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Abstract Automatic generation of image captions is essentially a cross‐modal conversion from image to text. Owing to the differences in linguistic characteristics between Chinese and English, quite a few Chinese image captioning methods have recently been proposed. Nevertheless, the existing Chinese image captioning models usually lack attention to local details of images or tend to produce general descriptions. To address these challenges, a Chinese image captioning method is proposed that incorporates fusion encoder, visual keyword search, and reinforcement learning. The fusion encoder can simultaneously extract local and global features of the input image to enrich the semantic information in the decoding stage, visual keyword search can pursue potential visual words associated with the image content, and the reinforcement learning mechanism can optimize the evaluation metric CIDEr at sentence level to promote the lexical diversity of image description. The results of extensive experiments demonstrate that the proposed model outperforms the state‐of‐the‐art models and delivers expressive and informative Chinese image captions.

Coal is the main energy source in China. It plays a crucial role in promoting economic and social development, and is the guarantee of China's energy security. This paper establishes a prediction model for the number of deaths in coal mine accidents under the influence of multiple factors through data analysis and modeling. Also, it predicts the safety production situation under the optimization of coal production capacity. Based on the statistical data of coal mine production capacity and the coal mine accident fatalities in China since 1949, the relationship between production capacity and accidents is analyzed. The regional characteristics of coal mine safety production situation is studied and a Long Short-Term Memory Network (LSTM) model is developed for predicting the number of deaths in coal mine accidents. And based on this model, the number of deaths in coal mine accidents under the influence of regional production capacity optimization policies was predicted. The research results show that ① based on China's coal production, accident fatalities, and million-ton mortality rate over the years, the development of the China's coal industry can be divided into five stages: initial establishment stage, exploration stage, recovery and rectification stage, institutional mechanism reform stage, and safe and efficient development stage. The national coal mine safety situation varies greatly in different periods due to the changes in safety policies and coal mine production capacity; ② There are significant differences in the production capacity and safety level of coal mines in different provinces of China. Through the chi square test analysis of contingency tables, the number and level of accidents in coal mine safety production are significantly correlated with the accident area. Therefore, the adjustment of coal mine production capacity in different regions is also a key factor affecting the safety situation of coal mines in China; ③ Establishing a time series prediction LSTM model that includes factors such as coal mining technology, degree of mechanization, and coal production in different regions can accurately predict the number of deaths in coal mine accidents. The correlation coefficient R2 between the true and predicted values is 0.99, the root mean square error of the predicted data is 40.49; ④ According to the prediction results, while the total coal production remains unchanged, increasing production capacity in the northwest region and reducing production capacity in the northeast or southwest regions will further improve the safety production situation of coal mines; ⑤ The prediction results indicate that the number of coal mine fatalities will decrease more significantly after the production capacity shifts from southwest to northwest, but the degree of reduction in accident fatalities will decrease with the increase of time. Although the coal mining conditions in the northwest region are relatively good, it is still necessary to pay attention to the safety mining risks under the rapid development of coal production capacity. The research results can provide a reference for scientifically formulating coal industry policies.

Abstract This study investigates the correlation between previous coal mine safety policies and accidents in China. Data on coal mine accidents and government regulatory information from 2008 to 2021 are collected. The characteristics of coal mine accidents are analyzed, and safety policy indexes are identified. An ordinary least squares (OLS) regression model is established to quantitatively analyze the correlation between accidents and safety policy. The study finds that safety policies have some impact on accident occurrence in coal mines. Although there has been a decrease in accidents and deaths over time, higher mortality rates are observed during periods of increased production intensity and on weekends. Gas accidents are the most common, followed by roof and flood accidents. The study concludes that national safety policies with wider coverage and a stronger system are effective in preventing accidents, but caution should be exercised to avoid reduced vigilance with decreasing death rates.

Preventing infection is a critical clinical challenge; however, the extensive use of antibiotics has resulted in remarkably increased antibiotic resistance. A variety of antibiotic alternatives including antimicrobial peptides (AMPs) have been studied. Unfortunately, like most conventional antibiotics, most current AMPs have shown significantly high toxicity toward the host, and therefore induce compromised host responses that may lead to negative clinical outcomes such as delayed wound healing. In this study, one of the AMPs with a short length of nine amino acids was first identified via machine learning to present potentially low cytotoxicity, and then synthesized and validated in vitro against both bacteria and mammalian cells. It was found that this short AMP presented strong and fast-acting antimicrobial properties against bacteria like Staphylococcus aureus, one of the most common bacteria clinically, and it targeted and depolarized bacterial membranes. This AMP also demonstrated significantly lower (e.g., 30%) toxicity toward mammalian cells like osteoblasts, which are important cells for new bone formation, compared to conventional antibiotics like gentamicin, vancomycin, rifampin, cefazolin, and fusidic acid at short treatment times (e.g., 2 h). In addition, this short AMP demonstrated relatively low toxicity, similar to osteoblasts, toward an epithelial cell line like BEAS-2B cells.

We propose an enhanced floor field model (FFM) to analyze the behavioral characteristics of crowds with varying attributes proportions during evacuation. This model governs pedestrian movement through the Dynamic Floor Field (DFF) and the Static Floor Field (SFF). The DFF takes into account individual factors such as the gender, familiarity with the environment, and social relationships of evacuees, which influence safe evacuation. Concurrently, the SFF encapsulates the impact of environmental factors like obstacles, exits, and guidance effects. Subsequently, this refined FFM was applied and validated using a sports center evacuation scenario. The results demonstrated that the enhanced FFM accurately replicated evacuees' asymmetric behavior and queuing, and aligned well with other models when the number of evacuees fluctuated over time. In the absence of guidance, both environmental familiarity and gender emerged as primary factors influencing partial evacuation. Additionally, the gender of pedestrians significantly affected the overall evacuation. Notably, compared to pre-existing environmental information available to evacuees, the implementation of guidance to augment pedestrians' environmental familiarity resulted in a more efficient evacuation. The FFM model and these findings could be instrumental in simulating personnel evacuation and formulating emergency management strategies in crowded areas.

High-molecular-weight glutenin subunits (HMW-GS) are an important component of total cereal proteins in wheat. It is closely related to the processing quality of flour. Here, we analyzed allelic variations at the Glu-1 locus in 163 wheat accessions from Hubei Province, China with SDS-PAGE. Among the 15 alleles detected, alleles 1, 7+8, and 2+12 were the major alleles, and 7, 6+8, and 2+10 were rare alleles. The breeding lines had higher genetic diversity than the commercial varieties. Alleles 7 and 6+8 significantly reduced the grain protein content and wet gluten content of wheat. The “1, 7+9, 5+10” and “1, 14+15, and 2+12” allelic combinations significantly increased the grain protein content, hardness index, test weight, and wet gluten content of wheat. Alleles 7+9, 14+15, and 5+10 were identified as alleles related to high wheat quality. The “1, 7, 5+10”, “1, 6+8, 5+10”, “null, 7+9, 2+12”, “1, 14+15, 2+12”, and “1, 7+9, 5+10” allelic combinations had greater effects on wheat grain quality traits. These results demonstrate the effects of HMW-GS on wheat grain quality traits and provide a reference for the genetic improvement of wheat quality.

Improving barley grain quality is a major goal in barley breeding. In this study, a total of 35 papers focusing on quantitative trait loci (QTLs) mapping for barley quality traits published since 2000 were collected. Among the 454 QTLs identified in these studies, 349 of them were mapped onto high-density consensus maps, which were used for QTL meta-analysis. Through QTL meta-analysis, the initial QTLs were integrated into 41 meta-QTLs (MQTLs) with an average confidence interval (CI) of 1. 66 cM, which is 88.9% narrower than that of the initial QTLs. Among the 41 identified MQTLs, 25 were subsequently validated in publications using genome-wide association study (GWAS). From these 25 validated MQTLs, ten breeder’s MQTLs were selected. Synteny analysis comparing barley and wheat MQTLs revealed orthologous relationships between eight breeder’s MQTLs and 45 wheat MQTLs. Additionally, 17 barley homologs associated with rice quality traits were identified within the regions of the breeder’s MQTLs through comparative analysis. The findings of this study provide valuable insights for molecular marker-assisted breeding and the identification of candidate genes related to quality traits in barley.

Abstract Single-cell RNA sequencing methods focusing on the 5′-end of transcripts can reveal promoter and enhancer activity and efficiently profile immune receptor repertoire. However, ultra-high-throughput 5′-end single-cell RNA sequencing methods have not been described. We introduce FIPRESCI, 5′-end single-cell combinatorial indexing RNA-Seq, enabling massive sample multiplexing and increasing the throughput of the droplet microfluidics system by over tenfold. We demonstrate FIPRESCI enables the generation of approximately 100,000 single-cell transcriptomes from E10.5 whole mouse embryos in a single-channel experiment, and simultaneous identification of subpopulation differences and T cell receptor signatures of peripheral blood T cells from 12 cancer patients.

Urologic cancers such as kidney, bladder, prostate, and uroepithelial cancers have recently become a considerable global health burden, and the response to immunotherapy is limited due to immune escape and immune resistance. Therefore, it is crucial to find appropriate and effective combination therapies to improve the sensitivity of patients to immunotherapy. DNA damage repair inhibitors can enhance the immunogenicity of tumor cells by increasing tumor mutational burden and neoantigen expression, activating immune-related signaling pathways, regulating PD-L1 expression, and reversing the immunosuppressive tumor microenvironment to activate the immune system and enhance the efficacy of immunotherapy. Based on promising experimental results from preclinical studies, many clinical trials combining DNA damage repair inhibitors (e.g., PARP inhibitors and ATR inhibitors) with immune checkpoint inhibitors (e.g., PD-1/PD-L1 inhibitors) are underway in patients with urologic cancers. Results from several clinical trials have shown that the combination of DNA damage repair inhibitors with immune checkpoint inhibitors can improve objective rates, progression-free survival, and overall survival (OS) in patients with urologic tumors, especially in patients with defective DNA damage repair genes or a high mutational load. In this review, we present the results of preclinical and clinical trials of different DNA damage repair inhibitors in combination with immune checkpoint inhibitors in urologic cancers and summarize the potential mechanism of action of the combination therapy. Finally, we also discuss the challenges of dose toxicity, biomarker selection, drug tolerance, drug interactions in the treatment of urologic tumors with this combination therapy and look into the future direction of this combination therapy.

The crop rhizosphere is the main site of soil microbial activities. Understanding the structure and diversity of microbial communities in the crop rhizosphere will help us reveal interactions between rhizosphere microorganisms and plant growth. In this study, the rhizosphere soil was collected from 35 cultivated barley varieties at the mature stage. To investigate the structure and diversity of bacterial communities in the rhizosphere of different barley varieties, the 16S rDNA gene of microorganisms from the soil was sequenced using Illumina MiSeq next-generation high-throughput sequencing technology. The results showed that 13, 25, 49, and 59 bacterial flora with relative abundance >1% were detected from 35 barley rhizosphere samples at the phylum, class, order, and family levels, respectively. The abundance of bacteria among varieties differed relatively little, but the abundance of the same bacteria in rhizospheres of different varieties was different. In addition, both the cluster analysis and principal component analysis (PCA) divided the 35 samples into three clusters at the phylum level. Groups III and IV showed significantly higher abundance than group II in Proteobacteria, while group II exhibited significantly higher abundance of Chloroflexi than groups III and IV. This finding provides a realistic basis for further using the relationship between barley rhizosphere microorganisms and barley growth to improve the resistance and quality of barley.

Highly crystalline perovskite films with large grains and few grain boundaries are conducive for efficient and stable perovskite solar cells. Current methods for preparing perovskite films are mostly based on a fast crystallization process, with rapid nucleation and insufficient growth. In this study, MAPbI3 perovskite with inhibited nucleation and promoted growth in the TiO2/ZrO2/carbon triple mesoscopic scaffold was crystallized by modulating the precursor and the crystallization process. N-methylformamide showed high solubility for both methylammonium iodide and PbI2 and hampered the formation of large colloids in the MAPbI3 precursor solution. Furthermore, methylammonium chloride was added to reduce large colloids, which are a possible source of nucleation sites. During the crystallization of MAPbI3, the solvent was removed at a slow controlled speed, to avoid rapid nucleation and provide sufficient time for crystal growth. As a result, highly oriented MAPbI3 crystals with suppressed non-radiative recombination and promoted charge transport were obtained in the triple mesoscopic layer with disordered pores. The corresponding hole-conductor-free, printable mesoscopic perovskite solar cells exhibited a highest power conversion efficiency of 18.82%. The device also exhibited promising long-term operational stability of 1000 h under continuous illumination at maximum power point at 55 ± 5 °C and damp-heat stability of 1340 h aging at 85 °C as well as 85% relative humidity.

Abstract With rocketing power conversion efficiency and continuously enhancing stability, perovskite solar cells (PSCs) have shown extraordinary potential to become a key player in tomorrow's energy industry. An essential approach to improve the performance and quantify the loss mechanisms of PSCs is the analysis of charge carrier dynamics. Although the structure of PSCs is diverse, the characterization techniques for the dynamic measurement are similar. With the aim of providing researchers with the necessary knowledge to choose suitable measurement methods, this review represents a comprehensive overview of the techniques to assess the entire spectrum of carrier dynamic processes from femtoseconds to many seconds, including charge generation, relaxation, recombination, transport, extraction and collection.

Immunogenic cell death (ICD) is a regulated cell death (RCD) pathway. In response to physical and chemical signals, tumor cells activate specific signaling pathways that stimulate stress responses in the endoplasmic reticulum (ER) and expose damage-associated molecular patterns (DAMPs), which promote antitumor immune responses. As a result, the tumor microenvironment is altered, and many tumor cells are killed. The ICD response in tumor cells requires inducers. These inducers can be from different sources and contribute to the development of the ICD either indirectly or directly. The combination of ICD inducers with other tumor treatments further enhances the immune response in tumor cells, and more tumor cells are killed; however, it also produces side effects of varying severity. New induction methods based on nanotechnology improve the antitumor ability and significantly reduces side effects because they can target tumor cells precisely. In this review, we introduce the characteristics and mechanisms of ICD responses in tumor cells and the DAMPs associated with ICD responses, summarize the current methods of inducing ICD response in tumor cells in five distinct categories: chemical sources, physical sources, pathogenic sources, combination therapies, and innovative therapies. At the same time, we introduce the limitations of current ICD inducers and make a summary of the use of ICD responses in clinical trials. Finally, we provide an outlook on the future of ICD inducer development and provide some constructive suggestions.

Abstract Background Zinc (Zn) is an important nutrient for human beings, which is also an essential micronutrient for crop growth. This study investigated the role of Zn in coordinating the mineral elements absorption in modern wheat (Triticum aestivum L.) cultivars with a new developed method. Results A method was developed, and showed a robust capability to simultaneously investigate seven mineral elements uptake in wheat seedling. With this method, we found low Zn supply (< 1 μM) promoted the absorption of potassium (K), magnesium (Mg) and manganese (Mn) in wheat seedling, while high Zn supply (> 1 μM) significantly inhibited the absorption of these elements. Cultivars with the green genes (Rht-B1b and Rht-D1b) showed a higher uptake capability on ammonium (NH4 +), and cultivars with Rht-B1b allele can uptake more phosphors (P), K, calcium (Ca), Mn and Zn compared to cultivars with Rht-D1b. Further analysis indicated higher uptake capability of NH4 + in cultivars contained Rhts was independent of Zn. Conclusion The key role of Zn in coordinating for mineral elements absorption was identified in modern wheat cultivars, providing the reference for Zn application in wheat. Meanwhile, this study provides a robust method for quantifying the absorption of mineral elements, which may be adopted into the broadly investigations on the coordinated nutrients absorption of plant.

Anoikis is a specialized mode of programmed cell death. Specifically, once cells detach from the original extracellular matrix, an apoptotic program is initiated, preventing colonization of the cells in distant parts of the organ. Therefore, both distant metastasis and colonization of cancer cells rely on the anoikis resistance of cancer cells. Bioinformatics analysis was performed to confirm the relation of anoikis to kidney renal cell carcinoma (KIRC). To construct a prognostic model for patients with KIRC, we investigated several genes of the anoikis pathway most closely related to KIRC and also contrasted the effects of common anticancer drugs on the KIRC pathway. Besides KIRC, we explored the expression of anoikis-related genes in various other cancers. We classified patients with KIRC into three clusters based on the coefficients and mRNA expression levels of anoikis-related genes selected using the GSVA algorithm. We used the GDSC database to predict the response of the anoikis pathway to common anticancer drugs and explored the potential targets of the anoikis pathway in KIRC. We then analyzed the response of common immunotherapies to the anoikis pathway to analyze the correlation between anoikis and immune checkpoint inhibitor therapy. Finally, eleven cancer-related genes were screened and a prognostic model was constructed using LASSO regression.

The occurrence of clear cell renal cell carcinoma (ccRCC) is related to changes in the transforming growth factor-β (TGF-β) signaling pathway. In this study, we adopted an integrated approach to identify and verify the effects of changes in this pathway on ccRCC and provide a guide for identifying new therapeutic targets. We performed transcriptome analysis of 539 ccRCC cases from The Cancer Genome Atlas (TCGA) and divided the samples into different TGF-β clusters according to unsupervised hierarchical clustering. We found that 76 of the 85 TGF-β pathway genes were dysregulated, and 55 genes were either protective or risk factors affecting the prognosis of ccRCC. The survival time of patients with tumors with low TGF-β scores was shorter than that of patients with tumors with high TGF-β scores. The overall survival (OS) of patients with ccRCC with high TGF-β scores was better than that of patients with low TGF-β scores. The TGF-β score correlated with the expression of key ccRCC and deacetylation genes. The sensitivity of tumor patients to targeted drugs differed between the high and low TGF-β score groups. Therefore, a prognostic model based on the TGF-β gene pathway can predict the prognosis of ccRCC patients. Grouping patients with ccRCC according to their TGF-β score is of great significance for evaluating the prognosis of patients, selecting targeted drugs, and identifying new therapeutic targets.

BackgroundNeoadjuvant anti-programmed death receptor-1 (PD-1) blockade has been reported to improve the prognosis of locally advanced esophageal squamous cell carcinoma (ESCC). This study was aimed to evaluate the efficacy and safety of neoadjuvant camrelizumab plus chemoradiotherapy in locally advanced ESCC.MethodsWe retrospectively enrolled ESCC patients who received camrelizumab plus chemoradiotherapy as neoadjuvant therapy before surgery from May 2019 to September 2021.ResultsA total of 38 eligible patients were enrolled. The neoadjuvant treatment was well tolerated with no serious treatment-related adverse events. 36 (94.7%) patients achieved a R0 resection without hospital mortality or any other serious intraoperative complications. The objective response rate (ORR) was 63.2% and the disease control rate (DCR) was 100.0%. The major pathological response (MPR) was 50.0% and the complete pathological response (pCR) was 39.5%. With a median follow-up of 18.5 months, 6 (15.8%) patients had died. The overall survival (OS) and disease-free survival (DFS) at 12 months were 87.6% and 78.7%, respectively. Subgroup analysis demonstrated that patients who got MPR or pCR achieved improved survival, while PD-L1 expression did not reach statistically difference in predicting survival.ConclusionsNeoadjuvant camrelizumab plus chemoradiotherapy is safe and efficacious in treating patients with locally advanced ESCC.

Renal cell carcinoma (RCC) is a type of cancer with an increasing rate of morbidity and mortality and is a serious threat to human health. The treatment of RCC, especially kidney renal clear cell carcinoma (KIRC), has always been the focus of clinical treatment. Using The Cancer Genome Atlas (TCGA) database as a starting point, we explored the feasibility of applying the pyroptosis mechanism to KIRC treatment by searching for cancer markers associated with pyroptosis and cancer treatment signatures. The obtained samples were clustered using unsupervised clustering analysis to define the different KIRC subtypes with different pyroptosis expression levels. Based on this, a gene expression analysis was performed to explore the carcinogenic mechanism that is markedly related to pyroptosis. The Genomics of Drug Sensitivity in Cancer database and single sample gene set enrichment analysis (ssGSEA) algorithm were used to analyze the different treatment methods of the current prominent KIRC to determine whether pyroptosis plays a role. Finally, LASSO regression was used to screen for related genes and construct a model to predict patient prognosis. The expression levels of GSDME, CASP3, CASP4, CASP5, CHMP3, and CHMP4C were incorporated into the model construction. After verification, the prediction accuracy of the 3-, 5-, 7- and 10 years survival rates of our prognostic model were 0.66, 0.701, 0.719, and 0.728, respectively. Through the above analysis, we demonstrated the feasibility of pyroptosis in the clinical treatment of KIRC and provided novel ideas and suggestions for the clinical treatment of KIRC.

Abstract The high‐pressure gas expansion‐induced deformation and dynamic fracture of coal are important parts of coal and gas outburst. To better understand the law of this process, laboratory experiments and numerical simulation are used to study the law of damage. A cavity with different pressures of CH4 or N2 was destroyed by a jack to achieve the rapid expansion of the gas and coal fracture inside. The particle size distribution of the coal particles before and after the experiment was measured, and the breakage ratio and the newly added surface area were calculated. The experimental results indicate that during the gas expansion process, the breakage ratio of coal and the newly added surface area clearly increase with the increase in gas pressure. Finally, a numerical model based on peridynamic theory was developed to simulate crack generation and the propagation of coal induced by the expansion of gases at different pressures. The numerical simulation results show that the higher the initial gas pressure is, the higher the number of failure units. Moreover, only when the gas pressure is large enough will the coal crack in various directions at the same time.

Abstract Gas distribution ahead of the coal tunnel is the key risk factor controlling coal and gas outburst. Based on an analysis of the influence of the stress state on the porosity of the coal seam, an improved quartet structure generation set (QSGS) random porous media generation tool is used to reconstruct the coal structure. Adopting the reconstructed pore structure of the coal as the boundary of the flow field, the Lattice Boltzmann method (LBM) is used to simulate the gas dynamic distribution law ahead of the coal tunnel. The gas pressure of the coal seam decreases obviously in the fracture zone only, and the change in gas pressure is very small in the stress concentration zone and ahead of the coal seam. This research is of significance for gas drainage and the prevention of coal and gas outburst hazards in the working face during coal tunneling.

The development of cancer treatment methods is constantly changing. For common cancers, our treatment methods are still based on conventional treatment methods, such as chemotherapy, radiotherapy, and targeted drug therapy. Nevertheless, the emergence of tumor resistance has a negative impact on treatment. Regulated cell death is a gene-regulated mode of programmed cell death. After receiving specific signal transduction, cells change their physical and chemical properties and the extracellular microenvironment, resulting in structural destruction and decomposition. As research accumulates, we now know that by precisely inducing specific cell death patterns, we can treat cancer with less collateral damage than other treatments. Many newly discovered types of RCD are thought to be useful for cancer treatment. However, some experimental results suggest that some RCDs are not sensitive to cancer cell death, and some may even promote cancer progression. This review summarizes the discovered types of RCDs, reviews their clinical efficacy in cancer treatment, explores their anticancer mechanisms, and discusses the feasibility of some newly discovered RCDs for cancer treatment in combination with the immune and tumor microenvironment.

Angiogenesis, a process highly regulated by pro-angiogenic and anti-angiogenic factors, is disrupted and dysregulated in cancer. Despite the increased clinical use of angiogenesis inhibitors in cancer therapy, most molecularly targeted drugs have been less effective than expected. Therefore, an in-depth exploration of the angiogenesis pathway is warranted. In this study, the expression of angiogenesis-related genes in various cancers was explored using The Cancer Genome Atlas datasets, whereupon it was found that most of them were protective genes in the patients with kidney renal clear cell carcinoma (KIRC). We divided the samples from the KIRC dataset into three clusters according to the mRNA expression levels of these genes, with the enrichment scores being in the order of Cluster 2 (upregulated expression) > Cluster 3 (normal expression) > Cluster 1 (downregulated expression). The survival curves plotted for the three clusters revealed that the patients in Cluster 2 had the highest overall survival rates. Via a sensitivity analysis of the drugs listed on the Genomics of Drug Sensitivity in Cancer database, we generated IC50 estimates for 12 commonly used molecularly targeted drugs for KIRC in the three clusters, which can provide a more personalized treatment plan for the patients according to angiogenesis-related gene expression. Subsequently, we investigated the correlation between the angiogenesis pathway and classical cancer-related genes as well as that between the angiogenesis score and immune cell infiltration. Finally, we used the least absolute shrinkage and selection operator (LASSO)–Cox regression analysis to construct a risk score model for predicting the survival of patients with KIRC. According to the areas under the receiver operating characteristic (ROC) curves, this new survival model based on the angiogenesis-related genes had high prognostic prediction value. Our results should provide new avenues for the clinical diagnosis and treatment of patients with KIRC.

Kidney cancer is a cancer with an increasing incidence in recent years. Clear cell renal cell carcinoma (ccRCC) accounts for up to 80% of all kidney cancers. The understanding of the pathogenesis, tumor progression, and metastasis of renal carcinoma is not yet perfect. Kidney cancer has some characteristics that distinguish it from other cancers, and the metabolic aspect is the most obvious. The specificity of glucose and lipid metabolism in kidney cancer cells has also led to its being studied as a metabolic disease. As the most common type of kidney cancer, ccRCC has many characteristics that represent the specificity of kidney cancer. There are features that we are very concerned about, including the presence of lipid droplets in cells and the obesity paradox. These two points are closely related to glucose metabolism and lipid metabolism. Therefore, we hope to explore whether metabolic changes affect the occurrence and development of kidney cancer by looking for evidence of changes on expression at the genomic and protein levels in glucose metabolism and lipid metabolism in ccRCC. We begin with the representative phenomenon of abnormal cancer metabolism: the Warburg effect, through the collection of popular metabolic pathways and related genes in the last decade, as well as some research hotspots, including the role of ferroptosis and glutamine in cancer, systematically elaborated the factors affecting the incidence and metastasis of kidney cancer. This review also identifies the similarities and differences between kidney cancer and other cancers in order to lay a theoretical foundation and provide a valid hypothesis for future research.

Accurate tooth segmentation from CBCT images is a crucial step for specialist to perform quantitative analysis, clinical diagnosis and operation. In this paper, we present a symmetric full convolutional network with residual block and Dense Conditional Random Field (DCRF), which can achieve accurate segmentation automatically for tooth images. The proposed method can not only strengthen feature propagation, but also boost feature reuse, which can be credited to the contracting path and the expanding path that extract and recover pixel cues sufficiently. To this end, we apply special deep bottleneck architectures (DBAs) and summation-based skip connection into our network to ensure accurate segmentation for much deeper neural network. Compared with previous methods which are based on conditional random field for original image intensity, our approach applies DCRF to the posterior probability generated by the proposed network. To avoid the interferences of noises around the tooth, we combine the pixel-level prediction capability of DCRF, which further enhance the segmentation performance. In the experiments, we verify the capabilities of our methods based on four evaluation indicators, which demonstrates the superiority of our method.

Target tracking based on Siamese network has reached the state-of-the-art performance. However is still limited in semantic feature extraction. In this paper, we propose a novel method to distinguish positive and negative samples. Taking deep neural network as the backbone, we fuse the feature maps from different layers and feed it to RPN (Region Proposal Network). In addition, we use a loss term for loss function to achieve self-adjusting and learn more discriminative embedding features of target objects with similar semantics. In the tracking stage, one-shot detection is used as the reference, fix the first frame as the weight of tracking to track the subsequent frames. Our method has achieved outstanding performance on several benchmark data set, such as: OTB2015, VOT2016, VOT2018, and VOT2019 et al.

Currently, object detectors based on CNN, such as RetinaNet, Faster-RCNN, CornerNet series, can achieve good performance, but have some common drawbacks, like large calculation cost, high model complexity and slow detection speed. In this paper, a new lightweight object detector is proposed, which adopted a density-based approach to merge the real boxes. To reduce calculation cost and improve detection speed, the tactic of multi-scale output is adopted to predict objects of different sizes with features of different scales. Furthermore, a new lightweight network model is proposed, which can show better performance in computation, FPS, and model complexity. Meanwhile, the separation of convolution is used to improve the basic convolution layer, which can achieve better results under the same number of filters. In the experiments, we verified the capability of our methods based on ablation experiment and model evaluation, which demonstrates the superiority of our method. Moreover, we have also conducted deep network and multichannel experiments on MS-COCO2014 datasets and achieved 20.9% mAP performance.

Keloids exhibit metabolic reprogramming including enhanced glycolysis and attenuated oxidative phosphorylation. Hypoxia induces a series of protective responses in mammalian cells. However, the metabolic phenotype of keloid fibroblasts under hypoxic conditions remains to be elucidated. The present study aimed to investigate glycolytic activity, mitochondrial function and morphology, and the HIF1α and PI3K/AKT signaling pathways in keloid fibroblasts (KFB) under hypoxic conditions. Our results showed that hypoxia promoted proliferation, migration invasion and collagen synthesis and inhibited apoptosis in KFB. The mRNA levels, protein expressions and enzyme activities of glycolytic enzymes in KFB were higher than those in normal skin fibroblasts (NFB) under normoxia. Moreover, hypoxia remarkedly upregulated glycolysis in KFB. Decreased activities of mitochondrial complexes and abnormal mitochondria were detected in KFB under normoxic conditions and the damage was aggravated by hypoxia. An intracellular metabolic profile assay suggested hypoxia increased glycolytic parameters except glycolytic reserve but inhibited the key parameters of mitochondrial function apart from H+ leak. Protein levels of HIF1α and phosphorylation levels of the PI3K/AKT signaling pathway were upregulated in the context of 3% oxygen. Enhanced total reactive oxygen species (ROS), mitochondrial ROS (mitoROS) and antioxidant activities of KFB were observed in response to hypoxia. Additionally, autophagy was induced by hypoxia. Our data collectively demonstrated potentiated glycolysis and attenuated mitochondrial function under hypoxia, indicating that altered glucose metabolism regulated by hypoxia could be a therapeutic target for keloids.

This study analyzed and compared the potential role of fatty acid metabolism pathways in three subtypes of renal cell carcinoma. Biological pathways that were abnormally up- and downregulated were identified through gene set variation analysis in the subtypes. Abnormal downregulation of the fatty acid metabolism pathway occurred in all three renal cell carcinoma subtypes. Alteration of the fatty acid metabolism pathway was vital in the development of pan-renal cell carcinoma. Bioinformatics methods were used to obtain a panoramic view of copy number variation, single-nucleotide variation, mRNA expression, and the survival landscape of fatty acid metabolism pathway-related genes in pan-renal cell carcinoma. Most importantly, we used genes related to the fatty acid metabolism pathway to establish a prognostic-related risk model in the three subtypes of renal cell carcinoma. The data will be valuable for future clinical treatment and scientific research.

The 3-D reconstruction methods based on structure from motion (SfM) pipeline mainly use the traditional scale invariant feature detecting methods for feature matching. This type of methods suffers from the accuracy with affine matching in the image-based modeling system. In this paper, we propose an affine-invariant feature detection and matching method which is accurate and fast based on the three types of critical points in Morse theory called precise extreme feature region (PEFR). We also exploit the evaluation method for the reconstruction results without the ground truth. We propose the average descending rate to quantitatively evaluate the accuracy of the reconstruction model. Using PEFR in an SfM pipeline to build 3-D point-cloud, our method presents better results and less runtime compared to the state-of-the-art approaches. In addition, our quality assessment method presents a practical comparison of the reconstruction result based on the geometric accuracy of the reconstruction models. The experimental results prove that our PEFR method is faster and more accurate and works well on the reconstruction pipeline. The assessment method is fit for the quality assessment on 3-D reconstruction.

Abstract Background Seedling characteristics play significant roles in the growth and development of barley (Hordeum vulgare L.), including stable stand establishment, water and nutrients uptake, biotic resistance and abiotic stresses, and can influence yield and quality. However, the genetic mechanisms underlying seedling characteristics in barley are largely unknown and little research has been done. In the present work, 21 seedling-related characteristics are assessed in a barley double haploid (DH) population, grown under hydroponic conditions. Of them, leaf age (LAG), shoot height (SH), maximum root length (MRL), main root number (MRN) and seedling fresh weight (SFW) were investigated at the 13th, 20th, 27th, and 34th day after germination. The objectives were to identify quantitative trait loci (QTLs) underlying these seedling characteristics using a high-density linkage map and to reveal the QTL expression pattern by comparing the QTLs among four different seedling growth stages. Results A total of 70 QTLs were distributed over all chromosomes except 4H, and, individually, accounted for 5.01%–77.78% of phenotypic variation. Out of the 70 detected QTLs, 23 showed a major effect on 14 seedling-related characteristics. Ten co-localized chromosomal regions on 2H (five regions), 3H (two regions) and 7H (three regions) involved 39 QTLs (55.71%), each simultaneously influenced more than one trait. Meanwhile, 9 co-localized genomic regions involving 22 QTLs for five seedling characteristics (LAG, SH, MRL, MRN and SFW) at the 13th, 20th, 27th and 34th day-old seedling were common for two or more growth stages of seedling. QTL in the vicinity of Vrs1 locus on chromosome 2H with the favorable alleles from Huadamai 6 was found to have the largest main effects on multiple seedling-related traits. Conclusions Six QTL cluster regions associated with 16 seedling-related characteristics were observed on chromosome 2H, 3H and 7H. The majority of the 29 regions identified for five seedling characteristics were selectively expressed at different developmental stages. The genetic effects of 9 consecutive expression regions displayed different developmental influences at different developmental stages. These findings enhanced our understanding of a genetic basis underlying seedling characteristics in barley. Some QTLs detected here could be used for marker-assisted selection (MAS) in barley breeding.

Abstract Background Screw-rod constructs have been widely used to correct spinal deformities, but the effects of different screw-rod systems on anti-rotational torque have not been determined. This study aimed to analyze the biomechanical effect of different rod-screw constructs on anti-rotational torque. Methods Three conventional spinal screw-rod systems (Legacy, RF-F-10 and USSII) were used to test the anti-rotational torque in the material test machine. ANOVA was performed to evaluate the anti-rotational capacity of different pedicle screws-rod constructs. Results The anti-rotational torque of Legacy group, RF-F-10 group and USSII group were 12.3 ± 1.9 Nm, 6.8 ± 0.4 Nm, and 3.9 ± 0.8 Nm, with a P value lower than 0.05. This results indicated that the Legacy screws-rod construct could provide a highest anti-rotation capacity, which is 68% and 210% greater than RF-F-10 screw-rod construct and USSII screw-rod respectively. Conclusions The anti-rotational torque may be mainly affected by screw cap and groove design. Our result showed the anti-rotational torque are: Legacy system > RF-F-10 system > USSII system, suggesting that appropriate rod-screw constructs selection in surgery may be vital for anti-rotational torque improvement and preventing derotation correction loss.

For precision medicine, there is a need to identify genes that accurately distinguish the physiological state or response to a particular therapy, but this can be challenging. Many methods of analyzing differential expression have been established and applied to this problem, such as t-test, edgeR, and DEseq2. A common feature of these methods is their focus on a linear relationship (differential expression) between gene expression and phenotype. However, they may overlook nonlinear relationships due to various factors, such as the degree of disease progression, sex, age, ethnicity, and environmental factors. Maximal information coefficient (MIC) was proposed to capture a wide range of associations of two variables in both linear and nonlinear relationships. However, with MIC it is difficult to highlight genes with nonlinear expression patterns as the genes giving the most strongly supported hits are linearly expressed, especially for noisy data. It is thus important to also efficiently identify nonlinearly expressed genes in order to unravel the molecular basis of disease and to reveal new therapeutic targets. We propose a novel nonlinearity measure called normalized differential correlation (NDC) to efficiently highlight nonlinearly expressed genes in transcriptome datasets. Validation using six real-world cancer datasets revealed that the NDC method could highlight nonlinearly expressed genes that could not be highlighted by t-test, MIC, edgeR, and DEseq2, although MIC could capture nonlinear correlations. The classification accuracy indicated that analysis of these genes could adequately distinguish cancer and paracarcinoma tissue samples. Furthermore, the results of biological interpretation of the identified genes suggested that some of them were involved in key functional pathways associated with cancer progression and metastasis. All of this evidence suggests that these nonlinearly expressed genes may play a central role in regulating cancer progression.

Grain size and weight are crucial components of barley yield and quality and are the target characteristics of domestication and modern breeding. Despite this, little is known about the genetic and molecular mechanisms of grain size and weight in barley. Here, we evaluated nine traits determining grain size and weight, including thousand grain weight (Tgw), grain length (Gl), grain width (Gw), grain length-width ratio (Lwr), grain area (Ga), grain perimeter (Gp), grain diameter (Gd), grain roundness (Gr), and factor form density (Ffd), in a double haploid (DH) population for three consecutive years. Using five mapping methods, we successfully identified 60 reliable QTLs and 27 hotspot regions that distributed on all chromosomes except 6H which controls the nine traits of grain size and weight. Moreover, we also identified 164 barley orthologs of 112 grain size/weight genes from rice, maize, wheat and 38 barley genes that affect grain yield. A total of 45 barley genes or orthologs were identified as potential candidate genes for barley grain size and weight, including 12, 20, 9, and 4 genes or orthologs for barley, rice, maize, and wheat, respectively. Importantly, 20 of them were located in the 14 QTL hotspot regions on chromosome 1H, 2H, 3H, 5H, and 7H, which controls barley grain size and weight. These results indicated that grain size/weight genes of other cereal species might have the same or similar functions in barley. Our findings provide new insights into the understanding of the genetic basis of grain size and weight in barley, and new information to facilitate high-yield breeding in barley. The function of these potential candidate genes identified in this study are worth exploring and studying in detail.

This study investigated the optimal conditions for preparing desulfurizing stalk carbon, using corn stalk as a raw material and zinc chloride as an activator. The structure of stalk carbon was characterized using thermogravimetry (TG), fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS) and Brunauer – Emmelt – Teller (BET). Results found no significant difference in stalk carbon desulfurization properties when using stalk skin, core, or a skin-core mixture as raw material. The desulfurization performance of stalk carbon prepared using a skin-core mixture, was the most effective when the material : liquid ratio was 1:2; activation temperature was 350 °C; and activation time was 70 min. The corresponding H2S adsorption time was 74 min. The large specific surface area of 562.28 m2/g and abundant pore-volume of 0.3851 ml/g was found in the desulfurization stalk carbon prepared using these conditions. The increase in micropores and the abundant oxygen-containing functional surface groups were conducive to H2S adsorption. The desulfurization products were found to be mainly elemental S and sulfite.

A Correction to this paper has been published: https://doi.org/10.1038/s41419-021-03387-3

An experiment designed to simulate coal during excavation was conducted. Microseismic signals of coal under vibration conditions during excavation and subsequent waiting time of the coal roadway at different excavation speeds were collected and analyzed. During the excavation and subsequent waiting time, the stress in coal is redistributed, and the concentrated stress is gradually transferred to the deeper section of the coal seam. The Hilbert–Huang transform (HHT) is used to effectively denoise the collected signals. According to the noise-reduced signal, the amplitude and pulse number of the microseismic signals emitted during the excavation process are much larger than those of the waiting time process. During excavation, the energy and event numbers of microseismic signals increase first and then decrease as the excavation speed increases. The faster the excavation speed, the more the energy, and the higher the event numbers of the microseismic signals released during the subsequent waiting time. When the excavation speed is faster, more elastic potential accumulates in the coal seam and the concentration stress is greater. As the concentrated stress moves forward in time without excavation, more coal seams fail, and more microseismic signals are released. The microseismic signal and the stress evolution law can provide a reasonable explanation for the forward movement of the concentrated stress and coal failure during roadway excavation.

This paper examines the changes in regional and sectoral inequality that accompanied economic transformation in Russia and China throughout the 1990s. The experiences of the two countries are widely viewed as having been polar opposites. While the Soviet collapse had adverse consequences for many parts of the post-Soviet population, the Chinese experience produced a continuing rise of average living standards. Nevertheless, both countries experienced a drastic increase in economic inequality. In both cases, regional inequalities rose more sharply than inequalities across sectors but within regions. In particular, major urban centers gained dramatically relative to the hinterlands. Also, in Russia as in China, those sectors exercising the largest degrees of monopoly power gained the most (or lost the least) in relative terms. In both countries, the respective position of finance improved greatly, while that of agriculture declined. The decline of agriculture in China, however, was not as precipitous as in Russia, and certain sectors, such as education and science, maintained their position in China in a way that was not possible for them in Russia.

An experiment simulating coal seam under forced vibration conditions was conducted. Acceleration response and microseism signal during the experiment were collected and analyzed. It is found that, with an increasing amount of vibration, the natural frequency of the specimen decreases, and this phenomenon reflects fractures appearing in the specimen. Acceleration response signals show that peaks in shock excitation frequency and shock excitation acceleration affect the acceleration response, which reflects damage to the specimen. When shock excitation frequency nears natural frequency, the acceleration response first decreases and then increases. When resonance occurs, it reaches its maximum value. As shock excitation acceleration peaks increase, the acceleration response peak of the specimen also increases. We conclude that destruction is mainly concentrated in the coal seam evidenced by specimen destruction situation. Then shock excitation frequency and shock excitation acceleration influence on microseism signals were analyzed by Hilbert-Huang transform. By receiving these signals and analyzing their characteristics, it is beneficial to develop new methods to predict disasters underground dynamically in the future.