Molecular and cellular mechanisms of PDAC progression based on RETN-CAP1-mediated macrophage-fibroblast crosstalk: Action of ITGB5 and ITGB1 recombinant proteins.

Pancreatic ductal adenocarcinoma (PDAC) has a very poor prognosis, and the main objective of this study was to reveal the specific mechanism of action of TN-CAP1-mediated macrophage-fibroblast crossinulation in the progression of PDAC, and to evaluate the function and potential therapeutic value of ITGB5 and ITGB1 recombinant proteins in this process. The expression of TN-CAP1 in tumor tissues of PDAC patients was analyzed by immunohistochemistry and compared with normal pancreatic tissues. The co-culture system of macrophages and fibroblasts was constructed using in vitro cell culture model.

Percutaneous Endoscopic Resection of Lumbar Discal Cyst.

Background: Discal cysts, an uncommon condition, can replicate the characteristic signs typically linked to a herniated lumbar disc, encompassing discomfort in the lumbar region and neuralgia that extends along the nerve paths, thereby complicating the process of distinguishing the discal cyst from other conditions. Consensus on the treatment of this disease remains elusive, and the best treatment for it is still a matter of controversy. In numerous past reports, this disease has been treated through either open or microscopic surgical approaches.

AdaBoost model for rockburst intensity prediction considering class differences and quantitative characterization of misclassification difference.

With the resource development gradually into the deep, rock explosion phenomenon is more and more frequent. The suddenness and harmfulness of rockbursts threaten the safe development of underground resources. In order to more accurately predict the possible intensities of rockbursts in specific rock conditions and stress environments, the rock mechanical and stress parameters between different intensities of rockbursts are further explored, an AdaBoost model considering the differences in the hierarchy is established, and a Flash Hill-Climbing method is proposed to optimize the hyperparameters in the classification model.

TS-DM: A Time Segmentation-Based Data Stream Learning Method for Concept Drift Adaptation.

Concept drift arises from the uncertainty of data distribution over time and is common in data stream. While numerous methods have been developed to assist machine learning models in adapting to such changeable data, the problem of improperly keeping or discarding data samples remains. This may results in the loss of valuable knowledge that could be utilized in subsequent time points, ultimately affecting the model's accuracy.

Multiview Classification Through Learning From Interval-Valued Data.

The classification problem concerning crisp-valued data has been well resolved. However, interval-valued data, where all of the observations' features are described by intervals, are also a common data type in real-world scenarios. For example, the data extracted by many measuring devices are not exact numbers but intervals.

From imaging to clinical outcome: dual-region CT radiomics predicting FOXM1 expression and prognosis in hepatocellular carcinoma.

Background: Liver cancer, especially hepatocellular carcinoma (HCC), remains a significant global health challenge. Traditional prognostic indicators for HCC often fall short in providing comprehensive insights for individualized treatment. The integration of genomics and radiomics offers a promising avenue for enhancing the precision of HCC diagnosis and prognosis.

An Extremely Simple Algorithm for Source Domain Reconstruction.

The aim of unsupervised domain adaptation (UDA) is to utilize knowledge from a source domain to enhance the performance of a given target domain. Due to the lack of accessibility to the target domain's labels, UDA's efficacy is highly reliant on the source domain's quality. However, it is often impractical and expensive to obtain an appropriate transferable source domain.

Disentangling Stochastic PDE Dynamics for Unsupervised Video Prediction.

Unsupervised video prediction aims to predict future outcomes based on the observed video frames, thus removing the need for supervisory annotations. This research task has been argued as a key component of intelligent decision-making systems, as it presents the potential capacities of modeling the underlying patterns of videos. Essentially, the challenge of video prediction is to effectively model the complex spatiotemporal and often uncertain dynamics of high-dimensional video data.

Fast treatment and recycling method of large-scale vegetable wastes.

With rapid development of vegetable industry in China, in process of refrigerated transportation and storage, large-scale abandoned vegetable wastes (VW) need to be urgently treated alone since they rot very fast and would pollute the environment seriously. Existing treatment projects generally regard VW as garbage with high content of water and adopt the process of squeeze and sewage treatment, which leads to not only high treatment costs but also great resource waste. Therefore, according to the composition and degradation characteristics of VW, a novel fast treatment and recycling method of VW was proposed in this paper.

A Novel Anti-Flashover Superhydrophobic Coating with Self-Assembly Characteristic of Surface Energy Differences.

Because of the versatility of superhydrophobic materials, they have attracted a lot of attention even in power electronics, transportation, engineering, and other fields. The volume fraction of fluorinated silicon oxide nanoparticles in superhydrophobic materials is one of the most important factors. Increasing the volume fraction will decrease the stability between the coating and the hydrophobic surface.

Multidomain Adaptation With Sample and Source Distillation.

Unsupervised multidomain adaptation attracts increasing attention as it delivers richer information when tackling a target task from an unlabeled target domain by leveraging the knowledge attained from labeled source domains. However, it is the quality of training samples, not just the quantity, that influences transfer performance. In this article, we propose a multidomain adaptation method with sample and source distillation (SSD), which develops a two-step selective strategy to distill source samples and define the importance of source domains.

Nanoparticles Mediated circROBO1 Silencing to Inhibit Hepatocellular Carcinoma Progression by Modulating miR-130a-5p/CCNT2 Axis.

Background: Circular RNAs (circRNAs) are becoming vital biomarkers and therapeutic targets for malignant tumors due to their high stability and specificity in tissues. However, biological functions of circRNAs in hepatocellular carcinoma (HCC) are still not well studied.

Methods: Gene Expression Omnibus (GEO) database and qRT-PCR were used to evaluate expression of circROBO1 (hsa_circ_0066568) in HCC tissues and cell lines.

Suppressing circIDE/miR-19b-3p/RBMS1 axis exhibits promoting-tumour activity through upregulating GPX4 to diminish ferroptosis in hepatocellular carcinoma.

Ferroptosis is a newly characterized form of iron-dependent non-apoptotic cell death, which is closely associated with cancer progression. However, the functions and mechanisms in regulation of escaping from ferroptosis during hepatocellular carcinoma (HCC) progression remain unknown. In this study, we reported that the RNA binding motif single stranded interacting protein 1 (RBMS1) participated in HCC development，and functioned as a regulator of ferroptosis.

[Accumulation Characteristics and Sources of PAEs in Agricultural Soils in Gansu Province].

In order to understand the composition and accumulation characteristics of phthalates esters (PAEs) in agricultural soils in Gansu province, a total of 41 soil samples from four agricultural soils in Gansu province were collected, and the content of six PAEs compounds was analyzed using a gas chromatography-single quadrupole mass spectrometer (GC-MS). The results showed that the average value of PAEs compounds in agricultural soils in Gansu province was 432.4 μg·kg.

The anti-alcoholism drug disulfiram effectively ameliorates ulcerative colitis through suppressing oxidative stresses-associated pyroptotic cell death and cellular inflammation in colonic cells.

Background: Oxidative stress, cell pyroptosis and inflammation are considered as important pathogenic factors for ulcerative colitis (UC) development, and the traditional anti-alcoholism drug disulfiram (DSF) has recently been reported to exert its regulating effects on all the above cellular functions, which makes DSF as ideal therapeutic agent for UC treatment, but this issue has not been fully studied.

Methods: Dextran sulfate sodium (DSS)-induced animal models in C57BL/6J mice and lipopolysaccharide (LPS)-induced cellular models in colonic cell lines (HT-29 and Caco-2) for UC were respectively established. Cytokine secretion was determined by ELISA.

Robust Gaussian Process Regression With Input Uncertainty: A PAC-Bayes Perspective.

The Gaussian process (GP) algorithm is considered as a powerful nonparametric-learning approach, which can provide uncertainty measurements on the predictions. The standard GP requires clearly observed data, unexpected perturbations in the input may lead to learned regression model mismatching. Besides, GP also suffers from the lack of good generalization performance guarantees.

Multiclass Classification With Fuzzy-Feature Observations: Theory and Algorithms.

The theoretical analysis of multiclass classification has proved that the existing multiclass classification methods can train a classifier with high classification accuracy on the test set, when the instances are precise in the training and test sets with same distribution and enough instances can be collected in the training set. However, one limitation with multiclass classification has not been solved: how to improve the classification accuracy of multiclass classification problems when only imprecise observations are available. Hence, in this article, we propose a novel framework to address a new realistic problem called multiclass classification with imprecise observations (MCIMO), where we need to train a classifier with fuzzy-feature observations.

Metabolic Profiling Analysis of the Effect and Mechanism of Gushiling Capsule in Rabbits With Glucocorticoid-Induced Osteonecrosis of the Femoral Head.

Gushiling capsule (GSLC) is an effective traditional Chinese medicine for the treatment of glucocorticoid-induced osteonecrosis of the femoral head (GIONFH). This study established the serum metabolite profiles of GSLC in rabbits and explored the metabolic mechanism and effect of GSLC on GIONFH. Seventy-five Japanese white rabbits were randomly divided into the control, model, and GSLC groups.

Screening of Potential Key Biomarkers for Ewing Sarcoma: Evidence from Gene Array Analysis.

Background: Ewing's sarcoma (ES) is a common bone cancer in children and adolescents. There are ethnic differences in the incidence and treatment effects. People have made great efforts to clarify the cause; however, the molecular mechanism of ES is still poorly understood.

Heterogeneous Multidomain Recommender System Through Adversarial Learning.

To solve the user data sparsity problem, which is the main issue in generating user preference prediction, cross-domain recommender systems transfer knowledge from one source domain with dense data to assist recommendation tasks in the target domain with sparse data. However, data are usually sparsely scattered in multiple possible source domains, and in each domain (source/target) the data may be heterogeneous, thus it is difficult for existing cross-domain recommender systems to find one source domain with dense data from multiple domains. In this way, they fail to deal with data sparsity problems in the target domain and cannot provide an accurate recommendation.

Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms.

Semi-supervised heterogeneous domain adaptation (SsHeDA) aims to train a classifier for the target domain, in which only unlabeled and a small number of labeled data are available. This is done by leveraging knowledge acquired from a heterogeneous source domain. From algorithmic perspectives, several methods have been proposed to solve the SsHeDA problem; yet there is still no theoretical foundation to explain the nature of the SsHeDA problem or to guide new and better solutions.

Learning Data Streams With Changing Distributions and Temporal Dependency.

In a data stream, concept drift refers to unpredictable distribution changes over time, which violates the identical-distribution assumption required by conventional machine learning methods. Current concept drift adaptation techniques mostly focus on a data stream with changing distributions. However, since each variable of a data stream is a time series, these variables normally have temporal dependency problems in the real world.

Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation.

In the unsupervised open set domain adaptation (UOSDA), the target domain contains unknown classes that are not observed in the source domain. Researchers in this area aim to train a classifier to accurately: 1) recognize unknown target data (data with unknown classes) and 2) classify other target data. To achieve this aim, a previous study has proven an upper bound of the target-domain risk, and the open set difference, as an important term in the upper bound, is used to measure the risk on unknown target data.