Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a "long-lie". Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD

Trends and predictions of malnutrition and obesity in 204 countries and territories: an analysis of the Global Burden of Disease Study 2019.

BackgroundMalnutrition and obesity are interdependent pathologies along the same spectrum. We examined global trends and projections of disability-adjusted life years (DALYs) and deaths from malnutrition and obesity until 2030.MethodsUsing data from the 2019 Global Burden of Disease study involving 204 countries and territories, trends in DALYs and deaths were described for obesity and malnutrition from 2000 to 2019, stratified by geographical regions (as defined by WHO) and Socio-Demographic Index (SDI). Malnutrition was defined according to the 10th revision of International Classification of Diseases codes for nutritional deficiencies, stratified by malnutrition type. Obesity was measured via body mass index (BMI) using metrics related to national and subnational estimates, defined as BMI ≥25 kg/m2. Countries were stratified into low, low-middle, middle, high-middle, and high SDI bands. Regression models were constructed to predict DALYs and mortality up to 2030. Association between age-standardised prevalence of the diseases and mortality was also assessed.FindingsIn 2019, age-standardised malnutrition-related DALYs was 680 (95% UI: 507-895) per 100,000 population. DALY rates decreased from 2000 to 2019 (-2.86% annually), projected to fall 8.4% from 2020 to 2030. Africa and low SDI countries observed highest malnutrition-related DALYs. Age-standardised obesity-related DALY estimates were 1933 (95% UI: 1277-2640). Obesity-related DALYs rose 0.48% annually from 2000 to 2019, predicted to increase by 39.8% from 2020 to 2030. Highest obesity-related DALYs were in Eastern Mediterranean and middle SDI countries.InterpretationThe ever-increasing obesity burden, on the backdrop of curbing the malnutrition burden, is predicted to rise further.FundingNone

Wolfberry genomes and the evolution of Lycium (Solanaceae)

AbstractWolfberry Lycium, an economically important genus of the Solanaceae family, contains approximately 80 species and shows a fragmented distribution pattern among the Northern and Southern Hemispheres. Although several herbaceous species of Solanaceae have been subjected to genome sequencing, thus far, no genome sequences of woody representatives have been available. Here, we sequenced the genomes of 13 perennial woody species of Lycium, with a focus on Lycium barbarum. Integration with other genomes provides clear evidence supporting a whole-genome triplication (WGT) event shared by all hitherto sequenced solanaceous plants, which occurred shortly after the divergence of Solanaceae and Convolvulaceae. We identified new gene families and gene family expansions and contractions that first appeared in Solanaceae. Based on the identification of self-incompatibility related-gene families, we inferred that hybridization hotspots are enriched for genes that might be functioning in gametophytic self-incompatibility pathways in wolfberry. Extremely low expression of LOCULE NUBER (LC) and COLORLESS NON-RIPENING (CNR) orthologous genes during Lycium fruit development and ripening processes suggests functional diversification of these two genes between Lycium and tomato. The existence of additional flowering locus C-like MADS-box genes might correlate with the perennial flowering cycle of Lycium. Differential gene expression involved in the lignin biosynthetic pathway between Lycium and tomato likely illustrates woody and herbaceous differentiation. We also provide evidence that Lycium migrated from Africa into Asia, and subsequently from Asia into North America. Our results provide functional insights into Solanaceae origins, evolution and diversification.</jats:p

Regulating STING in health and disease.

The presence of cytosolic double-stranded DNA molecules can trigger multiple innate immune signalling pathways which converge on the activation of an ER-resident innate immune adaptor named "STimulator of INterferon Genes (STING)". STING has been found to mediate type I interferon response downstream of cyclic dinucleotides and a number of DNA and RNA inducing signalling pathway. In addition to its physiological function, a rapidly increasing body of literature highlights the role for STING in human disease where variants of the STING proteins, as well as dysregulated STING signalling, have been implicated in a number of inflammatory diseases. This review will summarise the recent structural and functional findings of STING, and discuss how STING research has promoted the development of novel therapeutic approaches and experimental tools to improve treatment of tumour and autoimmune diseases

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe