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Accurate high-spatial-resolution precipitation is significantly important in hydrological and meteorological modelling, especially in rain-gauge-sparse areas. Some methods and strategies have been applied for satellite-based precipitation downscaling, residual correction and precipitation calibration. However, which downscaling scheme can provide reliable high-resolution precipitation efficiently remains unanswered. To address this issue, this study aimed to present a framework combining the machine learning downscaling algorithm and post-process procedures. Firstly, four ML-based models, namely support vector regression, random forest, spatial random forest (SRF) and eXtreme gradient boosting (XGBoost), were tested for downscaling and compared with conventional downscaling methods. Then, the effectiveness of the residual correction process using ordinary Kriging and the calibration process using the geographical difference analysis (GDA) method was investigated. The results showed that the ML-based methods had better performance than the conventional regression and interpolation approaches. The SRF and XGBoost outperformed others in generating accurate precipitation estimation with a high resolution. The GDA calibration process significantly improved the downscaled results. However, the residual correction process decreased the downscaling performance of the ML-based models. Combining the SRF or XGBoost downscaling algorithm with the GDA calibration method could be a promising downscaling scheme for precipitation data. The scheme could be used to generate high-resolution precipitation, especially in areas urgently requiring data, which would benefit regional water resource management and hydrological disaster prevention.

Background Recent achievement in genetics and epigenetics has led to the exploration of the pathogenesis of systemic lupus erythematosus (SLE). Identification of differentially expressed genes and their regulatory mechanism(s) at whole-genome level will provide a comprehensive understanding of the development of SLE and its devastating complications, lupus nephritis (LN). Methods We performed whole-genome transcription and DNA methylation analysis in PBMC of 30 SLE patients, including 15 with LN (SLE LN + ) and 15 without LN (SLE LN − ), and 25 normal controls (NC) using HumanHT-12 Beadchips and Illumina Human Methy450 chips. The serum proinflammatory cytokines were quantified using Bio-plex Human Cytokine 27-plex assay. Differentially expressed genes and differentially methylated CpG were analyzed with GenomeStudio, R, and SAM software. The association between DNA methylation and gene expression were tested. Gene interaction pathways of the differentially expressed genes were analyzed by IPA software. Results We identified 552 upregulated genes and 550 downregulated genes in PBMC of SLE. Integration of DNA methylation and gene expression profiling showed that 334 upregulated genes were hypomethylated, and 479 downregulated genes were hypermethylated. Pathway analysis on the differential genes in SLE revealed significant enrichment in interferon (IFN) signaling and toll-like receptor (TLR) signaling pathways. Nine IFN- and seven TLR-related genes were identified and displayed step-wise increase in SLE LN − and SLE LN + . Hypomethylated CpG sites were detected on these genes. The gene expressions for MX1, GPR84, and E2F2 were increased in SLE LN + as compared to SLE LN − patients. The serum levels of inflammatory cytokines, including IL17A, IP-10, bFGF, TNF-α, IL-6, IL-15, GM-CSF, IL-1RA, IL-5, and IL-12p70, were significantly elevated in SLE compared with NC. The levels of IL-15 and IL1RA correlated with their mRNA expression. The upregulation of IL-15 may be regulated by hypomethylated CpG sites in the promotor region of the gene. Conclusions Our study has demonstrated that significant number of differential genes in SLE were involved in IFN, TLR signaling pathways, and inflammatory cytokines. The enrichment of differential genes has been associated with aberrant DNA methylation, which may be relevant to the pathogenesis of SLE. Our observations have laid the groundwork for further diagnostic and mechanistic studies of SLE and LN.

Sweet potato is an important food, feed and industrial raw material, and its tubers are rich in starch, carotenoids and anthocyanins. To elucidate the gene expression regulation and metabolic characteristics during the development of sweet potato tubers, transcriptomic and metabolomic analyses were performed on the tubers of three different sweet potato varieties at three developmental stages (70, 100, and 130 days (d)). RNA-seq analysis revealed that 16,303 differentially expressed genes (DEGs) were divided into 12 clusters according to their expression patterns, and the pathways of each cluster were annotated. A total of 9118 DEGs were divided into three categories during the same developmental period. A total of 1566 metabolites were detected, which were mainly divided into 12 categories. DEGs and differentially regulated metabolites (DRMs) were significantly enriched in the starch and sucrose metabolism and flavonoid biosynthesis pathways. The DEGs associated with the flavonoid pathway showed greater expression with the development of tubers, with the highest expression occurring at 130 d; chalcone isomerase (CHI) was a key gene associated with 11 flavonoid compounds. The DEGs associated with the starch pathway presented relatively low expression during the development of tubers, with the highest expression occurring at 70 d; UDP-glucose pyrophosphorylase 2 (UPG2) and glycogen synthase (glgA) were able to regulate the key genes of 8 metabolites related to the starch biosynthesis pathway. The anthocyanin content is directly related to changes in the content of peonidin-3-O-(6\"-O-feruloyl)sophoroside-5-O-glucoside, which is regulated by the gene. The abundance of this starch is directly related to changes in the content of D-glucose 6-phosphate and is regulated by the and genes. A total of 14 candidate genes related to starch, carotenoids and anthocyanins in sweet potato tubers, including the , and genes, were identified via weighted correlation network analysis (WGCNA). This research provides fresh insights into the levels of anthocyanins, starch, and carotenoids throughout the growth of sweet potato tubers and sheds light on the potential regulatory pathways and candidate genes involved in this developmental progression.

For the robust fault-tolerant control of the controllable suspension system, a control strategy driven by knowledge-data fusion is proposed. Firstly, the boundary fuzziness between perturbation type uncertainty and gain type fault is analyzed, and then a data-driven method is introduced to avoid the state estimation of system uncertainty and fault. The proximal policy optimization algorithm in reinforcement learning is selected to construct a “data control law”, to deal with uncertainty and fault. On the other hand, based on the classical sky-hook control, the “knowledge control law” for system performance optimization is designed, taking into account the nonlinear and non-stationary characteristics of the system. Furthermore, the dependency between robust fault tolerance and performance optimization control is revealed, and the two control laws are fused by numerical multiplication, to realize the performance matching optimization control of robust fault tolerance of controllable suspension system driven by knowledge-data fusion. Finally, the effectiveness and feasibility of the proposed method are verified by the simulation and real-time experiment of non-stationary excitation and near-stationary excitation under the combination of uncertainty and fault.

Chlorogenic acids (CGAs) are important secondary metabolites produced in sweet potato. However, the mechanisms of their biosynthesis and regulation remain unclear. To identify potential genes involved in CGA biosynthesis, analysis of the dynamic changes in CGA components and RNA sequencing were performed on young leaves (YL), mature leaves (ML), young stems (YS), mature stems (MS) and storage roots (SR). Accordingly, we found that the accumulation of six CGA components varied among the different tissues and developmental stages, with YS and YL recording the highest levels, while SR exhibited low levels. Moreover, the transcriptome analysis yielded 59,287 unigenes, 3,767 of which were related to secondary-metabolite pathways. The differentially expressed genes (DEGs) were identified based on CGA content levels by comparing the different samples, including ML vs. YL, MS vs. YS, SR vs. YL and SR vs. YS. A total of 501 common DEGs were identified, and these were mainly implicated in the secondary metabolites biosynthesis. Additionally, eight co-expressed gene modules were identified following weighted gene co-expression network analysis, while genes in darkgrey module were highly associated with CGA accumulation. Darkgrey module analysis revealed that 12 unigenes encoding crucial enzymes (PAL, 4CL, C4H, C3H and HCT/HQT) and 42 unigenes encoding transcription factors (MYB, bHLH, WD40, WRKY, ERF, MADS, GARS, bZIP and zinc finger protein) had similar expression patterns with change trends of CGAs, suggesting their potential roles in CGA metabolism. Our findings provide new insights into the biosynthesis and regulatory mechanisms of CGA pathway, and will inform future efforts to build a genetically improve sweet potato through the breeding of high CGA content varieties.

Activation of fibroblasts is essential for physiological tissue repair. Uncontrolled activation of fibroblasts, however, may lead to tissue fibrosis with organ dysfunction. Although several pathways capable of promoting fibroblast activation and tissue repair have been identified, their interplay in the context of chronic fibrotic diseases remains incompletely understood. Here, we provide evidence that transforming growth factor-β (TGFβ) activates autophagy by an epigenetic mechanism to amplify its profibrotic effects. TGFβ induces autophagy in fibrotic diseases by SMAD3-dependent downregulation of the H4K16 histone acetyltransferase MYST1, which regulates the expression of core components of the autophagy machinery such as ATG7 and BECLIN1. Activation of autophagy in fibroblasts promotes collagen release and is both, sufficient and required, to induce tissue fibrosis. Forced expression of MYST1 abrogates the stimulatory effects of TGFβ on autophagy and re-establishes the epigenetic control of autophagy in fibrotic conditions. Interference with the aberrant activation of autophagy inhibits TGFβ-induced fibroblast activation and ameliorates experimental dermal and pulmonary fibrosis. These findings link uncontrolled TGFβ signaling to aberrant autophagy and deregulated epigenetics in fibrotic diseases and may contribute to the development of therapeutic interventions in fibrotic diseases. Uncontrolled activation of fibroblasts contributes to tissue fibrosis and organ dysfunction. Here the authors demonstrate that the epigenetic control of autophagy is disturbed by a TGFβ-dependent downregulation of MYST1 in systemic sclerosis patients. Restoration of the epigenetic control of autophagy reduces fibroblast activation and ameliorates fibrotic tissue remodeling.

To investigate the alterations of serum proteins and metabolomics in women infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the end of pregnancy and their potential effects on fetal development. The corona virus disease 2019 (COVID-19) group (n=31) included women in the third trimester diagnosed with SARS-CoV-2 infection and who delivered, while the control group (n=30) comprised uninfected women in the same gestational period. This study applied data-independent acquisition (DIA) proteomics and ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) metabolomics to analyze serum samples from two groups of full-term pregnant women. Serum samples in the control group were collected one week before delivery, while those in the COVID-19 group were collected within two days after the onset of fever. The differences between groups were compared by bioinformatics data analysis. For proteins and metabolites exhibiting a significant association with SARS-CoV-2, metabolic pathway enrichment was performed utilizing MetaboAnalyst 6.0, and the possible targets and pathways of SARS-CoV-2 infection in women in late pregnancy were plotted. The incidence of cesarean section, postpartum reproductive tract infection, and fetal distress were significantly higher in the COVID-19 group compared to the control group. Differential proteomic analysis revealed the regulation of proteins such as SAA1, SAA2, IPO7, WDR19, and BAZ1A, which were involved in processes such as visual, skin and limb development. Metabolomics analysis revealed key altered metabolites, including 1-(7-methoxy-2-oxo-2H-chromen-8-yl)-3-methyl-2-oxobutylacetate, 5-(hydroxymethyl) -4-methoxy-2,5-dihydrofuran-2-one, and cyclocytidine, which were involved in the riboflavin metabolism, the phenylalanine, tyrosine and tryptophan biosynthesis, and the arginine biosynthesis. Integrative analysis of proteomic and metabolomic revealed significant disruptions in metabolic pathways, including arginine biosynthesis, steroid hormone biosynthesis, and fatty acid degradation. This study revealed the main proteomic and metabolic effects of SARS-CoV-2 infection on women in the third trimester of pregnancy. Our comprehensive omics data elucidating the molecular mechanisms underlying SARS-CoV-2 infection in women during late pregnancy. These findings offer novel insights and potential targets for future investigations into the impact of SARS-CoV-2 infection on maternal and infant health.

Background Pulmonary arterial hypertension (PAH) and interstitial lung disease (ILD) are leading causes of systemic sclerosis (SSc)-related death. In this study, we aimed to identify biomarkers for detecting SSc pulmonary complications that are mild and in the early stages to improve the prognosis. Methods We screened for serum biomarkers using a proteomic antibody microarray that simultaneously assessed 1000 proteins. Differentially expressed proteins were further verified using ELISA. Finally, we performed a correlation analysis using clinical data. Results We identified 125 differentially expressed proteins, of which calcitonin, sclerostin (SOST), CD40, and fibronectin were selected for further verification. Serum calcitonin and SOST levels were significantly elevated in all SSc pulmonary complication subgroups, whereas serum calcitonin levels were higher in the SSc with PAH subgroup than in the SSc without PAH and ILD subgroup. Serum SOST levels were possibly associated with the presence of ILD and positively related to the presence of cardiac and gastrointestinal involvement. Serum CD40 and calcitonin levels appeared to be positively related to the presence of renal involvement, and serum calcitonin was also positively related to the presence of gastrointestinal involvement. Conclusions This study indicated that serum calcitonin and SOST levels may be promising biomarkers for SSc-related PAH and ILD, respectively. Further research is needed to verify this result and understand the underlying mechanisms.

The parsimony of the Budyko water balance model has prompted its extensive use for analyzing climate–vegetation–water interactions. Existence of time–space symmetry in the Budyko model, governing the hydrological predictions across both temporal (short‐term annual) and spatial (long‐term mean annual) domains, has enabled its application in ungauged basins through use of space‐for‐time substitution (SFTS). However, time–space symmetry in the relationship between the Budyko shape parameter and in vegetation changes has not been well studied. The Budyko curve, which varies spatially across regions due to differences of vegetation, also varies temporally as vegetation evolves over time. Here, we investigate this problem in a representative basin with several sub‐catchments, which includes sensitivity to vegetation dynamics and climate change using two shape parameters (n1 and n2) and the vegetation cover, M. Interestingly, time–space symmetry was found between M/Φ and n1/Φ or exp(−n2)/Φ, compared to just M versus n separately (which produces asymmetry), implying a coupled ecohydrological adjustment mechanism between vegetation functioning and hydrological structure co‐regulated in response to climatic constraints counteracting the effects of spatio‐temporal heterogeneity on water balance partitioning. Our study proposes a novel and streamlined ecohydrological model within the time–space equivalence hypothesis and recognizes that the symmetry/asymmetry is crucial for accurate SFTS application.

Cathepsin G belongs to the neutrophil serine proteases family, known for its function in killing pathogens. Studies over the past several years indicate that cathepsin G has important effects on inflammation and immune reaction, and may be a key factor in the pathogenesis of some autoimmune diseases. In this article, we discuss the roles of cathepsin G in inflammation, immune reaction, and autoimmune diseases. To our knowledge, this is the first study providing important information about cathepsin G in the pathogenesis of autoimmune diseases and suggesting that cathepsin G may be a new biomarker or treatment target.