Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Characteristics, Receptor Specificity, and Pathogenicity of Avian Influenza Viruses Isolated from Wild Ducks in Russia

Avian influenza viruses (AIV) of wild ducks are known to be able to sporadically infect domestic birds and spread along poultry. Regular surveillance of AIV in the wild is needed to prepare for potential outbreaks. During long-year monitoring, 46 strains of AIV were isolated from gulls and mallards in Moscow ponds and completely sequenced. Amino acid positions that affect the pathogenicity of influenza viruses in different hosts were tested. The binding affinity of the virus for receptors analogs typical for different hosts and the pathogenicity of viruses for mice and chickens were investigated. Moscow isolates did not contain well-known markers of pathogenicity and/or adaptation to mammals, so as a polybasic cleavage site in HA, substitutions of 226Q and 228G amino acids in the receptor-binding region of HA, and substitutions of 627E and 701D amino acids in the PB2. The PDZ-domain ligand in the NS protein of all studied viruses contains the ESEV or ESEI sequence. Although several viruses had the N66S substitution in the PB1-F2 protein, all Moscow isolates were apathogenic for both mice and chickens. This demonstrates that the phenotypic manifestation of pathogenicity factors is not absolute but depends on the genome context.
Antithrombotic Treatment Patterns of Patients with Symptomatic Peripheral Arterial Occlusive Disease in Germany: Evidence from Health Insurance Claims Data

Objectives: Patients with peripheral arterial occlusive disease (PAOD) are at risk of worsening limb symptoms, major adverse cardiovascular events and exhibit an impaired life expectancy. There is a lack of evidence on the extent of pharmacological secondary prevention in PAOD patients. This study assesses treatment patterns of antithrombotic agents in symptomatic PAOD patients. Methods: This is a retrospective cohort study using data from the second largest insurance fund in Germany, BARMER. We included symptomatic PAOD patients undergoing in-hospital treatment with an index admission between 1 January 2010 and 31 December 2017. Outcomes were proportions of single antiplatelets (SAPT), dual antiplatelets (DAPT), vitamin-K antagonists (VKA), or direct oral anticoagulants (DOAC) in the 12 months prior and 6 months after the index hospitalization. Non-parametric cumulative incidence for competing risks was estimated to account for censoring and death after discharge from hospital stay. Patient flows were visualised by alluvial diagrams. All analyses were stratified by intermittent claudication (IC) and chronic limb-threatening ischaemia (CLTI). The protocol was registered to ClinicalTrials.gov (NCT03909022). Results: A total of 80,426 unique patient encounters were identified. Mean age was 72.7 (46.3% female). Amongst all patients, 25.6% were on SAPT, 4.1% on DAPT, 9.1% on VKA, 3.9% on DOAC, 3.9% on both antiplatelets and oral anticoagulation, and 53.3% without any antithrombotic therapy during the 12 months before index stay. The estimated cumulative incidence was 37.9% SAPT, 14.8% DAPT, 7.5% VKA, 4.3% DOAC, 7.4% both, and 28.1% without any antithrombotic therapy during the 6 months after index stay. The considerable increases in antiplatelet therapy were mainly driven by the group of patients without antithrombotics before index stay. As compared with IC, patients who suffered from CLTI received less often antiplatelets but more often anticoagulants both before and after index stay. Conclusions: Utilisation rates of antithrombotic therapy increased considerably after in-hospital treatment for PAOD. Yet, remarkably high rates of symptomatic patients without any blood-thinning therapy constitute a major concern with respect to adequate secondary prevention of PAOD patients.
Cardiopulmonary Exercise Physiology in AL Amyloidosis Patients with Cardiac Involvement and Its Association with Cardiac Imaging Parameters

Background: Cardiopulmonary exercise testing (CPET) has been widely used for the functional evaluation of patients with heart failure. Patients with amyloidosis and cardiac involvement typically present with heart failure with preserved or mildly reduced ejection fraction. We sought to evaluate the use of CPET parameters in patients with AL amyloidosis for the assessment of disease severity and prognosis and their association with cardiac imaging findings. Methods: A single-center prospective analysis was conducted, which included 23 consecutive ambulatory patients with AL amyloidosis with cardiac involvement, not requiring hospitalization or intravenous diuretics. Patient evaluation included CPET, laboratory testing, echocardiography and cardiac MRI. The cohort was divided according to the presence of high-risk CPET characteristics (below median peak VO2 and above median VE/VCO2). Results: Patients with AL amyloidosis and cardiac involvement (median age was 60 years (56.5% males) had median peak relative VO2 (VO2/kg) of 17.8 mL/kg/min, VE/VCO2 slope of 39.4 and circulatory power of 2362.5 mmHg⋅mL/kg/min. Peak relative VO2 gradually declined across Mayo stages (p = 0.046) and exhibited a significant inverse correlation with NT-proBNP levels (r = −0.52, p = 0.01). Among imaging parameters, peak VO2 positively correlated with global work efficiency (r = 0.61, p < 0.001), and global work index (r = 0.45, p = 0.04). The group of patients with high-risk CPET findings showed evidence of more advanced disease, such as higher NT-proBNP levels (p = 0.007), increased septal and posterior left ventricular wall thickness (p = 0.043 and p = 0.033 respectively) and decreased global work efficiency (p = 0.027) without substantial differences in cardiac MRI parameters. In this group of patients, peak VO2 and VE/VCO2 were not associated significantly with overall survival and cardiac response at one year. Conclusion: In patients with AL amyloidosis, evaluation of exercise capacity with CPET identified a group of patients with more advanced cardiac involvement. The potential of CPET as a risk stratification tool in AL amyloidosis with cardiac involvement warrants further research.
Pragmatic Language Development: Analysis of Mapping Knowledge Domains on How Infants and Children Become Pragmatically Competent

New-borns are capable of recognising and producing sounds as they become phonologically competent. Following this, infants develop a system for connecting these sounds, which helps them become increasingly lexically competent over time. Their knowledge of these words grows as they develop, using words to form phrases, turning them into sentences, and ultimately becoming syntactically competent. By making sense of these linguistic elements, these three competencies are enhanced, and this is how infants become semantically competent. As infants continue to develop linguistic and non-linguistic communication behaviours, this miraculous language development becomes even more complex, enabling them to perfect their linguistic abilities while being pragmatically competent. In this study, a scientometric approach was used to examine past, present, and future trends in pragmatic language development (PLD). A total of 6455 documents were analysed from the Scopus, WOS, and Lens databases between 1950 and 2022. The analysis involved the visualisation and tabulation of eight bibliometric and eight scientometric indicators using CiteSpace 5.8. R3 and VOSviewer 1.6. 18 software for data analysis. In this study, we highlight the major patterns and topics directing the research on PLD between 1950 and 2022. The themes and topics included (1) analysing PLD as a social behaviour through the lens of executive functions; (2) studying PLD as a social behaviour based on social understanding; (3) examining PLD as a social behaviour associated with autism spectrum disorder; (4) developing an understanding of PLD in academic settings through the examination of executive functions; (5) identifying pragmatic competence versus communicative competence as a social behaviour; (6) analysing pragmatic language skills in aphasic patients via epistemic stances (i.e., attitudes towards knowledge in interaction); (7) investigating PLD as a behavioural problem in the context of a foreign language; (8) assessing PLD as a behavioural problem in individuals with autism spectrum disorder; (9) assessing PLD in persons with traumatic brain injury and closed head injury as a behavioural problem; (10) identifying the role of the right hemisphere in executive functions as a cognitive substrate; (11) assessing the impact of pragmatic failure in speech acts on pragmatic competence; and (12) investigating the patterns of PLD among learning-disabled children.
Stochastic Partial Differential Equations on Evolving Surfaces and Evolving Riemannian Manifolds

We formulate stochastic partial differential equations on Riemannian manifolds, moving surfaces, general evolving Riemannian manifolds (with appropriate assumptions) and Riemannian manifolds with random metrics, in the variational setting of the analysis to stochastic partial differential equations. Considering mainly linear stochastic partial differential equations, we establish various existence and uniqueness theorems.
Molecular Dynamics and Experimental Investigation on the Interfacial Binding Mechanism in the Fe/Cu1−x-Nix Bimetallic Interface

To systematically investigate the diffusion behavior of Fe/Cu bimetallic materials and the influence of the Ni element on the diffusion and mechanical properties of the Fe/Cu bimetallic interface, the diffusion distance, diffusion coefficient, and strain–stress process based on molecular dynamics (MD) calculations and experimental testing were analyzed. All simulation results indicated that the liquid Cu matrix had a higher diffusion coefficient but hardly diffused into the Fe matrix, and the solid Fe matrix had a smaller diffusion coefficient but diffused deep into the Cu matrix at the same temperature. Compared with the initial state, the addition of nickel atoms to the Cu matrix favored the improvement of the diffusion coefficient and the diffusion distance of Fe/Cu bimetallic materials. Moreover, we found that the diffusion distance and the yield strength simultaneously increased and then decreased with the increase in Ni atoms, which is in agreement with the experimental test results. These improvements in the diffusion and mechanical properties were attributed to the enrichment of Ni atoms at the interface, but excessive Ni content resulted in deteriorated properties. Finally, our research described the enhancement mechanism of the addition of nickel atoms to the Fe/Cu bimetallic diffusion system. An analysis of the contributions of the diffusion distance, the diffusion coefficient, and the yield strength revealed that the diffusion properties of nickel atoms play an important role in Fe/Cu bimetallic materials.
Entropy of chaotic eigenstates

These notes present a recent approach to study the high-frequency eigenstates of the Laplacian on compact Riemannian manifolds of negative sectional curvature. The main result is a lower bound on the Kolmogorov-Sinai entropy of the semiclassical measures associated with sequences of eigenstates, showing that high-frequency eigenstates cannot be too localized. The method is extended to the case of semiclassical Hamiltonian operators for which the classical flow in some energy range is of Anosov type, and to the case of quantized Anosov diffeomorphisms on the torus.
A robust control for a variable wind speed conversion system energy based on a DFIG using Backstepping

This paper presents a modeling and robust control of the DFIG (doubly fed induction generator) used in the wind energy conversion system (WECS). We started by using the MPPT method to extract the maximum power in the WECS, modeling the double-fed inductor generator, and then applying the Backstepping controller to control the reactive power and electromagnetic torque in order to test the performance and the robustness of the system. All is simulated and presented in MATLAB/SIMULINK software.
Synthesis, Molecular Docking, In Vitro and In Vivo Studies of Novel Dimorpholinoquinazoline-Based Potential Inhibitors of PI3K/Akt/mTOR Pathway

A (series) range of potential dimorpholinoquinazoline-based inhibitors of the PI3K/Akt/mTOR cascade was synthesized. Several compounds exhibited cytotoxicity towards a panel of cancer cell lines in the low and sub-micromolar range. Compound 7c with the highest activity and moderate selectivity towards MCF7 cells which express the mutant type of PI3K was also tested for the ability to inhibit PI3K-(signaling pathway) downstream effectors and associated proteins. Compound 7c inhibited the phosphorylation of Akt, mTOR, and S6K at 125–250 nM. It also triggered PARP1 cleavage, ROS production, and cell death via several mechanisms. Inhibition of PI3Kα was observed at a concentration of 7b 50 µM and of 7c 500 µM and higher, that can indicate minority PI3Kα as a target among other kinases in the titled cascade for 7c. In vivo studies demonstrated an inhibition of tumor growth in the colorectal tumor model. According to the docking studies, the replacement of the triazine core in gedatolisib (8) by a quinazoline fragment, and incorporation of a (hetero)aromatic unit connected with the carbamide group via a flexible spacer, can result in more selective inhibition of the PI3Kα isoform.
Multi-server Intelligent Task Caching Strategy for Edge Computing

As one of the directions of edge computing, task caching can reduce the computing pressure of mobile devices and cloud servers. Due to the limitation of cache capacity and computing power of edge server, how to make reasonable cache decision has become one of the difficulties of task caching. In addition, because users in adjacent areas prefer to request similar tasks, the cooperative cache between edge servers are becoming more and more important. Therefore, in this study, we propose a multi-user multi-server task caching model, which takes the average execution delay of tasks as an index to evaluate the system performance, and expresses the task caching problem as a nonlinear integer programming problem. On this basis, we propose a multi-actor-critic online task cache algorithm (MAC) based on deep reinforcement learning to minimize task average execution delays in the system. The proposed algorithm can not only adjust the cache decision based on task size and calculation amount, but also adapt to changes in user requests. Simulation experiments show that the MAC algorithm can effectively reduce the average execution delay of the task, with better robustness compared to other cache schemes.
MLK3 Regulates Inflammatory Response via Activation of AP-1 Pathway in HEK293 and RAW264.7 Cells

Inflammation is a critically important barrier found in innate immunity. However, severe and sustained inflammatory conditions are regarded as causes of many different serious diseases, such as cancer, atherosclerosis, and diabetes. Although numerous studies have addressed how inflammatory responses proceed and what kinds of proteins and cells are involved, the exact mechanism and protein components regulating inflammatory reactions are not fully understood. In this paper, to determine the regulatory role of mixed lineage kinase 3 (MLK3), which functions as mitogen-activated protein kinase kinase kinase (MAP3K) in cancer cells in inflammatory response to macrophages, we employed an overexpression strategy with MLK3 in HEK293 cells and used its inhibitor URMC-099 in lipopolysaccharide (LPS)-treated RAW264.7 cells. It was found that overexpressed MLK3 increased the mRNA expression of inflammatory genes (COX-2, IL-6, and TNF-α) via the activation of AP-1, according to a luciferase assay carried out with AP-1-Luc. Overexpression of MLK3 also induced phosphorylation of MAPKK (MEK1/2, MKK3/6, and MKK4/7), MAPK (ERK, p38, and JNK), and AP-1 subunits (c-Jun, c-Fos, and FRA-1). Phosphorylation of MLK3 was also observed in RAW264.7 cells stimulated by LPS, Pam3CSK, and poly(I:C). Finally, inhibition of MLK3 by URMC-099 reduced the expression of COX-2 and CCL-12, phosphorylation of c-Jun, luciferase activity mediated by AP-1, and phosphorylation of MAPK in LPS-treated RAW264.7 cells. Taken together, our findings strongly suggest that MLK3 plays a central role in controlling AP-1-mediated inflammatory responses in macrophages and that this enzyme can serve as a target molecule for treating AP-1-mediated inflammatory diseases.
Does continuous supervision improve teacher’s pedagogical competence?

The purpose of this study is to provide a comprehensive picture of the importance of continuous supervision in the context of optimizing teacher pedagogic competence. In this study, a mixed method was used. This research is also known as population research, because all the teachers on duty at SMP Negeri 2 Cengal, namely 28 people, are the research sample. To obtain accurate data, the instruments are arranged according to indicators of pedagogical competence, namely understanding of students, design & implementation of learning, development of students and evaluation of student learning outcomes. Data analysis was carried out according to this research method, there was a qualitative and quantitative analysis with the stages of data reduction analysis, data display which was then verified so that it became a research conclusion. According to the findings of this study, at least there has been an increase in teacher competence as a result of continuous supervision. This research is unique in that it is tested continuously for several periods of supervision, not just once to determine the effect of the implementation of supervision on teacher performance and competence. The final average obtained in each supervision related to the teacher's pedagogic competence is 77.05; 77.75 and 78.8. Of course, these results can help strengthen policies that regulate the implementation of supervision in every educational institution.
Hyperspectral Image Classification Using Group-Aware Hierarchical Transformer

Hyperspectral image (HSI) classification is a critical task with numerous applications in the field of remote sensing. Although convolutional neural networks have achieved remarkable success in computer vision, they are still limited in the ability to model long-term dependencies due to small receptive fields. Recently, vision transformers have been used in HSI classification, where multi-head self-attention (MHSA), as the key feature extractor of transformers, learns global dependencies in long-range positions and bands of HSI pixels. Existing vision transformers for classifying HSIs with a large number of bands, however, have some limitations in that features extracted by MHSA may exhibit over-dispersion. In this article, we propose a Group-Aware Hierarchical Transformer (GAHT) for HSI classification, which confines MHSA to the local spatial–spectral context by introducing a new grouped pixel embedding (GPE) module. The GPE emphasizes local relationships within HSI spectral channels, resulting in a global–local fashion from a spatial–spectral context for HSI classification. In addition, we construct our transformer in a hierarchical manner, which can significantly improve classification accuracy with only a few parameters. Extensive experiments on four benchmark HSI datasets demonstrate that the proposed method outperforms state-of-the-art HSI classification algorithms. The source code is available at https://github.com/MeiShaohui/Group-Aware-Hierarchical-Transformer.
L 0 image smoothing via iterating truncated L1 gradient regularization

Abstract. Edge-preserving image smoothing is a fundamental tool in computational photography and graphics. It aims to suppress insignificant details while maintaining salient structures. The classical L0 filter provides an elegant framework for a variety of applications. However, it inclines to sharpen the salient edges, thus suffering from gradient reversals and color deviations. We propose a solution toward the objective of optimizing summed squared error regularized by the L0-norm of the gradients. The proposed solution explores an iterative strategy, where each iteration is an optimization problem based on the truncated L1 regularization, which can be solved efficiently with the combination of alternating direction method of multipliers and Fourier domain optimization. Leveraging L1-norm limits the aggressive modifications of gradients during the iterative process, which alleviates various artifacts in classical L0 filter. Experiment results indicate that the proposed method achieves superior performance in various applications, including texture removal, detail enhancement, HDR tone mapping, and compression artifact removal. The proposed filter can be conveniently implemented on GPU. It takes our filter 0.51 s to process a 720P color image on a NVIDIA GTX 1080 GPU.
Emotion Recognition by Facial Expression and Voice: Review and Analysis

Emotion is a scorching topic in the recent years due to the critical unseen stress incurred during the pandemic and post-pandemic. This is worsening with the recent economy’s inflation and increase of living cost, many employees are seriously affected and drawn forth many families saddened cases and tremendous drop of working performance. The increasing stress brings a lot of harm not only to the individual but to the company’s and country’s growth. To recognize emotion through a single model is less accurate, however, recruiting multiple-models may lead to latency in data processing and possibly misleading results if the input models data are not properly filtered and segmented. This paper will review, analyze and theoretically compare 15 facial expression methods and 17 voice methods of emotion recognition research works. It will outline the pros and cons of each method and discuss the accuracy of some of the standalone and hybrid emotion recognition methods. Some of the methods (such as CNN, KNN and SVM) can span over multiple-models, but reveal different level of strengths. This is very important to discover, so that one may replace or enhance the weaker level if applying the same method across the multiple-models. This paper will also illustrate different levels of popularity of the methods in each model for visual comparison in ease. Hopefully, it can cater the new researchers a quick identification on the most suitable method for recognizing the emotion through facial expression and/or voice.
Comparison of Pedestrian Detectors for LiDAR Sensor Trained on Custom Synthetic, Real and Mixed Datasets

Deep learning algorithms for object detection used in autonomous vehicles require a huge amount of labeled data. Data collecting and labeling is time consuming and, most importantly, in most cases useful only for a single specific sensor application. Therefore, in the course of the research which is presented in this paper, the LiDAR pedestrian detection algorithm was trained on synthetically generated data and mixed (real and synthetic) datasets. The road environment was simulated with the application of the 3D rendering Carla engine, while the data for analysis were obtained from the LiDAR sensor model. In the proposed approach, the data generated by the simulator are automatically labeled, reshaped into range images and used as training data for a deep learning algorithm. Real data from Waymo open dataset are used to validate the performance of detectors trained on synthetic, real and mixed datasets. YOLOv4 neural network architecture is used for pedestrian detection from the LiDAR data. The goal of this paper is to verify if the synthetically generated data can improve the detector’s performance. Presented results prove that the YOLOv4 model trained on a custom mixed dataset achieved an increase in precision and recall of a few percent, giving an F1-score of 0.84.