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Developing an oral in situ-forming hydrogel that targets the inflamed intestine to suppress bleeding ulcers and alleviate intestinal inflammation is crucial for effectively treating ulcerative colitis (UC). Here, inspired by sandcastle worm adhesives, we proposed a water-immiscible coacervate (EMNs-gel) with a programmed coacervate-to-hydrogel transition at inflammatory sites composed of dopa-rich silk fibroin matrix containing embedded inflammation-responsive core-shell nanoparticles. Driven by intestinal peristalsis, the EMNs-gel can be actuated forward and immediately transform into a hydrogel once contacting with the inflamed intestine to yield strong tissue adhesion, resulting from matrix metalloproteinases (MMPs)-triggered release of Fe3+ from embedded nanoparticles and rearrangement of polymer network of EMNs-gel on inflamed intestine surfaces. Extensive in vitro experiments and in vivo UC models confirmed the preferential hydrogelation behavior of EMNs-gel to inflamed intestine surfaces, achieving highly effective hemostasis, and displaying an extended residence time (> 48 h). This innovative EMNs-gel provides a non-invasive solution that accurately suppresses severe bleeding and improves intestinal homeostasis in UC, showcasing great potential for clinical applications.

Abstract Background The incidence of Post Stroke Depression (PSD) in the Rehabilitation Stage is high, which can bring serious physical and psychological disorders to patients. However, there is still a lack of targeted tools for screening PSD in the rehabilitation stage. Therefore, the aim of this study was to evaluate the factor structure and reliability of a measurement instrument to screen for PSD in the rehabilitation stage. Methods A cross-sectional study was conducted on 780 hospitalized stroke patients who were within the rehabilitation stage from May to August 2020. Exploratory factor analysis (EFA) as well as first- and second-order confirmatory factor analysis (CFA) were performed to evaluate the factor structure of the newly developed Symptom Measurement of Post-Stroke Depression in the Rehabilitation Stage (SMPSD-RS). The reliability and validity of the SMPSD-RS were also verified using several statistical methods. Results EFA extracted a 24-item, five-factor (cognition, sleep, behavior, emotion, and obsession) model that can clinically explain the symptoms of PSD during the rehabilitation stage. A first-order CFA confirmed the EFA model with good model fit indices, and the second-order CFA further confirmed the five-factor structure model and showed acceptable model fit indices. Acceptable reliability and validity were also achieved by the corresponding indicators. Conclusion The SMPSD-RS was proven to have a stable factor structure and was confirmed to be reliable and valid for assessing PSD symptoms in stroke patients during the rehabilitation stage.

Maize is a globally important cereal crop, however, maize leaf disease is one of the most common and devastating diseases that afflict it. Artificial intelligence methods face challenges in identifying and classifying maize leaf diseases due to variations in image quality, similarity among diseases, disease severity, limited dataset availability, and limited interpretability. To address these challenges, we propose a residual-based multi-scale network (MResNet) for classifying multi-type maize leaf diseases from maize images. MResNet consists of two residual subnets with different scales, enabling the model to detect diseases in maize leaf images at different scales. We further utilize a hybrid feature weight optimization method to optimize and fuse the feature mapping weights of two subnets. We validate MResNet on a maize leaf diseases dataset. MResNet achieves 97.45% accuracy. The performance of MResNet surpasses other state-of-the-art methods. Various experiments and two additional datasets confirm the generalization performance of our model. Furthermore, thermodynamic diagram analysis increases the interpretability of the model. This study provides technical support for the disease classification of agricultural plants.

The matrix nuclear norm minimization problem has been extensively researched in recent years due to its widespread applications in control design, signal and image restoration, machine learning, big data problems, and more. One popular model is nuclear norm minimization with the l2-norm fidelity term, but it is only effective for those problems with Gaussian noise. A nuclear norm minimization problem with the l1-norm fidelity term has been studied in this paper, which can deal with the problems with not only non-Gaussian noise but also Gaussian noise or their mixture. Moreover, it also keeps the efficiency for the noiseless case. Given the nonsmooth proposed model, we transform it into a separated form by introducing an auxiliary variable and solve it by the semi-proximal alternating direction method of multipliers (sPADMM). Furthermore, we first attempt to solve its dual problem by sPADMM. Then, the convergence guarantees for the aforementioned algorithms are given. Finally, some numerical studies are dedicated to show the robustness of the proposed model and the effectiveness of the presented algorithms.

Starches plasticized with glycerol/citric acid/stearic acid and tributyl 2-acetylcitrate (ATBC), respectively, were processed with poly (butylene adipate-Co-terephthalate (PBAT) via extrusion and a film-blown process. All the composite films were determined for morphology, mechanical, thermal stability, crystalline, and optical properties. Results show that the most improved morphology was in the 30% glycerol plasticized PBAT/thermoplastic starch (TPS) composite films, characterized by the smallest and narrowest distribution of TPS particle sizes and a more uniform dispersion of TPS particles. However, the water absorption of PBAT/TPS composite films plasticized with glycerol surpassed that observed with ATBC as a plasticizer. Mechanical properties indicated insufficient plasticization of the starch crystal structure when using 10% ATBC, 20% ATBC, and 20% glycerol as plasticizers, leading to poor compatibility between PBAT and TPS. This resulted in stress concentration points under external forces, adversely affecting the mechanical properties of the composites. All PBAT/TPS composite films exhibited a negative impact on the initial thermal decomposition temperature compared to PBAT. Additionally, the haze value of PBAT/TPS composite films exceeded 96%, while pure PBAT had a haze value of 47.42%. Films plasticized with 10% ATBC, 20% ATBC, and 20% glycerol displayed lower transmittance values in the visible light region. The increased transmittance of films plasticized with 30% glycerol further demonstrated their superior plasticizing effect compared to other PBAT/TPS composite films. This study provides a simple and feasible method for preparing low-cost PBAT composites, and their extensions are expected to further replace general-purpose plastics in daily applications.

The effects of 0.0001%，0.0018% and 0.0037% S content on the solidification characteristics of K417G alloy were studied by isothermal solidification experiment in the temperature range of 1340-1230 ℃. The results show that S can reduce the solidus temperature of the alloy and enlarge the solid-liquid two phase region. When the S content increases from 0.0001% to 0.0037%， the solidus temperature of the alloy decreases from 1255 ℃ to 1245 ℃. During the solidification process， the S element segregates in the liquid phase and precipitates out as the Y phase in the final solidification zone. And S promotes the enrichment of Al and Cr elements at the solid-liquid interface. At the same isothermal temperature， the increase of S content promotes the increase of the volume fraction of MC carbide，γ+γ΄ eutectic and Y phase， and has little effect on the morphology and precipitation temperature of MC carbide and γ+γ΄ eutectic， but the precipitation temperature of the Y phase is increased from 1230 ℃ to 1240 ℃.