Browse Articles

Experimental study and modeling adsorption behavior of a robust cross-linker on carbonate rocks at different temperatures

Comparing warming strategies to reduce hypothermia and shivering in elderly abdominal or pelvic surgery patients: a network meta-analysis

Empirical evidence for the spread of Cooperation through copying successful groups

Ensemble distribution model of Muga Silkworm (Antheraea assamensis) and its primary host plant Soalu (Litsea monopetala) in defined climate space

EFCNet for small object detection in remote sensing images

Abstract Object detection, as a crucial component of remote sensing image processing, has become one of the primary methods with the maturation of deep learning technologies. Nonetheless, detecting small objects in remote sensing images remains a significant challenge. Addressing this issue, this study proposes an enhanced network model based on You Only Look Once YOLOv5, aimed at improving the detection capabilities for small objects in remote sensing images. The model employs a novel backbone network, ODCSP-Darknet53, to enhance feature extraction efficiency, and incorporates the small object enhancement bi-directional feature pyramid network (STEBIFPN) structure in the neck region of the network for optimized scaling of small object information. Additionally, we have designed two distinct weighted fusion strategies to further boost the model’s performance in detecting small objects. In the detection head portion of the model, a four-head detection network specialized for small objects is constructed, and adaptively spatial feature fusion (ASFF) technology is introduced to optimize the recognition capabilities for small objects. Experiments conducted on the DOTA and DIOR datasets demonstrate that our model achieves an average precision mean of 75.9% and 80.5%, respectively, with the model’s parameters and computational requirements amounting to 13.4M and 30.2 GFLOPs, respectively. Compared to the original YOLOv5s model, our model exhibits significant performance improvements in detecting typical small objects such as Bridge and Ship. Thus, this research provides an effective solution for object detection in the field of remote sensing image processing.

Impact of future climate trend and fluctuation on winter wheat yield in the North China Plain and adaptation strategies

Abstract Research on the impacts of climate change on crop yield is crucial for improving agricultural management practices and enhancing climate adaptability. Although previous studies have explored the effects of climate trends and fluctuations on wheat yield, their combined impacts under future climate scenarios in the North China Plain (NCP) remain insufficiently understood. This study employs the DSSAT model to analyze the impacts of future climate trends and fluctuations on winter wheat yield. The results indicate that in the 2030s, the benefits of increased precipitation outweighed the losses from rising temperatures, leading to a 1.5% increase in winter wheat yield in the NCP. However, by the 2080s, continuous temperature rise dominated yield reduction, resulting in a 13.4% decline, which exceeded the compensatory capacity of increased precipitation. Irrigated wheat was primarily influenced by temperature trends, while rainfed systems were more sensitive to precipitation fluctuations. Delaying the planting date and increasing field fertility could mitigate 6–7.5% of the potential losses caused by rising temperatures, whereas increasing irrigation had limited mitigation effects (only improving yield by 3%). This study quantifies the climate impact benefits on winter wheat in the NCP and highlights the need for prioritizing heat-tolerant varieties and optimizing sowing and fertilization practices over water-intensive adaptation strategies. The findings provide decision-making support for ensuring food security under a warming climate.

Evaluation of drinking water quality and health risk assessment in rural primary schools along the Thai Myanmar border

Reproductive timing and intensity in a Galápagos intertidal mollusc are modulated by thermal phases

Abstract The decline in finfish fisheries has increased the harvesting of coastal invertebrates, particularly molluscs. To understand how the endemic Galápagos chiton Radsia goodallii withstands harvest pressure, its reproductive traits were assessed on San Cristóbal Island across three El Niño thermal phases. Reproductive timing, duration, and intensity were found to vary significantly across thermal conditions, with a distinct cycle and peak gonadal investment approximately every four months. Reproductive intensity was highest during the cooler El Niño phase, whereas the duration of gonad maturity extended during warmer periods. Shifts in timing were evident in the onset of reproductive activity across phases. A male-biased sexual asymmetry in gonadal investment, combined with a higher number of females, suggested low sperm competition and potentially influenced male reproductive effort. Larger individuals exhibited greater reproductive capacity, indicating size-related reproductive optimization. Although a tropical species, R. goodallii displayed reproductive patterns more typical of temperate species, likely shaped by the Galápagos’ unique oceanographic conditions. These findings improve understanding of the species’ reproductive strategy and offer practical management insights, such as setting minimum catch sizes to protect juveniles until maturity or enforcing seasonal closures during reproductive peaks to support sustainable harvesting.

Characterizing the excision of 7,8-dihydro-8-oxoadenine by thymine DNA glycosylase

Circular RNA circFat3 as a biomarker for construction of postmortem interval Estimation models in mouse brain tissues at multiple temperatures

Abstract Circular RNAs (circRNAs) are conserved, abundant, stable, and specifically expressed in mammals. The postmortem interval (PMI) estimation is crucial in forensic medicine, particularly for case investigation and civil action. CircRNAs may serve as ideal PMI biomarkers. However, no research has explored PMI estimation in the brain using circRNAs. The total RNA, including circRNA, was sampled from mouse brain tissues at multiple temperatures (4℃, 25℃, and 35℃). The semi-quantitative reverse transcription (RT)-PCR and real-time quantitative PCR (RT-qPCR) were used to test the postmortem degradation levels at different PMIs. As a result, we found circFat3 is highly and specifically expressed in mouse brain tissue, with postmortem levels significantly correlated with PMI across multiple temperatures. In addition, mt-co1 and 28 S rRNA demonstrated stability under various temperature conditions, supporting their use as reliable reference genes for PMI models. Moreover, the error rates showed that the circFat3/28S rRNA model was more accurate at 4℃. The circFat3/mt-co1 and circFat3/28S rRNA models provided slightly better predictions for short-term and long-term PMI, respectively at 25℃, while the circFat3/mt-co1 model was more accurate at 35℃. The combined application of the two reference genes was beneficial primarily for long-term PMI estimation. Furthermore, the validation results confirmed that these models were more accurate for long-term PMI estimation. Thus, our mathematical models were constructed at multiple temperatures based on circFat3 and these two reference genes. Taken together, this is the first study to identify circRNA circFat3 as a novel biomarker that may serve as a complementary tool for PMI estimation.

Prediction of contralateral central lymph node metastasis in unilateral papillary thyroid carcinoma based on radiomics

Neurological and organ health risks associated with pesticide mixture exposure in banana farm workers in Moungo Division, Cameroon

Development and validation of an AI-enabled oral score using large-scale dental data

Abstract This research introduces Oral Score Basic (OS-B), a novel Artificial Intelligence (AI) derived methodology designed to provide a comprehensive, objective assessment of individual teeth and overall oral health, initially focused on dental conditions. Leveraging data from more than 340,000 patients across 2,558 U.S. dental practices, OS-B combines radiographic findings and periodontal probing depths with a treatment probability-weighted cost function to quantify the severity of dental conditions. The OS-B score aims to address limitations in prior oral health scoring systems by incorporating nuanced clinical data accounting for disease severity, and providing a scalable, data-driven approach to measuring oral health. This score was developed using Overjet’s FDA-cleared AI platform, which detects dental conditions using bitewing and periapical radiographs, providing a detailed analysis of each tooth. OS-B’s effectiveness was validated by demonstrating a strong correlation between tooth scores and treatment costs, surpassing the predictive power of previous scoring systems. This research presents a foundational framework for AI-enabled oral health scoring, with potential applications in value-based care, population risk analysis, and consumer health management. Future iterations may expand to include additional dimensions of oral health beyond clinical conditions such as risk factors and measures of oral function and esthetics, further enhancing the score’s public health and clinical utility and patient engagement.

Prognostic model of lung adenocarcinoma from the perspective of cancer-associated fibroblasts using single-cell and bulk RNA-sequencing

Dosimetric comparison of IMRT, VMAT, and hybrid techniques in stereotactic body radiotherapy for adrenal metastases

Network toxicology and molecular docking reveal the potential link between acrylamide exposure and breast cancer

Increased IFN responses drive myeloid cell activation in people living with HIV-1

Antiviral and anti-inflammatory efficacy of nanoencapsulated brazilian green propolis against SARS-CoV-2

Retraction: Induction of Hypoxia-inducible Factor 1 Activity by Muscarinic Acetylcholine Receptor Signaling

A novel mouse model for studying complications related to type 2 diabetes using a medium-fat diet, fructose, and streptozotocin

Abstract The study of type 2 diabetes mellitus (T2DM) pathophysiology relies mainly on the use of animal models, the most common of which involves the consumption of high-fat diets comprising 60% calories from fat. Although these models reproduce the onset and most complications associated with T2DM, they do not accurately mimic human dietary patterns, as they lack the addition of carbohydrates such as fructose in drinking water. The aim of this study was to develop a mouse model for studying complications related to T2DM. To this end, male C57BL/6 mice were fed a medium-fat diet (34.5% kcal from fat), given 20% fructose in drinking water, and injected with a single low dose of streptozotocin (STZ; 100 mg/kg) (D + T). At week 20, D + T mice exhibited significant weight gain, elevated fasting blood glucose levels, and the development of insulin resistance compared with control mice. Furthermore, the circulating levels of liver enzymes (GPT, GOT, and alkaline phosphatase), total cholesterol, and LDL increased. Multi-organ damage, including reduced pancreatic islet size and number, severe hepatic steatosis, inflammatory infiltration in visceral adipose tissue, and cardiac and renal dysfunction, was also detected. The proposed model replicates T2DM-associated complications in young mice by combining a medium-fat diet with fructose and STZ.