Resolution Enhancement of Metabolomic J-Res NMR Spectra Using Deep Learning.

J-Resolved (J-Res) nuclear magnetic resonance (NMR) spectroscopy is pivotal in NMR-based metabolomics, but practitioners face a choice between time-consuming high-resolution (HR) experiments or shorter low-resolution (LR) experiments which exhibit significant peak overlap. Deep learning neural networks have been successfully used in many fields to enhance quality of natural images, especially with regard to resolution, and therefore offer the prospect of improving two-dimensional (2D) NMR data. Here, we introduce the J-RESRGAN, an adapted and modified generative adversarial network (GAN) for image super-resolution (SR), which we trained specifically for metabolomic J-Res spectra to enhance peak resolution. A novel symmetric loss function was introduced, exploiting the inherent vertical symmetry of J-Res NMR spectra. Model training used simulated high-resolution J-Res spectra of complex mixtures, with corresponding low-resolution spectra generated via blurring and down-sampling. Evaluation of peak pair resolvability on J-RESRGAN demonstrated remarkable improvement in resolution across a variety of samples. In simulated plasma data, 100% of peak pairs exhibited enhanced resolution in super-resolution spectra compared to their low-resolution counterparts. Similarly, enhanced resolution was observed in 80.8-100% of peak pairs in experimental plasma, 85.0-96.7% in urine, 94.4-98.9% in full fat milk, and 82.6-91.7% in orange juice. J-RESRGAN is not sample type, spectrometer or field strength dependent and improvements on previously acquired data can be seen in seconds on a standard desktop computer. We believe this demonstrates the promise of deep learning methods to enhance NMR metabolomic data, and in particular, the power of J-RESRGAN to elucidate overlapping peaks, advancing precision in a wide variety of NMR-based metabolomics studies. The model, J-RESRGAN, is openly accessible for download on GitHub at https://github.com/yanyan5420/J-RESRGAN.

Kernel-based joint independence tests for multivariate stationary and non-stationary time series.

Multivariate time-series data that capture the temporal evolution of interconnected systems are ubiquitous in diverse areas. Understanding the complex relationships and potential dependencies among co-observed variables is crucial for the accurate statistical modelling and analysis of such systems. Here, we introduce kernel-based statistical tests of joint independence in multivariate time series by extending the d-variable Hilbert-Schmidt independence criterion to encompass both stationary and non-stationary processes, thus allowing broader real-world applications. By leveraging resampling techniques tailored for both single- and multiple-realization time series, we show how the method robustly uncovers significant higher-order dependencies in synthetic examples, including frequency mixing data and logic gates, as well as real-world climate, neuroscience and socio-economic data. Our method adds to the mathematical toolbox for the analysis of multivariate time series and can aid in uncovering high-order interactions in data.

Listening to Mental Health Crisis Needs at Scale: Using Natural Language Processing to Understand and Evaluate a Mental Health Crisis Text Messaging Service.

The current mental health crisis is a growing public health issue requiring a large-scale response that cannot be met with traditional services alone. Digital support tools are proliferating, yet most are not systematically evaluated, and we know little about their users and their needs. Shout is a free mental health text messaging service run by the charity Mental Health Innovations, which provides support for individuals in the UK experiencing mental or emotional distress and seeking help. Here we study a large data set of anonymised text message conversations and post-conversation surveys compiled through Shout. This data provides an opportunity to hear at scale from those experiencing distress; to better understand mental health needs for people not using traditional mental health services; and to evaluate the impact of a novel form of crisis support. We use natural language processing (NLP) to assess the adherence of volunteers to conversation techniques and formats, and to gain insight into demographic user groups and their behavioural expressions of distress. Our textual analyses achieve accurate classification of conversation stages (weighted accuracy = 88%), behaviours (1-hamming loss = 95%) and texter demographics (weighted accuracy = 96%), exemplifying how the application of NLP to frontline mental health data sets can aid with post-hoc analysis and evaluation of quality of service provision in digital mental health services.

[Impact of overweight and obesity on the relationship between serum vitamin D and fasting blood glucose in children aged 6-17 years in Shandong Province].

OBJECTIVE: To study the relationship between serum vitamin D and fasting blood glucose in children and adolescents, and its influence on overweight and obesity. METHODS: Using a multi-stage stratified cluster sampling method, 13 districts and counties in Shandong Province were selected as survey points, and a total of 26 elementary schools, 26 junior high schools and 13 high schools were selected. Questionnaire surveys and physical examination were conducted on children and adolescents aged 6-17.Serum vitamin D and fasting blood glucose were uniformly measured. The "BMI Classification Standard for Overweight and Obesity Screening for Chinese School-age Children and Adolescents" was used to determine overweight and obesity. Statistical method adopt t-test and generalized linear regression model for analysis. RESULTS: A total of 3562 children aged 6-17 years were investigated, 2666 were healthy, 446 were overweight, 450 were obese, including 131 peripheral obesity and 319 abdominal obesity. The fasting blood glucose level of the 13-17-year-old group was significantly higher than that of the 6-12-year-old group(t=-3.13, P=0.002), and the male was significantly higher than that of the female(t=7.87, P& lt; 0.001). In the healthy and obesity group, there was a negative correlation between serum vitamin D and fasting blood glucose(P& lt; 0.05). In overweight group, serum vitamin D was not significantly related to fasting blood glucose(P& gt; 0.05). Serum vitamin D and fasting blood glucose were negatively correlated with healthy bodies in female group and 13-17 years old group, and the abdominal obesity bodies in the 6-12 year old group(P& lt; 0.05). CONCLUSION: The fasting blood glucose of adolescents aged 13-17 is significantly higher than that of children aged 6-12, and the fasting blood glucose of men is significantly higher than that of women. Different gender, age, and overweight and obesity status have an impact on the relationship between serum vitamin D and fasting blood glucose.

Expression of Bordetella pertussis Antigens Fused to Different Vectors and Their Effectiveness as Vaccines.

Pertussis is an acute respiratory tract infection caused by Bordetella pertussis. Even though its current vaccine coverage is relatively broad, they still have some shortcomings such as short protection time and might be incapable of blocking the spread of the disease. In this study, we developed new pertussis vaccine candidates by separately fusing three pertussis antigens (B. pertussis fimbriae 2 "Fim2", pertussis toxin S1 subunit "PtxS1", and filamentous hemagglutinin "FHA1877-2250") to each of two immune-boosting carrier proteins (B subunits of AB5 toxin family: cholera toxin B subunit "CTB" and shiga toxin B subunit "StxB"). We then immunized mice with these fusion antigens and found that they significantly increased the serum antibody titers and elicited high bactericidal activity against B. pertussis. After CTB-or StxB-fused antigen-immunized mice were challenged with a non-lethal dose of B. pertussis, the bacterial loads in different tissues of these mice were significantly reduced, and their lung damage was nearly invisible. Furthermore, we also demonstrated that these candidate vaccines could provide strong prophylactic effects against a lethal challenge with B. pertussis. Overall, our candidate vaccines conferred better immune protection to mice compared with pertussis antigen alone. This B5 subunit-based vaccine strategy provides a promising option for vaccine design.

Fruit intake decreases risk of incident type 2 diabetes: an updated meta-analysis.

Association between fruit intake and risk of type 2 diabetes is inconsistent. In this study, we performed a meta-analysis of all prospective cohort studies to clarify the association between fruit intake and risk of type 2 diabetes. Relevant studies were identified by searches of the PubMed and Embase databases up to November 2013. The summary relative risks of association were obtained using a fixed- or random-effects model. A total of nine prospective studies (403,259 participants, including 27,940 with incident type 2 diabetes) from seven publications were included in the meta-analysis of fruit intake and risk of type 2 diabetes. We found that individuals in the highest category of fruit intake had a reduced risk of type 2 diabetes (relative risk 0.92, 95 % confidence interval 0.86-0.97, p = 0.003) compared to those in the lowest category, with moderate evidence of between-study heterogeneity (I (2) = 37.6 %, p = 0.12). There was an evident non-linear association of fruit intake with type 2 diabetes (P for nonlinearity <0.001). A non-linear threshold of 200 g/day of fruit intake was identified and the risk of type 2 diabetes reduced by about 13 % at this cut-off. Our findings are consistent with diet recommendations to consume about 200 g/day of fruits to prevent type 2 diabetes.

Intake of fruit juice and incidence of type 2 diabetes: a systematic review and meta-analysis.

BACKGROUND: Several prospective studies have been conducted to examine the relationship between fruit juice intake and risk of incident type 2 diabetes, but results have been mixed. In the present study, we aimed to estimate the association between fruit juice intake and risk of type 2 diabetes. METHODS: PubMed and Embase databases were searched up to December 2013. All prospective cohort studies of fruit juice intake with risk of type 2 diabetes were included. The pooled relative risks (RRs) with 95% confidence intervals (CIs) for highest vs. lowest category of fruit juice intake were estimated using a random-effects model. RESULTS: A total of four studies (191,686 participants, including 12,375 with type 2 diabetes) investigated the association between sugar-sweetened fruit juice and risk of incident type 2 diabetes, and four studies (137,663 participants and 4,906 cases) investigated the association between 100% fruit juice and risk of incident type 2 diabetes. A higher intake of sugar-sweetened fruit juice was significantly associated with risk of type 2 diabetes (RR = 1.28, 95%CI = 1.04-1.59, p = 0.02), while intake of 100% fruit juice was not associated with risk of developing type 2 diabetes (RR = 1.03, 95% CI = 0.91-1.18, p = 0.62). CONCLUSIONS: Our findings support dietary recommendations to limit sugar-sweetened beverages, such as fruit juice with added sugar, to prevent the development of type 2 diabetes.