Miracle-dz/UniFAR-FTUs huggingface dataset:
Invasive treatment strategy in patients aged 80 years or older with non-ST-elevation acute coronary syndromes: a retrospective cohort study.

Background Invasive treatment is commonly recommended for patients with non-ST-elevation acute coronary syndromes (NSTE-ACS). However, the efficacy of this approach in patients aged ≥80 years remains uncertain. Methods We retrospectively assessed consecutive NSTE-ACS patients ≥80 years of age who were hospitalized at our cardiovascular center from December 2012 to July 2019. Patients were divided into two groups based on whether they received invasive treatment (coronary angiography and, if indicated, revascularization) or not. Patients who died in the first 3 days after admission without receiving invasive treatment were excluded. The effect of invasive timed treatment was also explored by dividing patients into timely invasive or delayed invasive groups according to their risk classification. Multivariate COX regression, invasive probability weighting and propensity score matching were used to adjust for confounding variables. The primary outcome was all-cause death during follow-up. Results A total of 1,201 patients with a median age of 82.0 (IQR, 81.0-84.0) were divided into two groups: 656 (54.6%) patients in the invasive group and 545 (45.4%) patients in the conservative group. Follow-up survival information was available for up to 6 years (median 3.0 years). During the follow-up, 296 (24.6%) patients died. After adjusting for confounding variables, the invasive treatment strategy was significantly associated with a lower risk of long-term mortality (HR =0.70, 95% CI: 0.54-0.92, P=0.010). No difference was found between timely invasive and delayed invasive interventions with mortality (HR =0.92, 95% CI: 0.57-1.47, P=0.725). Conclusions Invasive treatment was associated with lower mortality in patients ≥80 years of age with NSTE-ACS over a median of a 3-year follow-up. The invasive intervention time did not impact the outcome.
Molecular Mapping of a New Brown Planthopper Resistance Gene Bph43 in Rice (Oryza sativa L.)

Brown planthopper (BPH) has become the most devastating insect pests of rice and a serious threat to rice production. To combat newly occurring virulent BPH populations, it is still urgent to explore more new broad-spectrum BPH resistance genes and integrate them into rice cultivars. In the present study, we explored the genetic basis of BPH resistance in IRGC 8678. We identified and mapped a new resistance gene Bph43 to a region of ~380 kb on chromosome 11. Genes encoding nucleotide-binding domain leucine-rich repeat-containing (NBS-LRR)-type disease resistance proteins or Leucine Rich Repeat family proteins annotated in this region were predicted as the possible candidates for Bph43. Meanwhile, we developed near isogenic lines of Bph43 (NIL-Bph43-9311) in an elite restorer line 9311 background using marker-assisted selection (MAS). The further characterization of NIL-Bph43-9311 demonstrated that Bph43 confers strong antibiosis and antixenosis effects on BPH. Comparative transcriptome analysis revealed that genes related to the defense response and resistance gene-dependent signaling pathway were significantly and uniquely enriched in BPH-infested NIL-Bph43-9311. Our work demonstrated that Bph43 can be deployed as a valuable donor in BPH resistance breeding programs.
Intelligent Visual Quality Control System Based on Convolutional Neural Networks for Holonic Shop Floor Control of Industry 4.0 Manufacturing Systems

The article presents research on industrial quality control system based on AI deep learning method. They are a part of larger project focusing on development of Holonic Shop Floor Control System for integration of machines, machine operators and manufacturing process monitoring with information fl ow in whole production process according to Industry 4.0 requirements. A system connecting together machine operators, machine control, process and machine monitoring with companywide IT systems is developed. It is an answer on manufacture of airplane industry requirements. The main aim of the system is full automation of information fl ow between a management level and manufacturing process level. Intelligent, fl exible quality control system allowing for active manufacturing optimization on the base of achieved results as well as a historical data collection for further Big Data analysis is the main aim of the current research. During research number of selected AI algorithms were tested for assessing their suitability for performing tasks identifi ed in real manufacturing environment. As a result of the conducted analyzes, Convolutional Neural Networks were selected for further study. Number of built Convolutional Neural Networks algorithms were tested using sets of data and photos from the production line. A further step of research will be focused on testing a system in real manufacturing process for able possible construct a fully functional quality control system based on the use of Convolutional Neural Networks.
Are students happier the more they learn? – Research on the influence of course progress on academic emotion in online learning

ABSTRACT Academic emotions of learners are important for academic achievement. For the online learning platform, it is of great value to gain insight into the academic emotion of the course in appropriate time interval from the platform. We crawled a large number of student comment texts from MOOC, and used deep learning algorithms (BERT models) to perform aspect-oriented sentiment classification on the comment texts. We conducted statistical analysis and identified keywords to explore the changes of academic emotions in the online learning environment in different aspect dimensions. The results show that academic emotions are significantly improved in the first and second period of the course schedule, and tend to be stable in the second and third period of the course schedule. From the word frequency statistics, in the dimension of the teacher, students’ concerns mainly focus on two aspects: One is whether they can acquire knowledge, the other is the characteristics of teachers; in the course dimension, students attach more importance to the learning; in the dimension of the platform, students’ negative emotions mainly focus on four aspects: certificate, learning record, prompt and subtitle. Our research aims at providing suggestions for course design, platform improvement, and teachers’ practice.
Confidence Aware Deep Learning Driven Wireless Resource Allocation in Shared Spectrum Bands

Deep learning (DL) driven proactive resource allocation (RA) is a promising approach for the efficient management of network resources. However, DL models typically have a limitation that they do not capture the uncertainty due to the arrival of new unseen samples with a distribution different than the data distribution available at DL model-training time, leading to wrong resource usage predictions. To address this, we propose a confidence aware DL solution for the robust and reliable predictions of wireless channel utilization (CU) in shared spectrum bands.We utilize an encoder-decoder based Bayesian DL model to generate prediction intervals which capture the uncertainties in wireless CU. We use the CU predictions to design a novel metric score which in turn is utilized to make an adaptive RA algorithm. We show that a DL model capturing uncertainty in CU can achieve higher data rates for a wireless network. Both DL driven predictions and RA models are tested using synthetic data as well as real CU data collected in the University of Oulu. Using analytical and simulations results, we also study the stability of the proposed RA algorithm and show that it converges to a Nash equilibrium (NE). Our results reveal that the proposed algorithm converges to an NE under 2N iterations where N is the number of network access points.
A prática docente em aulas remotas do ensino médio: potencialidades e fragilidades

Esse artigo é fruto de uma análise da prática docente em aulas remotas, do Ensino Médio, cujo objetivo é analisar as potencialidades e fragilidades do processo de ensino-aprendizagem no contexto de pandemia da COVID –19. Para tal empreendemos uma pesquisa qualitativa, com uso de Grupo Focal que conforme Gatti (2005) é uma técnica que possibilita coletar dados de um grupo de pessoas, de forma homogênea ou heterogênea, em que há um diálogo, uma interação entre os participes. Para a tratativa, interpretação e análise do material coletado, recorremos à análise de conteúdo, postulada por Franco (2005). Para fundamentar a análise a confrontamos pressupostos teóricos de Tardif (2000), Tardif e Raymond (2000), Shulman (1986), Garcia (1999), Moreira, Henriques e Barros (2020) e Moreira e Schlemmer (2020). Nesse ínterim, a partir da teoria estudada e dos dados coletados, criamos indicadores das potencialidades e fragilidades para nossa análise e percebemos que, mesmo em meio a todas as demandas, o que se destacou foi que através do trabalho em equipe e das atividades interdisciplinares, os professores aprenderam muito uns com os outros e com seus alunos e o resultado foi de superação em meio a tantas mazelas que a pandemia trouxe.
Near-duplicate image detection based on wavelet decomposition with modified deep learning model

Abstract. We aim to address the near-duplicate image (NDI) detection problem with a deep learning network. With the advancement of digital acquisition devices and easy-to-use image editing software, NDI forgery is ubiquitous nowadays. This rising problem demands robust detection algorithms that can efficiently prevent the NDI forgery and its distribution. We present a modified deep learning model to detect NDI forensics attacks based on extracted wavelet Haar features. Unlike standard deep learning models, a wavelet decomposed preprocessing layer is used before the deep learning networks, and a support vector machine classifier is employed in the classification stage of a modified deep learning model. The model is tested on distinctly available image databases. The experimental results show that the proposed model more effectively detect the NDIs. In addition, the comparison result shows that the modified model outperforms the other approaches significantly.
Nitrate Regeneration and Loss in the Central Yellow Sea Bottom Water Revealed by Nitrogen Isotopes

The Yellow Sea (YS) is an epicontinental sea framed by the densely populated mainland of China and the Korean peninsula. Human activities over the last decades resulted in heavily increasing discharge of reactive nitrogen into the YS, which created numerous ecological problems. To elucidate the role of central YS in the cycling of reactive nitrogen, specifically the Yellow Sea Cold Water Mass (YSCWM), we determined nutrient concentrations, dual stable isotopes of nitrate (δ15N-NO3- and δ18O-NO3-), and stable isotopes of particulate and sedimentary nitrogen in spring and summer, i.e., in biologically inactive and active periods. The nitrate concentration in spring was higher than that in summer in the northern part of the YSCWM, Nitrate increased in the southern part accompanied by a decrease in δ15N-NO3- and δ18O-NO3-, which are indicative of nitrification that was a significant source of recycled nitrate in the south part of YSCWM. To quantify this regenerated nitrate, we use a mixing model with end members of preformed nitrate in spring and regenerated nitrate in summer, both with their distinct dual isotope values. The results suggest that only 35% nitrate was a residual of nitrate preformed in spring and 65% in summer in the southern branch of YSCWM was regenerated. The northern part of YSCWM has low concentrations of dissolved inorganic nitrogen, mainly because of denitrification in sediments. In contrast, the southern pool of YSCWM is a growing reservoir of regenerated terrestrial reactive nitrogen, the addition of which compensates the removal by co-occurring sediment denitrification. In consequence, the southern branch of YSCWM is facing a higher ecological risk than the northern branch, when excess reactive nitrogen discharge from Changjiang River continues at present levels or even increases.
Survey on encode biometric data for transmission in wireless communication networks

The aim of this research survey is to review an enhanced model supported by artificial intelligence to encode biometric data for transmission in wireless communication networks can be tricky as performance decreases with increasing size due to interference, especially if channels and network topology are not selected carefully beforehand. Additionally, network dissociations may occur easily if crucial links fail as redundancy is neglected for signal transmission. Therefore, we present several algorithms and its implementation which addresses this problem by finding a network topology and channel assignment that minimizes interference and thus allows a deployment to increase its throughput performance by utilizing more bandwidth in the local spectrum by reducing coverage as well as connectivity issues in multiple AI-based techniques. Our evaluation survey shows an increase in throughput performance of up to multiple times or more compared to a baseline scenario where an optimization has not taken place and only one channel for the whole network is used with AI-based techniques. Furthermore, our solution also provides a robust signal transmission which tackles the issue of network partition for coverage and for single link failures by using airborne wireless network. The highest end-to-end connectivity stands at 10 Mbps data rate with a maximum propagation distance of several kilometers. The transmission in wireless network coverage depicted with several signal transmission data rate with 10 Mbps as it has lowest coverage issue with moderate range of propagation distance using enhanced model to encode biometric data for transmission in wireless communication.
Two-way shape memory behavior of semi-crystalline elastomer under stress-free condition

Semi-crystalline shape memory polymers exhibit two-way shape memory effect (2W-SME) under constant stresses through crystallization-induced elongation upon cooling and melting-induced constriction upon heating. The applied constant stress influenced the prediction and usability of 2W-SME in practical applications without any external force. Here the reversible shape transition in EVA-shaped memory polymer was quantitative analyzed under a suitable temperature range and external stress-free condition. The fraction of reversible strain increased with increasing upper temperature (Thigh) within the temperature range and reached the maximum value of 13.62% at 70 °C. However, reversible strain transition was almost lost when Thigh exceeded 80 °C because of complete melting of crystalline scaffold, known as the latent recrystallization template. The non-isothermal annealing of EVA 2W-SMP under changing circulating temperatures was confirmed. Moreover, the orientation of crystallization was retained at high temperatures. These findings may contribute to design an appropriate shape memory protocol based on application-specific requirements.
Prohibitin Protects Pulmonary Microvascular Endothelial Cells Against Cigarette Smoke Extract-Induced Cell Apoptosis and Inflammation

Background Prohibitin has been identified to play roles in cell survival and apoptosis. Here, this study aimed to clarify the role of prohibitin in cigarette smoke extract (CSE)-induced endothelial cell apoptosis. Methods The protein level of prohibitin was assessed by Western blot in lung tissues from emphysema and control mice. CSE-induced human pulmonary microvascular endothelial cells (hPMECs) were applied to mimic smoke-related cell apoptosis in vitro. Prohibitin was overexpressed in hPMECs with or without CSE. Mitochondrial function was analyzed by JC-1 staining and ATP assay kits. Oxidative stress was assessed by flow cytometry, fluorescence staining and immunocytochemistry. Apoptosis was analyzed by flow cytometry, Western blot and caspase-3 activity assays. In addition, the expression of inflammatory markers was assessed by Western blot and real-time polymerase chain reaction (PCR). The secretion of inflammatory cytokines was measured by ELISA. Results Prohibitin was downregulated in emphysema mouse tissues compared with control experiments. Consistently, CSE inhibited both the protein and RNA levels of prohibitin in hPMECs in a dose-dependent manner. Gain-of-function experiments indicated that CSE induced collapse of mitochondrial membrane potential (MMP) and loss of ATP, while prohibitin improved mitochondrial function. CSE induced robust ROS production and oxidative DNA damage, while prohibitin decreased this damage. Upregulation of prohibitin protected the apoptosis of hPMECs from CSE. Overexpression of prohibitin significantly reduced the levels of the main proinflammatory cytokines. Finally, prohibitin inhibited nuclear factor-kappa B (NF-κB) p65 accumulation and IκBα degradation induced by CSE. Conclusion The current findings suggest that CSE-mediated mitochondrial dysfunction, oxidative stress, cell apoptosis and inflammation in hPMECs were reduced by overexpression of prohibitin. We identified prohibitin as a novel regulator of endothelial cell apoptosis and survival in the context of CSE exposure.
Lactobacillus plantarum Lp3a improves functional constipation: evidence from a human randomized clinical trial and animal model

Background Functional constipation (FC) is a common gastrointestinal (GI) disorder characterized by symptoms of constipation without a clear physiologic or anatomic cause. Gut microbiome dysbiosis has been postulated to be a factor in the development of FC, and treatment with probiotic regimens, including strains of Lactobacillus plantarum (L. plantarum), has demonstrated efficacy in managing symptoms. To further understand the role of L. plantarum in GI health, we conducted an animal study and a randomized, double-blind, placebo-controlled clinical trial to evaluate the effect of a specific sub-strain, Lp3a, on FC. Methods For the animal study, male Kunming mice were treated with doses of L. plantarum Lp3a ranging from 0.67 to 2.00 g/kg or an equivalent amount of placebo for 15 days prior to the induction of constipation via 20 mL/kg of 25% diphenoxylate solution. GI motility parameters including intestinal motion and stool amount were then assessed. In the human study, 120 patients with FC were randomized to treatment [L. plantarum Lp3a; 2×1.0×1010 (colony forming units; CFU) ×7 days] or control groups (n=60 each). The primary endpoint was survey information on FC signs/symptoms. Participants and observers were blinded to group allocation. A subset of 20 Lp3a treated patients underwent pre- and post-treatment 16 s ribosomal ribonucleic acid (rRNA) gene sequencing. Whole genome sequencing (WGS) of L. plantarum Lp3a was also performed. Results Lp3a-treated mice showed significantly improved intestinal motion, reduced time to first defecation, and increased stool amounts. Similarly, patients in the treatment group (n=59) reported significant improvements in FC signs/symptoms compared to controls (n=58; all P<0.05). Although 16 s rRNA sequencing revealed no significant variations between pre- and post-treatment samples, WGS of Lp3a itself revealed several biological pathways that may underlie the relief of FC symptoms in animals and humans, including methane and fatty acid metabolism and bile acid biosynthesis. Conclusions We found that the use of the novel probiotic sub-strain, L. plantarum Lp3a, led to clinically significant improvements in FC in both mice and humans, and identified the potential biological mechanisms underlying this activity.
Experience Quality as the Key to Successful Halal Tourism Development

Lombok has become Indonesia’s mainstay halal tourist destination; they proved Lombok won the highest score in the GMTI and IMTI surveys. The label as the best halal tourist destination must be a competitive advantage that can attract Muslim tourists to visit Lombok. This study seeks to explain the effect of the variables Halal Tourism Index, Tourist Satisfaction, Tourist Experience Quality on Revisit Intention. This study took domestic Muslim tourist respondents aged between 17 to 60 years who had visited Lombok in the last 3 years. With 87 respondents, it processed the data using PLS-SEM. We know the results of the hypothesis test that the Halal tourism index has no effect on tourist satisfaction and revisit intention; the Halal tourism index has a significant effect on Tourist Experience quality. While the Tourist Experience quality variable has a significant effect on tourist satisfaction and revisit intention, and tourist satisfaction influences revisit intention. The limitation of this research is that respondents are not evenly distributed to all regions of Indonesia, and the number is only 87 respondents. This study revealed that the key factor in making domestic Muslim tourists satisfied and intend to visit Lombok is Experience quality.
A security detection approach based on autonomy-oriented user sensor in social recommendation network

User social network-based recommender system has achieved significant performance in current recommendation fields. However, the characteristic of openness brings great hidden dangers to the security of recommender systems. Shilling attackers can change the recommendations by foraging user relationships. Most shilling attack detection approaches depend on the explicit user historical data to locate shilling attackers. Some important features such as information propagation and social feedback of users in social networks have not been noticed. We propose a security detection method based on autonomy-oriented user sensor (AOUSD) to identify shilling attackers. Specifically, (1) the user is simulated as a social sensor with autonomous capabilities, (2) the user interaction model is built based on information propagation, information feedback and information disappearance mechanisms of social sensors, and a user dynamic knowledge graph is formed by considering the variable time function, (3) hierarchical clustering method is used to generate preliminary suspicious candidate groups and graph community detection clustering method is applied on the dynamic knowledge graph to detect the attackers. Then, AOUSD is first simulated on NetLogo and it is compared with other algorithms based on the Amazon data. The results prove the advantages of AOUSD in the efficiency and accuracy on shilling attack detection.
Relationship Between Pre- and Post-exercise Body Mass Changes and Pre-exercise Urine Color in Female Athletes

Although studies use body mass changes or urine color to measure hydration status, the purpose of this study was to examine the relationship between pre-practice urine color and exercise body mass changes in female tackle football players. Twenty-six female American football players (Age: 29.9 ± 7.3 years; Height: 165.2 ± 2.6 cm; Weight: 83.8 ± 24.4 kg) volunteered. Fluid consumptions (FC) was measured during tackle football practices, while urine color (Ucol), and percent body mass loss (%BML) were taken before and after practices. Subjects were grouped by %BML: lost mass (LM), gained mass (GM), or no change (NC). A one-way ANOVA compared groups on Ucol and FC. There were differences across groups for pre-practice Ucol (P < 0.01) and FC (P < 0.01). GM had a higher pre-practice Ucol than LM (P < 0.01) and NC (P < 0.05) and consumed more fluid than LM (P < 0.01) and NC (P < 0.05). A stepwise linear regression examined the extent that Ucol and FC were related to %BML. When predicting BML, FC accounted for 45% of variance (P < 0.01). The addition of pre-practice Ucol increased predicted variance explained (R2 change= 2.5%, P = 0032). Subjects who gained mass during practice arrived with elevated urine color (Ucol 5 ± 2), while those who lost mass arrived with pale urine color (Ucol 3 ± 2). Findings indicate those who arrived with an elevated urine color attempted to improve hydration status by consuming more fluid and gaining body mass during exercise.