Miracle-dz/UniFAR-FTUs huggingface dataset:
A Small-sample Radar Target Classification Method Based on DCGAN-SE-ResNeXt

Small-sample radar target classification has always been a difficult problem in the radar reference field. To solve this problem, a set of comprehensive solution classification method combining DCGAN and SE-ResNeXt is proposed, aiming at overcoming ac-curacy of radar small sample target classification. At the same time, through multiple sets of comparative experiments, in-depth analysis of the model performance changes of different learning algorithms in actual small-sample radar target classification scene, and two important conclusions that can effectively guide engineering applications: First, DCGAN can effectively expand the sequence range profile of radar echo, and the expanded samples can be used for subsequent target classification; Second, SE-ResNeXt can effectively carry out feature extraction and classification of the extended sequence range profile of radar echo, with an accuracy of 95.63%, and test the optimal expansion coefficient of the current sample. The classification accuracy is better than that of other methods for the classification effect of the sequence range profile of radar echo.
Random-Walk Graph Embeddings and the Influence of Edge Weighting Strategies in Community Detection Tasks

Graph embedding methods have been developed over recent years with the goal of mapping graph data structures into low dimensional vector spaces so that conventional machine learning tasks can be efficiently evaluated. In particular, random walk based methods sample the graph using random walk sequences that capture a graph's structural properties. In this work, we study the influence of edge weighting strategies that bias the random walk process and we are able to demonstrate that under several settings the biased random walks enhance downstream community detection tasks.
Adaptive Machine Learning Based Network Intrusion Detection

Network intrusion detection system (NIDS) adoption is essential for mitigating computer network attacks in various scenarios. However, the increasing complexity of computer networks and attacks make it challenging to classify network traffic. Machine learning (ML) techniques in a NIDS can be affected by different scenarios, and thus the recency, size and applicability of datasets are vital factors to consider when selecting and tuning a machine learning classifier. The proposed approach evaluates relatively new datasets constructed such that they depict real-world scenarios. It includes analyses of dataset balancing and sampling, feature engineering and systematic ML-based NIDS model tuning focused on the adaptive improvement of intrusion detection. A comparison between machine learning classifiers forms part of the evaluation process. Results on the proposed approach model effectiveness for NIDS are discussed. Recurrent neural networks and random forests models consistently achieved high f1-score results with macro f1-scores of 0.73 and 0.87 for the CICIDS 2017 dataset; and 0.73 and 0.72 against the CICIDS 2018 dataset, respectively.
Predicting Sports Facility Revisit Intentions Based on Experience and Mediating Effects of Perceived Value

Abstract Introduction. Several factors, such as program offerings, atmosphere and provided amenities, motivate repeat visits to sports destinations, with continued patronage most often associated with perceived value. Material and Methods. This study sought to determine the extent to which perceived value in sports facilities influenced revisit intentions, for which a path analysis model was used and a valid and reliable self-administered questionnaire was completed by 384 recreational sports facility visitors. Results. Visit experience and perceived value were found to influence revisit intentions, with nearly 50% of the direct effect being because of perceived value. Conclusions. The results of this study inform both academic practice and managerial decisions.
An Evaluation of an Online Brief Mindfulness-Based Intervention in Higher Education: A Pilot Conducted at an Australian University and a British University

Mental ill health among higher education students is a well-established problem; therefore, it is imperative to implement preventative approaches to support wellbeing. Blended and fully online education programmes widens access for mature or returning students; however, the psychological wellbeing of this sub-group of students is under-researched. Finally, evaluating wellbeing interventions that meet the needs of university students as well as accessible for online students is required. The aim of this study was to evaluate a brief, online and mindfulness-based intervention to assist the self-management of wellbeing and stress for both online and on-campus higher education students. The total sample included 427 participants (96% psychology students) at Monash University, Australia (n=283) and King’s College London (n=144), with 152 participants completing the whole study. Participants were allocated to a brief, self-guided, online and mindfulness-based intervention (over the course of one study period; n=297), or to a wait-list control group (n=148). Baseline and end of semester questionnaires included the 14-item Warwick-Edinburgh Mental Wellbeing Scale, 10-item Perceived Stress Scale and the 15-item Mindful Attention Awareness Scale. Regression modelling revealed the intervention condition accounted for up to 12% of the variability in change in student wellbeing, stress and mindfulness between the start and end of semester (when controlling for baseline). These findings support the implementation of a brief, online and asynchronous mindfulness-based intervention for supporting student mental health and psychological wellbeing. An on-going challenge in practice includes engaging and maintaining student engagement in wellbeing initiatives.
Potential Value of Matrix Metalloproteinase-13 as a Biomarker for Osteoarthritis

Background: Emerging knowledge has highlighted the role of matrix metalloproteinase (MMP)-13 in osteoarthritis (OA); however, the suitability of MMP-13 as a biomarker for OA remains unclear. Therefore, this study aimed to assess the potential value of MMP-13 as a biomarker for OA. Methods: The study enrolled 51 patients, of which 33 had advanced varus OA and 18 did not have OA. Immunohistochemistry and western blotting analyses were performed to measure MMP-13 activity in the cartilage and subchondral bone of patients with OA. Enzyme-linked immunosorbent assay was used to measure serum MMP-13 levels in patients with or without OA. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was used to assess the association between serum MMP-13 levels and clinical symptoms. Furthermore, the association between serum MMP-13 levels and radiological severity of OA was evaluated using the Kellgren–Lawrence (KL) grading system. Finally, we built the proportional odds logistic regression models to evaluate serum MMP-13 levels as a potential predictor for OA. Results: MMP-13 levels were significantly higher in the severe-worn cartilage of the medial tibial plateau than in the relatively intact portion of the lateral cartilage (p < 0.05). This was contrary to the findings for MMP-13 differential expression in the subchondral bone in knee OA (p < 0.05). Patients with OA had significantly higher serum MMP-13 levels compared with patients without OA. Additionally, remarkable associations among serum MMP-13 levels, WOMAC scores, and KL grading scores were found in the end-stage OA. Furthermore, the subsequent analysis suggested that serum MMP-13 level was a significant predictor for OA. Conclusion: MMP-13 is valuable for diagnosing, measuring disease severity, and predicting OA in the advanced period of the disease, suggesting that it has potential possibility as a biomarker for OA. However, the underlying mechanisms and clinical application of MMP-13 as a biomarker for OA require to be further investigated.
Photosensitivity of the Bacterial Pigment Cycloprodigiosin Enables Settlement in Coral Larvae—Light as an Understudied Environmental Factor

The survival of coral reefs largely depends among other factors on the recruitment of a new generation of coral individuals that are more adapted to a rapidly changing climate and other anthropogenic stressors (e.g., pollution, sedimentation). Therefore, a better understanding of the coral settlement process, the molecules involved as well as crucial environmental drivers that control settlement success are needed. In this study, we identified a novel settlement inducer for the brooding scleractinian coral Leptastrea purpurea and highlight the importance of light for the settlement process. Crude extract of the red-pigmented bacterium Pseudoalteromonas rubra reliably triggered attachment and metamorphosis in L. purpurea larvae in less than 24 h. Prodigiosin (II) and the two derivatives, cycloprodigiosin (I) and 2-methyl-3-hexyl prodiginine (III) were isolated and structurally elucidated from the crude extract of P. rubra. We demonstrated that the photosensitive pigment cycloprodigiosin (I) was the responsible compound for attachment and metamorphosis in L. purpurea larvae. Under the tested light regimes (i.e., darkness, constant light and a dark-light alternation), cycloprodigiosin (I) triggered approximately 90% settlement at a concentration of 0.2 μg mL–1 under a 12 h alternating dark-light cycle, mimicking the light-flooded coral reef environment. Our findings enable for the first time a mechanistic understanding of the light-dependent larva to polyp transformation by discovering the novel bacterial settlement cue cycloprodigiosin and its photosensitivity as a determining factor for coral settlement.
Prognosis and Immunotherapy Significances of a Cancer-Associated Fibroblasts-Related Gene Signature in Gliomas

As a cold tumor, malignant glioma has strong immunosuppression and immune escape characteristics. The tumor microenvironment (TME) provides the “soil” for the survival of malignant tumors, and cancer-associated fibroblasts (CAFs) are the architects of matrix remodeling in TME. Therefore, CAFs have potent regulatory effects on the recruitment and functional differentiation of immune cells, whereby they synthesize and secrete numerous collagens, cytokines, chemokines, and other soluble factors whose interaction with tumor cells creates an immunosuppressive TME. This consequently facilitates the immune escape of tumor cells. Targeting CAFs would improve the TME and enhance the efficacy of immunotherapy. Thus, regulation of CAFs and CAFs-related genes holds promise as effective immunotherapies for gliomas. Here, by analyzing the Chinese Glioma Genome Atlas and the Cancer Genome Atlas database, the proportion of CAFs in the tumor was revealed to be associated with clinical and immune characteristics of gliomas. Moreover, a risk model based on the expression of CAFs-related six-gene for the assessment of glioma patients was constructed using the least absolute shrinkage and selection operator and the results showed that a high-risk group had a higher expression of the CAFs-related six-genes and lower overall survival rates compared with those in the low-risk group. Additionally, patients in the high-risk group exhibited older age, high tumor grade, isocitrate dehydrogenase wildtype, 1p/19q non-codeletion, O-6-methylguanine-DNA methyltransferase promoter unmethylation and poor prognosis. The high-risk subtype had a high proportion CAFs in the TME of glioma, and a high expression of immune checkpoint genes. Analysis of the Submap algorithm indicated that the high-risk patients could show potent response to anti-PD-1 therapy. The established risk prediction model based on the expression of six CAFs-related genes has application prospects as an independent prognostic indicator and a predictor of the response of patients to immunotherapy.
Influence of GlycoGag on the Incorporation of Host Membrane Proteins Into the Envelope of the Moloney Murine Leukemia Virus

Analysis of viral particle heterogeneity produced from infected cells has been limited by the inefficiency of traditional analytical methods to characterize large populations of viruses at an individual particle level. Flow virometry (FVM) is an emerging technique based on flow cytometry principles that enables a high throughput, multiparametric, and phenotypic characterization of viruses at a single particle resolution. Here, we performed FVM to analyze surface markers found on Murine Leukemia Virus (MLV) and glycosylated Gag-deficient (glycoGag) MLV. The glycoGag viral accessory protein has several roles in the MLV viral infection cycle including directing retroviral assembly and particle release at lipid rafts. Based on previous studies, we hypothesize that glycoGag modulates host protein incorporation into the viral envelope during viral assembly and budding. Here, by using FVM, we reveal that glycoGag is associated with an increased incorporation of the host-derived tetraspanins CD81 and CD63 along with the lipid raft marker and immune antigen Thy1.2 during the assembly and release of viral particles from infected NIH 3T3, EL4, and primary CD4+ T cells. Moreover, we show differences in the uptake of host proteins by viruses that are released from the two cell lines and primary T lymphocytes. Additionally, at the individual viral particle level, we observed a degree of expression heterogeneity of host-derived antigens within the viral population. Finally, certain cellular antigens can show either enrichment or exclusion from the viral envelope depending on whether glycoGag is expressed by the virus. This suggests that glycoGag is involved in a mechanism of selective host protein incorporation into the viral envelope.
Altering Early Life Gut Microbiota Has Long-Term Effect on Immune System and Hypertension in Spontaneously Hypertensive Rats

Hypertension is regulated by immunological components. Spontaneously hypertensive rats (SHR) display a large population of proinflammatory CD161 + immune cells. We investigated the effect of early post-natal gut microbiota on the development of the immune system and resulting hypertension in the SHR. We first examined the microbial populations in the fecal samples of SHR and normotensive control WKY using 16S rDNA sequencing. We found that in the newborn SHR (1-week old) the gut microbiota was qualitatively and quantitatively different from the newborns of normotensive WKY. The representation of the predominant bacterial phylum Proteobacteria was significantly less in 1-week old SHR pups than in WKY (94.5% Proteobacteria in WKY vs. 65.2% in SHR neonates). Even within the phylum Proteobacteria, the colonizing genera in WKY and SHR differed dramatically. Whereas WKY microbiota was predominantly comprised of Escherichia-Shigella, SHR microbiota was represented by other taxa of Enterobacteriaceae and Pasteurellaceae. In contrast, the representation of phylum Firmicutes in the neonatal SHR gut was greater than WKY. Cross-fostering newborn SHR pups by lactating WKY dams caused a dramatic shift in 1-week old cross-fostered SHR gut microbiota. The two major bacterial taxa of phylum Proteobacteria, Enterobacteriaceae and Pasteurellaceae as well as Lactobacillus intestinalis, Proteus, Romboustia and Rothia were depleted after cross-fostering and were replaced by the predominant genera of WKY (Escherichia-Shigella). A proinflammatory IL-17F producing CD161 + immune cell population in the spleen and aorta of cross-fostered SHR was also reduced (30.7% in self-fostered SHR vs. 12.6% in cross-fostered SHR at 30 weeks of age) as was the systolic blood pressure in adult cross-fostered SHR at 10 weeks of age. Thus, altered composition of gut microbiota of SHR toward WKY at early neonatal age had a long-lasting effect on immune system by reducing proinflammatory immune cells and lowering systolic blood pressure.
Cross-Sectional Characterization of Local Brain Network Connectivity Pre and Post Breast Cancer Treatment and Distinct Association With Subjective Cognitive and Psychological Function

Objective: We aimed to characterize local brain network connectivity in long-term breast cancer survivors compared to newly diagnosed patients. Methods: Functional magnetic resonance imaging (fMRI) and subjective cognitive and psychological function data were obtained from a group of 76 newly diagnosed, pre-treatment female patients with breast cancer (mean age 57 ± 7 years) and a separate group of 80, post-treatment, female breast cancer survivors (mean age 58 ± 8; mean time since treatment 44 ± 43 months). The network-based statistic (NBS) was used to compare connectivity of local brain edges between groups. Hubs were defined as nodes with connectivity indices one standard deviation or more above network mean and were further classified as provincial (higher intra-subnetwork connectivity) or connector (higher inter-subnetwork connectivity) using the participation coefficient. We determined the hub status of nodes encompassing significantly different edges and correlated the centralities of edges with behavioral measures. Results: The post-treatment group demonstrated significantly lower subjective cognitive function (W = 3,856, p = 0.004) but there were no group differences in psychological distress (W = 2,866, p = 0.627). NBS indicated significantly altered connectivity (p < 0.042, corrected) in the post-treatment group compared to the pre-treatment group largely in temporal, frontal-temporal and temporal-parietal areas. The majority of the regions projecting these connections (78%) met criteria for hub status and significantly less of these hubs were connectors in the post-treatment group (z = 1.85, p = 0.031). Subjective cognitive function and psychological distress were correlated with largely non-overlapping edges in the post-treatment group (p < 0.05). Conclusion: Widespread functional network alterations are evident in long-term survivors of breast cancer compared to newly diagnosed patients. We also demonstrated that there are both overlapping and unique brain network signatures for subjective cognitive function vs. psychological distress.
Improvement of Nicotine Removal and Ethanol Fermentability From Tobacco Stalk by Integration of Dilute Sulfuric Acid Presoak and Instant Catapult Steam Explosion Pretreatment

The nicotine from tobacco stalk showed obvious inhibitory effect on the activity of cellulase and fermentability of microorganisms, which seriously hinders the utilization of tobacco stalk. Dilute sulfuric acid presoak of tobacco stalk was used to enhance the performance of instant catapult steam explosion (ICSE) for tobacco stalk pretreatment. The presoak was beneficial to break the recalcitrant structure of tobacco stalk, reduce nicotine content to relieve the inhibition on the activity of cellulase and metabolism of microorganisms, and promote the performance of enzymatic hydrolysis and ethanol fermentation. The optimized 0.8% sulfuric acid (w/w) presoak–integrated ICSE pretreatment resulted in 85.54% nicotine removal from tobacco stalk; meanwhile, the total sugar concentration from enzymatic hydrolysis of pretreated tobacco stalk increased from 33.40 to 53.81 g/L (the ratio of dry tobacco stalk to water was 1:8, w/w), ethanol concentration increased 103.36% from 5.95 to 12.10 g/L in flask, compared with separate ICSE pretreatment. Finally, the ethanol concentration achieved the highest 23.53 g/L in a 5-L fermenter with the ethanol yield from the glucose of tobacco stalk hydrolysate achieving 71.40% by increasing the solid loading of the tobacco stalk in the enzymatic hydrolysis process (the ratio of dry tobacco stalk to water was 1:4, w/w). These results achieved the expected purpose of efficient utilization of discarded tobacco stalk.
SEVEN IN ABSENTIA Ubiquitin Ligases Positively Regulate Defense Against Verticillium dahliae in Gossypium hirsutum

Ubiquitination is a post-translational regulatory mechanism that controls a variety of biological processes in plants. The E3 ligases confer specificity by recognizing target proteins for ubiquitination. Here, we identified SEVEN IN ABSENTIA (SINA) ubiquitin ligases, which belong to the RING-type E3 ligase family, in upland cotton (Gossypium hirsutum). Twenty-four GhSINAs were characterized, and the expression levels of GhSINA7, GhSINA8, and GhSINA9 were upregulated at 24 h after inoculation with Verticillium dahliae. In vitro ubiquitination assays indicated that the three GhSINAs possessed E3 ubiquitin ligase activities. Transient expression in Nicotiana benthamiana leaves showed that they localized to the nucleus. And yeast two-hybrid (Y2H) screening revealed that they could interact with each other. The ectopic overexpression of GhSINA7, GhSINA8, and GhSINA9 independently in Arabidopsis thaliana resulted in increased tolerance to V. dahliae, while individual knockdowns of GhSINA7, GhSINA8, and GhSINA9 compromised cotton resistance to the pathogen. Thus, GhSINA7, GhSINA8, and GhSINA9 act as positive regulators of defense responses against V. dahliae in cotton plants.
Direction-Specific Iterative Tuning of Motor Commands With Local Generalization During Randomized Reaching Practice Across Movement Directions

During motor learning, people often practice reaching in variety of movement directions in a randomized sequence. Such training has been shown to enhance retention and transfer capability of the acquired skill compared to the blocked repetition of the same movement direction. The learning system must accommodate such randomized order either by having a memory for each movement direction, or by being able to generalize what was learned in one movement direction to the controls of nearby directions. While our preliminary study used a comprehensive dataset from visuomotor learning experiments and evaluated the first-order model candidates that considered the memory of error and generalization across movement directions, here we expanded our list of candidate models that considered the higher-order effects and error-dependent learning rates. We also employed cross-validation to select the leading models. We found that the first-order model with a constant learning rate was the best at predicting learning curves. This model revealed an interaction between the learning and forgetting processes using the direction-specific memory of error. As expected, learning effects were observed at the practiced movement direction on a given trial. Forgetting effects (error increasing) were observed at the unpracticed movement directions with learning effects from generalization from the practiced movement direction. Our study provides insights that guide optimal training using the machine-learning algorithms in areas such as sports coaching, neurorehabilitation, and human-machine interactions.