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Yuanbaoshancha (YBSC) is characterized as a new wild tea relative morphologically and phytochemically distinguished from the closest wild tea plants Rongjiangcha (Camellia yungkiangensis, RJC) and Tulecha (Camellia costata, TLC). YBSC young leaves contain higher tea polyphenol and theobromine contents but lower caffeine and theanine as compared with RJC, TLC, and other tea landraces and modern cultivars. The major alkaloid detected in YBSC, TLC, and RJC is theobromine while caffeine is a minor; the primary catechins in YBSC leaves are non-galloylated catechins, significantly different from Camellia sinensis and other low-caffeine tea resources. The unique phytochemical profiles featured YBSC black tea with extremely lower caffeine and higher theobromine, as well as unique flavors and health benefits. This botanical characterization of YBSC and two related low-caffeine wild tea resources lays a foundation for future better utilization for the production of a highly valuable natural low-caffeine/high-theobromine tea. Chemical compounds: Caffeine (PubChem CID: 2519); Theobromine (PubChem CID: 5429); Catechins (PubChem CID: 9064); Epigallocatechin gallate (PubChem CID: 65064); Theanine (PubChem CID: 439378); Jasmone (PubChem CID: 1549018); cis-3-Hexenyl hexanoate (PubChem CID: 5352543); Hexyl 2-methylbutanoate (PubChem CID: 24838).

This study aims to evaluate the impact of the in-situ ocean observations on seasonal forecasts. A series of seasonal reforecasts have been conducted for the period 1993-2015, in which different sets of ocean observations were withdrawn in the production of the ocean initial conditions, while maintaining a strong constrain in sea surface temperature (SST). By comparing the different reforecast sets, it is possible to assess the impact on the forecast of ocean and atmospheric variables. Results show that the in-situ observations have profound and significant impacts on the mean state of forecast ocean and atmospheric variables, which can be classified into different categories: i) impact due to local air-sea interaction, as direct consequence of changes in the mixed layer in the ocean initial conditions, and visible in the early stages of the forecasts; ii) changes due to different ocean dynamical balances, most visible in the Equatorial Pacific in forecasts initialized in May, which amplify and evolve with forecast lead time; iii) changes to the atmospheric circulation resulting from changes in large scale SST gradients; these are non-local, mediated by the atmospheric bridge, and they are obvious from the visible impact of the removing in-situ observations on the Atlantic basin only in the global atmospheric circulation; iv) changes in the atmospheric tropical deep convection associated with the structure of the warm pools. The ocean observations have also a significant impact on the representation of the trends of the ocean initial conditions, which affect the trends in the seasonal forecasts of ocean and atmospheric variables. The impact of the ocean observing system in the Atlantic and extratropics appears dominated by Argo, but this is not the case in the Tropical Pacific, where the other ocean observing systems play a role in constraining the ocean state.

As an important spice species in Rutaceae, the Sichuan pepper (Zanthoxylum armatum) can provide pungent and numbing taste, as well as aroma in its mature fruit. Here we assembled a chromosome-level genome of green prickly ash which was widely cultivated in a major production area including Chongqing and Sichuan province, China. We generated 712 Gb (∼112 × ) PacBio long reads and 511 Gb (∼82 × ) Hi-C data, and yielded an assembly of 99 pseudochromosomes with total size of 5.32 Gb and contig N50 of 796 kb. The genomic analyses and cytogenetic experiments both indicated that the cultivar ‘Zhuye Huajiao’ was a triploid. We identified a Zanthoxylum-specific whole genome duplication event emerging about 24.8 million years ago (Mya). We also detected a transposition burst event (0.3–0.4 Mya) responsible for the large genome size of Z. armatum. Metabolomic analysis of the Zanthoxylum fruits during development stages revealed profiles of 39 volatile aroma compounds and 528 secondary metabolites, from which six types of sanshools were identified. Based on metabolomic and transcriptomic network analysis, we screened candidate genes encoding long chain acyl-CoA synthetase, fatty acid desaturase, branched-chain amino acid aminotransferase involved in sanshool biosynthesis and three genes encoding terpene synthase during fruit development. The multi-omics data provide insights into the evolution of Zanthoxylum and molecular basis of numbing and aroma flavor of Sichuan pepper.

BackgroundIncreasing evidence reveals the involvement of mitochondria and macrophage polarisation in tumourigenesis and progression. This study aimed to establish mitochondria and macrophage polarisation-associated molecular signatures to predict prognosis in gastric cancer (GC) by single-cell and transcriptional data.MethodsInitially, candidate genes associated with mitochondria and macrophage polarisation were identified by differential expression analysis and weighted gene co-expression network analysis. Subsequently, candidate genes were incorporated in univariateCox analysis and LASSO to acquire prognostic genes in GC, and risk model was created. Furthermore, independent prognostic indicators were screened by combining risk score with clinical characteristics, and a nomogram was created to forecast survival in GC patients. Further, in single-cell data analysis, cell clusters and cell subpopulations were yielded, followed by the completion of pseudo-time analysis. Furthermore, a more comprehensive immunological analysis was executed to uncover the relationship between GC and immunological characteristics. Ultimately, expression level of prognostic genes was validated through public datasets and qRT-PCR.ResultsA risk model including six prognostic genes (GPX3, GJA1, VCAN, RGS2, LOX, and CTHRC1) associated with mitochondria and macrophage polarisation was developed, which was efficient in forecasting the survival of GC patients. The GC patients were categorized into high-/low-risk subgroups in accordance with median risk score, with the high-risk subgroup having lower survival rates. Afterwards, a nomogram incorporating risk score and age was generated, and it had significant predictive value for predicting GC survival with higher predictive accuracy than risk model. Immunological analyses revealed showed higher levels of M2 macrophage infiltration in high-risk subgroup and the strongest positive correlation between risk score and M2 macrophages. Besides, further analyses demonstrated a better outcome for immunotherapy in low-risk patients. In single-cell and pseudo-time analyses, stromal cells were identified as key cells, and a relatively complete developmental trajectory existed for stromal C1 in three subclasses. Ultimately, expression analysis revealed that the expression trend of RGS2, GJA1, GPX3, and VCAN was consistent with the results of the TCGA-GC dataset.ConclusionOur findings demonstrated that a novel prognostic model constructed in accordance with six prognostic genes might facilitate the improvement of personalised prognosis and treatment of GC patients.

We assess for the first time the impact of in-situ ocean observations on European Centre for Medium-Range Weather Forecasts (ECMWF) sub-seasonal forecasts of both ocean and atmospheric conditions. A series of coupled reforecasts have been conducted for the period 1993-2015, in which different sets of ocean observations were withdrawn in the production of the ocean initial conditions. Removal of all ocean in-situ observations in the initial conditions leads to significant degradation in the forecasts of ocean surface and subsurface mean state at lead times from week 1 to week 4. The negative impact is predominantly caused by the removal of the Argo observing system in recent decades. Changes in the mean state of atmospheric variables are comparatively small but significant in the forecasts of lower and upper atmospheric circulation over large regions. Our results highlight the value of continuous, real-time in-situ observations of the surface and subsurface ocean for coupled forecasts in the sub-seasonal range.

Abstract Background In recent years, the extensive use of drugs and antibiotics has led to increasing microbial resistance. Therefore, it becomes crucial to explore deep connections between drugs and microbes. However, traditional biological experiments are very expensive and time-consuming. Therefore, it is meaningful to develop efficient computational models to forecast potential microbe-drug associations. Results In this manuscript, we proposed a novel prediction model called GARFMDA by combining graph attention networks and bilayer random forest to infer probable microbe-drug correlations. In GARFMDA, through integrating different microbe-drug-disease correlation indices, we constructed two different microbe-drug networks first. And then, based on multiple measures of similarity, we constructed a unique feature matrix for drugs and microbes respectively. Next, we fed these newly-obtained microbe-drug networks together with feature matrices into the graph attention network to extract the low-dimensional feature representations for drugs and microbes separately. Thereafter, these low-dimensional feature representations, along with the feature matrices, would be further inputted into the first layer of the Bilayer random forest model to obtain the contribution values of all features. And then, after removing features with low contribution values, these contribution values would be fed into the second layer of the Bilayer random forest to detect potential links between microbes and drugs. Conclusions Experimental results and case studies show that GARFMDA can achieve better prediction performance than state-of-the-art approaches, which means that GARFMDA may be a useful tool in the field of microbe-drug association prediction in the future. Besides, the source code of GARFMDA is available at https://github.com/KuangHaiYue/GARFMDA.git

Abstract Global hydrological reanalyses are modelled datasets providing information on river discharge evolution everywhere in the world. With multi‐decadal daily timeseries, they provide long‐term context to identify extreme hydrological events such as floods and droughts. By covering the majority of the world's land masses, they can fill the many gaps in river discharge in‐situ observational data, especially in the global South. These gaps impede knowledge of both hydrological status and future evolution and hamper the development of reliable early warning systems for hydrological‐related disaster reduction. River discharge is a natural integrator of the water cycle over land. Global hydrological reanalysis datasets offer an understanding of its spatio‐temporal variability and are therefore critical for addressing the water–energy–food–environment nexus. This paper describes how global hydrological reanalyses can fill the lack of ground measurements by using earth system or hydrological models to provide river discharge time series. Following an inventory of alternative sources of river discharge datasets, reviewing their advantages and limitations, the paper introduces the Copernicus Emergency Management Service (CEMS) Global Flood Awareness System (GloFAS) modelling chain and its reanalysis dataset as an example of a global hydrological reanalysis dataset. It then reviews examples of downstream applications for global hydrological reanalyses, including monitoring of land water resources and ocean dynamics, understanding large‐scale hydrological extreme fluctuations, early warning systems, earth system model diagnostics and the calibration and training of models, with examples from three Copernicus Services (Emergency Management, Marine and Climate Change).

Realistic representation of monthly sea level anomalies in coastal regions has been a challenge for global ocean reanalyses. This is especially the case in coastal regions where sea levels are influenced by western boundary currents such as near the U.S. Atlantic Coast and the Gulf of Mexico. For these regions, most ocean reanalyses compare poorly to observations. Problems in reanalyses include errors in data assimilation and horizontal resolutions that are too coarse to simulate energetic currents like the Gulf Stream and Loop Current System. However, model capabilities are advancing with improved data assimilation and higher resolution. Here, we show that some current-generation ocean reanalyses produce monthly sea level anomalies with improved skill when compared to satellite altimetry observations of sea surface heights. Using tide gauge observations for coastal verification, we find the highest skill associated with the GLORYS12 and HYCOM ocean reanalyses. Both systems assimilate altimetry observations and have eddy-resolving horizontal resolutions (1/12°). We found less skill in three other ocean reanalyses (ACCESS-S2, ORAS5, and ORAP6) with coarser, though still eddy-permitting, resolutions (1/4°). The operational reanalysis from ECMWF (ORAS5) and their pilot reanalysis (ORAP6) provide an interesting comparison because the latter assimilates altimetry globally and with more weight, as well as assimilating ocean observations over continental shelves. We find these attributes associated with improved skill near many tide gauges. We also assessed an older reanalysis (CFSR), which has the lowest skill likely due to its lower resolution (1/2°) and lack of altimetry assimilation. ACCESS-S2 likewise does not assimilate altimetry, although its skill is much better than CFSR and only somewhat lower than ORAS5. Since coastal flooding is influenced by sea level anomalies, the recent development of skilful ocean reanalyses on monthly timescales may be useful for better understanding the physical processes associated with flood risks.

A chip mounter is the core equipment in the production line of the surface-mount technology, which is responsible for finishing the mount operation. It is the most complex and time-consuming stage in the production process. Therefore, it is of great significance to optimize the load balance and mounting efficiency of the chip mounter and improve the mounting efficiency of the production line. In this study, according to the specific type of chip mounter in the actual production line of a company, a maximum and minimum model is established to minimize the maximum cycle time of the chip mounter in the production line. The production efficiency of the production line can be improved by optimizing the workload scheduling of each chip mounter. On this basis, a hybrid adaptive optimization algorithm is proposed to solve the load scheduling problem of the mounter. The hybrid algorithm is a hybrid of an adaptive genetic algorithm and the improved ant colony algorithm. It combines the advantages of the two algorithms and improves their global search ability and convergence speed. The experimental results show that the proposed hybrid optimization algorithm has a good optimization effect and convergence in the load scheduling problem of chip mounters.

Recently, the task of acquiring causal directed acyclic graphs (DAGs) from empirical data has been modeled as an iterative process within the framework of continuous optimization with a differentiable acyclicity characterization. However, learning DAGs from data is an NP-hard problem since the DAG space increases super-exponentially with the number of variables. In this work, we introduce the graph topological sorts in solving the continuous optimization problem, which is substantially smaller than the DAG space and beneficial in avoiding local optima. Moreover, the topological sorts space does not require consideration of acyclicity, which can significantly reduce the computational cost. To further deal with the inherent asymmetries of DAGs, we investigate the acyclicity characterization and propose a new DAGs learning optimization strategy based on QR factorization, named DAGOR. First, using the matrix congruent transformation, the adjacency matrix of the DAG is transformed into an upper triangular matrix with a topological sort. Next, using the QR factorization as a basis, we construct a least-square penalty function as constraints for optimization in the graph autoencoder framework. Numerical experiments are performed to further validate our theoretical results and demonstrate the competitive performance of our method.

Abstract Background Dissection of the regulatory pathways that control skeletal muscle development and atrophy is important for the treatment of muscle wasting. Long noncoding RNA (lncRNA) play important roles in various stages of muscle development. We previously reported that Synaptopodin‐2 (SYNPO2) intron sense‐overlapping lncRNA (SYISL) regulates myogenesis through an interaction with enhancer of zeste homologue 2 (EZH2). However, it remains unclear whether SYISL homologues exist in humans and pigs, and whether the functions and mechanisms of these homologues are conserved among species. Methods Bioinformatics, cell fractionation, and quantitative real‐time polymerase chain reaction (qRT‐PCR) analyses were used for the identification and molecular characterization of SYISL homologues in humans and pigs. Effects on myogenesis and muscle atrophy were determined via loss‐of‐function or gain‐of‐function experiments using C2C12 myoblasts, myogenic progenitor cells, dexamethasone (DEX), and aging‐induced muscle atrophy models. RNA pulldown, RNA immunoprecipitation, dual luciferase reporting, and co‐transfection experiments were used to explore the mechanisms of SYISL interactions with proteins and miRNAs. Results We identified SYISL homologues in humans (designated hSYISL) and pigs (designated pSYISL). Functional experiments demonstrated that hSYISL and pSYISL regulate myogenesis through interactions with EZH2. Interestingly, we showed that SYISL functions to regulate muscle atrophy and sarcopenia through comparative analysis. SYISL is significantly up‐regulated after muscle atrophy (P

Abstract Lipid peroxidation-induced ferroptosis is a newly recognized type of programmed cell death. With the method of RNA sequencing, we found that irradiation (IR) markedly increased the expression of ferroptosis promotive genes, whereas reduced the expression of ferroptosis suppressive genes in murine intestine tissues, when compared with those of liver and lung tissues. By using ferroptosis inducer RSL-3 and inhibitor liproxstatin-1, we found that ferroptosis is essential for IR-induced intestinal injury. Acyl-CoA Synthetase Long-Chain Family Member 4 (ACSL4) is an important component for ferroptosis execution, and we found that ACSL4 expression was significantly upregulated in irradiated intestine tissues, but not in liver or lung tissues. Antibacterial and antifungal regents reduced the expression of ASCL4 and protected against tissue injury in irradiated intestine tissues. Further studies showed that troglitazone, a ACSL4 inhibitor, succeeded to suppresses intestine lipid peroxidation and tissue damage after IR.

In this study, we used first-principles calculation methods to investigate the effects of Cr doping at different positions on the electronic and optical properties of ZnSe nanowires. We found that the bandgap of the nanowires is larger than that of the bulk. After doping, it was found that the easiest doping position is on the surface, while all positions require heating for doping. After doping, the change in the intrinsic bandgap was very small, and a large number of defect bands appeared in the middle of the intrinsic bandgap, which are mainly composed of Cr(d) orbital electrons, Se(p) orbital electrons, and d-p hybridized bands. The doping of Cr atoms also affects the distribution of LUMO and HOMO. In the parallel direction of the nanowires, the absorption spectrum exhibits a prominent absorption peak near 0.8 eV, which is caused by transitions between defect bands. Therefore, Cr-doped ZnSe (111) nanowires doped at radius ½ positions are more suitable as mid-infrared laser gain materials.

Abstract Background Tea is one of the most popular non-alcoholic beverages in the world for its flavors and numerous health benefits. The tea tree (Camellia sinensis L.) is a well-known aluminum (Al) hyperaccumulator. However, it is not fully understood how tea plants have adapted to tolerate high concentrations of Al, which causes an imbalance of mineral nutrition in the roots. Results Here, we combined ionomic and transcriptomic profiling alongside biochemical characterization, to probe the changes of metal nutrients and Al responsive genes in tea roots grown under increasing concentrations of Al. It was found that a low level of Al (~ 0.4 mM) maintains proper nutrient balance, whereas a higher Al concentration (2.5 mM) compromised tea plants by altering micro- and macro-nutrient accumulation into roots, including a decrease in calcium (Ca), manganese (Mn), and magnesium (Mg) and an increase in iron (Fe), which corresponded with oxidative stress, cellular damage, and retarded root growth. Transcriptome analysis revealed more than 1000 transporter genes that were significantly changed in expression upon Al exposure compared to control (no Al) treatments. These included transporters related to Ca and Fe uptake and translocation, while genes required for N, P, and S nutrition in roots did not significantly alter. Transporters related to organic acid secretion, together with other putative Al-tolerance genes also significantly changed in response to Al. Two of these transporters, CsALMT1 and CsALS8, were functionally tested by yeast heterologous expression and confirmed to provide Al tolerance. Conclusion This study shows that tea plant roots respond to high Al-induced mineral nutrient imbalances by transcriptional regulation of both cation and anion transporters, and therefore provides new insights into Al tolerance mechanism of tea plants. The altered transporter gene expression profiles partly explain the imbalanced metal ion accumulation that occurred in the Al-stressed roots, while increases to organic acid and Al tolerance gene expression partly explains the ability of tea plants to be able to grow in high Al containing soils. The improved transcriptomic understanding of Al exposure gained here has highlighted potential gene targets for breeding or genetic engineering approaches to develop safer tea products.

The clinical efficacy of surgery, radiotherapy, and chemotherapy for cancer is usually limited by the deterioration of tumor microenvironment (TME). Neutrophil extracellular traps (NETs) are decondensed chromatin extruded by neutrophils and are widely distributed among various cancers, such as pancreatic cancer, breast cancer, and hepatocellular carcinoma. In the TME, NETs interact with stromal components, immune cells and cancer cells, which allows for the reshaping of the matrix and the extracellular environment that favors the initiation, progression, and metastasis of cancer. In addition, NETs impair the proliferation and activation of T cells and NK cells, thus producing a suppressive TME that restricts the effect of immunotherapy. A better understanding of the function of NETs in the TME will provide new opportunities for the prevention of cancer metastasis and the discovery of novel therapy strategies.

Aiming at the power balance control of multi-source energy storage grid in the case of a high proportion of new energy grid connection. In this article, a power grid dynamic partition method based on the Markov energy field principle and a priori knowledge model is proposed. Combined with the coordinated dispatching of power grid source-load, a two-layer power balance partition control model based on the topological characteristics of multi-source energy storage nodes is established. First, in the upper-layer model, the energy homogenization method of multi-source energy storage nodes is studied, and the Markov energy field model of power grid node partition based on energy interaction constraints between nodes is established to partition the power grid initially. Combined with the prior model of node dynamic partition, the initial partition is dynamically optimized to realize the dynamic partition of the multi-source energy storage grid. Then, in the lower-layer model, the source-load coordinated dispatching model in the power grid partition area is established to realize the dynamic partition control of the power grid. Finally, based on the real operation data of a northeast power grid and IEEE39 node system, a dynamic partition power control simulation model of a multi-source energy storage power grid is established. The simulation results and analysis show that the dynamic partition power control strategy proposed in this article can effectively improve the regulation ability and economy of the power grid.

Abstract This paper endeavors to reveal the squeezing failure mechanism of roof‐coal masses and its correlation with the movement of the roof strata. An integrated method incorporating field instruments and numerical modeling was conducted. The borehole camera detection revealed that the development degree and scope of cracks in the roof strata on the coal pillar side are significantly larger than that on the panel rib side, and the fracture line of the main roof occurred above the coal pillar. A rigorous modeling procedure verified the borehole camera detection results and also found that the vertical and horizontal displacement of the roof‐coal mass exhibited asynchronous development characteristics at various heights. Subsequently, a comprehensive control strategy was proposed to prevent the squeezing failure behavior of roof‐coal masses in DUOS gob‐side entry. This study provides a basis for understanding the squeezing failure behavior of roof‐coal masses, and the proposed control strategy can potentially be applied in other similar mining projects.

The effect of PARP inhibitors on bone mass and bone metastasis are unclear. Here, the authors show that PARP1/2 dual inhibitors may increase cancer bone metastasis through PARP2-dependent regulation of immature myeloid cells, and cause bone loss through PARP1/2-dependent regulation of osteoclasts.

Imbalanced classification using deep learning has attracted much attention in intelligent fault diagnosis of machinery. However, the existing methods use individual deep neural network to extract features and recognize the health conditions under imbalanced dataset, which may easily over-fit the mechanical data and affect the diagnosis accuracy. To deal with this problem, this paper takes the advantages of ensemble learning and proposes an ensemble convolutional neural network (EnCNN) for the intelligent fault diagnosis for machines under imbalanced data. In the proposed method, a convolutional neural network with the input of multi-sensor signals is used as the base classifier. Firstly, the mechanical imbalance dataset is first split into balanced training subsets through under-sampling strategy, and each subset is used to train a base classifier. Then the weight coefficients of each trained base classifier are calculated by G-mean score and anomalous base classifiers are screened using classifier selection. Finally, the base classifiers are integrated into EnCNN through weighted voting strategy. The proposed EnCNN is validated by the imbalanced dataset collected from a machinery fault test bench. By comparing with the related methods, the superiority of EnCNN is verified in intelligent fault diagnosis of machines under imbalanced data.

Deep learning has been applied in intelligent fault diagnosis of machines since it trains deep neural networks to simultaneously learn features and recognize faults. In the intelligent fault diagnosis methods based on deep learning, feature learning and fault recognition are achieved by solving a multi-class classification problem. The multi-class classification, however, has not considered the relationships of fault labels, leading to two weaknesses of these methods. One is that it cannot ensure to learn the correlated features for related faults and the other is that it cannot handle missing label problem. To overcome these weaknesses, we introduce a concept of multi-label classification into intelligent fault diagnosis and propose a deep multi-label learning framework called multi-label convolutional neural network (MLCNN). MLCNN builds the relationship between the labels, and thus it is able to learn the correlated features from mechanical vibration signals and be well trained using the samples with missing labels. A motor bearing diagnosis case and a compound fault diagnosis case are used to verify the proposed method, respectively. The results show that the relationships between features are learned by MLCNN, and the classification accuracies of MLCNN are higher than traditional methods when the missing label problem occurs.

Objective: It has been shown that LIM-domain-binding protein 1 (LDB1) is involved in the tumorigenesis of several cancers, but its function in colorectal cancer (CRC) has not been fully explained. This study is aimed to investigate whether LDB1 is involved in regulating the cell growth and drug sensitivity of CRC.Methods: To analyze the protein expression of LDB1 in CRC tissues, western blot was used. KM plotter and UALCAN databases were used to predict the prognosis of CRC patients with low or high LDB1 expression. To do the correlation analysis in CRC tissues, GEPIA database was used. CCK-8 assay and xenograft models were used to evaluate the effects of LDB1 in CRC cell growth. An oxaliplatin-resistant cell line was constructed to evaluate the effect of LDB1 in drug sensitivity of CRC cells.Results: Our current research confirmed that LDB1 was upregulated in CRC tumor tissues, and its elevation predicted a poor prognosis for CRC patients. LDB1 was also found positively correlated with CCNA1, BCL2 and BCLW, but negatively correlated with the pro-apoptotic signals (BID, BAX and BAK). Silence of LDB1 significantly inhibited CRC cell growth in vitro, and CRC cells with low expression of LDB1 had a lower tumorigenesis rate in tumor-bearing nude mice. Our experiments also showed that LDB1 silence enhanced the anti-tumor activity of oxaliplatin in CRC cells. The expression of LDB1 was also found increased in oxaliplatin-resistant CRC cell lines, and silence of LDB1 partly restored the antitumor effect of oxaliplatin in an oxaliplatin-resistant CRC cell line.Conclusion: Our current results revealed the roles of LDB1 in the growth and drug resistance of CRC cells, and may provide the new theoretical support for LDB1 as a potential target for the treatment of CRC in the future.

In this study, the stress relief method of hollow inclusion and mathematical-statistical analysis are used to study the in situ stress distribution law of the Guotun coal mine, and the optimization strategy of roadway support is put forward. The test site is located in the city of Heze, Shandong Province, China. The main conclusions included the following: (1) among the three large-scale tectonic movements experienced by the Guotun coal mine, the middle Yanshan period has the strongest impact on it. According to the geological evolution process, it is preliminarily inferred that the maximum principal stress direction of the Guotun coal mine is northwest by west to southeast by east (NWW∼SEE). (2) Compared with the surrounding mines, the average value of the maximum principal stress in the Guotun coal mine is slightly higher, and there is a significant difference in the direction of in situ stress. The arc structure within the mine is the main reason for the deflection of the stress direction. (3) The lateral pressure coefficients KH, Kh, and Kav show a nonlinear distribution with increased depth, and the three coefficients have a decreasing trend. The value of the stress gradient in the Guotun coal mine is greater when it is compared with the surrounding mines. (4) Field tests show that the new strategy proposed in this study is effective, which provides a good reference for engineering practice under similar geological conditions.

Tea plants have adapted to grow in tropical acidic soils containing high concentrations of aluminum (Al) and fluoride (F) (as Al/F hyperaccumulators) and use secret organic acids (OAs) to acidify the rhizosphere for acquiring phosphorous and element nutrients. The self-enhanced rhizosphere acidification under Al/F stress and acid rain also render tea plants prone to accumulate more heavy metals and F, which raises significant food safety and health concerns. However, the mechanism behind this is not fully understood. Here, we report that tea plants responded to Al and F stresses by synthesizing and secreting OAs and altering profiles of amino acids, catechins, and caffeine in their roots. These organic compounds could form tea-plant mechanisms to tolerate lower pH and higher Al and F concentrations. Furthermore, high concentrations of Al and F stresses negatively affected the accumulation of tea secondary metabolites in young leaves, and thereby tea nutrient value. The young leaves of tea seedlings under Al and F stresses also tended to increase Al and F accumulation in young leaves but lower essential tea secondary metabolites, which challenged tea quality and safety. Comparisons of transcriptome data combined with metabolite profiling revealed that the corresponding metabolic gene expression supported and explained the metabolism changes in tea roots and young leaves via stresses from high concentrations of Al and F. The study provides new insight into Al- and F-stressed tea plants with regard to responsive metabolism changes and tolerance strategy establishment in tea plants and the impacts of Al/F stresses on metabolite compositions in young leaves used for making teas, which could influence tea nutritional value and food safety.

Previous studies have shown that the nano-crystallization process has an appreciable impact on the luminescence properties of nanocrystals, which determines their defect state composition, size and morphology. This project aims to explore the influence of nanocrystal size on the electrical and optical properties of Cr2+:ZnSe nanowires. A first-principles study of Cr2+:ZnSe nanowires with different sizes was carried out at 0 K in the density functional framework. The Cr2+ ion was found to prefer to reside at the surface of ZnSe nanowires. As the size of the nanocrystals decreased, a considerable short-wave-length shift in the absorption of the vis-near infrared wavelength was observed. A quantum mechanism for the wavelength tunability was discussed.

Enhancer of zeste homolog 2 (EZH2) is the catalytic subunit of polycomb repressive complex 2 and contains a SET domain that catalyzes histone H3 trimethylation on lysine 27 (H3K27me3) to generate an epigenetic silencing mark. EZH2 interacts with transcription factors or RNA transcripts to perform its function. In this study, we applied RNA immunoprecipitation sequencing and long intergenic non-coding RNA (lincRNA) sequencing methods to identify EZH2-binding lincRNAs. A total of 356 novel EZH2-binding lincRNAs were identified by bioinformatics analysis and an EZH2-binding lincRNA TCONS-00036665 was characterized. TCONS-00036665 promoted pig skeletal satellite cell proliferation but inhibited cell differentiation, and this function was conserved between pigs and mice. Further mechanistic studies indicated that TCONS-00036665 can bind to EZH2 and recruits EZH2 to the promoters of the target genes p21, MyoG, and Myh4, which leads to the enrichment of H3K27me3 and the repression of target gene expression and pig myogenesis. In conclusion, the lincRNA TCONS-00036665 regulates pig myogenesis through its interaction with EZH2.

Citrus originated in Southeast Asia, and it has become one of the most important fruit crops worldwide. Citrus has a long and obscure domestication history due to its clonal propagation, long life cycle, wide sexual compatibility, and complex genetic background. As the genomic information of both wild and cultivated citrus becomes available, their domestication history and underlying traits or genes are becoming clear. This review outlines the genomic features of wild and cultivated species. We propose that the reduction of citric acid is a critical trait for citrus domestication. The genetic model representing the change during domestication may be associated with a regulatory complex known as WD-repeat-MYB-bHLH-WRKY (WMBW), which is involved in acidification and anthocyanin accumulation. The reduction in or loss of anthocyanins may be due to a hitchhiking effect of fruit acidity selection, in which mutation occurs in the common regulator of these two pathways in some domesticated types. Moreover, we have summarized the domestication traits and candidate genes for breeding purposes. This review represents a comprehensive summary of the genes controlling key traits of interest, such as acidity, metabolism, and disease resistance. It also sheds light on recent advances in early flowering from transgenic studies and provides a new perspective for fast breeding of citrus. Our review lays a foundation for future research on fruit acidity, flavor, and disease resistance in citrus.

Pancreatic cancer is known as “the king of cancer,” and ubiquitination/deubiquitination-related genes are key contributors to its development. Our study aimed to identify ubiquitination/deubiquitination-related genes associated with the prognosis of pancreatic cancer patients by the bioinformatics method and then construct a risk model. In this study, the gene expression profiles and clinical data of pancreatic cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database and the Genotype-tissue Expression (GTEx) database. Ubiquitination/deubiquitination-related genes were obtained from the gene set enrichment analysis (GSEA). Univariate Cox regression analysis was used to identify differentially expressed ubiquitination-related genes selected from GSEA which were associated with the prognosis of pancreatic cancer patients. Using multivariate Cox regression analysis, we detected eight optimal ubiquitination-related genes (RNF7, NPEPPS, NCCRP1, BRCA1, TRIM37, RNF25, CDC27, and UBE2H) and then used them to construct a risk model to predict the prognosis of pancreatic cancer patients. Finally, the eight risk genes were validated by the Human Protein Atlas (HPA) database, the results showed that the protein expression level of the eight genes was generally consistent with those at the transcriptional level. Our findings suggest the risk model constructed from these eight ubiquitination-related genes can accurately and reliably predict the prognosis of pancreatic cancer patients. These eight genes have the potential to be further studied as new biomarkers or therapeutic targets for pancreatic cancer.

This paper presents an integrated approach for mathematical statistics, theoretical analysis, and a field test to investigate the distribution law of in-situ stress and its engineering practice of rock burst control. The test site is located in the Juye mining area, Shandong Province, China. The main conclusions included: (1) There are two types of in-situ stress states in the Juye mining area, σH > σV > σh (42.42%) and σH > σh > σV (57.57%), which are mainly caused by the tectonic stress of the Heze and Fushan faults (The σH, σV, and σh is the maximum principal stress, vertical principal stress or intermediate principal stress and minimum principal stress respectively). (2) The lateral pressure coefficients KH, Kh, and Kav show a non-linear distribution with increased depth, approaching 1.32, 0.96, and 1.41, respectively. The variation range of the horizontal difference stress μd is 0.09–0.58. (3) The average value of the stress gradient is 3.05 MPa/100 m, and the main directions of the maximum horizontal principal stress are northeast–southwest, and northwest–southeast. (4) A new combined supporting strategy, incorporating optimization of roadway layout, anti-impact support system design, and local reasonable pressure relief, was proposed for the rock burst control, and its validity was verified via field monitoring. All these design principles and support strategies for the rock burst control presented in this study can potentially be applied to other similar projects.

Abstract Background Transjugular intrahepatic portosystemic shunt (TIPS) procedure has played a vital role in management of portal hypertension. Thus, we aimed to investigate the natural history, long-term clinical outcome, predictors of survival in viral hepatitis related cirrhotic patients post-TIPS. Method A total of 704 patients with complete followed-up data were enrolled, and clinical characteristics of patients were collected and analyzed. Kaplan-Meier method was used to calculate survival, and comparisons were made by log rank test. A multivariate analysis of factors influencing survival was carried out using the Cox proportional hazards regression model. Results TIPS implatantion significantly decreased portal vein pressure with 9.77 cmH2O reduction, without influencing long-term liver functions. The total incidence rate of major complication post-TIPS, including HE and re-bleeding/bleeding, was 37.9% and 15.5%, respectively. Patients in Child-Pugh C stage revealed higher overt hepatic encephalopathy (HE) occurrence (65.6%), while patients receiving covered, 6 mm in diameter stents indicated notably lower incidence of HE in comparison with other groups (6.4%). The median survival was > 60 months, 27.0 months, and 11.5 months in cirrhotic patients with variceal bleeding, refractory ascites, and both complications, respectively. The cumulative 5-year survival was significantly higher in patients with variceal bleeding (75.6%) in comparison with either that in patients with refractory ascites (12.5%) or that in patients with both complications (1.96%) (P 0.05). Conclusion TIPS was an effective and safe therapeutic method for decompression of portal hypertension and for treatment of its complications. Careful selection of patients with minimal liver dysfunction for TIPS implantation was essential for better long-term outcomes.

Background/Aims: The gut-vascular barrier (GVB) has recently been depicted to dampen the bacterial invasion of the bloodstream. The intestinal mucosa is a tissue rich in small vessels including capillaries. In this study, the protective effect of berberine on GVB in small bowel mucosa was investigated. Methods: The rat cecal ligation and puncture (CLP) sepsis model was employed to evaluate the effect of berberine on serum endotoxin level and intestinal vascular permeability to Evans blue in vivo. The rat intestinal microvascular endothelial cells (RIMECs) treated by lipopolysaccharide (LPS) were used to assess the effect of berberine on endothelial permeability to FITC-labeled dextran, transendothelial electrical resistance (TEER), and tight junction (TJ) and adherens junction (AJ) expression in vitro. Results: After 24-hr CLP operation the serum endotoxin concentration and gut vascular permeability were significantly increased, while berberine markedly reduced endotoxin level and vascular leakage. In vitro, LPS not only dramatically increased endothelial permeability of RIMECs to FITC-dextran, but also decreased TEER and inhibited claudin-12, beta-catenin and VE-cadherin expression. These effects of LPS were antagonized by berberine. In addition, our in vivo and vitro studies also confirmed that the effect of berberine on GVB could be partially abolished by ICG001. Conclusion: Berberine exerted a protective effect on GVB function in sepsis, which was strictly related to the modulation of the Wnt/beta-catenin signaling pathway.

Developments in observing system technologies and ocean data assimilation (DA) are symbiotic. New observation types lead to new DA methods and new DA methods, such as coupled DA, can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners of DA are encouraged to make better use of observations that are already available, for example, taking advantage of strongly coupled DA so that ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate as well as the initialization of operational long-range prediction models. There are many remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean-observing system throughout its history, the presence of biases and drifts in models, and the simplifying assumptions made in DA solution methods. From a governance point of view, more support is needed to bring the ocean-observing and DA communities together. For prediction applications, there is wide agreement that protocols are needed for rapid communication of ocean-observing data on numerical weather prediction (NWP) timescales. There is potential for new observation types to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of NWP, covering hours to weeks, out to multiple decades. Better communication between DA and observation communities is encouraged in order to allow operational prediction centers the ability to provide guidance for the design of a sustained and adaptive observing network.

This paper summarizes recent efforts on Observing System Evaluation (OS-Eval) by the Ocean Data Assimilation and Prediction (ODAP) communities such as GODAE OceanView and CLIVAR-GSOP. It provides some examples of existing OS-Eval methodologies, and attempts to discuss the potential and limitation of the existing approaches. Observing System Experiment (OSE) studies illustrate the impacts of the severe decrease in the number of TAO buoys during 2012–2014 and TRITON buoys since 2013 on ODAP system performance. Multi-system evaluation of the impacts of assimilating satellite sea surface salinity data based on OSEs has been performed to demonstrate the need to continue and enhance satellite salinity missions. Impacts of underwater gliders have been assessed using Observing System Simulation Experiments (OSSEs) to provide guidance on the effective coordination of the western North Atlantic observing system elements. OSSEs are also being performed under H2020 AtlantOS project with the goal to enhance and optimize the Atlantic in-situ networks. Potential of future satellite missions of wide-swath altimetry and surface ocean currents monitoring is explored through OSSEs and evaluation of Degrees of Freedom for Signal (DFS). Forecast Sensitivity Observation Impacts (FSOI) are routinely evaluated for monitoring the ocean observation impacts in the US Navy's ODAP system. Perspectives on the extension of OS-Eval to coastal regions, the deep ocean, polar regions, coupled data assimilation, and biogeochemical applications are also presented. Based on the examples above, we identify the limitations of OS-Eval, indicating that the most significant limitation is reduction of robustness and reliability of the results due to their system-dependency. The difficulty of performing evaluation in near real time is also critical. A strategy to mitigate the limitation and to strengthen the impact of evaluations is discussed. In particular, we emphasize the importance of collaboration within the ODAP community for multi-system evaluation and of communication with ocean observational communities on the design of OS-Eval, required resources, and effective distribution of the results. Finally, we recommend further developing OS-Eval activities at international level with the support of the international ODAP (e.g., OceanPredict and CLIVAR-GSOP) and observational communities.

Ocean reanalyses combine ocean models, atmospheric forcing fluxes, and observations using data assimilation to give a four-dimensional description of the ocean. Metrics assessing their reliability have improved over time, allowing reanalyses to become an important tool in climate services that provide a more complete picture of the changing ocean to end users. Besides climate monitoring and research, ocean reanalyses are used to initialize sub-seasonal to multi-annual predictions, to support observational network monitoring, and to evaluate climate model simulations. These applications demand robust uncertainty estimates and fit-for-purpose assessments, achievable through sustained advances in data assimilation and coordinated inter-comparison activities. Ocean reanalyses face specific challenges: (i) dealing with intermittent or discontinued observing networks, (ii) reproducing inter-annual variability and trends of integrated diagnostics for climate monitoring, (iii) accounting for drift and bias due, e.g., to air-sea flux or ocean mixing errors, and (iv) optimizing initialization and improving performances during periods and in regions with sparse data. Other challenges such as multi-scale data assimilation to reconcile mesoscale and large-scale variability and flow-dependent error characterization for rapidly evolving processes, are amplified in long-term reanalyses. The demand to extend reanalyses backward in time requires tackling all these challenges, especially in the emerging context of earth system reanalyses and coupled data assimilation. This mini-review aims at documenting recent advances from the ocean reanalysis community, discussing unsolved challenges that require sustained activities for maximizing the utility of ocean observations, supporting data rescue and advancing specific research and development requirements for reanalyses.

The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015–2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU’s Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services.

Numerical weather prediction models are including an increasing number of components of the Earth system. In particular, every forecast now issued by the European Centre for Medium-Range Weather Forecasts (ECMWF) runs with a 3D ocean model and a sea ice model below the atmosphere. Initialisation of different components using different methods and on different timescales can lead to inconsistencies when they are combined in the full system. Historically, the methods for initialising the ocean and the atmosphere have been typically developed separately. This paper describes an approach for combining the existing ocean and atmospheric analyses into what we categorise as a weakly coupled assimilation scheme. Here, we show the performance improvements achieved for the atmosphere by having a weakly coupled ocean–atmosphere assimilation system compared with an uncoupled system. Using numerical weather prediction diagnostics, we show that forecast errors are decreased compared with forecasts initialised from an uncoupled analysis. Further, a detailed investigation into spatial coverage of sea ice concentration in the Baltic Sea shows that a much more realistic structure is obtained by the weakly coupled analysis. By introducing the weakly coupled ocean–atmosphere analysis, the ocean analysis becomes a critical part of the numerical weather prediction system and provides a platform from which to build ever stronger forms of analysis coupling.

Human calcium-sensing receptor (CaSR) is a G-protein-coupled receptor (GPCR) that maintains extracellular Ca2+ homeostasis through the regulation of parathyroid hormone secretion. It functions as a disulfide-tethered homodimer composed of three main domains, the Venus Flytrap module, cysteine-rich domain, and seven-helix transmembrane region. Here, we present the crystal structures of the entire extracellular domain of CaSR in the resting and active conformations. We provide direct evidence that L-amino acids are agonists of the receptor. In the active structure, L-Trp occupies the orthosteric agonist-binding site at the interdomain cleft and is primarily responsible for inducing extracellular domain closure to initiate receptor activation. Our structures reveal multiple binding sites for Ca2+ and PO43- ions. Both ions are crucial for structural integrity of the receptor. While Ca2+ ions stabilize the active state, PO43- ions reinforce the inactive conformation. The activation mechanism of CaSR involves the formation of a novel dimer interface between subunits.

Osteoclasts are bone-resorbing cells essential for skeletal remodeling. However, over-active osteoclasts can cause bone-degenerative disorders. Therefore, the level of NFATc1, the master transcription factor of osteoclast, must be tightly controlled. Although the activation and amplification of NFATc1 have been extensively studied, how NFATc1 signaling is eventually resolved is unclear. Here, we uncover a novel and critical role of the orphan nuclear receptor Nur77 in mediating an NFATc1 self-limiting regulatory loop to prevent excessive osteoclastogenesis. Nur77 deletion leads to low bone mass owing to augmented osteoclast differentiation and bone resorption. Mechanistically, NFATc1 induces Nur77 expression at late stage of osteoclast differentiation; in turn, Nur77 transcriptionally up-regulates E3 ubiquitin ligase Cbl-b, which triggers NFATc1 protein degradation. These findings not only identify Nur77 as a key player in osteoprotection and a new therapeutic target for bone diseases, but also elucidate a previously unrecognized NFATc1→Nur77→Cblb—•NFATc1 feedback mechanism that confers NFATc1 signaling autoresolution.

This study aims to evaluate the impact of the in-situ ocean observations on seasonal forecasts. A series of seasonal reforecasts have been conducted for the period 1993-2015, in which different sets of ocean observations were withdrawn in the production of the ocean initial conditions, while maintaining a strong constrain in sea surface temperature (SST). By comparing the different reforecast sets, it is possible to assess the impact on the forecast of ocean and atmospheric variables. Results show that the in-situ observations have profound and significant impacts on the mean state of forecast ocean and atmospheric variables, which can be classified into different categories: i) impact due to local air-sea interaction, as direct consequence of changes in the mixed layer in the ocean initial conditions, and visible in the early stages of the forecasts; ii) changes due to different ocean dynamical balances, most visible in the Equatorial Pacific in forecasts initialized in May, which amplify and evolve with forecast lead time; iii) changes to the atmospheric circulation resulting from changes in large scale SST gradients; these are non-local, mediated by the atmospheric bridge, and they are obvious from the visible impact of the removing in-situ observations on the Atlantic basin only in the global atmospheric circulation; iv) changes in the atmospheric tropical deep convection associated with the structure of the warm pools. The ocean observations have also a significant impact on the representation of the trends of the ocean initial conditions, which affect the trends in the seasonal forecasts of ocean and atmospheric variables. The impact of the ocean observing system in the Atlantic and extratropics appears dominated by Argo, but this is not the case in the Tropical Pacific, where the other ocean observing systems play a role in constraining the ocean state. [ABSTRACT FROM AUTHOR]

We assess for the first time the impact of in-situ ocean observations on European Centre for Medium-Range Weather Forecasts (ECMWF) sub-seasonal forecasts of both ocean and atmospheric conditions. A series of coupled reforecasts have been conducted for the period 1993-2015, in which different sets of ocean observations were withdrawn in the production of the ocean initial conditions. Removal of all ocean in-situ observations in the initial conditions leads to significant degradation in the forecasts of ocean surface and subsurface mean state at lead times from week 1 to week 4. The negative impact is predominantly caused by the removal of the Argo observing system in recent decades. Changes in the mean state of atmospheric variables are comparatively small but significant in the forecasts of lower and upper atmospheric circulation over large regions. Our results highlight the value of continuous, real-time in-situ observations of the surface and subsurface ocean for coupled forecasts in the sub-seasonal range. [ABSTRACT FROM AUTHOR]