The study addresses the challenge of medical image segmentation with extremely limited labeled data, known as barely-supervised learning. The authors identify a key issue in current semi-supervised methods, which is the low precision of foreground classes in pseudo labels. To tackle this, they introduce a novel method called Compete-to-Win (ComWin), which generates higher quality pseudo labels by comparing confidence maps from different networks and selecting the most confident predictions. They also present an enhanced version, ComWin+, which includes a boundary-aware enhancement module for better accuracy near boundaries. The proposed methods outperform existing solutions on three public medical image datasets, focusing on cardiac structure, pancreas, and colon tumor segmentation. The source code for ComWin has been made available on GitHub.

The article discusses a study on the relationship between PD-L1 expression and various clinicopathologic characteristics in patients with liver metastasis of colon cancer. The study involved 72 patients and utilized 18F-FDG PET/CT scans to assess PD-L1 expression. The findings indicate that PD-L1 expression is not significantly related to age, sex, or the number of liver metastases but is significantly correlated with tumor size, differentiation, and patient survival. Additionally, the study found that SUVmax, a parameter from PET imaging, is positively correlated with PD-L1 expression and can be used to predict it. The study also explored the correlation between immune cell infiltration, cancer-associated fibroblasts (CAF) distribution, and PD-L1 expression and SUVmax. It was found that the number of infiltrating cytotoxic T cells is significantly correlated with both SUVmax and PD-L1 expression. Multivariate analysis confirmed that SUVmax and tumor differentiation are independent risk factors for PD-L1 expression. The study contributes to understanding the prognostic value of PD-L1 levels in liver metastases and the efficacy of anti-PD1/PD-L1 therapy.

The article discusses a study on the impact of the updated Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2023 guidelines on the treatment of COPD in China. The study is a multi-center prospective cohort study conducted in northeast China, including 986 diagnosed COPD patients, with 617 completing follow-up. The study found that only half of the patients used COPD-specific medications, with LABA+ICS being the most common therapy. The transition from GOLD 2017 to GOLD 2023 guidelines would result in a significant decrease in the adoption of preferred-line maintenance therapy in China. The study highlights the issue of patients discontinuing maintenance medication during stable phases, a problem seen globally. The findings suggest that the prescription of ICS-containing regimens is widespread and requires attention. The study concludes that further research is needed to assess the impact of the new guidelines on Chinese COPD patients.

The study evaluates the utility of satellite-based precipitation estimates from IMERG-E and forecasted precipitation from NASA's GEOS-FP in real-time hydrological monitoring and forecasting using the DRIVE model. The IMERG Final Run precipitation estimates and corresponding hydrological simulations serve as references. The study finds that IMERG-E has a dominant error source in hit bias, while GEOS-FP shows more false precipitation. The GEOS-FP-driven model performs comparably to the IMERG-E-driven model on the first day, suggesting GEOS-FP can compensate for IMERG-E's time latency in nowcasting. For forecasts with longer lead times, biases tend to cancel out, with initial hydrological conditions contributing to forecast accuracy in most regions, except the tropics where GEOS-FP is more accurate. The study highlights the potential benefits of combining IMERG-E and GEOS-FP for hydrological nowcasts and forecasts.

The article discusses the faculty recruitment process, breaking it down into two decision-making processes: graduates choosing their employers and universities selecting candidates. It highlights the mutual influence in the selection process, where doctoral graduates prefer employment at higher-ranked universities due to the prestige and resources available, while also being conservative in their applications to avoid rejection from institutions far above their alma mater's ranking. The article suggests that the ranking difference between a graduate's employment institution and their alma mater follows a normal distribution. It also notes that universities prioritize candidates from more prestigious institutions, reinforcing the central role of these universities in academia. The selection preferences of both graduates and universities are quantified using probability density functions.

The article discusses the structural and mechanical properties of SF-GMA hydrogels, which are synthesized by methacrylate substitution of silk fibroin (SF) primary amines. Fourier Transform Infrared Spectroscopy (FTIR) and proton nuclear magnetic resonance (H-NMR) spectroscopy were used to analyze the secondary structure and confirm the successful synthesis of the SF-GMA hydrogel. The study found that directional freezing and photo-crosslinking can prevent the transformation from random coil to β-sheet structures, which typically enhances the toughness of SF materials. The mechanical properties of the hydrogels were tested, revealing that directional freezing improves the toughness and strength of the SF-GMA hydrogels without affecting their elongation at break. The article provides detailed insights into the potential of SF-GMA hydrogels for applications in tissue engineering due to their enhanced mechanical properties.

The article presents a comprehensive bibliometric analysis of artificial intelligence (AI) applications in macular edema (ME) research from 2011 to 2022. It identifies the United States as having the highest influence in ME research based on the H-index, with China and the United States being the closest collaborators. The Medical University of Vienna emerged as the most influential institution in this field. The review categorizes reference clusters into ten topics, including retinal Optical Coherence Tomography (OCT) fluid detection and deep learning (DL) architectures. The study utilized databases like the Web of Science Core Collection and bibliometric platforms such as bibliometrix-BiblioShiny, VOSviewer, and CiteSpace for analysis. The research highlights the shift in ME research towards automatic detection and the identification of diabetic macular edema (DME) as a recent research hotspot. The article also acknowledges limitations, such as potential publication bias due to the time frame of the studies included.

The article discusses the compressive responses of different types of specimens: lattices, hybrid PU-foam-filled lattices, and PU foams. It highlights the three stages of compression - elasticity, stress plateau, and densification. The study finds that PU foam enters the stress plateau after a very short elastic stage and begins densification at around 9 mm of compression. The BCC lattice specimens are stiffer than the hybrid ones before the elastic limit, while the hyperbolic lattice specimens are less stiff than those filled with PU foam. The article suggests that PU foam hybridization softens the BCC lattice but stiffens the hyperbolic lattice, attributing this to different deformation mechanisms. High-speed camera observations are used to explore these mechanisms further. The article also notes that the stress plateau stage's steady-state value is almost equal to the peak force of the linear elastic stage, except for the primitive BCC lattice. Finally, it is observed that PU foam hybridization accelerates the entry into the densification stage, particularly for the BCC lattice.

The article discusses the challenges faced by polyamide–polyurethane (PA-PU) block copolymers in mechanical applications due to their low wear resistance. It highlights the common practice of reinforcing polymers with carbon fiber (CF) to enhance mechanical and wear properties, but also the associated problems such as high viscosity and poor dispersion. The study examines the use of common fillers like graphite and the potential of multi-component hybrid reinforcements. It then introduces the concept of using thermotropic liquid crystal polymer (LCP) as a novel filler to improve the tribological properties of CF/PA-PU composites. The article reports on research that compares the effects of LCP and graphite on the tribological, tensile, processing, and thermal properties of composites, suggesting that LCP could be a superior alternative to traditional fillers for enhancing the performance of CF/PA-PU composites in tribological applications.

The article discusses the importance of accurate precipitation forecasting in monitoring and preventing flood disasters, especially in the context of global warming. It highlights the advancements in numerical weather forecast technology and the challenges in forecasting on sub-seasonal to seasonal scales. The article reviews various statistical post-processing methods and machine learning techniques used for bias correction in model precipitation forecasts. It also introduces a new bias correction method based on Quantile Mapping (QM) that aims to improve the calibration of forecasting precipitation on longer-term scales with high temporal resolution. The new method's performance is analyzed in comparison to the original QM method, focusing on precipitation accumulation and hydrological simulations.

The article discusses the relationship between the tensile strength of polymer films and the ratio of reactants, denoted as R. It explains that as R increases, the tensile strength of the films also increases due to a higher content of isocyanate and glycerol, which leads to more cross-linking points and a harder segment in the polymer. However, this also results in a decrease in elongation at break due to the higher cross-linking density. The optimal range for R is identified as 2.0 to 2.4, balancing tensile strength and flexibility. The chosen R value for further application in energetic polymer composites is 2.2. The article compares these findings with other scholars' work, noting that the results presented show higher strength and elongation than previously reported.

The article discusses the field of metabolic engineering and its strategies to enhance the production of various compounds by optimizing metabolic networks within cells. It addresses the challenge of 'metabolic burden' that arises when engineered strains are modified, which can affect productivity. The article reviews several gene expression systems, including chemical inducers and natural metabolite-sensing proteins, but highlights their limitations such as toxicity, cost, and scalability. It then focuses on the potential of optogenetic switches, which use light to control gene expression, as a more precise and reversible method for regulating engineered metabolic pathways. The article also mentions recent research developments in optogenetic circuits and their applications in metabolic engineering, such as the OptoLAC and OptoAMP circuits. Additionally, it discusses the use of CRISPR-Cas systems in combination with light-controlled gene circuits to redirect metabolic flux and enhance product synthesis, citing specific studies and contributions from the author's research group.

The article discusses the environmental toxicology of Prothioconazole (PTZ) and its metabolite, PTD, which are used as fungicides in agriculture. PTZ is known to degrade rapidly, forming PTD, which has higher toxicity and can persist in the environment. Studies have shown that PTZ and PTD can cause various disorders in mammals, such as liver and gonad disorders, and affect the gut microflora. They also pose risks to aquatic life, with evidence of toxicity towards zebrafish embryos. The article highlights the limited research on the effects of PTZ and PTD on aquatic plants and emphasizes the importance of studying their impact on duckweeds, which are crucial primary producers in aquatic ecosystems. Duckweeds are recommended by the ISO and OECD for ecotoxicology research, and studies have shown that chemicals can affect their growth by interfering with metabolic pathways and photosynthesis.

The article discusses the research development on graphitic carbon nitride and its enhanced catalytic activity on ammonium perchlorate. The authors, D. Ma, X. Li, X. Wang, and Y. Luo, have contributed to the publication in RSC Advances, a journal by the Royal Society of Chemistry (RSC). The article is available under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence, which allows for non-commercial reuse with proper acknowledgement. The article emphasizes the importance of correct citation when using the material and provides guidance on how to request permission for reproduction in commercial publications.

The article discusses a study on the influence of extracellular matrix (ECM) stiffness on chondrocyte functions, specifically focusing on the impact of viscosity. Researchers established a biphasic gelatin solution/hydrogel system for culturing bovine articular chondrocytes (BACs) to explore how different viscosities affect chondrocyte proliferation, ECM secretion, and phenotype maintenance. The study found that gelatin solutions with varying viscosities supported chondrocyte proliferation and ECM production, but the cell morphology, proliferation rate, ECM quantity, and gene expression levels varied with the viscosity. Low-viscosity solutions favored proliferation, while high-viscosity solutions enhanced ECM production and the expression of specific cartilage markers. The study concluded that viscosity is a crucial factor influencing cell morphology, proliferation, and phenotype in chondrocyte culture.

The article discusses a novel quantitative method for analyzing multiple elements in vanadium slags using Laser-Induced Breakdown Spectroscopy (LIBS). The method addresses the challenge of spectral interference by employing selective ensemble learning. This approach considers interference spectral lines in model training and uses bootstrap resampling to create diverse learners. These learners are then filtered through K-means clustering to construct a robust regression model. The method was tested on fourteen vanadium slag samples to predict the concentrations of five different oxides. The results showed relative errors below 10% for all oxides, indicating the method's effectiveness for quantitative analysis in industrial processes.

The article discusses a new quantitative method for laser-induced breakdown spectroscopy (LIBS) that improves accuracy in high temperature applications. Traditional LIBS analysis faces challenges due to the lack of sufficient certified standard samples for calibration at high temperatures. The proposed method utilizes transfer learning to adapt information from room temperature spectra to high temperature conditions, thereby enhancing the regression model for better prediction. An iterative weight adjusting scheme and ensemble learning are incorporated into the model training and prediction process. The method was tested on certified alloy steel standard samples to analyze chromium concentrations, resulting in reduced average absolute and relative errors by 1.8% and 20.58%, respectively. This advancement suggests a cost-effective approach for LIBS analysis in high temperature industrial processes like iron and steel smelting.