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Background/Objectives: With the increase of reactive oxygen species (ROS) production, cancer cells can avoid cell death and damage by up-regulating antioxidant programs. Therefore, it will be more effective to induce cell death by using targeted strategies to further improve ROS levels and drugs that inhibit antioxidant programs. Methods: Considering that dihydroartemisinin (DHA) can cause oxidative damage to protein, DNA, or lipids by producing excessive ROS, while, disulfiram (DSF) can inhibit glutathione (GSH) levels and achieve the therapeutic effect by inhibiting antioxidant system and amplifying oxidative stress, they were co-loaded onto the copper peroxide nanoparticles (CuO2) coated with copper tannic acid (Cu-TA), to build a drug delivery system of CuO2@Cu-TA@DSF/DHA nanoparticles (CCTDD NPs). In response to the tumor microenvironment, DHA interacts with copper ion (Cu2+) to produce ROS, and a double (diethylthiocarbamate)-copper (II) (CuET) is generated by the complexation of DSF and Cu2+, which consumes GSH and inhibits antioxidant system. Meanwhile, utilizing the Fenton-like effect induced by the multi-copper mode can achieve ROS storm, activate the MAPK pathway, and achieve chemotherapy (CT) and chemodynamic (CDT). Results: Taking pancreatic cancer cell lines PANC-1 and BxPC-3 as the research objects, cell line experiments in vitro proved that CCTDD NPs exhibit efficient cytotoxicity on cancer cells. Conclusions: The CCTDD NPs show great potential in resisting pancreatic cancer cells and provides a simple strategy for designing powerful metal matrix composites. [ABSTRACT FROM AUTHOR]

Person re-identification (ReID) refers to the task of retrieving target persons from image libraries captured by various distinct cameras. Over the years, person ReID has yielded favorable recognition outcomes under typical visible light conditions, yet there remains considerable scope for enhancement in challenging conditions. The challenges and research gaps include the following: multi-modal data fusion, semi-supervised and unsupervised learning, domain adaptation, ReID in 3D space, fast ReID, decentralized learning, and end-to-end systems. The main problems to be solved, which are the occlusion problem, viewpoint problem, illumination problem, background problem, resolution problem, openness problem, etc., remain challenges. For the first time, this paper uses person ReID in special scenarios as a basis for classification to categorize and analyze the related research in recent years. Starting from the perspectives of person ReID methods and research directions, we explore the current research status in special scenarios. In addition, this work conducts a detailed experimental comparison of person ReID methods employing deep learning, encompassing both system development and comparative methodologies. In addition, we offer a prospective analysis of forthcoming research approaches in person ReID and address unresolved concerns within the field. [ABSTRACT FROM AUTHOR]

As an important means to enhance oil recovery, ternary composite flooding (ASP flooding for short) technology has achieved remarkable results in Daqing Oilfield. Alkalis, surfactants and polymers are mixed in specific proportions and injected into the reservoir to give full play to the synergistic effect of each component, which can effectively enhance the fluidity of crude oil and greatly improve the oil recovery. At present, the technology for further improving oil recovery after ternary composite flooding is not mature and belongs to the stage of technical exploration. The presence of alkaline substances significantly alters the reservoir's physical properties and causes considerable corrosion to the equipment used in its development. This is detrimental to both the environment and production. Therefore, it is necessary to develop green displacement control agents. In the reservoir environment post-ASP flooding, 2-(methylamino)ethyl methacrylate and glycidyl methacrylate were chosen as monomers to synthesize a polymer responsive to alkali, and then grafted with cellulose nanocrystals to form microspheres of alkali-resistant swelling hydrogel. Cellulose nanocrystals (CNCs) modified with functional groups and other materials were utilized to fabricate hydrogel microspheres. The product's structure was characterized and validated using Fourier transform infrared spectroscopy and X-ray diffraction. The infrared spectrum revealed characteristic absorption peaks of CNCs at 1165 cm−1, 1577 cm−1, 1746 cm−1, and 3342 cm−1. The diffraction spectrum corroborated the findings of the infrared analysis, indicating that the functional modification occurred on the CNC surface. After evaluating the swelling and erosion resistance of the hydrogel microspheres under various alkaline conditions, the optimal particle size for compatibility with the target reservoir was determined to be 6 μm. The potential of cellulose-based gel microspheres to enhance oil recovery was assessed through the evaluation of Zeta potential and laboratory physical simulations of oil displacement. The study revealed that the absolute value of the Zeta potential for gel microspheres exceeds 30 in an alkaline environment with pH values ranging from 7 to 14, exhibiting a phenomenon where stronger alkalinity correlates with a greater absolute value of Zeta potential. The dispersion stability spans from good to excellent. The laboratory oil displacement simulation experiment was conducted using a cellulose-based gel microsphere system following weak alkali ASP flooding within the pH value range from 7 to 10. The experimental interventions yielded recovery rates of 2.98%, 3.20%, 3.31%, and 3.38%, respectively. The study indicates that cellulose-based gel microspheres exhibit good adaptability in alkaline reservoirs. This research offers a theoretical foundation and experimental approaches to enhance oil recovery techniques post-ASP flooding. [ABSTRACT FROM AUTHOR]

In this article, low temperature ultrafine grinding and other three kind of crushing methods were used to prepare potato granules powder. And the swelling power and solubility, water‐holding capacity, gelatinization characteristics, particle size, and chromaticity of different particle size levels of potato granules powder were determined. The experimental results showed that the potato granules powder prepared by low temperature ultrafine grinding had the highest solubility and swelling power, and the highest degree of pre‐gelatinization; With the increase in the addition of potato powder, the water absorption of the dough increased, and been more rheological. The volume of bread gradually decreases and the firmness increases. With the decrease of potato granules powder size, the water absorption of dough decreases and the bread hardness increases. In this experiment, when the grain size of potato powder was 200 mesh and the addition amount was 2%, the sensory score was the highest and the taste was the best. The result provides theoretical and practical basis for the production of potato bread and the comprehensive utilization of potato resources. Practical applications: The present study demonstrates the effective preparation of potato whole powder particles through low temperature ultrafine grinding. Comparative analysis with shear grinding, ball grinding, and room temperature grinding reveals that low temperature ultrafine grinding yields superior solubility, swelling, and gelatinization characteristics. Furthermore, incorporating 200 mesh fine potato powder in bread formulation significantly enhances dough rheological properties and water absorption while improving bread hardness and flavor. The findings offer a solid theoretical and practical foundation for the production of potato bread and the comprehensive utilization of potato resources. [ABSTRACT FROM AUTHOR]

FOXG1 syndrome is a developmental encephalopathy caused by FOXG1 (Forkhead box G1) mutations, resulting in high phenotypic variability. However, the upstream transcriptional regulation of Foxg1 expression remains unclear. This report demonstrates that both deficiency and overexpression of Men1 (protein: menin, a pathogenic gene of MEN1 syndrome known as multiple endocrine neoplasia type 1) lead to autism‐like behaviors, such as social defects, increased repetitive behaviors, and cognitive impairments. Multifaceted transcriptome analyses revealed that Foxg1 signaling is predominantly altered in Men1 deficiency mice, through its regulation of the Alpha Thalassemia/Mental Retardation Syndrome X‐Linked (Atrx) factor. Atrx recruits menin to bind to the transcriptional start region of Foxg1 and mediates the regulation of Foxg1 expression by H3K4me3 (Trimethylation of histone H3 lysine 4) modification. The deficits observed in menin deficient mice are rescued by the over‐expression of Foxg1, leading to normalized spine growth and restoration of hippocampal synaptic plasticity. These findings suggest that menin may have a putative role in the maintenance of Foxg1 expression, highlighting menin signaling as a potential therapeutic target for Foxg1‐related encephalopathy. [ABSTRACT FROM AUTHOR]

As remote sensing technology has advanced, the use of satellites and similar technologies has become increasingly prevalent in daily life. Now, it plays a crucial role in hydrology, agriculture, and geography. Nevertheless, because of the distinct qualities of remote sensing, including expansive scenes and small, densely packed targets, there are many challenges in detecting remote sensing objects. Those challenges lead to insufficient accuracy in remote sensing object detection. Consequently, developing a new model is essential to enhance the identification capabilities for objects in remote sensing imagery. To solve these constraints, we have designed the OD-YOLO approach that uses multi-scale feature fusion to improve the performance of the YOLOv8n model in small target detection. Firstly, traditional convolutions have poor recognition capabilities for certain geometric shapes. Therefore, in this paper, we introduce the Detection Refinement Module (DRmodule) into the backbone architecture. This module utilizes Deformable Convolutional Networks and the Hybrid Attention Transformer to strengthen the model's capability for feature extraction from geometric shapes and blurred objects effectively. Meanwhile, based on the Feature Pyramid Network of YOLO, at the head of the model framework, this paper enhances the detection capability by introducing a Dynamic Head to strengthen the fusion of different scales features in the feature pyramid. Additionally, to address the issue of detecting small objects in remote sensing images, this paper specifically designs the OIoU loss function to finely describe the difference between the detection box and the true box, further enhancing model performance. Experiments on the VisDrone dataset show that OD-YOLO surpasses the compared models by at least 5.2% in mAP50 and 4.4% in mAP75, and experiments on the Foggy Cityscapes dataset demonstrated that OD-YOLO improved mAP by 6.5%, demonstrating outstanding results in tasks related to remote sensing images and adverse weather object detection. This work not only advances the research in remote sensing image analysis, but also provides effective technical support for the practical deployment of future remote sensing applications. [ABSTRACT FROM AUTHOR]