Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanical Stretching Simulates Cardiac Physiology and Pathology through Mechanosensor Piezo1

The dynamics of a living body enables organs to experience mechanical stimulation at cellular level. The human cardiomyocytes cell line provides a source for simulating heart dynamics; however, a limited understanding of the mechanical stimulation effect on them has restricted potential applications. Here, we investigated the effect of mechanical stimulation on the cardiac function-associated protein expressions in human cardiomyocytes. Human cardiomyocyte cell line AC16 was subjected to different stresses: 5% mild and 25% aggressive, at 1 Hz for 24 h. The stretched cardiomyocytes showed down-regulated Piezo1, phosphorylated-Ak transforming serine473 (P-AKTS473), and phosphorylated-glycogen synthase kinase-3 beta serine9 P-GSK3βS9 compared to no stretch. In addition, the stretched cardiomyocytes showed increased low-density lipoprotein receptor-related protein 6 (LRP6), and phosphorylated-c-Jun N-terminal kinase threonine183/tyrosine185 (P-JNKT183/Y185). When Piezo inhibitor was added to the cells, the LRP6, and P-JNKT183/Y185 were further increased under 25%, but not 5%, suggesting that higher mechanical stress further activated the wingless integrated-(Wnt)-related signaling pathway when Piezo1 was inhibited. Supporting this idea, when Piezo1 was inhibited, the expression of phosphorylated-endothelial nitric oxide synthase serine1177 (P-eNOSS1177) and release of calcium ions were reduced under 25% compared to 5%. These studies demonstrate that cyclic mechanical stimulation affects cardiac function-associated protein expressions, and Piezo1 plays a role in the protein regulation.
The Association of Unreported Sleep Disturbances and Systemic Inflammation: Findings from the 2005-2008 NHANES

Background/Objective Sleep apnea is associated with elevated inflammatory markers. A subgroup of patients never report sleep disturbances to their physician. The inflammatory status of this subgroup is not known. The present study aims to evaluate two inflammatory markers, C-reactive protein (CRP) and red cell distribution width (RDW), in those with unreported sleep disturbances and compares these findings to those with and without reported sleep disorders. We also investigate the utility of RDW as an inflammatory marker in sleep disorders. Methods Sample includes 9,901 noninstitutionalized, civilian, nonpregnant adults from the 2005-2008 National Health and Nutrition Examination Survey, a nationally representative, cross-sectional U.S. study. Sleep questionnaire and laboratory data were used to compare inflammatory markers (CRP and RDW) in five subgroups of individuals: reporting physician-diagnosed sleep apnea, reporting another physician-diagnosed sleep disorder, reported sleep disturbance to physician with no resulting diagnosis, unreported sleep disturbance (poor sleep quality not reported to physician), and no diagnosed sleep disorder or sleep disturbance. Results Individuals with unreported sleep disturbance had significantly higher odds of elevated RDW (>13.6%) when compared to those without a sleep disturbance in adjusted models (OR=1.33). Those with unreported sleep disturbance had significantly higher odds of elevated CRP levels (>1 mg/L) than those without sleep disturbances (OR 1.34), although the association was not significant when adjusted for obesity and other controls. Conclusion Self-identified unreported sleep disturbances are associated with significantly higher odds of elevated RDW than those without sleep disturbances. RDW may serve as a valuable indicator in identifying individuals at higher risk for sleep apnea and other sleep disorders.
The insulin/IGF signaling cascade modulates SUMOylation to regulate aging and proteostasis in Caenorhabditis elegans

Although aging-regulating pathways were discovered a few decades ago, it is not entirely clear how their activities are orchestrated, to govern lifespan and proteostasis at the organismal level. Here, we utilized the nematode Caenorhabditis elegans to examine whether the alteration of aging, by reducing the activity of the Insulin/IGF signaling (IIS) cascade, affects protein SUMOylation. We found that IIS activity promotes the SUMOylation of the germline protein, CAR-1, thereby shortening lifespan and impairing proteostasis. In contrast, the expression of mutated CAR-1, that cannot be SUMOylated at residue 185, extends lifespan and enhances proteostasis. A mechanistic analysis indicated that CAR-1 mediates its aging-altering functions, at least partially, through the notch-like receptor glp-1. Our findings unveil a novel regulatory axis in which SUMOylation is utilized to integrate the aging-controlling functions of the IIS and of the germline and provide new insights into the roles of SUMOylation in the regulation of organismal aging.
Effects of Dataset properties on the training of GANs

Generative Adversarial Networks are a new family of generative models, frequently used for generating photorealistic images. The theory promises for the GAN to eventually reach an equilibrium where generator produces pictures indistinguishable for the training set. In practice, however, a range of problems frequently prevents the system from reaching this equilibrium, with training not progressing ahead due to instabilities or mode collapse. This paper describes a series of experiments trying to identify patterns in regard to the effect of the training set on the dynamics and eventual outcome of the training.
Soil Permittivity and Soil Frost Retrievals Using a Synergistic Method for Active and Passive Microwave Instruments

A synergistic method for obtaining soil permittivity and soil frost was developed. The method incorporates a semiempir-ical backscattering model for forested land. Soil permittivity is retrieved from active microwave observations using least squares inversion method. Bayesian assimilation scheme can be applied to combine the active retrieval with a permittivity estimate from a passive instrument. Soil frost can be determined from permittivity estimates using a threshold method. The synergistic method was tested on boreal forest site in Northern Finland using ASAR for active and SMOS for passive observations. Satellite retrievals were compared to in situ soil permittivity, temperature and frost measurements. The results show that high resolution SAR data (e.g., ASAR, Sentinel) can be used to downscale coarse resolution SMOS estimates and that synergistic method reduces variability and biases of the ASAR retrieval.
Optimized Cloudlet Management in Edge Computing Environment

As mobile applications are becoming computational intensive, edge computing supported by Cloudlets is a promising and ongoing solution to overcome the obstacles of mobile devices capabilities and remote Cloud access limitations. Bringing the Cloud, closer to mobile user, at the network’s edge, is a plausible key solution to tackle high network latencies in a Wireless Metropolitan Area Network (WMAN). The inclusion of Cloud features at the access network can be accomplished by collocating an Access Point (AP) with a respective Cloudlet. However, this functionality induces a cost of additional energy consumption at the edge of the network, which may be an important issue for massive deployments not only for operational expenses, but also for environmental reasons. Thus, designing such an architecture should take into consideration not only technical requirements to support user Quality of Experience (QoE), but also power consumption expenses constraints imposed by operators. In this paper, we assume an Edge Computing environment and propose latency and energy aware heuristic policies, such as Active Cloudlet Selection and User Assignment policies to activate Cloudlets and route mobile users for service to the most appropriate active Cloudlet respectively. The performance of the proposed algorithms is evaluated through simulation experiments and results show that the proposed schemes are very efficient in terms of energy consumption, system’s latencies and QoS.
An Improved Non-Local Means Filter for Sar Image Despeckle Based on Heterogeneity Measurement

Despeckle is a key post-processing step for Synthetic Aperture Radar (SAR) images [1], because speckles severely affect visual quality and intelligent interpretation. However the existing despeckle methods have a problem in texture preservation when the speckles are reduced. To solve this problem, image heterogeneity measurement is utilized to improvement the non-local means (NLM) despeckle method in this paper. First, an anisotropic window is constructed using coefficient of variation (CV), which is used for heterogeneity measurement. The anisotropic window are used to replace the isotropic window for the similarity measurement in NLM method. Furthermore, for weight function, an adaptive filter parameter based on heterogeneity measurement is designed to adjust the despeckling degree for different regions. The simulation results show that the proposed method outperforms than the existing algorithms.
Improving the Estimation of Leaf Area Index in Winter Wheat at Regional Scale

As an important land surface parameter for crop growth model, leaf area index (LAI) is desired to be estimated accurately on regional scale. Before applying LAI estimation model built at local scale to the regional scale, the mismatch between multi-scale sensors should be corrected. We firstly applied the kernel-driven BRDF model to describe the two-dimensional reflection characteristics of Landsat 5-TM data based on the kernel weights from MODIS MCD43A1 products and the angle vectors obtained from TM Collection 1 Level-1 dataset. Then the point spread function (PSF) was used to simulate the spatial response of the sensor. Results show that there is a good agreement between TM observations and corrected MODIS values (Red band: $\mathrm{R}^{2}= 0.736,\ \text{RMSE}=2.65\mathrm{e}-4$, Near-infrared band: $\mathrm{R}^{2}=0.539, \text{RMSE}=5.39\mathrm{e}-4$). If we apply the LAI estimation model that has been validated at local scale (TM) directly to the data at large scale (MODIS) without any correction, more than 50% uncertainty of LAI estimation would be introduced. This study implies that the estimation of LAI in winter wheat could be significantly improved by correcting the differences between multi-scale sensors.
Interactive humanoids and androids as ideal interfaces for humans

We, humans, anthropomorphize targets of communication. In this sense, humanoids or androids can have ideal interface for humans. This paper focuses on two new fundamental issues in the human interface studies. There are two relationships between robots and humans: one is inter-personal and the other is social. In the inter-personal relationships, the appearance of the robot is a new and important research issues. In the social relationships, a function to recognize human relationships through interaction is needed for robots of the next generation. These two issues explore new possibilities of androids and humanoids. Especially, the appearance problem bridges between science and engineering. The approach from robotics tries to build very humanlike robots based on knowledge from cognitive science. The approach from cognitive science uses the robot for verifying hypotheses for understanding humans. We call this cross-interdisciplinary framework android science.
Learning a Stable Local Manifold Representation for Hyperspectral Linear Dimensionality Reduction

Hyperspectral data with high dimensionality always needs more storage space and increases the computational consumption, manifold learning is extensively used in dimensionality reduction. A novel dimensionality reduction method based on manifold learning is proposed through learning a stable local manifold representation. Four adjacency graphs are constructed to model the interclass similarity, interclass diversity, intraclass similarity and intraclass diversity respectively, and then merge these graphs into the discriminant objective function for linear dimensionality reduction. The classification results in the use of different methods are compared with and experimental results show that the proposed method is effective and it is superior to comparison methods.
Highly Accurate Power Profiling of Bluetooth Low Energy Communication

Bluetooth technology is widely used nowadays by mobile, embedded and IoT devices. One important aspect that should be addressed by battery powered solutions is energy efficiency, where communication takes an important share or the total power consumption. The main goal of our research is to analyze and profile Bluetooth communication from power consumption and performance perspectives. This analysis is further used to optimize communication on battery powered devices in order to increase the battery lifetime of the application. In our work we managed to identify and measure power consumption and timings of BLE communication at event level with high accuracy.
Aircraft Detection in Sar Images Using Saliency Based Location Regression Network

In this paper, a novel framework for aircraft detection in high resolution apron area in Synthetic Aperture Radar (SAR) images is proposed, which combines the strength of location regression based convolutional neural network (CNN) framework and the salient features of target in SAR images. Specifically, a Constant False Alarm Rate (CFAR) based target pre-locating algorithm is introduced, which can match the scale of target in SAR images more accurate compared to the existing region proposal method. In addition, in order to eliminate the fact of overfitting, we explore several strategies for SAR data augmentation, including translation, adding noise and rotation within a small range. Experiments are conducted on the data set acquired by the TerraSAR-X satellite in a resolution of 3.0 meters. The results show that the proposed detection framework could effectively obtain a more accurate detection result.
Lung Function in Users of a Smoke-Free Electronic Device With HeatSticks (iQOS) Versus Smokers of Conventional Cigarettes: Protocol for a Longitudinal Cohort Observational Study

Background Chronic obstructive pulmonary disease (COPD) is a global public health problem. It is the third-leading cause of death in the world, the fourth leading cause of death in Kazakhstan, and is strongly associated with smoking. Smoking cessation reduces the severity of respiratory symptoms and COPD exacerbations. Heated tobacco products, such as HeatSticks heated by the iQOS device, a smoke-free electronic device, may serve as less risky alternatives to conventional combustible cigarettes. Objective The purpose of this study is to evaluate frequency of exacerbations, respiratory symptoms, physical exercise intolerance, and abnormal lung functions, as well as other parameters and comorbidities among men and women aged 40-59 residing in Almaty, Kazakhstan, who use iQOS with HeatSticks compared to smokers of conventional cigarettes. Methods This is a 5-year single-center cohort observational study. It includes two cohorts of participants consisting of men and women aged 40-59 residing in the city of Almaty, Kazakhstan: (1) smokers of combustible cigarettes (control group) and (2) users of iQOS with HeatSticks (exposure group). The study has baseline and periodic (ie, annual) comprehensive clinical assessments, as well as continuous COPD case-finding activities and registration of acute respiratory exacerbations over the course of the 5-year observation period. Study measures include spirometry, chest computed tomography, electrocardiography, physical exams, laboratory testing of serum for biomarkers of inflammation and metabolic syndrome, anthropometry, and the 6-minute walk test. Information about COPD symptoms will be collected using the COPD Assessment Test. Results Participant recruitment began December 2017, and enrollment is expected to last until late summer 2018. Conclusions This is the first cohort observational study in Kazakhstan to assess differences in lung function between users of the heated tobacco product, iQOS with HeatSticks, and smokers of conventional combustible cigarettes. The study results will add to knowledge on whether switching from combustible cigarettes to iQOS with HeatSticks affects respiratory symptoms and diseases, including the development and progression of COPD. Trial Registration ClinicalTrials.gov NCT03383601; https://clinicaltrials.gov/ct2/show/NCT03383601 (Archived by WebCite at http://www.webcitation.org/72BYoAKxa) International Registered Report Identifier (IRRID) PRR1-10.2196/10006
Spatiotemporal evolution of seismic slip of the 31 October 2013 Ruisui, Taiwan, earthquake

In this study, coseismic and postseismic deformation following the October 31, 2013 M 6.4 earthquake in eastern Taiwan is investigated using InSAR and GPS measurements. The InSAR data includes a pair of ascending and descending SAR data from Radarsat-II satellite covering the mainshock. The GPS measurements span the earthquake and up to 340 days after the event. The geodetic measurements of coseismic deformation are inverted to determine fault geometry and slip distribution. GPS time series data are analyzed by Principal Component Analysis based Inversion Method (PCAIM) to derive space and time distribution of postseismic fault slip. The main coseismic slip in the best fitting oblique-thrust dislocation shows two clear asperities: a small asperity at a depth of about 5 km with a maximum slip of 0.6 m and a larger asperity between depths of 15–25 km with maximum slip of 1 m. There is little slip above 5 km, suggesting the presence of active subsurface (blind) faulting under the Central Range. The mainshock is followed by widespread frictional afterslip on one patch which is localized close to the deepest coseismic asperity.
Towards Generating Narratives for the Real World

An immersive, interactive environment and the non-player characters populating it often play a key role in interactive narrative experiences. We posit that if a procedurally generated narrative is better able to reflect real-world attributes of the player's surroundings, then the experience would be more transportive for the player. With this comes the problem of generating believable narratives and characters for an open, complex, real world. Simulating such a society within the constraints of the real environment, and allowing for virtual characters to more accurately mimic human behavior could increase the believability of the agents. The interactions amongst these agents sharing their cultural views, biases, and histories based on their real-world geolocation could inform the study of audience modeling and machine enculturation, allowing computers to learn or reason about social norms in regions. Finally, we posit this research would afford better applications in the field of entertainment or computational social science.
EM Wave Scattering by a Window on a Wall

Electromagnetic wave scattering by a window on a building wall has been analyzed here as plane wave scattering by a dielectric slab in a thick conducting screen. High frequency asymptotic ray method is utilized for this analysis. Geometrical Theory of Diffraction has been used to obtain the diffracted fields by the edges of the aperture, then ray-mode conversion technique has been applied to get the modal field inside the slit region in order to consider the modal coupling and the re-radiation field. The validity of our formulation has been first tested by comparing the results with those obtained by the Kobayashi Potential method for the relatively narrow slit aperture cases. Then the various numerical calculation results have been made to show the effects of the wall and window thickness, and window’s glasses for practical wireless mobile communication frequencies.
Dynamic Programming Track Before Detect Algorithm for Multistatic Mimo Stap Radar

The airborne or spaceborne early warning radar faces great challenges of detecting and tracking weak moving targets with signal fluctuations. An adaptive detector is proposed to improve the detection performance by jointly processing the space-time data sets received by multistatic multiple-input multiple-output (MIMO) radar, and fusing the measurement vectors information. Monte-Carlo simulations demonstrate that the proposed detector can implement early detection of weak moving targets with the target detection probability 0.5 and the false alarm probability 10–1 under the condition of 5dB signal-to-noise ratio.
Analisis Struktur Senyawa Organik Secara Spektroskopi

Buku ini ditulis karena belum ada buku serupa membahas tentang analisa struktur senyawa organik secara komprehensif dan terpadu. Diharapkan buku yang ditulis berdasarkan pengalaman mengajar dan penelitian-penelitian yang dilakukan oleh penulis selama ini, akan memudahkan mahasiswa dalam bidang Farmasi dan Kimia untuk melakukan penentuan struktur dari suatu senyawa organik. Buku ini terdiri dari lima bab, dimana pada bagian pertama sepintas dibahas teori dasar spektrofotometer ultraviolet-visibel dan aplikasinya dalam penentuan struktur dan analisa kuantitatif suatu zat, kemudian dilanjutkan dengan spektrofotometer inframerah beserta beberapa contoh dan aplikasinya. Bagian ketiga membahas tentang teori dasar spektrometer massa serta aplikasinya; dan yang ke empat dan ke lima adalah spektrometer RMI proton dan karbon beserta beberapa contoh dan aplikasinya.
Subspace Multinomial Logistic Regression Ensemble for Classification of Hyperspectral Images

Exploiting multiple complementary classifiers in an ensemble framework has shown to be effective for improving hyperspectral image classification results, specially when the training samples are limited. With a different principle and based on this assumption that hyperspectal feature vectors effectively lie in a low-dimensional subspace, the subspace-based techniques have shown great classification performance. In this work, we propose a new ensemble method for accurate classification of hyperspectral images, which exploits the concept of subspace projection. For this purpose, we extend the subspace multinomial logistic regression classifier (MLRsub) to learn from multiple random subspaces for each class. More specifically, we impose diversity in constructing MLRsub by randomly selecting bootstrap samples from the training set and subsets of the original hyperspectral feature space, which lead to generate different class subspace features. Experimental results, conducted on two real hyperspectral data sets, indicate that the proposed method provides significant classification results in comparison with other state-of-the-art approaches.
A Competitive Algorithm for Online Multi-Robot Exploration of a Translating Plume

In this paper, we study the problem of exploring a translating plume with a team of aerial robots. The shape and the size of the plume are unknown to the robots. The objective is to find a tour for each robot such that they collectively explore the plume. Specifically, the tours must be such that each point in the plume must be visible from the field-of-view of some robot along its tour. We propose a recursive Depth-First Search (DFS)-based algorithm that yields a constant competitive ratio for the exploration problem. The competitive ratio is $\frac{2(S_{r}+S_{p})(R+\lfloor\log R\rfloor)}{(S_{r}-S_{p})(1+\lfloor\log R\rfloor)}$ where R is the number of robots, and Sr and Sp are the robot speed and the plume speed, respectively. We also consider a more realistic scenario where the plume shape is not restricted to grid cells but an arbitrary shape. We show our algorithm has $\frac{2(S_{r}+S_{p})(18R+\lfloor\log R\rfloor)}{(S_{r}-S_{p})(1+\lfloor\log R\rfloor)}$ competitive ratio under the fat condition. We empirically verify our algorithm using simulations.