Miracle-dz/UniFAR-FTUs huggingface dataset:
Recurrent tuberculosis among HIV-coinfected patients: a case series from KwaZulu-Natal

Background Recurrent tuberculosis (TB) following TB treatment completion in HIV-infected individuals remains a major public health burden. We assessed the role of various risk factors in mediating the development of recurrent TB and subsequent resistance to antiretroviral therapy and anti-TB drugs. Patients and methods We analyzed secondary demographic, clinical, and laboratory data from medical records of five HIV-infected TB patients enrolled between 2009 and 2014 in a prospective observational study investigating TB recurrence. Paired clinical isolates of Myco-bacterium tuberculosis were typed by IS6110 restriction fragment length polymorphism analysis to determine the mechanism of TB recurrence. Plasma samples were genotyped to determine acquisition of HIV drug resistance mutations on antiretroviral treatment (ART). Results All five patients were HIV-coinfected, with a previous history of TB infection and prior exposure to anti-TB treatment, and residual lung damage, and demonstrated poor treatment adherence – significant risk factors linked to the development of recurrent TB disease. Furthermore, three of the five patients had multiple episodes of drug-susceptible TB infection with subsequent drug-resistant TB infection. Genotyping of the initial and recurrent M. tuberculosis isolates demonstrated three cases of recurrent TB because of relapse and two because of reinfection. All five patients had no mutations at ART initiation; however, by the end of the study follow-up, all patients developed dual class resistance. Conclusion This series demonstrates the complexity of recurrent TB in HIV coinfection. We highlight the challenges of managing coinfected patients and the increased propensity for the development of drug resistance. We report on the role of various risk factors mediating the development of resistance and subsequent clinical impact. This report underscores the need for structural clinical and adherence interventions for the management of complex treatment and dosing.
Optimum Design of a Multiple-Active-Bridge DC–DC Converter for Smart Transformer

The modular smart transformer (ST) is composed by several basic converters rated for lower voltage and power. In this paper, the quadruple active bridge (QAB) is used as the basic block for the modular ST. In this application, the efficiency and cost are the most important design parameters. Therefore, the paper focus on the design of the converter, with the aim to optimize its efficiency, taking the cost into consideration. To do so, the losses of all components are carefully modeled and a computer-aided design is used, where an algorithm to calculate the losses and cost is developed, allowing to perform multiobjective optimization. Additionally, silicon IGBTs and silicon carbide mosfets are considered for the design and the performance of the converter using both semiconductors technology is compared. Experimental results obtained for the optimized 20 kW QAB converter has shown an efficiency of 97.5%.
Operation and Control Scheme of a Five-Level Hybrid Inverter for Medium-Voltage Motor Drives

This paper proposes a control method for a five-level hybrid flying-capacitor (5L-HFC) inverter, of which structure stems from the conventional five-level active neutral-point-clamped (5L-ANPC) topology by dividing the dc-link stage into three series-connected capacitors. In this inverter, the voltage stress on the power switches connected to the dc link is reduced by a half compared with that of the 5L-ANPC topology, thus the lower number of equally voltage-rated power devices can be employed. Also, the power losses in the 5L-HFC inverter are more evenly distributed than in the 5L-ANPC. In order to balance the dc-link capacitor voltages, a third-order harmonic offset injection is applied. When a diode rectifier is used to supply the dc bus voltage, the balancing method is not effective if the modulation index is higher than 0.64. Thus, an auxiliary circuit is needed to support the balancing of the dc-link capacitor voltages. However, the unbalancing problem can be overcome in a full-range operation without the auxiliary circuit if the back-to-back configuration is utilized. Finally, simulation and experiment results have verified the performance of the 5L-HFC inverter with the proposed control method.
Success Factors for Sustainable Industry-University Cooperative education; Based on the Case Study of Dongguk University

Recent rapid technology development, globalization, and the advent of the Fourth Industrial Revolution have accelerated the need to cultivate human resources through collaboration between industries and universities. In Korea, many universities have made efforts to strengthen university-industry cooperation through a government-led initiative, LINC. Despite the quantitative growth, however, there have still been some difficulties to systemize university-industry cooperative education programs in the entire university level. In order to lead to a longterm, sustainable industry-university cooperative education, it is necessary to examine in depth what factors may affect to strengthen and improve it, and how the difficulties or obstacles may be overcome. We introduce the trend of industry-university cooperation in Korea and analyze the success factors and obstacles for industry-university cooperative education based on related literature reviews. We also conduct case studies of Dongguk University, one of the universities participating in the LINC project, to verify the factors derived from the literature reviews. Through the case analysis, we refine the factors in order to be used generally as a tool for evaluating and improving industry-university cooperative education. Through discussions, we provide some insights and a practical framework based on success factors to develop sustainable industry-university cooperative education.
Compressed Sensing With Approximate Message Passing Using In-Memory Computing

In-memory computing is a promising non-von Neumann approach where certain computational tasks are performed within resistive memory units by exploiting their physical attributes. In this paper, we propose a new method for fast and robust compressed sensing (CS) of sparse signals with approximate message passing recovery using in-memory computing. The measurement matrix for CS is encoded in the conductance states of resistive memory devices organized in a crossbar array. In this way, the matrix-vector multiplications associated with both the compression and recovery tasks can be performed by the same crossbar array without intermediate data movements at potential <inline-formula> <tex-math notation="LaTeX">${O}{(}{1}{)}$ </tex-math></inline-formula> time complexity. For a signal of size <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula>, the proposed method achieves a potential <inline-formula> <tex-math notation="LaTeX">${O}{(}{N}{)}$ </tex-math></inline-formula>-fold recovery complexity reduction compared with a standard software approach. We show the array-level robustness of the scheme through large-scale experimental demonstrations using more than 256k phase-change memory devices.
Single-cell murine genetic fate mapping reveals bipotential hepatoblasts and novel multi-organ endoderm progenitors

ABSTRACT The definitive endoderm (DE) is the embryonic germ layer that forms the gut tube and associated organs, including thymus, lungs, liver and pancreas. To understand how individual DE cells furnish gut organs, genetic fate mapping was performed using the Rosa26lacZ Cre-reporter paired with a tamoxifen-inducible DE-specific Cre-expressing transgene. We established a low tamoxifen dose that infrequently induced heritable lacZ expression in a single cell of individual E8.5 mouse embryos and identified clonal cell descendants at E16.5. As expected, only a fraction of the E16.5 embryos contained lacZ-positive clonal descendants and a subset of these contained descendants in multiple organs, revealing novel ontogeny. Furthermore, immunohistochemical analysis was used to identify lacZ-positive hepatocytes and biliary epithelial cells, which are the cholangiocyte precursors, in each clonally populated liver. Together, these data not only uncover novel and suspected lineage relationships between DE-derived organs, but also illustrate the bipotential nature of individual hepatoblasts by demonstrating that single hepatoblasts contribute to both the hepatocyte and the cholangiocyte lineage in vivo. Summary: Single-cell genetic fate mapping reveals novel progenitor relationships between endoderm-derived organs and demonstrates that individual hepatoblasts contribute both hepatocytes and cholangiocytes to the mature liver.
Deep Learning for an Effective Nonorthogonal Multiple Access Scheme

Nonorthogonal multiple access (NOMA) has been considered as an essential multiple access technique for enhancing system capacity and spectral efficiency in future communication scenarios. However, the existing NOMA systems have a fundamental limit: high computational complexity and a sharply changing wireless channel make exploiting the characteristics of the channel and deriving the ideal allocation methods very difficult tasks. To break this fundamental limit, in this paper, we propose a novel and effective deep learning (DL)-aided NOMA system, in which several NOMA users with random deployment are served by one base station. Since DL is advantageous in that it allows training the input signals and detecting sharply changing channel conditions, we exploit it to address wireless NOMA channels in an end-to-end manner. Specifically, it is employed in the proposed NOMA system to learn a completely unknown channel environment. A long short-term memory (LSTM) network based on DL is incorporated into a typical NOMA system, enabling the proposed scheme to detect the channel characteristics automatically. In the proposed strategy, the LSTM is first trained by simulated data under different channel conditions via offline learning, and then the corresponding output data can be obtained based on the current input data used during the online learning process. In general, we build, train and test the proposed cooperative framework to realize automatic encoding, decoding and channel detection in an additive white Gaussian noise channel. Furthermore, we regard one conventional user activity and data detection scheme as an unknown nonlinear mapping operation and use LSTM to approximate it to evaluate the data detection capacity of DL based on NOMA. Simulation results demonstrate that the proposed scheme is robust and efficient compared with conventional approaches. In addition, the accuracy of the LSTM-aided NOMA scheme is studied by introducing the well-known tenfold cross-validation procedure.
The Impact of Innovation: How the Changing Nature of Data Will Challenge FDA’s Regulatory Framework

Rapid advances in technology and our understanding of disease will lead to a shift in how the health care system thinks about data, which will in turn challenge current regulatory constructs. In the future, there will be a shift away from milestone-based data to continuous, contextual data; we believe this data shift will impact the current model of medical product regulation, with potential implications across the regulatory landscape, reflecting the convergence of clinical development and clinical practice.
Learning Long-Range Perception Using Self-Supervision From Short-Range Sensors and Odometry

We introduce a general self-supervised approach to predict the future outputs of a short-range sensor (such as a proximity sensor) given the current outputs of a long-range sensor (such as a camera). We assume that the former is directly related to some piece of information to be perceived (such as the presence of an obstacle in a given position), whereas the latter is information rich but hard to interpret directly. We instantiate and implement the approach on a small mobile robot to detect obstacles at various distances using the video stream of the robot's forward-pointing camera, by training a convolutional neural network on automatically-acquired datasets. We quantitatively evaluate the quality of the predictions on unseen scenarios, qualitatively evaluate robustness to different operating conditions, and demonstrate usage as the sole input of an obstacle-avoidance controller. We additionally instantiate the approach on a different simulated scenario with complementary characteristics, to exemplify the generality of our contribution.
Trajectories of callous–unemotional traits from childhood to adolescence in referred youth with a disruptive behavior disorder who received intensive multimodal therapy in childhood

Purpose Our aims were to explore the developmental trajectories of callous–unemotional (CU) traits using a growth curve analysis in Italian children with disruptive behavior disorders treated with a multimodal intervention, and to test both predictors and distal outcomes of CU traits trajectories. Patients and method One hundred and sixty-eight children were enrolled, of whom 24 were lost in the follow-up and 144 were followed up from ages 8–9 to 14–15 years with four assessment points. Patients included 128 males (88.9%) with a mean age of 8.7 years, 96 with oppositional defiant disorder (66.7%) and 48 with conduct disorder (CD) (33.3%). The developmental trajectories of CU traits were assessed with the Inventory of Callous–Unemotional Traits (ICU). Results Our findings revealed that CU features were likely to fit a quadratic model from childhood to adolescence. The CU traits tended to decrease during childhood, with stabilization in adolescence and a significant variability in the growth curves. Pretreatment CD and higher levels of externalizing behavioral problems were associated with higher level of CU traits at baseline, whereas positive parenting was associated with lower levels. No significant effects were found for all the other predictors (socioeconomic status, negative parenting, combined pharmacotherapy). Regarding outcomes into adolescence, both higher levels of CU traits at the baseline and a lower decrease of CU traits across time points predicted a higher risk of CD diagnosis, and higher rate of referrals to mental health services and of substance use. Furthermore, pretreatment CD and negative parenting predicted a higher risk of substance use into adolescence. Conclusion Our findings suggest that a close monitoring of CU traits in referred children with disruptive behavior disorders may help to detect the patients at higher risk of poor outcome.
A novel paradigm for urban environment characterization using ascending and descending TerraSAR-X data

The new Very High Resolution radar satellites, with a spatial resolution up to 1 meter, give a unique opportunity in the context of urban applications. This paper presents an approach for automatic detection of built-up areas based on the analysis of single-polarized TerraSAR-X images. The proposed methodology includes a specific preprocessing of the SAR data and an automated image analysis procedure. The preprocessing aims at providing a multi-resolution texture layer based on the analysis of local speckle characteristics to automatically extract settlements. The technique is tested on 2 TerraSAR-X images acquired over the city of Pavia, northern Italy, in February3 2008. The overall accuracies between 78% and 85% for the derived city footprints demonstrate the high potential of the proposed analysis for built up areas detection. In addition, the joint use of both acquisitions allow to reach a total accuracy of 89%. Although the methodology needs to be further tested on different case studies, the investigation demonstrates the feasibility and the utility of the combined use of ascending and descending SAR intensities data for complete urban footprint extraction.
Key Components of an E-Learning Module in Statistics for Computer Science Students

The traditionally taught face-to-face course in statistics for undergraduate non-statistics students suffered from several problems: Time and location constraints, different preknowledge in mathematics, lack of motivation, no use of statistical software etc. Therefore, we decided to develop an e-learning course in statistics to reduce the impact of these problems. This paper describes the different components developed for the e-learning course as well as the teaching framework. Preliminary results from the first in-class usage of the e-learning course indicate positive qualitative effects. In addition, we discuss which of the e-learning components the students mainly preferred to use. Furthermore, we show that the exam results from the e-learning course are significantly better than the corresponding results from the face-to-face course in this first in-class usage.
Measuring Cognitive Performance on programming Knowledge: Classical Test Theory versus Item Response Theory

This research aims to study the strengths and weaknesses of Classical Test Theory (CTT) and Item Response Theory (IRT) and to make suggestion of what situation should the theories be adopted. The test-instruments were developed to measure the cognitive performance of students when learning programming language through web-mediated environment. This quasi-experimental study at the same time purpose to measure the effects of web-mediated learning environment in acquisition abstract knowledge. However, this paper only reports part of the research findings which involves the comparison between CTT and IRT mainly on test-instruments. The test-items show different interval scale when locating at the same measurement scale. The location of test-items analyzing using raw scores have different intervals between them. However, when using logit (the measurement unit in IRT), the test-items locate at the same distance apart. Therefore, by simply summing the raw scores to measure students' cognitive performance will leads to inconsistency in constructing a defensible unit of measurement. The finding suggests that the used of CTT and IRT should be carefully examining depends on the context of study.
Subthreshold Behavior of Floating-Gate MOSFETs With Ferroelectric Capacitors

The subthreshold behavior of floating-gate MOSFETs connected with ferroelectric capacitors (FeCs) was investigated experimentally and theoretically. We found that the subthreshold swing (SS) decreases with decreasing ferroelectric capacitance (i.e., with the decreasing FeC area), in contrast to the voltage dividing effect of regular dielectric capacitors. The ferroelectric polarization switching counteracts the effect of voltage dividing by introducing an additional charge onto the floating gate, which improves the SS and leads to the observed SS dependence. The SS improvement caused by the polarization switching is different from the negative capacitance effect, which is helpful to further understand the ferroelectric transistor.
Admission Control Implementation for QoS Performance Evaluation over SDWN

Local area networks based on the IEEE 802.11 standard represent the largest deployment of wireless network technology worldwide, and such networks have accelerated the growth of multimedia applications. This type of content requires special settings to ensure quality of service, specially in the case of real-time video streaming. Currently, the state of the art mostly focuses on solving the QoS problem over traditional IEEE 802.11 networks, but few works try to evaluate the performance of QoS mechanisms over software-defined WLAN network scenarios. The Software Defined Networking (SDN) architecture helps to improve, simplify, and program the network management according to specific needs, making the design and deployment of new control policies easier and faster to implement. This paper presents an analysis of throughput and delay metrics behavior of QoS mechanisms from IEEE 802.11 standard such as admission control, implemented over a emulation scenario based on NS-3 integrated with SDN.
An Active Damping Stabilization Scheme for the Suppression of the DC-Link Oscillation in Metro Traction Drive System

The dc-link oscillation is a typical phenomenon happening in the metro traction drive system (MTDS) due to the negative input impedance associated with the constant power load, which is considered as a thorny problem that needs to be resolved well. This paper proposes an active damping stabilization scheme based on inverter power regulation that can effectively eliminate the dc-link oscillation of the MTDS. The proposed stabilization scheme is mainly composed of two parts: 1) the generation of damping power; and 2) the inverter power control loop. The damping power, linked to the deviation of the dc-link voltage through the gain units, is fed back to the vector-controlled induction motor drive system to establish the inverter power control loop. From this control loop, the added q -axis current is obtained, which is regarded as the stabilization term. The additional stabilization term is combined with the q-axis current command of the induction motor for achieving the improvements of the drive performance and the system stability. The effectiveness of the proposed active damping stabilization scheme is verified by simulation and hardware-in-the-loop tests.
Oriented Feature Selection SVM Applied to Cancer Prediction in Precision Medicine

Advances in the gene sequencing technology and the outbreak of artificial intelligence have made precision medicine a reality recently. Applying machine learning algorithms to cancer prediction using gene expression data helps to discover the link between genetic data and cancer, which will promote the development and application of precision medicine. Considering the natural order of genes, a new classification method that combines fused lasso and elastic net as regularization for linear support vector machine (SVM), which uses huberized hinge loss as the loss function, is proposed in this paper, which we name it oriented feature selection SVM (OFSSVM). Due to the characteristics of the elastic net and fused lasso, the OFSSVM can not only provide automatic feature selection, but also average the adjacent coefficients, resulting in a sparse and smooth solution. We demonstrate its effectiveness in both binary classification and multiclass classification in the sense of comprehensive evaluation that not only the classification accuracy but also the interpretability are considered. The experiments show that the OFSSVM is an appealing compromise between interpretability and classification accuracy, and is superior to other traditional methods in the sense of comprehensive evaluation.
Ezrin enrichment on curved membranes requires a specific conformation or interaction with a curvature-sensitive partner

One challenge in current cell biology is to decipher the biophysical mechanisms governing protein enrichment on curved membranes and the resulting membrane deformation. The ERM protein ezrin is abundant and associated with cellular membranes that are flat or with positive or negative curvatures. Using in vitro and cell biology approaches, we assess mechanisms of ezrin’s enrichment on curved membranes. We evidence that ezrin (ezrinWT) and its phosphomimetic mutant T567D (ezrinTD) do not deform membranes but self-assemble anti-parallelly, zipping adjacent membranes. EzrinTD’s specific conformation reduces intermolecular ezrin interactions, allows binding to actin filaments, and promotes ezrin binding to positively curved membranes. While neither ezrinTD nor ezrinWT senses negative membrane curvature alone, we demonstrate that interacting with curvature sensors I-BAR-domain proteins facilitates ezrin enrichment in negatively curved membrane protrusions. Overall, our work reveals new mechanisms, specific conformation or binding to a curvature sensor partner, for targeting curvature insensitive proteins to curved membranes.