Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential Association of Products of Alternative Transcripts of the Candidate Tumor Suppressor ING1 with the mSin3/HDAC1 Transcriptional Corepressor Complex*

The candidate tumor suppressor ING1was identified in a genetic screen aimed at isolation of human genes whose expression is suppressed in cancer cells. It may function as a negative growth regulator in the p53 signal transduction pathway. However, its molecular mechanism is not clear. The ING1locus encodes alternative transcripts of p47 ING1a , p33 ING1b , and p24 ING1c . Here we report differential association of protein products of ING1 with the mSin3 transcriptional corepressor complex. p33 ING1b associates with Sin3, SAP30, HDAC1, RbAp48, and other proteins, to form large protein complexes, whereas p24 ING1c does not. The ING1 immune complexes are active in deacetylating core histones in vitro, and p33 ING1b is functionally associated with HDAC1-mediated transcriptional repression in transfected cells. Our data provide basis for a p33 ING1b -specific molecular mechanism for the function of the ING1 locus.
Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning

Segmentation of breast tumor is an important step for breast cancer follow-up and treatment. Automating this challenging task can help radiologists to reduce the high workload of breast cancer analysis. In this paper, we propose a deep learning approach to automate the segmentation of breast tumors in DCE-MRI data. We build an architecture based on U-net fully convolutional neural network. The trained model can handle both detection and segmentation on each single breast slice. In this study, we used a dataset of 86 DCE-MRI, acquired before and after chemotherapy, of 43 patients with local breast cancer, a total of 5452 slices. The data have been annotated manually by an experienced radiologist. The model was trained and validated on 85% and 15% of the data and achieved a mean IoU of 76,14%.
Enhancing Security of Biometric Systems Using Deep Features of Hand Biometrics

Nowadays, the success of the different institutions depends on the security of their information against possible attacks. Because much of this information is related to persons’ activities, so safeguarding their identities is essential for these successes. For this purpose, persons’ identity recognition can be categorized as an effective method to enhance the security of information systems. To do this, most of these systems use biometric methods, which are very efficient compared to traditional methods. In this context, the present paper attempts to design an effective multimodal biometric system using hand biometrics. In this work, Finger- Knuckle-Print (FKP) and PaLMprint (PLM) biometrics are used. In addition, for high accuracy, two simple and effective deep learning based feature extraction techniques, PCANet and DCTNet, are integrated. The security analysis presented in this paper can help simplify the understanding of biometric systems and thus finding a robust solution to protect information systems. Our experimental results, using a synthetic multibiometric dataset of 165 persons, show that our proposed system has a high identification rate and a very low classification error compared to several existing methods.
Platelet adhesion to decorin but not collagen I correlates with the integrin α2 dimorphism E534K, the basis of the human platelet alloantigen (HPA)-5 system

A single nucleotide polymorphism in the integrin α2 gene ITGA2 (rs1801106; G1600A) creates the non-conservative amino acid substitution E534K, the basis of the human platelet alloantigen system HPA-5. Yet HPA-5 alleles do not influence binding of α2β1 to its primary ligand collagen I, and the effect of HPA-5 on platelet function has not been determined. We used a direct platelet adhesion assay to evaluate whether differential inheritance of HPA-5 alleles influences platelet adhesion to collagen I or an alternative ligand, decorin. Platelets from donors bearing one or more minor allele HPA-5b showed attenuated adhesion to purified decorin but not collagen I. Adhesion to decorin was significantly inhibited by human alloantibodies specific for HPA-5a but not by the collagen I sequence GFOGER or α2-specific inhibitory monoclonal antibodies. The minor allele 534K attenuates platelet adhesion to decorin but not collagen I, providing the first evidence of a functional effect of HPA-5 alleles.
Learning-based Cooperative Sound Event Detection with Edge Computing

In this paper, we propose a novel real-time sound event detection framework, which combines multi-label learning and edge computing, to classify and localize abnormal sound events for city surveillance. Multiple devices equipped with acoustic sensors are deployed to collect the audio information. A learning-based approach is introduced to address the difficulties of accurately classifying the temporally overlapping acoustic events in a noisy environment. Then, edge computing is adopted to handle the high processing complexity of the learned analytics. Computation-intensive tasks of classification and localization can be offloaded to the nearby edge server for low-latency sound detection. An ensemble-based cooperative decision-making algorithm is also presented to aggregate the information from distributed devices in order to obtain better classification results. Extensive evaluations show the effectiveness of edge computing which helps reduce the time latency as well as the superiority of cooperative post-processing on the edge server to obtain a high accuracy.
Modelling the Dynamics of Inventory and Backlog Management in Servitized Supply Chains: The Case of Custom Industrial Manufacturing Girms

An important challenge for managers in a servitized supply chain is to integrate the delivery of the products and services such that resources and knowledge are used effectively and efficiently. In the absence of a study that simultaneously considered the various services rendered at different stages of the product life of a servitized manufacturing firm, this study developed a system dynamics simulation model that incorporates the product and service operations of a custom industrial manufacturing firm and the response of the customers in the firm’s service level to analyze the effectiveness of various service capacity and inventory strategies. From the insights generated in the results and analysis, alternative policies that target the major feedback loops were generated. The policies include incorporating manufacturing and service capacity utilization decisions, and a dynamic safety stock decision. These policies were proven to improve the overall system behavior by significantly attenuating the amplifications, phase lags, and oscillations in all subsystems of the servitized supply chain, while maintaining desirable service levels. The theoretical findings were then translated tomanagerial implications.
A New Reconfigurable Antenna for Full-band Metal-Rimmed Smartphones’ Applications

A new reconfigurable antenna for smartphone with a small ground plane clearance is proposed. The most important figure-of-merit of this design is that it covers all recent wireless frequency applications for smartphone devices. The methodology utilizes the use of two switches between the metallic frame and the system circuit board to tune the fundamental resonant frequency modes of a couple loop and a slot monopole. The design uses a microstrip-feeding strip line to excite the loop and slot modes, the feeding line also acts as an internal antenna structure. By combining two working states of the switches, the design covers all wireless standard up to 6 GHz. The simulated total efficiency of the proposed antenna is shown to be high enough to guarantee a successful operation for both the lower and higher bands.
A Deep Learning Method for Lung Segmentation on Large Size Chest X-Ray Image

Lung segmentation on chest X-ray image is an important task for image analysis and computer-aided diagnosis. It can improve the performance of diagnosis or lung rib suppression. Some methods have developed to address this problem. However, processing large chest Xray image is still a challenge. This study proposes a deep learning method for lung segmentation on large size chest X-ray image. The performance was evaluated on a set of fifty large chest X-ray images. The experimental results show that the proposed method is promising.
Network Embedding based on External Word Vectors

Network embedding, which preserves network's sophisticated features, aims to effectively learn the low-dimensional embedding of vertices to lower the computing and storage costs. Content information network like Twitter is defined as network with rich text information. Most research about content information network are based on the information of the network itself. By introducing external word vectors into the modeling process, we can make use of the external syntactic and semantic features. In this paper, we propose NE-EWV(Network Embedding based on External Word Vectors), which preserves the feature fusion representation from both semantic feature space as well as structural feature space. Experiments are conducted in three real-world content information network datasets. In link predict task, the AUC increases 7% to 19% compared with the models which only considering the structural features, increases 1% to 12% compared with the model considering structural and text features in most cases.
Monitoring Driving Stress using HRV

Driving stress is responsible for bad driving behavior and fatal accidents. This stress initiates from road factors and keeps negative consequences in overall traffic management as well as in transportation system. Bangladesh has poor road infrastructure, environment and management. We explore to know the Bangladeshi drivers stress level through HRV by developing a system. This system helps the drivers to manage their stress by creating self-awareness among the drivers regarding overall road safety.
The relationship between social capital in hospitals and emotional exhaustion in clinicians: A study in four German hospitals

Background: About 30% of doctors working in inpatient and outpatient departments suffer from burnout, characterized, for example, by emotional exhaustion. The prevention of burnout constitutes a great challenge for those responsible for the healthcare system. Aims: Research into the relationship between social capital in hospitals and the occurrence of emotional exhaustion in clinicians is still at an early stage. The aim of the current study is to examine the effects of social capital in the workplace on the emotional exhaustion of clinicians. Method: A questionnaire was posted to 2,644 employees working in four German hospitals, and 1,645 responded. Responses from the 277 clinicians (61%) are analyzed here. The questionnaire looked at symptoms of emotional exhaustion and levels of organizational social capital. Results: Logistic regression identified two significant predictors of emotional exhaustion in clinicians: low self-efficacy and subjectively perceived lower levels of social capital in the hospitals where they worked. The model accounted for 26 % of Nagelkerke’s R2. Conclusion: Efforts to create a good working atmosphere, with the readiness to provide mutual support and the pursuit of joint values and objectives within a hospital, may reduce the development of, or even prevent, emotional exhaustion in clinicians.
High Performance Oscillator Design Using High-Q Substrate Integrated Waveguide (SIW) Resonator

In order to cater the needs of advanced communication systems, silicon-based RF ICs experience a fast-growing phase. In this work, the design of CMOS oscillator using an SIW (Substrate Integrated Waveguides) based LC-tank circuit is proposed. SIW based LC-tank circuit is preferred in oscillator design to get high performances such as improved phase noise and wide tuning range. In this work, the designed oscillator operates at 4.1 GHz, using the SIW structures that model passive circuit components such as inductors and capacitors. The mentioned components are designed in SIW structures that operate in the range of 2 GHz to 10 GHz. For oscillator design, a balanced differential cross-coupled pairs using NMOS and PMOS transistors are used as the forward amplifier. This design uses 45 nm CMOS technology and results are analyzed in Cadence Virtuoso. The designed SIW components act as feedback elements, which are designed in Ansys HFSS. It is observed that the phase noise of GPDK provided LC-tank is −196.4 dBc at 100 MHz whereas SIW structures based LC-tank show a better phase noise, −246.9 dBc at 100 MHz. The power consumption is 4.976 milliWatts for GPDK provided LC-tank oscillator whereas for SIW based LC-tank is just 1.371 milliWatts at 1 V supply voltage.
Processing and Mechanical Performance of Hydroxyapatite-UHMWPE Composites

Natural hard tissues are composed of a mineral part called hydroxyapatite (HAp), interlocked with collagen fibers. Synthetic composites based hydroxyapatite and polyethylene have been studied for the last three decades. A product with this composition has been commercialized under a trade name (HAPEX™), which is a bio-stable composite. A common problem arouse in these composites, which was the lack of chemical bonding between HAp and each of the polymers. Therefore, improving the overall properties of these composites through chemical modifying the polymeric matrix is the main objective of the current study. An injection molding technique was used to prepare composites based on commercially available hydroxyapatite and both unmodified and chemically-modified versions of UHMWPE. The proportion of HAp added was up to 25% by weight. Results reflected the high sensitivity of the produced samples to slight variations in the assembly of the injection molding as well as changing its parameters. High quality samples with excellent homogeneity were produced. This was also reflected in consistent mechanical behavior during their tensile strength measurements. Morphology of selected composites showed a homogeneous distribution of the HAp particulates in the matrix of the polymers as well as absence of phase separation.
The efficacy of gastrodin in combination with folate and vitamin B12 on patients with epilepsy after stroke and its effect on HMGB-1, IL-2 and IL-6 serum levels

This study evaluated the efficacy of gastrodin in combination with folate (FOL) and vitamin-B12 (V-B12) on patients with epilepsy after stroke (EAS) and its effect on high-mobility group protein B1 (HMGB-1), interleukin-1 (IL-1), and IL-6 serum levels. The clinical data of 92 EAS patients admitted for treatment between April, 2014 and March, 2016 were collected. These patients were randomly divided into control and observation groups (n=46 each) using computer software. Patients in the control group were administered only regular antiepileptic drugs, whereas observation group patients also received a combination of gastrodin, FOL and V-B12. After treatment, we compared efficacy, frequency of epileptic seizure, and Montreal cognitive assessment (MoCA) scores. Serum homocysteine (HCY), FOL and V-B12 levels were detected 3 months later. Enzyme-linked immunosorbent assay (ELISA) was used to detect changes in serum HMGB-1, IL-2 and IL-6 levels at one day before treatment and on the 7th, 21st, 30th and 90th days after treatment. Pearson's correlation coefficient was utilized to analyze the correlations of HMGB-1 with IL-2 and IL-6. The total treatment effectiveness rate was 95.65% in the observation group, which was significantly higher than the control group (73.91%, p<0.05). Epileptic seizure frequency and MoCA scores significantly improved in the observation group (p<0.05). Serum HCY levels were significantly lower, whereas FOL and V-B12 serum levels were significantly higher, at 3 months post-treatment start in the observation group relative to control group (p<0.05). After treatment, serum HMGB-1, IL-2 and IL-6 levels progressively decreased over time in both groups, but observation group levels were significantly lower than in control group (p<0.05). Pearson's correlation coefficient analysis showed that HMGB-1 levels were positively correlated with IL-2 and IL-6 levels. A combination of gastrodin, FOL and V-B12 for EAS can significantly improve inflammatory response symptoms, decrease HCY levels, and increase FOL and V-B12 levels in serum while effectively controlling epileptic seizures, thus exhibiting relatively better clinical efficacy. Therefore, this combination treatment is worthy of being promoted in clinical practice.
Customer based Multi-objective Capacitor Allocation and DG Integration in a Microgrid Type Radial Distribution System

This paper presents customer based multi-objective capacitor allocations and DG integrations considering total cost, energy loss, SAIFI, SAIDI and AENS in sixty nine bus microgrid type radial distribution system. The multi-objective optimization has been approached here by evolutionary algorithm viz. Fish Electrolocation Optimization (FEO). Non dominated solutions achieved after performing multi-objective optimization using evolutionary technique have been analyzed by TOPSIS method for customer based decision making. Customers such as office/building, commercial and industrial types have been considered here for evaluation purpose. The results achieved after simulation for different customers are promising and encouraging in nature.
[Can individualized rehabilitation improve functional independence in elderly patients with COPD?].

Study objectives The aims of this study were to establish whether pulmonary rehabilitation (PR) improves domestic function and daily activity levels in COPD and whether individually targeted exercise is more effective than general exercise. Design Prospective randomized, controlled trial. Setting Outpatient PR program in secondary care. Participants One-hundred eighty patients (mean [±SD] age, 68.3 ± 8.6 years; FEV 1 , 0.95 ± 0.4 L; FEV 1 /FVC ratio, 0.51 ± 0.15; 111 male patients; 69 female patients) with stable COPD. One hundred twenty-one patients completed the study. Interventions Patients were randomized to a conventional 7-week general exercise program ([GEP] n=90) or an individually targeted exercise program ([ITEP] n=90). Measurement and results Daily activity was measured using ambulatory activity monitors (Z80 –32k V1 Int; Gaehwiler Electronics; Hombrechtikon, Switzerland). These were lightweight devices, which contained a uniaxial accelerometer. Domestic function was assessed by the Canadian Occupational Performance Measure (COPM). Exercise performance was assessed by the incremental shuttle walk test (ISWT) and the endurance shuttle walk test and health status by the chronic respiratory questionnaire–self-reported. Activity monitor counts increased by 29.18% (95% confidence interval [CI], 3.19 to 55.17; p=0.03) for the GEP and 40.63% (95% CI, 7.42 to 73.83; p=0.02) for the ITEP. Mean COPM performance scores increased by 1.71 (95% CI, 1.37 to 2.05; p=0.0001) for the GEP and 1.46 (95% CI, 1.05 to 1.87; p=0.0001) for the ITEP. Mean COPM satisfaction scores increased by 2.27 (95% CI, 1.74 to 2.81; p=0.0001) for the GEP and 2.04 (95% CI, 1.56 to 2.52; p=0.0001) for the ITEP. ISWT scores increased by 81.72 m (range, 63.83 to 99.62) for the GEP and by 85.52 m (range, 67.62 to 103.42) for the ITEP. No statistically significant difference was found between the general exercise group and the individually targeted exercise group for any outcome measure. Conclusions Pulmonary rehabilitation improves domestic function and physical activity. This study also demonstrates that general exercise training is as effective as individually targeted training.
Automated Robotic Moisture Monitoring in Agricultural Fields

Monitoring moisture level of land in a large-scale plantation is tedious. The main objective of this project is to use a robotic kit in collaboration with the on-field moisture sensor circuits, thereby creating an efficient and economical moisture monitoring system. A large agriculture field is divided into smaller grids. Each grid is placed with a moisture sensor. Whenever a sensor reports the soil to be dry, the robot goes to the concerned field for inspection. The path to the concerned field is found by applying Dijkstra's shortest path algorithm on the aerial image of the field. Then the total moisture content of the field is calculated by the robot using suitable image processing algorithms and reported accordingly. For developing and testing this work, a small study field was set up above which a camera was mounted at an appropriate height to capture its aerial view. Thus a prototype for an automated system of monitoring agricultural fields' moisture has been developed through this work.
Android Applications Scanning: The Guide

The booming of Android technology makes Android users and their devices with all running applications targeted by many security attackers. These attackers intent to inject malicious software in these applications to obtain the user’s data for several purposes. Nowadays, Android users are keen to install clean apps in their devices. At the same time, security solutions have been proposed to detect malware apps. This paper presents a general framework for Android applications scanning process. The aim is to guide researchers and developers to the main phases/steps required to analyze Android applications, check their trustworthiness and protect Android users from being victims to serious malware attacks. Moreover, this research highlights the metrics, tools, mechanisms and datasets that are mostly used throughout the scanning process.
Modeling and Detecting Drift in User Interest Based on Hierarchical Classification

During participating in Web 2.0 applications, people enjoy the convenience brought by Internet resources, but also encounter the problem of lost in Internet. This paper takes the Douban movie review as the background, and proposed a method for detecting the drift of user’s interests. The user’s comment and the frequency with which the user watches a certain type of movie are used to determine the extent to which the user is interested in the movie, and the parameters ${p}$ and ${q}$ are used to adjust the degree to which the two factors affect the interest weight. A hierarchical user interest classification tree (named HC-tree) and time window table are designed to maintain the interests and their weights in multi-granularities way. Also two functions $\lambda $ and $\psi $ are designed to quickly identify the drift of user interests. A large number of experimental results show that the proposed method is better on the correctness for user interest drift detection and is prior to other similar algorithms.
In silico models for drug-induced liver injury – current status

Introduction: Drug-induced liver injury (DILI) is one of the most important reasons for drug attrition at both pre-approval and post-approval stages. Therefore, it is crucial to develop methods that will detect potential hepatotoxicity among drug candidates as early and quickly as possible. However, the complexity of hepatotoxicity endpoint makes it very difficult to predict. In addition, there is still a lack of sensitive and specific biomarkers for DILI that consequently leads to a scarcity of reliable hepatotoxic data, which are the key to any modelling approach. Areas covered: This review explores the current status of existing in silico models predicting hepatotoxicity. Over the past decade, attempts have been made to compile hepatotoxicity data and develop in silico models, which can be used as a first-line screening of drug candidates for further testing. Expert opinion: Most of the predictive methods discussed in this review are based on the structural properties of chemicals and do not take into account genetic and environmental factors; therefore, their predictions are still uncertain. To improve the predictability of in silico models for DILI, it is essential to better understand its mechanisms as well as to develop sensitive toxicogenomics biomarkers, which show relatively good differentiation between hepatotoxins and non-hepatotoxins.