Miracle-dz/UniFAR-FTUs huggingface dataset:
The NF-κB RelA Transcription Factor Is Critical for Regulatory T Cell Activation and Stability

Regulatory T cells (Tregs) play a major role in immune homeostasis and in the prevention of autoimmune diseases. It has been shown that c-Rel is critical in Treg thymic differentiation, but little is known on the role of NF-κB on mature Treg biology. We thus generated mice with a specific knockout of RelA, a key member of NF-κB, in Tregs. These mice developed a severe autoimmune syndrome with multi-organ immune infiltration and high activation of lymphoid and myeloid cells. Phenotypic and transcriptomic analyses showed that RelA is critical in the acquisition of the effector Treg state independently of surrounding inflammatory environment. Unexpectedly, RelA-deficient Tregs also displayed reduced stability and cells that had lost Foxp3 produced inflammatory cytokines. Overall, we show that RelA is critical for Treg biology as it promotes both the generation of their effector phenotype and the maintenance of their identity.
Continuity in Medical Care Using Electronic Data Interchange.

Abstract Having a central position in Dutch Health Care, GPs are an ideal choice for the registration and coordination of the medical activities of their patients. The complexity of the task, however, does make demands on the information-processing capacities of GPs. The use of computer-based medical records is gradually increasing, thus bringing fully integrated Electronic Data Interchange (EDI) within reach. This paper describes the setup of the Communication Project Apeldoorn (COPA), the aim of which is to examine the contribution of EDI to quality, efficiency and reduction in costs in Medical Health Care. Currently, admission/discharge reports and laboratory test results are sent regularly to the GPs, using the EDIFACT message standard.
National bias in citations in urology journals: parochialism or availability?

Objective To determine any bias by authors of different nationalities in their citation rate of selected urological journals in papers published in the British Journal of Urology and the Journal of Urology. Methods Using a simple computer program and text files of accepted reports in the BJU, or those available on CD-ROM from J Urol, 212 recent papers in the BJU and 111 from J Urol were analysed to determine the number of citations to four major urological journals (BJU, J Urol, Eur Urol and Urology). The frequencies of citations to these journals were then compared with the national origin of the author(s), grouped as UK, Europe, North America and Other. Results In both the BJU and J Urol the citation rates of the selected journals differed significantly among authors from different regions. In BJU papers, the citation rate of the BJU was highest by UK authors and their citation rate of J Urol was amongst the lowest of the rates for J Urol. The highest citation rate for J Urol was that by European authors. American authors cited the BJU least, citing the J Urol about five times more often than they cited the BJU. Of the papers in the J Urol sample, over 60% were from American authors, with only four from UK authors; thus the UK group was not analysed separately but included in the European group. The mean citation rate of J Urol was highest in papers by American authors, at about 14 times that for citations to the BJU. The citation rates for the other two journals were not significantly different with nationality or journal, but were generally much lower in J Urol than in the BJU. Conclusion There are significant differences in citation rates both with authors’ nationality and between journals. Citation rates may be influenced by journal accessibility, perceived journal ‘prestige’ (impact factor) or national bias. Authors, editors and reviewers should be aware of this potential bias in citation habits. Authors should strive to conduct exhaustive searches using electronic methods, so that all relevant papers are assessed, regardless of their origin.
‘Covering All Points': conference review

T he Royal College of Surgeons in London played host in May to the ‘Covering All Points’ conference on the prevention of occupational blood-borne virus transmission in health care. The aim of the conference was two-fold. First, it considered how the number of accidents occurring in healthcare organisations could be measured accurately. It then discussed how to control and reduce the risks inherent in them.
Javanese Gender Speech Recognition Using Deep Learning And Singular Value Decomposition

Speech detection is an interesting research field. Research in speech recognition uses a variety of models that aim to improve accuracy, one of which is by using Deep Learning, but high dimensional data problems are one of the problems that cause a decrease in the quality of speech recognition accuracy. This paper discusses the gender voice recognition of Javanese people who are processed using the Mel Frequency Cepstral Coefficient (MFCC) extraction feature, then voice classification is done using the Deep Learning method combined with the Singular Value Decomposition (SVD) method in reducing the dimensions of the data produced. By using a 70% split ratio for training data and 30% for testing data the results of the research show that the Deep Learning method's accuracy is 97.78% higher than the Logistic Regression method of 95.56% and SVM of 93.33%. Speech recognition research shows that the Deep learning and SVD method can be used in performing speech recognition with a high degree of accuracy.
Improving the calibration time of traffic simulation models using parallel computing technique

The calibration procedure for traffic simulation models can be a very time-consuming process in the case of a large-scale and complex network. In the application of Evolutionary Algorithms (EA) such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for calibration of traffic simulation models, objective function evaluation is the most time-consuming step in such calibration problems, because EA has to run a traffic simulation and calculate its corresponding objective function value once for each set of parameters. The main contribution of this study has been to develop a quick calibration procedure for the parameters of driving behavior models using EA and parallel computing techniques (PCTs). The proposed method was coded and implemented in a microscopic traffic simulation software. Two scenarios with/without PCT were analyzed using the developed methodology. The results of scenario analysis show that using an integrated calibration and PCT can reduce the total computational time of the optimization process significantly - in our experiments by 50% - and improve the optimization algorithm’s performance in a complex optimization problem. The proposed method is useful for overcoming the limitation of computational time of the existing calibration methods and can be applied to various EAs and traffic simulation software.
Custom virtual machine implementation and its influence on executable static properties

There are many ways how to change static properties of an executable code. Packing and encryption are the most common ways. All these techniques suffer from von Neumann architecture model, which means that the code to be executed must be loaded into the primary memory. So usually the analyst is able to dump the unpacked code from the memory. Virtual machine-based obfuscators try to solve this issue by implementing custom RISC-based interpreters. So even if the analyst is able to dump the shell code from the memory, only proprietary and unknown instruction set based shell code is found, which decreases the value of current tools. The goal of this paper is to design and implement custom metamorphic reconfigurable virtual machine using instruction substitution method and measure the influence of this technique on static properties. Measurement are made on Fibonacci order implementation in custom shell code.
Enhancements of Robust Transmission for Block Compressed Sensing with LASSO

Transmission of compressed multimedia forms an important part for practical applications. Block compressed sensing (BCS) is a newly developed research in multimedia compression studies. Due to the fact that compressed data are vulnerable to the effects caused by lossy channels, protection schemes like multiple description coding (MDC) may need to be applied in advance. To enhance the protected results, we apply the least absolute shrinkage and selection operator (LASSO) for obtaining better results for reconstruction of BCS data. Simulations have shown the potential of LASSO with other protection mechanisms for the transmission of block compressed sensing.
Prediction of mortality and occurrence of complications for gastric cancer patients

Gastric cancer is one of the most prevalent types of cancer in the whole world, affecting millions of people over the last decades. Its symptoms are ambiguous, which leads to late diagnoses, reducing the patients' chances of survival. In most countries, routine screenings are not usual, which also contributes to the detection of this gastric malignancy in later and more dangerous (and often fatal)stages. One of the main focus of improving healthcare services related to gastric cancer relies on increasing the survival rates. This and predicting if a patient will suffer from any complication following the surgery can aid the healthcare professionals in selecting better and more efficient treatment strategies. Thus, this constitutes as the aims of this study which will test and compare a set of classification models in order to improve the prediction accuracy. Data mining techniques will be put into use, since it's been proved they are one of the best ways of producing useful information for many businesses, including healthcare.
Semi-Supervised Deep Neural Network for Joint Intensity Estimation of Multiple Facial Action Units

Facial action units (AUs) are defined to depict movements of facial muscles, which are basic elements to encode facial expressions. Automatic AU intensity estimation is an important task in affective computing. Previous works leverage the representation power of deep neural networks (DNNs) to improve the performance of intensity estimation. However, a large number of intensity annotations are required to train DNNs that contain millions of parameters. But it is expensive and difficult to build a large-scale database with AU intensity annotation since AU annotation requires annotators have strong domain expertise. We propose a novel semi-supervised deep convolutional network that leverages extremely limited AU annotations for AU intensity estimation. It requires only intensity annotations of keyframes of training sequences. Domain knowledge on AUs is leveraged to provide weak supervisory information, including relative appearance similarity, temporal intensity ordering, facial symmetry, and contrastive appearance difference. We also propose a strategy to train a model for joint intensity estimation of multiple AUs under the setting of semi-supervised learning, which greatly improves the efficiency during inference. We perform empirical experiments on two public benchmark expression databases and make comparisons with state-of-the-art methods to demonstrate the effectiveness of the proposed method.
AccessE-Algorithm to detect the accessible segments during LncRNA-mRNA interactions

Long noncoding RNAs (lncRNAs) constitutes a class of noncoding RNAs which are versatile molecules and perform various regulatory functions. A significant number of lncRNAs have been found in different organisms including Homo sapiens, but their functions are yet to be explored fully. LncRNAs target protein coding genes or mRNAs to regulate different functions. Hence it is highly challenging to find out its key target molecule and understand the function of these molecules. Predicting accessible energy between an lncRNA and an mRNA is an essential step to identify lncRNA targets further. The existing algorithms used for accessible energy calculation has several limitations and are unable to predict comprehensive interaction between lncRNAs and mRNAs. We have developed AccessE which identifies accessible segments of lncRNA and mRNA and provides the accessible energy of the two molecules. AccessE has been used to calculate the accessible energy for a set of experimentally validated lncRNA-target mRNA pairs. Proper evaluation of accessible energy between multiple accessible segments of lncRNA and mRNA, provides in-depth understanding of lncRNA-mRNA interaction.
Constructing and Managing Multi-Granular Linguistic Values Based on Linguistic Terms and Their Fuzzy Sets

Constructing and managing multi-granular linguistic values are more and more important for linguistic decision making in big data or social computing environments, linguistic variable is the fundamental of constructing and managing multi-granular linguistic values. Based on analysis of linguistic values and drawbacks of symbolic or fuzzy set methods in processing linguistic information, a linguistic value is expressed by a formal linguistic concept, which is constructed by a linguistic term and it’s fuzzy sets, i.e., intension (name) and extension (meaning) of the concept are a linguistic term and it’s fuzzy sets. A new symbolic translation based on fuzzy sets is provided to obtain formal 2-tuple linguistic concepts, which are continuous formal linguistic concepts. By using linguistic hedges, the hierarchy of multi-granular formal linguistic concepts is constructed, and managing multi-granular linguistic values is carried out by a new transformation function between formal linguistic concepts of the hierarchy. Cases study shows that the proposed method combines advantages of symbolic approaches and fuzzy set methods in linguistic information processing and overcomes their drawbacks due to fuzzy sets and linguistic term as entity in linguistic information processing based on formal linguistic concepts, intensions are utilized to deal with linguistic information and extensions are used to represent meanings and obtain natural or artificial language concepts. It seems that constructing and managing multi-granular linguistic values via formal linguistic concepts is an useful and alternative method in linguistic information processing.
Explainable Prediction of Chronic Renal Disease in the Colombian Population Using Neural Networks and Case-Based Reasoning

This paper presents a neural network-based classifier to predict whether a person is at risk of developing chronic kidney disease (CKD). The model is trained with the demographic data and medical care information of two population groups: on the one hand, people diagnosed with CKD in Colombia during 2018, and on the other, a sample of people without a diagnosis of this disease. Once the model is trained and evaluation metrics for classification algorithms are applied, the model achieves 95% accuracy in the test data set, making its application for disease prognosis feasible. However, despite the demonstrated efficiency of the neural networks to predict CKD, this machine-learning paradigm is opaque to the expert regarding the explanation of the outcome. Current research on eXplainable AI proposes the use of twin systems, where a black-box machine-learning method is complemented by another white-box method that provides explanations about the predicted values. Case-Based Reasoning (CBR) has proved to be an ideal complement as this paradigm is able to find explanatory cases for an explanation-by-example justification of a neural network’s prediction. In this paper, we apply and validate a NN-CBR twin system for the explanation of CKD predictions. As a result of this research, 3,494,516 people were identified as being at risk of developing CKD in Colombia, or 7% of the total population.
An Efficient Method of Fetching Sensor Values Using Shared Memory

To monitor a car in racing competitions, we have developed a system that consists of sensor nodes, a server, and applications. Because the sensor nodes work on batteries, low power consumption was an important design consideration. We needed to acquire sensor values but also be able to run the system for a long time. Our proposed method that efficiently acquires sensor values from the sensors. In the conventional device driver general-purpose input/output (GPIO) method, processes in the sensor nodes invoke system calls to access the sensors requires significant system overhead. In our proposed method, the processes and device drivers share the same memory. Using our method, device drivers periodically store sensor values in shared memory, while processes fetch sensor values by accessing memory. Because memory access is used instead of system calls, fetching the sensor values requires only low overhead and low power consumption. Experimental results have confirmed that our method reduces processing time for fetching sensor values by 35% when compared with the conventional GPIO driver method.
Mathematical Models for Resonant Radiation of Plane Wave Packets on Layered, Cubically Polarizable Gratings – Existence of Solutions

Based on a unified model developed by the authors in previous works, the investigation of resonant radiation pro- cesses of waves on a nonmagnetic, isotropic, linearly polarized (E-polarization), cubically polarizable, layered and (possibly) longitudinally periodic, dielectric structure, which is excited by a packet of plane waves, is continued. In this paper a first rigourous result on the existence of (exact) solutions of this model is demonstrated. Numerical results illustrating the dependence of the energy properties of the resonant wave radiation on the magnitude of the susceptibility parameter of the medium at the resonant excitation frequency complete the presentation.
A new Tunable Pseudo-Resistor for Extremely/Ultra Low Frequency applications

This paper presents a new architecture for a pseudo-resistor (PR) implemented with MOS transistors, working in subthreshold region, tunable from Mega $\Omega$ to Giga $\Omega$ range. The proposed PR is programmed by controlling the $V_{GS}$ of the MOS transistors with a differential pair. An ultra-low frequency bandpass filter which uses the proposed PR with adjustable highpass cut-off frequency for a tuning range of 0.28 Hz to 900 Hz was designed. The simulated total harmonic distortion is $< \mathbf{1}\%$ for an input voltage of $\mathbf{224}\ mV_{pp}$. Simulations were performed using HSPICE Analog Design Environment and ON-SEMI 0.5 $\mu \mathbf{m}$ technology parameters which validates the feasibility of the proposed circuit.
Fixed-time robust output feedback control of a restricted state biped robot based on a tangent barrier Lyapunov function

The aim of this study is developing a robust controller with state dependent gains which guarantees the fixed-time robust convergence of the tracking error trajectories at each articulation for a suspended biped robot. The satisfaction of the state restrictions is justified with the controller gains design that are calculated using a Barrier tangent-type Lyapunov candidate function. The explicit adaptation law is obtained with the analysis of the controlled variant of the Barrier function, including the deviation terms for the selected adaptive gains. The virtualized model of the biped robot serves as a testing platform for the suggested controller, this computer-aided model robot operates as numerical test bench for the robust constraint controller. Some numerical simulation demonstrate the application of the state feedback controller with state restrictions and the gain law. The comparison of the tracking performance implementing the suggested controller and the regular proportional-integral-derivative form confirms the origin as a fixed-time stable equilibrium point for the tracking error while the state space restrictions are satisfied.
Comparison of Two Internal Miller Compensation Techniques for LDO Regulators

Internal frequency compensation is required to achieve stable operation of fully integrated Low Dropout (LDO) regulators without relying on an external $\mu F$ capacitor at the output node. This paper evaluates the performance of two Miller-based frequency compensation strategies: current buffer LDO (CB-LDO) and Basic Miller LDO (BM-LDO), both using a two-stage LDO core. Parameters such as overshoot, undershoot, settling time, power consumption and dropout voltage are measured and compared for 1.8V regulated output LDOs, verifying the impact of the compensation technique on the time response of the regulators.
Statistical Analysis of Train Delay and Delay Propagation Patterns in a High-Speed Railway System

This study aims to investigate delays, delay increases, and delay recovery characteristics, by using statistical methods to clarify delay propagation patterns according to historical records of the Wuhan-Guangzhou high-speed railway (HSR) in China in 2014 and 2015. Specifically, we examined arrival and departure delay duration distributions and used heatmaps to demonstrate the spatiotemporal frequency distribution of delays, delay increases, and delay recovery, and the heatmaps clearly show hot spots (coordinates with high frequencies) in a timetable. Then, we separated delays as discrete intervals according to their severity, and analyzed the delay increasing frequency and the delay increasing severity within each interval, so as to clarify the relationships of delay increasing probability and delay increasing severity with delay extents. Next, we investigated the observed delay recoveries and prescheduled buffer times at (in) station (section), which demonstrate the recovery ability of each station and section. Finally, to understand the key influencing factor of delay propagation, we analyzed the relationship between capacity utilization and delays, delay increases, and delay recoveries, by examining their Pearson correlation coefficients. These indicate that delay frequencies and delay increasing frequencies with Pearson correlation coefficients as high as 0.9 are highly dependent on capacity utilization. The uncovered delay propagation patterns can enrich dispatchers’ experience, and improve their decision-making ability during real-time dispatching in HSR.
Application of Regularizing Algorithms for Solving Ill-conditioned LAS Arising in Problems of Scatterer Form Reconsttuction

The algorithms enabled to regularize the ill-posed problems of body form reconstruction are proposed and tested. The first of them is based on the usual Tikhonov regularization and presents the iterative procedure, which minimize the deviation of the obtained form of scatterer on the desired one in the each iteration. The second one is based on the known dynamical system method allows to obtain the scatterer with form, norm of with is as close as possible to the norm of the desired form. The effectiveness of both the algorithms is compared at the different geometries of problem.
Robust Nonlinear flight Control of a Power-Generating tethered kite

The motion description of an airborne wind energy (AWE) system is very complex and is not suitable to design a controller. In recent years, simplified models have been developed for this kind of systems. In this paper a controller is designed using a simplified model and is based on the sliding mode technique. The designed controller is robust against model uncertainties and disturbances in wind velocity. Some simulation results are presented with a complete generation cycle where the wind velocity and the kite glide ratio are perturbed.