnetworks is complementary.

Enhancement-fusion feature pyramid network for object detection

Scale variation is one of the challenges of object detection. Most state-of-the-art object detectors depend on feature pyramid networks (FPN) for multiscale learning to deal with this problem, in which feature fusion is an essential operation. However, feature fusion does not sufficiently address the difficulty of the detection task. This paper presents an enhancement-fusion feature pyramid network (EFPN) to obtain reliable object representations for object detectors. Specifically, it contains a feature enhancement module (FEM) and a bottom-up path module (BPM). The FEM is used to eliminate the negative impact of the uneven distribution of object scales on the model performance. Then, a BPM is proposed to address the fusion inconsistency in the FPN. Additionally, an attention module (A(c)) is added to eliminate the information loss in the bottom-up aggregation process. EFPN is evaluated by combining it with state-of-the-art detection methods. Extensive experimental results on two datasets MS-COCO and VOC2007 demonstrate the effectiveness of the proposed method.

Low-Power Area-Efficient Fault Tolerant Adder in Current Mode Multi Valued Logic Using Berger Codes

In this paper, we propose a low-power yet area-efficient fault tolerant adder by using Berger codes. The proposed Berger code checker is designed by using the current mode multi-valued logic (CM-MVL) circuits. The proposed structure, which is more area and power efficient than state-of-the-art fault tolerant adders, is able to detect all single and multi-bit unidirectional faults. The efficiency of the proposed fault tolerant adder is evaluated by comparing its characteristics to those of two state-of-the-art fault detection schemes in adders as well as the conventional duplex and parity bit checkers in a 90 nm technology. The results reveal that the proposed 64-bit Berger code checker for adders imposes up to 6.7% and 27.2% delay and area penalties, respectively with a cost of static power dissipation. In the proposed scheme, in sub threshold regime, the power penalty is just 1%, while its area overhead is only 31%. The drawback of using this scheme in sub threshold regime is that delay time introduced to the circuit is unacceptable. So, depending on the application, we should choose one of the above-mentioned schemes.

DBRS2: dense boundary regression for semantic segmentation

Most of the current semantic segmentation approaches have achieved state-of-the-art performance relying on fully convolutional networks. However, the consecutive operations such as pooling or convolution striding lead to spatially disjointed object boundaries. We present a dense boundary regression architecture (DBRS2), which aims to use boundary cues to aid high-level semantic segmentation task. Specifically, we first propose a multilevel guided low-level boundary (MG-LB) learning method, where we exploit multilevel convolutional features as guidance for low-level boundary detection. The predicted MG-LB boundaries are used to enable consistent spatial grouping and enhance precise adherence to segment boundaries. Then, we present a significant global energy model based on boundary penalty and appearance penalty, which are respectively defined on the predicted boundaries and coarse segmentations obtained by the DeepLabv3 network. Finally, the refined segmentations are regressed by minimizing the global energy model. Extensive experiments over PASCAL VOC 2012, ADE20K, CamVid, and BSD500 datasets demonstrate that the proposed approach can obtain state-of-the-art performance on both semantic segmentation and boundary detection tasks. (C) 2018 SPIE and IS&T

Genome-wide analysis of long non-coding RNAs under diel light exhibits role in floral development and the circadian clock in Arabidopsis thaliana

The circadian clock is regulated by signaling networks that enhance a plant's ability to coordinate internal events with the external environment. In this study, we examine the rhythmic expression of long non-coding RNAs (lncRNAs) using multiple transcriptomes of Arabidopsis thaliana in the diel light cycle and integrated this information to have a better understanding of the functions of lncRNAs in regulating the circadian clock. We identified 968, 1050, and 998 lncRNAs at 8 h light, 16 h light and 8 h dark conditions, respectively. Among these, 423, 486, and 417 lncRNAs were uniquely present at 8 h light, 16 h light, and 8 h dark, respectively, whereas 334 lncRNAs were common under the three conditions. The specificity of identified lncRNAs under different light conditions was verified using qRT-PCR. The identified lncRNAs were less GC-rich and expressed at a significantly lower level than the mRNAs of protein-coding genes. In addition, we identified enriched motifs in lncRNA transcribing regions that were associated with light-responsive genes (SORLREP and SORLIP), flower development (AGAMOUS), and circadian clock (CCA1) under all three light conditions. We identified 10 and 12 different lncRNAs targeting different miRNAs with perfect and interrupted complementarity (endogenous target mimic). These predicted lncRNA-interacting miRNAs govern the function of a set of genes involved in the developmental process, reproductive structure development, gene silencing and transcription regulation. We demonstrated that the lncRNA transcribing regions were enriched for epigenetic marks such as H3.3, H3K4me2, H3K4me3, H4K16ac, H3K36ac, H3K56ac and depleted for heterochromatic (H3K9me2 and H3K27me1) and repressive (H3K27me3) histone modifications. Further, we found that hypermethylated genomic regions negatively correlated with lncRNA transcribing regions. Overall, our study showed that lncRNAs expressed corresponding to the diel light cycle are implicated in regulating the circadian rhythm and governing the developmental stage-specific growth.

Evolving Generative Adversarial Networks to improve image steganography

Images have been repeatedly used as the perfect environment to hide information through the use of steganography techniques. Whether messages, documents or even other images, the bitmap of an digital picture provides a place where hidden data can be embedded without human notice. So far, a plethora of steganography methods can be found in the state-of-the-art literature, together with steganalysis techniques, devoted to detect the presence of hidden information in files. Recent steganography techniques rely on Convolutional Neural Networks, trying to embed as information as possible while minimising visual changes in the image. Following this trend, this article tries to demonstrate that a Generative Adversarial Network (GAN) can be used to improve the ability of a spatial domain steganalysis method and to insert secret information with minimal image alteration. Through a training process, the GAN learns how to adapt an image to later introduce a message using the Least Significant Bit steganography algorithm. The results evidence that the approach is successful at avoiding detection by a state-of-the-art Deep Learning steganalysis architecture.

Effect and comparison of different working fluids on a two-stage organic rankine cycle (ORC) concept

This paper presents Aspen Plus (V7.3) simulations of a two-stage organic rankine cycle concept with internal heat recovery. The proposed system is compared to state-of-the-art processes with four different working fluids distinguished by the slope of the saturated vapor curve in the corresponding T-s-diagram. The heat source is defined as exhaust gas (490 C and 1 bar) from an internal combustion engine, which is fired with biogas from a biomass digestion plant. In a first consideration the exhaust gas outlet is constrained to 130 C to stay above the acid dew point (study 1). In a second study the pinch point of the exhaust gas heat exchanger is set to 10 K. For wet and isentropic fluids the thermodynamic efficiencies of the two-stage cycle exceed the corresponding values of reference processes by up to 2.25%, while the recuperator design benefits dry fluids compared to the two-stage concept. (C) 2013 Elsevier Ltd. All rights reserved.

Image denoising with morphology- and size-adaptive block-matching transform domain filtering

BM3D is a state-of-the-art image denoising method. Its denoised results in the regions with strong edges can often be better than in the regions with smooth or weak edges, due to more accurate block-matching for the strong-edge regions. So using adaptive block sizes on different image regions may result in better image denoising. Based on these observations, in this paper, we first partition each image into regions belonging to one of the three morphological components, i.e., contour, texture, and smooth components, according to the regional energy of alternating current (AC) coefficients of discrete cosine transform (DCT). Then, we can adaptively determine the block size for each morphological component. Specifically, we use the smallest block size for the contour components, the medium block size for the texture components, and the largest block size for the smooth components. To better preserve image details, we also use a multi-stage strategy to implement image denoising, where every stage is similar to the BM3D method, except using adaptive sizes and different transform dimensions. Experimental results show that our proposed algorithm can achieve higher PSNR and MSSIM values than the BM3D method, and also better visual quality of denoised images than by the BM3D method and some other existing state-of-the-art methods.

Cu nanoclusters decorated Ti3C2 nanosheets composite with tetraenzyme mimic activities and the application for smartphone-assisted detection of hypoxanthine

MXene-based nanozymes have increased research enthusiasm in the field of food safety and environment monitoring. Herein, the Cu NCs/Ti3C2 NSs nanocomposites were prepared by modifying copper nanoclusters (Cu NCs) on the surface of Ti3C2 nanosheets (NSs) with a simple two-step method. The Cu NCs/Ti3C2 NSs nanocomposites had outstanding tetraenzyme mimic activities, i.e. peroxidase (POD)-mimics, catalase (CAT)-mimics, ascorbic acid oxidase (AAO)-mimics and superoxide dismutase (SOD)-mimics. Modification of Cu NCs on Ti3C2 NSs can enhance tetraenzyme mimic activities because of the synergistic catalytic effect between Cu NCs and Ti3C2 NSs. The catalytic mechanism and steady-state kinetics of Cu NCs/Ti3C2 NSs were also investigated. Based on the POD-mimic activity of Cu NCs/Ti3C2 NSs, a simple and rapid colorimetric method was established for the on-site detection of hypoxanthine (Hx), with the linear range of 5-200 μM and limit of detection (LOD) was 0.25 μM. The visible color change with the increase of Hx concentration can be recognized by a smartphone APP to transfer the red (R), green (G) and blue (B) value for the quantitative analysis of Hx, with the linear range of 10-200 μM, which provided a convenient method for the real-time detection of Hx. This work not only provides a significant route to fabricate nanocomposite with outstanding tetraenzyme mimic activities but also offers a low-cost and rapid method for monitoring the freshness of aquatic products.

Stemming the Leak: A Novel Treatment for Gastro-Bronchial Fistula

Laparoscopic sleeve gastrectomy (LSG) is a commonly used procedure in bariatric patients that often has excellent results. Despite its advantages, LSG is burdened by specific intraoperative and postoperative early and late complications. One of the life-threatening complications is gastric fistula, usually treated with a multidisciplinary surgical-endoscopic approach. In case of failure of the latter, alternative nonoperative techniques such as the use of autologous stem cells truly represents an innovative possibility, with only few cases described in literature. Here, we report the case of a 25-year-old man with post-LSG broncho-gastric fistula treated with application of autologous stem cells after the failure of the conventional surgical/endoscopic approach.

The sarcoplasmic protein profile of breast muscle in Turkeys in response to different dietary ratios of limiting amino acids and Clostridium perfringens-induced inflammation

In this study, the effects of the Arginine/Lysine (Arg/Lys) ratio in low- and high-methionine (Met) diets on the sarcoplasmic protein profile of breast muscles from turkeys reared under optimal or challenge (Clostridium perfringens infection) conditions were determined. One-day-old Hybrid Converter female turkey poults (216 in total) obtained from a commercial hatchery on hatching day, and on the basis of their average initial body weight were randomly allocated to 12 pens (4 m2 each; 2.0 m × 2.0 m) containing litter bedding and were reared over a 42-day experimental period. Diets with high levels of Lys contained approximately 1.80% and 1.65% Lys and were offered in two successive feeding periods (days 1-28 and days 29-42). The supplemental levels of Lys were consistent with the nutritional specifications for birds at their respective ages as established in the Management Guidelines for Raising Commercial Turkeys. The experiment was based on a completely randomized 3 × 2 × 2 factorial design with three levels of Arg (90%, 100% and 110%) relative to the content of dietary Met (30 or 45%) and without (-) or with (+) C. perfringens challenge at 34, 36, or 37 d of age. Meat samples were investigated in terms of pH, color, and sarcoplasmic protein profile. The experimental factors did not influence meat quality but the dietary Arg content affected meat color. The sarcoplasmic protein profile was influenced by all studied factors, and glycolytic enzymes were the most abundant. This study evidenced strong association between the challenge conditions and the involvement of glycolytic enzymes in cell metabolism, particularly in inflammatory processes, and DNA replication and maintenance in turkeys. The results showed an effect of C. perfringens infection and feeding with different doses of Arg and Met may lead to significant consequences in cell metabolism.

Comparing State-of-the-Art Deep Learning Algorithms for the Automated Detection and Tracking of Black Cattle

Effective livestock management is critical for cattle farms in today's competitive era of smart modern farming. To ensure farm management solutions are efficient, affordable, and scalable, the manual identification and detection of cattle are not feasible in today's farming systems. Fortunately, automatic tracking and identification systems have greatly improved in recent years. Moreover, correctly identifying individual cows is an integral part of predicting behavior during estrus. By doing so, we can monitor a cow's behavior, and pinpoint the right time for artificial insemination. However, most previous techniques have relied on direct observation, increasing the human workload. To overcome this problem, this paper proposes the use of state-of-the-art deep learning-based Multi-Object Tracking (MOT) algorithms for a complete system that can automatically and continuously detect and track cattle using an RGB camera. This study compares state-of-the-art MOTs, such as Deep-SORT, Strong-SORT, and customized light-weight tracking algorithms. To improve the tracking accuracy of these deep learning methods, this paper presents an enhanced re-identification approach for a black cattle dataset in Strong-SORT. For evaluating MOT by detection, the system used the YOLO v5 and v7, as a comparison with the instance segmentation model Detectron-2, to detect and classify the cattle. The high cattle-tracking accuracy with a Multi-Object Tracking Accuracy (MOTA) was 96.88%. Using these methods, the findings demonstrate a highly accurate and robust cattle tracking system, which can be applied to innovative monitoring systems for agricultural applications. The effectiveness and efficiency of the proposed system were demonstrated by analyzing a sample of video footage. The proposed method was developed to balance the trade-off between costs and management, thereby improving the productivity and profitability of dairy farms; however, this method can be adapted to other domestic species.

Intensity of Health Behaviors in People Who Practice Combat Sports and Martial Arts

Background: Health behaviors are associated with a healthy lifestyle, in which relative possibilities of choice play an important part. Athletes are a group of people who should particularly endeavor to have a health-oriented lifestyle. It is believed that combat sports (CS) and martial arts (MA) have an especially significant educational potential, connected with several desirable values which provide positive patterns of health behaviors. The aim of the work was to assess the intensity of health behaviors in athletes who practiced CS and MA in relation to the length of their training history, their age, sex, place of residence, education level, and financial situation. Methods: The research involved 441 men and women who practiced boxing (B), Brazilian ju-jitsu (BJJ), karate (K), mixed martial arts (MMA) and Muay Thai (MT). The average age of the subjects was 24.68 +/- 8.24 years. The standardized Health Behavior Inventory (HBI) questionnaire and another questionnaire for a lifestyle survey were applied. Individual behaviors covered four areas: Correct eating habits (CEH), preventive behaviors (PB), positive mental attitude (PMA), and health practices (HP). The one-way analysis of variance (F-test) for independent groups was used (ANOVA). The effect size was calculated with Hedge's g for Student's t-test, and with Cramer's V for the chi(2) test. The value of p <= 0.05 was assumed to be statistically significant. Results: CS and MA athletes presented a moderate level of health behaviors. The greater intensity of health behaviors (HBI and its categories) was found among B, K and MMA athletes, and the smaller among those who practiced MT. Correct eating habits (CEH) were characteristic of subjects who practiced every day and whose length of training history was 4-8 years. Greater intensity of preventive behaviors (PB) was observed among individuals aged under-19 years, who still studied. Greater intensity of health practices (HP) was found among those who exercised every day. Influence of financial situation was observed in relations to PMA. Conclusions: It seems that the existing educational potential of CS and MA was not fully realized in the studied population. Determining the place of health in the system of values of CS and MA athletes may be the basis for predicting health behaviors and developing health education programs.

Towards a semantic indoor trajectory model: application to museum visits

In this paper we present a new conceptual model of trajectories, which accounts for semantic and indoor space information and supports the design and implementation of context-aware mobility data mining and statistical analytics methods. Motivated by a compelling museum case study, and by what we perceive as a lack in indoor trajectory research, we combine aspects of state-of-the-art semantic outdoor trajectory models, with a semantically-enabled hierarchical symbolic representation of the indoor space, which abides by OGC's IndoorGML standard. We drive the discussion on modeling issues that have been overlooked so far and illustrate them with a real-world case study concerning the Louvre Museum, in an effort to provide a pragmatic view of what the proposed model represents and how. We also present experimental results based on Louvre's visiting data showcasing how state-of-the-art mining algorithms can be applied on trajectory data represented according to the proposed model, and outline their advantages and limitations. Finally, we provide a formal outline of a new sequential pattern mining algorithm and how it can be used for extracting interesting trajectory patterns.

General-Purpose Ultrasound Neuromodulation System for Chronic, Closed-Loop Preclinical Studies in Freely Behaving Rodents

Transcranial focused ultrasound stimulation (tFUS) is an effective noninvasive treatment modality for brain disorders with high clinical potential. However, the therapeutic effects of ultrasound neuromodulation are not widely explored due to limitations in preclinical systems. The current preclinical studies are head-fixed, anesthesia-dependent, and acute, limiting clinical translatability. Here, this work reports a general-purpose ultrasound neuromodulation system for chronic, closed-loop preclinical studies in freely behaving rodents. This work uses microelectromechanical systems (MEMS) technology to design and fabricate a small and lightweight transducer capable of artifact-free stimulation and simultaneous neural recording. Using the general-purpose system, it can be observed that state-dependent ultrasound neuromodulation of the prefrontal cortex increases rapid eye movement (REM) sleep and protects spatial working memory to REM sleep deprivation. The system will allow explorative studies in brain disease therapeutics and neuromodulation using ultrasound stimulation for widespread clinical adoption.

MiR-199a-5p promotes ferroptosis-induced cardiomyocyte death responding to oxygen-glucose deprivation/reperfusion injury via inhibiting Akt/eNOS signaling pathway

Myocardial ischemia/reperfusion (I/R) injury is associated with the poor outcome and higher mortality after myocardial infarction. Recent studies have revealed that miR-199a-5p participates in the process of myocardial I/R injury, but the precise roles and molecular mechanisms of miR-199a-5p in myocardial I/R injury remain not well-studied. Ferroptosis has been proposed to promote cardiomyocyte death, closely associated with myocardial I/R injury. Herein, the present study aimed to explore the function and mechanisms by which miR-199a-5p regulates whether miR-199a-5p contributes to ferroptosis-induced cardiomyocyte death responding to oxygen-glucose deprivation/reoxygenation (OGD/R) injury, an in vitro model of myocardial I/R injury focusing on Akt/eNOS signaling pathway. The results found that ferroptosis-induced cardiomyocyte death occurs and is accompanied by an increase in miR-199a-5p level in OGD/R-treated H9c2 cells. MiR-199a-5p inhibitor ameliorated ferroptosis-induced cardiomyocyte death as evidenced by the increased cell viability, the reduced reactive oxygen species (ROS) generation, lactate dehydrogenase (LDH) activity, malondialdehyde (MDA) and Fe2+ contents, and the up-regulated glutathione (GSH)/glutathione disulphide (GSSG) ratio as well as glutathione peroxidase 4 (Gpx4) protein expression in H9c2 cells-exposed to OGD/R, while miR-199a-5p mimic had the opposite effects. In addition, OGD/R led to the inhibition of Akt/eNOS signaling pathway, which was also blocked by miR-199a-5p inhibitor and aggravated by miR-199a-5p mimic. Furthermore, LY294002, an inhibitor of Akt/eNOS signaling pathway, abrogated miR-199a-5p inhibitor-induced the reduction of ferroptosis-induced cardiomyocyte death. In summary, our findings demonstrated that miR-199a-5p plays a central role in stimulating ferroptosis-induced cardiomyocyte death during ischemic/hypoxic injury via inhibiting Akt/eNOS signaling pathway.

'Reading landscape': interdisciplinary approaches to understanding place

This paper outlines a collaborative project between a group of Fine Art and Geography students who helped develop and contribute to a conversation about recording 'place'. Introducing methodologies from both disciplines, the project started from the premise of all environmental 'recordings' being 'inputs' and so questioned what could be defined as 'data' when encountering a location. Brunel's Grand Entrance to the Thames Tunnel (London) provided the motivation for 10 objective and subjective 'recordings' which were subsequently distilled into a smaller subset and then used to produce a short film that was presented at an international conference. Important to the collaborative nature of the project were ongoing opportunities to share equipment, techniques, material and references across disciplines. It was an experiment to measure the potential for 'mapping' to capture physical and historical information, as well as embodied experience.

Fluoride Exposure Provokes Mitochondria-Mediated Apoptosis and Increases Mitophagy in Osteocytes via Increasing ROS Production

Fluoride is a persistent environmental pollutant, and its excessive intake causes skeletal and dental fluorosis. However, few studies focused on the effects of fluoride on osteocytes, making up over 95% of all bone cells. This study aimed to investigate the effect of fluoride on osteocytes in vitro, as well as explore the underlying mechanisms. CCK-8, LDH assay, fluorescent probes, flow cytometry, and western blotting were performed to examine cell viability, apoptosis, mitochondria changes, reactive oxygen species (ROS) and mitochondrial ROS (mtROS), and protein expressions. Results showed that sodium fluoride (NaF) exposure (4, 8 mmol/L) for 24 h inhibited the cell viability of osteocytes MLO-Y4 and promoted G0/G1 phase arrest and increased cell apoptosis. NaF treatment remarkably caused mitochondria damage, loss of MMP, ATP decrease, Cyto c release, and Bax/Bcl-2 ratio increase and elevated the activity of caspase-9 and caspase-3. Furthermore, NaF significantly upregulated the expressions of LC-3II, PINK1, and Parkin and increased autophagy flux and the accumulation of acidic vacuoles, while the p62 level was downregulated. In addition, NaF exposure triggered the production of intracellular ROS and mtROS and increased malondialdehyde (MDA); but superoxide dismutase (SOD) activity and glutathione (GSH) content were decreased. The scavenger N-acetyl-L-cysteine (NAC) significantly reversed NaF-induced apoptosis and mitophagy, suggesting that ROS is responsible for the mitochondrial-mediated apoptosis and mitophagy induced by NaF exposure. These findings provide in vitro evidence that apoptosis and mitophagy are cellular mechanisms for the toxic effect of fluoride on osteocytes, thereby suggesting the potential role of osteocytes in skeletal and dental fluorosis.

Visual Tracking via Locally Structured Gaussian Process Regression

We propose a new target representation method, where the temporally obtained targets are jointly represented as a time series function by exploiting their spatially local structure. With this method, we propose a new tracking algorithm, where tracking is formulated as a problem of Gaussian process regression over the joint representation. Numerous experiments on various challenging video sequences demonstrate that our tracker outperforms several other state-of-the-art trackers.

Universal structural requirements for maximal robust perfect adaptation in biomolecular networks

Adaptation is a running theme in biology. It allows a living system to survive and thrive in the face of unpredictable environments by maintaining key physiological variables at their desired levels through tight regulation. When one such variable is maintained at a certain value at the steady state despite perturbations to a single input, this property is called robust perfect adaptation (RPA). Here we address and solve the fundamental problem of maximal RPA (maxRPA), whereby, for a designated output variable, RPA is achieved with respect to perturbations in virtually all network parameters. In particular, we show that the maxRPA property imposes certain structural constraints on the network. We then prove that these constraints are fully characterized by simple linear algebraic stoichiometric conditions which differ between deterministic and stochastic descriptions of the dynamics. We use our results to derive a new internal model principle (IMP) for biomolecular maxRPA networks, akin to the celebrated IMP in control theory. We exemplify our results through several known biological examples of robustly adapting networks and construct examples of such networks with the aid of our linear algebraic characterization. Our results reveal the universal requirements for maxRPA in all biological systems, and establish a foundation for studying adaptation in general biomolecular networks, with important implications for both systems and synthetic biology.

MgB2 coils for particle accelerators

The construction of superconducting magnets is at present the most promising application of magnesium diboride. Aiming to develop magnets for particle accelerators, we present in the paper the design of a superferric dipole magnet based on the status of art of MgB2 conductors.

Feature Ranking by Variational Dropout for Classification Using Thermograms from Diabetic Foot Ulcers

Diabetes mellitus presents a high prevalence around the world. A common and long-term derived complication is diabetic foot ulcers (DFUs), which have a global prevalence of roughly 6.3%, and a lifetime incidence of up to 34%. Infrared thermograms, covering the entire plantar aspect of both feet, can be employed to monitor the risk of developing a foot ulcer, because diabetic patients exhibit an abnormal pattern that may indicate a foot disorder. In this study, the publicly available INAOE dataset composed of thermogram images of healthy and diabetic subjects was employed to extract relevant features aiming to establish a set of state-of-the-art features that efficiently classify DFU. This database was extended and balanced by fusing it with private local thermograms from healthy volunteers and generating synthetic data via synthetic minority oversampling technique (SMOTE). State-of-the-art features were extracted using two classical approaches, LASSO and random forest, as well as two variational deep learning (DL)-based ones: concrete and variational dropout. Then, the most relevant features were detected and ranked. Subsequently, the extracted features were employed to classify subjects at risk of developing an ulcer using as reference a support vector machine (SVM) classifier with a fixed hyperparameter configuration to evaluate the robustness of the selected features. The new set of features extracted considerably differed from those currently considered state-of-the-art but provided a fair performance. Among the implemented extraction approaches, the variational DL ones, particularly the concrete dropout, performed the best, reporting an F1 score of 90% using the aforementioned SVM classifier. In comparison with features previously considered as the state-of-the-art, approximately 15% better performance was achieved for classification.

Effects of hydrolysable tannins from Terminalia citrina on type III secretion system (T3SS) and their intestinal metabolite urolithin B represses Salmonella T3SS through Hha-H-NS-HilD-HilC-RtsA-HilA regulatory pathway

Gamma-proteobacteria is a class of gram-negative opportunistic pathogens existing in the intestinal flora, often leading to diarrhea and intestinal infectious diseases, and plays an important role in maintaining intestinal homeostasis. Type III secretion system (T3SS), an important virulence system, is closely related to the adhesion and invasion and pathogenicity to host cells. Therefore, anti-virulence agents targeting T3SS are important strategies for controlling pathogenic infections. In this study, the anti-Salmonella T3SS active compounds neochebulagic acid (1), ellagic acid (2) and urolithin M5 (3) were isolated from seed extract of Terminalia citrina by activity-guided isolation method. Based on the fact that urolithins are the main and stable intestinal microbiota metabolites of hydrolysable tannins, we found that the metabolite urolithin B repressed translation and secretion of SipC through the Hha-H-NS-HilD-HilC-RtsA-HilA regulatory pathway. The results provide evidence for Terminalia seeds and ellagitannin-rich berries and nuts in regulating intestinal homeostasis and treating bacterial infection.

Multilevel category structure in the ART-2 network

Multilevel categorization is investigated within the context of analog activity patterns on the output layer of an ART 2 network. The ART 2 network parameters are analyzed in terms of stable category formation and in terms of the number of nodes in the output layer that can become most active. The resulting activity patterns on the output layer demonstrate a multilevel category structure based on the relative differences between patterns that exist for many different values of the vigilance parameter. We have shown that the information contained in the output analog patterns can be interpreted in several different ways, which is not possible when the category is represented by a single winning node. Also, favorable comparisons are also demonstrated between the category structure emerging from the set of category patterns and principles of categorization in psychology and neurobiology.

John squire and endothelial glycocalyx structure: an unfinished story

John Squire did not only produce leading works in the muscle field, he also significantly contributed to the vascular permeability field by ultrastructural analysis of the endothelial glycocalyx. Presented here is a review of his involvement in the field by his main collaborator C.C. Michel and his last postdoctoral researcher KP Arkill. We end on a reinterpretation of his work that arguably links to our current understanding of endothelial glycocalyx structure and composition predicting 6 glycosaminoglycans fibres per syndecan core protein, only achieved in the endothelium by dimerization.

Rapid, state-of-the-art techniques for the detection of toxic chemical adulterants in water systems

Recent events have heightened awareness concerning potential hazardous threats to U.S. populace. The causes of concern include a possible contamination of water systems through harmful chemical agents resulting in sickness or death among consumers. To forestall the consequences of high-risk chemical contaminants that can potentially pollute our water resources, swift intervening measures need to be taken as a first line of defense. This aspect of environmental protection involves the design, testing, and installation of detection devices that protect U.S. water supply systems from toxic chemicals. These sensing devices are based on physical, chemical, biological, and radiological methods of detection. Traditional analytical tools are rather cumbersome, time-consuming, and expensive to operate. On the other hand, contemporary trends in the fight against toxic chemical threats to domestic and industrial water facilities comprise of sensors designed to achieve rapid, highly sensitive, and cost-effective detection, and intervention. This paper samples the state-of-the-art in detection techniques for toxic chemical antagonists with emphasis on heavy metals and cyanide compounds that can be potentially deleterious to U.S. water systems. The goal is to identify rapid, realistic and reliable methods, as early warning systems, to mitigate the effects of toxicants in water systems.

Object Tracking in Satellite Videos Based on a Multiframe Optical Flow Tracker

Object tracking is a hot topic in computer vision. Thanks to the booming of the very high resolution (VHR) remote sensing techniques, it is now possible to track targets of interests in satellite videos. However, since the targets in the satellite videos are usually too small in comparison with the entire image, and too similar with the background, most state-of-the-art algorithms failed to track the target in satellite videos with a satisfactory accuracy. Due to the fact that optical flow shows great potential to detect even the slight movement of the targets, we proposed a multiframe optical flow tracker for object tracking in satellite videos. The Lucas-Kanade optical flow method was fused with the HSV color system and integral image to track the targets in the satellite videos, while multiframe difference method was utilized in the optical flow tracker for a better interpretation. The experiments with five VHR remote sensing satellite video datasets indicate that compared with state-of-the-art object tracking algorithms, the proposed method can track the target more accurately.

A Systematic Review and Meta-analysis of Attitudes of Iranian Nurses and Related Factors Towards End-Of-Life Care

This meta-analysis aimed to summarize the evidence regarding attitudes of Iranian nurses and related factors towards end-of-life (EOL) care. PubMed, Web of Science, Scopus, Magiran, Iranmedex, Scientific Information Database, and Google Scholar search engine were searched using Persian and English appropriate keywords from the earliest records up to September 11, 2020. A total of 849 nurses were included in six studies. After a meta-analysis of the mean score of nurses' attitudes, the pooled mean was 80.07 out of 120 (Q(5)=4.32, I-squared=0.00%; 95%CI: 73.53-86.60; p < 0.001). Marital status, ward type, education level, a history of participating in EOL care workshops, personal study of EOL care, experience of caring for a dying family member or close people, natural and approach acceptance, fear of death, and professional autonomy had a significant positive relationship with nurses' attitudes towards EOL care. Therefore, further large-scale studies considering potential confounding variables are needed to confirm our findings.

Non-surgical extirpation of a non-infectious expanding tricuspid valve mass by percutaneous aspiration thrombectomy

Marantic endocarditis refers to a noninfectious lesion, usually in the aortic and mitral valves, that is most commonly seen in advanced malignancy and systemic lupus erythematosus. Inflammatory conditions, including antiphospholipid syndrome (APS), are a rare etiology making up less than 20% of reported cases. The condition is thought to be due to a hypercoagulable state and found postmortem with rates in autopsy series ranging from 0.9% to 1.6%. In comparison to infective endocarditis, marantic endocarditis has a greater tendency for valve vegetations to embolize. Common treatment modalities include anticoagulation or valve replacement. Although percutaneous aspiration thrombectomy of right-sided heart chamber thrombi exists, there are limited reports demonstrating its use with regards to treatment of right-sided endocarditis. We present the case of an older male with a history of Factor V Leiden and APS who was admitted due to a rapidly expanding mass on the tricuspid valve (TV). Despite serial blood cultures being negative, the patient received adequate antibiotic therapy for more than 4 weeks. Transthoracic echocardiogram showed an enlarged TV vegetation with an increased diameter from 10 to 30 mm over 6 weeks. Due to the patient's high operative risk and concern for embolization complications, a multidisciplinary decision was made to perform percutaneous aspiration thrombectomy of the TV vegetation. Subsequent biopsy of the lesion confirmed it was noninfectious and nonmalignant. Thus, the patient was started on systemic anticoagulation for prevention of thromboembolic events.

Addendum: Implicit learning of temporal behavior in complex dynamic environments

New analyses of the data in this study (Salet et al., 2021, Psychonomic Bulletin & Review, https://doi.org/10.3758/s13423-020-01873-x ) have led us to reinterpret our main finding. Previously, we had attributed better performance for targets appearing at regular intervals versus irregular intervals to "temporal statistical learning." That is, we surmised that this benefit for the regular intervals arises because participants implicitly distilled the regular 3000 ms interval from the otherwise variable environment (i.e., irregular intervals) to predict future (regular) targets. The analyses presented in this Addendum, however, show that this benefit can be attributed to ongoing "temporal preparation" rather than temporal statistical learning.

On-Chip Neural Data Compression Based On Compressed Sensing With Sparse Sensing Matrices

On-chip neural data compression is an enabling technique for wireless neural interfaces that suffer from insufficient bandwidth and power budgets to transmit the raw data. The data compression algorithm and its implementation should be power and area efficient and functionally reliable over different datasets. Compressed sensing is an emerging technique that has been applied to compress various neurophysiological data. However, the state-of-the-art compressed sensing (CS) encoders leverage random but dense binary measurement matrices, which incur substantial implementation costs on both power and area that could offset the benefits from the reduced wireless data rate. In this paper, we propose two CS encoder designs based on sparse measurement matrices that could lead to efficient hardware implementation. Specifically, two different approaches for the construction of sparse measurement matrices, i.e., the deterministic quasi-cyclic array code (QCAC) matrix and (1, s)-sparse random binary matrix [(1, s)-SRBM] are exploited. We demonstrate that the proposed CS encoders lead to comparable recovery performance. And efficient VLSI architecture designs are proposed for QCAC-CS and (1, s)-SRBM encoders with reduced area and total power consumption.

High-yield identification of pathogenic NF1 variants by skin fibroblast transcriptome screening after apparently normal diagnostic DNA testing

Neurofibromatosis type 1 (NF1) is caused by inactivating mutations in NF1. Due to the size, complexity, and high mutation rate at the NF1 locus, the identification of causative variants can be challenging. To obtain a molecular diagnosis in 15 individuals meeting diagnostic criteria for NF1, we performed transcriptome analysis (RNA-seq) on RNA obtained from cultured skin fibroblasts. In each case, routine molecular DNA diagnostics had failed to identify a disease-causing variant in NF1. A pathogenic variant or abnormal mRNA splicing was identified in 13 cases: 6 deep intronic variants and 2 transposon insertions causing noncanonical splicing, 3 postzygotic changes, 1 branch point mutation and, in 1 case, abnormal splicing for which the responsible DNA change remains to be identified. These findings helped resolve the molecular findings for an additional 17 individuals in multiple families with NF1, demonstrating the utility of skin-fibroblast-based transcriptome analysis for molecular diagnostics. RNA-seq improves mutation detection in NF1 and provides a powerful complementary approach to DNA-based methods. Importantly, our approach is applicable to other genetic disorders, particularly those caused by a wide variety of variants in a limited number of genes and specifically for individuals in whom routine molecular DNA diagnostics did not identify the causative variant.

Enhanced coagulation and oxidation by the Mn(VII)-Fe(III)/peroxymonosulfate process: Performance and mechanisms

To improve the performance of the conventional coagulation process, a permanganate (Mn(VII)) pre-oxidation combined with Fe(III)/peroxymonosulfate (PMS) coagulation process (Mn(VII)-Fe(III)/PMS) that can significantly improve the removal of dissolved organic carbon (DOC), turbidity, and micropollutants is proposed in this study. Compared with conventional Fe(III) coagulation, the Mn(VII)-Fe(III)/PMS process can also significantly enhance the removal of iohexol and sulfamethoxazole in raw water. During this process, the primary reduction product, Mn(IV), after Mn(VII) pre-oxidation was adsorbed on the floc surfaces and involved in the Fe(III)/PMS process. The natural organic matter (NOM) in raw water mediated the redox cycle of iron. The synergistic effect of NOM, Fe, and Mn facilitated the redox cycle of Mn(III)/Mn(IV) and Fe(III)/Fe(II) to promote the activation of PMS. The sulfate radical (SO4•-) played an important role in the degradation of micropollutants. The formation potential of the detected volatile disinfection by-product (DBP) during the subsequent chlorination was reduced by 21.9% after the Mn(VII)-Fe(III)/PMS process. This study demonstrated the promising application of the Mn(VII)-Fe(III)/PMS process for coagulation and micropollutant control and illustrated the reaction mechanism. This study provides guidance for improving conventional drinking water treatment processes.

(Dis-)Connected Dots in Dementia with Lewy Bodies-A Systematic Review of Connectivity Studies

Studies on dementia with Lewy bodies (DLB) have mainly focused on the degeneration of distinct cortical and subcortical regions related to the deposition of Lewy bodies. In view of the proposed trans-synaptic spread of the α-synuclein pathology, investigating the disease only in this segregated fashion would be detrimental to our understanding of its progression. In this systematic review, we summarize findings on structural and functional brain connectivity in DLB, as connectivity measures may offer better insights on how the brain is affected by the spread of the pathology. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched Web of Science, PubMed, and SCOPUS for relevant articles published up to November 1, 2021. Of 1215 identified records, we selected and systematically reviewed 53 articles that compared connectivity features between patients with DLB and healthy controls. Structural and functional magnetic resonance imaging, positron emission tomography, single-positron emission computer tomography, and electroencephalography assessments of patients revealed widespread abnormalities within and across brain networks in DLB. Frontoparietal, default mode, and visual networks and their connections to other brain regions featured the most consistent disruptions, which were also associated with core clinical features and cognitive impairments. Furthermore, graph theoretical measures revealed disease-related decreases in local and global network efficiency. This systematic review shows that structural and functional connectivity characteristics in DLB may be particularly valuable at early stages, before overt brain atrophy can be observed. This knowledge may help improve the diagnosis and prognosis in DLB as well as pinpoint targets for future disease-modifying treatments. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Bayes Saliency-Based Object Proposal Generator for Nighttime Traffic Images

Object proposal is one of the most key preprocessing steps for nighttime vehicle detection systems in intelligent transportation systems. However, most current object proposal methods are developed on daytime data sets, and these methods demonstrate unsatisfactory results when they are used on nighttime images. Therefore, this paper presents a novel Bayes saliency-based object proposal generator for nighttime RGB traffic images to generate a modest and accurate set of proposals, which are more likely to be vehicles for preceding vehicle detection. First, we propose a new Bayes saliency detection approach in which prior estimation, feature extraction, weight estimation, and Bayes rule are used to compute saliency maps. Then, we propose a simple but effective object proposal generator based on the Bayes saliency map. Multi-scale sliding window, proposal rejecting, scoring, and non-maximum suppression are combined to generate a modest and effective set of proposals. Experimental results demonstrate that our proposed approach generates a modest set of proposals and outperforms some state-of-the-art methods on nighttime images in terms of various evaluation metrics. Furthermore, our proposed object proposal approach can improve the detection performance and the speed of several state-of-the-art vehicle detection approaches.

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Just as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment.

Feedback cascade regression model for face alignment

Face alignment has made great progress in recent years and the cascade regression framework is one of the main contributors. However, the performance of this framework is unsatisfactory on heavily occluded faces or those far from the frontal pose. This is because regression is sensitive to hidden landmarks and unified initialisation can often lead to the method falling into local minima. The authors propose a new pipeline of salient-to-inner-to-all to progressively compute the locations of landmarks. Additionally, a feedback process is utilised to improve the robustness of regression. They bring out a pose-invariant shape retrieval method to generate the discriminative initialisation. Experiments are performed on two benchmarks, and the experimental results demonstrate that the proposed method has a considerable improvement on the cascade regression model, and achieves favourable results compared with the state-of-the-art deep learning-based methods.

Cleaner production, Process Integration and intensification

A considerable number of studies have been performed and are ongoing for enhancing cleaner production, process integration and also process intensification. These topics reflect some of the most important challenges of our society and have been targeted in this journal. Considerable research effort has been devoted to addressing process integration and process intensification as well as environmentally friendlier production. This article has made an attempt to provide a short assessment of the current state-of-art covered in the recent publications.

Predictor-Based Tensor Regression (PBTR) for LPV subspace identification

The major bottleneck in state-of-the-art Linear Parameter Varying (LPV) subspace methods is the curse of-dimensionality during the first regression step. In this paper, the origin of the curse-of-dimensionality is pinpointed and subsequently a novel method is proposed which does not suffer from this bottleneck. The problem is related to the LPV sub-Markov parameters. These have inherent structure and are dependent on each other. But state-of-the-art LPV subspace methods parametrize the LPV sub-Markov parameters independently. This means the inherent structure is not preserved in the parametrization. In turn this leads to a superfluous parametrization with the curse-of-dimensionality. The solution lies in using parametrizations which preserve the inherent structure sufficiently to avoid the curse of-dimensionality. In this paper a novel method based on tensor regression is proposed. This novel method is named the Predictor-Based Tensor Regression method (PBTR). This method preserves the inherent structure sufficiently to avoid the curse-of-dimensionality. Simulation results show that PBTR has superior performance with respect to both state-of-the-art LPV subspace techniques and also non convex techniques. (C) 2017 Elsevier Ltd. All rights reserved.

Role of gasdermin family proteins in the occurrence and progression of hepatocellular carcinoma

Primary liver cancer is the sixth most common cancer and the third leading cause of cancer mortality worldwide, hepatocellular carcinoma (HCC) is the most common type of liver cancer, accounting for 75%-85% of cases. The occurrence and progression of HCC involve multiple events. Pyroptosis is a gasdermins mediated programmed cell death and is intricately associated with cancerogenesis, including HCC. This review mainly concerns the recent research advances of the gasdermin family members in HCC. The biological roles and specific expression patterns of the family members are discussed, especially those that are involved in the regulatory pathways in the occurrence and progression of HCC. We provide the latest progress into the distinct molecular mechanisms of gasdermin family members involved in the occurrence and development of HCC.

Shigir idol: Origin of monumental sculpture and ideas about the ways of preservation of the representational tradition

The article presents some new ideas about the Big Shigir Idol erection, ways of positioning and fixing the wooden sculpture on a Mansy sanctuary in trans-Uralian taiga. According to art study criteria, the Shigir Idol can be referred to monumental sculpture. Its Mesolithic age allows to put it at the beginning of the pre-literate period monumental sculpture range in Eurasian history - sculptures of Yamnaya, Chemurchek and Okunevo cultures, Scythian deer stones and Middle Age stone idols. The Big Shigir Idol's anthropomorphic iconographic characteristics, synthesis with its decorative features demonstrate a fully formed representational tradition. In spite of the idea of Ural Mesolithic Age origin, the author believes that the Shigir Idol's representational tradition could have been formed in the local Taiga environment unlike the art and craft tradition. The discussion concerning the problem of the long existence of this tradition follows the part about the spreading of anthropomorphic representations in cultures of the Neolithic, Bronze and Iron Ages in Northern Europe, the part about the representational traditions trends. As for the problem of Mesolithic representational tradition preservation in wooden sculpture up to modern and contemporary times in modern indigenous peoples in Western Siberia, it is not possible to solve it with archaeological sources yet. The author justifies this idea and suggests an original way to solve it.

Interstitial Outburst of Angiogenic Factors During Skeletal Muscle Regeneration After Acute Mechanical Trauma

Angiogenesis is a key event during tissue regeneration, but the intimate mechanisms controlling this process are still largely unclear. Therefore, the cellular and molecular interplay along normal tissue regeneration should be carefully unveiled. To this matter, we investigated by xMAP assay the dynamics of some angiogenic factors known to be involved in tissue repair, such as follistatin (FST), Placental Growth Factor-2 (PLGF-2), epidermal growth factor (EGF), betacellulin (BTC), and amphiregulin (AREG) using an animal model that mimics acute muscle contusion injuries. In situ immunofluorescence was used for the evaluation and tissue distribution of their cellular sources. Tissue levels of explored factors increased significantly during degeneration and inflammatory stage of regeneration, peaking first week postinjury. However, except for PLGF-2 and EGF, their levels remained significantly elevated after the inflammatory process started to fade. Serum levels were significantly increased only after 24 h for AREG and EGF. Though, for all factors except FST, the levels in injured samples did not correlate with serum or contralateral tissue levels, excluding the systemic influence. We found significant correlations between the levels of EGF and AREG, BTC, FST and FST and AREG in injured samples. Interstitial cells expressing these factors were highlighted by in situ immunolabeling and their number correlated with measured levels dynamics. Our study provides evidence of a dynamic level variation along the regeneration process and a potential interplay between selected angiogenic factors. They are synthesized, at least partially, by cell populations residing in skeletal muscle interstitium during regeneration after acute muscle trauma.

Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning

Diabetic retinopathy (DR) is a disease resulting from diabetes complications, causing non-reversible damage to retina blood vessels. DR is a leading cause of blindness if not detected early. The currently available DR treatments are limited to stopping or delaying the deterioration of sight, highlighting the importance of regular scanning using high-efficiency computer-based systems to diagnose cases early. The current work presented fully automatic diagnosis systems that exceed manual techniques to avoid misdiagnosis, reducing time, effort and cost. The proposed system classifies DR images into five stages-no-DR, mild, moderate, severe and proliferative DR-as well as localizing the affected lesions on retain surface. The system comprises two deep learning-based models. The first model (CNN512) used the whole image as an input to the CNN model to classify it into one of the five DR stages. It achieved an accuracy of 88.6% and 84.1% on the DDR and the APTOS Kaggle 2019 public datasets, respectively, compared to the state-of-the-art results. Simultaneously, the second model used an adopted YOLOv3 model to detect and localize the DR lesions, achieving a 0.216 mAP in lesion localization on the DDR dataset, which improves the current state-of-the-art results. Finally, both of the proposed structures, CNN512 and YOLOv3, were fused to classify DR images and localize DR lesions, obtaining an accuracy of 89% with 89% sensitivity, 97.3 specificity and that exceeds the current state-of-the-art results.

The State-of-the-Art of Human-Drone Interaction: A Survey

Drones have expanded from military operations to performing a broad range of civilian applications. As drone usage increases, humans will interact with such systems more often, therefore, it is important to achieve a natural human-drone interaction. Although some knowledge can be derived from the field of human-robot interaction, drones can fly in a 3D space, which essentially changes how humans can interact with them, making human-drone interaction a field of its own. This paper is the first survey on the emerging field of human-drone interaction focusing on multi-rotor systems, providing an overview of existing literature and the current state of the art in the field. This work begins with an analysis and comparison of the drone models that are commonly used by end-users and researchers in the field of human-drone interaction. Following, the current state of the field is discussed, including the roles of humans in HDI, innovative control methods, remaining aspects of interaction, and novelty drone prototypes and applications. This paper concludes by presenting a discussion of current challenges and future work in the field of human-drone interaction.

Transient neonatal hemolytic anemia due to the novel gamma globin gene mutation HBG2:C.290T>C, p.Leu97Pro (hemoglobin Wareham)

Unstable gamma globin variants can cause transient neonatal hemolytic anemia. We have identified a novel variant in a newborn who presented with jaundice and anemia requiring phototherapy and red blood cell transfusion. The patient was found to be heterozygous for the mutation HGB2:c.290T>C, p.Leu97Pro, which we have termed hemoglobin (Hb) Wareham. This substitution is expected to generate an unstable hemoglobin with increased oxygen affinity based on the homologous mutation previously described in the beta globin gene, which is termed as Hb Debrousse. The patient fully recovered by 9 months of age as expected with the transition from fetal to adult hemoglobin.

Robust and energy-efficient carbon nanotube FET-based MVL gates: A novel design approach

In this paper energy-efficient multiple valued logic (MVL) circuits based on carbon nanotube field effect transistor (CNTFET) are proposed. These circuits are designed based on the unique properties of CNTFETs, such as having same mobility for electrons and holes and also capability of adopting desirable threshold voltage by adjusting the CNTs diameters. The proposed designs have high driving capability, larger noise margins and higher robustness as compared to the previous CNTFET-based designs. The proposed quaternary circuits are examined using HSPICE simulator with the standard CNTFET technology. Simulation results demonstrate more energy-efficient and robust operation of the proposed designs, as compared to the other state-of-the-art CNTFET-based MVL circuits, recently presented in the literature. According to the simulation results the proposed STNOT, STNAND and STNOR circuits have on average 82%, 76% and 45% lower power-delay product (PDP), respectively as compared to their state-of-the-art counterparts. In addition, the proposed QNOT, QNAND and QNOR circuits have the average PDP improvements of 79%, 42% and 61%, respectively, as compared the other recently presented CNTFET-based quaternary designs. (C) 2015 Elsevier Ltd. All rights reserved.

Integrated Microwave Photonics for Radio Access Networks

We explore the advantages that integrated microwave photonics (IMWP) can bring to access networks. We first of all review the most common architectures of radio access networks (RANs) to identify the segments where microwave photonic components and radio over fiber links are located. Then, we provide a short description of the basic principles of IMWP with the aim of illustrating the current state of the art of this technology and its potentials. We discuss the possibilities of incorporating IMWP technology into the RAN front-haul. In particular, we first of all identify the required MWP functionalities and then discuss the feasibility of implementing these in light of the current and near future state of the art.

Scientific evidence invalidates health assumptions underlying the FCC and ICNIRP exposure limit determinations for radiofrequency radiation: implications for 5G

In the late-1990s, the FCC and ICNIRP adopted radiofrequency radiation (RFR) exposure limits to protect the public and workers from adverse effects of RFR. These limits were based on results from behavioral studies conducted in the 1980s involving 40-60-minute exposures in 5 monkeys and 8 rats, and then applying arbitrary safety factors to an apparent threshold specific absorption rate (SAR) of 4 W/kg. The limits were also based on two major assumptions: any biological effects were due to excessive tissue heating and no effects would occur below the putative threshold SAR, as well as twelve assumptions that were not specified by either the FCC or ICNIRP. In this paper, we show how the past 25 years of extensive research on RFR demonstrates that the assumptions underlying the FCC's and ICNIRP's exposure limits are invalid and continue to present a public health harm. Adverse effects observed at exposures below the assumed threshold SAR include non-thermal induction of reactive oxygen species, DNA damage, cardiomyopathy, carcinogenicity, sperm damage, and neurological effects, including electromagnetic hypersensitivity. Also, multiple human studies have found statistically significant associations between RFR exposure and increased brain and thyroid cancer risk. Yet, in 2020, and in light of the body of evidence reviewed in this article, the FCC and ICNIRP reaffirmed the same limits that were established in the 1990s. Consequently, these exposure limits, which are based on false suppositions, do not adequately protect workers, children, hypersensitive individuals, and the general population from short-term or long-term RFR exposures. Thus, urgently needed are health protective exposure limits for humans and the environment. These limits must be based on scientific evidence rather than on erroneous assumptions, especially given the increasing worldwide exposures of people and the environment to RFR, including novel forms of radiation from 5G telecommunications for which there are no adequate health effects studies.

Bias-Dependent Small-Signal Modeling Based on Neuro-Space Mapping for MOSFET

In this article, bias-dependent small-signal modeling approach based on neuro-space mapping is proposed for MOSFET. Good agreement is obtained between the simulated and measured results for a 130 nm MOSFET in the frequency range of 100 MHz-40 GHz confirming the validity and effectiveness of our approach. In addition, higher accuracy is achieved by our approach in contrast to conventional empirical model. (C) 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE 21:182-189, 2011.

Navigating Environmental Transitions: the Role of Phenotypic Variation in Bacterial Responses

The ability of bacteria to respond to changes in their environment is critical to their survival, allowing them to withstand stress, form complex communities, and induce virulence responses during host infection. A remarkable feature of many of these bacterial responses is that they are often variable across individual cells, despite occurring in an isogenic population exposed to a homogeneous environmental change, a phenomenon known as phenotypic heterogeneity. Phenotypic heterogeneity can enable bet-hedging or division of labor strategies that allow bacteria to survive fluctuating conditions. Investigating the significance of phenotypic heterogeneity in environmental transitions requires dynamic, single-cell data. Technical advances in quantitative single-cell measurements, imaging, and microfluidics have led to a surge of publications on this topic. Here, we review recent discoveries on single-cell bacterial responses to environmental transitions of various origins and complexities, from simple diauxic shifts to community behaviors in biofilm formation to virulence regulation during infection. We describe how these studies firmly establish that this form of heterogeneity is prevalent and a conserved mechanism by which bacteria cope with fluctuating conditions. We end with an outline of current challenges and future directions for the field. While it remains challenging to predict how an individual bacterium will respond to a given environmental input, we anticipate that capturing the dynamics of the process will begin to resolve this and facilitate rational perturbation of environmental responses for therapeutic and bioengineering purposes.

Tuning space mapping: The state of the art

The electromagnetic (EM)-simulator-based tuning process for rapid microwave design can combine EM accuracy with circuit-design speed. Our own approach is based on the intuitive engineering idea of space mapping. In this article, we explain the art of microwave design optimization through tuning space mapping procedures. We list various appropriate types of models (called surrogates). We demonstrate the implementation of these surrogates through a simple bandstop filter. We provide application examples using commercial simulation software. Our purpose is to help microwave engineers understand the tuning space mapping methodology and to inspire new implementations and applications. (C) 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE 22: 639651, 2012.

An Iterative Centroid Approach for Diffeomorphic Online Atlasing

Online atlasing, i.e., incrementing an atlas with new images as they are acquired, is key when performing studies on very large, or still being gathered, databases. Regular approaches to atlasing however do not focus on this aspect and impose a complete reconstruction of the atlas when adding images. We propose instead a diffeomorphic online atlasing method that allows gradual updates to an atlas. In this iterative centroid approach, we integrate new subjects in the atlas in an iterative manner, gradually moving the centroid of the images towards its final position. This leads to a computationally cheap approach since it only necessitates one additional registration per new subject added. We validate our approach on several experiments with three main goals: 1- to evaluate atlas image quality of the obtained atlases with sharpness and overlap measures, 2- to assess the deviation in terms of transformations with respect to a conventional atlasing method and 3- to compare its computational time with regular approaches of the literature. We demonstrate that the transformations divergence with respect to a state-of-the-art atlas construction method is small and reaches a plateau, that the two construction methods have the same ability to map subject homologous regions onto a common space and produce images of equivalent quality. The computational time of our approach is also drastically reduced for regular updates. Finally, we also present a direct extension of our method to update spatio-temporal atlases, especially useful for developmental studies.

Indoor air quality at Salarjung Museum, Hyderabad, India

Deterioration of art objects at Salarjung Museum has been noticed such as blackening of white and pink pigments of Indian miniature paintings and other objects like pigments, paints, varnishes, coatings, silver ware, zari works, textiles, which are displayed in museum galleries. The cause of deterioration of the artifacts is attributed to air pollution. The outdoor air pollution levels with respect to suspended particulate matter, sulphur dioxide, oxides of nitrogen, ammonia, aldehydes and oxidants are observed to be high when compared with background environment and ambient air quality standards for sensitive areas. The indoor air quality levels in terms of various parameters including temperature and relative humidity (RH) observed to be more than the threshold limits. The climatic conditions coupled with polluted indoor air are the main causes for the deterioration of art objects. Hence remedial measures are suggested to avoid further deterioration of objects.

Surgical Outcomes of Congenital Heart Disease in Down Syndrome: Tertiary Center Experience-Focus on the Electrical Conduction System

To document outcomes of cardiac surgical repair in Down syndrome (DS) patients with specific focus on the associated electrical conduction morbidities, ultimately leading to a higher incidence of pacemaker implantation (PMI). A retrospective study conducted between 2011 and 2020. A total of 167 DS patients undergoing 204 surgeries were included. The mean gestational age (GA) and mean weight were 37.3 weeks and 5.5 kg, respectively. Complete atrioventricular septal defect (AVSD) was the most common diagnosis. Pre-operative ECG revealed superior axis deviation (SAD) in 92 and 32% of patients with AVSD and isolated perimembranous ventricular septal defect (VSD), respectively (p < 0.01). Postoperative right bundle branch block (RBBB) was observed in 83 and 55% of patients with AVSD and following perimembranous VSD repair, respectively (p = 0.04). Ten patients underwent post-operative pacemaker implantation (PMI). Reintervention rate was around 8.9%. Three mortalities were encountered throughout the study period, 2 of which were in-hospital deaths. Low mortality was observed, however, a higher rate of PMI requirements noted with risk factors including lower age and weight.

Identify Representative Samples by Conditional Random Field of Cancer Histology Images

Pathologyanalysis is crucial to precise cancer diagnoses and the succeeding treatment plan as well. To detect abnormality in histopathology images with prevailing patch-based convolutional neural networks (CNNs), contextual information often serves as a powerful cue. However, aswhole-slide images (WSIs) are characterized by intense morphological heterogeneity and extensive tissue scale, a straightforward visual span to a larger context may not well capture the information closely associated with the focal patch. In this paper, we propose a novel pixel-offset based patch-locationmethod to identify high-representative tissues, with a CNN backbone. Pathology Deformable Conditional Random Field (PDCRF) is proposed to learn the offsets and weights of neighboring contexts in a spatialadaptivemanner, to search for high-representative patches. A CNN structure with the localized patches as training input is then capable of consistently reaching superior classification outcomes for histology images. Overall, the proposed method has achieved state-of-the-art performance, in terms of the test classification accuracy improvement to the baseline by 1.15-2.60%, 0.78-1.78%, and 1.47-2.18% on TCGA public datasets of TCGA-STAD, TCGA-COAD, and TCGA-READ respectively. It also achieves 88.95% test accuracy and 0.920 test AUC on Camelyon 16. To show the effectiveness of the proposed framework on downstream tasks, we take a further step by incorporating an active learning model, which noticeably reduces the number of manual annotations by PDCRF to reach a parallel patchbased histology classifier.

Hyperspectral image classification based on spectral and spatial information using ResNet with channel attention

Classification of hyperspectral image (HSI) is widely used for the study of remotely sensed images. Convolutional Neural Networks (CNNs) are one of the most commonly chosen deep learning algorithms for visual data analysis. The HSI classification framework based on the CNN is presented in this paper. Since the imbalance between the high dimension of HSI input data and the limited amount of labeled training data would induce overfitting, current convolutional networks are fairly superficial for HSI classification. To stop the limited efficiency of feature learning, a new HSI classification network called Residual Spectral Spatial-Channel Attention Network (RSS-CAN) is proposed. By utilizing the "shortcut connection" framework, RSS-CAN can use deeper layers to extract more succinct and efficient features. Furthermore, attention mechanism is used to emphasize meaningful features. In addition, we revised an HSI dataset called Shandong Feicheng. The resolution and pixel quantity of this dataset are significantly greater. In order to check its variety, it has been contrasted with state-of-the-art approaches. Experimental results with widely used hyperspectral image datasets demonstrate that, our proposed method has achieved better performance in comparison with state-of-the-art classifiers and conventional deep learning-based classifiers.

App design of distance art education platform under internet ecological environment

With the rapid development of computer and network technology, the application of mobile app has been involved in all walks of life, especially the traditional art teaching mode. Therefore, it is of great significance to design an app with art learning and education as the main content based on the concept of young people's cognitive psychology and art training. This paper first analyzes the current situation and development trend of art education app at home and abroad, then analyzes and studies the design and application needs of education app at home, clarifies the needs of students and teachers, analyzes the visual and psychological characteristics of teenagers and their impact on interface and interaction design, and finally designs an art education app that meets the domestic needs. The results show that the app can enhance the interest of young people in art learning.

Plasma Functionalization of Silica Bilayer Polymorphs

Ultrathin silica films are considered suitable two-dimensional model systems for the study of fundamental chemical and physical properties of all-silica zeolites and their derivatives, as well as novel supports for the stabilization of single atoms. In the present work, we report the creation of a new model catalytic support based on the surface functionalization of different silica bilayer (BL) polymorphs with well-defined atomic structures. The functionalization is carried out by means of in situ H-plasma treatments at room temperature. Low energy electron diffraction and microscopy data indicate that the atomic structure of the films remains unchanged upon treatment. Comparing the experimental results (photoemission and infrared absorption spectra) with density functional theory simulations shows that H2 is added via the heterolytic dissociation of an interlayer Si-O-Si siloxane bond and the subsequent formation of a hydroxyl and a hydride group in the top and bottom layers of the silica film, respectively. Functionalization of the silica films constitutes the first step into the development of a new type of model system of single-atom catalysts where metal atoms with different affinities for the functional groups can be anchored in the SiO2 matrix in well-established positions. In this way, synergistic and confinement effects between the active centers can be studied in a controlled manner.

Timing is everything: Transcriptional repression is not the default mode for regulating Hedgehog signaling

Hedgehog (HH) signaling is a conserved pathway that drives developmental growth and is essential for the formation of most organs. The expression of HH target genes is regulated by a dual switch mechanism where GLI proteins function as bifunctional transcriptional activators (in the presence of HH signaling) and transcriptional repressors (in the absence of HH signaling). This results in a tight control of GLI target gene expression during rapidly changing levels of pathway activity. It has long been presumed that GLI proteins also repress target genes prior to the initial expression of HH in a given tissue. This idea forms the basis for the limb bud pre-patterning model for regulating digit number. Recent findings indicate that GLI repressor proteins are indeed present prior to HH signaling but contrary to this model, GLI proteins are inert as they do not regulate transcriptional responses or enhancer chromatin modifications at this time. These findings suggest that GLI transcriptional repressor activity is not a default state as assumed, but is itself regulated in an unknown fashion. We discuss these findings and their implications for understanding pre-patterning, digit regulation, and HH-driven disease.

Capsicum annuum L. and its bioactive constituents: A critical review of a traditional culinary spice in terms of its modern pharmacological potentials with toxicological issues

Capsicum annuum L., commonly known as chili pepper, is used as an important spice globally and as a crude drug in many traditional medicine systems. The fruits of C. annuum have been used as a tonic, antiseptic, and stimulating agent, to treat dyspepsia, appetites, and flatulence, and to improve digestion and circulation. The article aims to critically review the phytochemical and pharmacological properties of C. annuum and its major compounds. Capsaicin, dihydrocapsaicin, and some carotenoids are reported as the major active compounds with several pharmacological potentials especially as anticancer and cardioprotectant. The anticancer effect of capsaicinoids is mainly mediated through mechanisms involving the interaction of Ca2+ -dependent activation of the MAPK pathway, suppression of NOX-dependent reactive oxygen species generation, and p53-mediated activation of mitochondrial apoptosis in cancer cells. Similarly, the cardioprotective effects of capsaicinoids are mediated through their interaction with cellular transient receptor potential vanilloid 1 channel, and restoration of calcitonin gene-related peptide via Ca2+ -dependent release of neuropeptides and suppression of bradykinin. In conclusion, this comprehensive review presents detailed information about the traditional uses, phytochemistry, and pharmacology of major bioactive principles of C. annuum with special emphasis on anticancer, cardioprotective effects, and plausible toxic adversities along with food safety.

DENAO: Monocular Depth Estimation Network With Auxiliary Optical Flow

Estimating depth from multi-view images captured by a localized monocular camera is an essential task in computer vision and robotics. In this study, we demonstrate that learning a convolutional neural network (CNN) for depth estimation with an auxiliary optical flow network and the epipolar geometry constraint can greatly benefit the depth estimation task and in turn yield large improvements in both accuracy and speed. Our architecture is composed of two tightly-coupled encoder-decoder networks, i.e., an optical flow net and a depth net, the core part being a list of exchange blocks between the two nets and an epipolar feature layer in the optical flow net to improve predictions of both depth and optical flow. Our architecture allows to input arbitrary number of multiview images with a linearly growing time cost for optical flow and depth estimation. Experimental result on five public datasets demonstrates that our method, named DENAO, runs at 38.46fps on a single Nvidia TITAN Xp GPU which is 5.15X similar to 142X faster than the state-of-the-art depth estimation methods Meanwhile, our DENAO can concurrently output predictions of both depth and optical flow, and performs on par with or outperforms the state-of-the-art depth estimation methods and optical flow methods.

Ultrawideband Conical Log-Spiral Circularly Polarized Feed for Radio Astronomy

In order to meet the requirements of the new generation of radio telescopes, we have developed a new topology called DYQSA, which stands for DYson Quad-Spiral Array. The design exhibits dual circular polarization contrary to dual linear polarization of state-of-the-art feeds. It covers the required ultrawideband (UWB) from 2 to 14 GHz with an almost constant and real input impedance which facilitates the design of the feeding structure and the low-noise amplifiers (LNAs). Different versions are investigated for enhancing feed performance, ensuring higher aperture efficiencies and mechanical stability. The simulation results of the reflector loaded by the proposed feed show an aperture efficiency of 65% +/- 5% with a noise antenna temperature around 14 K and a system equivalent flux density (SEFD) of about 1300 Jy, both averaged over the required bandwidth at zenith. Measurements of the single-element and the four-element feeds are presented. Comparisons with other state-of-the-art feeds are shown in terms of total aperture efficiencies, design adaptability to different reflectors, calibration signal injection, and the required number of LNAs per feed, cost, and physical volume.

Workload control in the semiconductor industry

The state-of-the-art in published research on workload control as applied to the semiconductor industry is described. The focus is on examining the concepts behind workload control heuristics and evaluating their effectiveness, overhead requirements, and implementability. Individual sections provide an overview of the distinctive elements in a semiconductor manufacturing environment, general dispatching and order release methods, workload control strategies developed for semiconductor manufacturing, and future directions.

Affinity learning via a diffusion process for subspace clustering

Subspace clustering refers to the problem of finding low-dimensional subspaces (clusters) for high-dimensional data. Current state-of-the-art subspace clustering methods are usually based on spectral clustering, where an affinity matrix is learned by the self-expressive model, i.e., reconstructing every data point by a linear combination of all other points while regularizing the coefficients using the l(1) norm. The sparsity nature of l(1) norm guarantees the subspace-preserving property (i.e., no connection between clusters) of affinity matrix under certain condition, but the connectedness property (i.e., fully connected within clusters) is less considered. In this paper, we propose a novel affinity learning method by incorporating the sparse representation and diffusion process. Instead of using sparse coefficients directly as the affinity values, we apply the l(1) norm as a neighborhood selection criterion, which could capture the local manifold structure. An effective diffusion process is then deployed to spread such local information along with the global geometry of data manifold. Each pairwise affinity is augmented and re-evaluated by the context of data point pair, yielding significant enhancements of within-cluster connectivity. Extensive experiments on synthetic data and real-world data have demonstrated the effectiveness of the proposed method in comparison to other state-of-the-art methods. (C) 2018 Elsevier Ltd. All rights reserved.

Changes in heart rate variability of healthy subjects shortly exposed to printing shop particles and the effect of air purifier intervention

Particulate matter (PM) released by printers may cause airway inflammation and cardiac electrophysiological changes. We conducted a two-stage crossover study to examine the association between short-term exposure to printing shop particles (PSPs) and the heart rate variability (HRV) among healthy volunteers, as well as to evaluate the effect of air purifier intervention. The on-site assessments of PSPs and individual HRV parameters of the volunteers were used to analyze the influence of size-fractionated PSPs and air purifier intervention on HRV at different lag times after PSPs exposure (0 min, 5 min, 15 min, and 30 min) by using the linear mixed-effects models. We observed that changes in 6 HRV parameters were associated with particle mass concentration (PMC) of PSPs, and changes in 8 HRV parameters were associated with particle number concentration (PNC) of PSPs. The sensitive HRV parameters were the square root of the mean of the sum of the squares of differences between adjacent NN intervals (rMSSD), NN50 count divided by the total number of all NN intervals (pNN50), normalized high frequency power (nHF), very high frequency power (VHF), normalized low frequency power (nLF), and the ratio of low frequency power to high frequency power (LF/HF). Most HRV parameters exhibited the strongest effect associated with PMC and PNC at a lag time of 30 min. The air purifier intervention markedly reduced the PNC and PMC of size-fractionated PSPs, enhanced 5 HRV parameters, and decreased the nLF and LF/HF. Our study suggests that short-term exposure to PSPs can affect HRV parameters, reflecting changes in cardiac autonomic nervous activity, and the use of an air purifier can reduce the concentration of PSPs and improve the autonomic nervous system activity of the exposed individuals.

Intratympanic steroid treatment can reduce ROS and immune response in human perilymph investigated by in-depth proteome analysis

Intratympanic (IT) steroid treatment is one of the most widely used and effective treatments for inner ear disorders such as sudden sensorineural hearing loss (SNHL). However, a clear mechanism of IT steroids in inner ear recovery has not yet been revealed. Therefore, we investigated proteome changes in extracted human perilymph after steroid treatment. In this study, we applied a tandem mass spectrometry (MS/MS)-based proteomics approach to discover global proteome changes by comparing human perilymph after steroid treatment with non-treated perilymph group. Using liquid chromatography-MS/MS analysis, we selected 156 differentially expressed proteins (DEPs) that were statistically significant according to Student's t-test. Functional annotation analysis showed that upregulated proteins after steroid treatment are related to apoptosis signaling, as well as reactive oxygen species (ROS) and immune responses. The protein-protein interaction (PPI) clusters the proteins associated with these processes and attempts to observe signaling circuitry, which mediates cellular response after IT steroid treatments. Moreover, we also considered the interactome analysis of DEPs and observed that those with high interaction scores were categorized as having equivalent molecular functions (MFs). Collectively, we suggest that DEPs and interacting proteins in human perilymph after steroid treatment would inhibit the apoptotic and adaptive immune processes that may lead to anti-inflammatory effects.

RegionSeeker: Automatically Identifying and Selecting Accelerators From Application Source Code

Embedded systems present stringent and often conflicting requirements. On the one side, the need for high performance within a tight energy budget favors inflexible Application Specific Integrated Circuit (ASIC) implementations; on the other side, a short time-to-market demands programmability. Hybrid architectures such as special-purpose customized processors represent an attractive solution, as they arc programmable by software, but use dedicated hardware to accelerate parts of the computation. In such a scenario, the capability of automatically identifying the computation parts to he realized in hardware is highly desirable, in order to reduce design time and effort. This paper aims at advancing the state-of-the-art in this field. We recognize that subgraphs of control flow graphs having a single input control point and a single output control point, that we call regions, are good targets for the synthesis of application specific hardware accelerators. We therefore provide a method to identify them and an LINM-based toolchain (named RegionSeeker) that, analyzing a software application, automatically selects its most profitable regions given an area constraint. Experimental evidence shows that the accelerators identified by RegionSeeker provide a speedup of up to 4.6x and, on average, approximately 30% higher speedup is achieved compared to state-of-the-art identification techniques.

Sustainable Design Masters: Increasing the Sustainability Literacy of Designers

This paper examines student learning in the Master of Arts in Sustainable Design course at Kingston School of Art, Kingston University London. It considers what designers learn, how they learn and where they learn, in a postgraduate course that seeks to enable them to direct their practice towards sustainability by increasing their sustainability literacy. The paper reviews the learning experiences of students, and the curriculum structures and approaches used to serve those experiences. The story of the course is told here by the course leader of ten years, using student outputs to illustrate the argument made for a sustainable design pedagogy. The key principles of this pedagogy are (1) sustainability is a social, not just an environmental, agenda; (2) sustainability presents us with 'wicked problems', which have no right or wrong answers; (3) sustainability-directed design practice arises from the sustainability literacy of the designer; (4) sustainability derives from mindsets and worldviews, not just methods and materials; and (5) sustainability is an emergent property of systems, not a quality of products. This combination has generated a distinctive model of postgraduate sustainable design education, which seeks to equip students with a 'mastery' of how to put into practice their own visions of sustainable design.

Unsupervised Domain Adaptation for Depth Prediction from Images

State-of-the-art approaches to infer dense depth measurements from images rely on CNNs trained end-to-end on a vast amount of data. However, these approaches suffer a drastic drop in accuracy when dealing with environments much different in appearance and/or context from those observed at training time. This domain shift issue is usually addressed by fine-tuning on smaller sets of images from the target domain annotated with depth labels. Unfortunately, relying on such supervised labeling is seldom feasible in most practical settings. Therefore, we propose an unsupervised domain adaptation technique which does not require groundtruth labels. Our method relies only on image pairs and leverages on classical stereo algorithms to produce disparity measurements alongside with confidence estimators to assess upon their reliability. We propose to fine-tune both depth-from-stereo as well as depth-from-mono architectures by a novel confidence-guided loss function that handles the measured disparities as noisy labels weighted according to the estimated confidence. Extensive experimental results based on standard datasets and evaluation protocols prove that our technique can address effectively the domain shift issue with both stereo and monocular depth prediction architectures and outperforms other state-of-the-art unsupervised loss functions that may be alternatively deployed to pursue domain adaptation.

Phosphosites of the yeast centrosome component Spc110 contribute to cell cycle progression and mitotic exit

Spc110 is an essential component of the spindle pole body (SPB), the yeast equivalent of the centrosome, that recruits the γ-tubulin complex to the nuclear side of the SPB to produce the microtubules that form the mitotic spindle. Here, we identified phosphosites S11 and S36 in maternally originated Spc110 and explored their functions in vivo. Yeast expressing non-phosphorylatable Spc110S11A had a distinct spindle phenotype characterised by higher levels of α-tubulin, which was frequently asymmetrically distributed between the two SPBs. Furthermore, expression of the double mutant Spc110S11AS36A had a delayed cell cycle progression. Specifically, the final steps of mitosis were delayed in Spc110S11AS36A cells, including expression and degradation of the mitotic cyclin Clb2, disassembling the mitotic spindle and re-localizing Cdc14 to the nucleoli, resulting in late mitotic exit and entry in G1. Thus, we propose that Spc110 phosphorylation at S11 and S36 is required to regulate timely cell cycle progression in budding yeast. This article has an associated First Person interview with the first author of the paper.

Comparison of the Literacy Level on Major Environmental Issues of the GCE (A/L) Students of Different Disciplines in Kandy District, Sri Lanka

The study aims to examine the environmental literacy level of G.C.E A/L students (comparable to British Advanced Level) in the Kandy District, Sri Lanka, and to evaluate environment modules embedded into the curriculum. Furthermore, we find the relationships between environmental literacy level and socioeconomic and discipline levels. A survey with a sample of 300 students was selected randomly from four different disciplines, with 25 students in each subject (biological science, mathematics, arts, and commerce) from three different types of schools (Public, Private, and International). A piloted, self-administered, and structured questionnaire with 44 items under six sections (personal background, environmental issues, attitudes, behavior, suggestion, and mitigation) was randomly distributed among the study sample. Results showed that most respondents reside in an urban area, the education level of parents was positioned in the G.C.E. (A/L) category, and the monthly total family income was more than SLR 90,000.00. Overall, the environmental literacy (EL) was similar among students of three different types of schools. The type of permanent residence, discipline, and educational background of parents were recognized as determining factors of EL levels (p < 0.05). The environmental literacy knowledge of biological science students was the highest (44%), followed by mathematics (36%), art (32%), and commerce (28%). Furthermore, the EL of the biology discipline was significantly different from commerce (18%) and art (14%) students. Students acquired environmental knowledge from television and radio (44%), internet (22%), school (27%), and parents (7%). The A/L Curriculum evaluation results proved that much less environment-related components were included in the commerce, art, and mathematics subjects. Of the disciplines, geography (35%) and biological science (11%) included a higher amount of EL knowledge, comparatively. Students suggested that recycling/ reuse of waste conserves natural resources, the green building concept, and the use of public transportation to conserve the environment. This study recommends that environmental concepts should be integrated with formal G.C.E A/L syllabus with activity learning, especially for non-science disciplines, and that environment-related television and radio programs should be enhanced.

Street Art Participation in Increasing Investments in the City Center of Bucharest, a Paradox or Not?

This article analyses street art's contribution to the current economic life in the city center of an Eastern European capital, Bucharest. The development of socio-economic activities in the Romanian capital has been strongly influenced in the last 30 years by a complex of effects generated by the transition to the capitalist economy in the early 1990s, the impact of globalization, and recently the COVID-19 pandemic. This study focuses on the investigation of those areas that through street art came to know processes of urban regeneration. By applying semi-structured interviews to providers of alternative guided tours, but also questionnaires among the population that is familiar with this subculture, including an organization of urban regeneration through street art, an important number of economically new spaces, next to reinvented ones, have been investigated. In these areas, street art ends up by supporting activities from hospitality, cultural, and creative industries, changing for the better the perspectives of economic and cultural development, along with the attractiveness of the Bucharest city center. Street art proves to be an important tool in the regeneration process bringing positive effects when involving active cooperation between the public and the private sectors.

Purified exosome product enhances chondrocyte survival and regeneration by modulating inflammation and promoting chondrogenesis

Aim: This study was to detect the effects of purified exosome product (PEP) on C28/I2 cells and chondrocytes derived from osteoarthritis patients. Materials & methods: Cell viability and apoptosis assays were used to detect the effect of PEP on cells. qRT-PCR and cell fluorescence assays were used to investigate the potential mechanism of PEP on cell chondrogenesis. Results: PEP was internalized by cells at a fast rate and enhanced cellular proliferation and migration while attenuating apoptosis. These findings reflect the fact that PEP can increase the expression of PCNA and reduce the expression of CASP3/7/9 and BAX. Conclusion: This study suggests an innovative strategy for chondrogenesis that could be applied to osteoarthritis repair in the future.