A Supervised STDP-based Training Algorithm for Living Neural Networks

Neural networks have shown great potential in many applications like speech recognition, drug discovery, image classification, and object detection. Neural network models are inspired by biological neural networks, but they are optimized to perform machine learning tasks on digital computers. The proposed work explores the possibilities of using living neural networks in vitro as basic computational elements for machine learning applications. A new supervised STDP-based learning algorithm is proposed in this work, which considers neuron engineering constrains. A 74.7% accuracy is achieved on the MNIST benchmark for handwritten digit recognition.Comment: 5 pages, 3 figures, Accepted by ICASSP 201

Examination and evaluation of multi-source monthly scale fusion precipitation product in China based on machine learning algorithm

Grid format precipitation products have better spatial monitoring capabilities compared to ground meteorological station observations, but there are significant differences in performance among different products. This article evaluates the accuracy of nine monthly scale precipitation products TRMM, GPM, CMORPH, CHIRPS, ERA5, ERA5 Land, PERSIANN, PERSIANN-CDR, PERSIANN-CCS in China, and selects five better precipitation products from them. XGBoost is used to select the best precipitation products Three machine learning algorithms, random forest and multiple linear regression, were used for data fusion. Research has found that TRMM, GPM, CMORPH, CHIRPS, and PERSIANN-CDR products have relatively good accuracy. In high altitude and arid regions, the error of precipitation products significantly increases. After machine learning algorithm fusion, the optimal XGBoost algorithm model significantly improves product correlation coefficient, and significantly reduces root mean square error and bias. The three algorithms have shown good accuracy in each month, with XGBoost algorithm model products performing better in summer and random forest algorithm model products performing better in winter. Moreover, the three algorithm model products have shown high accuracy in different regions. Compared with the five original products before the fusion, the accuracy of the three algorithm model products has improved. The product fused with XGBoost algorithm has more variation and local precipitation details compared to the optimal original GPM product and meteorological station interpolation product in space

Endocytic sorting and recycling require membrane phosphatidylserine asymmetry maintained by TAT-1/CHAT-1. PLoS Genet

Endocytic sorting is achieved through the formation of morphologically and functionally distinct sub-domains within early endosomes. Cargoes destined for recycling are sorted to and transported through newly-formed tubular membranes, but the processes that regulate membrane tubulation are poorly understood. Here, we identified a novel Caenorhabditis elegans Cdc50 family protein, CHAT-1, which acts as the chaperone of the TAT-1 P4-ATPase to regulate membrane phosphatidylserine (PS) asymmetry and endocytic transport. In chat-1 and tat-1 mutants, the endocytic sorting process is disrupted, leading to defects in both cargo recycling and degradation. TAT-1 and CHAT-1 colocalize to the tubular domain of the early endosome, the tubular endocytic recycling compartment (ERC), and the recycling endosome where PS is enriched on the cytosolic surface. Loss of tat-1 and chat-1 function disrupts membrane PS asymmetry and abrogates the tubular membrane structure. Our data suggest that CHAT-1 and TAT-1 maintain membrane phosphatidylserine asymmetry, thus promoting membrane tubulation and regulating endocytic sorting and recycling

RSRE: RNA structural robustness evaluator

Biological robustness, defined as the ability to maintain stable functioning in the face of various perturbations, is an important and fundamental topic in current biology, and has become a focus of numerous studies in recent years. Although structural robustness has been explored in several types of RNA molecules, the origins of robustness are still controversial. Computational analysis results are needed to make up for the lack of evidence of robustness in natural biological systems. The RNA structural robustness evaluator (RSRE) web server presented here provides a freely available online tool to quantitatively evaluate the structural robustness of RNA based on the widely accepted definition of neutrality. Several classical structure comparison methods are employed; five randomization methods are implemented to generate control sequences; sub-optimal predicted structures can be optionally utilized to mitigate the uncertainty of secondary structure prediction. With a user-friendly interface, the web application is easy to use. Intuitive illustrations are provided along with the original computational results to facilitate analysis. The RSRE will be helpful in the wide exploration of RNA structural robustness and will catalyze our understanding of RNA evolution. The RSRE web server is freely available at http://biosrv1.bmi.ac.cn/RSRE/ or http://biotech.bmi.ac.cn/RSRE/

Development of a Method to Monitor Gene Expression in Single Bacterial Cells During the Interaction With Plants and Use to Study the Expression of the Type III Secretion System in Single Cells of Dickeya dadantii in Potato

Dickeya dadantii is a bacterial plant pathogen that causes soft rot disease on a wide range of host plants. The type III secretion system (T3SS) is an important virulence factor in D. dadantii. Expression of the T3SS is induced in the plant apoplast or in hrp-inducing minimal medium (hrp-MM), and is repressed in nutrient-rich media. Despite the understanding of induction conditions, how individual cells in a clonal bacterial population respond to these conditions and modulate T3SS expression is not well understood. In our previous study, we reported that in a clonal population, only a small proportion of bacteria highly expressed T3SS genes while the majority of the population did not express T3SS genes under hrp-MM condition. In this study, we developed a method that enabled in situ observation and quantification of gene expression in single bacterial cells in planta. Using this technique, we observed that the expression of the T3SS genes hrpA and hrpN is restricted to a small proportion of D. dadantii cells during the infection of potato. We also report that the expression of T3SS genes is higher at early stages of infection compared to later stages. This expression modulation is achieved through adjusting the ratio of T3SS ON and T3SS OFF cells and the expression intensity of T3SS ON cells. Our findings not only shed light into how bacteria use a bi-stable gene expression manner to modulate an important virulence factor, but also provide a useful tool to study gene expression in individual bacterial cells in planta

Inhibition of NK1.1 signaling attenuates pressure overload-induced heart failure, and consequent pulmonary inflammation and remodeling

BackgroundInflammation contributes to heart failure (HF) development, the progression from left ventricular failure to pulmonary remodeling, and the consequent right ventricular hypertrophy and failure. NK1.1 plays a critical role in Natural killer (NK) and NK T (NKT) cells, but the role of NK1.1 in HF development and progression is unknown.MethodsWe studied the effects of NK1.1 inhibition on transverse aortic constriction (TAC)-induced cardiopulmonary inflammation, HF development, and HF progression in immunocompetent male mice of C57BL/6J background.ResultsWe found that NK1.1+ cell-derived interferon gamma+ (IFN-γ+) was significantly increased in pulmonary tissues after HF. In addition, anti-NK1.1 antibodies simultaneously abolished both NK1.1+ cells, including the NK1.1+NK and NK1.1+NKT cells in peripheral blood, spleen, and lung tissues, but had no effect on cardiopulmonary structure and function under control conditions. However, systemic inhibition of NK1.1 signaling by anti-NK1.1 antibodies significantly rescued mice from TAC-induced left ventricular inflammation, fibrosis, and failure. Inhibition of NK1.1 signaling also significantly attenuated TAC-induced pulmonary leukocyte infiltration, fibrosis, vessel remodeling, and consequent right ventricular hypertrophy. Moreover, inhibition of NK1.1 signaling significantly reduced TAC-induced pulmonary macrophage and dendritic cell infiltration and activation.ConclusionsOur data suggest that inhibition of NK1.1 signaling is effective in attenuating systolic overload-induced cardiac fibrosis, dysfunction, and consequent pulmonary remodeling in immunocompetent mice through modulating the cardiopulmonary inflammatory response

Global mortality of snakebite envenoming between 1990 and 2019

Snakebite envenoming is an important cause of preventable death. The World Health Organization (WHO) set a goal to halve snakebite mortality by 2030. We used verbal autopsy and vital registration data to model the proportion of venomous animal deaths due to snakes by location, age, year, and sex, and applied these proportions to venomous animal contact mortality estimates from the Global Burden of Disease 2019 study. In 2019, 63,400 people (95% uncertainty interval 38,900-78,600) died globally from snakebites, which was equal to an age-standardized mortality rate (ASMR) of 0.8 deaths (0.5-1.0) per 100,000 and represents a 36% (2-49) decrease in ASMR since 1990. India had the greatest number of deaths in 2019, equal to an ASMR of 4.0 per 100,000 (2.3-5.0). We forecast mortality will continue to decline, but not sufficiently to meet WHO's goals. Improved data collection should be prioritized to help target interventions, improve burden estimation, and monitor progress.Peer reviewe

Adaptive Wavelet Estimations in the Convolution Structure Density Model

Using kernel methods, Lepski and Willer study a convolution structure density model and establish adaptive and optimal Lp risk estimations over an anisotropic Nikol’skii space (Lepski, O.; Willer, T. Oracle inequalities and adaptive estimation in the convolution structure density model. Ann. Stat.2019, 47, 233–287). Motivated by their work, we consider the same problem over Besov balls by wavelets in this paper and first provide a linear wavelet estimate. Subsequently, a non-linear wavelet estimator is introduced for adaptivity, which attains nearly-optimal convergence rates in some cases