Browse Articles

Prognostic value of combined NP and LHb index with absolute monocyte count in colorectal cancer patients

Maternal excess dietary phosphate intake in the periconceptional period is a potential risk for mineral disorders in offspring mice

In-shoe plantar temperature, normal and shear stress relationships during gait and rest periods for people living with and without diabetes

Abstract Diabetic foot ulcers (DFUs) are a common complication of diabetes. This study aims to investigate the relationships between in-shoe plantar temperature, normal and shear stress during walking and rest periods for participants with and without diabetes. For this purpose, a novel temperature, normal and shear stress sensing system was developed and embedded in an insole at the hallux, first metatarsal head and calcaneus region. Ten participants living with diabetes with no history of previous ulceration and ten healthy participants were recruited. Participants walked on a treadmill for 15 min and then rested for 20 min wearing the sensing insole. Results showed high correlation (Spearman’s r s ≥ 0.917) between heat energy, total plantar temperature change, during walking and strain energy, cumulative stress squared in all participants. Importantly, between-group comparisons showed indications of thermal regulation differences in participants with and without diabetes, with the first metatarsal head site showing significantly higher temperature at the end of the active period (P = 0.0097) although walking speed and mechanical stress were similar. This research demonstrates for the first time the correlation between strain energy and heat energy in-shoe during gait. Further research is needed to quantify relationships and investigate thermal regulation as a mechanism for DFU formation.

Automatic segmentation and landmark detection of 3D CBCT images using semi supervised learning for assisting orthognathic surgery planning

University students describe how they adopt AI for writing and research in a general education course

Abstract University students have begun to use Artificial Intelligence (AI) in many different ways in their undergraduate education, some beneficial to their learning, and some simply expedient to completing assignments with as little work as possible. This exploratory qualitative study examines how undergraduate students used AI in a large General Education course on sustainability and technology at a research university in the United States in 2023. Thirty-nine students documented their use of AI in their final course project, which involved analyzing conceptual networks connecting core sustainability concepts. Through iterative qualitative coding, we identified key patterns in students’ AI use, including higher-order writing tasks (understanding complex topics, finding evidence), lower-order writing tasks (revising, editing, proofreading), and other learning activities (efficiency enhancement, independent research). Students primarily used AI to improve communication of their original ideas, though some leveraged it for more complex tasks like finding evidence and developing arguments. Many students expressed skepticism about AI-generated content and emphasized maintaining their intellectual independence. While some viewed AI as vital for improving their work, others explicitly distinguished between AI-assisted editing and their original thinking. This analysis provides insight into how students navigate AI use when it is explicitly permitted in coursework, with implications for effectively integrating AI into higher education to support student learning.

Seismic reliability analysis of reinforced slope considering soil parameter dependence structure

Delving into quasi-periodic type optical solitons in fully nonlinear complex structured perturbed Gerdjikov–Ivanov equation

Mitogenome of Neolissochilus pnar, the largest cavernicolous species of Mahseer

Geriatric Nutritional Risk Index as a prognostic marker for predicting survival outcomes in patients with UTUC after radical nephroureterectomy

Global or local modeling for XGBoost in geospatial studies upon simulated data and German COVID-19 infection forecasting

Abstract Methods from artificial intelligence (AI) and, in particular, machine learning and deep learning, have advanced rapidly in recent years and have been applied to multiple fields including geospatial analysis. Due to the spatial heterogeneity and the fact that conventional methods can not mine large data, geospatial studies typically model homogeneous regions locally within the entire study area. However, AI models can process large amounts of data, and, theoretically, the more diverse the train data, the more robust a well-trained model will be. In this paper, we study a typical machine learning method XGBoost, with the question: Is it better to build a single global or multiple local models for XGBoost in geospatial studies? To compare the global and local modeling, XGBoost is first studied on simulated data and then also studied to forecast daily infection cases of COVID-19 in Germany. The results indicate that if the data under different relationships between independent and dependent variables are balanced and the corresponding value ranges are similar, i.e., low spatial variation, global modeling of XGBoost is better for most cases; otherwise, local modeling of XGBoost is more stable and better, especially for the secondary data. Besides, local modeling has the potential of using parallel computing because each sub-model is trained independently, but the spatial partition of local modeling requires extra attention and can affect results.

A quantitative assessment of the hand kinematic features estimated by the oculus Quest 2

Constructing a nomogram model for patients with cervical spondylotic myelopathy

Stacked machine learning models for accurate estimation of shear and Stoneley wave transit times in DSI log

A novel strategy for controllable electrofabrication of molecularly imprinted polymer biosensors utilizing embedded Prussian blue nanoparticles

Research on the disaster mechanism and control technology of large section high waste dump slope in open pit mines

Increased individual variability in functional connectivity of the default mode network and its genetic correlates in major depressive disorder

Secure gray image sharing framework with adaptive key generation using image digest

Publisher Correction: First principles design of multifunctional spintronic devices based on super narrow borophene nanoribbons

The efficacy of C1/2 arthrodesis with C2 root resection at symptomatic side for occipital neuralgia from atlantoaxial osteoarthritis

Physical activity reverses the aging induced decline in angiogenic potential in the fast locomotory muscles of mice

Abstract Fast locomotory muscles, which are responsible for generating the highest power outputs, are more vulnerable to aging than slow muscles. In this study, we aimed to evaluate the impact of middle age and voluntary physical activity on capillarization and angiogenic potential in fast locomotory muscles. Middle-aged (M-group) and young (Y-group) wild-type FVB female mice were randomly assigned to either the sedentary or trained group undergoing 8-week spontaneous wheel running (8-sWR). Capillary density (assessed via immunohistochemical capillary staining and Western immunoblotting) of the fast locomotory muscles in the M-group (15-months old) was not significantly different compared to the Y-group (4-months old). Nevertheless, the expression of key pro-angiogenic genes in the fast muscle of the M-group was lower than that in the fast muscle of Y-group. 8-sWR had no impact on muscle capillarization; however, it increased fast muscle Vegfa expression in both the M and Y groups. We concluded that although fast muscle capillarization is still preserved in middle age, nevertheless the angiogenic potential (at least at the level of gene expression) is significantly reduced at this stage of aging. Moderate-intensity voluntary physical activity had no effect on capillary density, but it increased the angiogenic potential of the fast muscle.