Experimental investigation on fracturing effects in hydraulic sand fracturing with acoustic emission and 3d laser scanning.

Due to the extremely low permeability of shale reservoirs, large-scale reservoir fracturing is required. Hydraulic fracturing is one of the most important technologies in shale gas exploration and development. In this paper, the acoustic emission energy and the number of location and fracture surface morphology of specimens before and after fracture are studied through hydraulic sand fracturing test.

GEO data mining identifies potential immune-related genes in hypertrophic scar and verities in a rabbit model.

Objective: Hypertrophic scar (HTS), the secondary major abnormal tissue after wound healing, is the most frequent and severe type of skin scar. Dysregulated immune response plays an important role in HTS formation. In this study, we identified the potential immune-related genes in HTS and explored their potential therapeutic significance.

Building Block Extractor: An MS/MS Data Mining Tool for Targeted Discovery of Natural Products with Specified Features.

The utilization of a building-block-based molecular network is an efficient approach to investigate the unknown chemical space of natural products. However, structure-based automated MS/MS data mining remains challenging. This study introduces building block extractor, a user-friendly MS/MS data mining program that automatically extracts user-defined specified features.

Accumulation, potential risk and source identification of toxic metal elements in soil: a case study of a coal-fired power plant in Western China.

Coal-fired power plants (CPP) usually release massive numerous amounts of potentially toxic metal(loid)s (PTMs) into nearby ecosystems. There have been relatively few studies targeted on the ecological influences of PTMs related to the CPP in arid area. In this work, the distribution pattern, source apportionment and environmental risks of As, Cd, Cr, Hg, Pb and a couple of seldom monitored PTMs (Se, Zn, Co, Cu, Fe, Mn and Ni) in the soils near a coal electricity integration base were investigated in Hami, a city in northwestern China.

The common neurological basis and targeted therapeutic approaches for chronic pain and opioid addiction.

Chronic pain and drug addiction seriously threaten human health and generate a large loss of labor. Most highly addictive drugs are derived from opioids, which have severe side effects and are difficult to quit completely. On the other hand, opioid analgesics are widely used in detoxification for opioid addiction.

Discovery of deaminase functions by structure-based protein clustering.

The elucidation of protein function and its exploitation in bioengineering have greatly advanced the life sciences. Protein mining efforts generally rely on amino acid sequences rather than protein structures. We describe here the use of AlphaFold2 to predict and subsequently cluster an entire protein family based on predicted structure similarities.

Efficacy and Safety of Butylphthalide in Patients With Acute Ischemic Stroke: A Randomized Clinical Trial.

Importance: DL-3-n-butylphthalide (NBP) is a drug for treating acute ischemic stroke and may play a neuroprotective role by acting on multiple active targets. The efficacy of NBP in patients with acute ischemic stroke receiving reperfusion therapy remains unknown.

Objective: To assess the efficacy and safety of NBP in patients with acute ischemic stroke receiving reperfusion therapy of intravenous thrombolysis and/or endovascular treatment.

Progressive Hydrophilic Processes of the Pyrite Surface in High-Alkaline Lime Systems.

Pyrite, as a disturbing gangue mineral in the beneficiation of valuable sulfide minerals and coal resources, is usually required to be depressed for floating in flotation practice. Specifically, the depression of pyrite is achieved by causing its surface to be hydrophilic with the assistance of depressants, normally with inexpensive lime used. Accordingly, the progressive hydrophilic processes of the pyrite surface in high-alkaline lime systems were studied in detail using density functional theory (DFT) calculations in this work.

Heavy metal pollution in Mongolian-Manchurian grassland soil and effect of long-range dust transport by wind.

Grasslands provide a range of valuable ecosystem services, but they are also particularly fragile ecosystems easily threatened by human activities, such as long-term open-pit mining and related industrial activities. In grassland area, dust containing heavy metal(loid)s generated by mines may further migrate to remote places, but few studies have focused on the long-range transport of contaminants as an important pollution source. In the present study, one of the largest and most intact grassland ecosystems, the Mongolian-Manchurian steppe, was selected to investigate its pollution status and track potential sources.

A Survey on Learning Objects' Relationship for Image Captioning.

Image captioning is a challenging modality transformation task in computer vision and natural language processing, aiming to understand the image content and describe it with a natural language. Recently, the relationship information between objects in the image has been investigated to be of importance in generating a more vivid and readable sentence. Many types of research have been done in relationship mining and learning for leveraging into the caption models.

Reconstruction of all-weather land surface temperature based on a combined physical and data-driven model.

At present, the remote sensing (RS) thermal infrared (TIR) images that are commonly used to obtain land surface temperature (LST) are contaminated by clouds and thus cannot obtain spatiotemporal integrity of LST. To solve this problem, this study combined a physical model with strong interpretability with a data-driven model with high data adaptability. First, the physical model (Weather Research and Forecast (WRF) model) was used to generate LST source data.

Benign-malignant classification of pulmonary nodules by low-dose spiral computerized tomography and clinical data with machine learning in opportunistic screening.

Background: Many people were found with pulmonary nodules during physical examinations. It is of great practical significance to discriminate benign and malignant nodules by using data mining technology.

Methods: The subjects' demographic data, baseline examination results, and annual follow-up low-dose spiral computerized tomography (LDCT) results were recorded.

Research progress and visualization of underground coal fire detection methods.

Underground coal fires are a widespread disaster prevailing in major coal-producing countries globally, posing serious threats to the ecological environment and restricting the safe exploitation of coal mines. The accuracy of underground coal fire detection directly affects the effectiveness of fire control engineering. In this study, we searched 426 articles from the Web of Science database within 2002-2022 as the data foundation and visualized the research contents of the underground coal fire field using VOSviewer and CiteSpace.

Prevalence of common chronic disease and multimorbidity patterns in Guangdong province with three typical cultures: analysis of data from the Diverse Life-Course Cohort study.

Background: Variations in the prevalence and pattern of multimorbidity might be attributable to lifestyle and environmental factors. This study was performed to determine the prevalence of common chronic diseases and to reveal multimorbidity patterns among adults in Guangdong province with Chaoshan, Hakka, and island cultures.

Methods: We used data collected at the baseline survey (April-May 2021) of the Diverse Life-Course Cohort study and included 5,655 participants aged ≥20 years.

A New Cellular Interactome of SARS-CoV-2 Nucleocapsid Protein and Its Biological Implications.

There is still much to uncover regarding the molecular details of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. As the most abundant protein, coronavirus nucleocapsid (N) protein encapsidates viral RNAs, serving as the structural component of ribonucleoprotein and virion, and participates in transcription, replication, and host regulations. Virus-host interaction might give clues to better understand how the virus affects or is affected by its host during infection and identify promising therapeutic candidates.

Identification of glutamyl-prolyl-tRNA synthetase as a new therapeutic target in hepatocellular carcinoma via a novel bioinformatic approach.

Background: Hepatocellular carcinoma (HCC) has a high incidence, and current treatments are ineffective. We aimed to explore potential diagnostic and prognostic biomarkers for HCC by conducting bioinformatics analysis on genomic and proteomic data.

Methods: Genome and proteome data were downloaded from The Cancer Genome Atlas (TCGA) and ProteomeXchange databases, respectively.

Online Omics Platform Expedites Industrial Application of TD1.0.

Multi-omic data mining has the potential to revolutionize synthetic biology especially in non-model organisms that have not been extensively studied. However, tangible engineering direction from computational analysis remains elusive due to the interpretability of large datasets and the difficulty in analysis for non-experts. New omics data are generated faster than our ability to use and analyse results effectively, resulting in strain development that proceeds through classic methods of trial-and-error without insight into complex cell dynamics.

The Influence of the Strain Rate and Prestatic Stress on the Dynamic Mechanical Properties of Sandstone-A Case Study from China.

A series of conventional dynamic uniaxial compressive (CDUC) tests and coupled static dynamic loading (CSDL) tests were performed using a split Hopkinson compression bar (SHPB) system to explore the variable dynamic mechanical behavior and fracture characteristics of medium siltstone at a microscopic scale in the laboratory. In the CDUC tests, the dynamic uniaxial strength of the medium sandstone is rate-dependent in the range of 17.5 to 96.

Rare earth elements in soil around coal mining and utilization: Contamination, characteristics, and effect of soil physicochemical properties.

REEs are emerging contaminants, and soils nearby coal and coal ash with high REEs composition are vulnerable to REEs contamination. Besides, coal industry can alter surrounding soil characteristics. However, there is information paucity about REEs contamination and geochemical behaviors along with soil characteristics around coal industrial areas, which are essential for understanding their toxicity and mobilization.

Chinese Clinical Named Entity Recognition From Electronic Medical Records Based on Multisemantic Features by Using Robustly Optimized Bidirectional Encoder Representation From Transformers Pretraining Approach Whole Word Masking and Convolutional Neural Networks: Model Development and Validation.

Background: Clinical electronic medical records (EMRs) contain important information on patients' anatomy, symptoms, examinations, diagnoses, and medications. Large-scale mining of rich medical information from EMRs will provide notable reference value for medical research. With the complexity of Chinese grammar and blurred boundaries of Chinese words, Chinese clinical named entity recognition (CNER) remains a notable challenge.

A Comprehensive Review on the Excitation-Emission Matrix Fluorescence Spectroscopic Characterization of Petroleum-Containing Substances: Principles, Methods, and Applications.

Petroleum-containing substance (PCS) is a general term used for petroleum and its derivatives. A comprehensive characterization of PCSs is crucial for resource exploitation, economic development and environmental protection. Fluorescence spectroscopy, especially excitation-emission matrix fluorescence (EEMF) spectroscopy, has been proved to be a powerful tool to characterize PCSs since its remarkable sensitivity, selectivity, simplicity and high efficiency.

Detection and analysis of graduate students' academic emotions in the online academic forum based on text mining with a deep learning approach.

Purpose: The possibility of mental illness caused by the academic emotions and academic pressure of graduate students has received widespread attention. Discovering hidden academic emotions by mining graduate students' speeches in social networks has strong practical significance for the mental state discovery of graduate students.

Design/methodology/approach: Through data collected from online academic forum, a text based BiGRU-Attention model was conducted to achieve academic emotion recognition and classification, and a keyword statistics and topic analysis was performed for topic discussion among graduate posts.

Resequencing of Rosa rugosa accessions revealed the history of population dynamics, breed origin, and domestication pathways.

Background: Rosa rugosa is a shrub that originated in China and has economic and ecological value. However, during the development of R. rugosa, the genetic background was chaotic, and the genetic structure among different wild populations was unclear, as well as wild and cultivated accessions.

Artificial intelligence-enabled microbiome-based diagnosis models for a broad spectrum of cancer types.

Microbiome-based diagnosis of cancer is an increasingly important supplement for the genomics approach in cancer diagnosis, yet current models for microbiome-based diagnosis of cancer face difficulties in generality: not only diagnosis models could not be adapted from one cancer to another, but models built based on microbes from tissues could not be adapted for diagnosis based on microbes from blood. Therefore, a microbiome-based model suitable for a broad spectrum of cancer types is urgently needed. Here we have introduced DeepMicroCancer, a diagnosis model using artificial intelligence techniques for a broad spectrum of cancer types.