Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel amphiphilic polyvinylpyrrolidone functionalized silicone particles as carrier for low-cost lipase immobilization

The high catalytic activity, specificity and stability of immobilized lipase have been attracting great interest. How to reduce the cost of support materials has always been a hot topic in this field. Herein, for the development of low-cost immobilized lipase, we demonstrate an amphiphilic polyvinylpyrrolidone (PVP) grafted on silicone particle (SP) surface materials (SP-PVP) with a rational design based on interfacial activation and solution polymerization. Meanwhile, hydrophilic pristine SP and hydrophobic polystyrene-corded silicone particles (SP-Pst) were also prepared for lipase immobilization. SP-PVP was characterized by X-ray diffraction, scanning electron microscopy, X-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy and thermogravimetry. Our results indicated that the lipase loading amount on the SP-PVP composites was about 215 mg of protein per gram. In the activity assay, the immobilized lipase SP-PVP@CRL exhibited higher catalysis activity and better thermostability and reusability than SP@CRL and SP-Pst@CRL. The immobilized lipase retained more than 54% of its initial activity after 10 times of re-use and approximately trended to a steady rate in the following cycles. By introducing the interesting amphiphilic polymer to this cheap and easily obtained SP surface, the relative performance of the immobilized lipase can be significantly improved, facilitating interactions between the low-cost support materials and lipase.
Gene-environment interactions and predictors of breast cancer in family-based multi-ethnic groups

Breast cancer (BC) is the most common cancer in women worldwide and second leading cause of cancer-related death. Understanding gene-environment interactions could play a critical role for next stage of BC prevention efforts. Hence, the purpose of this study was to examine the key gene-environmental factors affecting the risks of BC in a diverse sample. Five genes in one-carbon metabolism pathway including MTHFR 677, MTHFR 1298, MTR 2756, MTRR 66, and DHFR 19bp together with demographics, lifestyle, and dietary intake factors were examined in association with BC risks. A total of 80 participants (40 BC cases and 40 family/friend controls) in southern California were interviewed and provided salivary samples for genotyping. We presented the first study utilizing both conventional and new analytics including ensemble method and predictive modeling based on smallest errors to predict BC risks. Predictive modeling of Generalized Regression Elastic Net Leave-One-Out demonstrated alcohol use (p = 0.0126) and age (p < 0.0001) as significant predictors; and significant interactions were noted between body mass index (BMI) and alcohol use (p = 0.0027), and between BMI and MTR 2756 polymorphisms (p = 0.0090). Our findings identified the modifiable lifestyle factors in gene-environment interactions that are valuable for BC prevention.
Scalable GPU Virtualization with Dynamic Sharing of Graphics Memory Space

With increasing GPU-intensive workloads deployed on cloud, cloud service providers are seeking for practical and efficient GPU virtualization solutions. However, the cutting-edge GPU virtualization techniques such as gVirt still suffer from the restriction of scalability, which constrains the number of guest virtual GPU instances. This paper presents gScale, a scalable and practical open source GPU virtualization solution based on gVirt. gScale presents a sharing mechanism which combines partition and sharing together to break the hardware limitation of global graphics memory space. Particularly, we propose two approaches for gScale: (1) the private shadow graphics translation table (GTT), which enables global graphics memory space sharing among virtual GPUs, (2) ladder mapping and fence memory space pool, which allows CPU access host physical memory space (serving the graphics memory) to bypass global graphics memory space. Furthermore, to mitigate the performance degradation caused by switching private shadow GTT when the number of vGPUs scales up, four other mechanisms are proposed: (1) slot sharing, which improves the performance of vGPU by dividing the high global graphics memory into multiple slots, (2) fine-grained slotting, which provides a flexible virtual graphics memory configuration, (3) predictive GTT copy mechanism, which reduces the performance loss by switching private shadow GTT before context switch, (4) predictive-copy aware scheduling, which maximizes the improvement of predictive GTT copy mechanism in cloud environment. Evaluation shows that gScale scales up to 15 guest virtual GPU instances in Linux or 12 guest virtual GPU instances in Windows, which is 5x and 4x, respectively, that of gVirt. At the same time, gScale incurs a slight but acceptable runtime overhead when hosting multiple virtual GPU instances.
Falcon: Fused Application of Light Based Positioning Coupled With Onboard Network Localization

Indoor localization based on visible light and visible light communication has become a viable alternative to radio frequency wireless-based techniques. Modern visible light position (VLP) systems have been able to attain sub-decimeter level accuracy within standard room environments. However, a major limitation is their reliance on line-of-sight visibility between the tracked object and the lighting infrastructure. This paper introduces fused application of light-based positioning coupled with onboard network localization (Falcon), a VLP system, which incorporates convolutional neural network-based wireless localization to remove this limitation. This system has been tested in real-life scenarios that cause traditional VLP systems to lose accuracy. In a hallway with luminaires along one axis, the Falcon managed to attain position estimates with a mean error of 0.09 m. In a large room where only a few luminaires were visible or the receiver was completely occluded, the mean error was 0.12 m. With the luminaires switched off, the Falcon managed to correctly classify the target 99.59% of the time to within a 0.9-m2 cell and with 100% accuracy within a1.6-m2 cell in the room and hallway, respectively.
Forensic efficiency estimate and phylogenetic analysis for Chinese Kyrgyz ethnic group revealed by a panel of 21 short tandem repeats

Short tandem repeats (STRs) with a high level of polymorphisms and convenient detection method play an indispensable role in human population and forensic genetics. Recently, we detected the 21 autosomal non-combined DNA index system (non-CODIS) STR loci in a Kyrgyz ethnic group, calculated their forensic parameters and analysed its genetic relationships with reference populations from China. In total, 168 alleles were observed at 21 non-CODIS STRs with corresponding allelic frequencies from 0.0016 to 0.4788. No significant deviations at these STRs were observed from the Hardy–Weinberg equilibrium. The values of cumulative power of discrimination and probability of exclusion for all the 21 non-CODIS STRs were 0.99999999999999999998835 and 0.9999994002, respectively. Furthermore, the analyses of phylogenetic trees, genetic distances and interpopulation differentiations demonstrated that the Kyrgyz group had relatively close genetic relationships with the Uygur and Kazak groups. These 21 non-CODIS STRs were characterized by high genetic diversities in the Kyrgyz group and could be applied as a robust tool for individual identification and kinship testing in forensic sciences.
Executive Functions Rating Scale and Neurobiochemical Profile in HIV-Positive Individuals

The set of complex cognitive processes, that are necessary for the cognitive control of behavior, known as executive functions (EF), are traditionally associated with the prefrontal cortex and commonly assessed with laboratory based tests and conventional neuroimaging. In an effort to produce a more complete and ecologically valid understanding of executive functioning, the rating scales have been developed in order to assess the behavioral aspects of EF within an everyday real-world context. The main objective of this study was to examine the relationship between behavioral aspects of EF measured by rating scale and neurometabolic profile in neurologically asymptomatic HIV-positive individuals under cART, measured using multi-voxel magnetic resonance spectroscopy (mvMRS). The sample comprised 39 HIV-positive adult male participants, stable on cART and 39 healthy HIV-negative volunteers. Both groups completed the Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A). HIV-positive group additionally underwent long-echo three-dimensional mvMRS to determine neurobiochemical profile in the anterior cingulate gyrus (ACG) of both hemispheres. Three dominant neurometabolites were detected: N-acetyl aspartate (NAA), the neuronal marker; choline (Cho), the marker of membrane metabolism and gliosis and creatine (Cr), the reference marker. Ratios of NAA/Cr and Cho/Cr were analyzed. The initially detected significant correlations between age, current CD4, BRIEF-A subscales Inhibit, Shift, Emotional Control, Plan/Organize, Self Monitoring and ratios of NAA/Cr and Cho/Cr in the dorsal and ventral part of the ACG, were lost after the introduction of Bonferroni corrections. Also, there were no significant differences between HIV–positive and HIV–negative group on any of BRIEF-A subscales. Such results possibly imply that stable cART regimen contributes to preservation of behavioral aspects of EF in asymptomatic HIV-positive individuals. Even though a subtle deficit in some aspects of EF might exist, it would not be manifest if behavioral aspect was assessed using EF rating scale. Further explanation might be that expected HIV-related changes in neurometabolic profile of the ACG under cART are not reflected in those behavioral aspects that are measurable by EF rating scale.
Neuroprotection Against MPP+-Induced Cytotoxicity Through the Activation of PI3-K/Akt/GSK3β/MEF2D Signaling Pathway by Rhynchophylline, the Major Tetracyclic Oxindole Alkaloid Isolated From Uncaria rhynchophylla

Rhynchophylline is a major tetracyclic oxindole alkaloid in Uncaria rhynchophylla, which has been extensively used as traditional herb medicine for the prevention of convulsions and hypertension. However, there is still little evidence about the neuroprotective effects of rhynchophylline for Parkinson’s disease (PD), a neurodegenerative condition currently without any effective cure. In this present study, the neuroprotective molecular mechanisms of rhynchophylline were investigated in a cellular model associated with PD. It is shown that rhynchophylline (10–50 μM) greatly prevented neurotoxicity caused by 1-methyl-4-phenylpyridinium ion (MPP+) in primary cerebellar granule neurons, as evidenced by the promotion of cell viability as well as the reversal of dysregulated protein expression of Bax/Bcl-2 ratio. Very encouragingly, we found that rhynchophylline markedly enhanced the activity of the transcription factor myocyte enhancer factor 2D (MEF2D) at both basal and pathological conditions using luciferase reporter gene assay, and reversed the inhibition of MEF2D caused by MPP+. Additionally, pharmacological inhibition of PI3-Kinase or short hairpin RNA-mediated gene down-regulation of MEF2D abrogated the protection provided by rhynchophylline. Furthermore, Western blot analysis revealed that rhynchophylline could potentiate PI3-K/Akt to attenuate GSK3β (the MEF2D inhibitor) in response to MPP+ insult. In conclusion, rhynchophylline inhibits MPP+-triggered neurotoxicity by stimulating MEF2D via activating PI3-K/Akt/GSK3β cascade. Rhynchophylline is served as a novel MEF2D enhancer and might be a potential candidate for further preclinical study in the prevention of PD.
Toward a Holistic Software Systems Engineering Approach for Dependable Autonomous Systems

Autonomous systems are gaining momentum in various application domains, such as autonomous vehicles, autonomous transport robotics and self-adaptation in smart homes. Product liability regulations impose high standards on manufacturers of such systems with respect to dependability (safety, security and privacy). Today’s conventional engineering methods are not adequate for providing guarantees with respect to dependability requirements in a costefficient manner, e.g. road tests in the automotive industry sum up millions of miles before a system can be considered sufficiently safe. System engineers will no longer be able to test and respectively formally verify autonomous systems during development time in order to guarantee the dependability requirements in advance. In this vision paper, we introduce a new holistic software systems engineering approach for autonomous systems, which integrates development time methods as well as operation time techniques. With this approach, we aim to give the users a transparent view of the confidence level of the autonomous system under use with respect to the dependability requirements. We present already obtained results and point out research goals to be addressed in the future.
Trajectories Modeling and Clustering

1 The location of moving objects facilitates the monitoring of their evolution and the history of displacement offers interesting perspectives in the field of the study of the behavior of these objects. The purpose of this paper is to provide a new methodology for constructing object trajectories based on a matching process that implements different features such as occultation management, surface, histogram, and so on. a second objective was to propose a classification approach of its trajectories. The proposed algorithm consists in classifying its trajectories in homogenous groups and isolating aberrant trajectories that exhibit deviant behavior. Its principle is based on the transformation of trajectories into spaces of distinct characteristics. These spaces make it possible to provide additional information on the characteristics of the model of movement of an object in a video sequence. The approach then consists in using the Mean-Shift algorithm to estimate the groups in each characteristic space. Clusters with the small number of associated trajectories and trajectories that are far from the center of the groups are considered outliers.
High-Diversity Joint Precoder Design for Non-Concurrent Two-Way AF MIMO Relaying

We design a precoder for non-concurrent two-way relaying (ncTWR) where a base station (BS) serves a transmit-only user equipment (TUE) in the uplink and a receive-only user equipment (RUE) in the downlink. The RUE experiences back-propagating interference (BI). The proposed precoder is designed such that it not only cancels the BI experienced by the RUE but more importantly, enables receive data decoding with high diversity. The high diversity precoder is designed by deriving the closed-form pairwise error probability (PEP) expressions, and by optimizing the precoder elements to minimize the PEP. We analytically show that with <inline-formula> <tex-math notation="LaTeX">$N_{r}$ </tex-math></inline-formula>-antenna relay, <inline-formula> <tex-math notation="LaTeX">$N_{b}$ </tex-math></inline-formula>-antenna BS, and <inline-formula> <tex-math notation="LaTeX">$N_{u}$ </tex-math></inline-formula>-antenna TUE and RUE, both BS and RUE decode their respective data with a diversity order of <inline-formula> <tex-math notation="LaTeX">$\min (N_{u}^{2},(N_{r}-N_{u})N_{b})$ </tex-math></inline-formula> at high receive signal-to-noise ratio. We also numerically show that the proposed design has lower bit error rate than the existing state-of-the-art ncTWR designs.
Improved Localisation Using Spatio-Temporal Data from Cellular Network

Localisation of mobile devices has been a topic of academic research and industry practice for solving various application problems, examples can be footfall counting and profiling used for location based digital or physical advertising, crowd monitoring for public security, emergency handling, transport measurement and management, etc. Often the solutions for localisation require large scale networks hardware and/or software upgrades, which can be very costly. However, we note the fact that many commercial use cases actually do not require very high resolution of localisation and satisfactory level of accuracy may be sufficient for attaining business decision quality. A reasonable trade-off between achieving business value and minimising additional costs on network equipment purchase and maintenance is to build solutions that rely only on telco-network data and utilize data mining methods to improve the localisation accuracy of mobile devices that carried by subscribers. In this work, we aim to achieve acceptable accuracy for localisation at the resolution of region of interest (ROI), the exact shape of which is defined according to business requirements. One example is the geographical division of planning sub-zone in Singapore. We make use of the Global Positioning System (GPS) locations extracted from mobile broadband log that contain the longitude and latitude of the subscriber to annotate the telco-network data. We experimented with three learning models: maximum likelihood estimation, dominant serving ROI, and random forest, along with the baseline of localisation based on cellular tower locations. The experiment results demonstrate the effectiveness of the proposed models and demonstrate accuracy improvement from baseline of 37.8% (naive cellular tower localisation) to 78.4% (random forest classification).
Forecasting of Bitcoin Daily Returns with EEMD-ELMAN based Model

The present study investigates the application of EEMD-ELMAN model to forecast the daily returns of the Bitcoin. More than seven years data were collected online from 18th July 2010 to 17th January 2018. Then the data signal was decomposed into several sub-signals using EEMD method. After, sub-signals were captured by different ELMAN models, and their output results were combined to generate the final forecast. Besides, the results of this study were compared against ELMAN and ARGARCH models. Hence, the statistical metrics revealed that the used model outperforms ELMAN network, and has approximately the same estimation error as ARGARCH, although the later model is prone to bad generalization due to the high gap between its approximation and generalization errors. Therefore, we can confirm that EEMD can be considered as a promising preprocessing technique, which enables to bring up the forecasting performance of ELMAN network with respect to highly volatile time series.
A Cost-Aware Incentive Mechanism in Mobile Crowdsourcing Systems

The rapid growth of ubiquitous mobile smart devices has led to the creation of a new era of mobile crowdsourcing applications, where human workers participate and perform tasks in exchange of a monetary reward. Such crowdsourcing systems can play a vital role during emergency events, where fast and accurate responses are needed. However, a commonly ignored aspect is how the price (i.e. the reward paid to workers) must be set in order for the system to meet two important requirements: (i) to timely receive an adequate number of responses which is crucial during emergencies, and (ii) to meet budget constraints. In the majority of the existing systems, the price per task is set up-front and remains unchanged for all upcoming tasks, leading to either higher monetary cost than necessary or to significantly larger latency than expected. In this work, we provide a formulation based on Kalman Filters that enables the system to estimate the user/worker behavior, i.e., the likelihood over time for a user to provide answers for a specific reward. Specifically, we focus on the problem of developing an adaptive pricing policy to incentivize the users to rapidly provide their responses. Our mechanism can be adjusted dynamically to bridge the gap among the users' behavior and the system's needs so as to maximize the overall utility of the system. We simulate our model and through extensive experimental evaluation we show how our system performs and provides benefits to both the users and the system operator.
Near Real-time Hippocampus Segmentation Using Patch-based Canonical Neural Network

Over the past decades, state-of-the-art medical image segmentation has heavily rested on signal processing paradigms, most notably registration-based label propagation and pair-wise patch comparison, which are generally slow despite a high segmentation accuracy. In recent years, deep learning has revolutionalized computer vision with many practices outperforming prior art, in particular the convolutional neural network (CNN) studies on image classification. Deep CNN has also started being applied to medical image segmentation lately, but generally involves long training and demanding memory requirements, achieving limited success. We propose a patch-based deep learning framework based on a revisit to the classic neural network model with substantial modernization, including the use of Rectified Linear Unit (ReLU) activation, dropout layers, 2.5D tri-planar patch multi-pathway settings. In a test application to hippocampus segmentation using 100 brain MR images from the ADNI database, our approach significantly outperformed prior art in terms of both segmentation accuracy and speed: scoring a median Dice score up to 90.98% on a near real-time performance (<1s).
A Compact Rectenna System With High Conversion Efficiency for Wireless Energy Harvesting

A novel coplanar waveguide-fed rectenna with high efficiency is proposed and implemented in this paper for 2.45-GHz Bluetooth/ wireless local area network applications. The antenna has compact dimensions of 18 mm <inline-formula> <tex-math notation="LaTeX">$\times \,\, 30$ </tex-math></inline-formula> mm, which is simulated and manufactured using a low-cost FR4 substrate with a thickness of 1.6 mm. A tuning stub technique with rectangular slots is used for better impedance matching and enhancing the impedance bandwidth of the antenna with a peak gain of 5.6 dB. The proposed novel antenna for RF energy harvesting applications exhibits dipolelike radiation pattern in <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula>-plane and omnidirectional pattern in <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-plane with improved radiation efficiency. Single-stage Cockcroft–Walton rectifier with L-shaped impedance-matching network is designed in advance design system and fabricated on FR4 substrate. The dc output of the rectenna is measured as 3.24 V with a load resistance of 5 <inline-formula> <tex-math notation="LaTeX">$\text{k}\Omega $ </tex-math></inline-formula>. A simulated peak conversion efficiency of 75.5% is attained, whereas the measured one is observed to be 68% with an input signal power of 5 dBm at 2.45 GHz.