Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Evaluation of the Salivary and Buccal Mucosal Microbiota by 16S rRNA Sequencing for Forensic Investigations

The human microbiome has emerged as a new potential biomarker for forensic investigations with the development of high-throughput sequencing and bioinformatic analysis during the last decade. The oral cavity has many different microbial habitats, with each habit colonized by specific and individualized microbiota. As saliva and buccal mucosa are common biological evidence in forensic science, understanding the differences of microbial communities between the two is important for forensic original identification. Moreover, the oral microbiota is individualized, whereas there are few studies on the application of forensic personal identification that need to be supplemented. In this study, Streptococcus was the most abundant genus, with an average relative abundance of 49.61% in the buccal mucosa, while in the saliva, Streptococcus, Veillonella, and Neisseria had similar proportions (20%, 15%, 16%) and were the dominant genera. The α and β diversity displayed a significant distinctness between the saliva and buccal mucosal groups. The community assembly mechanism stated that the deterministic process played a more significant effect in shaping the salivary bacterial community assembly than buccal mucosa, which explained the microbial differences. Of the test samples, 93.3% can be correctly classified with the random forest model based on the microbial differences. Targeting the low-abundance bacteria at the species level, 52% of experimental participants could be discriminated by using the observed unique bacterial species. In conclusion, the salivary bacterial community composition differed from that of the buccal mucosa and showed high richness and diversity. With the random forest model, the microbiota of saliva and buccal mucosa can be classified, which can be used in identifying the source of oral biological trace. Furthermore, each individual has a unique bacterial community pattern, and the presence or absence of unique bacteria and differences in the composition of the core oral microbiota are the key points for forensic personal discrimination that supplement the study of oral microbial application to forensic personal discrimination. Whether for original identification or personal discrimination, the oral microbiome has great potential for application.
Profound Change in Soil Microbial Assembly Process and Co-occurrence Pattern in Co-inoculation of Bradyrhizobium japonicum 5038 and Bacillus aryabhattai MB35-5 on Soybean

Rhizosphere microbial communities are vital for plant growth and soil sustainability; however, the composition of rhizobacterial communities, especially the assembly process and co-occurrence pattern among microbiota after the inoculation of some beneficial bacteria, remains considerably unclear. In this study, we investigated the structure of rhizomicrobial communities, their assembly process, and interactions contrasting when Bradyrhizobium japonicum 5038 and Bacillus aryabhattai MB35-5 are co-inoculated or Bradyrhizobium japonicum 5038 mono-inoculated in black and cinnamon soils of soybean fields. The obtained results indicated that the Chao and Shannon indices were all higher in cinnamon soil than that in black soil. In black soil, the co-inoculation increased the Shannon indices of bacteria comparing with that of the mono-inoculation. In cinnamon soil, the co-inoculation decreased the Chao indices of fungi comparing with that of mono-inoculation. Compared with the mono-inoculation, the interactions of microorganisms of co-inoculation in the co-occurrence pattern increased in complexity, and the nodes and edges of co-inoculation increased by 10.94, 40.18 and 4.82, 16.91% for bacteria and fungi, respectively. The co-inoculation of Bradyrhizobium japonicum 5038 and Bacillus aryabhattai MB35-5 increased the contribution of stochastic processes comparing with Bradyrhizobium japonicum 5038 inoculation in the assembly process of soil microorganisms, and owing to the limitation of species diffusion might restrict the direction of pathogenic microorganism movement. These findings support the feasibility of rebuilding the rhizosphere microbial system via specific microbial strain inoculation and provide evidence that the co-inoculation of Bradyrhizobium japonicum 5038 and Bacillus aryabhattai MB35-5 can be adopted as an excellent compound rhizobia agent resource for the sustainable development of agriculture.
Transcriptome Analysis Reveal Candidate Genes and Pathways Responses to Lactate Dehydrogenase Inhibition (Oxamate) in Hyperglycemic Human Renal Proximal Epithelial Tubular Cells

Diabetic kidney disease (DKD) is the leading cause of both chronic kidney disease (CKD) and end-stage renal disease (ESRD). Previous studies showed that oxamate could regulate glycemic homeostasis and impacted mitochondria respiration in a hyperglycemia-dependent manner in the rat proximal tubular cells. To explore the transcriptome gene expression profiling of kidney tissues in human renal proximal epithelial tubular cell line (HK-2), we treated HK-2 cells with high D-glucose (HG) for 7 days before the addition of 40 mM oxamate for a further 24 hours in the presence of HG in this study. Afterwards, we identified 3,884 differentially expressed (DE) genes based on adjusted P-value ≤ 0.05 and investigated gene relationships based on weighted gene co-expression network analysis (WGCNA). After qRT-PCR validations, MAP1LC3A, MAP1LC3B (P-value < 0.01) and BECN1 were found to show relatively higher expression levels in the treated groups than the control groups, while PGC1α (P-value < 0.05) showed the lower expressions. Accordingly, enrichment analyses of GO terms and KEGG pathways showed that several pathways [e.g., lysosome pathway (hsa04142) and p53 signaling pathway (hsa04115)] may be involved in the response of HK-2 cells to oxamate. Moreover, via WGCNA, we identified two modules: both the turquoise and blue modules were enriched in pathways associated with lysosome. However, the p53 signaling pathway was only found using all 3,884 DE genes. Furthermore, the key hub genes IGFBP3 (adjusted P-value = 1.34×10-75 and log2(FC) = 2.64) interacted with 6 up-regulated and 12 down-regulated DE genes in the network that were enriched in the p53 signaling pathway. This is the first study reporting co-expression patterns of a gene network after lactate dehydrogenase inhibition in HK-2 cells. Our results may contribute to our understanding of the underlying molecular mechanism of in vitro reprogramming under hyperglycemic stress that orchestrates the survival and functions of HK-2 cells.
Optimal allocation of distributed generation for microgrid based on hybrid quantum genetic algorithm

This paper studies the optimal allocation of hybrid solar-wind island microgrid according to the richness of solar and wind resources,the output characteristics of distributed generations and the users' requirements for the reliability of power supply. An adaptive penalty function method is adopted to correct the objective function so that the best economy performance can be achieved under the premise of meeting the requirements for the reliability of power supply. In the solving process,a new hybrid quantum genetic algorithm(HQGA) is proposed combining with the adaptive rotation angle adjustment strategy,crossover and mutation operation for quantum bits and the idea of population catastrophe. Finally a practical case is simulated through the proposed algorithm and the optimal results are compared with those obtained by quantum genetic algorithm and genetic algorithm. The result shows that the proposed method can not only rapidly converge,but also has a good global optimization capability.
Prevalence and Contributing Factors of Childhood Trauma, Anxiety, and Depression Among Adolescents From Two-Child Families in China

The two-child policy has been implemented in China since 2016 and has attracted the attention of the world. Adolescents may face huge psychological challenges in the process of changing family structures and relationships. To date, no mental health survey of adolescents from two-child families has been conducted. We investigated the prevalence and risk factors for childhood trauma, anxiety, and depression in two-child families in a statistically representative sample of Chinese senior high school students from Shanghai. A total of 426 participants were randomly selected from 1,059 students of four senior high schools in different districts of Shanghai. The childhood trauma questionnaire-short form (CTQ-SF), self-rating anxiety scale (SAS) and self-rating depression scale (SDS) were used as the screening tools. We found that the overall prevalence of childhood trauma, anxiety, and depression among senior high school students in two-child families was 46.70% (95% CI, 39.93–53.47%), 22.17% (95% CI, 16.53–27.81%), and 35.85% (95% CI, 29.34–42.36%), respectively. The two-child families was an important factor associated childhood trauma, emotional neglect, and physical neglect (χ2 = 5.984, p = 0.014; χ2 = 4.071, p = 0.044; χ2 = 4.202, p = 0.040). Ranking in two-child families was a risk factor for childhood trauma (β = −0.135, p = 0.048). Parental preference was a significantly correlated with physical abuse, physical neglect, anxiety, and depression (β = −1.581 to 0.088, p < 0.05). Meanwhile, emotional abuse, physical abuse, emotional neglect, and physical neglect of participants in the two groups were positively correlated with anxiety and depression (r = 0.195–0.478, p < 0.05). There was a significant relationship between sexual abuse and anxiety symptoms in the one-child family group (r = 0.161, p < 0.05). The findings suggest that the overall prevalence of childhood trauma, anxiety and depression among adolescents from two-child families in China was high. The two-child families and family ranking are important factors associated childhood trauma, while parental preference is related to anxiety and depression. These results highlight an urgent need to be addressed by adolescents' mental health service providers and policy-makers.
Automated Classification of Resting-State fMRI ICA Components Using a Deep Siamese Network

Manual classification of functional resting state networks (RSNs) derived from Independent Component Analysis (ICA) decomposition can be labor intensive and requires expertise, particularly in large multi-subject analyses. Hence, a fully automatic algorithm that can reliably classify these RSNs is desirable. In this paper, we present a deep learning approach based on a Siamese Network to learn a discriminative feature representation for single-subject ICA component classification. Advantages of this supervised framework are that it requires relatively few training data examples and it does not require the number of ICA components to be specified. In addition, our approach permits one-shot learning, which allows generalization to new classes not seen in the training set with only one example of each new class. The proposed method is shown to out-perform traditional convolutional neural network (CNN) and template matching methods in identifying eleven subject-specific RSNs, achieving 100% accuracy on a holdout data set and over 99% accuracy on an outside data set. We also demonstrate that the method is robust to scan-rescan variation. Finally, we show that the functional connectivity of default mode and salience networks identified by the proposed technique is altered in a group analysis of mild traumatic brain injury (TBI), severe TBI, and healthy subjects.
Beat-Level Interpretation of Intra-Patient Paradigm Based on Object Detection

Electrocardiogram (ECG), as a product that can most directly reflect the electrical activity of the heart, has become the most common clinical technique used for the analysis of cardiac abnormalities. However, it is a heavy and tedious burden for doctors to analyze a large amount of ECG data from the long-term monitoring system. The realization of automatic ECG analysis is of great significance. This work proposes a beat-level interpretation method based on the automatic annotation algorithm and object detector, which abandons the previous mode of separate R peak detection and heartbeat classification. The ground truth of the QRS complex is automatically annotated and also regarded as the object the model can learn like category information. The object detector unifies the localization and classification tasks, achieving an end-to-end optimization as well as decoupling the high dependence on the R peak. Compared with most advanced methods, this work shows superior performance. For the interpretation of 12 heartbeat types in the MIT-BIH dataset, the average accuracy is 99.60%, the average sensitivity is 97.56%, and the average specificity is 99.78%. This method can be used as a clinical auxiliary tool to help doctors diagnose arrhythmia after receiving large-scale database training.
In Vivo Dopamine Neuron Imaging-Based Small Molecule Screen Identifies Novel Neuroprotective Compounds and Targets

Parkinson’s disease (PD) is the second most common neurodegenerative disorder with prominent dopamine (DA) neuron degeneration. PD affects millions of people worldwide, but currently available therapies are limited to temporary relief of symptoms. As an effort to discover disease-modifying therapeutics, we have conducted a screen of 1,403 bioactive small molecule compounds using an in vivo whole organism screening assay in transgenic larval zebrafish. The transgenic model expresses the bacterial enzyme nitroreductase (NTR) driven by the tyrosine hydroxylase (th) promotor. NTR converts the commonly used antibiotic pro-drug metronidazole (MTZ) to the toxic nitroso radical form to induce DA neuronal loss. 57 compounds were identified with a brain health score (BHS) that was significantly improved compared to the MTZ treatment alone after FDR adjustment (padj<0.05). Independently, we curated the high throughput screening (HTS) data by annotating each compound with pharmaceutical classification, known mechanism of action, indication, IC50, and target. Using the Reactome database, we performed pathway analysis, which uncovered previously unknown pathways in addition to validating previously known pathways associated with PD. Non-topology-based pathway analysis of the screening data further identified apoptosis, estrogen hormone, dipeptidyl-peptidase 4, and opioid receptor Mu1 to be potentially significant pathways and targets involved in neuroprotection. A total of 12 compounds were examined with a secondary assay that imaged DA neurons before and after compound treatment. The z’-factor of this secondary assay was determined to be 0.58, suggesting it is an excellent assay for screening. Etodolac, nepafenac, aloperine, protionamide, and olmesartan showed significant neuroprotection and was also validated by blinded manual DA neuronal counting. To determine whether these compounds are broadly relevant for neuroprotection, we tested them on a conduritol-b-epoxide (CBE)-induced Gaucher disease (GD) model, in which the activity of glucocerebrosidase (GBA), a commonly known genetic risk factor for PD, was inhibited. Aloperine, olmesartan, and nepafenac showed significant protection of DA neurons in this assay. Together, this work, which combines high content whole organism in vivo imaging-based screen and bioinformatic pathway analysis of the screening dataset, delineates a previously uncharted approach for identifying hit-to-lead candidates and for implicating previously unknown pathways and targets involved in DA neuron protection.
Serum and Urinary Neutrophil Gelatinase-Associated Lipocalin Are Not Associated With Serum Redox Parameters in Amateur Athletes After an Ultramarathon

Objective To evaluate the relationship between oxidative stress and NGAL levels in blood and urine of amateur athletes after participating in a 100 km ultramarathon. Methodology The sample was composed of seven athletes, submitted to anthropometric assessment, cardiopulmonary exercise test, collection of urine and blood, measurement of body weight. The rate of perceived exertion (RPE), competition duration, heart rate (HR), energy expenditure and oxygen consumption (V’O2”) were also measured during the event. The energy consumption during the race was verified at its end. The analyses were based on the means (M) and respective standard deviations (SD), with statistical significance set at 5% (p < 0.05). Paired t-test was used for comparison between the periods before and after the competition, and Pearson’s correlation coefficient was used to measure the linear correlation between quantitative variables. Results Body mass index (BMI) of the sample was 25.75 kg/m2 ± 3.20, body fat percentage 18.54% ± 4.35% and V’O2”max 48.87% ± 4.78. Glucose, cortisol, and neutrophil gelatinase-associated lipocalin (NGAL) (p < 0.01) as well as glutathione peroxidase (GPx) active were higher after the race when compared to basal values. Moreover, lactate, creatinine, microalbuminuria, and glomerular filtration rate (GFR) (p < 0.001) were also higher after the race. After the competition, there was a significant correlation only between serum NGAL and creatinine, which was classified as strong and positive (r: 0.77; p < 0.05). There was a significant reduction (p < 0.05) of body weight after the event (72.40 kg ± 9.78) compared to before it (73.98 kg ± 10.25). In addition, we found an increase of RPE (p < 0.001) after the race. The competition lasted 820.60 min (±117.00), with a 127.85 bpm (±12.02) HR, a 2209.72 kcal ± 951.97 energy consumption, 7837.16 kcal ± 195.71 energy expenditure, and 28.78 ml/kg/min–1 (±4.66) relative V’O2”max. Conclusion The lack of correlation between oxidative stress biomarkers and serum and urine NGAL suggests that NGAL is more sensitive to inflammatory processes than to ROS levels.
Rapid On-Site Detection of the Bursaphelenchus xylophilus Using Recombinase Polymerase Amplification Combined With Lateral Flow Dipstick That Eliminates Interference From Primer-Dependent Artifacts

The pine wood nematode (PWN), Bursaphelenchus xylophilus, is one of the most lethal nematode species, which causes pine wilt disease (PWD), a devastating forest disease. To date, no effective methods have been developed to control the disease; hence, rapid precise detection of B. xylophilus is of great significance. Traditional molecular diagnostic methods are time-consuming and require sophisticated instruments or skilled operators, which are unavailable in resource-limited settings. A specific, sensitive, and field-applicable diagnostic method is urgently needed. In this study, we developed a diagnostic method using recombinase polymerase amplification combined with lateral flow dipstick (RPA-LFD) for the rapid on-site detection of B. xylophilus. The false-positive signals from primer-dependent artifacts were eliminated using a probe, and base substitutions were included in the primer and probe. The entire detection process for the RPA-LFD assay can be completed under 38°C within approximately 30 min, including 15 min for crude nematode genomic DNA (gDNA) extraction and master mix preparation, 15 min for the RPA-LFD assay. This assay displayed high specificity toward B. xylophilus and showed no cross-reactions with closely related species, including Bursaphelenchus mucronatus and Bursaphelenchus doui. The sensitivity of this assay had a detection limit as low as 1 pg of B. xylophilus purified genomic DNA. Furthermore, the application of the RPA-LFD assay in simulated spiked pinewood samples showed accurate detection results. The RPA-LFD assay in this study successfully detected B. xylophilus in less than 30 min, providing a novel alternative for the simple, sensitive, and specific detection of B. xylophilus and showed potential for B. xylophilus point-of-care testing (POCT) in resource-limited areas or in field.
Vision-based Autonomous Vehicle Recognition

Vision-based Automated Vehicle Recognition (VAVR) has attracted considerable attention recently. Particularly given the reliance on emerging deep learning methods, which have powerful feature extraction and pattern learning abilities, vehicle recognition has made significant progress. VAVR is an essential part of Intelligent Transportation Systems. The VAVR system can fast and accurately locate a target vehicle, which significantly helps improve regional security. A comprehensive VAVR system contains three components: Vehicle Detection (VD), Vehicle Make and Model Recognition (VMMR), and Vehicle Re-identification (VRe-ID). These components perform coarse-to-fine recognition tasks in three steps. In this article, we conduct a thorough review and comparison of the state-of-the-art deep learning--based models proposed for VAVR. We present a detailed introduction to different vehicle recognition datasets used for a comprehensive evaluation of the proposed models. We also critically discuss the major challenges and future research trends involved in each task. Finally, we summarize the characteristics of the methods for each task. Our comprehensive model analysis will help researchers that are interested in VD, VMMR, and VRe-ID and provide them with possible directions to solve current challenges and further improve the performance and robustness of models.
Towards a Classification Model for Identifying Risky IFTTT Applets

With the rapid growth of Internet-of-Things (IoT) devices, especially in the context of smart homes, we witnessed the rise of different services aimed at providing end-users with tools for the definition of custom behaviors. Among these, If-This-Than-That (IFTTT) became the most used end-user programming tool for creating event-condition-action (ECA) rules. However, while defining such rules, end-users might expose both their smart devices and personal information to security and privacy threats. This paper presents the progress achieved in the definition of a classification model based on neural networks for the identification of possible security and privacy issues within an IFTTT applet.
Nocturnal Enuresis in Children Aged 6-9 Years in National Guard Housing in Al-Hassa, Saudi Arabia: A Cross-Sectional Study

Nocturnal enuresis (NE) is common among children worldwide, but prevalence estimates vary. This study examined NE prevalence and risks factors among primary school children and evaluated common treatments in a cross-sectional design involving students at six primary schools (age 6-9 years). NE prevalence was 14.4%. NE was associated with late child’s order, history of NE, and having a sibling with history of NE and recurrent urinary tract infection. The attitude of parent toward NE are within average. Approximately 10% of parents sought treatment for NE; commonly reported treatments included waking the child (69.4%), fluids restriction (44.9%), and waiting for maturity (36.7%). Longitudinal studies are required to evaluate causal associations between risk factors and NE and assess efficacy of NE treatments.
Copy number amplification-activated long non-coding RNA LINC00662 epigenetically inhibits BIK by interacting with EZH2 to regulate tumorigenesis in non-small cell lung cancer

Recently, studies have shown that lncRNAs play important roles in regulation of cancer cells proliferation, apoptosis and metastasis. Here, through systematic bioinformatics analysis and screening, we identified a long noncoding RNA LINC00662 with high copy number amplification in NSCLC. High expression of LINC00662 predicted a poorer survival. The exact sequence full-length of LINC00662 was determined by rapid amplification of cDNA ends (RACE). We also found that LINC00662 could regulate lung cancer cell proliferation both in vitro and in vivo. Mechanically, we obtained global expression profile that respond to LINC00662 knockdown through RNA-Seq analysis. And we found that LINC00662 could bind to EZH2 and recruit EZH2 to the promoter regions of BIK, regulating the level of H3K27me3 in the BIK promoter, thus epigenetically repressing BIK expression. Our results shown that lncRNA LINC00662, driven by copy number amplification, promotes tumorigenesis by EZH2/BIK cell axis, indicating that it was a potential molecular target of NSCLC.