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Based on phylogenetic analysis, Candolleomyces (Psathyrellaceae, Agaricales) was established with Psathyrella candolleana as the type species. The basidiomes range from small to large and are typically terrestrial, lignicolous, and rarely fimicolous. We analysed the Candolleomyces species collected during five years in China, and based on morphological and molecular data (nrITS, nrLSU, and tef-1α), we propose seven new Candolleomyces species viz. C. brevisporus, C. gyirongicus, C. lignicola, C. luridus, C. shennongdingicus, C. shennongjianus, and C. sichuanicus. Full descriptions, colour photographs, illustrations, phylogenetic analyses results, and comparisons with related Candolleomyces species of the new taxa are provided. This study enriches the species diversity of Candolleomyces in China. [ABSTRACT FROM AUTHOR]

This study addresses two key issues, ie, the "cold-start problem" in transmission prediction of new or rare epidemics and the collaborative allocation of emergency medical resources considering multiple objectives. These two issues have not yet been well addressed in data-driven emergency medical resource allocation systems. A decision support prediction-then-optimization framework combing deep learning and optimization is developed to address these two issues. Two transfer learning based convolutional neural network models are built for epidemic transmission predictions in the initial and the subsequent outbreak regions using transfer learning to deal with the "cold-start problem". A prediction-driven collaborative emergency medical resource allocation model is built to address the issue of collaborative decisions by simultaneously considering the inter- and intra-echelon resource flows in a multi-echelon system and considering the efficiency and fairness as the objective functions. A case study of the COVID-19 pandemic shows that combining transfer learning and convolutional neural networks can improve the performances of epidemic transmission predictions, and good predictions can improve both the efficiency and fairness of emergency medical resource allocation decisions. Moreover, the computational results show that the prediction errors are asymmetrically amplified in the optimization stage, and the shortage of the resource reserve quantity mediates the asymmetrical amplification effect. [ABSTRACT FROM AUTHOR]

Objective: Maintenance hemodialysis (MHD) patients suffer from enormous physical, mental stress and poor quality of life, so an increasing number of patients are in a long-term state of depression. A prominent feature of MHD patients is chronic persistent inflammation, which is also an important mechanism for the onset of depression. Therefore, finding economically convenient inflammatory markers to predict and diagnose the onset of depression in MHD patients is of great value. As a novel inflammatory marker, systemic immune inflammation index (SII) can more comprehensively reflect the inflammation and immunity level of patients. This study aims to explore the relationship between SII and depressive symptoms in MHD patients., Methods: A cross-sectional study was conducted on 206 MHD patients from three dialysis centers. Based on the Hospital Anxiety and Depression Scale (HADS) scores, patients were divided into non-depression and depression groups. Inter group comparison and multivariate logistic regression analysis were performed to determine whether SII is an independent risk factor for depression in MHD patients. Receiver operating characteristic (ROC) curve was used to evaluate the predictive value of SII on depression symptoms in MHD patients., Results: According to the HADS scale score, 38.83% of the included patients were in a state of depression. After adjusting for all confounding factors, MHD patients with SII>963.93 had a 4.709 times higher risk of depression than those with SII ≤ 478.32 (OR=4.709, 95% CI 1.821-12.178, P<0.01). ROC analysis showed that SII>685.11 was the best cutoff value for MHD depression patients, and the area under the curve (AUC) was 0.681., Conclusions: High SII is an independent risk factor for depressed MHD patients and an ideal inflammatory marker for predicting and identifying depression in MHD patients as assessed by the HADS scale., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Han, Zhang, Kong, Liu, Zhang and Ren.)