Impact of Sensor Zenith Angle on MOD10A1 Data Reliability and Modification of Snow Cover Data for the Tarim River Basin

Snow in the mountainous watersheds of the Tarim River Basin is the primary source of water for western China. The Snow Cover Daily L3 Global 500-m Grid (MOD10A1) remote sensing dataset has proven extremely valuable for monitoring the changing snow cover patterns over large spatial areas; however, inherent uncertainty associated with large sensor zenith angles (SZAs) has called its reliability into question. Comparative analysis that utilized a paired-date difference method for parameters such as snow cover frequency, snow cover percentage, and normalized difference snow index (NDSI) has shown that overestimation of snow cover in the Tarim River Basin correlates with high values of SZA. Hence, such overestimation was associated with an increase in the NDSI, attributable to the change in reflectance between Band 4 and Band 6 imagery. A maximum threshold value of SZA of 22.37° was used alongside a multiday refilling method to modify the MOD10A1 dataset to produce a new daily snow cover map of the Tarim River Basin, spanning a 10-year period. A comparison of benchmark results of snow cover classification produced by the HJ-1A/B satellite revealed an increase in the overall accuracy of up to 4%, confirming the usefulness of our modified MOD10A1 data

CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding

Self-supervised learning (SSL) has gained widespread attention in the remote sensing (RS) and earth observation (EO) communities owing to its ability to learn task-agnostic representations without human-annotated labels. Nevertheless, most existing RS SSL methods are limited to learning either global semantic separable or local spatial perceptible representations. We argue that this learning strategy is suboptimal in the realm of RS, since the required representations for different RS downstream tasks are often varied and complex. In this study, we proposed a unified SSL framework that is better suited for RS images representation learning. The proposed SSL framework, Contrastive Mask Image Distillation (CMID), is capable of learning representations with both global semantic separability and local spatial perceptibility by combining contrastive learning (CL) with masked image modeling (MIM) in a self-distillation way. Furthermore, our CMID learning framework is architecture-agnostic, which is compatible with both convolutional neural networks (CNN) and vision transformers (ViT), allowing CMID to be easily adapted to a variety of deep learning (DL) applications for RS understanding. Comprehensive experiments have been carried out on four downstream tasks (i.e. scene classification, semantic segmentation, object-detection, and change detection) and the results show that models pre-trained using CMID achieve better performance than other state-of-the-art SSL methods on multiple downstream tasks. The code and pre-trained models will be made available at https://github.com/NJU-LHRS/official-CMID to facilitate SSL research and speed up the development of RS images DL applications.Comment: Accepted by IEEE TGRS. The codes and models are released at https://github.com/NJU-LHRS/official-CMI

SANT proteins modulate gene expression by coordinating histone H3KAc and Khib levels and regulate plant heat tolerance

Histone post-translational modifications (PTMs), such as acetylation and recently identified lysine 2-hydroxyisobutyrylation (Khib), act as active epigenomic marks in plants. SANT domain-containing proteins SANT1, SANT2, SANT3, and SANT4 (SANT1/2/3/4), derived from PIF/Harbinger transposases, form a complex with HISTONE DEACETYLASE 6 (HDA6) to regulate gene expression via histone deacetylation. However, whether SANT1/2/3/4 coordinates different types of PTMs to regulate transcription and mediate responses to specific stresses in plants remains unclear. Here, in addition to modulating histone deacetylation, we found that SANT1/2/3/4 proteins acted like HDA6 or HDA9 in regulating the removal of histone Khib in Arabidopsis (Arabidopsis thaliana). Histone H3 lysine acetylation (H3KAc) and histone Khib were coordinated by SANT1/2/3/4 to regulate gene expression, with H3KAc playing a predominant role and Khib acting complementarily to H3KAc. SANT1/2/3/4 mutation significantly increased the expression of heat-inducible genes with concurrent change of H3KAc levels under normal and heat stress conditions, resulting in enhanced thermotolerance. This study revealed the critical roles of Harbinger transposon-derived SANT domain-containing proteins in transcriptional regulation by coordinating different types of histone PTMs and in the regulation of plant thermotolerance by mediating histone acetylation modification

Machine-learning prediction of BMI change among doctors and nurses in North China during the COVID-19 pandemic

ObjectiveThe COVID-19 pandemic has become a major public health concern over the past 3 years, leading to adverse effects on front-line healthcare workers. This study aimed to develop a Body Mass Index (BMI) change prediction model among doctors and nurses in North China during the COVID-19 pandemic, and further identified the predicting effects of lifestyles, sleep quality, work-related conditions, and personality traits on BMI change.MethodsThe present study was a cross-sectional study conducted in North China, during May-August 2022. A total of 5,400 doctors and nurses were randomly recruited from 39 COVID-19 designated hospitals and 5,271 participants provided valid responses. Participants’ data related to social-demographics, dietary behavior, lifestyle, sleep, personality, and work-related conflicts were collected with questionnaires. Deep Neural Network (DNN) was applied to develop a BMI change prediction model among doctors and nurses during the COVID-19 pandemic.ResultsOf participants, only 2,216 (42.0%) individuals kept a stable BMI. Results showed that personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions had effects on the BMI change among doctors and nurses. The prediction model for BMI change was developed with a 33-26-20-1 network framework. The DNN model achieved high prediction efficacy, and values of R2, MAE, MSE, and RMSE for the model were 0.940, 0.027, 0.002, and 0.038, respectively. Among doctors and nurses, the top five predictors in the BMI change prediction model were unbalanced nutritional diet, poor sleep quality, work-family conflict, lack of exercise, and soft drinks consumption.ConclusionDuring the COVID-19 pandemic, BMI change was highly prevalent among doctors and nurses in North China. Machine learning models can provide an automated identification mechanism for the prediction of BMI change. Personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions have contributed to the BMI change prediction. Integrated treatment measures should be taken in the management of weight and BMI by policymakers, hospital administrators, and healthcare workers

Analysis of shared ceRNA networks and related-hub genes in rats with primary and secondary photoreceptor degeneration

IntroductionPhotoreceptor degenerative diseases are characterized by the progressive death of photoreceptor cells, resulting in irreversible visual impairment. However, the role of competing endogenous RNA (ceRNA) in photoreceptor degeneration is unclear. We aimed to explore the shared ceRNA regulation network and potential molecular mechanisms between primary and secondary photoreceptor degenerations.MethodsWe established animal models for both types of photoreceptor degenerations and conducted retina RNA sequencing to identify shared differentially expressed long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs). Using ceRNA regulatory principles, we constructed a shared ceRNA network and performed function enrichment and protein–protein interaction (PPI) analyses to identify hub genes and key pathways. Immune cell infiltration and drug–gene interaction analyses were conducted, and hub gene expression was validated by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsWe identified 37 shared differentially expressed lncRNAs, 34 miRNAs, and 247 mRNAs and constructed a ceRNA network consisting of 3 lncRNAs, 5 miRNAs, and 109 mRNAs. Furthermore, we examined 109 common differentially expressed genes (DEGs) through functional annotation, PPI analysis, and regulatory network analysis. We discovered that these diseases shared the complement and coagulation cascades pathway. Eight hub genes were identified and enriched in the immune system process. Immune infiltration analysis revealed increased T cells and decreased B cells in both photoreceptor degenerations. The expression of hub genes was closely associated with the quantities of immune cell types. Additionally, we identified 7 immune therapeutical drugs that target the hub genes.DiscussionOur findings provide new insights and directions for understanding the common mechanisms underlying the development of photoreceptor degeneration. The hub genes and related ceRNA networks we identified may offer new perspectives for elucidating the mechanisms and hold promise for the development of innovative treatment strategies

Association Study between BDNF Gene Polymorphisms and Autism by Three-Dimensional Gel-Based Microarray

Single nucleotide polymorphisms (SNPs) are important markers which can be used in association studies searching for susceptible genes of complex diseases. High-throughput methods are needed for SNP genotyping in a large number of samples. In this study, we applied polyacrylamide gel-based microarray combined with dual-color hybridization for association study of four BDNF polymorphisms with autism. All the SNPs in both patients and controls could be analyzed quickly and correctly. Among four SNPs, only C270T polymorphism showed significant differences in the frequency of the allele (χ2 = 7.809, p = 0.005) and genotype (χ2 = 7.800, p = 0.020). In the haplotype association analysis, there was significant difference in global haplotype distribution between the groups (χ2 = 28.19, p = 3.44e-005). We suggest that BDNF has a possible role in the pathogenesis of autism. The study also show that the polyacrylamide gel-based microarray combined with dual-color hybridization is a rapid, simple and high-throughput method for SNPs genotyping, and can be used for association study of susceptible gene with disorders in large samples

Feasibility of Isolation Remediation Technology for Heavy Metal Contaminated Soil

This paper discussed current situations of researches about the isolation remediation technology “soil barrier and landfill technology” and “physical isolation remediation technology” for heavy metal contaminated soil in mining areas. In view of defects of current technologies, it introduced a new isolation remediation technology, of which the new isolation materials were mixed by slaked lime, soil, find sand, and clay mineral in certain proportion. The new isolation remediation technology is expected to realize isolation remediation of heavy metal combined pollution of soil through chemical passivation of slaked lime and physical adsorption function of clay minerals or activated carbons

Magnetic integration of LTL filter with two LC-traps for grid-connected power converters

High-order inductor-traps-inductor (LTL) filters recently attracted a lot of research attentions in grid-connected converter applications due to their excellent harmonic attenuation ability, low cost, volume, and weight. However, more magnetic cores for trap inductors are required compared to conventional inductor-capacitor-inductor (LCL) filters. In order to further reduce the volume and weight of output power filter, this paper proposes a magnetic integrated LTL filter with two LC-traps. On the basis of conventional integrated LCL filters, the proposed method introduces a coupling winding wound on the central-limb of an EE-type magnetic core, and two equivalent trap inductors are introduced by the magnetic coupling between different windings. A step-by-step design guideline of the proposed magnetic integration method and the parameters design of LTL filters are also presented. The feasibility and effectiveness of the proposed method are finally verified by the experimental results.Accepted versio