Miracle-dz/UniFAR-FTUs huggingface dataset:
HBV Infection and Host Interactions: The Role in Viral Persistence and Oncogenesis

Hepatitis B virus (HBV) is a major cause of chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma. Despite the advent of vaccines and potent antiviral agents able to suppress viral replication, recovery from chronic HBV infection is still an extremely difficult goal to achieve. Complex interactions between virus and host are responsible for HBV persistence and the risk of oncogenesis. Through multiple pathways, HBV is able to silence both innate and adaptive immunological responses and become out of control. Furthermore, the integration of the viral genome into that of the host and the production of covalently closed circular DNA (cccDNA) represent reservoirs of viral persistence and account for the difficult eradication of the infection. An adequate knowledge of the virus–host interaction mechanisms responsible for viral persistence and the risk of hepatocarcinogenesis is necessary for the development of functional cures for chronic HBV infection. The purpose of this review is, therefore, to analyze how interactions between HBV and host concur in the mechanisms of infection, persistence, and oncogenesis and what are the implications and the therapeutic perspectives that follow.
A Modular Unipolar/Bipolar High-Voltage Pulse Generator Suitable for High Resistive Load

This article presents two novel high-voltage pulse generator (HVPG) structures. These modular topologies are based on the buck-boost converter (BBC) operated in discontinuous conduction mode (DCM). The proposed generators produce unipolar and bipolar bell-shaped pulses, which can be applied for applications with high resistive loads. In these structures, the capacitors can be charged up to several times the dc input voltage, simultaneously. Accordingly, they have a high voltage (HV) gain even with a low-voltage dc (LVDC) source. This allows for a compact structure with a low number of modules (switches, diodes, capacitors, etc.). By reducing the number of modules, the total cost and power losses are reduced and the reliability is increased and its maintenance and fault detection would become easier. The other significant advantage of the proposed topologies is the voltage rating reduction of switches and diodes. Also, the series connection of switches and diodes is not required in this structure, which decreases the complexity and costs. Furthermore, due to the simultaneous charging of the capacitors, high-frequency pulses can be generated. Moreover, pulsewidth and pulse rise time are designed arbitrary parameters in nanosecond to microsecond ranges and they are completely independent of the gate driver and switch speed. In the design section, the parameters of the structure are determined based on the pulse specifications. The structures are simulated in MATLAB/SIMULINK environment. The experimental results for 1 kV output voltage confirm the performance of the proposed structures.
Traumatic Brain Injury in a Well: A Modular Three-Dimensional Printed Tool for Inducing Traumatic Brain Injury In vitro

Traumatic brain injury (TBI) is a major health problem that affects millions of persons worldwide every year among all age groups, mainly young children, and elderly persons. It is the leading cause of death for children under the age of 16 and is highly correlated with a variety of neuronal disorders, such as epilepsy, and neurodegenerative disease, such as Alzheimer's disease or amyotrophic lateral sclerosis. Over the past few decades, our comprehension of the molecular pathway of TBI has improved, yet despite being a major public health issue, there is currently no U.S. Food and Drug Administration–approved treatment for TBI, and a gap remains between these advances and their application to the clinical treatment of TBI. One of the major hurdles for pushing TBI research forward is the accessibility of TBI models and tools. Most of the TBI models require costume-made, complex, and expensive equipment, which often requires special knowledge to operate. In this study, we present a modular, three-dimensional printed TBI induction device, which induces, by the pulse of a pressure shock, a TBI-like injury on any standard cell-culture tool. Moreover, we demonstrate that our device can be used on multiple systems and cell types and can induce repetitive TBIs, which is very common in clinical TBI. Further, we demonstrate that our platform can recapitulate the hallmarks of TBI, which include cell death, decrease in neuronal functionality, axonal swelling (for neurons), and increase permeability (for endothelium). In addition, in view of the continued discussion on the need, benefits, and ethics of the use of animals in scientific research, this in vitro, high-throughput platform will make TBI research more accessible to other labs that prefer to avoid the use of animals yet are interested in this field. We believe that this will enable us to push the field forward and facilitate/accelerate the availability of novel treatments.
Variational cloud‐clearing with TOVS data

A number of studies have shown that the use of passive microwave and infrared satellite observations in data assimilation systems can increase forecast skill. Considerable effort has been expended over the past two decades, particularly with the TIROS Operational Vertical Sounder (TOVS), to achieve this result. The positive impact on forecast skill is a result of more rigorous treatment of quality control, improvements in systematic error correction schemes, and advances in data assimilation systems. Yet, there Still remains potential for improving the use of satellite data, particularly cloud‐contaminated observations, in data assimilation. Here, we use a one‐dimensional variational framework (1DVAR) as a first step towards improving the treatment of cloudy data by cloud‐clearing. Cloud‐clearing is a procedure that removes cloud radiative effects through comparison of partly cloudy adjacent pixels. The JDVAR approach simultaneously extracts cloud‐clearing parameters and information about the atmospheric and surface state from microwave and infrared observations. The variational framework ensures that the state estimate is consistent with all available measurements. The 1DVAR cloud‐clearing approach can also be extended to three or four dimensions (3DVAR, 4DVAR). Our TOVS cloud‐clearing implementation allows for complex cloud structures, including multiple cloud layers with wavelength‐dependent radiative properties. We present preliminary results of our 1DVAR cloud‐clearing implementation with TOVS. The results suggest that there is useful information in the cloud‐cleared data.
Biomarkers of Tumor Heterogeneity in Glioblastoma Multiforme Cohort of TCGA

Simple Summary Identifying biomarkers of survival from a large-scale cohort of Glioblastoma Multiforme (GBM) pathology images is hindered by heterogeneity of tumor signature compounded by age being the single most important confounder in predicting survival in GBM. The main contributions of this manuscript are to define (i) metrics for identifying tumor subtypes of tumor heterogeneity and (ii) relevant statistics for incorporating age for evaluating competing hypotheses. As a result, the GBM cohort are stratified based on interpretable morphometric features with or without preconditioning on published genomic subtypes. Abstract Tumor Whole Slide Images (WSI) are often heterogeneous, which hinders the discovery of biomarkers in the presence of confounding clinical factors. In this study, we present a pipeline for identifying biomarkers from the Glioblastoma Multiforme (GBM) cohort of WSIs from TCGA archive. The GBM cohort endures many technical artifacts while the discovery of GBM biomarkers is challenged because “age” is the single most confounding factor for predicting outcomes. The proposed approach relies on interpretable features (e.g., nuclear morphometric indices), effective similarity metrics for heterogeneity analysis, and robust statistics for identifying biomarkers. The pipeline first removes artifacts (e.g., pen marks) and partitions each WSI into patches for nuclear segmentation via an extended U-Net for subsequent quantitative representation. Given the variations in fixation and staining that can artificially modulate hematoxylin optical density (HOD), we extended Navab’s Lab method to normalize images and reduce the impact of batch effects. The heterogeneity of each WSI is then represented either as probability density functions (PDF) per patient or as the composition of a dictionary predicted from the entire cohort of WSIs. For PDF- or dictionary-based methods, morphometric subtypes are constructed based on distances computed from optimal transport and linkage analysis or consensus clustering with Euclidean distances, respectively. For each inferred subtype, Kaplan–Meier and/or the Cox regression model are used to regress the survival time. Since age is the single most important confounder for predicting survival in GBM and there is an observed violation of the proportionality assumption in the Cox model, we use both age and age-squared coupled with the Likelihood ratio test and forest plots for evaluating competing statistics. Next, the PDF- and dictionary-based methods are combined to identify biomarkers that are predictive of survival. The combined model has the advantage of integrating global (e.g., cohort scale) and local (e.g., patient scale) attributes of morphometric heterogeneity, coupled with robust statistics, to reveal stable biomarkers. The results indicate that, after normalization of the GBM cohort, mean HOD, eccentricity, and cellularity are predictive of survival. Finally, we also stratified the GBM cohort as a function of EGFR expression and published genomic subtypes to reveal genomic-dependent morphometric biomarkers.
Effect of an mHealth intervention on retention in HIV pre-exposure prophylaxis services among female sex workers: Preliminary evidence of the use of the Jichunge app in Dar es Salaam, Tanzania

Background Mobile health (mHealth) applications have been reported to be effective in promoting access and adherence to health services. However, knowledge about their effect on retention in HIV preventive services among at-risk populations in sub-Saharan Africa is limited. Objective We aimed to evaluate the effect of the Jichunge mHealth application on retention in HIV pre-exposure prophylaxis (PrEP) services among female sex workers in Dar es Salaam, Tanzania. Methods We used respondent-driven sampling to recruit female sex workers eligible for PrEP and who owned a smartphone. All study participants were provided with a smartphone application (Jichunge app) aiming to promote PrEP use through medication reminders, easy access to PrEP information, online consultations with a doctor and/or peer educator, and online discussions between PrEP users. The effect of optimal use of the Jichunge app on retention in PrEP services at 1-month was modelled using log-binomial regression. Results A total of 470 female sex workers with a median age of 26 (interquartile range: 22–30) years were recruited. Overall, 27.7% of female sex workers were retained in PrEP services at 1 month. Retention was twice as high among optimal users of the app than among sub-optimal users (adjusted risk ratios = 2.00, 95% confidence interval (CI): 1.41–2.83, p < 0.001). Conclusion The optimal use of the Jichunge mHealth application was significantly associated with higher retention in PrEP services among female sex workers in Dar es Salaam.
WORD SENSE INDUCTION USING DOMAIN-SPECIFIC CORPORA

1. a celestial body of hot gases that radiates energy derived from thermonuclear reactions . 2. someone who is dazzlingly skilled in any field 3. any celestial body visible (as a point of light) from the Earth at night 4. a plane figure with 5 or more points; often used as an emblem 5. an actor who plays a principal role 6. a performer who receives prominent billing 7. a star-shaped character * used in printing 8. the topology of a network whose components are connected to a hub Figure 1: The 8 senses of star given in WordNet 2.0
Systematic underestimation of uncertainties by widespread neutron-scattering data-reduction software

Data-reduction software used at neutron-scattering facilities around the world, Mantid and Scipp, ignore correlations when propagating uncertainties in arithmetic operations. Normalization terms applied during data-reduction frequently have a lower dimensionality than the quantities being normalized. We show how the lower dimensionality introduces correlations, which the software does not take into account in subsequent data-reduction steps such as histogramming, summation, or fitting. As a consequence, any uncertainties in the normalization terms are strongly suppressed and thus effectively ignored. This can lead to erroneous attribution of significance to deviations that are actually pure noise, or to overestimation of significance in final data-reduction results that are used for further data analysis. We analyze this flaw for a number of different cases as they occur in practice. For the two concrete experiments that are comprised in these case studies the underestimation turns out to be of negligible size. There is however no reason to assume that this generalizes to other measurements at the same or at different neutron-scattering beamlines. We describe and implement a potential solution that yields not only corrected error estimates but also the full variance-covariance matrix of the reduced result with minor additional computational cost.
KHGCN: Knowledge-Enhanced Recommendation with Hierarchical Graph Capsule Network

Knowledge graphs as external information has become one of the mainstream directions of current recommendation systems. Various knowledge-graph-representation methods have been proposed to promote the development of knowledge graphs in related fields. Knowledge-graph-embedding methods can learn entity information and complex relationships between the entities in knowledge graphs. Furthermore, recently proposed graph neural networks can learn higher-order representations of entities and relationships in knowledge graphs. Therefore, the complete presentation in the knowledge graph enriches the item information and alleviates the cold start of the recommendation process and too-sparse data. However, the knowledge graph’s entire entity and relation representation in personalized recommendation tasks will introduce unnecessary noise information for different users. To learn the entity-relationship presentation in the knowledge graph while effectively removing noise information, we innovatively propose a model named knowledge—enhanced hierarchical graph capsule network (KHGCN), which can extract node embeddings in graphs while learning the hierarchical structure of graphs. Our model eliminates noisy entities and relationship representations in the knowledge graph by the entity disentangling for the recommendation and introduces the attentive mechanism to strengthen the knowledge-graph aggregation. Our model learns the presentation of entity relationships by an original graph capsule network. The capsule neural networks represent the structured information between the entities more completely. We validate the proposed model on real-world datasets, and the validation results demonstrate the model’s effectiveness.
Adaptive feature selection method based on information gain

The information gain method for feature selection in text categorization combines the characteristics of the positive correlation and the negative correlation with equal proportion,which causes the decline of the classification precision.Therefore,the scaling factor was introduced,and an adaptive method was proposed.The appropriate scaling factor for the information gain was added and the classic naive Bayesian algorithm was combined,the scaling factor was automatically adjusted to make the improved information gain suitable for the different corpuses.The experimental results show that this method can choose the appropriate scaling factor for not only the feature space with different numbers,but also the different text sets,and the improved information gain has better classification effect on both the balance data set and the unbalanced ones.
Analyzing Nitrogen Effects on Rice Panicle Development by Panicle Detection and Time-Series Tracking

Detailed observation of the phenotypic changes in rice panicle substantially helps us to understand the yield formation. In recent studies, phenotyping of rice panicles during the heading–flowering stage still lacks comprehensive analysis, especially of panicle development under different nitrogen treatments. In this work, we proposed a pipeline to automatically acquire the detailed panicle traits based on time-series images by using the YOLO v5, ResNet50, and DeepSORT models. Combined with field observation data, the proposed method was used to test whether it has an ability to identify subtle differences in panicle developments under different nitrogen treatments. The result shows that panicle counting throughout the heading–flowering stage achieved high accuracy (R2 = 0.96 and RMSE = 1.73), and heading date was estimated with an absolute error of 0.25 days. In addition, by identical panicle tracking based on the time-series images, we analyzed detailed flowering phenotypic changes of a single panicle, such as flowering duration and individual panicle flowering time. For rice population, with an increase in the nitrogen application: panicle number increased, heading date changed little, but the duration was slightly extended; cumulative flowering panicle number increased, rice flowering initiation date arrived earlier while the ending date was later; thus, the flowering duration became longer. For a single panicle, identical panicle tracking revealed that higher nitrogen application led to earlier flowering initiation date, significantly longer flowering days, and significantly longer total duration from vigorous flowering beginning to the end (total DBE). However, the vigorous flowering beginning time showed no significant differences and there was a slight decrease in daily DBE.
A Dual Self-Calibrating Framework for Noninvasive Fetal ECG R-Peak Detection

Fetal heart rate (fHR) is critical for assessing fetal health and diagnosing disorders, such as fetal distress, congenital heart disease, and intrauterine growth retardation. With the rapid development of the Internet of Medical Things (IoMT), fetal R-peak detection plays an important role in diagnosing heart defects during pregnancy. However, due to the nonlinear mixing of multiple sources in the noninvasive signals and the low signal-to-noise ratio (SNR), it is difficult to obtain accurate R-peak detection result. This article presents a dual self-calibrating system based on a spectral attention kernel independent component analysis (SA-KICA) module and a self-calibrating fetal R-peak detection (SC-FRD) module. SA-KICA is an ICA-based calibration module constructed by the spectral attention mechanism, which was sought from short-time Fourier transform (STFT) and was shipped back to original signal with convolution to achieve perfect maternal electrocardiogram (MECG) separation in high-dimensional linear separable space. Then, a periodic and morphological-based channel selector is designed to select the optimal MECG. After MECG removal, to further improve the performance of fetal R-peak detection, the SC-FRD module is introduced to utilize the interior peak information and self-calibrating strategy, which includes variance-based fetal R-peak seed selection, time-varying coarse prediction, and adaptive probability mask calibration. The proposed framework is a primary attempt to concurrently introduce the nonlinear feature, spectral information, and self-calibrating strategy in the field of fetal ECG processing. The framework achieved excellent performance in fetal R-peak detection accuracy on a simulated data set and two public data sets with varying divergence and richness of resources. The experimental results show that our framework is superior to existing methods and can be used as a potential fetal monitoring method in the application of IoMT. The code is released in https://github.com/bfyjr/NI-FECG-Extraction.
Intelligent and Environmentally Friendly Solutions in Smart Cities’ Development—Empirical Evidence from Poland

This study presents a comprehensive analysis aiming to identify the implementation level of intelligent and environmentally friendly solutions (IEFS) in cities in Poland, and barriers impeding their development. Based on a representative sample of 280 cities, it was evident that the implementation level of IEFS in Poland is relatively very low. The most common barriers to IEFS implementation as indicated by representatives of city authorities were high costs, lack of adequate funds, and lack of awareness of benefits resulting from applying IEFS. Nevertheless, regression analyses showed that the IEFS implementation level was mostly affected by cities’ population size and perception of individual IEFS as integral elements of the smart city concept. It was also revealed that the high costs of implementing IEFS, the lack of their inclusion in local development strategies, the lack of appropriate legal regulations, the lack of widespread good practices, and the resistance of inhabitants to change and to new technologies perceived as impediments had significant negative effects on the implementation level of specific IEFS. Furthermore, the analyses demonstrated that perceiving certain issues as barriers did not hinder the implementation of such solutions. Based on a discussion of the results, relevant recommendations and directions for future research are proposed.
Discrete-Time LQG with Cross-Terms in the Loss Function and the Noise Description

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First Isolation and Genome Sequence Analysis of West Nile Virus in Mosquitoes in Brazil

West Nile virus is a flavivirus transmitted by mosquitoes, mainly of the genus Culex. In Brazil, serological studies have already indicated the circulation of the virus since 2003, with the first human case detected in 2014. The objective of the present paper is to report the first isolation of WNV in a Culex (Melanoconion) mosquito. Arthropods were collected by protected human attraction and CDC light bait, and taxonomically identified and analyzed by viral isolation, complement fixation and genomic sequencing tests. WNV was isolated from samples of Culex (Melanoconion) mosquitoes, and the sequencing analysis demonstrated that the isolated strain belonged to lineage 1a. The finding of the present study presents the first evidence of the isolation and genome sequencing of WNV in arthropods in Brazil.
OPTIMAL SENSOR PLACEMENT BASED ON MULTI-OBJECT GENETIC ALGORITHM

The present optimal sensor placement is based on single-object criterion in the bridge health monitoring and structure modal test. A new method for optimal sensor placement is presented based on multi-criterion. Using the multi-object optimization algorithm, the satisfying solution (not necessarily optimal in the sense of strict mathematics) of the sensor placement is carried out using Pareto genetic algorithm. The genetic operator and coding precept are designed in this paper. Front side of Pareto solution is obtained at the end of the optimal calculation, proving the feasibility of the proposed method.