Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of soil management systems on the rhizosphere bacterial community structure of tobacco: Continuous cropping vs. paddy-upland rotation

Rhizosphere bacteria play important role in soil nutrient cycling and plant growth, and their richness and diversity are influenced by soil management systems. However, the specific changes in tobacco rhizosphere bacterial community structure in continuous and tobacco-rice rotation cropping systems remain uninvestigated. In this study, soil properties and the composition of the rhizosphere bacterial community in tobacco monocropping and tobacco-rice rotation cropping systems were analyzed. Moreover, the comparison of rhizosphere bacterial community structure between tobacco continuous and tobacco-rice rotation cropping systems was performed via high-throughput sequencing. The changes in the composition of the rhizosphere bacterial community were investigated at different tobacco growth stages. The results showed that continuous tobacco cropping increased the soil soluble organic carbon (SOC), total nitrogen (TN), and the content of other nutrients (e.g., available phosphorus and available potassium) compared to tobacco-rice rotation cropping. However, monocropping decreased bacterial alpha-diversity and altered the community composition when compared to the rotation cropping system. At the phylum level, the relative abundance of Proteobacteria, Gemmatimonadetes, and Bacteroidetes increased in the continuous cropping soil, while that of Acidobacteria, Firmicutes, and Actinobacteria decreased. At the genera level, the average abundance of the dominant genus Bacillus varied from 12.96% in continuous cropping libraries to 6.33% in the rotation cropping libraries (p < 0.05). Additionally, several other taxa, such as o_Acidobacteriales and Candidatus_Solibacter decreased from 7.63 to 6.62% (p < 0.05) and 4.52 to 2.91% (p < 0.05), respectively. However, the relative abundance of f_Gemmatimonadaceae and c_Subgroup_6 showed an increase of 1.46% (p < 0.05) and 1.63% (p < 0.05) in the tobacco-rice rotation cropping system, respectively. The results of NMDS indicated that the rhizobacteria community structure differed in the two cropping systems. In tobacco, the rhizosphere bacterial community structure showed no significant changes in the prosperous long-term stage and topping stage, but the composition changed significantly in the mature stage.
The impact of school attachment and parental involvement on the positive mental health of 2SLGBTQ + students during COVID-19

On March 11th, 2020, the World Health Organization (WHO) declared the worldwide outbreak of COVID-19 as a pandemic. On the following day, the Ontario government (Canada’s most populous province) ordered all public schools to close. By Monday, March 16th, 2020, all public schools (and most private schools) in Canada announced plans to physically shutter schools, with a shift to remote and online learning to follow soon after. This unprecedented shift in learning environment for young Canadians came at a time when the onset of the COVID-19 pandemic was creating a challenging environment for the mental health of all Canadians. While all students may have struggled to cope, 2SLGBTQ + students faced an unusually complex shift, as their school and home environments may have contributed differentially to the social supports and acceptance (related to their 2SLGBTQ + identity or identities) that their cisgender heterosexual peers routinely experience in their social surroundings. In this paper, we explore the relationship between school attachment, parental involvement and positive mental health in 2SLGBTQ + youth using data collected as part of the Second Annual School Climate Survey on Homophobia, Biphobia, and Transphobia in Canadian Schools.
Needs of patients with early psychosis: A comparison of patient’s and mental health care provider’s perception

Background Needs define the capacity of a patient to benefit from health care services and a systematic assessment of needs allows planning and delivery of effective treatment to suit patients. This study aimed to understand the (a) needs felt by patients and those perceived by the care providers (CPs), (b) agreement between patients and CPs in the identified needs and (c) factors associated with unmet needs. Methods Participants (N = 215) were recruited through convenience sampling from the Early Psychosis Intervention Programme (EPIP). Data was captured from patients and CPs using the Camberwell Assessment of Needs Short Appraisal Schedule (CANSAS). Results Patients and CPs identified an average of 4.06 and 3.84 needs, respectively. The highest number of unmet needs were identified for the social (50% of patients and CPs) and health domains (31.13% of patients’ vs. 28.30% of CPs). Company, intimate relationships, psychotic symptoms, money, sexual expression and psychological distress, information and benefits were the unmet needs identified by patients, whereas company, intimate relationships, physical health, and daytime activities were identified by CPs. The concordance between patients and CPs was low with majority of the items scoring slight to fair agreement (Cohen’s kappa = 0–0.4). Older age, depression, severe anxiety and having Obsessive-Compulsive Disorder (OCD) were positively associated with unmet needs in patients. Conclusion While there was an overall consensus on the total needs and met needs between patients and CPs, the level of agreement between the two groups on various items were low. Different perceptions regarding unmet needs were noted between the groups. A holistic approach that takes into account different facets of the needs of patients together with strategic planning to address unmet needs might improve treatment outcomes and satisfaction.
Real-time recommendations for energy-efficient appliance usage in households

According to several studies, the most influencing factor in a household's energy consumption is user behavior. Changing user behavior to improve energy usage leads to efficient energy consumption, saving money for the consumer and being more friendly for the environment. In this work we propose a framework that aims at assisting households in improving their energy usage by providing real-time recommendations for efficient appliance use. The framework allows for the creation of household-specific and appliance-specific energy consumption profiles by analyzing appliance usage patterns. Based on the household profile and the actual electricity use, real-time recommendations notify users on the appliances that can be switched off in order to reduce consumption. For instance, if a consumer forgets their A/C on at a time that it is usually off (e.g., when there is no one at home), the system will detect this as an outlier and notify the consumer. In the ideal scenario, a household has a smart meter monitoring system installed, that records energy consumption at the appliance level. This is also reflected in the datasets available for evaluating such systems. However, in the general case, the household may only have one main meter reading. In this case, non-intrusive load monitoring (NILM) techniques, which monitor a house's energy consumption using only one meter, and data mining algorithms that disaggregate the consumption into appliance level, can be employed. In this paper, we propose an end-to-end solution to this problem, starting with the energy disaggregation process, and the creation of user profiles that are then fed to the pattern mining and recommendation process, that through an intuitive UI allows users to further refine their energy consumption preferences and set goals. We employ the UK-DALE (UK Domestic Appliance-Level Electricity) dataset for our experimental evaluations and the proof-of-concept implementation. The results show that the proposed framework accurately captures the energy consumption profiles of each household and thus the generated recommendations are matching the actual household energy habits and can help reduce their energy consumption by 2–17%.
Serum α-Klotho associated with oral health among a nationally representative sample of US adults

Background Low klotho is associated with aging-related traits. However, no study has assessed the association between klotho and oral health in a large sample of population. This study aimed to explore the association between serum α-klotho and oral health in US Adults. Methods Data were from the National Health and Nutrition Examination Survey. Oral health parameters included periodontitis, self-rated oral health, and tooth loss. Logistic regression and restricted cubic spline models were adopted to evaluate the associations. Results A total of 6187 participants were included in the study. The median of the α-klotho level was 815.2 pg/mL. Serum α-Klotho was significantly lower in participants with poor oral health (all P <0.01). Compared with the highest tertile, the lowest tertile of α-klotho was associated with moderate/severe periodontitis, poor-rated oral health, and tooth loss, with OR (95% CI) being 1.21 (1.01, 1.48), 1.26 (1.01, 1.56) and 1.38 (1.05, 1.84), respectively. An increment of per 1 standard deviation in the α-klotho concentration was associated with lower odds of moderate/severe periodontitis (OR: 0.93; 95% CI: 0.87, 0.99). Linear dose-response relationships were found between α-klotho and the odds of moderate/severe periodontitis (P for non-linearity=0.88) and poor-rated oral health (P for non-linearity=0.66). An L-shaped dose-response relationship was found between levels of α-klotho and the odds of tooth loss (P for non-linearity=0.04). Conclusions Serum α-klotho was associated with oral health. Further studies are necessary to clarify the potential mechanisms and demonstrate the predictive ability of klotho in oral diseases.
Identification and characterization of putative effectors from Plasmodiophora brassicae that suppress or induce cell death in Nicotiana benthamiana

Clubroot, caused by Plasmodiophora brassicae, is a major disease of crucifers. Effector proteins are important virulence factors in host recognition of pathogens and the interactions between pathogens and hosts. Secretory proteins, as effector candidates, have been studied in the interaction between Plasmodiophora brassicae and its hosts. In this study, 518 secretary proteins were screened from the Plasmodiophora brassicae genome. A total of 63 candidate effectors that induce or suppress cell death were identified using agroinfiltration-mediated transient expression in Nicothiana benthamiana. The candidate effectors, Pb4_102097 and Pb4_108104 showed high expressing level in the stage of rest spore maturity, could induce cell death and were associated with H2O2 accumulation in N. benthamiana leaves. In addition, 55 candidate effectors that could suppress BAX (Bcl-2-associated X protein) induced cell death, and 21 out of which could suppress the immunity caused by bacterial pathogen Pseudomonas syringae pv. tomato strain DC3000 expressing avrRps4 in Arabidopsis. Based on the expression pattern in different stages, 28 candidate effectors showed high expression levels during the primary and secondary infection stage. Five candidate effectors containing the RXLR motif functioned in the cytoplasm and cell membrane.
Predictive models based on machine learning for bone metastasis in patients with diagnosed colorectal cancer

Background This study aimed to develop an artificial intelligence predictive model for predicting the probability of developing BM in CRC patients. Methods From SEER database, 50,566 CRC patients were identified between January 2015 and December 2019 without missing data. SVM and LR models were trained and tested on the dataset. Accuracy, area under the curve (AUC), and IDI were used to evaluate and compare the models. Results For bone metastases in the entire cohort, SVM model with poly as kernel function presents the best performance, whose accuracy is 0.908, recall is 0.838, and AUC is 0.926, outperforming LR model. The top three most important factors affecting the model's prediction of BM include extraosseous metastases (EM), CEA, and size. Conclusion Our study developed an SVM model with poly as kernel function for predicting BM in CRC patients. SVM model could improve personalized clinical decision-making, help rationalize the bone metastasis screening process, and reduce the burden on healthcare systems and patients.
Rethinking mental health wellness among adolescents living with HIV in the African context: An integrative review of mental wellness components

Objective Adolescents living with HIV (ALHIV) are considered to be at heightened risk for developing mental health problems in comparison to their peers due to the burden of living with a stigmatized condition and managing a chronic condition. Poorer mental health outcomes among ALHIV are associated with lower rates of adherence to anti-retroviral therapy (ART). It is necessary to improve mental wellness among ALHIV as this acts as a buffer against developing mental health problems which, if left untreated can evolve into mental health disorders. Research on mental wellness concepts among ALHIV is underdeveloped which is associated with a lack of appropriate measures of mental wellness. We conducted an integrative review to conceptualize mental wellness and consider the critical components for measuring mental wellness in ALHIV. Method An integrative review of published literature focusing on mental wellness of ALHIV in the African context was conducted. The process was guided by the PRISMA operational steps. As part of our problem identification phase, we drew on findings from a previous systematic review of mental wellness instruments and a qualitative photovoice study on exploring the experiences of ALHIV, to develop an initial framework of 13 mental wellness concepts and behaviors which informed the search strategy. Results The review included 17 articles from which we identified six mental wellness concepts: Connectedness, Sense of Coherence (SOC), Self-esteem, Self-acceptance, Hope for the Future and Spirituality as well as six behaviors facilitating mental wellness: Coping, Resilience, Purpose in Life (goals), Self-efficacy, Adherence Self-efficacy, and Leisure Activities. All of these concepts and behaviors have been noted in our previous research (systematic review and qualitative work), with the exception of adherence self-efficacy. Based on the findings from this review and our previous work, we adapted the Salutogenic Model of Health developed by Antonovsky in 1987, to propose a Salutogenic Model of Mental Wellness (SMoMW) for ALHIV in the African context. This SMoMW may be used to develop an age and culturally appropriate measure of mental wellness for ALHIV. Conclusion The findings from this review used to conceptualize mental wellness among ALHIV which can be used to develop a measurement of mental wellness.
EMG feedback outperforms force feedback in the presence of prosthesis control disturbance

Closing the prosthesis control loop by providing artificial somatosensory feedback can improve utility and user experience. Additionally, closed-loop control should be more robust with respect to disturbance, but this might depend on the type of feedback provided. Thus, the present study investigates and compares the performance of EMG and force feedback in the presence of control disturbances. Twenty able-bodied subjects and one transradial amputee performed delicate and power grasps with a prosthesis in a functional task, while the control signal gain was temporarily increased (high-gain disturbance) or decreased (low-gain disturbance) without their knowledge. Three outcome measures were considered: the percentage of trials successful in the first attempt (reaction to disturbance), the average number of attempts in trials where the wrong force was initially applied (adaptation to disturbance), and the average completion time of the last attempt in every trial. EMG feedback was shown to offer significantly better performance compared to force feedback during power grasping in terms of reaction to disturbance and completion time. During power grasping with high-gain disturbance, the median first-attempt success rate was significantly higher with EMG feedback (73.3%) compared to that achieved with force feedback (60%). Moreover, the median completion time for power grasps with low-gain disturbance was significantly longer with force feedback than with EMG feedback (3.64 against 2.48 s, an increase of 32%). Contrary to our expectations, there was no significant difference between feedback types with regards to adaptation to disturbances and the two feedback types performed similarly in delicate grasps. The results indicated that EMG feedback displayed better performance than force feedback in the presence of control disturbances, further demonstrating the potential of this approach to provide a reliable prosthesis-user interaction.
Metabolomics and proteomics analyses revealed mechanistic insights on the antimicrobial activity of epigallocatechin gallate against Streptococcus suis

Streptococcus suis (S. suis) is a highly virulent zoonotic pathogen and causes severe economic losses to the swine industry worldwide. Public health security is also threatened by the rapidly growing antimicrobial resistance in S. suis. Therefore, there is an urgent need to develop new and safe antibacterial alternatives against S. suis. The green tea polyphenol epigallocatechin gallate (EGCG) with a number of potential health benefits is known for its antibacterial effect; however, the mechanism of its bactericidal action remains unclear. In the present, EGCG at minimal inhibitory concentration (MIC) showed significant inhibitory effects on S. suis growth, hemolytic activity, and biofilm formation, and caused damage to S. suis cells in vitro. EGCG also reduced S. suis pathogenicity in Galleria mellonella larvae in vivo. Metabolomics and proteomics analyses were performed to investigate the underlying mechanism of antibacterial activity of EGCG at MIC. Many differentially expressed proteins involved in DNA replication, synthesis of cell wall, and cell membrane, and virulence were down-regulated after the treatment of S. suis with EGCG. EGCG not only significantly reduced the hemolytic activity of S. suis but also down-regulated the expression of suilysin (Sly). The top three shared KEGG pathways between metabolomics and proteomics analysis were ABC transporters, glycolysis/gluconeogenesis, and aminoacyl-tRNA biosynthesis. Taken together, these data suggest that EGCG could be a potential phytochemical compound for treating S. suis infection.
The diagnostic value of non-invasive methods for diagnosing bladder outlet obstruction in men with lower urinary tract symptoms: A meta-analysis

Purpose We conducted the first meta-analysis to determine the diagnostic value of non-invasive methods for diagnosing bladder outlet obstruction (BOO) in men with lower urinary tract symptoms (LUTS). Methods We searched a range of databases for relevant publications up to June 2022, including PubMed, Embase, Web of Science, and the Cochrane Library. Retrieved studies were then reviewed for eligibility and data were extracted. The risk of bias (RoB) was assessed using the QUADAS-2 tool. We then performed a formal meta-analysis to evaluate the accuracy of various non-invasive methods for diagnosing BOO in men. Results We identified 51 eligible studies including 7,897 patients for meta-analysis. The majority of the studies had a low overall RoB. Detrusor wall thickness (DWT) (pooled sensitivity (SSY): 71%; specificity (SPY): 88%; diagnostic odds ratio (DOR): 17.15; area under curve (AUC) 0.87) and the penile cuff test (PCT) (pooled SSY: 87%; SPY: 78%; DOR: 23.54; AUC: 0.88) showed high accuracy for diagnosing BOO. Furthermore, data suggested that DWT had the highest pooled SPY (0.89), DOR (32.58), and AUC (0.90), when using 2 mm as the cut-off. Conclusion Of the non-invasive tests tested, DWT and PCT had the highest levels of diagnostic accuracy for diagnosing BOO in men with LUTS. DWT, with a 2 mm cut-off, had the highest level of accuracy. These two methods represent good options as non-invasive tools for evaluating BOO in males.
Predicting non-small cell lung cancer-related genes by a new network-based machine learning method

Lung cancer is the leading cause of cancer death globally, killing 1.8 million people yearly. Over 85% of lung cancer cases are non-small cell lung cancer (NSCLC). Lung cancer running in families has shown that some genes are linked to lung cancer. Genes associated with NSCLC have been found by next-generation sequencing (NGS) and genome-wide association studies (GWAS). Many papers, however, neglected the complex information about interactions between gene pairs. Along with its high cost, GWAS analysis has an obvious drawback of false-positive results. Based on the above problem, computational techniques are used to offer researchers alternative and complementary low-cost disease–gene association findings. To help find NSCLC-related genes, we proposed a new network-based machine learning method, named deepRW, to predict genes linked to NSCLC. We first constructed a gene interaction network consisting of genes that are related and irrelevant to NSCLC disease and used deep walk and graph convolutional network (GCN) method to learn gene–disease interactions. Finally, deep neural network (DNN) was utilized as the prediction module to decide which genes are related to NSCLC. To evaluate the performance of deepRW, we ran tests with 10-fold cross-validation. The experimental results showed that our method greatly exceeded the existing methods. In addition, the effectiveness of each module in deepRW was demonstrated in comparative experiments.
A light-guiding urinary catheter for the inhibition of Proteus mirabilis biofilm formation

Catheter-associated urinary tract infection (CAUTI) is a leading cause of hospital-acquired infections worldwide causing debilitating illness for patients as well as a significant financial and treatment burden on health services. CAUTI is linked with the build-up of biofilms on catheter surfaces which act as a reservoir for infection. Additionally, urease-producing bacteria such as Gram-negative Proteus mirabilis (PM), can form crystalline biofilms which encrust catheter surfaces ultimately leading to blockages which require immediate removal of the catheter. Currently there are limited treatments available to prevent the formation of biofilms by PM as well as other urinary tract infection causing bacteria. A novel concept for a light-guiding urinary catheter is presented where a silicone elastomer waveguide incorporated along the length of the catheter is used to irradiate the catheter surfaces with antimicrobial blue light (405 nm) to prevent biofilm formation in situ. The prototype device is mass producible while also easy to fabricate in a lab setting for research studies. The inhibitory effect of blue light on PM biofilm formation over a range of irradiances is described for the first time showing an LD90 at 192–345 J/cm2 and total inhibition at 1,700 J/cm2 In vitro studies show that the light-guiding catheter (LGC) prototypes exhibit a 98% inhibition in PM biofilm formation inside the catheter lumen at an average estimated irradiance of 30–50 mW/cm2 (324–540 J/cm2 fluence) showing that the concept is highly effective, promising to be a powerful and economical antimicrobial approach to prevent catheter associated biofilm development and blockage.