FAIR: A Causal Framework for Accurately Inferring Judgments Reversals

Artificial intelligence researchers have made significant advances in legal intelligence in recent years. However, the existing studies have not focused on the important value embedded in judgments reversals, which limits the improvement of the efficiency of legal intelligence. In this paper, we propose a causal Framework for Accurately Inferring case Reversals (FAIR), which models the problem of judgments reversals based on real Chinese judgments. We mine the causes of judgments reversals by causal inference methods and inject the obtained causal relationships into the neural network as a priori knowledge. And then, our framework is validated on a challenging dataset as a legal judgment prediction task. The experimental results show that our framework can tap the most critical factors in judgments reversal, and the obtained causal relationships can effectively improve the neural network's performance. In addition, we discuss the generalization ability of large language models for legal intelligence tasks using ChatGPT as an example. Our experiment has found that the generalization ability of large language models still has defects, and mining causal relationships can effectively improve the accuracy and explain ability of model predictions

Identification and validation of a muscle failure index to predict prognosis and immunotherapy in lung adenocarcinoma through integrated analysis of bulk and single-cell RNA sequencing data

BackgroundIt was previously reported that the production of exerkines is positively associated with the beneficial effects of exercise in lung adenocarcinoma (LUAD) patients. This study proposes a novel scoring system based on muscle failure-related genes, to assist in clinical decision making.MethodsA comprehensive analysis of bulk and single cell RNA sequencing (scRNA-seq) of early, advanced and brain metastatic LUAD tissues and normal lung tissues was performed to identify muscle failure-related genes in LUAD and to determine the distribution of muscle failure-related genes in different cell populations. A novel scoring system, named MFI (Muscle failure index), was developed and validated. The differences in biological functions, immune infiltration, genomic alterations, and clinical significance of different subtypes were also investigated.ResultsFirst, we conducted single cell analysis on the dataset GSE131907 and identified eight cell subpopulations. We found that four muscle failure-related genes (BDNF, FNDC5, IL15, MSTN) were significantly increased in tumor cells. In addition, IL15 was widely distributed in the immune cell population. And we have validated it in our own clinical cohort. Then we created the MFI model based on 10 muscle failure-related genes using the LASSO algorithm, and MFI remained an independent prognostic factor of OS in both the training and validation cohorts. Moreover, we generated MFI in the single-cell dataset, in which cells with high MFI received and sent more signals compared to those with low MFI. Biological function analysis of both subtypes revealed stronger anti-tumor immune activity in the low MFI group, while tumor cells with high MFI had stronger metabolic and proliferative activity. Finally, we systematically assessed the immune cell activity and immunotherapy responses in LUAD patients, finding that the low MFI group was more sensitive to immunotherapy.ConclusionOverall, our study can improve the understanding of the role of muscle failure-related genes in tumorigenesis and we constructed a reliable MFI model for predicting prognosis and guiding future clinical decision making

Integration of Metabolite Profiling and Transcriptome Analysis Reveals Genes Related to Volatile Terpenoid Metabolism in Finger Citron (C. medica var. sarcodactylis)

Finger citron (Citrus medica var. sarcodactylis) is a popular ornamental tree and an important source of essential oils rich in terpenoids, but the mechanisms behind volatile formation are poorly understood. We investigated gene expression changes combined with volatile profiling of ten samples from three developing organs: flower, leaf, and fruit. A total of 62 volatiles were identified with limonene and γ-terpinene being the most abundant ones. Six volatiles were identified using partial least squares discriminant analysis (PLS-DA) that could be used as markers for distinguishing finger citron from other citrus species. RNA-Seq revealed 1,611,966,118 high quality clean reads that were assembled into 32,579 unigenes. From these a total of 58 terpene synthase (TPS) gene family members were identified and the spatial and temporal distribution of their transcripts was measured in developing organs. Transcript levels of transcription factor genes AP2/ERF (251), bHLH (169), bZIP (76), MYB (155), NAC (184), and WRKY (66) during finger citron development were also analyzed. From extracted subnetworks of three modules constructed by weighted gene co-expression network analysis (WGCNA), thirteen TPS genes and fifteen transcription factors were suggested to be related to volatile terpenoid formation. These results provide a framework for future investigations into the identification and regulatory network of terpenoids in finger citron

Cardiovascular risk burden, dementia risk and brain structural imaging markers:a study from UK Biobank

Background:Cardiovascular risk burden is associated with dementia risk and neurodegeneration-related brain structure, while the role of genetics and incident cardiovascular disease (CVD) remains unclear. Aims:To examine the association of overall cardiovascular risk burden with the risk of major dementia subtypes and volumes of related brain regions in a large sample, and to explore the role of genetics and CVD onset. Methods:A prospective study among 354 654 participants free of CVD and dementia (2006–2010, mean age 56.4 years) was conducted within the UK Biobank, with brain magnetic resonance imaging (MRI) measurement available for 15 104 participants since 2014. CVD risk burden was evaluated by the Framingham General Cardiovascular Risk Score (FGCRS). Dementia diagnosis was ascertained from inpatient and death register data. Results:Over a median 12.0-year follow-up, 3998 all-cause dementia cases were identified. Higher FGCRS was associated with increased all-cause dementia risk after adjusting for demographic, major lifestyle, clinical factors and the polygenic risk score (PRS) of Alzheimer’s disease. Comparing the high versus low tertile of FGCRS, the odds ratios (ORs) and 95% confidence intervals (CIs) were 1.26 (1.12 to 1.41) for all-cause dementia, 1.67 (1.33 to 2.09) for Alzheimer’s disease and 1.53 (1.07 to 2.16) for vascular dementia (all ptrend&lt;0.05). Incident stroke and coronary heart disease accounted for 14% (95% CI: 9% to 21%) of the association between FGCRS and all-cause dementia. Interactions were not detected for FGCRS and PRS on the risk of any dementia subtype. We observed an 83% (95% CI: 47% to 128%) higher all-cause dementia risk comparing the high–high versus low–low FGCRS–PRS category. For brain volumes, higher FGCRS was associated with greater log-transformed white matter hyperintensities, smaller cortical volume and smaller grey matter volume. Conclusions:Our findings suggest that the positive association of cardiovascular risk burden with dementia risk also applies to major dementia subtypes. The association of cardiovascular risk burden with all-cause dementia is largely independent of CVD onset and genetic predisposition to dementia.</p

Cardiovascular risk burden, dementia risk and brain structural imaging markers:a study from UK Biobank

Background:Cardiovascular risk burden is associated with dementia risk and neurodegeneration-related brain structure, while the role of genetics and incident cardiovascular disease (CVD) remains unclear. Aims:To examine the association of overall cardiovascular risk burden with the risk of major dementia subtypes and volumes of related brain regions in a large sample, and to explore the role of genetics and CVD onset. Methods:A prospective study among 354 654 participants free of CVD and dementia (2006–2010, mean age 56.4 years) was conducted within the UK Biobank, with brain magnetic resonance imaging (MRI) measurement available for 15 104 participants since 2014. CVD risk burden was evaluated by the Framingham General Cardiovascular Risk Score (FGCRS). Dementia diagnosis was ascertained from inpatient and death register data. Results:Over a median 12.0-year follow-up, 3998 all-cause dementia cases were identified. Higher FGCRS was associated with increased all-cause dementia risk after adjusting for demographic, major lifestyle, clinical factors and the polygenic risk score (PRS) of Alzheimer’s disease. Comparing the high versus low tertile of FGCRS, the odds ratios (ORs) and 95% confidence intervals (CIs) were 1.26 (1.12 to 1.41) for all-cause dementia, 1.67 (1.33 to 2.09) for Alzheimer’s disease and 1.53 (1.07 to 2.16) for vascular dementia (all ptrend&lt;0.05). Incident stroke and coronary heart disease accounted for 14% (95% CI: 9% to 21%) of the association between FGCRS and all-cause dementia. Interactions were not detected for FGCRS and PRS on the risk of any dementia subtype. We observed an 83% (95% CI: 47% to 128%) higher all-cause dementia risk comparing the high–high versus low–low FGCRS–PRS category. For brain volumes, higher FGCRS was associated with greater log-transformed white matter hyperintensities, smaller cortical volume and smaller grey matter volume. Conclusions:Our findings suggest that the positive association of cardiovascular risk burden with dementia risk also applies to major dementia subtypes. The association of cardiovascular risk burden with all-cause dementia is largely independent of CVD onset and genetic predisposition to dementia.</p

miRNA profiling in intrauterine exosomes of pregnant cattle on day 7

Intrauterine exosomes have been identified to be involved in the embryo development and implantation. The aim of this study was to explore the role of miRNAs in intrauterine exosomes in bovine pregnancy. Intrauterine exosomes were collected from uterine flushing fluids of three donor and three recipient Xianan cows 7 days after fertilization. Intrauterine exosomes miRNAs were extracted and the exosomal miRNAs expression levels were analyzed. Sixty miRNAs differed significantly in their amounts between donors and recipients (p-value 1). Twenty-two miRNAs were upregulated and 38 downregulated in the group of donor cows. The bta-miR-184 was the most significant (PBenjamini-Hochberg < 0.001). A total of 9,775 target genes were predicted using the 60 miRNAs. GO and KEGG analysis showed that the target genes were enriched in several biological processes or pathways associated with embryo implantation and endometrial development, such as cell adhesion, cell junction, focal adhesion, and Rap1 signaling pathway. Our findings suggest that, in cattle early pregnancy stage, these differently expressed miRNAs in intrauterine exosomes involved in embryo implantation and endometrial development, which may exert a significant effect and influence the uterine microenvironment for embryo implantation. These results could provide reference for screening and exploring the intrauterine exosomal miRNA affecting embryo implantation

Comparison of proteomic landscape of extracellular vesicles in pleural effusions isolated by three strategies

Extracellular vesicles (EVs) derived from pleural effusion (PE) is emerging as disease biomarkers. However, the methods for isolation of EVs from PE (pEVs) were rarely studied. In our study, three methods for isolating pEVs of lung cancer patients were compared, including ultracentrifugation (UC), a combination of UC and size exclusion chromatography (UC-SEC) and a combination of UC and density gradient ultracentrifugation (UC-DGU). The subpopulation of pEVs was identified by nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), Western blotting (WB) and nano-flow cytometry (nFCM). Additionally, the proteomic landscape of pEVs was analyzed by Label-free proteomics. The results showed that, compared with UC and UC-DGU, the UC-SEC method separated pEVs with the highest purity. In the proteomic analysis, on average, 1595 proteins were identified in the pEVs isolated by UC-SEC, much more than pEVs isolated by UC (1222) or UC-DGU (807). Furthermore, approximately 90% of identified proteins in each method were found in the EVs public database ExoCarta. Consistent with this, GO annotation indicated that the core proteins identified in each method were mainly enriched in “extracellular exosome.” Many of the top 100 proteins with high expression in each method were suggested as protein markers to validate the presence of EVs in the MISEV2018 guidelines. In addition, combined with lung tissue-specific proteins and vesicular membrane proteins, we screened out and validated several novel protein markers (CD11C, HLA DPA1 and HLA DRB1), which were enriched in pEVs rather than in plasma EVs. In conclusion, our study shows that the method of UC-SEC could significantly improve the purity of EVs and the performance of mass spectrometry-based proteomic profiling in analyzing pEVs. The exosomal proteins CD11C, HLA DPA1 and HLA DRB1 may act as potential markers of pEVs. The proteomic analysis of pEVs provides important information and new ideas for studying diseases complicated with PE

Transient Receptor Potential V Channels Are Essential for Glucose Sensing by Aldolase and AMPK

Fructose-1,6-bisphosphate (FBP) aldolase links sensing of declining glucose availability to AMPK activation via the lysosomal pathway. However, how aldolase transmits lack of occupancy by FBP to AMPK activation remains unclear. Here, we show that FBP-unoccupied aldolase interacts with and inhibits endoplasmic reticulum (ER)-localized transient receptor potential channel subfamily V, inhibiting calcium release in low glucose. The decrease of calcium at contact sites between ER and lysosome renders the inhibited TRPV accessible to bind the lysosomal v-ATPase that then recruits AXIN:LKB1 to activate AMPK independently of AMP. Genetic depletion of TRPVs blocks glucose starvation-induced AMPK activation in cells and liver of mice, and in nematodes, indicative of physical requirement of TRPVs. Pharmacological inhibition of TRPVs activates AMPK and elevates NAD(+) levels in aged muscles, rejuvenating the animals' running capacity. Our study elucidates that TRPVs relay the FBP-free status of aldolase to the reconfiguration of v-ATPase, leading to AMPK activation in low glucose

Contribution of Microbe-Mediated Processes in Nitrogen Cycle to Attain Environmental Equilibrium

Nitrogen (N), the most important element, is required by all living organisms for the synthesis of complex organic molecules like amino acids, proteins, lipids etc. Nitrogen cycle is considered to be the most complex yet arguably important cycle next to carbon cycle. Nitrogen cycle includes oxic and anoxic reactions like organic N mineralization, ammonia assimilation, nitrification denitrification, anaerobic ammonium oxidation (anammox), dissimilatory nitrate reduction to ammonium (DNRA), comammox, codenitrification etc. Nitrogen cycling is one of the most crucial processes required for the recycling of essential chemical requirements on the planet. Soil microorganisms not only improve N-cycle balance but also pave the way for sustainable agricultural practices, leading to improved soil properties and crop productivity as most plants are opportunistic in the uptake of soluble or available forms of N from soil. Microbial N transformations are influenced by plants to improve their nutrition and vice versa. Diverse microorganisms, versatile metabolic activities, and varied biotic and abiotic conditions may result in the shift in the equilibrium state of different N-cycling processes. This chapter is an overview of the mechanisms and genes involved in the diverse microorganisms associated in the operation of nitrogen cycle and the roles of such microorganisms in different agroecosystems