Related Datasets Research Articles

Integrating Microseismic Monitoring for Predicting Water Inrush Hazards in Coal Mines

The essence of roof water inrush in coal mines fundamentally stems from the development of water-bearing fracture zones, facilitating the intrusion of overlying aquifers and thereby leading to water hazard incidents. Monitoring rock-fracturing conditions through the analysis of microseismic data can, to a certain extent, facilitate the prediction and early warning of water hazards. The water inflow volume stands as the most characteristic type of data in mine water inrush accidents. Hence, we investigated the feasibility of predicting water inrush events through anomalies in microseismic data from the perspective of water inflow volume variations. The data collected from the microseismic monitoring system at the 208 working face were utilized to compute localization information and source parameters. Based on the hydrogeological conditions of the working face, the energy screening range and its calculation grid characteristics were determined, followed by the generation of kernel density cloud maps at different depths. By observing these microseismic kernel density cloud maps, probabilities of roof water-conducting channel formation and potential locations were inferred. Subsequently, based on the positions of these roof water-conducting channels on the planar domain, the extension depth and expansion direction of the water-conducting channels were determined. Utilizing microseismic monitoring data, a quantitative assessment of water inrush risk was conducted, thereby establishing a linkage between microseismic data and water (inrush) data, which are two indirectly related datasets. The height of microseismic events was directly proportional to the trend of water inflow in the working face. In contrast, the occurrence of water inflow events and microseismic events exhibited a specific lag effect, with microseismic events occurring prior to water inrush events. Abnormalities in microseismic monitoring data partially reflect changes in water-conducting channel patterns. When connected with coal seam damage zones, water inrush hazards may occur. Therefore, abnormalities in microseismic monitoring data can be regarded as one of the precursor signals indicating potential floor water inrushes in coal seams.

Automatic multi-LiDAR calibration and refinement method

Sensor-based environmental perception is a crucial component of autonomous driving systems. To perceive the surrounding environment better, an intelligent system would utilize multiple LiDARs (3D Light Detection and Ranging). The accuracy of the perception largely depends on the quality of the sensor calibration. This research aims to develop a robust, fast, automatic, and accurate calibration strategy for multiple LiDAR systems. Our proposed multi-LiDAR calibration method consists of two stages: rough and refinement calibration. In the first stage, sensors are roughly calibrated from an arbitrary initial position using a deep neural network that does not rely on prior information or constraints on the initial sensor pose. In the second stage, we propose the octree-based refinement, an optimization method that considers sensor noise and prioritization. Our strategy is robust, fast, and not restricted to any environment. Additionally, we collected two datasets consisting of both real-world and simulated scenarios. Our experimental results from both datasets demonstrate the reliability and accuracy of our method. All the related datasets and codes are open-sourced on the GitHub website at https://github.com/OpenCalib/LiDAR2LiDAR.

Integrated analysis of single-cell and bulk RNA sequencing data reveals prognostic characteristics of lysosome-dependent cell death-related genes in osteosarcoma.

Tumor cells exhibit a heightened susceptibility to lysosomal-dependent cell death (LCD) compared to normal cells. However, the role of LCD-related genes (LCD-RGs) in Osteosarcoma (OS) remains unelucidated. This study aimed to elucidate the role of LCD-RGs and their mechanisms in OS using several existing OS related datasets, including TCGA-OS, GSE16088, GSE14359, GSE21257 and GSE162454. Analysis identified a total of 8,629 DEGs1, 2,777 DEGs2 and 21 intersection genes. Importantly, two biomarkers (ATP6V0D1 and HDAC6) linked to OS prognosis were identified to establish the prognostic model. Significant differences in risk scores for OS survival were observed between high and low-risk cohorts. Additionally, scores of dendritic cells (DC), immature DCs and γδT cells differed significantly between the two risk cohorts. Cell annotations from GSE162454 encompassed eight types (myeloid cells, osteoblastic OS cells and plasma cells). ATP6V0D1 was found to be significantly over-expressed in myeloid cells and osteoclasts, while HDAC6 was under-expressed across all cell types. Moreover, single-cell trajectory mapping revealed that myeloid cells and osteoclasts differentiated first, underscoring their pivotal role in patients with OS. Furthermore, ATP6V0D1 expression progressively decreased with time. A new prognostic model for OS, associated with LCD-RGs, was developed and validated, offering a fresh perspective for exploring the association between LCD and OS.

Automatic speech recognition using advanced deep learning approaches: A survey

Recent advancements in deep learning (DL) have posed a significant challenge for automatic speech recognition (ASR). ASR relies on extensive training datasets, including confidential ones, and demands substantial computational and storage resources. Enabling adaptive systems improves ASR performance in dynamic environments. DL techniques assume training and testing data originate from the same domain, which is not always true. Advanced DL techniques like deep transfer learning (DTL), federated learning (FL), and deep reinforcement learning (DRL) address these issues. DTL allows high-performance models using small yet related datasets, FL enables training on confidential data without dataset possession, and DRL optimizes decision-making in dynamic environments, reducing computation costs.This survey offers a comprehensive review of DTL, FL, and DRL-based ASR frameworks, aiming to provide insights into the latest developments and aid researchers and professionals in understanding the current challenges. Additionally, Transformers, which are advanced DL techniques heavily used in proposed ASR frameworks, are considered in this survey for their ability to capture extensive dependencies in the input ASR sequence. The paper starts by presenting the background of DTL, FL, DRL, and Transformers and then adopts a well-designed taxonomy to outline the state-of-the-art (SOTA) approaches. Subsequently, a critical analysis is conducted to identify the strengths and weaknesses of each framework. Additionally, a comparative study is presented to highlight the existing challenges, paving the way for future research opportunities.

What can rhetoric bring us? Incorporating rhetorical structure into neural related work generation

The ever-increasing volume of research literature poses challenges for researchers in keeping up with related works in their fields. Automating the generation of related work section holds promise for saving time and effort. However, current models often fall short of producing coherent and logically correct related work with multiple sentences, a phenomenon we refer to as rhetorical structure chaos. Rhetorical structure describes how adjacent spans of units are connected to each other, and logically correct rhetorical structure is essential for a well-structured related work. Hence, to tackle the rhetorical structure chaos issue, this paper explicitly incorporates rhetorical structure information into related work generation. Firstly, we conduct the first rhetorical structure analysis for related work section, which provides insights into understanding the organization and arrangement of contents within related work. Then, based on two preliminary studies on rhetorical structure, we present a novel related work generation model called RSGen, which incorporates rhetorical structure at both the encoding and decoding stages. The encoding stage is facilitated with a rhetorical structure-based graph encoder, while the decoding process is guided by a rhetorical plan — ordered sequence of rhetorical functions of the related work. We conduct extensive experiments on three related work generation datasets to evaluate the performance of our model. The results show that our approach achieves state-of-the-art performance on ROUGE metrics. An ablation study and more analyses further highlight the remarkable efficacy of introducing rhetorical structure into both the encoding and decoding stages.

Volume Determination Challenges in Waste Sorting Facilities: Observations and Strategies.

In this case study on volume determination in waste sorting facilities, we evaluate the effectiveness of ultrasonic sensors and address waste-material-specific challenges. Although ultrasonic sensors offer a cost-effective automation solution, their accuracy is affected by irregular waste shapes, varied compositions, and environmental factors. Notable inconsistencies in volume measurements between storage bunkers and conveyor belts underscore the need for a comprehensive approach to standardize bale production. With prediction reliability being constrained by limited datasets, undocumented modifications to machine settings, and sensor failures, this task renders a challenging application area for machine learning. We explore related research and present dataset analyses from three distinct waste sorting facilities in Europe, addressing issues such as sensor usability, data quality, and material specifics. Our analysis suggests promising strategies and future directions for enhancing waste volume measurement accuracy, ultimately aiming to advance sustainable waste management.

Abstract 7432: Improving FAIRness of computational approaches for cancer: The computational resources for cancer research portal

Abstract The Computational Resources for Cancer Research website is a public portal supported by NCI that democratizes access to available computational resources for the cancer research community. In supporting an emerging FAIR (Findable, Accessible, Interoperable, Reusable) data science ecosystem inclusive of software, models and data, the goals of the portal are to serve cancer data scientists by increasing understanding, access, and adoption of computational resources for cancer research, and to foster new collaborative research projects. Initially populated with resources developed in NCI supported collaborations with the US Department of Energy, the portal is serving as a unified reference and resource for computational resources developed broadly by the cancer community. These collaborative projects include CANDLE (the Cancer Distributed Learning Environment), IMPROVE (Innovative Methods and data for PRedictive Oncology model Validation and Evaluation), ADMIRRAL (AI Driven Multi-scale Investigation of Ras-Raf Activation Lifecycle), MOSSAIC (Model Outcomes using Surveillance Data and Scalable AI for Cancer) and ATOM (Accelerating Therapeutics for Opportunities in Medicine). The resources include software, models, and datasets as well as descriptive use cases and references to tutorials associated with many of the described resources. The portal also integrates content from the Predictive Oncology Model and Data Clearinghouse (modac.cancer.gov) which can be accessed through REST APIs. Ongoing development of the portal is driven by community insights and contributions including input from an interdisciplinary advisory committee, working groups, early users, and preliminary feedback received at AACR 2023. The portal will also support community contributions of computational resources. In addition, the portal highlights computational approaches in emerging and challenging areas, such as digital twins for cancer. The portal will enable the cancer research community to learn about new computational research methods through use cases and educational material, access cancer related software, datasets, and AI models, engage with peers and cross-disciplinary researchers at different career levels through an interactive forum, explore emerging areas, such as computational imaging, community-based standards for data management, predictive radiation oncology, and digital twin technologies, learn about in industry and community events, conferences, and workshops, and create new collaborative research projects. The presentation will deliver insights into key resources offered through the Computational Resources for Cancer Research Portal, how the portal will help the cancer research community identify and adopt computational resources of interest, and how researchers can join and participate in this growing community. Citation Format: Eric Stahlberg, Lynn Borkon, Ruth Frost, Sara Jones, Amar Khalsa, Lauren Lewis, Emily Greenspan, Sunita Menon. Improving FAIRness of computational approaches for cancer: The computational resources for cancer research portal [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7432.

Propensity score oversampling and matching for uplift modeling

In this paper, we propose a novel matching strategy to correct for confounding in uplift modeling. Our method, called propensity score oversampling and matching (ProSOM), extends the well-known propensity score matching (PSM) technique by addressing one of its main limitations: dealing with small datasets that face an imbalance in the distribution of the causal variable. Apart from this, we also face the additional complexity of dealing with class labels. The proposed method establishes a parallel between uplift modeling and class-imbalance classification as it extends existing oversampling techniques to create synthetic elements from the treatment group. We design an algorithm that performs classaware data oversampling in the treatment group, and then it matches samples from this group with the control group. This can be seen as a novel hybrid undersampling-oversampling solution for causal learning. Experiments on five datasets show the virtues of ProSOM in terms of predictive performance, achieving the best Qini coefficient for all five datasets in relation to PSM and other resampling solutions.

Joint extraction of entity relations from geological reports based on a novel relation graph convolutional network

Geological reports house a wealth of geological domain knowledge and expert experience knowledge, and the efficient extraction of geological entity relations from these texts holds great significance in constructing a geological knowledge graph. To address challenges such as overlapping entity relations, data imbalance, and the limited incorporation of contextual features in existing deep learning-based entity relation extraction methods, this paper proposes a relational graph convolutional network pointer model based on the pre-trained model of Robustly Optimize Bidirectional Encoder Representation from Transformers Pre-training Approach (RoBERTa). Specifically, our approach tackles the issue of overlapping entity relationships by identifying the primary entity and the subordinate entity associated with a specific relationship. Additionally, by incorporating a relationship graph convolutional network, we can capture multiple relationships among entities. Moreover, we leverage the Biaffine layer to accurately handle entity annotation boundaries, thereby enhancing the precision of entity classification. To address the issue of imbalanced data, we adopt the Focal Loss function during experiments. Furthermore, we construct a geological entity relation dataset comprising 24 types of relationships, which is generated by collecting seven regional geological reports and employing semi-automatic annotation techniques. Finally, the proposed algorithm was tested and comparison with several baseline models, the results demonstrate the superior performance of the proposed model in this paper.

CSDMS Data Components: data–model integration tools for Earth surface processes modeling

Abstract. Progress in better understanding and modeling Earth surface systems requires an ongoing integration of data and numerical models. Advances are currently hampered by technical barriers that inhibit finding, accessing, and executing modeling software with related datasets. We propose a design framework for Data Components, which are software packages that provide access to particular research datasets or types of data. Because they use a standard interface based on the Basic Model Interface (BMI), Data Components can function as plug-and-play components within modeling frameworks to facilitate seamless data–model integration. To illustrate the design and potential applications of Data Components and their advantages, we present several case studies in Earth surface processes analysis and modeling. The results demonstrate that the Data Component design provides a consistent and efficient way to access heterogeneous datasets from multiple sources and to seamlessly integrate them with various models. This design supports the creation of open data–model integration workflows that can be discovered, accessed, and reproduced through online data sharing platforms, which promotes data reuse and improves research transparency and reproducibility.

ADAM22 acts as a novel predictive biomarker for unfavorable prognosis and facilitates metastasis via PI3K/AKT signaling pathway in nasopharyngeal carcinoma

BackgroundNasopharyngeal carcinoma (NPC) is a type of epithelial malignancy known for its high likelihood of metastasizing to distant organs, which remains the primary obstacle in the treatment of NPC. The present study aimed to identify potential intervention target for NPC metastasis. MethodsThe differentially expressed genes (DEGs) were firstly analyzed and intersected across various NPC related datasets in the Gene Expression Omnibus database. Subsequently, various techniques including quantitative polymerase chain reaction (qPCR), western blotting, immunohistochemistry, migration and invasion assays, in conjunction with bioinformatics and prognostic modeling, were utilized to elucidate the role of candidate genes in NPC metastasis. ResultsWe discerned the gene a disintegrin and metalloprotease 22 (ADAM22) as a distinct and significant factor in the progression and metastasis of NPC through five datasets. The elevated expression of ADAM22 was observed in clinical tissue and plasma samples with advanced NPC, as well as in high metastatic cells. Furthermore, we highlighted its essential role in a prognostic model that demonstrated strong prediction performance for NPC. Notably, overexpression of ADAM22 was found to enhance the aggressiveness and epithelial-mesenchymal transition (EMT) of low metastatic NPC cells, whereas the downregulation of ADAM22 resulted in suppressed effect in high metastatic cells. Delving into the mechanism, ADAM22 activated the PI3K/Akt signaling pathway through the mediation of Rac Family Small GTPase 2 (RAC2), thereby facilitating EMT and metastasis in NPC. ConclusionsThe study provided pioneering insights that ADAM22 had the potential to act as an oncogene by promoting EMT and metastasis of NPC through the RAC2-mediated PI3K/Akt signaling pathway. Thus, ADAM22 could serve as a novel prognostic indicator in NPC.

The prediction of residual stress of welding process based on deep neural network

The welding process has been an efficient method for producing essential and complex manufacturing parts in various industrial design fields. The post-weld residual stress can have detrimental effects on welded components. Therefore, systematic studies of residual stress are essential for evaluating welding behaviors and mechanisms in welded structures. They can provide a valuable reference and optimization for addressing residual stress relief. Numerical finite element analyses based on thermal-mechanical models offer a comprehensive approach to simulate real welding, providing a reliable means to determine and quantify the distribution of residual stress based on welding parameters and material properties. Furthermore, the finite element analysis is capable of generating adequate and dependable datasets in relation to the classical experiment. However, the finite element simulation is not considered an efficient method for predicting the magnitude and distortion of residual stress due to its high computational cost. A deep learning framework with powerful automatic learning abilities could potentially be used as an alternative method to efficiently predict residual stress. The purpose of the current study is to propose an innovative modeling approach for accurately and effectively predicting residual stress. A deep network model with Convolutional Neural Network using Adam optimization is integrated with numerical finite element analyses of a single-pass beam weld in SUS304 stainless steel. Finite element analysis is used to generate extensive residual stress datasets, which are partly used to train the deep network model and partly used for model validation. The deep network model aligns closely with the finite element analysis results, with a root-mean-square error (RMSE) of less than 12, an absolute fraction of variation (R2) of greater than 0.95, a mean absolute error (MAE) of less than 6.8 and a mean absolute percentage error (MAPE) of less than 1.1. Furthermore, this study highlights the potential advantage of using a deep network model with strong memory capabilities to directly predict residual stress for identical structural components and welding processes.

Discovering Functional Dependencies through Hitting Set Enumeration

Functional dependencies (FDs) are among the most important integrity constraints in databases. They serve to normalize datasets and thus resolve redundancies, they contribute to query optimization, and they are frequently used to guide data cleaning efforts. Because the FDs of a particular dataset are usually unknown, automatic profiling algorithms are needed to discover them. These algorithms have made considerable advances in the past few years, but they still require a significant amount of time and memory to process datasets of practically relevant sizes. We present FDHits, a novel FD discovery algorithm that finds all valid, minimal FDs in a given relational dataset. FDHits is based on several discovery optimizations that include a hybrid validation approach, effective hitting set enumeration techniques, one-pass candidate validations, and parallelization. Our experiments show that FDHits, even without parallel execution, has a median speedup of 8.1 compared to state-of-the-art FD discovery algorithms while using significantly less memory. This allows the discovery of all FDs even on datasets that could not be processed by the current state-of-the-art.

Identification of key regulatory molecules in the early development stage of Alzheimer's disease.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, the incidence of which increases with age, and the pathological changes in the brain are irreversible. Recent studies have highlighted the essential role of long noncoding RNAs (lncRNAs) in AD by acting as competing endogenous RNAs (ceRNAs). Our aim was to construct lncRNA-associated ceRNA regulatory networks composed of potential biomarkers for the early stage of AD. AD related datasets come from AlzData and GEO databases. The R package 'Limma' identifies differentially expressed genes (DEGs), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases for functional enrichment analysis. Protein-protein interactions (PPIs) in DEGs were constructed in the STRING database, and Cytoscape software identified DEGs. Convergent functional genomics (CFG) analysis of differentially expressed hub genes (referred to as early-DEGs) in the brain before the development of AD pathology. The AlzData database analyses the expression levels of early-DEGs in different nerve cells. The lncRNA-miRNA-mRNA regulatory network was established according to the ceRNA hypothesis. We identified four lncRNAs (XIST, NEAT1, KCNQ1OT1 and HCG18) and four miRNAs (hsa-let-7c-5p, hsa-miR-107, hsa-miR-129-2-3p and hsa-miR-214-3p) were preliminarily identified as potential biomarkers for early AD, competitively regulating Atp6v0b, Atp6v1e1 Atp6v1f and Syt1. This study indicates that NEAT1, XIST, HCG18 and KCNQ1OT1 act as ceRNAs in competitive binding with miRNAs to regulate the expression of Atp6v0b, Atp6v1e1, Atp6v1f and Syt1 before the occurrence of pathological changes in AD.

A Novel Explainable AI Model for Medical Data Analysis

Abstract This research focuses on the development of an explainable artificial intelligence (Explainable AI or XAI) system aimed at the analysis of medical data. Medical imaging and related datasets present inherent complexities due to their high-dimensional nature and the intricate biological patterns they represent. These complexities necessitate sophisticated computational models to decode and interpret, often leading to the employment of deep neural networks. However, while these models have achieved remarkable accuracy, their ”black-box” nature raises legitimate concerns regarding their interpretability and reliability in the clinical context. To address this challenge, we can consider the following approaches: traditional statistical methods, a singular complex neural network, or an ensemble of simpler neural networks. Traditional statistical methods, though transparent, often lack the nuanced sensitivity required for the intricate patterns within medical images. On the other hand, a singular complex neural network, while powerful, can sometimes be too generalized, making specific interpretations challenging. Hence, our proposed strategy employs a hybrid system, combining multiple neural networks with distinct architectures, each tailored to address specific facets of the medical data interpretation challenges. The key components of this proposed technology include a module for anomaly detection within medical images, a module for categorizing detected anomalies into specific medical conditions and a module for generating user-friendly, clinically-relevant interpretations.

Identification of m6A-associated autophagy genes in non-alcoholic fatty liver.

Studies had shown that autophagy was closely related to nonalcoholic fat liver disease (NAFLD), while N6-methyladenosine (m6A) was involved in the regulation of autophagy. However, the mechanism of m6A related autophagy in NAFLD was unclear. The NAFLD related datasets were gained via the Gene Expression Omnibus (GEO) database, and we also extracted 232 autophagy-related genes (ARGs) and 37 m6A. First, differentially expressed ARGs (DE-ARGs) and differentially expressed m6A (DE-m6A) were screened out by differential expression analysis. DE-ARGs associated with m6A were sifted out by Pearson correlation analysis, and the m6A-ARGs relationship pairs were acquired. Then, autophagic genes in m6A-ARGs pairs were analyzed for machine learning algorithms to obtain feature genes. Further, we validated the relationship between feature genes and NAFLD through quantitative real-time polymerase chain reaction (qRT-PCR), Western blot (WB). Finally, the immuno-infiltration analysis was implement, and we also constructed the TF-mRNA and drug-gene networks. There were 19 DE-ARGs and four DE-m6A between NAFLD and normal samples. The three m6A genes and five AGRs formed the m6A-ARGs relationship pairs. Afterwards, genes obtained from machine learning algorithms were intersected to yield three feature genes (TBK1, RAB1A, and GOPC), which showed significant positive correlation with astrocytes, macrophages, smooth muscle, and showed significant negative correlation with epithelial cells, and endothelial cells. Besides, qRT-PCR and WB indicate that TBK1, RAB1A and GOPC significantly upregulated in NAFLD. Ultimately, we found that the TF-mRNA network included FOXP1-GOPC, ATF1-RAB1A and other relationship pairs, and eight therapeutic agents such as R-406 and adavosertib were predicted based on the TBK1. The study investigated the potential molecular mechanisms of m6A related autophagy feature genes (TBK1, RAB1A, and GOPC) in NAFLD through bioinformatic analyses and animal model validation. However, it is critical to note that these findings, although consequential, demonstrate correlations rather than cause-and-effect relationships. As such, more research is required to fully elucidate the underlying mechanisms and validate the clinical relevance of these feature genes.

Improving accuracy of age estimates for insect evidence-calibration of physiological age at emergence (k) using insect size but without "k versus size" model.

Postmortem interval may be estimated based on the age of insect evidence collected on a death scene. Reference data that are used in such estimation frequently comprise thermal summation constant (i.e. k), which is equal to the insect age upon completion of immature development expressed in accumulated degree-days or degree-hours (ADD or ADH). Essentially, k is a central point of an insect group and it may poorly represent insect evidence that is near the limits of variation for the group. Accordingly, it was postulated to calibrate k for particular insect evidence and insect size and sex were found to be useful for this purpose in some of the species. However, the calibration is only possible by using the model that correlates k with insect size. Since very few such models were published, this lack of data limits the calibration of k in forensic casework. In this article, we develop a formula that is useful for the calibration of k without the use of "k versus size" model (and related datasets). The formula uses k from the general thermal summation model for a species (with its standard error), size range for the species (retrieved from entomology literature), and size measurements for particular insect evidence. The calibration of k with the formula was validated using the Creophilus maxillosus (Coleoptera: Staphylinidae) and Necrodes littoralis (Coleoptera: Silphidae) datasets. It was particularly useful while analyzing unusually small and large insects, in case of which the formula reduced the inaccuracy of k from the general model on average by ~25 ADD in C. maxillosus and ~40 ADD in N. littoralis. We discuss the limitations and prospects of the calibration protocol that employs the formula.

Identification and validation of NAD+ metabolism-related biomarkers in patients with diabetic peripheral neuropathy.

The mechanism of Nicotinamide Adenine Dinucleotide (NAD+) metabolism-related genes (NMRGs) in diabetic peripheral neuropathy (DPN) is unclear. This study aimed to find new NMRGs biomarkers in DPN. DPN related datasets GSE95849 and GSE185011 were acquired from the Gene Expression Omnibus (GEO) database. 51 NMRGs were collected from a previous article. To explore NMRGs expression in DPN and control samples, differential expression analysis was completed in GSE95849 to obtain differentially expressed genes (DEGs), and the intersection of DEGs and NMRGs was regarded as DE-NMRGs. Next, a protein-protein interaction (PPI) network based on DE-NMRGs was constructed and biomarkers were screened by eight algorithms. Additionally, Gene Set Enrichment Analysis (GSEA) enrichment analysis was completed, biomarker-based column line graphs were constructed, lncRNA-miRNA-mRNA and competing endogenouse (ce) RNA networks were constructed, and drug prediction was completed. Finally, biomarkers expression validation was completed in GSE95849 and GSE185011. 5217 DEGs were obtained from GSE95849 and 21 overlapping genes of DEGs and NMRGs were DE-NMRGs. Functional enrichment analysis revealed that DE-NMRGs were associated with glycosyl compound metabolic process. The PPI network contained 93 protein-interaction pairs and 21 nodes, with strong interactions between NMNAT1 and NAMPT, NADK and NMNAT3, ENPP3 and NUDT12 as biomarkers based on 8 algorithms. Expression validation suggested that ENPP3 and NUDT12 were upregulated in DPN samples (P < 0.05). Moreover, an alignment diagram with good diagnostic efficacy based on ENPP3 and NUDT12 were identified was constructed. GSEA suggested that ENPP3 was enriched in Toll like receptor (TLR) pathway, NUDT12 was enriched in maturity onset diabetes of the young and insulin pathway. Furthermore, 18 potential miRNAs and 36 Transcription factors (TFs) were predicted and the miRNA-mRNA-TF networks were constructed, suggesting that ENPP3 might regulate hsa-miR-34a-5p by affecting MYNN. The ceRNA network suggested that XLOC_013024 might regulate hsa-let-7b-5p by affecting NUDT12. 15 drugs were predicted, with 8 drugs affecting NUDT12 such as resveratrol, and 13 drugs affecting ENPP3 such as troglitazone. ENPP3 and NUDT12 might play key roles in DPN, which provides reference for further research on DPN.

Navigating energy diplomacy in times of recovery and conflict: A study of cross-border energy trade dynamics

Recent geopolitical crises have underlined the importance of understanding when and how energy interdependence affects international politics. While scholars have extensively debated the link between international politics and international trade in general terms, systematic analyses gauging how energy interdependence shapes interstate relations have recently begun to emerge. To facilitate research on this topic, this study asks whether energy relations between states shape their foreign policy decisions. Using the recently released Global Energy Relations Dataset and United Nations General Assembly Voting Data, our study shows strong support for the hypothesis that energy interdependence increases dyadic foreign policy affinity. Moreover, out of four primary energy resources (i.e. coal, oil, natural gas, and electricity), trade in oil appears to be a strong motivator for trading states' foreign policy preferences to converge.

Deep learning-based low overlap point cloud registration for complex scenario: The review

Most studies on point cloud registration have established the problem in the case of ideal point cloud data. Although the state-of-the-art approaches have achieved amazing results on multiple public datasets, the issue of low overlap point cloud data invalidating state-of-the-art methods is acting as a latent challenge that has not been solved. Therefore, a profound analysis about why existing registration architectures break down in the low-overlap regime and how to select the appropriate strategies to improve the low overlap point cloud correspondence estimation is necessary and useful. Unfortunately, there are few survey works about low overlap cloud registration solving strategies and the corresponding datasets are very limited. This work briefly reviews mainstream deep learning-based point cloud registration and provides an in-depth analysis of the reasons why these architectures are not generalizable to scenarios with low overlapping areas. It is the first survey that mainly focuses on representative low overlap registration methods, their techniques, and related datasets for training/testing. It is worth noting that we also design and construct a large 3D dataset to eliminate the gap in Semantic-assisted point cloud registration with low overlap. Finally, challenges about low overlap point cloud registration and future directions in addressing these challenges are also pointed out. [dataset]