Miracle-dz/UniFAR-FTUs huggingface dataset:
The anti-lipidemic drug simvastatin modulates Epigenetic Biomarkers in the Amphipod Gammarus locusta

The adverse effects of certain environmental chemicals have been recently associated with epigenome’s modulation. Although the changes in the epigenetic signature are still not integrated into hazard and risk assessment, they are interesting candidates for linking environmental exposures to altered phenotypes given that these changes may be passed across multiple non-exposed generations. Here, we addressed the effects of simvastatin (SIM), one of the most prescribed human pharmaceuticals, in epigenetic regulators of the amphipod Gammarus locusta, as a proxy to support its integration in hazard and environmental risk assessment. SIM is a known modulator of epigenome in mammalian cell lines, and has been reported to impact G. locusta ecological endpoints at environmentally relevant levels. G. locusta juveniles were exposed to three SIM concentrations (0.32, 1.6 and 8 µg.L-1), for 15 days. The basal expression of selected epigenetic regulators was determined, along with the quantification of DNA methylation levels and the assessment of key ecological endpoints. Exposure to 0.32 and 8 µg. L-1 SIM induced significant downregulation of DNA methyltransferase1 (dnmt1), concomitantly with Global DNA hypomethylation and impact on growth. Overall, this work is the first to validate the basal expression of key epigenetic regulators in a keystone marine crustacean, supporting the integration of epigenetic biomarkers into hazard assessment frameworks.
A Method of Toothbrush Position Measurement using AR Markers

Appropriate daily brushing of the teeth is important for preventing oral diseases. Therefore, a personal assistant for assessing and coaching appropriate toothbrushing is needed. Herein, a three-dimensional toothbrush position measurement method using augmented reality (AR) markers is proposed. The AR markers are detected via a brushing video captured using a smartphone camera. The AR markers are installed on each surface of a dodecahedron attached at the rear end of the toothbrush. This report describes the proposed method, the resulting toothbrush position measurement accuracy, and the optimal number of markers needed for an accurate measurement of the position.
Human Real Time Localization System in Underground Mines using UWB

This work proposes an improved Real Time Localization System using UWB technology, for people and asset tracking in a closed environment, particularly in underground mines. The UWB technology has demonstrated a very good performance with a localization error margin below 41 centimeters. The main objective of this work was to develop an affordable, easy to use, and scalable localization system to detect people in underground mines that allows fast location and rescue in case of disaster. It can also improve the mine productivity under normal conditions. Different tests were carried out in mines and urban buildings to validate the design and evaluate the error margin. The test in an underground mine was done at “Casposo Mining”, which is located in the town of Calingasta, San Juan province, Argentina. Finally, an autonomy of 25 hours of continuous used was achieved using a 2000mAh battery.
Identification and Characterization of Fbxl22, a novel skeletal muscle atrophy-promoting E3 ubiquitin ligase

Muscle-specific E3 ubiquitin ligases have been identified in muscle atrophy-inducing conditions. The purpose of the current study was to explore the functional role of Fbxl22, and a newly identified splice variant (Fbxl22-193), in skeletal muscle homeostasis and neurogenic muscle atrophy. In mouse C2C12 muscle cells, promoter fragments of the Fbxl22 gene were cloned and fused with the secreted alkaline phosphatase reporter gene to assess the transcriptional regulation of Fbxl22. The tibialis anterior muscles of male C57/BL6 mice (12-16 weeks old) were electroporated with expression plasmids containing the cDNA of two Fbxl22 splice variants and tissues collected after 7, 14 and 28 days. Gastrocnemius muscles of wild type and MuRF1 knockout mice were electroporated with an Fbxl22 RNAi or empty plasmid, denervated three days post-transfection, and tissues collected 7 days post-denervation. The full-length gene and novel splice variant are transcriptionally induced early (after 3 days) during neurogenic muscle atrophy. In vivo overexpression of Fbxl22 isoforms in mouse skeletal muscle lead to evidence of myopathy/atrophy suggesting that both are involved in the process of neurogenic muscle atrophy. Knockdown of Fbxl22 in MuRF1 KO muscles resulted in significant additive muscle sparing at 7 days of denervation. Targeting two E3 ubiquitin ligases appears to have a strong additive effect on protecting muscle mass loss with denervation and these findings have important implications in the development of therapeutic strategies to treat muscle atrophy.
A Fast Single-Image Dehazing Algorithm Based on Dark Channel Prior and Rayleigh Scattering

The scattering of atmospheric particles significantly alters images captured under hazy weather condition. Images appear distorted, blurry and low in contrast attenuation, which extensively affects computer vision systems. There has been development of several prior based methods to address this problem. However, these methods come at a high computational cost. We present a fast, single image dehazing method based on dark channel prior and Rayleigh scattering. Firstly, we present a simple but effective methodology for estimating the atmospheric light through the computation of average, minimum and maximum of the pixels in each of the three RGB colour channels. Then, using the theory of Rayleigh scattering, we model a scattering coefficient to estimate the initial transmission map. Also, a fast-guided filter is adopted to refine the initial transmission map due to inaccurate halo edges. Finally, we restore the haze-free image through the atmospheric scattering model. Extensive qualitative and computational experiments on hazy outdoor images demonstrate that the proposed method produces excellent results whiles achieving a faster processing time.
Entropy and Randomness: From Analogic to Quantum World

This work is dedicated to the construction and evaluation of random number generators used in cryptography. The critical element on which the security of information is based is the cryptographic key (usually a binary sequence). In order to be resistant to brute force attacks it is necessary that it be made up of random variables with a certain degree of randomness and independence. Formally, this comes back to generate the cryptographic key through the systems which ensures a certain minimum level of entropy. The observer has access to a sample, of a certain size, and based on it he will estimate the minimum value of the entropy, in the situations in which the variables resulting from the measurement process are independent. In the situation where these variables are not independent, complex mathematical procedures also allow estimation of the minimum entropy. This article is a review of how mathematical entropy can be estimated and evaluated, of the construction mode (from technologies based on analogue procedures: thermal noise in a transistor to modern procedures: quantum devices), as well as to evaluate the security of binary sequence generators used for generating cryptographic keys or critical security parameters related to new technologies based on quantum principles. The techniques and methods used to generate binary random values as well as the methods of statistical and informational validation (Shannon entropy) are exemplified in this paper.
ProRace

This paper presents ProRace, a dynamic data race detector practical for production runs. It is lightweight, but still offers high race detection capability. To track memory accesses, ProRace leverages instruction sampling using the performance monitoring unit (PMU) in commodity processors. Our PMU driver enables ProRace to sample more memory accesses at a lower cost compared to the state-of-the-art Linux driver. Moreover, ProRace uses PMU-provided execution contexts including register states and program path, and reconstructs unsampled memory accesses offline. This technique allows \ProRace to overcome inherent limitations of sampling and improve the detection coverage by performing data race detection on the trace with not only sampled but also reconstructed memory accesses. Experiments using racy production software including apache and mysql shows that, with a reasonable offline cost, ProRace incurs only 2.6% overhead at runtime with 27.5% detection probability with a sampling period of 10,000.
Numerical study on novel double-layered heat sink with optimized pin fins

A numerical study on the thermal performance of a novel design of double-layered heat sink with optimized pin fins (DL-OPFHS) is presented in this work. It is found that the DL-OPFHS performs better comparing to the conventional double-layered heat sink with staggered circular pin fins (DL-CPFHS) under the same overall geometric dimensions and fixed volume flow rates. The thermal resistance of DL-OPFHS is lower by 28% than that of DL-CPFHS at the inlet velocity of 0.3 m/s.
An Optimal Charging Station Mapping Strategy for Electric Vehicles Incorporating User Constraints by Fleet Tracking

Pollution and scarcity of fuel for the traditional engine vehicles facilitated the necessity of electric power driven vehicles which can makes a remarkable reduction on environmental issues persisting in the country. The introduction of EV fleets for transportation can be considered to be the most applicable solution to promote the electric vehicles and its acceptance from the consumer end. So an optimal allocation of the charging station for the electric vehicles based on the distance and duration from the source to destination and cost needed for charging is proposed in this paper. The Integer linear programming is used for the optimal distribution of electric vehicles to the charging stations available. The system with and without optimization is analyzed and results are obtained which infer or recommend optimal charging station for the customer in view of power system limits, stability and economics.
Latency-Aware Dynamic Resource Allocation Scheme for Multi-Tier 5G Network: A Network Slicing-Multitenancy Scenario

In 5G slice networks, the multi-tenant, multi-tier heterogeneous network will be critical in meeting the quality of service (QoS) requirement of the different slice use cases and in reduction of the capital expenditure (CAPEX) and operational expenditure (OPEX) of mobile network operators. Hence, 5G slice networks should be as flexible as possible to accommodate different network dynamics such as user location and distribution, different slice use case QoS requirements, cell load, intra-cluster interference, delay bound, packet loss probability, and service level agreement (SLA) of mobile virtual network operators (MVNO). Motivated by this condition, this paper addresses a latency-aware dynamic resource allocation problem for 5G slice networks in a multi-tenant, multi-tier heterogeneous environment, for efficient radio resource management. The latency-aware dynamic resource allocation problem is formulated as a maximum utility optimisation problem. The optimisation problem is transformed and the hierarchical decomposition technique is adopted to reduce the complexities in solving the optimisation problem. Furthermore, we propose a genetic algorithm (GA) intelligent latency-aware resource allocation scheme (GI-LARE). We compare GI-LARE with the static slicing (SS) resource allocation; the spatial branch and bound-based scheme; and, an optimal resource allocation algorithm (ORA) via Monte Carlo simulation. Our findings reveal that GI-LARE outperformed these other schemes.
The Impact of Structural Change on Green Economy Performance : –Evidence From China

We apply a non-radial directional distance function (NDDF) for examining the dynamic changes in green economy performance in China, and then examine how industrial structural upgrading affects green economy performance. The analysis framework is developed by the fixed effect model and the generalized method of moments (GMM), using a city-level panel data set during the period of 2007-2014. The results indicate that industrial structural upgrading has a significant positive effect on green economy performance. Further path analyses show that industrial structural upgrading affects green economy performance by improving both energy performance and environmental performance in China. but the influence paths in eastern, central and western regions are different in the heterogeneity analysis.
Feedback Linearization Kalman Observer Based Sliding Mode Control for Semi-Active Suspension Systems

In this paper, a new approach for an observer based controller for semi-active suspension systems is presented. The observer part is a feedback linearization Kalman filter which is based on differential geometry. The original nonlinear system is transferred to a linear system by certain coordinate transfer after the verification of observability and solvability of the system observer design problem under certain sensor placement configurations. Then, a linear Kalman filter algorithm can be applied to the linearized system. The state information can be obtained through an inverse coordinate transfer of the estimation results of the linear Kalman filter. The observer is verified by a simulation test under different road profiles, and a comparison between the designed observer and extended Kalman filter shows that the feedback linearization Kalman filter has better performance. A model reference sliding mode controller based on the estimation results of the observer is also proposed. A rig test system for the semi-active suspension system is implemented, and, both the designed observer and controller are verified through the rig test. Experimental results show that the proposed new approach for semi-active suspension control can significantly improve vehicle ride comfort with common and low-cost sensors.
Active TGF‐β1 correlates with myofibroblasts and malignancy in the colorectal adenoma‐carcinoma sequence

Transforming growth factor‐β1 (TGF‐β1), a cytokine involved in various stages of cancer, is produced as a latent complex and requires processing to become active. We have determined total and active TGF‐β1 levels in homogenates of colorectal neoplasia. In contrast to total TGF‐β levels, showing a stepwise increase in the mucosa‐adenoma‐carcinoma sequence, active TGF‐β1 levels are increased only in carcinomas but not in premalignant adenomas. Furthermore, solely active TGF‐β1 levels are associated with the stage of the carcinomas and worse patient prognosis. Active TGF‐β1 levels correlated significantly with plasminogen activator inhibitor (PAI)‐1, α‐smooth muscle actin (SMA) and several matrix‐remodeling proteinases. Interestingly, SMA levels are also significantly increased in colorectal carcinomas but not in adenomas, suggesting that despite the enhanced total TGF‐β1 levels, myofibroblast accumulation is not (yet) occurring in these premalignant neoplasias. The correlation between active TGF‐β1 and SMA expression in tumors indicates that tumor‐promoting myofibroblasts might arise as a result of increased TGF‐β1 activation. These data underline the significance of the interaction between malignant cells and (myo)‐fibroblasts in the tumor microenvironment, modulating the biologic behavior of colorectal cancer. (Cancer Sci 2009; 100: 663–670)
Dielectric Image Line Liquid Crystal Phase Shifter at W-Band

This paper presents a continuously tunable dielectric image line phase shifter operating at W-Band (75 GHz to 110 GHz). The core material of the image line is liquid crystal (LC), by which tunability is obtained. The image line topology offers a small and compact realization when compared to fully dielectric or metallic waveguide-reliant structures and is suitable to a substrate-integrated environment. Four bias electrodes provide the field necessary to tune the LC. A maximum differential phase shift of 352° at 110 GHz is achieved with a bias voltage of ±150 V, while the insertion loss is between 2.8 dB and 4.8 dB. The resulting Figure-of-Merit is 84 °/dB. Response times as low as 5 s are possible with this first realization.
A Multi-Task Music Artist Classification Network

Music Artist Classification is a challenging task in Music Information Retrieval. There exist methods that are based on either signal processing features or deep learning algorithms. While signal processing based approaches do perform well, the challenging task implies that signal processing alone does not suffice to provide good features for the task. Other approaches that rely on deep learning based techniques learn representations from large amount of labelled data. A limitation to this approach is the requirement for large amount of annotated corpus for obtaining a good set of parameters. In this work, we pose auxiliary signal processing based tasks for a deep learning network that includes predicting the harmonic-percussive and vocal-non vocal components of audio files. We show that training on a combination of these tasks provides us with a well trained CNN with a good set of parameters; which can then further be used for the artist classification task. We use a Multi-Task framework and a popular deep learning architecture to train the model jointly on the artists classification and the auxiliary tasks. We observe that vocal-non vocal separation along with alignment prediction proves to be a good auxiliary task for artist classification, improving the baseline by 6%.
Induction of an ACC synthase cDNA by ozone in light‐grown Arabidopsis thaliana leaves

The rate-limiting enzyme in the ethylene biosynthesis pathway is 1-aminocyclo-propane-1 -carboxylate (ACC) synthase. We have investigated an ozone (O 3 )-induced ACC synthase gene in Arabidopsis thaliana. Four previously reported ACC synthase genes from A. thaliana (ACS1. ACS2. ACS4 and ACS5) were not induced in response to O 3 stress. An O 3 -induced 1098-bp cDNA encoding ACC synthase was isolated from foliage of A. thaliana; this sequence was identical to that of ACS6 which could be induced by multiple stimuli including touch. When plants were treated with 350 nl l -1 of O 3 for 2 or 6 h, the evolution of ethylene became maximal after 1.5 h and gradually decreased thereafter. Similarly, the mRNA transcript of O 3 -induced ACC synthase was detected after 30 min of exposure, attained a maximum level after 1 h, and then declined. Visible damage in O 3 -treated plants was observed at the end of the 2-h exposure. This ACC synthase gene in A. thaliana provides a useful tool for studying O 3 -induced responses related to ethylene production at the molecular and genetic levels.
A Reputation-Based Multi-User Task Selection Incentive Mechanism for Crowdsensing

Crowdsensing high quality data relies on the efficient participation of users. However, the existing incentive mechanism is unable to take into account the dual requirements of both quantity and quality of users’ participation. In this paper, we propose Crowdsensing Task Selection algorithm and rewards allocation incentive mechanism based on Reputation Evaluation model(CTSRE), which deploys the reputation weighted rewards allocation method to effectively encourage users to actively participate in the execution of tasks. In CTSRE, we adopt a game-theoretic approach and apply best response dynamics based algorithm to achieve the goal of maximizing users’ utilities. We show that the task selection algorithm can converge in finite time and meet the fairness requirement. We also design a reputation conversion method and updating rule to improve incentive and fairness of the mechanism. Through numerical experiments and comparative analysis, we verify that the task selection algorithm meets the convergence requirements. The application of sigmoid function for reputation conversion improves the fairness of rewards allocation and motivate users to improve their reputation to obtain high rewards. Experimental results indicate that CTSRE can effectively ensure the quantity and the quality of users’ participation.
Adiposome Targeting and Enzymatic Activity of Lipid Droplet-Specific Proteins

New strategies to decode the specific protein targeting mechanism on lipid droplet (LD) are urgently needed. Using adiposome, the LD binding of perilipin 2 (PLIN2), perilipin 3 (PLIN3), and adipose triglyceride lipase (ATGL) were studied. Scatchard analysis found that the binding of PLIN2 to the adiposome surface was saturable, pointing to a specific membrane binding partner. Phosphatidylinositol (PI) was found to inhibit PLIN2 binding while it did not impede PLIN3. Structural analysis combined with mutagenesis revealed that the 73rd glutamic acid of PLIN2 is significant for the effect of PI on the protein binding. The presence of PI significantly stimulated the activity of ATGL in vitro. The phosphorylation site mutants of ATGL were found reducing the lipase activity in the adiposome system. Our study demonstrates the utility of adiposome as a powerful, manipulatable model system for the characterization of LD binding and enzymatic activity of LD proteins in vitro.
Adaptive Clutter Suppression and Detection Algorithm for Radar Maneuvering Target With High-Order Motions Via Sparse Fractional Ambiguity Function

Radar maneuvering target detection in clutter background should not only consider the complex characteristics of the target to accumulate its energy as much as possible, but also suppress clutter to improve the signal-to-clutter ratio (SCR). The traditional fractional domain transform-based detection method requires parameters match searching, which costs heavy computational burden in case of a large amount of data. Sparse FT and sparse fractional FT can obtain high-resolution sparse representation of the target, but the signal sparsity needs to be known before, and the sparse representation performance is poor in clutter background. In this article, adaptive filtering method is introduced into the sparse fractional ambiguity function (SFRAF) method, and a SFRAF domain adaptive clutter suppression and highly maneuvering target detection algorithm is proposed, which is named as adaptive SFRAF (ASFRAF). The ASFRAF domain iterative filtering operation can suppress the clutter while retaining the signal energy as much as possible. Simulation results and measured radar data processing results show that the proposed algorithm can overcome the limitation of the SFRAF on the sparsity preset value and achieve high efficiency and robust detection of high-order phase maneuvering targets under a low SCR environment.