A Novel Glycolysis-Related Long Noncoding RNA Signature for Predicting Overall Survival in Gastric Cancer

Background: The aim of this study was to construct a glycolysis-related long noncoding RNA (lncRNA) signature to predict the prognosis of patients with gastric cancer (GC).Methods: Glycolysis-related genes were obtained from the Molecular Signatures Database (MSigDB), lncRNA expression profiles and clinical data of GC patients were obtained from The Cancer Genome Atlas database (TCGA). Furthermore, univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analysis were used to construct prognostic glycolysis-related lncRNA signature. The specificity and sensitivity of the signature was verified by receiver operating characteristic (ROC) curves. We constructed a nomogram to predict the 1-year, 3-year, and 5-year survival rates of GC patients. Besides, the relationship between immune infiltration and the risk score was analyzed in the high and low risk groups. Multi Experiment Matrix (MEM) was used to analyze glycolysis-related lncRNA target genes. R “limma” package was used to analyze the mRNA expression levels of the glycolysis-related lncRNA target genes in TCGA. Gene set enrichment analysis (GSEA) was employed to further explore the biological pathways in the high-risk group and the glycolysis-related lncRNA target gene.Results: A prognostic signature was conducted based on nine glycolysis-related lncRNAs, which are AL391152.1, AL590705.3, RHOXF1-AS1, CFAP61-AS1, LINC00412, AC005165.1, AC110995.1, AL355574.1 and SCAT1. The area under the ROC curve (AUC) values at 1-year, 3-year, and 5-year were 0.765, 0.828 and 0.707 in the training set, and 0.669, 740 and 0.807 in the testing set, respectively. In addition, the nomogram could efficaciously predict the 1-year, 3-year, and 5-year survival rates of the GC patients. Then, we discovered that GC patients with high-risk scores were more likely to respond to immunotherapy. GSEA revealed that the signature was mainly associated with the calcium signaling pathway, extracellular matrix (ECM) receptor interaction, and focal adhesion in high-risk group, also indicated that SBSPON is related to aminoacyl-tRNA biosynthesis, citrate cycle, fructose and mannose metabolism, pentose phosphate pathway and pyrimidine metabolism.Conclusion: Our study shows that the signature can predict the prognosis of GC and may provide new insights into immunotherapeutic strategies

Highly conserved evolution of aquaporin PIPs and TIPs confers their crucial contribution to flowering process in plants

Flowering is the key process for the sexual reproduction in seed plants. In gramineous crops, the process of flowering, which includes the actions of both glume opening and glume closing, is directly driven by the swelling and withering of lodicules due to the water flow into and out of lodicule cells. All these processes are considered to be controlled by aquaporins, which are the essential transmembrane proteins that facilitate the transport of water and other small molecules across the biological membranes. In the present study, the evolution of aquaporins and their contribution to flowering process in plants were investigated via an integration of genome-wide analysis and gene expression profiling. Across the barley genome, we found that HvTIP1;1, HvTIP1;2, HvTIP2;3, and HvPIP2;1 were the predominant aquaporin genes in lodicules and significantly upregulated in responding to glume opening and closing, suggesting the importance of them in the flowering process of barley. Likewise, the putative homologs of the above four aquaporin genes were also abundantly expressed in lodicules of the other monocots like rice and maize and in petals of eudicots like cotton, tobacco, and tomato. Furthermore, all of them were mostly upregulated in responding to the process of floret opening, indicating a conserved function of these aquaporin proteins in plant flowering. The phylogenetic analysis based on the OneKP database revealed that the homologs of TIP1;1, TIP1;2, TIP2;3, and PIP2;1 were highly conserved during the evolution, especially in the angiosperm species, in line with their conserved function in controlling the flowering process. Taken together, it could be concluded that the highly evolutionary conservation of TIP1;1, TIP1;2, TIP2;3 and PIP2;1 plays important roles in the flowering process for both monocots and eudicots

When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks

In-context learning (ICL) has become the default method for using large language models (LLMs), making the exploration of its limitations and understanding the underlying causes crucial. In this paper, we find that ICL falls short of handling specification-heavy tasks, which are tasks with complicated and extensive task specifications, requiring several hours for ordinary humans to master, such as traditional information extraction tasks. The performance of ICL on these tasks mostly cannot reach half of the state-of-the-art results. To explore the reasons behind this failure, we conduct comprehensive experiments on 18 specification-heavy tasks with various LLMs and identify three primary reasons: inability to specifically understand context, misalignment in task schema comprehension with humans, and inadequate long-text understanding ability. Furthermore, we demonstrate that through fine-tuning, LLMs can achieve decent performance on these tasks, indicating that the failure of ICL is not an inherent flaw of LLMs, but rather a drawback of existing alignment methods that renders LLMs incapable of handling complicated specification-heavy tasks via ICL. To substantiate this, we perform dedicated instruction tuning on LLMs for these tasks and observe a notable improvement. We hope the analyses in this paper could facilitate advancements in alignment methods enabling LLMs to meet more sophisticated human demands.Comment: Under revie

Analysis of traveling wave based fault location method for distribution network with image processing

Laws of traveling wave data related to fault location for medium voltage distribution network are discussed and summarized. Given the tree structure of a distribution network, an image of nodes voltage is created combining the use of real-time traveling wave meters at all nodes of the tree. The novelty of this paper is that travelling wavefront are analyzed based on the dynamic changes of these images. Based on principle of the traditional fault location with traveling wave-based method for transmission networks, traveling wave data of fault location for medium voltage distribution networks are plotted in order to estimate propagation velocity and distance between the fault position and the reference node. The results indicate that taking advantage of the laws of data related to first wave front can improve the reliability of the fault location for medium voltage networks

MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation

Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-event relationships. However, due to the annotation challenges brought by task complexity, a large-scale dataset covering the full process of event understanding has long been absent. In this paper, we introduce MAVEN-Arg, which augments MAVEN datasets with event argument annotations, making the first all-in-one dataset supporting event detection, event argument extraction (EAE), and event relation extraction. As an EAE benchmark, MAVEN-Arg offers three main advantages: (1) a comprehensive schema covering 162 event types and 612 argument roles, all with expert-written definitions and examples; (2) a large data scale, containing 98,591 events and 290,613 arguments obtained with laborious human annotation; (3) the exhaustive annotation supporting all task variants of EAE, which annotates both entity and non-entity event arguments in document level. Experiments indicate that MAVEN-Arg is quite challenging for both fine-tuned EAE models and proprietary large language models (LLMs). Furthermore, to demonstrate the benefits of an all-in-one dataset, we preliminarily explore a potential application, future event prediction, with LLMs. MAVEN-Arg and our code can be obtained from https://github.com/THU-KEG/MAVEN-Argument.Comment: Working in progres

Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis

BackgroundGastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D.MethodsThe Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein–protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database.ResultsA total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that “ECM-receptor interaction,” “AGE-RAGE signaling pathway in diabetic complications,” “aging,” and “cellular response to copper ion” were mutual pathways. Through the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes.Conclusion“ECM-receptor interaction,” “AGE-RAGE signaling pathway in diabetic complications,” “aging,” and “cellular response to copper ion” were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D

Gas-Bearing Property in Deep Marine Shale and Its Micro Controlling Factors: Evidence from the Lower Silurian Longmaxi Formation in Southern Sichuan, China

AbstractThe gas content in shale reservoirs is often determined by the micro storage and sealing capacities of the reservoir. Deep shale reservoirs are in the high- or over-thermale maturity stage and have complex pore structure and connectivity, which are highly heterogeneous in vertical distribution. Research on the gas-bearing property of deep shale reservoirs is limited by these complex microscopic conditions. To analyze the gas-bearing characteristics of deep shale reservoirs, this work collected and summarized data on total organic carbon content, mineral composition, porosity, water saturation, and gas content measured on-site for the Longmaxi Formation in the Sichuan Basin in southern Sichuan, China. Then, experimental methods, such as X-ray photoelectron spectroscopy, transmission electron microscope, low-pressure N2 adsorption, spontaneous imbibition, and high-pressure methane adsorption, were used to analyze the micro storage and sealing capacities of the deep shale reservoirs. The results show that, different from shallow shale reservoirs (<3500 m), deep shale reservoirs have a higher graphitization degree and water saturation. An abundance of graphite structures often leads to weak resistance of organic matter to compression, deformation, or even collapse of pores in organic matter and severe damage to the gas storage space. However, a higher degree of graphitization can enhance the ability of the shale reservoirs to adsorb gas and self-sealing. The high water saturation in the reservoirs can interact with clay minerals and negatively affect the gas accumulation, storage, and transmission capacities of the shale reservoirs. However, the upper shale reservoirs with higher water saturation can seal the lower shale reservoirs, helping it preserve shale gas. Based on the vertical distribution of graphite structure, clay minerals contents, lithofacies, and water content in deep shale reservoirs, the essential microscopic conditions for deep shale reservoirs to have high gas content were proposed. This paper provides a detailed explanation and evaluation of deep shale’s storage and sealing capacities at the microscopic scale and can serve as a reference for further identifying the patterns for high-yield and rich shale gas reservoirs and improving deep shale gas exploration technologies

SCRaMbLE generates designed combinatorial stochastic diversity in synthetic chromosomes

Synthetic chromosome rearrangement and modification by loxP-mediated evolution (SCRaMbLE) generates combinatorial genomic diversity through rearrangements at designed recombinase sites. We applied SCRaMbLE to yeast synthetic chromosome arm synIXR (43 recombinase sites) and then used a computational pipeline to infer or unscramble the sequence of recombinations that created the observed genomes. Deep sequencing of 64 synIXR SCRaMbLE strains revealed 156 deletions, 89 inversions, 94 duplications, and 55 additional complex rearrangements; several duplications are consistent with a double rolling circle mechanism. Every SCRaMbLE strain was unique, validating the capability of SCRaMbLE to explore a diverse space of genomes. Rearrangements occurred exclusively at designed loxPsym sites, with no significant evidence for ectopic rearrangements or mutations involving synthetic regions, the 99% nonsynthetic nuclear genome, or the mitochondrial genome. Deletion frequencies identified genes required for viability or fast growth. Replacement of 3′ UTR by non-UTR sequence had surprisingly little effect on fitness. SCRaMbLE generates genome diversity in designated regions, reveals fitness constraints, and should scale to simultaneous evolution of multiple synthetic chromosomes.</jats:p