Proteomics Analysis of Soybean Seedlings under Short-Term Water Deficit

Soybeans are one of the most important grain crops worldwide.Water deficit,which seriously affects the yield and quality of soybeans,is the main abiotic stress factor in soybean production.As a follow-up study,the droughttolerant soybean variant Heinong 44 was analyzed via proteome analysis.Soybean was exposed to water deficit for 0,8,and 24 h,and protein samples were extracted for detection of differentially expressed proteins.Protein sequencing of leaf tissues under water stress yielded a total of 549 differentially expressed proteins:75 and 320 upregulated proteins as well as 70 and 84 downregulated proteins were obtained after 8 and 24 h of water deficit,respectively.Gene Ontology analysis revealed that most of the differentially expressed proteins(DEPs)were involved in catalytic activity,molecular function,and metabolic processes,whereas some of them were involved in photosynthesis,carbon metabolism,and energy metabolism.We also identified some differentially expressed proteins that may be involved in the regulation of water deficit response.Our study provides a theoretical basis for the breeding of drought-resistant soybean varieties.

Transcriptome Analysis of Soybean in Response to Different Sulfur Concentrations

Sulfur is an indispensable nutrient for plant growth and development,and is important in the synthesis of sulfurcontaining amino acids.Although several studies on the effects of some macronutrients,including nitrogen and phosphorus,have been conducted on the performance of several crops at the genomic level,studies on the effect of sulfur on crop performance are limited.Therefore,this study aimed to examine the effect of different sulfur concentration on the transcriptome of soybean.Additionally,soybean yield parameters were also examined.Two soybean varieties,DND252 and HN84,were exposed to low and high concentrations of sulfur,and differentially expressed genes(DEGs)were identified using transcriptome analysis.The study results showed that the DEGs identified in the DND252 variety were involved in stimuli response,DNA binding and cell periphery under low sulfur concentrations.Also,the DEGs identified under high sulfur concentration were involved in membrane and membrane parts.Additionally,DEGs identified in the HN84 variety under low sulfur concentrations had similar functions as those identified in DND252 under high sulfur concentrations,indicating that HN84 was more sensitive to sulfur concentration changes than DND252.However,under higher sulfur concentrations,the DEGs identified in HN84 were primarily involved in membrane and membrane parts,indicating that high sulfur can cause cell membrane damage.Furthermore,soybean grown using 2.0 mmol/L sulfur had the best yield.The findings of this study identified candidate genes for the breeding and development of sulfur-efficient soybean varieties.

PhaseFIT:live-organoid phase-fluorescent image transformation via generative AI

Organoid models have provided a powerful platform for mechanistic investigations into fundamental biological processes involved in the development and function of organs.Despite the potential for image-based phenotypic quantification of organoids,their complex 3D structure,and the time-consuming and labor-intensive nature of immunofluorescent staining present significant challenges.In this work,we developed a virtual painting system,PhaseFIT(phase-fluorescent image transformation)utilizing customized and morphologically rich 2.5D intestinal organoids,which generate virtual fluorescent images for phenotypic quantification via accessible and low-cost organoid phase images.This system is driven by a novel segmentation-informed deep generative model that specializes in segmenting overlap and proximity between objects.The model enables an annotation-free digital transformation from phase-contrast to multi-channel fluorescent images.The virtual painting results of nuclei,secretory cell markers,and stem cells demonstrate that PhaseFIT outperforms the existing deep learning-based stain transformation models by generating fine-grained visual content.We further validated the efficiency and accuracy of PhaseFIT to quantify the impacts of three compounds on crypt formation,cell population,and cell stemness.PhaseFIT is the first deep learning-enabled virtual painting system focused on live organoids,enabling large-scale,informative,and efficient organoid phenotypic quantification.PhaseFIT would enable the use of organoids in high-throughput drug screening applications.

Chemical formation and source apportionment of PM_(2.5) at an urban site at the southern foot of the Taihang mountains

The region along the Taihang Mountains in the North China Plain(NCP) is characterized by serious fine particle pollution. To clarify the formation mechanism and controlling factors, an observational study was conducted to investigate the physical and chemical properties of the fine particulate matter in Jiaozuo city, China. Mass concentrations of the water-soluble ions(WSIs) in PM_(2.5) and gaseous pollutant precursors were measured on an hourly basis from December 1, 2017, to February 27, 2018. The positive matrix factorization(PMF) method and the FLEXible PARTicle(FLEXPART) model were employed to identify the sources of PM_(2.5). The results showed that the average mass concentration of PM_(2.5) was 111 μg/m^(3) during the observation period. Among the major WSIs, sulfate, nitrate, and ammonium(SNA) constituted 62% of the total PM_(2.5) mass, and NO_(3)^(-) ranked the highest with an average contribution of 24.6%. NH_(4)^(+) was abundant in most cases in Jiaozuo. According to chemical balance analysis, SO_(2)-4, NO_(3)^(-), and Cl^(-) might be present in the form of(NH_4)_(2)SO_4, NH_4NO_3, NH_4Cl, and KCl. The liquid-phase oxidation of SO_(2) and NO_(2) was severe during the haze period. The relative humidity and pH were the key factors influencing SO_(4)^(2-) formation. We found that NO_(3)^(-) mainly stemmed from homogeneous gas-phase reactions in the daytime and originated from the hydrolysis of N_(2)O_5 in the nighttime, which was inconsistent with previous studies. The PMF model identified five sources of PM_(2.5) : secondary origin(37.8%), vehicular emissions(34.7%), biomass burning(11.5%), coal combustion(9.4%), and crustal dust(6.6%).

DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning

The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field.However,a persistent issue remains unsolved during experiments:the interferential technical noises caused by systematic errors(e.g.,temperature,reagent concentration,and well location)are always mixed up with the real biological signals,leading to misinterpretation of any conclusion drawn.Here,we reported a mean teacher-based deep learning model(Deep Noise)that can disentangle biological signals from the experimental noises.Specifically,we aimed to classify the phenotypic impact of 1108 different genetic perturbations screened from 125,510 fluorescent microscopy images,which were totally unrecognizable by the human eye.We validated our model by participating in the Recursion Cellular Image Classification Challenge,and Deep Noise achieved an extremely high classification score(accuracy:99.596%),ranking the 2nd place among 866 participating groups.This promising result indicates the successful separation of biological and technical factors,which might help decrease the cost of treatment development and expedite the drug discovery process.The source code of Deep Noise is available at https://github.com/Scu-sen/Recursion-Cellular-Image-Classification-Challenge.

Study on forecasting the parameters of gas environment of metro station

In the crowded metro station,it is important to forecast the changes of environmental parameters for the normal operation of metro and the safety of passengers in the future.Artificial Neural Network(ANN)has a good perfor-mance on processing time series data.In order to accurately predict environmental parameters,this paper uses ANN method to build the forecasting model of environmental parameters.The forecasting model uses the external environment parameters of the station as input variables.Finally,the accuracy of the model is verified by the field data collected from the metro station.The results show that the mean relative error of the proposed method is within 10%.The forecasting model based on ANN in this paper can accurately forecast the internal environment parameters of the metro station in the future period and is of great significance of emergency prevention and decision-making.