Construction and validation of immunogenic cell death-related molecular clusters, signature, and immune landscape in pancreatic cancer.

Pancreatic cancer (PC) is a malignancy of the gastrointestinal tract that is characterized by a poor prognosis. This study investigates the roles of immunogenic cell death (ICD) genes in the prognosis and progression of PC. Expression data for PC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, while ICD genes were sourced from published literature.

Oncogenic GALNT5 confers FOLFIRINOX resistance via activating the MYH9/ NOTCH/ DDR axis in pancreatic ductal adenocarcinoma.

Chemotherapy resistance has been a great challenge in pancreatic ductal adenocarcinoma(PDAC) treatments. Current first-line chemotherapy regimens for PDAC include gemcitabine-based regimens such as AG regimen (albumin paclitaxel and gemcitabine), fluorouracil-based regiments such as FOLFIRINOX regimen ((5-fluorouracil5-FU), oxaliplatin, Irinotecan) and platinum-based regimens for patients with BRCA mutations. large amounts of work have been done on exploring the mechanism underlying resistance of gemcitabine-based and platinum-based regimens, while little research has been achieved on the mechanism of FOLFIRINOX regimens resistance.

Super-resolution microscopy unveils the nanoscale organization and self-limiting clustering of CD47 in human erythrocytes.

The transmembrane protein CD47, an innate immune checkpoint protein, plays a pivotal role in preventing healthy erythrocytes from immune clearance. Our study utilized stochastic optical-reconstruction microscopy (STORM) and single-molecule analysis to investigate the distribution of CD47 on the human erythrocyte membrane. Contrary to previous findings in mouse erythrocytes, we discovered that CD47 exists in randomly distributed monomers rather than in clusters across the human erythrocyte membrane.

Predicting High-Strength Concrete's Compressive Strength: A Comparative Study of Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Response Surface Methodology.

Machine learning and response surface methods for predicting the compressive strength of high-strength concrete have not been adequately compared. Therefore, this research aimed to predict the compressive strength of high-strength concrete (HSC) using different methods. To achieve this purpose, neuro-fuzzy inference systems (ANFISs), artificial neural networks (ANNs), and response surface methodology (RSM) were used as ensemble methods.

Evaluation of a new scoring system for assessing nerve invasion in resected pancreatic cancer: A single-center retrospective analysis.

Nerve invasion (NI) is a characteristic feature of pancreatic cancer. Traditional dichotomous statements on the presence of NI are unreasonable because almost all cases exhibit NI when sufficient pathological sections are examined. The critical implications of NI in pancreatic cancer highlight the need for a more effective criterion.

Effects of Curing Conditions on Pore Structure of Ultra-High-Strength Shotcrete (UHSSC) Based on X-ray Computed Tomography.

Shotcrete is widely used in mine and civil engineering as supporting structure. A new type of ultra-high-strength shotcrete (UHSSC) with viscosity-enhancing agent was taken as the research object in this paper. A microstructure model of UHSSC under different curing conditions (standard curing, natural curing and film curing) was reconstructed using X-ray computed tomography (X-CT).

Revealing the link between gut microbiota and brain tumor risk: a new perspective from Mendelian randomization.

Background: Recent studies have shown that gut microbiota may be related to the occurrence of brain tumors, but direct evidence is lacking. This study used the Mendelian randomization study (MR) method to explore the potential causal link between gut microbiota and brain tumors.

Method: We analyzed the genome-wide association data between 211 gut microbiota taxa and brain tumors, using the largest existing gut microbiota Genome-Wide Association Studies meta-analysis data (n=13266) and combining it with brain tumor data in the IEU OpenGWAS database.

AGFG1 increases cholesterol biosynthesis by disrupting intracellular cholesterol homeostasis to promote PDAC progression.

Purpose: Cholesterol metabolism reprograming has been acknowledged as a novel feature of cancers. Pancreatic ductal adenocarcinoma (PDAC) is a cancer with a high demand of cholesterol for rapid growth. The underlying mechanism of how cholesterol metabolism homestasis are disturbed in PDAC is explored.

The lncRNA LINC01605 promotes the progression of pancreatic ductal adenocarcinoma by activating the mTOR signaling pathway.

Background: This study investigated the molecular mechanism of long intergenic non-protein coding RNA 1605 (LINC01605) in the process of tumor growth and liver metastasis of pancreatic ductal adenocarcinoma (PDAC).

Methods: LINC01605 was filtered out with specificity through TCGA datasets (related to DFS) and our RNA-sequencing data of PDAC tissue samples from Renji Hospital. The expression level and clinical relevance of LINC01605 were then verified in clinical cohorts and samples by immunohistochemical staining assay and survival analysis.

Systematic dissection of sequence features affecting binding specificity of a pioneer factor reveals binding synergy between FOXA1 and AP-1.

Despite the unique ability of pioneer factors (PFs) to target nucleosomal sites in closed chromatin, they only bind a small fraction of their genomic motifs. The underlying mechanism of this selectivity is not well understood. Here, we design a high-throughput assay called chromatin immunoprecipitation with integrated synthetic oligonucleotides (ChIP-ISO) to systematically dissect sequence features affecting the binding specificity of a classic PF, FOXA1, in human A549 cells.

[Mechanism of electroacupuncture for vascular dementia based on proteomics].

Objective: To explore the potential mechanism of electroacupuncture (EA) for vascular dementia (VD) using tandem mass tag (TMT) quantitative proteomics technology.

Methods: Among 80 male SPF SD rats, 78 rats which met the selection criteria through the Morris water maze test were selected and randomly divided into a sham surgery group (18 rats) and a surgery group (60 rats). VD model was established by four-vessel occlusion (4-VO) method in the surgery group, and 36 rats with successful modeling were randomly assigned to a model group (18 rats) and an EA group (18 rats).

Size-based expectation maximization for characterizing nucleosome positions and subtypes.

Genome-wide nucleosome profiles are predominantly characterized using MNase-seq, which involves extensive MNase digestion and size selection to enrich for mononucleosome-sized fragments. Most available MNase-seq analysis packages assume that nucleosomes uniformly protect 147 bp DNA fragments. However, some nucleosomes with atypical histone or chemical compositions protect shorter lengths of DNA.

Inversion of soil organic carbon content based on the two-point machine learning method.

Soil organic carbon (SOC) is vital for the global carbon cycle and environmentally sustainable development. Meanwhile, the fast, convenient remote sensing technology has become one of the notable means to monitor SOC content. Nowadays, limitations are found in the inversion of SOC content with high-precision and complex spatial relationships based on scarce ground sample points.

Cu(II)-Loaded Polydopamine-Coated Urchin-like Titanate Microspheres as a High-Performance IMAC Adsorbent for Hemoglobin Separation.

Immobilized metal ion affinity chromatography (IMAC) adsorbents generally have excellent affinity for histidine-rich proteins. However, the leaching of metal ions from the adsorbent usually affects its adsorption performance, which greatly affects the reusable performance of the adsorbent, resulting in many limitations in practical applications. Herein, a novel IMAC adsorbent, i.

Metabolism-triggered sensor array aided by machine learning for rapid identification of pathogens.

Chemical-nose strategy has achieved certain success in the discrimination and identification of pathogens. However, this strategy usually relies on non-specific interactions, which are prone to be significantly disturbed by the change of environment thus limiting its practical usefulness. Herein, we present a novel chemical-nose sensing approach leveraging the difference in the dynamic metabolic variation during peptidoglycan metabolism among different species for rapid pathogen discrimination.

Machine Learning-Assistant Colorimetric Sensor Arrays for Intelligent and Rapid Diagnosis of Urinary Tract Infection.

Urinary tract infections (UTIs), which can lead to pyelonephritis, urosepsis, and even death, are among the most prevalent infectious diseases worldwide, with a notable increase in treatment costs due to the emergence of drug-resistant pathogens. Current diagnostic strategies for UTIs, such as urine culture and flow cytometry, require time-consuming protocols and expensive equipment. We present here a machine learning-assisted colorimetric sensor array based on recognition of ligand-functionalized Fe single-atom nanozymes (SANs) for the identification of microorganisms at the order, genus, and species levels.

Collective Cell Radial Ordered Migration in Spatial Confinement.

Collective cells, a typical active matter system, exhibit complex coordinated behaviors fundamental for various developmental and physiological processes. The present work discovers a collective radial ordered migration behavior of NIH3T3 fibroblasts that depends on persistent top-down regulation with 2D spatial confinement. Remarkably, individual cells move in a weak-oriented, diffusive-like rather than strong-oriented ballistic manner.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors.

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing.

A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020.

We provide a remote sensing derived dataset for large-scale ground-mounted photovoltaic (PV) power stations in China of 2020, which has high spatial resolution of 10 meters. The dataset is based on the Google Earth Engine (GEE) cloud computing platform via random forest classifier and active learning strategy. Specifically, ground samples are carefully collected across China via both field survey and visual interpretation.

Chemical Nose Strategy with Metabolic Labeling and "Antibiotic-Responsive Spectrum" Enables Accurate and Rapid Pathogen Identification.

The worldwide antimicrobial resistance (AMR) dilemma urgently requires rapid and accurate pathogen phenotype discrimination and antibiotic resistance identification. The conventional protocols are either time-consuming or depend on expensive instrumentations. Herein, we demonstrate a metabolic-labeling-assisted chemical nose strategy for phenotyping classification and antibiotic resistance identification of pathogens based on the "antibiotic-responsive spectrum" of different pathogens.

Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation.

In fluorescence microscopy, computational algorithms have been developed to suppress noise, enhance contrast, and even enable super-resolution (SR). However, the local quality of the images may vary on multiple scales, and these differences can lead to misconceptions. Current mapping methods fail to finely estimate the local quality, challenging to associate the SR scale content.

Systematic Dissection of Sequence Features Affecting the Binding Specificity of a Pioneer Factor Reveals Binding Synergy Between FOXA1 and AP-1.

Despite the unique ability of pioneer transcription factors (PFs) to target nucleosomal sites in closed chromatin, they only bind a small fraction of their genomic motifs. The underlying mechanism of this selectivity is not well understood. Here, we design a high-throughput assay called ChIP-ISO to systematically dissect sequence features affecting the binding specificity of a classic PF, FOXA1.

Land use/cover and land degradation across the Eurasian steppe: Dynamics, patterns and driving factors.

Despite the ecological and socio-economic importance of Eurasian steppe, the land use/cover change, land degradation and the threats facing this precious ecosystem still have not been comprehensively understood. Taking advantages of the land use/cover change monitoring platform (FROM-GLC Plus), this study developed the annual land use/cover maps during 2000-2022, and the land use/cover change, especially the change of grassland, was further analyzed. The grassland area exhibited a net increase, predominantly transformed from cropland, forest, and bareland, accounting for 17.

SEM: sized-based expectation maximization for characterizing nucleosome positions and subtypes.

Genome-wide nucleosome profiles are predominantly characterized using MNase-seq, which involves extensive MNase digestion and size selection to enrich for mono-nucleosome-sized fragments. Most available MNase-seq analysis packages assume that nucleosomes uniformly protect 147bp DNA fragments. However, some nucleosomes with atypical histone or chemical compositions protect shorter lengths of DNA.