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Face Recognition Using Sparse Fingerprint Classification Algorithm

Unconstrained face recognition is still an open problem as the state-of-the-art algorithms have not yet reached high recognition performance in real-world environments. This paper addresses this problem by proposing a new approach called sparse fingerprint classification algorithm (SFCA). In the training phase, for each enrolled subject, a grid of patches is extracted from each subject's face images in order to construct representative dictionaries. In the testing phase, a grid is extracted from the query image and every patch is transformed into a binary sparse representation using the dictionary, creating a fingerprint of the face. The binary coefficients vote for their corresponding classes and the maximum-vote class decides the identity of the query image. Experiments were carried out on seven widely-used face databases. The results demonstrate that when the size of the data set is small or medium (e.g., the number of subjects is not greater than one hundred), SFCA is able to deal with a larger degree of variability in ambient lighting, pose, expression, occlusion, face size, and distance from the camera than other current state-of-the-art algorithms.

A case of death of patient with ovarian fibroma combined with Meigs Syndrome and literature review

Ovarian fibroma is the most common benign pure stromal tumor. It has no specific clinical manifestation, most of which are pelvic or adnexal masses. 10-15% of cases with hydrothorax or ascites, after tumor resection, hydrothorax and ascites disappear, known as Meigs Syndrome. The elevated level of CA125 in a few patients was easily misdiagnosed as ovarian malignant tumor. A case of bilateral Ovarian fibroma associated with Meigs Syndrome is reported and the literature is reviewed in order to improve the understanding of the changes and avoid misdiagnosis.

Beyond corrosion: development of a single cell-ICP-ToF-MS method to uncover the process of microbiologically influenced corrosion

The development of the microbiologically influenced corrosion (MIC)-specific inductively coupled plasma-time of flight-mass spectrometry (ICP-ToF-MS) analytical method presented here, in combination with the investigation of steel-MIC interactions, contributes significantly to progress in instrumental MIC analysis. For this, a MIC-specific staining procedure was developed, which ensures the analysis of intact cells. It allows the analysis of archaea at a single cell level, which is extremely scarce compared to other well-characterized organisms. The detection method revealed elemental selectivity for the corrosive methanogenic strain Methanobacterium-affiliated IM1. Hence, the possible uptake of individual elements from different steel samples was investigated and results showed the cells responded at a single-cell level to the different types of supplemented elements and displayed the abilities to uptake chromium, vanadium, titanium, cobalt, and molybdenum from solid metal surfaces. The methods developed and information obtained will be used in the future to elucidate underlying mechanisms, compliment well-developed methods, such as SEM-EDS, and develop novel material protection concepts.

Anti-glomerular basement membrane vasculitis

Antiglomerular basement membrane disease (anti-GBM) is a rare life-threatening autoimmune vasculitis that involves small vessels and it is characterized by circulating autoantibodies directed against type IV collagen antigens expressed in glomerular and alveolar basement membrane. The typical clinical manifestations are the rapidly progressive glomerulonephritis and the alveolar hemorrhage. The diagnosis is usually confirmed by the detection of anti-GBM circulating antibodies. If not rapidly recognized, anti-GBM disease can lead to end stage kidney disease (ESKD). An early diagnosis and prompt treatment with immunosuppressive therapies and plasmapheresis are crucial to prevent a poor outcome. In this review, we discuss the primary form of anti-GBM (the so called Goodpasture syndrome) but also cases associated with other autoimmune diseases such as antineutrophil-cytoplasmic-antibody (ANCA) vasculitis, membranous nephropathy, IgA nephritis and systemic lupus erythematosus (SLE), as well as the few cases of anti-GBM vasculitis complicating kidney transplantation in the Alport syndrome.

A Website Fingerprint defense technology with low delay and controllable bandwidth

Website Fingerprinting (WF) attacks have shown a serious threat to users' privacy on Tor networks. To resist WF attacks, many defenses have been proposed, of which padding without any delay is the state-of-the-art method. With the extra data overhead, they managed to reduce the accuracy of WF attacks. However, it is challenging to defeat WF attacks effectively while costing low bandwidth and latency. In this paper, we present RanDePad, a traffic protection and obfuscation method. It achieves low delay and controllable bandwidth. RanDePad uses an adaptive random delaying technique to destroy the website traffic's time distribution characteristics. It could adjust the time interval between the real traffic packets without changing its order and limits the required traffic latency. Through a bandwidth evaluation algorithm, RanDePad evaluates the traffic bandwidth and dynamically adjusts the random bandwidth padding scheme to hide traffic space features. It ensures low and controllable bandwidth overhead. We evaluated the performance of RanDePad against WF attacks under different bandwidth overhead settings. Experimental results show that with the same total bandwidth overhead and 5.89% total latency overhead, RanDePad can decrease the state-of-the-art attack DF accuracy from 94.57% to 62.40%, while the Front reduces it to 71.01% and the WTF-PAD to 86.44%. Furthermore, when the accuracy is defended to 70%, our method consumes only 21.66% total bandwidth overhead, which is half of the Front and WTF-PAD. In addition, the single bandwidth overhead of RanDePad is also lower than the existing defenses, where the Font spends three times as much as RanDePad does. The results indicate that the RanDePad performs better than the current state-of-the-art WTF-PAD and FRONT methods.

A High-Speed Successive-Cancellation Decoder for Polar Codes Using Approximate Computing

Polar codes are a new class of forward error-correction codes, which have been proved asymptotically capacity-achieving for symmetric memoryless channels. Successive-cancellation (SC) decoders are low complexity while suffering from low speed due to their serial processing nature. Based on a newly published fast simplified SC algorithm, we propose a more efficient decoder than the prior art in this brief. Well-optimized components in the decoder core are developed. Additionally, several efficient approximate computing units (Apx-CUs) are introduced to substitute their accurate counterparts in the decoder to achieve even higher operating frequency. Field-programmable gate-array implementation results show that our design is about 1.5 times faster than the prior art, and the proposed Apx-CUs can bring an additional 19% processing speed.

Electromagnetic interference with infusion pumps from GSM mobile phones

Electromagnetic interference with critical medical care devices has been reported by various groups. Previous studies have demonstrated that volumetric and syringe pumps are susceptible to false alarm buzzing and blocking when exposed to various electromagnetic sources. The risk of electromagnetic interference depends on several factors such as the phone-emitted power, distance, and carrier frequency. The aim of this study was to assess the risk of GSM phone-induced electromagnetic interference with volumetric and syringe pumps, at various distances and emitted powers. Malfunctions were observed in 6 out of 8 volumetric pumps and in 1 out of 4 syringe pumps exposed to mobile phones at their maximum output, at distances up to 30 cm. The maximum power that did not induce any malfunction at zero distance was 50 mW at 900 MHz and 2.5 mW at 1,800 MHz. In state-of-the-art pumps, the presence of moderate-good base station coverage would significantly reduce the risk of electromagnetic interference.

Attention by Selection: A Deep Selective Attention Approach to Breast Cancer Classification

Deep learning approaches are widely applied to histopathological image analysis due to the impressive levels of performance achieved. However, when dealing with high-resolution histopathological images, utilizing the original image as input to the deep learning model is computationally expensive, while resizing the original image to achieve low resolution incurs information loss. Some hard-attention based approaches have emerged to select possible lesion regions from images to avoid processing the original image. However, these hard-attention based approaches usually take a long time to converge with weak guidance, and valueless patches may be trained by the classifier. To overcome this problem, we propose a deep selective attention approach that aims to select valuable regions in the original images for classification. In our approach, a decision network is developed to decide where to crop and whether the cropped patch is necessary for classification. These selected patches are then trained by the classification network, which then provides feedback to the decision network to update its selection policy. With such a co-evolution training strategy, we show that our approach can achieve a fast convergence rate and high classification accuracy. Our approach is evaluated on a public breast cancer histopathological image database, where it demonstrates superior performance compared to state-of-the-art deep learning approaches, achieving approximately 98% classification accuracy while only taking 50% of the training time of the previous hard-attention approach.

Data model for additive manufacturing digital thread: state of the art and perspectives

Additive Manufacturing (AM) is currently seen as an interesting process for the fabrication of complex and high value-added functional products. AM is being increasingly used in manufacturing bringing forward technological advancements particularly in materials and process optimisations. Simultaneously, the lifecycle of manufactured products is increasingly based on an 'all-digital' context using cyber-physical systems, the Internet of Things and Industry 4.0 devices. This has led to huge amounts of digital data being generated in manufacturing projects development. Data management is one of the key issues for diffusing and adopting this technology. However, the commonly used digital thread for AM is based on old solutions, such as STL and G-code, which were developed during the 1980s and are not well synchronised with current advanced developments in AM. Moreover, digital thread needs to be contextualised according to the complexities of the sector in which it is being used. Hence, alternative formats should also take into account global and sectorial application. This paper presents the past and current research for a state-of-the-art data model digital thread, as well as perspectives and recommendations for a performing data model thread adapted to the different needs of the AM community.

A Wavelet-GSM Approach to Demosaicking

We propose a wavelet-based Gaussian scale mixture (GSM) demosaicking method. The wavelet coefficients of the proposed method corresponding to the luminance and chrominance components are reconstructed using Bayesian minimum mean square error estimation. The proposed wavelet-GSM prior exploits the correlation of neighboring wavelets coefficients to improve upon a previously proposed posterior sparsity directed demosaicking method. As a result, our proposed demosaicking method suppresses the zippering artifacts more effectively than the state of the arts.

A Service Evaluation of Adherence with Antimicrobial Guidelines in the Treatment of Community-Acquired Pneumonia Before and During the SARS-CoV-2 Outbreak

Antimicrobial stewardship is essential to reducing antimicrobial resistance, reducing costs, and, crucially, ensuring good patient care. Community-acquired pneumonia (CAP) is a common medical condition, the symptoms of which show a significant overlap with those of COVID-19. Following the COVID-19 outbreak in Ireland, patients presenting to our hospital with features of a respiratory infection were more commonly reviewed within 24 hours (24h) of admission by an infectious disease (ID) or respiratory specialist. We aimed to assess how the change in service provision, involving frequent specialist reviews of patients admitted with features of CAP during the first wave of the COVID-19 pandemic, affected antimicrobial stewardship and prescribing practices. Patients admitted under general medical teams treated for CAP from March-April 2020 were included. Retrospective data including demographics, CURB-65 score, and antimicrobial therapy were collected, as well as information on whether the patient had undergone specialist review by an ID or respiratory physician. Data were compared to a similar cohort treated for CAP between November 2019 and January 2020, though in this cohort, before the era of COVID-19, none of the patients had undergone specialist review. Seventy-six patients were included from the March-April 2020 cohort, with 77 from November 2019-January 2020 for comparison. An ID or respiratory specialist reviewed 35 patients from the March-April cohort within 24 h of admission. There was a higher rate of appropriate escalation, de-escalation, and continuation of antibiotics among those reviewed. Less than 20% of patients were started on antibiotics in accordance with CAP guidelines on admission, though the antibiotics initiated were frequently deemed appropriate in the clinical setting. Specialist review increases rates of appropriate antimicrobial prescribing and adherence with hospital guidelines in patients with CAP.

Centroid neural network adaptive resonance theory for vector quantization

In this paper, a novel unsupervised competitive learning algorithm, called the centroid neural network adaptive resonance theory (CNN-ART) algorithm, is proposed to relieve the dependence on the initial codewords of the codebook in contrast to the conventional algorithms with vector quantization in lossy image compression. The design of the CNN-ART algorithm is mainly based on the adaptive resonance theory structure, and then a gradient-descent-based learning rule is derived so that the CNN-ART algorithm does not require a predetermined schedule for learning rate. Furthermore, the appropriate initial weights obtained by the CNN-ART algorithm can be applied as an initial codebook for the Linde-Buzo-Gray (LBG) algorithm such that the compression performance can be greatly improved. In this paper, the extensive simulations demonstrate that the CNN-ART algorithm does outperform other algorithms like LBG, self-organizing feature map and differential competitive learning. (C) 2002 Elsevier Science B.V. All rights reserved.

Hematopoietic Stem Cell Identification Postirradiation

Radiation exposure is particularly damaging to cells of the hematopoietic system, inducing pancytopenia and bone marrow failure. The study of these processes, as well as the development of treatments to prevent hematopoietic damage or enhance recovery after radiation exposure, often require analysis of bone marrow cells early after irradiation. While flow cytometry methods are well characterized for identification and analysis of bone marrow populations in the nonirradiated setting, multiple complications arise when dealing with irradiated tissues. Among these complications is a radiation-induced loss of c-Kit, a central marker for conventional gating of primitive hematopoietic populations in mice. These include hematopoietic stem cells (HSCs), which are central to blood reconstitution and life-long bone marrow function, and are important targets of analysis in these studies. This chapter outlines techniques for HSC identification and analysis from mouse bone marrow postirradiation.

Effects of hourly precipitation and temperature on ambulance response time

Background: Longer ambulance response time (ART) delaying treatment would worsen conditions of seriously ill or injured patients, but limited evidence is available on the effects of weather factors on ART. This study aims to assess precipitation- and temperature-ART associations and their potential lagged effects using a novel modeling strategy. Methods: Based on 779,156 emergency records during 2010-2016 from the whole population in Shenzhen, China, we creatively combined quantile regression with distributed-lag nonlinear models to examine the nonlinear and lagged effects of hourly precipitation and temperature on ART at the 50th and 90th percentiles. Results: A linear precipitation-ART association with a delay of 9.01 (95%CI, 7.82-10.20) seconds at median ART for a 1 mm increase in hourly precipitation, and the effects lasted for 5 h with the greatest effect at the current hour. A two linear thresholds temperature-ART association revealed 1 degrees C decrease below 19 degrees C caused 1.68 (95%CI, 0.92-2.44) seconds delay in total ART over lag 0-7 h, and 1 degrees C increase above 24 degrees C caused 2.44 (95%CI, 1.55-3.33) seconds delay. The hourly call volumes exceeding 54 calls caused 8.79 (95%CI, 8.71-8.86) seconds delay in total ART for 1 more call, but not affected the effects of weather factors. The internal ART suffered more from the hourly call volumes, while the external ART suffered more from precipitation and temperature. The effects were apparently greater on ART at the 90th percentile than median. Conclusions: Precipitation and temperature are independent risk factors for ambulance services performance, and their lagged effects are notable. The external ART and patients with long ART are vulnerable. More attention should be paid to weather and ART, and these findings may have implications for effective policies to reduce ART to protect public health.

How to Manage Pseudomonas aeruginosa Infections

Pseudomonas aeruginosa is a pathogen frequently encountered in healthcare-associated infections and immunocompromised patients. In bacteremia, this pathogen is associated with higher mortality than other Gram-negative pathogens. This increase in mortality was also found globally for multi-resistant compared to susceptible strains. Several factors have been associated with the development of resistance: previous ICU stay, use of carbapenems, and comorbidities were identified in multivariate analysis. In the therapeutic choice, previous antibiotic treatment remains the strongest driver suggesting a potential resistant strain. These risk factors will decide whether multi-resistant strains must be considered in the empiric coverage. For susceptible strains, a single agent can be used, β-lactams are usually the first choice. Associations do not provide any advantage on mortality. Optimization of pharmacokinetic/pharmacodynamic parameters, such as prolonged infusion (for time-dependent antibiotics), increased dosage (for concentration-dependent antibiotics), and therapeutic drug monitoring, also influences the outcome. The increasing number of resistant strains led the clinician to use either recently approved new molecules but also associations. For multi-resistant strains, new molecules such as ceftolozane-tazobactam, ceftazidime-avibactam, and cefiderocol have shown an adequate activity against P. aeruginosa. Older molecules like colistin and fosfomycin are also used in this indication. The complexity of the resistance and consequences on a larger scale of antibiotic prescription will probably lead to more individualized prescriptions.

Decline of lymphatic vessel density and function in murine skin during aging

Lymphatic vessels play important roles in the pathogenesis of many conditions that have an increased prevalence in the elderly population. However, the effects of the aging process on the lymphatic system are still relatively unknown. We have applied non-invasive imaging and whole-mount staining techniques to assess the lymphatic vessel function and morphology in three different age groups of mice: 2 months (young), 7 months (middle-aged), and 18 months (aged). We first developed and validated a new method to quantify lymphatic clearance from mouse ear skin, using a lymphatic-specific near-infrared tracer. Using this method, we found that there is a prominent decrease in lymphatic vessel function during aging since the lymphatic clearance was significantly delayed in aged mice. This loss of function correlated with a decreased lymphatic vessel density and a reduced lymphatic network complexity in the skin of aged mice as compared to younger controls. The blood vascular leakage in the skin was slightly increased in the aged mice, indicating that the decreased lymphatic function was not caused by a reduced capillary filtration in aged skin. The decreased function of lymphatic vessels with aging might have implications for the pathogenesis of a number of aging-related diseases.

Secure Location of Things (SLOT): Mitigating Localization Spoofing Attacks in the Internet of Things

The rise of geo-spatial location-based applications for the Internet of Things introduces new location spoofing security risks. To overcome the threat of malicious spoofing attacks, we develop the Secure Location of Things (SLOT) framework which extends current state-of-the-art methods and is able to cope with such threats. The SLOT framework incorporates another piece of information which has not been utilized so far, the audibility information. This information indicates whether a node is able/unable to communicate with the target. By leveraging on this available information, we reformulate the location estimation problem as a stochastic censoring model and derive the maximum likelihood estimator for the node's location in two different ways: the first algorithm is based on a probabilistic mixture model and assumes knowledge of the Byzantine attack distributional model; and the second algorithm is based on a difference-time-of-arrival, which does not make any distributional assumptions regarding the attack. We show that our algorithms provide significant performance gain over current state-of-the-art algorithms, by mitigating the well-known ambiguity problem of the likelihood surface. Extensive simulations show the significant benefits that the SLOT framework provides compared to current state-of-the-art algorithms.

Detection and Segmentation of Concealed Objects in Terahertz Images

Terahertz imaging makes it possible to acquire images of objects concealed underneath clothing by measuring the radiometric temperatures of different objects on a human subject. The goal of this work is to automatically detect and segment concealed objects in broadband 0.1-1 THz images. Due to the inherent physical properties of passive terahertz imaging and associated hardware, images have poor contrast and low signal to noise ratio. Standard segmentation algorithms are unable to segment or detect concealed objects. Our approach relies on two stages. First, we remove the noise from the image using the anisotropic diffusion algorithm. We then detect the boundaries of the concealed objects. We use a mixture of Gaussian densities to model the distribution of the temperature inside the image. We then evolve curves along the isocontours of the image to identify the concealed objects. We have compared our approach with two state-of-the-art segmentation methods. Both methods fail to identify the concealed objects, while our method accurately detected the objects. In addition, our approach was more accurate than a state-of-the-art supervised image segmentation algorithm that required that the concealed objects be already identified. Our approach is completely unsupervised and could work in real-time on dedicated hardware.

The role of emotion regulation in mental health during the COVID-19 outbreak: A 10-wave longitudinal study

The COVID-19 pandemic elicited a lot of concerns among citizens, thereby potentially compromising their well-being. This study sought to examine the role of individuals' emotion regulation styles (i.e., emotional dysregulation, emotional suppression, and emotional integration) in handling these concerns and their experiences of well-being (i.e., satisfaction with life and sleep quality) and ill-being (i.e., anxiety and depressive symptoms). The study had a unique 10-wave longitudinal design (N = 986; Mage = 41.28; 76% female) and was conducted during the outbreak of the pandemic in March-May 2020. Multilevel analyses showed, first, that weekly variation in COVID-19 related concerns related negatively to weekly variation in well-being and positively to weekly variation in ill-being. Second, at the between-person level, emotional dysregulation and suppression related positively to between-person vulnerability in ill-being and lower well-being (across all waves). Third, between-person differences in emotional dysregulation amplified the strength of the within-person association between concerns and depressive complaints and lowered life satisfaction. Unexpectedly, integrative emotion regulation amplified the strength of the within-person association between concerns and anxiety. The discussion focuses on the critical role of emotion regulation in handling the uncertainty elicited by the pandemic and provides directions for further research.

Load-Oriented Order Release (LOOR) revisited: bringing it back to the state of the art

In the workload control literature, the Load-Oriented Order Release (LOOR) approach has been neglected since its robustness was questioned at the end of the 1990s. This paper revisits LOOR and evaluates whether its performance can be improved in two ways. First, an intermediate pull release mechanism is added to avoid starvation between periodic release events. This mechanism was recently shown to be effective at improving the performance of a state-of-the-art release method known as LUMS COR. Second, an integer linear programming model is used to manage the trade-off between the timing and load balancing functions of order release. The two refinements are assessed using simulations of different shop configurations, which allow us to evaluate robustness. Results demonstrate that the refinements contribute to improving the performance of LOOR such that it can even outperform LUMS COR. Perhaps counter-intuitively, putting more emphasis on load balancing than on the urgency of individual orders is shown to lead to a lower percentage of tardy orders. Overall, the improvements mean that concerns about LOOR's robustness are no longer valid - it now appears suitable for a wide range of shops found in practice.

Pharmacokinetic and Pharmacodynamic Modeling and Simulation Analysis of Prasugrel in Healthy Male Volunteers

This study evaluated the pharmacokinetics and pharmacodynamics of the antiplatelet agent prasugrel, and explored its optimal dose regimens via modeling and simulation using NONMEM. We measured platelet aggregation and the serial plasma concentrations of the inactive (R-95913) and active metabolites (R-138727) of prasugrel after a single oral dose of 10-60 mg in 20 healthy adult male volunteers. A pharmacokinetic model for R-95913 and R-138727, and a pharmacodynamic model between the concentration of R-138727 and maximal platelet aggregation measured by light transmittance were constructed. The predictability of the model for platelet aggregation was evaluated by comparing the model prediction values with the observed ones not used in the construction of the model. Pharmacokinetic data were best described by a 3-compartment models for R-95913, a 1-compartment model for R-138727 with transit compartment model for absorption delay, and first-pass metabolic conversion of R-95913 into R-138727 during absorption. The association-dissociation model between R-138727 and its receptor in platelets was applied for the inhibitory effect of prasugrel on platelet aggregation. Prasugrel rapidly inhibited platelet aggregation after oral administration, with a prolonged duration of action; however, the concentration of the active metabolite decreased rapidly, which may have been due to the slow dissociation rate of R-138727 from its target receptor in platelets. The external validation suggests that our model could be used to individualize prasugrel treatment in various clinical situations. Simulation showed rapid onset of inhibitory effect with great magnitude and consistent inhibition after therapeutic dose of prasugrel.

Assessment of the Relevance and Reliability of Reaction Time Tests Performed in Immersive Virtual Reality by Mixed Martial Arts Fighters

Immersive virtual reality (VR) is increasingly applied in various areas of life. The potential of this technology has also been noticed in recreational physical activity and sports. It appears that a virtual environment can also be used in diagnosing certain psychomotor abilities. The main aim of this study consisted of assessing the relevance and reliability of VR-implemented tests of simple and complex reaction time (RT) performed by mixed martial arts (MMA) fighters. Thirty-two professional MMA fighters were tested. The original test developed in the virtual environment was applied for RT assessment. The fighters' task consisted of reacting to the lighting up of a virtual disc situated in front of them by pushing a controller button. The relevance of the test task was estimated by juxtaposing the obtained results with the classic computer test used for measuring simple and complex reactions, while its reliability was assessed with the intraclass correlation procedure. Significant relationships found between the results of VR-implemented tests and computer-based tests confirmed the relevance of the new tool for the assessment of simple and complex RT. In the context of their reliability, RT tests in VR do not differ from tests conducted with the use of standard computer-based tools. VR technology enables the creation of tools that are useful in diagnosing psychomotor abilities. Reaction time tests performed by MMA fighters with the use of VR can be considered relevant, and their reliability is similar to the reliability obtained in computer-based tests.

Manifold Learning via Linear Tangent Space Alignment (LTSA) for Accelerated Dynamic MRI With Sparse Sampling

The spatial resolution and temporal frame-rate of dynamic magnetic resonance imaging (MRI) can be improved by reconstructing images from sparsely sampled ${k}$ -space data with mathematical modeling of the underlying spatiotemporal signals. These models include sparsity models, linear subspace models, and non-linear manifold models. This work presents a novel linear tangent space alignment (LTSA) model-based framework that exploits the intrinsic low-dimensional manifold structure of dynamic images for accelerated dynamic MRI. The performance of the proposed method was evaluated and compared to state-of-the-art methods using numerical simulation studies as well as 2D and 3D in vivo cardiac imaging experiments. The proposed method achieved the best performance in image reconstruction among all the compared methods. The proposed method could prove useful for accelerating many MRI applications, including dynamic MRI, multi-parametric MRI, and MR spectroscopic imaging.

Resilience of art cities to flood risk: A quantitative model based on depth-idleness correlation

Cultural heritage (CH) is threatened by floods; however, the understanding of exposure and vulnerability is challenging and makes risk and resilience assessment rarely practiced. CH is crucial for post-disaster resilience, especially when the local economy is based on tourism. The work presents a novel framework for evaluating flood resilience, indirect impacts, and associated risk in art cities. The exposure of CH is estimated using the number of visitors as a proxy variable for the social appreciation. A new depth-idleness vulnerability function assigning a reopening time to flood depth is developed from post-event reports. A resilience model is conceived to (i) describe the recovery dynamics, (ii) estimate the indirect impacts in terms of lost visitors to CH for different probabilistic scenarios, (iii) calculate risk, and (iv) identify mitigation actions. The application of the model to the art city of Florence (Italy), a UNESCO site visited by approximately 10 million people a year, shows that a medium recurrence interval flood requires a recovery time of 351 days and causes a loss of 10.5 million visitors. The annual average number of lost visitors is 88,000 approximately. Resilience can be increased by accelerating the reopening and by reinforcing the attractivity of the city.

Rates and Predictors of Consistent Condom-use by People Living with HIV/AIDS on Antiretroviral Treatment in Uganda

Antiretroviral treatment (ART) has been recognized as one of the methods for reducing the risk of HIV transmission, and access to this is being rapidly expanded. However, in a generalized HIV epidemic, ART could increase unprotected sex by people living with HIV/AIDS (PHAs). This paper assessed the rates and predictors of consistent condom-use by sexually-active PHAs after initiating ART. The study used cross-sectional data on sexual behaviour of 269 sexually-active ART-experienced individuals (95 males and 174 females) aged 18 years and above. The results revealed that 65% (70% of men and 61% of women) used condom consistently after initiating ART. Consistent use of condom was more likely if PHAs had secondary- or tertiary-level education and had more than one sex partner in the 12 months preceding the study. However, PHAs were less likely to have used condom consistently if they worked in the informal and formal sectors, belonged to the medium- and high-income groups, and were married. PHAs, who were on ART for less than 1 year and 1-2 year(s), had a good self-perception of health, had a sexual partner who was HIV-negative or a partner with unknown HIV status, and desired to bear children, were also less likely to have used condom consistently. The paper concluded that, although the majority of PHAs consistently used condom, there was potential for unprotected sex by PHAs on ART.

Multi-Modal Adaptive Fusion Transformer Network for the Estimation of Depression Level

Depression is a severe psychological condition that affects millions of people worldwide. As depression has received more attention in recent years, it has become imperative to develop automatic methods for detecting depression. Although numerous machine learning methods have been proposed for estimating the levels of depression via audio, visual, and audiovisual emotion sensing, several challenges still exist. For example, it is difficult to extract long-term temporal context information from long sequences of audio and visual data, and it is also difficult to select and fuse useful multi-modal information or features effectively. In addition, how to include other information or tasks to enhance the estimation accuracy is also one of the challenges. In this study, we propose a multi-modal adaptive fusion transformer network for estimating the levels of depression. Transformer-based models have achieved state-of-the-art performance in language understanding and sequence modeling. Thus, the proposed transformer-based network is utilized to extract long-term temporal context information from uni-modal audio and visual data in our work. This is the first transformer-based approach for depression detection. We also propose an adaptive fusion method for adaptively fusing useful multi-modal features. Furthermore, inspired by current multi-task learning work, we also incorporate an auxiliary task (depression classification) to enhance the main task of depression level regression (estimation). The effectiveness of the proposed method has been validated on a public dataset (AVEC 2019 Detecting Depression with AI Sub-challenge) in terms of the PHQ-8 scores. Experimental results indicate that the proposed method achieves better performance compared with currently state-of-the-art methods. Our proposed method achieves a concordance correlation coefficient (CCC) of 0.733 on AVEC 2019 which is 6.2% higher than the accuracy (CCC = 0.696) of the state-of-the-art method.

Antibiotic allergy labels in immunocompromised populations

Antibiotic allergy labels (AALs) are commonly reported, with well-defined prevalence in the general population; several studies have now focused efforts on immunocompromised hosts. Understanding the prevalence of reported allergy labels and methods of antibiotic allergy evaluation and delabeling strategies has the potential to improve prescribing practices and clinical outcomes in this high-antibiotic use group. In this review, we will discuss the current literature on the prevalence, impact, and evaluations of AALs in immunocompromised hosts with a focus on beta-lactam (penicillin) allergy and sulfa-antibiotic (antimicrobial sulfurs) allergy labels.

Experimental DC extraction of the thermal resistance of bipolar transistors taking into account the Early effect

This paper presents three methods to experimentally extract the thermal resistance of bipolar transistors taking into account the Early effect. The approaches are improved variants of recently-proposed techniques relying on common-base DC measurements. The accuracy is numerically verified by making use of a compact model calibrated on I-V characteristics of state-of-the-art SOG BJTs and SiGe:C HBTs. (C) 2016 Elsevier Ltd. All rights reserved.

Lossy-to-lossless compression of medical volumetric data using three-dimensional integer wavelet transforms

We study lossy-to-lossless compression of medical volumetric data using three-dimensional (3-D) integer wavelet transforms. To achieve good lossy coding performance, it is important to have transforms that are unitary. In addition to the lifting approach, we first introduce a general 3-D integer wavelet packet transform structure that allows implicit bit shifting of wavelet coefficients to approximate a 3-D unitary transformation. We then focus on context modeling for efficient arithmetic coding of wavelet coefficients. Two state-of-the-art 3-D wavelet video coding techniques, namely, 3-D set partitioning in hierarchical trees (Kim et al., 2000) and 3-D embedded subband coding with optimal truncation (Xu et aL, 2001), are modified and applied to compression of medical volumetric data, achieving the best performance published so far in the literature-both in terms of lossy and lossless compression.

Managing life-threatening 5-fluorouracil cardiotoxicity

5-Fluorouracil (5-FU), a known cardiotoxin, is the backbone for the treatment of colorectal cancer. It is associated with arrhythmias, myocardial infarction and sudden cardiac death. Most commonly, it is associated with coronary vasospasm secondary to direct toxic effects on vascular endothelium.A woman with metastatic colon cancer, originally treated with a 5-FU infusion as part of the FOLFIRI (Folinic acid, 5-Fluorouracil, Irinotecan) regimen, was unable to tolerate the chemotherapy due to chest pain. She was transitioned from infusional 5-FU to inferior 1-hour bolus 5-FU, in an attempt to minimise cardiotoxicity, but had disease progression. A multidisciplinary decision was made to again trial 5-FU infusion and pretreat with diltiazem. She tolerated chemotherapy without adverse events. A multidisciplinary discussion is recommended for co-management of reversible 5-FU-associated cardiotoxicity. After coronary artery disease (CAD) risk stratification and treatment, empiric treatment with calcium channel blockers and/or nitrates may allow patients with suspected coronary vasospasm, from 5-FU, to continue this vital chemotherapy.

Ratiometric fluorescent detection of miRNA-21 via pH-regulated adsorption of DNA on polymer dots and exonuclease III-assisted amplification

This work proposed a simple and sensitive method for ratiometric fluorescent detection of nucleic acids via pH-dependent adsorption of dye-labeled DNA on polymer dots. The polymer dots (Pdots) could be conveniently prepared with nanoprecipitation in water. The mixture of dye-labeled DNA and Pdots at neutral pH showed the fluorescence of Pdots, while the adsorption of dye-labeled DNA on Pdots at acidic pH led to fluorescence resonance energy transfer from the Pdots to dye, and thus the fluorescence of dye. As a result, a signal switch could be designed for the detection of nucleic acids complementary to the DNA after combining with exonuclease III-assisted digestion of DNA. Using miRNA-21 as a target model and Cy3-labeled DNA as the probe, the hybridization of DNA with miRNA-21 provided active sites for EXO III, which released the hybridized miRNA-21 for cyclic digestion of DNA, and thus decreased the adsorption of Cy3-labeled DNA on Pdots and the fluorescence of Cy3. The ratio of fluorescent intensity of Pdots to Cy3 showed linear increase upon increasing miRNA-21 concentration ranging from 0.01 to 2.5 nM. The limit of detection at 3σ was 4.0 pM. The excellent performance and good extendability of the proposed strategy demonstrated its promising application in bioanalysis.

Interviewing in virtual environments: Towards understanding the impact of rapport-building behaviours and retrieval context on eyewitness memory

Given the complexities of episodic memory and necessarily social nature of in-person face-to-face interviews, theoretical and evidence-based techniques for collecting episodic information from witnesses, victims, and survivors champion rapport-building. Rapport is believed to reduce some of the social demands of recalling an experienced event in an interview context, potentially increasing cognitive capacity for remembering. Cognitive and social benefits have also emerged in remote interview contexts with reduced anxiety and social pressure contributing to improved performance. Here, we investigated episodic memory in mock-eyewitness interviews conducted in virtual environments (VE) and in-person face-to-face (FtF), where rapport-building behaviours were either present or absent. Main effects revealed when rapport was present and where interviews were conducted in a VE participants recalled more correct event information, made fewer errors and were more accurate. Moreover, participants in the VE plus rapport-building present condition outperformed participants in all other conditions. Feedback indicated both rapport and environment were important for reducing the social demands of a recall interview, towards supporting effortful remembering. Our results add to the emerging literature on the utility of virtual environments as interview spaces and lend further support to the importance of prosocial behaviours in applied contexts.

Reducing your local footprint with anyrun computing

Computational offloading is the standard approach to running computationally intensive tasks on resource-limited smart devices, while reducing the local footprint, i.e., the local resource consumption. The natural candidate for computational offloading is the cloud, but recent results point out the hidden costs of cloud reliance in terms of latency and energy. Strategies that rely on local computing power have been proposed that enable fine-grained energy-aware code offloading from a mobile device to a nearby piece of infrastructure. Even state-of-the-art cloud-free solutions are centralized and suffer from a lack of flexibility, because computational offloading is tied to the presence of a specific piece of computing infrastructure. We propose AnyRun Computing (ARC), a system to dynamically select the most adequate piece of local computing infrastructure. With ARC, code can run anywhere and be offloaded not only to nearby dedicated devices, as in existing approaches, but also to peer devices. We present a detailed system description and a thorough evaluation of ARC under a wide variety of conditions. We show that ARC matches the performance of the state-of-the-art solution (MAUI), in reducing the local footprint with stationary network topology conditions and outperforms it by up to one order of magnitude under more realistic topological conditions. (C) 2016 Elsevier B.V. All rights reserved.

Discrepancy-Guided Domain-Adaptive Data Augmentation

Data augmentation has been observed playing a crucial role in achieving better generalization in many machine learning tasks, especially in unsupervised domain adaptation (DA). It is particularly effective on visual object recognition tasks as images are high-dimensional with an enormous range of variations that can be simulated. Existing data augmentation techniques, however, are not explicitly designed to address the differences between different domains. Expert knowledge about the data is required, as well as manual efforts in finding the optimal parameters. In this article, we propose a novel domain-adaptive augmentation method by making use of a state-of-the-art style transfer method and domain discrepancy measurement. Specifically, we measure the discrepancy between source and target domains, and use it as a guide to augment the original source samples using style transferred source-to-target samples. The proposed domain-adaptive augmentation method is data and model agnostic that can be easily incorporated with state-of-the-art DA algorithms. We show empirically that, by using this domain-adaptive augmentation, we are able to gradually reduce the discrepancy between the source and target samples, and further boost the adaptation performance using different DA algorithms on three popular domain adaption datasets.

Protective coatings on silicon particles and their effect on energy density and specific energy in lithium ion battery cells: A model study

Protective coating on silicon particles is a strategy reported in literature to improve capacity retention of Sicontaining lithium ion batteries. Up to date, the impact of the coating on the cell energy density and specific energy is not considered and guidelines for coating design are missing. In this paper a model is proposed to fill this gap. The model depicts how energy density and specific energy of lithium ion cells based on a Si-graphite composite electrode change in function of coating type, thickness and silicon weight fraction in the negative electrode. Volume changes during lithiation-delithiation and corresponding electrolyte displacement are also considered. Energy density depends on the ratio of coating thickness to silicon particle dimension and weight fraction of silicon in the electrode. Specific energy depends - marginally - also on the coating type. As a case study silicon spherical particles of 200 nm diameter are considered. For a 10 nm coating, the maximum energy density gain vs. state of art graphite negative electrodes is 13%, obtained with 40% weight fraction of silicon in the negative electrode. Above 60 nm thickness no improvement can be obtained vs. state of art graphite negative electrodes.

Feature Saliencies in Asymmetric Hidden Markov Models

Many real-life problems are stated as nonlabeled high-dimensional data. Current strategies to select features are mainly focused on labeled data, which reduces the options to select relevant features for unsupervised problems, such as clustering. Recently, feature saliency models have been introduced and developed as clustering models to select and detect relevant variables/features as the model is learned. Usually, these models assume that all variables are independent, which narrows their applicability. This article introduces asymmetric hidden Markov models with feature saliencies, i.e., models capable of simultaneously determining during their learning phase relevant variables/features and probabilistic relationships between variables. The proposed models are compared with other state-of-the-art approaches using synthetic data and real data related to grammatical face videos and wear in ball bearings. We show that the proposed models have better or equal fitness than other state-of-the-art models and provide further data insights.

Predictors of undocumented PTSD in persons using public mental health services

Individuals diagnosed with serious mental illness (SMI) have greater trauma exposure and are at increased risk for posttraumatic stress disorder (PTSD). However, PTSD is rarely documented in their clinical records. This study investigated the predictors of PTSD documentation among 776 clients with SMI receiving public mental health services, who had probable PTSD as indicated by a PTSD Checklist score of at least 45. Only 5.3% of clients had PTSD listed as a primary diagnosis, and 8.4% had PTSD as a secondary diagnosis, with a total 13.7% documentation rate. PTSD documentation rate was highest for clients with major depression (18.8%) compared to those with schizophrenia (4.1%) or bipolar disorder (6.3%). Factors that predicted a lower likelihood of having a chart diagnosis of PTSD included being diagnosed with schizophrenia/schizoaffective disorder or bipolar disorder. Factors that predicted a higher likelihood of having a chart diagnosis of PTSD included being of non-white race, being female, and experiencing eight or more types of traumatic events. Findings highlight the need for PTSD screening and trauma informed care for clients with SMI receiving public mental health services.

Discovery of Anti-tubercular Analogues of Bedaquiline with Modified A-, B- and C-Ring Subunits

To date, the clinical use of the anti-tubercular therapy bedaquiline has been somewhat limited due to safety concerns. Recent investigations determined that modification of the B- and C-ring units of bedaquiline delivered new diarylquinolines (for example TBAJ-587) with potent anti-tubercular activity yet an improved safety profile due to reduced affinity for the hERG channel. Building on our recent discovery that substitution of the quinoline motif (the A-ring subunit) for C5-aryl pyridine groups within bedaquiline analogues led to retention of anti-tubercular activity, we investigated the concurrent modification of A-, B- and C-ring units within bedaquiline variants. This led to the discovery that 4-trifluoromethoxyphenyl and 4-chlorophenyl pyridyl analogues of TBAJ-587 retained relatively potent anti-tubercular activity and for the 4-chlorophenyl derivative in particular, a significant reduction in hERG inhibition relative to bedaquiline was achieved, demonstrating that modifications of the A-, B- and C-ring units within the bedaquiline structure is a viable strategy for the design of effective, yet safer (and less lipophilic) anti-tubercular compounds.

A state-of-the-art differential evolution algorithm for parameter estimation of solar photovoltaic models

Photovoltaic (PV) generation systems are vital to the utilization of the sustainable and pollution-free solar energy. However, the parameter estimation of PV systems remains very challenging due to its inherent nonlinear, multi-variable, and multi-modal characteristics. In this paper, we propose a state-of-the-art optimization method, namely, directional permutation differential evolution algorithm (DPDE), to tackle the parameter estimation of several kinds of solar PV models. By fully utilizing the information arisen from the search population and the direction of differential vectors, DPDE can possess a strong global exploration ability of jumping out of the local optima. To verify the performance of DPDE, six groups of experiments based on single, double, triple diode models and PV module models are conducted. Extensive comparative results between DPDE and other fifteen representative algorithms show that DPDE outperforms its peers in terms of the solution accuracy. Additionally, statistical results based on Wilcoxon rank-sum and Friedman tests indicate that DPDE is the most robust and best performing algorithm for the parameter estimation of PV systems.

Fast and Accurate Estimation of RFID Tags

Radio frequency identification (RFID) systems have been widely deployed for various applications such as object tracking, 3-D positioning, supply chain management, inventory control, and access control. This paper concerns the fundamental problem of estimating RFID tag population size, which is needed in many applications such as tag identification, warehouse monitoring, and privacy-sensitive RFID systems. In this paper, we propose a new scheme for estimating tag population size called Average Run-based Tag estimation (ART). The technique is based on the average run length of ones in the bit string received using the standardized framed slotted Aloha protocol. ART is significantly faster than prior schemes. For example, given a required confidence interval of 0.1% and a required reliability of 99.9%, ART is consistently 7 times faster than the fastest existing schemes (UPE and EZB) for any tag population size. Furthermore, ART's estimation time is provably independent of the tag population sizes. ART works with multiple readers with overlapping regions and can estimate sizes of arbitrarily large tag populations. ART is easy to deploy because it neither requires modification to tags nor to the communication protocol between tags and readers. ART only needs to be implemented on readers as a software module.

Application of Isotopically Labeled Engineered Nanomaterials for Detection and Quantification in Soils via Single-Particle Inductively Coupled Plasma Time-of-Flight Mass Spectrometry

Finding and quantifying engineered nanomaterials (ENMs) in soil are challenging because of the abundance of natural nanomaterials (NNMs) with the same elemental composition, for example, TiO2. Isotopically enriched ENMs may be distinguished from NNMs with the same elemental composition using single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOF-MS) to measure multiple isotopes simultaneously within each ENM and NNM in soil, but the minimum isotope enrichment needed for detection of ENMs in soil is not known. Here, we determined the isotope enrichment needed for 47Ti-enriched TiO2 ENMs to be detectable in soil and assessed the effects of weathering on those requirements for less soluble TiO2 and more soluble CuO ENMs. The isotope-enriched ENMs were dosed into two different soils and were extracted and measured by spICP-TOF-MS after 1, 7, and 30 days. Isotope-enriched ENMs were recovered and detected for all three time points. The 47Ti-enriched TiO2 ENMs were detectable in Lufa 2.2 soil at a nominal dosed concentration of 10 mg-TiO2 kg-1 which is an environmentally relevant concentration in biosolid-amended soils. For distinguishing an ∼70 nm diameter TiO2 ENM from TiO2 NNMs in Lufa 2.2 soil, an ∼10 wt % 47Ti isotope-enrichment was required, and this enrichment requirement increases as the particle size decreases. This study is the first to evaluate the tracking ability of isotope-enriched ENMs at an individual particle level in soil and provides guidance on the isotope enrichment requirements for quantification of ENMs made from Earth-abundant elements in soils.

CAM-CAN: Class activation map-based categorical adversarial network

Numerous studies have investigated image classification. In particular, recent methods based on deep learning have exhibited high accuracies. However, various existing state-of-the-art methods based on deep learning show different accuracies depending on the database and environment. Accordingly, different deep learning models need to be used in image classification studies according to the database, environment, and research field. This study investigated a technique to increase the accuracy of the existing deep learning-based models. The proposed method was applied to various existing state-of-the-art methods. In the proposed method, a convolution neural network (CNN) is trained using the classification activation map (CAM) to focus on specific areas in the input image. The CAM image is used as the ground-truth image. Furthermore, the concept of the CAM-based cate-gorical adversarial network (CAM-CAN), in which the CNN is trained based on a generative adversarial network, is proposed in this paper. An action recognition experiment was performed using the self-collected Dongguk thermal image database (DTh-DB) and open database, and the results revealed that the accuracies of the existing state-of-the-art methods significantly increased after applying the proposed method. For instance, the accuracies obtained using the DTh-DB, TPR, PPV, ACC, and F1 with the conventional DenseNet201 model were 80.14%, 75.28%, 96.0%, and 75.91%, respectively. After applying the proposed method, the accuracies increased to 86.53%, 89.90%, 97.64%, and 85.84%, respectively.

The Frequency of Refractory Status Epilepticus and Its Outcome in a Tertiary Care Hospital in Pakistan: A Retrospective Study

Background Refractory status epilepticus (RSE) is a common neurologic emergency with refractory cases leading to increased rates of morbidity and mortality in patients. The lack of previous studies on the incidence, causes, and management of refractory status epilepticus in the pediatric population from our region prompted us to investigate further in this study. Methods We included retrospective data of all patients admitted to the pediatric intensive care unit (PICU) with a provisional diagnosis of RSE at a tertiary care hospital in Karachi from February 2019 to February 2021. No personal identification data was used, and confidentiality of the data was maintained throughout the analysis. The Statistical Package for the Social Sciences (SPSS) software version 22.0 (IBM SPSS Statistics, Armonk, NY, USA) was used to pool data and perform a descriptive analysis. Results Among the 687 patients who presented to the PICU with seizures, 50 (7.27%) patients were eventually diagnosed with RSE during the two-year period. The majority of the patients were male and less than one year of age. Infectious causes predominated our data cohort, and a four-drug regimen consisting of phenytoin, levetiracetam, valproic acid, and midazolam was able to terminate RSE in the majority of the patients in our setting (70%). The mortality rate was noted to be 22% among patients with RSE. Conclusion Morbidity and mortality among pediatric RSE patients are high in our settings. Urgent emergency services and timely cause-directed intervention could improve outcomes.

Learning the micro deformations by max-pooling for offline signature verification

For signature verification systems, micro deformations can be defined as the small differences in the same strokes of signatures or special writing habits of different signers. These micro deformations can reveal the core distinction between the genuine signatures and skilled forgeries. In this paper, we prove that Convolutional Neural Networks (CNNs) have the potential to extract those micro deformations by max-pooling. More specifically, the micro deformations can be determined by watching the location coordinates of the maximum values in pooling windows of max-pooling. Extensive analysis and experiments demonstrate that it is possible to achieve state-of-the-art performance by using this location information as a new feature for capturing micro deformations, along with convolutional features. The proposed method outperforms the state-of-the-art systems on four publicly available datasets of different languages, i.e., English (GPDSsynthetic, CEDAR), Persian (UTSig), and Hindi (BHSig260). (c) 2021 Elsevier Ltd. All rights reserved.

Performance benchmarking of state-of-the-art software switches for NFV

With the ultimate goal of replacing proprietary hardware appliances with Virtual Network Functions (VNFs) implemented in software, Network Function Virtualization (NFV) has gained popularity in the past few years. Software switches are widely employed to route traffic between VNFs and physical Network Interface Cards (NICs). It is thus of paramount importance to compare the performance of different switch designs and architectures. In this paper, we propose a methodology to compare fairly and comprehensively the performance of software switches. We first explore the design spaces of 7 state-of-the-art software switches and then compare their performance under four representative test scenarios. Each scenario corresponds to a specific case of routing NFV traffic between NICs and/or VNFs. In our experiments, we evaluate the throughput and latency between VNFs in two of the most popular virtualization environments, namely virtual machines (VMs) and containers. Our experimental results show that no single software switch prevails in all scenarios. It is, therefore, crucial to choose the most suitable solution for the given use case. At the same time, the presented results and analysis provide a more in-depth insight into the design tradeoffs and identify potential performance bottlenecks that could inspire new designs.

A Novel Framework for Trash Classification Using Deep Transfer Learning

Nowadays, society is growing and crowded, the construction of automatic smart waste sorter machine utilizing the intelligent sensors is important and necessary. To build this system, trash classification from trash images is an important issue in computer vision to be addressed for integrating into sensors. Therefore, this study proposes a robust model using deep neural networks to classify trash automatically which can be applied in smart waste sorter machines. Firstly, we collect the VN-trash dataset that consists of 5904 images belonging to three different classes including Organic, Inorganic and Medical wastes from Vietnam. Next, this study develops a deep neural network model for trash classification named DNN-TC which is an improvement of ResNext model to improve the predictive performance. Finally, the experiments are conducted to compare the performances of DNN-TC and the state-of-the-art methods for trash classification on VN-trash dataset as well as Trashnet dataset to show the effectiveness of the proposed model. The experimental results indicate that DNN-TC yields 94 and 98 in terms of accuracy for Trashnet and VN-trash datasets respectively and thus it outperforms the state-of-the-art methods for trash classification on both experimental datasets.

LOGISMOS-B: Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces for the Brain

Automated reconstruction of the cortical surface is one of the most challenging problems in the analysis of human brain magnetic resonance imaging (MRI). A desirable segmentation must be both spatially and topologically accurate, as well as robust and computationally efficient. We propose a novel algorithm, LOGISMOS-B, based on probabilistic tissue classification, generalized gradient vector flows and the LOGISMOS graph segmentation framework. Quantitative results on MRI datasets from both healthy subjects and multiple sclerosis patients using a total of 16 800 manually placed landmarks illustrate the excellent performance of our algorithm with respect to spatial accuracy. Remarkably, the average signed error was only 0.084 mm for the white matter and 0.008 mm for the gray matter, even in the presence of multiple sclerosis lesions. Statistical comparison shows that LOGISMOS-B produces a significantly more accurate cortical reconstruction than FreeSurfer, the current state-of-the-art approach (p << 0.001). Furthermore, LOGISMOS-B enjoys a run time that is less than a third of that of FreeSurfer, which is both substantial, considering the latter takes 10 h/subject on average, and a statistically significant speedup.

Can we teach computers to understand art? Domain adaptation for enhancing deep networks capacity to de-abstract art

Humans comprehend a natural scene at a single glance; painters and other visual artists, through their abstract representations, stressed this capacity to the limit. The performance of computer vision solutions matched that of humans in many problems of visual recognition. In this paper we address the problem of recognizing the genre (subject) in digitized paintings using Convolutional Neural Networks (CNN) as part of the more general dealing with abstract and/or artistic representation of scenes. Initially we establish the state of the art performance by training a CNN from scratch. In the next level of evaluation, we identify aspects that hinder the CNNs' recognition, such as artistic abstraction. Further, we test various domain adaptation methods that could enhance the subject recognition capabilities of the CNNs. The evaluation is performed on a database of 80,000 annotated digitized paintings, which is tentatively extended with artistic photographs, either original or stylized, in order to emulate artistic representations. Surprisingly, the most efficient domain adaptation is not the neural style transfer. Finally, the paper provides an experiment-based assessment of the abstraction level that CNNs are able to achieve. (C) 2018 Elsevier B.V. All rights reserved.

Nonsense-mediated mRNA decay and metal ion homeostasis and detoxification in Saccharomyces cerevisiae

The highly conserved Nonsense-mediated mRNA decay (NMD) pathway is a translation dependent mRNA degradation pathway. Although NMD is best known for its role in degrading mRNAs with premature termination codons (PTCs) generated during transcription, splicing, or damage to the mRNAs, NMD is now also recognized as a pathway with additional important functions. Notably, NMD precisely regulates protein coding natural mRNAs, hence controlling gene expression within several physiologically significant pathways. Such pathways affected by NMD include nutritional bio-metal homeostasis and metal ion detoxification, as well as crosstalk between these pathways. Here, we focus on the relationships between NMD and various metal homeostasis and detoxification pathways. We review the described role that the NMD pathway plays in magnesium, zinc, iron, and copper homeostasis, as well as cadmium detoxification.

Maximum A Posteriori Linear Regression for language recognition

This paper proposes the use of Maximum A Posteriori Linear Regression (MAPLR) transforms as feature for language recognition. Rather than estimating the transforms using maximum likelihood linear regression (MLLR), MAPLR inserts the priori information of the transforms in the estimation process using maximum a posteriori (MAP) as the estimation criterion to drive the transforms. By multi MAPLR adaptation each language spoken utterance is convert to one discriminative transform supervector consist of one target language transform vector and other non-target transform vectors. SVM classifiers are employed to model the discriminative MAPLR transform supervector. This system can achieve performance comparable to that obtained with state-of-the-art approaches and better than MLLR. Experiment results on 2007 NIST Language Recognition Evaluation (LRE) databases show that relative decline in EER of 4% and on mincost of 9% are obtained after the language recognition system using MAPLR instead of MLLR in 30-s tasks, and further improvement is gained combining with state-of-the-art systems. It leads to gains of 6% on EER and 11% on minDCF comparing with the performance of the only combination of the MMI system and the GMM-SVM system. (C) 2011 Elsevier Ltd. All rights reserved.

Identifying the geochemical evolution and controlling factors of the shallow groundwater in a high fluoride area, Feng County, China

Understanding how groundwater is formed and evolves is critical for water resource exploitation and utilization. In this study, hydrochemistry and stable isotope tracing techniques were adopted to determine the key factors influencing groundwater chemical evolution in Feng County. A total of fourteen wells and five surface water samples were investigated in November 2021. The δD and δ18O compositions show that both surface water and groundwater are recharged from atmospheric precipitation. The dominating order of cations and anions in groundwater appears to be Na+ > Mg2+ > Ca2+ > K+ and HCO3- > SO42- > Cl- > NO3- > F-, respectively. The groundwater hydrochemical facies are mainly characterized by HCO3-Ca-Mg and SO4-Cl-Na types. The chemical evolution of groundwater is dominated by water-rock interaction and cation exchange reactions. The major ions in groundwater are mainly controlled by various geogenic processes including halite, gypsum, calcite, dolomite, Glauber's salt, feldspar, and fluorite dissolution/precipitation. Furthermore, the abundant fluoride-bearing sediments, together with low Ca2+, promote the formation of high F- groundwater. Approximately 85.7% and 28.6% of groundwater samples exceeded the permissible limit for F- and NO3- respectively. Apart from geogenic F-, human interventions (i.e., industrial fluoride-containing wastewater discharge and agricultural phosphate fertilizer uses) also regulate the F- enrichment in the shallow groundwater. Nitrate pollution of the groundwater may be attributed to domestic waste and animal feces. Our findings could provide valuable information for the sustainable exploitation of groundwater in the study area and the development of effective management strategies by the authorities.

Are androstenedione, dihydrotestosterone, thyroid-stimulating hormone, insulin-like growth factor I, and insulin-like growth factor binding protein 3 necessary for isolated micropenis healthy boys' evaluation without any phenotypic abnormalities? A cross-sectional study

The study aimed to familiarise primary care physicians and specialists with the minimum hormonal diagnostic tests necessary to assay isolated micropenis in healthy children without any phenotypic abnormality. Children aged 6-15 years (mean 11.6 ± 1.68) were assessed from May 2010 to September 2021 (N = 247). Multiple regression analysis showed correlations between stretched penile length (SPL) and hormonal assays as follows: follicle-stimulating hormone (FSH): r = 0.097, p = 0.035; luteinizing hormone (LH): r = 0.139, p = 0.012, thyroid-stimulating hormone (TSH): r = -0.001, p = 0.321; testosterone (T): r = 0.118, p = 0.004; dihydrotestosterone (DHT): r = 0.002, p = 0.243; androstenedione (Δ4And): r = -0.004, p = 0.502; insulin-like growth factor I (IGF-I): r = -0.003, p = 0.062; and IFG-binding protein 3 (IGF-BP3 ): r = 0.052, p = 0.051. The most hormonal disorder was testosterone deficiency. TSH, Δ4And, and DHT were normal in all boys. SPL was significantly correlated with FSH, LH, and T, but there was no significant correlation between SPL and TSH, DHT, Δ4And, IGF-I, and IGF-BP3 . Whenever the isolated micropenis is seen without other anomalies, it is sufficient to assay testosterone, FSH, and LH in the first step.

Incidental Discovery of Nonrotation in a Patient With Nonspecific Abdominal Pain: A Surgical Diagnostic Dilemma

Intestinal nonrotation is a subtype of malrotation occurring when the midgut fails to rotate before returning to the peritoneal cavity between weeks 8-10 of development. Though sometimes presenting as volvulus during the neonatal period, a subset of patients remains asymptomatic and are identified incidentally as adults. When patients with intestinal nonrotation present with abdominal symptoms, there exists a diagnostic dilemma for the treating surgeon. We present the case of a patient who presented with acute abdominal pain and vomiting, with radiographic findings of intestinal nonrotation and no other acute pathology. Symptoms spontaneously resolved with conservative management for likely etiology of viral gastroenteritis. At the one-month follow-up, the patient had no residual or recurrent symptoms, with no further interventions planned.

From the Women's Health Initiative to the combination of estrogen and selective estrogen receptor modulators to avoid progestin addition

The female life expectancy rose from an average of 48 years to over 80 years in a century. The decline in the endogenous production of estrogen (especially the main circulating physiological hormone, 17β-estradiol, E2) at menopause (51 years on average) often leads to functional disorders affecting the quality of life. Estrogen deficiency impacts different tissues and results in an increase of various diseases, such as osteoporosis or cardiovascular diseases. Hormone therapy (HT) for menopause is a rather new challenge which experienced vagaries following the women's health initiative (WHI) study conducted in largely post-menopausal women. In the first part of this review, we will try to summarize the main conclusions of the WHI trials, in particular the timing effect as well as the deleterious impact of the associated progestin, medroxyprogesterone acetate (MPA). Hormone therapy, particularly the conjugated equine estrogen (CEE) combined with the MPA favor the occurrence of breast cancer, whereas conversely selective estrogen receptor modulators (SERMs, such as tamoxifen or raloxifene) that block the activity of estrogen receptor alpha (ERa) prevent the risk of recurrence of ERa-positive breast cancers. A new strategy of ERa modulation called tissue selective estrogen complex (TSEC), combines (1) CEE to maintain the benefits of estrogen (climacteric symptoms and prevention of osteoporosis) and (2) bazedoxifene, which is not only a SERM, but which also induces a rapid degradation of ERa in the uterus and in the breast, thereby prevents the stimulatory effects of estrogens on epithelial proliferation of these two sex targets. In the second part of this review, we will summarize the recognized benefits of the TSEC approach, and our current knowledge of its potential benefits and risks.

A Low Complexity Design Framework for NFC-RFID Inductive Coupled Antennas

Inductive Wireless Power and Data Transfer (WPDT) technology has become a vital enabler to the globalisation of Internet of Things. Driven by an increasing demand for data within applications and by the need to reduce the devices footprint by transmitting data and power with the same antenna, power transfer efficiency has become a barrier to WPDT systems' performance. To overcome the limitations of power transfer efficiency, current research focuses on the design of efficient integrated circuits and does not consider the challenges of inductive antennas' design and system integration. Hence, current system integration methods used in industry to design receivers for WPDT applications still require expensive experimental benchmarking of antennas. This paper introduces a new framework for inductive WPDT systems integration that focuses on the design of inductive coils and tuning capacitances. First, this framework proposes a new planar rectangular coil inductance formula that achieves an average error of 11% based on the testing of one hundred of coils, which out performs the current state of the art. Then, based on a detailed electrical model of both transmitter and receiver of WPDT systems, our design framework computes the coils geometric parameters and tuning capacitances that will optimize the overall efficiency of the WPDT system. Unlike state of the art design approaches, the main advantage of this framework is that it does not require expensive benchmarking of inductive antennas to find the optimal antenna. Verification of our design framework was achieved through a comparative analysis for Very High Bit Rate 13.56 MHz RFID applications. Results indicate an improvement of more than 15% in overall system power transfer efficiency compared to current state of the art methods within a comparatively more cost effective framework. A sensitivity analysis provides an insight and practical guide to implications of manufacturing variances in component parameters.

Estimation of SARS-CoV-2 Neutralizing Activity and Protective Immunity in Different Vaccine Types Using Three Surrogate Virus Neutralization Test Assays and Two Semiquantitative Binding Assays Targeting the Receptor-Binding Domain

Estimating neutralizing activity in vaccinees is crucial for predicting the protective effect against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As the plaque reduction neutralization test (PRNT) requires a biosafety level 3 facility, it would be advantageous if surrogate virus neutralization test (sVNT) assays and binding assays could predict neutralizing activity. Here, five different assays were evaluated with respect to the PRNT in vaccinees: three sVNT assays from GenScript, Boditech Med, and SD Biosensor and two semiquantitative binding assays from Roche and Abbott. The vaccinees were subjected to three vaccination protocols: homologous ChAdOx1, homologous BNT162b2, and heterologous administration. The ability to predict a 50% neutralizing dose (ND50) of ≥20 largely varied among the assays, with the binding assays showing substantial agreement (kappa, ~0.90) and the sVNT assays showing relatively poor performance, especially in the ChAdOx1 group (kappa, 0.33 to 0.97). The ability to predict an ND50 value of ≥118.25, indicating a protective effect, was comparable among different assays. Applying optimal cutoffs based on Youden's index, the kappa agreements were greater than 0.60 for all assays in the total group. Overall, relatively poor performance was demonstrated in the ChAdOx1 group, owing to low antibody titers. Although there were intra-assay differences related to the vaccination protocols, as well as interassay differences, all assays demonstrated fair performance in predicting the protective effect using the new cutoffs. This study demonstrates the need for a different cutoff for each assay to appropriately determine a higher neutralizing titer and suggests the clinical feasibility of using various assays for estimation of the protective effect. IMPORTANCE The coronavirus disease 2019 (COVID-19) pandemic continues to last, despite high COVID-19 vaccination rates. As many people experience breakthrough infection after prior infection and/or vaccination, estimating the neutralization activity and predicting the protective effect are major issues of concern. However, since standard neutralization tests are not available in most clinical laboratories, it would be beneficial if commercial assays could predict these aspects. In this study, we evaluated the performance of three sVNT assays and two semiquantitative binding assays targeting the receptor-binding domain with respect to the PRNT. Our results suggest that these assays could be used for predicting the protective effect by adjusting the cutoffs.

Radiocarbon and U-series age constraints for the Lateglacial rock art of Sicily

The presence of rock and portable art on Sicily has been recognized since World War II. This record has been unanimously attributed to the Upper Palaeolithic in the published literature, based almost uniquely on stylistic reasoning. Here we present the first absolute dates in direct association with the Sicilian art record. These data provide new insights into the life of Southern European hunter-gatherers and their relationships with coeval groups from Western Europe, contributing a fresh perspective on the ongoing discussion about the development and co-existence of different art traditions in Europe during the final phases of the Pleistocene and at the beginning of the Holocene. (C) 2020 Elsevier Ltd. All rights reserved.

Cervix type detection using a self-supervision boosted object detection technique

Cervical cancer accounts for a large number of fatalities among cancer patients. It is ranked fourth in the total cancer patients and total number of deaths due to cancer. Developing countries account for 70% of the cases and 90% of the fatalities. Contemporary techniques used for screening cervical cancer are PAP smear test and HPV DNA test. Today there are treatments that can successfully prevent cervical cancer if detected at an early stage. Understanding the cervix type is very important for treatment; computational methods can help us classify the cervix type from cervical images. In this study, we propose an ROI proposal network EfficientCenterDet and a self-supervision boosted training trick that improves the performance of the network with relatively less labeled data. We use 6114 unlabeled images to perform a pretraining task and 1166 labeled images to retrain the ROI proposal network. The proposed model matches the state-of-the-art IOU of FasterRCNN on the ISIC skin lesions dataset while using one-third of the number of parameters used in FasterRCNN. On MobileODT cervical data, our self-supervision boosted model achieves 0.632 IOU, a 10% boost over the state-of-the-art FasterRCNN. Introducing an ensembled EfficientNet B4, the cervix type classification stage achieved an accuracy of 87%.

High Energy Storage Performance in La-Doped AgNbO3 Ceramics via Tape Casting

AgNbO3-based Pb-free antiferroelectric (AFE) ceramics have attracted increasing interest owing to their excellent potential in energy storage applications. Herein, a high recoverable energy storage density (Wrec) of 7.62 J/cm3 is realized in La-doped AgNbO3 ceramics prepared via tape casting. The high Wrec is attributed to high breakdown strength Eb of 380 kV/cm induced by dense microstructure as well as fine grain size and enhanced AFE stability stemming from M2 phase and reduced tolerance factor t. The high Wrec exceeding 6 J/cm3 was maintained in a wide temperature range of 20-150 °C and exhibited frequency stability with less than 8% variation in a range of 1-200 Hz. The discharge energy density Wd exhibited temperature stability at 30-110 °C with less than 9% variation. Our research provides a good method for producing AgNbO3-based ceramics having high energy storage performances.

Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis From Lung CT Scans With Multi-Scale Guided Dense Attention

Computed Tomography (CT) plays an important role in monitoring radiation-induced Pulmonary Fibrosis (PF), where accurate segmentation of the PF lesions is highly desired for diagnosis and treatment follow-up. However, the task is challenged by ambiguous boundary, irregular shape, various position and size of the lesions, as well as the difficulty in acquiring a large set of annotated volumetric images for training. To overcome these problems, we propose a novel convolutional neural network called PF-Net and incorporate it into a semi-supervised learning framework based on Iterative Confidence-based Refinement And Weighting of pseudo Labels (I-CRAWL). Our PF-Net combines 2D and 3D convolutions to deal with CT volumes with large inter-slice spacing, and uses multi-scale guided dense attention to segment complex PF lesions. For semi-supervised learning, our I-CRAWL employs pixel-level uncertainty-based confidence-aware refinement to improve the accuracy of pseudo labels of unannotated images, and uses image-level uncertainty for confidence-based image weighting to suppress low-quality pseudo labels in an iterative training process. Extensive experiments with CT scans of Rhesus Macaques with radiation-induced PF showed that: 1) PF-Net achieved higher segmentation accuracy than existing 2D, 3D and 2.5D neural networks, and 2) I-CRAWL outperformed state-of-the-art semi-supervised learning methods for the PF lesion segmentation task. Our method has a potential to improve the diagnosis of PF and clinical assessment of side effects of radiotherapy for lung cancers.

Language Recognition with Language Total Variability

In this paper, we try to introduce the idea of total variability used in speaker recognition to language recognition. In language total variability, we propose two new recognition systems, Language-independent total variability recognition system (LITV) and Language-dependent total variability recognition system (LDTV). Our experiments show that language-independent total factor vector includes the language dependent information, what's more, language-dependent total factor vector contains more language dependent information. These two systems LITV and LDTV can achieve performance similar to that obtained with state-of-the-art approaches. Experiment results on 2007 National Institute of Standards and Technology (NIST) Language Recognition Evaluation (LRE) databases show LDTV gains relative improvement in Equal error rate (EER) of 23.2% and in minimum Decision cost value (minDCF) of 14.2% comparing to LITV in 30-second tasks, and we can obtain further improvement by combining these two new systems with state-of-the-art systems. It leads to relative improvement of 21.1% in EER and 23.1% in minDCF comparing with the performance of the combination of the MMI and the GMM-SVM systems.

Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks

The development of active distribution grids requires more accurate and lower computational cost state estimation. In this paper, the authors investigate a decentralized learning-based distribution system state estimation (DSSE) approach for large distribution grids. The proposed approach decomposes the feeder-level DSSE into subarea-level estimation problems that can be solved independently. The proposed method is decentralized pruned physics-aware neural network (D-P2N2). The physical grid topology is used to parsimoniously design the connections between different hidden layers of the D-P2N2. Monte Carlo simulations based on one-year of load consumption data collected from smart meters for a three-phase distribution system power flow are developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares and state-of-the-art learning-based DSSE approaches. Numerical results show that the D-P2N2 outperforms the state-of-the-art methods in terms of estimation accuracy and computational efficiency.

Early outcomes of the treatment of aortic coarctation with BeGraft aortic stent in children and young adults

We report our experience and early outcomes of using the BeGraft aortic stent in children, adolescents, and young adults. BeGraft aortic stent (Bentley InnoMed, Hechingen, Germany) requires a smaller long sheath compared to other covered stents, and it has a low profile and adequate radial power. With these features, it can overcome some limitations in the treatment of coarctation, especially in children. This is a single centre retrospective analysis of 11 implanted BeGraft aortic stents in coarctation of the aorta between July 2020 and November 2021. The eleven stents were successfully implanted in 11 patients (10 males). The median age of the patients was 13.7 years (interquartile range 12-16 years), and the median weight was 43 kg (interquartile range 35-62 kg). In five patients, after the stents were opened completely by the first balloon, they were exchanged with a Z-MED II™ balloon, 1-3 mm larger in diameter, and the stents were redilated. The median catheter-derived systolic peak-to-peak pressure gradient was 23 mm Hg (interquartile range 16-37 mmHg) before the procedure and 3 mm Hg (interquartile range 1-5 mm Hg) after the procedure. Except for the partial femoral artery thrombosis in two patients, no other procedural complications were observed in our study. The median follow-up duration was 5 months (interquartile range 2-12 months). During follow-up, only one patient (9%) had stent narrowing that required dilation. Our initial results and short-term follow-up showed that the BeGraft aortic stent implantation and redilation can be performed effectively, safely, and successfully in the treatment of coarctation of the aorta.

Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration

In this paper, we present a deep learning method, DDMReg, for accurate registration between diffusion MRI (dMRI) datasets. In dMRI registration, the goal is to spatially align brain anatomical structures while ensuring that local fiber orientations remain consistent with the underlying white matter fiber tract anatomy. DDMReg is a novel method that uses joint whole-brain and tract-specific information for dMRI registration. Based on the successful VoxelMorph framework for image registration, we propose a novel registration architecture that leverages not only whole brain information but also tract-specific fiber orientation information. DDMReg is an unsupervised method for deformable registration between pairs of dMRI datasets: it does not require nonlinearly pre-registered training data or the corresponding deformation fields as ground truth. We perform comparisons with four state-of-the-art registration methods on multiple independently acquired datasets from different populations (including teenagers, young and elderly adults) and different imaging protocols and scanners. We evaluate the registration performance by assessing the ability to align anatomically corresponding brain structures and ensure fiber spatial agreement between different subjects after registration. Experimental results show that DDMReg obtains significantly improved registration performance compared to the state-of-the-art methods. Importantly, we demonstrate successful generalization of DDMReg to dMRI data from different populations with varying ages and acquired using different acquisition protocols and different scanners.

Adaptor complex-mediated trafficking of Newcastle disease virus fusion protein is regulated by the YLMY motif of its cytoplasmic tail

Previously, we reported that the mediation of Newcastle disease virus (NDV) pathogenicity by the 524YLMY527 motif depends mainly on the regulation of F protein transport to the cell surface. The virus and host determinants that govern this intracellular trafficking remain unknown. Here, we confirmed that host adaptor protein (AP) complexes are involved in NDV infection using small interfering RNA. The transport of viral F protein to the cell surface depends on host transport proteins. We observed that the trends for host expression of AP complexes AP1M1 and AP2M1 were similar to those of mutated F proteins, especially in the membrane protein. NDV F protein interacted with AP1M1 and AP2M1, and the YLMY motif influenced this interaction. Knockdown of AP1M1 or AP2M1 suppressed the intracellular and extracellular virus titre of mutated-YLMY-motif NDVs, especially rSG10*-F/Y527A and rSG10*-F/Y524AY527A, to varying degrees. Therefore, the YLMY motif regulates AP-mediated viral F protein transportation from the cytoplasm to the cell surface and subsequently affects viral titer. We further found that the YLMY-motif mutants were differently associated with the process of AAK1 and GAK kinase-mediated AP - viral F protein interaction. These data demonstrate that the essential YLMY motif located in the NDV F protein cytoplasmic tail recruits AP to direct the F protein to the cell surface, which is necessary for its ability to affect virus budding. This study provides support for a deeper understanding of virus and host determinants that facilitate virus trafficking, which can be exploited in the design of novel antiviral therapies.

Enhancing Inclusivity for LGBTQIA+ Student Survivors of Color Commentary: Creating a University Strategic Plan to Address Relationship Violence and Sexual Misconduct (RVSM): An Application of Principles-Focused Evaluation at Michigan State University

Campbell and colleagues propose a robust and rigorous strategic model to address and reduce Relationship Violence and Sexual Misconduct (RVSM) at Michigan State University, which significantly advances the field of RVSM prevention and education, particularly for survivors belonging to marginalized populations. While prior efforts have addressed RVSM on college and university campuses, Campbell and colleagues' model is groundbreaking in its ability to reduce RVSM against lesbian, gay, bisexual, transgender, queer/questioning, intersex, and asexual/agender (LGBTQIA+) students of color, by its principles of intersectional and trauma-informed action. This commentary highlights the contributions of Campbell et al.'s model and provides recommendations for enhancing programming and postassault services by addressing the totality of LGBTQIA+ survivors of color's identities.

Variation-Aware Delay Fault Testing for Carbon-Nanotube FET Circuits

Sensitivity to process variations and manufacturing defects are major showstoppers for the high-volume manufacturing of carbon nanotube field-effect transistors (CNFETs). These imperfections affect gate delay and may remain undetected when test patterns obtained using conventional test-generation techniques are used. We propose a new test generation method that takes CNFET-specific process variations into account and identifies multiple testable long paths through each node in a netlist. In contrast to state-of-the-art techniques, our method can also handle variations that have a nonlinear impact on the propagation delay. The generated test patterns ensure the detection of delay faults through the longest path, even under random CNFET process variations. The proposed method shows significant improvement in the statistical delay quality level (SDQL) compared with a state-of-the-art technique and a commercial ATPG tool for multiple benchmarks. We observed a minimum of 17.1% improvement in the SDQL offered by our patterns over a test set of the same size generated by the commercial tool. We also show that our method, when integrated with the conventional transition fault test flow, offers a significant improvement in the quality of test patterns under random variations. Moreover, the proposed method is flexible and can be easily extended to other emerging device technologies.

Complete mitochondrial genomes of Thyreophagus entomophagus and Acarus siro (Sarcoptiformes: Astigmatina) provide insight into mitogenome features, evolution, and phylogeny among Acaroidea mites

Mites from the Acaroidea (Sarcoptiformes: Astigmatina) are important pests of various stored products, posing potential threats to preserved foods. In addition, mites can cause allergic diseases. Complete mitochondrial genomes (mitogenomes) are valuable resources for different research fields, including comparative genomics, molecular evolutionary analysis, and phylogenetic inference. We sequenced and annotated the complete mitogenomes of Thyreophagus entomophagus and Acarus siro. A comparative analysis was made between mitogenomic sequences from 10 species representing nine genera within Acaroidea. The mitogenomes of T. entomophagus and A. siro contained 37 genes, including 13 protein-coding genes (PCGs), 22 transfer RNAs (tRNAs), two ribosomal RNAs (rRNAs), and one control region. In Acaroidea species, mitogenomes have highly conserved gene size and order, and codon usage. Among Acaroidea mites, most PCGs were found to be under purifying selection, implying that most PCGs might have evolved slowly. Our findings showed that nad4 evolved most rapidly, whereas cox1 and cox3 evolved most slowly. The evolutionary rates of Acaroidea vary considerably across families. In addition, selection analyses were also performed in 23 astigmatid mite species, and the evolutionary rate of the same genes in different superfamilies exhibited large differences. Phylogenetic results are mostly consistent with those identified by previous phylogenetic studies on astigmatid mites. The monophyly of Acaroidea was rejected, and the Suidasiidae and Lardoglyphidae appeared to deviate from the Acaroidea branch. Our research proposed a review of the current Acaroidea classification system.

Adaptive Orientation Model Fitting for Latent Overlapped Fingerprints Separation

Overlapped fingerprints are commonly encountered in latent fingerprints lifted from crime scenes. Such overlapped fingerprints can hardly be processed by state-of-the-art fingerprint matchers. Several methods have been proposed to separate the overlapped fingerprints. However, these methods neither provide robust separation results, nor could be generalized for most overlapped fingerprints. In this paper, we propose a novel latent overlapped fingerprints separation algorithm based on adaptive orientation model fitting. Different from existing methods, our algorithm estimates the initial orientation fields in a more accurate way and then separates the orientation fields for component fingerprints through an iterative correction process. Global orientation field models are used to predict and correct the orientations in overlapped regions. Experimental results on the latent overlapped fingerprints database show that the proposed algorithm outperforms the state-of-the-art algorithm in terms of accuracy.

SPIE Medical Imaging 50th anniversary: history of the Picture Archiving and Communication Systems Conference

To commemorate the SPIE Medical Imaging 50th anniversary, this article provides a brief review of the Picture Archiving and Communication Systems (PACS) and Informatics conferences. Important topics and advances, contributing researchers from both academia and industry, and key papers are noted.

Low power HEVC software decoder for mobile devices

In the context of mobile handheld devices, energy consumption is a primary concern and the process of video decoding is often among the most resource-intensive applications. Recent embedded processors are equipped with advanced features such as dynamic voltage frequency scaling (DVFS) in order to reduce their power consumption. These features can be used to perform low power video decoding when no hardware decoding support is available for a given standard. High efficiency video coding (HEVC) is a recent video standard offering state-of-the-art compression rates and advanced parallel processing solutions. This paper presents strategies for the power optimization of a real-time software HEVC decoder on NEON architecture. These strategies include the exploitation of data and task-level parallelism, as well as the use of a new frequency control system to optimize the processor DVFS, based on an estimation of the decoding complexity. Extensive power measurement results, based on a multi-core ARM big.LITTLE processor, are provided and compared to state-of-the-art. These results show that the proposed open-source implementation can reach an energy consumption below 21 nJ/px for HD decoding at 2.2 Mbits/s.

The agony of choice: Impact of the host animal species on the enzyme-linked immunosorbent assay performance for host cell protein quantification

Host cell proteins (HCPs) are inevitable process-related impurities in biotherapeutics commonly monitored by enzyme-linked immunosorbent assays (ELISAs). Of particular importance for their reliable detection are the anti-HCP polyclonal antibodies (pAbs), supposed to detect a broad range of HCPs. The present study focuses on the identification of suitable host animal species for the development of high-performance CHO-HCP ELISAs, assuming the generation of pAbs with adequate coverage and specificity. Hence, antibodies derived from immunization of sheep, goats, donkeys, rabbits, and chickens were compared concerning their amount of HCP-specific antibodies, coverage, and performance in a sandwich ELISA. Immunization of sheep, goats, donkeys, and rabbits met all test criteria, whereas the antibodies from chickens cannot be recommended based on the results of this study. Additionally, a mixture of antibodies from the five host species was prepared to assess if coverage and ELISA performance can be improved by a multispecies approach. Comparable results were obtained for the single- and multispecies ELISAs in different in-process samples, indicating no substantial improvement for the latter in ELISA performance while raising ethical and financial concerns.

Same-day and rapid initiation of antiretroviral therapy in people living with HIV in Asia. How far have we come?

Human immunodeficiency virus (HIV) continues to be a major public health issue, and the effectiveness of HIV prevention, diagnosis, treatment, and care varies, particularly in the Asia-Pacific region. The rapid initiation of antiretroviral therapy (ART) is important to control the HIV epidemic and to optimize the health of people living with HIV; many guidelines now recommend ART initiation within 7 days of HIV diagnosis, with same-day initiation for people diagnosed with HIV who feel ready. Many countries in the Asia-Pacific region have already implemented or are moving towards implementation of rapid or same-day ART initiation. However, there are many obstacles and challenges to its implementation, which vary substantially across the region. This article summarizes the latest evidence on rapid and same-day ART initiation and discusses lessons learned and barriers to implementation in Asian countries, particularly focusing on Taiwan, Thailand, Singapore, and the Republic of Korea.

Sparse tree structured representation for re-identification

Finding substantial features for image representation is one of the keys to cope with the challenges of person re-identification given video streams. The important features for re-identification can be found by image saliency computation and subject appearance modeling. State-of-the-art models explore this direction by balancing between the global low-level features and the features from local patches. We proposed a novel nested patch tree for a tree structured feature representation, and the feature representation is used to match between a probe image and a gallery image to solve the re-identification problem. The feature representation is learned based on an unsupervised approach, which is different from the majority of the community when they work on finding similar subjects. Usually, the video streams for the same figure may have highly repetitive information, and the pseudo repetitiveness should be useful for a center-learning based method. We further improve the prediction accuracy by learning components by components for the same subject and working in the multi-color space. We evaluate the proposed method for person re-identification on the VIPeR and GRID datasets. The result shows that the proposed method is indeed superior to other state-of-the-art methods. (C) 2016 Elsevier Ltd. All rights reserved.

Effects of pre-slaughter showering and ventilation on stress, meat quality and metabolite concentrations of broilers in summer

Effects of pre-slaughter showering and ventilation on stress, meat quality and energy metabolism of broilers in summer were investigated. After transport, 84 Arbor Acres broilers were randomly divided into four treatment groups: (i) control group without ventilation and showering (C); (ii) 10 min ventilation without showering (VWS); (iii) 10 min showering without ventilation (SWV); (iv) 5 min showering and then 5 min ventilation (SV). Compared with the control group, plasma lactate dehydrogenase and creatine kinase activities in the other three treatment groups were (P < 0.05) lower; however, the plasma glucose level did not show any significant changes among all the groups. The breast meat in the SV group had significantly (P < 0.05) higher pHu , glycogen content, lower L*, ΔpH, drip loss, cook loss, R-value and lactate content than the control group; however, there was no significant difference in shear force values among all the groups. In conclusion, this study indicated broilers in the SV group showed a lower stress level and greater meat quality, which suggest that showering and ventilation after transportation may be a good measure to relieve stress caused by transport under high temperature and improve the meat quality of broilers.

Speech Intelligibility Potential of General and Specialized Deep Neural Network Based Speech Enhancement Systems

In this paper, we study aspects of single microphone speech enhancement (SE) based on deep neural networks (DNNs). Specifically, we explore the generalizability capabilities of state-of- the-art DNN-based SE systems with respect to the background noise type, the gender of the target speaker, and the signal-to-noise ratio (SNR). Furthermore, we investigate how specialized DNN-based SE systems, which have been trained to be either noise type specific, speaker specific or SNR specific, perform relative to DNN-based SE systems that have been trained to be noise type general, speaker general, and SNR general. Finally, we compare how a DNN-based SE system trained to be noise type general, speaker general, and SNR general performs relative to a state-of-the- art short-time spectral amplitude minimum mean square error (STSA-MMSE) based SE algorithm. We show that DNN-based SE systems, when trained specifically to handle certain speakers, noise types and SNRs, are capable of achieving large improvements in estimated speech quality (SQ) and speech intelligibility (SI), when tested in matched conditions. Furthermore, we show that improvements in estimated SQ and SI can be achieved by a DNN-based SE system when exposed to unseen speakers, genders and noise types, given a large number of speakers and noise types have been used in the training of the system. In addition, we show that a DNN-based SE system that has been trained using a large number of speakers and a wide range of noise types outperforms a state-of-the- art STSA-MMSE based SE method, when tested using a range of unseen speakers and noise types. Finally, a listening test using several DNN-based SE systems tested in unseen speaker conditions show that these systems can improve SI for some SNR and noise type configurations but degrade SI for others.

Su-MICL: Severity-Guided Multiple Instance Curriculum Learning for Histopathology Image Interpretable Classification

Histopathology image classification plays a critical role in clinical diagnosis. However, due to the absence of clinical interpretability, most existing image-level classifiers remain impractical. To acquire the essential interpretability, lesion-level diagnosis is also provided, relying on detailed lesion-level annotations. Although the multiple-instance learning (MIL)-based approach can identify lesions by only utilizing image-level annotations, it requires overly strict prior information and has limited accuracy in lesion-level tasks. Here, we present a novel severity-guided multiple instance curriculum learning (Su-MICL) strategy to avoid tedious labeling. The proposed Su-MICL is under a MIL framework with a neglected prior: disease severity to define the learning difficulty of training images. Based on the difficulty degree, a curriculum is developed to train a model utilizing images from easy to hard. The experimental results for two histopathology image datasets demonstrate that Su-MICL achieves comparable performance to the state-of-the-art weakly supervised methods for image-level classification, and its performance for identifying lesions is closest to the supervised learning method. Without tedious lesion labeling, the Su-MICL approach can provide an interpretable diagnosis, as well as an effective insight to aid histopathology image diagnosis.

Spillover and crossover effects of working time demands on work-life balance satisfaction among dual-earner couples: the mediating role of work-family conflict

To examine the spillover and crossover effects of working time demands (specifically, work contact in leisure time, evening work, and long work hours) on satisfaction with work-life balance among dual-earner couples, path analyses were conducted using data from the 2017/2018 German Family Panel (pairfam; N = 1,053 dual-earner couples). Working time demands were measured based on (a) answering work emails/phone calls in leisure time, (b) evening work, and (c) weekly work hours. High working time demands impaired workers' work-life balance satisfaction due to higher levels of work-life conflict. They indirectly affected partners' work-life balance satisfaction through two pathways: (a) workers' and partners' work-life conflict and (b) workers' work-life conflict and work-life balance satisfaction. These findings indicate that high working time demands negatively impact the work-life balance satisfaction of workers and their partners because of work-life conflict experienced either by the workers only or by both partners. In an increasingly digitalized labor market, measures are needed to reduce working time demands-and thus work-life conflict-for workers and their partners.

Estimation of Maximum Speed-Up in Multicore-Based Mobile Devices

This letter proposes an effective speed-up estimation method for modern mobile devices. Unlike existing approaches, the proposed method uses a task graph to extract multiple parallelizable fractions of real-world mobile scenarios. Then, it uses the extracted parallelizable fractions to estimate the theoretical maximum speed-up of mobile devices. In experiments, the proposed method estimated the maximum speed-up of mobile devices more accurately than the state-of-the-art speed-up estimation method.

Flexible Lead-Free Piezoelectric Composite Materials for Energy Harvesting Applications

Vibrational piezoelectric energy harvesters are being investigated to replace batteries in embedded sensor systems. The energy density that can be harvested depends on the figure of merit, d(33)g(33), where d(33) and g(33) are the piezoelectric charge and voltage coefficient. Commonly used piezoelectric materials are based on inorganic ceramics, such as lead zirconium titanate (PZT), as they exhibit high piezoelectric coefficients. However, ceramics are brittle, leading to mechanical failure under large cyclic strains and, furthermore, PZT is classified as a Substance of Very High Concern (SVHC). To circumvent these drawbacks, we fabricated quasi 1-3 potassium sodium lithium niobate (KNLN) ceramic fibers in a flexible polydimethylsiloxane (PDMS) matrix. The fibers were aligned by dielectrophoresis. We demonstrate for the structured composites values of d(33)g(33) approaching 18 pm(3) J(-1), comparable to that of state-of-the-art ceramic PZT. This relatively high value is due to the reduced inter-particle distance in the direction of the electric field. As a confirmation, the stored electrical energy for both material systems was measured under identical mechanical loading conditions. The similar values for KNLN/PDMS and PZT demonstrate that environmentally friendly, lead-free, mechanically compliant materials can replace state-of-the-art environmentally-less-desirable ceramic materials in piezoelectric vibrational energy harvesters.

ON THE ENVIRONMENTAL DESIGN ILLUMINATION: TEACHER'S ATTITUDE

As a special professional discipline the design of lighting is a part of learning program of the department of Environmental Design in Moscow State Stroganov Academy of Design and Applied Arts. The training of designers working with light and lighting equipment requires the special methodize, and the elaboration of such methodize becomes more and more urgent in modern school. We see solution in the combination of traditional art studies and environmental design with modern digital design technologies. All the factors of the lighting content of our environment such as lighting technique and technologies are considered during the learning process. Those branches of professional training are closely connected with the whole design culture and that gives us an opportunity to use a multidisciplinary approach. The study in the lighting design takes one term. That's enough for the concept of the fragment of urban environment including the elements of the lighting content. The basics of art and the skills of creative thinking also help to achieve a high level of final design and to develop aesthetic skills of students. The new horizons of the lighting design are opened with the new profile of education on our department: a Multimedia Design providing an extensive digital ground for design process. All these means can help us to create a unique ground for the design education, for the contacts between students and possible customers, and also to attract some new, talented tutors.

Transnational technology transfer networks for SMEs. A review of the state-of-the art and an analysis of the European IRC network

This paper will review the effectiveness of the network approach to technology transfer. It will consider the current state-of-the-art, and examine specifically the results and status of the latest development of the IRC technology transnational transfer network supported by the European Commission. It will also draw from the practical experiences of Japan to stimulate innovation among SMEs; the experience of other informal networks of technology transfer professionals and commercial technology transfer companies. In Europe the IRC network currently consists of 68 offices covering 31 countries. This network was started in 1995 and it is a distinct case of an operating innovation virtual network covering a multicultural area. How such a network was set up with a top down approach will be discussed as well as the outcome and future of the network in view of a recent review study. Of utmost importance is its focus on SMEs as part of the, up to now, successful policy towards the promotion of co-operative innovation in the SME environment. Its offer-focused model will also be analysed and consideration given as to whether this offer-focused model, as opposed to a demand-focused model, can be sustained. The influences that the different socio-economic environments across Europe have been playing in the network performance will also be discussed.

Detection of Face Spoofing Using Visual Dynamics

Rendering a face recognition system robust is vital in order to safeguard it against spoof attacks carried out using printed pictures of a victim (also known as print attack) or a replayed video of the person (replay attack). A key property in distinguishing a live, valid access from printed media or replayed videos is by exploiting the information dynamics of the video content, such as blinking eyes, moving lips, and facial dynamics. We advance the state of the art in facial antispoofing by applying a recently developed algorithm called dynamic mode decomposition (DMD) as a general purpose, entirely data-driven approach to capture the above liveness cues. We propose a classification pipeline consisting of DMD, local binary patterns (LBPs), and support vector machines (SVMs) with a histogram intersection kernel. A unique property of DMD is its ability to conveniently represent the temporal information of the entire video as a single image with the same dimensions as those images contained in the video. The pipeline of DMD + LBP + SVM proves to be efficient, convenient to use, and effective. In fact only the spatial configuration for LBP needs to be tuned. The effectiveness of the methodology was demonstrated using three publicly available databases: 1) print-attack; 2) replay-attack; and 3) CASIA-FASD, attaining comparable results with the state of the art, following the respective published experimental protocols.

Metastases to the nail unit and distal phalanx: a systematic review

Metastases to the nail unit/distal phalanx (NU/DP), although rare, carry a poor prognosis and are frequently misdiagnosed due to variable clinical presentation. Metastases to the NU/DP may be the initial presenting sign of a new or recurrent malignancy. Since the most recent systematic review of case reports (133 patients total) was conducted in 2001, we conducted a systematic review from 1900 to 2021 (244 patients total) to assess any changes in trends in demographics, clinical presentation, and morphology and to report on more updated differential diagnoses. We also examined cases for age, sex, race, ethnicity, Fitzpatrick skin type, laterality, distribution, and diagnostic methods. The PubMed database (1900-2021) was used to detect case-level data per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We found that the most common primary tumors were lung, kidney, and esophagus. A NU/DP metastasis was the presenting sign of malignancy in 31.00% of patients without a former cancer diagnosis. Male to female ratio was 2:1, with average age at diagnosis 58 years. Metastases most often affected a single digit (79.91%), particularly the thumb, followed by the fourth digit. This systematic review corroborates that metastases to the NU/DP may be the initial presenting sign of a new or recurrent malignancy and provides updated diagnostic guidelines. NU/DP metastasis should be considered in both healthy patients and patients with a former malignancy diagnosis presenting with nail changes involving a single digit. Prompt diagnosis and treatment may improve prognosis.

Novel Combined Load Frequency Control and Automatic Voltage Regulation of a 100% Sustainable Energy Interconnected Microgrids

Frequency and voltage deviations are two main problems in microgrids, especially with the increase in the penetration level of renewable energies. This paper presents novel techniques to apply combined the load frequency control and automatic voltage regulation of two interconnected microgrids. The two microgrids are operated by solar energy and bioenergy technologies and include energy-storage facilities. The control is applied using a novel accelerating PID controller (PIDA), which is compared to state-of-the-art control schemes. The controllers are designed using a new doctor and patient optimization technique (DPO), which is compared to state-of-the-art techniques. The combined design of load frequency controllers and automatic voltage regulators is also compared to a standalone design. The comparisons are carried out by testing the system performance at each operation condition in addition to indicators such as integral absolute error for frequency and voltage and integral time absolute error for frequency and voltage. The results show that a combined DPO-PIDA design of LFC-AVR schemes for fully sustainable microgrids has better performance than other standalone designs and other control and optimization alternatives.

Adaptive multi-task learning using lagrange multiplier for automatic art analysis

Numerous computer vision applications, such as image classification, have benefited from multi-task learning techniques. However, the relative weighting between each task's loss is hard to be tuned by hand, causing multi-task learning prohibitive in real applications. In this paper, we present a novel and principled adaptive multi-task learning method that weights multiple loss functions based on lagrange multiplier strategy. Our method starts from the standard multi-task learning model. Based on Gaussian likelihood and lagrange multiplier, we then design an adaptive multi-task learning model to learn suitable weightings of each task and boost performance. In order to validate the feasibility of proposed method, we conduct automatic art analysis tests, including art classification and cross-modal art retrieval. Experimental results demonstrate that our method outperforms several state-of-the-art techniques, showing that performance is improved by up to 4.2% in art classification and 8.7% in cross-modal art retrieval when compared with the latest automatic loss weights learning method.

Deep Recurrent Network for Fast and Full-Resolution Light Field Deblurring

The popularity of parallax-based image processing is increasing while in contrast early works on recovering sharp light field from its blurry input (deblurring) remain stagnant. State-of-the-art blind light field deblurring methods suffer from several problems such as slow processing, reduced spatial size, and simplified motion blur model. In this paper, we solve these challenging problems by proposing a novel light field recurrent deblurring network that is trained under 6 degree-of-freedom camera motion-blur model. By combining the real light field captured using Lytro Illum and synthetic light field rendering of 3D scenes from UnrealCV, we provide a large-scale blurry light field dataset to train the network. The proposed method outperforms the state-of-the-art methods in terms of deblurring quality, the capability of handling full-resolution, and a fast runtime.

[Oral desensitization with Ketocal® in an infant with ketogenic diet and cow's milk protein allergy]

Introduction: ketogenic diet is a treatment with proven efficacy in drug-refractory childhood epilepsy. Cow's milk protein allergy may be a limitation for treating infants with ketogenic diet, as they need a product that contains cow's milk protein (Ketocal®). Case report: we report the case of an infant with a drug-refractory epileptic encephalopathy and IgE-mediated cow's milk protein allergy, who started a classic ketogenic diet. Oral desensitization was achieved with Ketocal 3:1®, allowing its use in the diet and achieving a clinical improvement with seizure control. Discussion: a patient with epilepsy and cow's milk protein allergy can benefit from the ketogenic diet, since it is possible to perform an oral immunotherapy with Ketocal®, also achieving a probable resolution of his/her allergy.

Photonic nanostructures mimicking floral epidermis for perovskite solar cells

Here, we report photonic nanostructures replicated from the adaxial epidermis of flower petals onto light-polymerized coatings using low-cost nanoimprint lithography at ambient temperature. These multifunctional nanocoatings are applied to confer enhanced light trapping, water repellence, and UV light and environmental moisture protection features in perovskite solar cells. The former feature helps attain a maximum power conversion efficiency of 24.61% (21.01% for the reference cell) without any additional device optimization. Added to these merits, the nanocoatings also enable stable operation under AM 1.5G and UV light continuous illumination or in real-world conditions. Our engineering approach provides a simple way to produce multifunctional nanocoatings optimized by nature's wisdom.

[Surgical treatment of arthrofibrosis of the knee joint]

Arthrofibrosis of the knee joint is a severe complication following trauma and surgical procedures, which often results in long-term impairment of joint function. Early mobilization techniques and anesthesia are still employed without sufficient clarification of the underlying processes. While the early stages of arthrofibrosis can be successfully treated with conservative measures for pain reduction and wound healing, in the late stage tense collagenous scar tissue is frequently present that permanently limits joint mobility. In this stage an improvement of joint mobility has no chance of success without a surgical intervention. In surgical treatment a differentiation should be made between localized (mostly secondary) arthrofibrosis (e.g. cruciate ligament surgery) and generalized arthrofibrosis (in the majority of cases primarily after total knee arthroplasty) and the treatment planned accordingly. Comorbid pathological alterations (transplant position, instability of the total knee endoprosthesis, implant attrition, low-grade infection, patellofemoral instability or maltracking, patella baja) must be taken into consideration in the treatment. A multimodal accompanying treatment including physiotherapy, pain therapy and psychosomatics is necessary to ensure successful treatment.

Network Coding Aided Cooperative Quantum Key Distribution Over Free-Space Optical Channels

Realistic public wireless channels and quantum key distribution (QKD) systems are amalgamated. Explicitly, we conceive network coding aided cooperative QKD over free space optical systems for improving the bit error ratio and either the key rate or the reliable operational distance. Our system has provided a 55% key rate improvement against the state-of-the-art benchmarker.

Highly effective reduction of phosphate and harmful bacterial community in shrimp wastewater using short-term biological treatment with immobilized engineering microalgae

Shrimp farming wastewater includes high amounts of phosphate and microbiological contaminants, necessitating further treatment before release into receiving water bodies. After 24 h of shrimp wastewater treatment, alginate beads containing the blue-green algal Synechocystis strain lacking the phosphate regulator gene (mutant strain ΔSphU) at 150 mg L-1 reduced phosphate content from 17.5 mg L-1 to 5.0 mg L-1, representing 71.5% removal efficiency, with phosphate removal rate reaching 6.9 mg gDW-1 h-1 during photobioreactor operation. For short-term treatment, removal rates of nitrate, ammonium and nitrite were 42.7, 48.5 and 92.9%, respectively. Microalgal encapsulated beads also impacted the bacterial community composition dynamics in shrimp wastewater. Next-generation sequencing targeting the V3-V4 region of the 16S rDNA gene showed significant differences in bacterial community composition after 24 h of treatment. Proteobacteria are the most abundant phylum in shrimp wastewater. After 24 h of bioremediation, reductions of harmful bacteria in the Cellvibrionaceae and Pseudomonadaceae families were recorded at 5.85 and 3.18%, respectively. Engineered microalgal immobilization under optimal conditions can be applied as an alternative short-term bioremediation strategy to remove phosphate and other harmful microbial contamination from shrimp farming wastewater.

Automatic 2-D/3-D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter

Automated detection of vascular structures is of great importance in understanding the mechanism, diagnosis, and treatment of many vascular pathologies. However, automatic vascular detection continues to be an open issue because of difficulties posed by multiple factors, such as poor contrast, in homogeneous backgrounds, anatomical variations, and the presence of noise during image acquisition. In this paper, we propose a novel 2-D/3-D symmetry filter to tackle these challenging issues for enhancing vessels from different imaging modalities. The proposed filter not only considers local phase features by using a quadrature filter to distinguish between lines and edges, but also uses the weighted geometric mean of the blurred and shifted responses of the quadrature filter, which allows more tolerance of vessels with irregular appearance. As a result, this filter shows a strong response to the vascular features under typical imaging conditions. Results based on eight publicly available datasets (six 2-D data sets, one 3-D data set, and one 3-D synthetic data set) demonstrate its superior performance to other state-of-the-art methods.

Efficient methods for mining weighted clickstream patterns

Pattern mining has been an attractive topic for many researchers since its first introduction. Clickstream mining, a specific version of sequential pattern mining, has been shown to be important in the age of the Internet. However, most previous works have simply exploited and applied existing sequential pattern algorithms to the mining of clickstream patterns, and few have studied clickstreams with weights, which also have a wide range of application. In this paper, we address this problem by proposing an approach based on the average weight measure for clickstream pattern mining and adapting a previous state-of-the-art algorithm to deal with the problem of weighted clickstream pattern mining. Following this, we propose an improved method named Compact-SPADE to enhance both the efficiency and memory consumption. Through various tests on both real-life and synthetic databases, we show that our proposed algorithms outperform state-of-the-art alternatives in terms of efficiency, memory requirements and scalability. (C) 2019 Elsevier Ltd. All rights reserved.

Soft Subspace Based Ensemble Clustering for Multivariate Time Series Data

Recently, multivariate time series (MTS) clustering has gained lots of attention. However, state-of-the-art algorithms suffer from two major issues. First, few existing studies consider correlations and redundancies between variables of MTS data. Second, since different clusters usually exist in different intrinsic variables, how to efficiently enhance the performance by mining the intrinsic variables of a cluster is challenging work. To deal with these issues, we first propose a variable-weighted K-medoids clustering algorithm (VWKM) based on the importance of a variable for a cluster. In VWKM, the proposed variable weighting scheme could identify the important variables for a cluster, which can also provide knowledge and experience to related experts. Then, a Reverse nearest neighborhood-based density Peaks approach (RP) is proposed to handle the problem of initialization sensitivity of VWKM. Next, based on VWKM and the density peaks approach, an ensemble Clustering framework (SSEC) is advanced to further enhance the clustering performance. Experimental results on ten MTS datasets show that our method works well on MTS datasets and outperforms the state-of-the-art clustering ensemble approaches.

The Evaluation of Curative Effect of Acupuncture: A Review of Systematic and Meta-Analysis Studies

The present study attempts to critically evaluate previously published research articles on the efficiency of acupuncture in the treatment of diseases. First, 35 systematic reviews or meta-analysis were found in the Cochrane database. Second, 54 related articles were selected by searching important scientific databases. Based on the results obtained regarding the efficacy of acupuncture for the treatment of various diseases, the articles were divided into 3 groups. The first group of articles confirmed the efficacy of treatment by acupuncture. In the second group of articles, the therapeutic effect of acupuncture was shown; however, further research is required to verify the results. In the third group of articles there is no evidence regarding the therapeutic effect of acupuncture till now. There is an urgent need to design and conduct double-blinded randomized clinical trials with high-quality methodologies. This provides a more careful evaluation of acupuncture efficiency in relation to the treatment of a vast array of diseases, based on scientific evidence.

Optimizing CDK4/6 inhibitors in advanced HR+/HER2- breast cancer: A personalized approach

Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) combined with endocrine therapy (ET) are now a backbone of treatment for hormone receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative advanced breast cancer. CDK4/6i plus ET is more effective than ET alone in this setting; however, the risk of grade 3-4 adverse events also increases. Approved agents in this class have similar efficacies, but important differences due to their structural and pharmacological properties. We review biomarkers and discuss determinants to inform a rational approach to therapy choice when selecting the most appropriate ET and CDK4/6i partners. We also identify subgroups that may benefit from specific ET-CDK4/6i combinations and discuss strategies to overcome resistance. This personalized approach aims to minimize treatment-related toxicities that may affect patient QoL and compliance, and ultimately therapy efficacy.

Sampling Optimization for Printer Characterization by Direct Search

Printer characterization usually requires many printer inputs and corresponding color measurements of the printed outputs. In this brief, a sampling optimization for printer characterization on the basis of direct search is proposed to maintain high color accuracy with a reduction in the number of characterization samples required. The proposed method is able to match a given level of color accuracy requiring, on average, a characterization set cardinality which is almost one-fourth of that required by the uniform sampling, while the best method in the state of the art needs almost one-third. The number of characterization samples required can be further reduced if the proposed algorithm is coupled with a sequential optimization method that refines the sample values in the device-independent color space. The proposed sampling optimization method is extended to deal with multiple substrates simultaneously, giving statistically better colorimetric accuracy (at the alpha = 0.05 significance level) than sampling optimization techniques in the state of the art optimized for each individual substrate, thus allowing use of a single set of characterization samples for multiple substrates.

Generic Proposal Evaluator: A Lazy Learning Strategy Toward Blind Proposal Quality Assessment

Existing detection or recognition systems typically select one state-of-the-art proposal algorithm to produce massive object-covered candidate windows, and a quality metric specifically designed for this algorithm is utilized to single out small amounts of proposals. However, in practice, the accuracies of different proposal algorithms significantly change from one image content to another one. To obtain more robust proposal results, a generic proposal evaluator (GPE) is highly desired, which could choose optimal candidate windows across multiple proposal algorithms. In this paper, we propose a lazy learning strategy to train the GPE, which aims to blindly estimate the quality of each proposal without accessing to its manual annotation. Unlike the traditional end-to-end framework that learns a universal model from all training samples, we try to build query-specific training subset for each given proposal, where only its k-nearest-neighborhoods are collected from all labeled candidate windows. Benefits from the capability of updating the regression parameters for different visual contents, the proposed method delivers a higher quality prediction accuracy even with respect to the deep neural network learned by end-to-end method. Experimental results confirm that the proposed algorithm significantly outperforms many state-of-the-art proposal quality metrics.

Cell cultures in assessing radioresistance of glioblastomas

To date, no modern methods of treatment allow overcoming malignant potential of glial neoplasms and significant increase of survival. Analysis of glioblastoma radioresistance using cancer cell cultures is one of the perspective directions, as radiotherapy is standard and available treatment method for these neoplasms. This review summarizes current studies identifying many factors of radioresistance of glial tumors, such as hypoxia, microenvironment and metabolic features of tumor, stem cells, internal heterogeneity of tumor, microRNA, features of cell cycle, DNA damage and reparation. We obtained data on involvement of various molecular pathways in development of radioresistance such as MEK/ERK, c-MYC, PI3K/Akt, PTEN, Wnt, JAK/STAT, Notch, etc. Changes in activity of RAD51 APC, FZD1, LEF1, TCF4, WISP1, p53 and many others are determined in radioresistant cells. Further study of radioresistance pathways will allow development of specific target aptamers and inhibitors.