L., an invasive plant originating from South America, is characterized by rapid growth and strong ecological adaptability, posing a threat to China's ecosystems, agricultural industry, and biodiversity. In this study, we optimized the MaxEnt model using the ENMeval package and constructed an ensemble model using the Biomod2 package based on global geospatial distribution data of and considering climate, soil, and topography factors. We simulated the potential suitable distribution of in China at present and in the future (2041-2060, 2061-2080). Through multivariate environment similarity surface and most dissimilar variable analysis, we identified the main environmental variables influencing the distribution of . Additionally, niche analysis elucidated temporal and spatial variations in ' climate niche. Our results demonstrate that the ensemble model, constructed from the top seven single models, outperforms the individual models in predicting the suitable habitat of . The ensemble model achieved the true skill statistic (TSS) of 0.833 and the area under the subject curve (AUC) of 0.971, indicative of outstanding predictive performance. Presently, the suitable habitat of in China primarily exists in the region between 18° and 28° N, covering approximately 1.47 million km. The temperature annual range, precipitation of the wettest month, and mean temperature of the coldest quarter were identified as the primary environmental variables influencing its distribution, while soil and elevation variables had minor roles. Under future climate conditions, the suitable habitat of is expected to expand northeastward, with the centroid of its habitat shifting northward as the climate warms. The migration speed of is projected to increase with the degree of warming. Furthermore, the climate niche of will undergo certain changes and may face both niche expansion and a decrease in niche overlap under different climate conditions.
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http://dx.doi.org/10.1002/ece3.11513 | DOI Listing |
Brief Bioinform
November 2024
School of Artificial Intelligence, Jilin University, 3003 Qianjin Street, 130012 Changchun, China.
Accurate identification of causal genes for cancer prognosis is critical for estimating disease progression and guiding treatment interventions. In this study, we propose CPCG (Cancer Prognosis's Causal Gene), a two-stage framework identifying gene sets causally associated with patient prognosis across diverse cancer types using transcriptomic data. Initially, an ensemble approach models gene expression's impact on survival with parametric and semiparametric hazard models.
View Article and Find Full Text PDFRisk Manag Healthc Policy
January 2025
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, New Taipei City, 235603, Taiwan.
Purpose: As HF progresses into advanced HF, patients experience a poor quality of life, distressing symptoms, intensive care use, social distress, and eventual hospital death. We aimed to investigate the relationship between morality and potential prognostic factors among in-patient and emergency patients with HF.
Patients And Methods: A case series study: Data are collected from in-hospital and emergency care patients from 2014 to 2021, including their international classification of disease at admission, and laboratory data such as blood count, liver and renal functions, lipid profile, and other biochemistry from the hospital's electrical medical records.
Eur J Radiol Open
June 2025
Department of Medical Oncology, The Fifth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, PR China.
Objective: Immunotherapy has become an option for the first-line therapy of advanced gastric cancer (GC), with improved survival. Our study aimed to investigate unresectable GC from an imaging perspective combined with clinicopathological variables to identify patients who were most likely to benefit from immunotherapy.
Method: Patients with unresectable GC who were consecutively treated with immunotherapy at two different medical centers of Chinese PLA General Hospital were included and divided into the training and validation cohorts, respectively.
Adv Sci (Weinh)
January 2025
McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada.
Excitation-inhibition (E/I) imbalance is theorized as a key mechanism in the pathophysiology of epilepsy, with ample research focusing on elucidating its cellular manifestations. However, few studies investigate E/I imbalance at the macroscale, whole-brain level, and its microcircuit-level mechanisms and clinical significance remain incompletely understood. Here, the Hurst exponent, an index of the E/I ratio, is computed from resting-state fMRI time series, and microcircuit parameters are simulated using biophysical models.
View Article and Find Full Text PDFSci Rep
January 2025
Japan Agency for Marine-Earth Science and Technology, 3173-25, Showa-machi, Kanazawa-ku, Yokohama, Kanagawa, 2360001, Japan.
Subsurface seismic velocity structure is essential for earthquake source studies, including hypocenter determination. Conventional hypocenter determination methods ignore the inherent uncertainty in seismic velocity structure models, and the impact of this oversight has not been thoroughly investigated. Here, we address this issue by employing a physics-informed deep learning (PIDL) approach that quantifies uncertainty in two-dimensional seismic velocity structure modeling and its propagation to hypocenter determination by introducing neural network ensembles trained on active seismic survey data, earthquake observation data, and the physical equation of wavefront movement.
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