Miracle-dz/UniFAR-FTUs huggingface dataset:
Constitutively active Stat 5 A and Stat 5 B promote adipogenesis

Objective The metabolic syndrome is an important social problem affecting many people in developed countries. Obesity is a leading cause of this syndrome, hence understanding molecular mechanisms underlying obesity is of prime importance for preventive medicine to develop novel methods to alleviate the corresponding social cost as well as for pharmaceutical companies to develop antimetabolic drugs. Methods Since adipocytes play an important role in obesity, we explored the signaling pathways leading to differentiation of adipocytes. We used a preadipocyte cell line to monitor the differentiation of adipocytes, and virusmediated gene transfer to assess the role of the transcription factor Stat5 in adipogenesis. Adipocyte differentiation was assessed by Northern blot and Western blot analyses as well as accumulation of fat droplets in cells. Promoter activity of the proadipogenic transcription factor peroxisome proliferator-activated receptor-gamma (PPARc) was evaluated by luciferase assay. Results Virus-mediated gene transfer of the constitutively active form of both Stat5A and Stat5B resulted in enhanced adipocyte differentiation in the absence of fetal bovine serum (FBS) as judged by expression of proadipogenic factors as well as accumulation of fat droplets in cells. Such a proadipogenic effect of Stat5 is, in part, mediated by its ability to enhance transcription of PPARc, a master transcriptional regulator in adipogenesis. Conclusion The constitutively active form of Stat5A and Stat5B promoted adipocyte differentiation in the absence of FBS via induction of PPARc.
A Multi-Priority Service Differentiated and Adaptive Backoff Mechanism over IEEE 802.11 DCF for Wireless Mobile Networks

Backoff mechanism serves as one of the key technologies in the MAC-layer of wireless mobile networks. The traditional Binary Exponential Backoff (BEB) mechanism in IEEE 802.11 Distributed Coordination Function (DCF) and other existing backoff mechanisms poses several performance issues. For instance, the Contention Window (CW) oscillations occur frequently; a low delay QoS guarantee cannot be provided for real-time transmission, and services with different priorities are not differentiated. For these problems, we present a novel Multi-Priority service differentiated and Adaptive Backoff (MPAB) algorithm over IEEE 802.11 DCF for wireless mobile networks in this paper. In this algorithm, the backoff stage is chosen adaptively according to the channel status and traffic priority, and the forwarding and receding transition probability between the adjacent backoff stages for different priority traffic can be controlled and adjusted for demands at any time. We further employ the 2-dimensional Markov chain model to analyze the algorithm, and derive the analytical expressions of the saturation throughput and average medium access delay. Both the accuracy of the expressions and the algorithm performance are verified through simulations. The results show that the performance of the MPAB algorithm can offer a higher throughput and lower delay than the BEB algorithm.
A three-stage scheme for consumers' partitioning using hierarchical clustering algorithm

The clustering of any type of consumers (residential, commercial, industrial) is of great importance in the operation of Smart Grids. In this paper, we propose a three-stage hierarchical scheme for residential consumers' partitioning using the Hierarchical clustering algorithm. The aim of this study is to cluster the consumers in well-separated and compact clusters using information from the near past (almost real time). The usage of electricity from a resident to another varies and this information can be used from the system operator for improving the efficiency of the distribution network. The first stage corresponds to the consumers' clustering of the distribution grid using data driven every three minutes (simulation time) from the meter of each residency. The procedure of the second stage takes part every a specific number of hours, called h, that is defined by the user. The average value of each of the k∗20 clusters formed the last h hours is used as input for the hierarchical algorithm. In the third stage and in the end of each h hours, the average value of the data in each cluster is calculated and each consumer is reassigned to the cluster where the a distance metric is minimized. The results of the second stage provide deeper information about the load patterns existing each hour in the distribution grid. This information can be used from suppliers to design the energy tariffs for suiting better to the consumers' needs. This approach is tested using the IEEE-13 test feeder. The data are driven from 56 residencies.
X-Ray Energy Self-Adaption High Dynamic Range (HDR) Imaging Based on Linear Constraints With Variable Energy

In X-ray imaging, the image quality is related to the ray energy, imaging detector, object construction, etc. For complex workpieces, if all imaging conditions are identical, not all of the construction projection information can be captured with a fixed energy and limited dynamic range. This will result in over- and underexposed regions, which will affect projection quality. Thus, this paper presents an approach to extend the dynamic range of X-ray imaging systems based on linear constraints with variable energy. First, we study the relationship of grey variation between the adjacent energy images and present the grey gain distribution and linear dispersion. Then, based on a piecewise curve linear approximation, auto-self-adaption variation of ray energy is used to capture the image sequences under the linear constraint of the grey gain distribution. Also, based on the linear assumption, we obtain the weighting fusion coefficient to fuse the image sequences to extend the dynamic range. Finally, experiments demonstrate that this method has an advantage for ray energy auto-control and fusion coefficient calculation, which can realize optimal matching between the X-ray imaging system and the testing object to extend the dynamic range of the imaging system.
Passive blood plasma separation at the microscale: a review of design principles and microdevices

Blood plasma separation is vital in the field of diagnostics and health care. Due to the inherent advantages obtained in the transition to microscale, the recent trend in these fields is a rapid shift towards the miniaturization of complex macro processes. Plasma separation in microdevices is one such process which has received extensive attention from researchers globally. Blood plasma separation techniques based on microfluidic platforms can be broadly classified into two categories. While active techniques utilize external force fields for separation, the passive techniques are dependent on biophysical effects, cell behavior, hydrodynamic forces and channel geometry for blood plasma separation. In general, passive separation methods are favored in comparison to active methods because they tend to avoid design complexities and are relatively easy to integrate with biosensors; additionally they are cost effective. Here we review passive separation techniques demonstrating separation and blood behavior at microscale. We present an extensive review of relevant biophysical laws, along with experimental details of various passive separation techniques and devices exploiting these physical effects. The relative performances, and the advantages and disadvantages of microdevices discussed in the literature, are compared and future challenges are brought about.
The decomposed affiliation exposure model: A network approach to segregating peer influences from crowds and organized sports

Abstract Self-identification with peer crowds (jocks, popular kids, druggies, etc.) has an important influence on adolescent substance use behavior. However, little is known about the impact of the shared nature of crowd identification on different stages of adolescent drinking behavior, or the way crowd identification interacts with participation in school-sponsored sports activities. This study examines drinking influences from (1) peers with shared crowd identities, and (2) peers who jointly participate in organized sports at their school (activity members). This study introduces a new network analytic approach that can disentangle the effects of crowd identification and sports participation on individual behavior. Using survey data from adolescents in five high schools in a predominantly Hispanic/Latino district (N = 1,707), this paper examines the association between social influences and each stage of drinking behavior (intention to drink, lifetime, past-month, and binge drinking) by conducting an ordinal regression analysis. The results show that both shared identities and joint participation were associated with all stages of drinking, controlling for friends' influence. Additionally, shared identification overlapped with joint participation was associated with more frequent drinking. Related policy implications are discussed.
Image based cardiac acceleration map using statistical shape and 3D+t myocardial tracking models; in-vitro study on heart phantom

It has been demonstrated that the acceleration signal has potential to monitor heart function and adaptively optimize Cardiac Resynchronization Therapy (CRT) systems. In this paper, we propose a non-invasive method for computing myocardial acceleration from 3D echocardiographic sequences. Displacement of the myocardium was estimated using a two-step approach: (1) 3D automatic segmentation of the myocardium at end-diastole using 3D Active Shape Models (ASM); (2) propagation of this segmentation along the sequence using non-rigid 3D+t image registration (temporal di eomorphic free-form-deformation, TDFFD). Acceleration was obtained locally at each point of the myocardium from local displacement. The framework has been tested on images from a realistic physical heart phantom (DHP-01, Shelley Medical Imaging Technologies, London, ON, CA) in which the displacement of some control regions was known. Good correlation has been demonstrated between the estimated displacement function from the algorithms and the phantom setup. Due to the limited temporal resolution, the acceleration signals are sparse and highly noisy. The study suggests a non-invasive technique to measure the cardiac acceleration that may be used to improve the monitoring of cardiac mechanics and optimization of CRT.
Effect of heat treatment on morphology of Fe-rich intermetallics in Al–Cu alloys

The iron-rich intermetallics are considered to be stable in aluminium alloys and usually hard to dissolve into the matrix during solution heat treatment (SHT). Nevertheless, solid-state transformations between Fe-rich intermetallics may happen and will play a great role in breaking-up of the large Fe-rich intermetallics particle during hot deformation process. In this study, the solid-state phase transformation of the iron-rich intermetallics from AlmFe to β-Fe (Al7Cu2Fe) has been studied using SEM, XRD and deep-etched technique in a 2xxx Al–Cu cast alloy during SHT. The mechanism of solid-state transformation for the Fe-rich intermetallics was discussed, including the solid-state reaction, nucleation and growth of iron-rich intermetallics and Al phases.
Porphyria: often discussed but too often missed

The diagnosis of acute intermittent porphyria (AIP) is often overlooked. We describe a patient with this condition who had all the ‘bells and whistles’, in whom the diagnosis was only made after considerable delay. Far from an esoteric condition haunting examination candidates, AIP is an important cause of a broad spectrum of neurological symptoms. Its early recognition allows the astute clinician to prevent potentially devastating sequelae. We provide practical guidance on the investigation and management of this complex disorder. With a ‘back to basics’ approach to the underlying genetics and biochemistry, we hope to dispel some of the confusion that may obstruct a timely diagnosis.
Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions

Uncovering the genetic and evolutionary basis of local adaptation is a major focus of evolutionary biology. The recent development of cost-effective methods for obtaining high-quality genome-scale data makes it possible to identify some of the loci responsible for adaptive differences among populations. Two basic approaches for identifying putatively locally adaptive loci have been developed and are broadly used: one that identifies loci with unusually high genetic differentiation among populations (differentiation outlier methods) and one that searches for correlations between local population allele frequencies and local environments (genetic-environment association methods). Here, we review the promises and challenges of these genome scan methods, including correcting for the confounding influence of a species’ demographic history, biases caused by missing aspects of the genome, matching scales of environmental data with population structure, and other statistical considerations. In each case, we make suggestions for best practices for maximizing the accuracy and efficiency of genome scans to detect the underlying genetic basis of local adaptation. With attention to their current limitations, genome scan methods can be an important tool in finding the genetic basis of adaptive evolutionary change.
The Determinants of Optimal Interchange Fees in Payment Systems

A fundamental aspect of any open payment system is the interchange fee that is paid from the merchant's bank to the cardholder's bank. Using a model in which there is partial participation by heterogeneous consumers and merchants, this paper characterizes the output maximizing, profit maximizing and welfare maximizing level of such an interchange fee. It examines how the optimal level of the fee depends on costs, profits margins, pass-through coefficients, participation rates, and membership fees, as well as two different strategic effects arising from competition between merchants. It also determines the factors which drive deviations between the output maximizing, profit maximizing, and welfare maximizing interchange fees.
Multiple VLAD Encoding of CNNs for Image Classification

Despite the effectiveness of convolutional neural networks (CNNs), especially for image classification tasks, the effect of convolution features on learned representations is still limited, mainly focusing on an images salient object but ignoring the variation information from clutter and local objects. The authors propose a multiple vector of locally aggregated descriptors (VLAD) encoding method with CNN features for image classification. To improve the VLAD coding methods performance, they explore the multiplicity of VLAD encoding with the extension of three encoding algorithms. Moreover, they equip the spatial pyramid patch (SPM) on VLAD encoding to add spatial information to CNN features. The addition of SPM, in particular, allows their proposed framework to yield better performance compared to the traditional method.
Neural network for lane change prediction assessing driving situation, driver behavior and vehicle movement

With the steady progress in advanced driver assistance and partial automation of the task of driving it is also increasingly important to put the vehicular systems in the position to autonomously identify and assess the driving situation as well as the needs and intentions of the road users. This is in particular relevant for driving maneuvers such as lane changes. To predict them features have to be taken into account covering a wide range of situations and drivers. Against this background, an algorithm is proposed predicting situations of upcoming lane changes based on assessments of the driving situation, the driver's behavior and the vehicle's movement. It relies on a 360° sensory perception of the vehicular surroundings and on the analysis of the driver's gaze behavior preparing lane changes. The information gained is fused and used for classification by means of an artificial neural network that was parameterized by applying machine learning. The resulting prediction algorithm is working in real-time as a vehicular application. The parameterization as well as the evaluation of the whole system were done using naturalistic driving data obtained by a driving study.
Design of clocked circuits using UML

Clocking is an essential component of any embedded system design. However, traditional design techniques are either short of clocking support or too complex for users. The Unified Modeling Language (UML) has been proposed as design tool in real time system design, but the clocking semantics has not been properly dealt with. In this paper, we will present our experience of using UML to design a clocked system. In particular, UML is used to model the digital down converter, an essential component of software radios. Our tool chain automatically generates the simulation as well as synthesizes the final implementation.
Training Bayesian classifier with scaling unique colors among image samples

To improve the quality of skin segmentation in images, the new training approach of the Bayesian classifier has been proposed. The segmentation problems arise in many applications of developing the intellectual systems based on video analysis. One of the important tasks is the human skin segmentation for further high-level processing. The most used approach is based on naive Bayesian classifier to separate human skin (foreground) pixels from the background ones. We propose the new approach for preparing training dataset which considers only unique colors from each image to avoid normalization influence related to the size of the foreground area. It provides stable improvement of the segmentation quality based on ROC-curves comparison. The testing was conducted on the union of 4 biggest public datasets containing labeled images with human skin. The results show that the average improvement about 3–4% of TPR and up to 10% of FPR metrics.
Macrophage-Tropic HIV Induces and Exploits Dendritic Cell Chemotaxis

Immature dendritic cells (iDCs) express the CC chemokine receptor (CCR)5, which promotes chemotaxis toward the CC chemokines regulated on activation, normal T cell expressed and secreted (RANTES), macrophage inflammatory protein (MIP)-1α, and MIP-1β. By contrast, mature DCs downregulate CCR5 but upregulate CXC chemokine receptor (CXCR)4, and as a result exhibit enhanced chemotaxis toward stromal cell–derived factor (SDF)-1α. CCR5 and CXCR4 also function as coreceptors for macrophage-tropic (M-tropic) and T cell–tropic (T-tropic) human immunodeficiency virus (HIV)-1, respectively. Here, we demonstrate chemotaxis of iDCs toward M-tropic (R5) but not T-tropic (X4) HIV-1. Furthermore, preexposure to M-tropic HIV-1 or its recombinant envelope protein prevents migration toward CCR5 ligands. The migration of iDCs toward M-tropic HIV-1 may enhance formation of DC–T cell syncytia, thus promoting viral production and destruction of both DC and T helper lymphocytes. Therefore, disturbance of DC chemotaxis by HIV-1 is likely to contribute to immunosuppression in primary infection and AIDS. In addition, migration of iDCs toward HIV-1 may aid the capture of R5 HIV-1 virions by the abundant DC cell surface protein DC-specific intercellular adhesion molecule (ICAM)3-grabbing nonintegrin (DC-SIGN). HIV-1 bound to DC cell–specific DC-SIGN retains the ability to infect replication-permissive T cells in trans for several days. Consequently, recruitment of DC by HIV-1 could combine with the ability of DC-SIGN to capture and transmit the virus to T cells, and so facilitate dissemination of virus within an infected individual.
Leveraging cloud intelligence for hybrid vehicular communications

Vehicular networks are required to meet strict performance criteria to reliably support safety-critical applications at scale. However, wireless communication technologies that are currently available for vehicles usually have unique sets of limitations in terms of latency, throughput, and/or signal propagation characteristics. A promising solution to break through these limitations is hybrid vehicular networking, in which on-board units in vehicles concurrently make use of multiple types of radios (e.g., DSRC, Wi-Fi, LTE, etc.), dynamically selecting the most suitable network interface(s) according to the performance requirements and network conditions. In this paper, we explore the potential of a quasi-centralized approach to hybrid vehicular networking. Vehicles opportunistically report their positions and channel load measurements to a cloud-based central server when they have access to network infrastructure (e.g., roadside units). Analyzing the wide-scale statistics on road and network conditions, the server derives and distributes recommended network selection strategy for each geographical region, allowing vehicles to make globally-optimized network selection. Through a case study and proof-of-concept simulations, we show the basic feasibility of the concept of cloud-assisted hybrid vehicular networking.
Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges

Semantic segmentation was seen as a challenging computer vision problem few years ago. Due to recent advancements in deep learning, relatively accurate solutions are now possible for its use in automated driving. In this paper, the semantic segmentation problem is explored from the perspective of automated driving. Most of the current semantic segmentation algorithms are designed for generic images and do not incorporate prior structure and end goal for automated driving. First, the paper begins with a generic taxonomic survey of semantic segmentation algorithms and then discusses how it fits in the context of automated driving. Second, the particular challenges of deploying it into a safety system which needs high level of accuracy and robustness are listed. Third, different alternatives instead of using an independent semantic segmentation module are explored. Finally, an empirical evaluation of various semantic segmentation architectures was performed on CamVid dataset in terms of accuracy and speed. This paper is a preliminary shorter version of a more detailed survey which is work in progress.