Biological invasions are a growing aspect of global biodiversity change. In many regions, introduced species richness increases supralinearly over time. This does not, however, necessarily indicate increasing introduction rates or invasion success. We develop a simple null model to identify the expected trend in invasion records over time. For constant introduction rates and success, the expected trend is exponentially increasing. Model extensions with varying introduction rate and success can also generate exponential distributions. We then analyse temporal trends in aquatic, marine and terrestrial invasion records. Most data sets support an exponential distribution (15/16) and the null invasion model (12/16). Thus, our model shows that no change in introduction rate or success need be invoked to explain the majority of observed trends. Further, an exponential trend does not necessarily indicate increasing invasion success or 'invasional meltdown', and a saturating trend does not necessarily indicate decreasing success or biotic resistance.
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http://dx.doi.org/10.1111/j.1461-0248.2006.00913.x | DOI Listing |
Machine learning approaches including deep learning models have shown promising performance in the automatic detection of Parkinson's disease. These approaches rely on different types of data with voice recordings being the most used due to the convenient and non-invasive nature of data acquisition. Our group has successfully developed a novel approach that uses convolutional neural network with transfer learning to analyze spectrogram images of the sustained vowel /a/ to identify people with Parkinson's disease.
View Article and Find Full Text PDFArch Esp Urol
December 2024
Polytechnic University of Coimbra, 3045-093 Coimbra, Portugal.
Penile cancer (PeCa) ranks as the 30th most prevalent cancer globally, predominantly affecting populations in developing countries. Phimosis and Human Papillomavirus (HPV) infection are recognized as the primary risk factors. Early-stage diagnosis typically warrants limited excision or non-invasive therapies.
View Article and Find Full Text PDFBMC Ecol Evol
January 2025
Botany & Microbiology Department, Faculty of Science, Suez Canal University, Ismailia, Egypt.
Background: The destructive human activities, encroachment of natural habitats, and hyperarid climate threaten the wild flora of the unprotected mountainous areas facing the Gulf of Suez, Egypt. So, this study aims to revise and give an updated systematic status of the flowering plants growing there to conserve and utilize valuable biodiversity.
Results: This study showed the presence of 136 species, including 7 sub-species of vascular plants, 12 species of monocots, and 124 species dicots belonged to 98 genera and 37 families.
Commun Med (Lond)
January 2025
Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA.
Background: The ability to non-invasively measure left atrial pressure would facilitate the identification of patients at risk of pulmonary congestion and guide proactive heart failure care. Wearable cardiac monitors, which record single-lead electrocardiogram data, provide information that can be leveraged to infer left atrial pressures.
Methods: We developed a deep neural network using single-lead electrocardiogram data to determine when the left atrial pressure is elevated.
Clin Exp Nephrol
January 2025
Department of Nephrology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 464-8550, Japan.
Background: Identifying patients on dialysis among those with an estimated glomerular filtration rate (eGFR) < 15 mL/min/1.73 m remains challenging. To facilitate clinical research in advanced chronic kidney disease (CKD) using electronic health records, we aimed to develop algorithms to identify dialysis patients using laboratory data obtained in routine practice.
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