ON THE CONSTRUCTION OF DUALLY FLAT FINSLER METRICS

In this paper, we give a new approach to find a dually flat Finsler metric. As its application, we produce many new spherically symmetric dually flat Fins ler metrics by using known projective spherically symmetric Fins ler metrics.National Natural Science Foundation of China [11371032, 11301283]; Doctoral Program of Higher Education of China [20110001110069]SCI(E)[email protected]; [email protected]; [email protected]

Modeling and analysis of release strategies of sterile mosquitoes incorporating stage and sex structure of wild ones

This paper proposes and studies a switched interactive model of wild and sterile mosquitoes with stage and sex structure. Sterile males are released periodically and impulsively and remain sexually active for time $\bar{T}$. We investigate the dynamical behavior of the system when the release period $T$ is shorter than the sexual lifespan $\bar{T}$, corresponding to a relatively frequent release. We first determine two important thresholds, $m_1^*$ and $m_2^*$, for the release amount $m$ and prove the exponential asymptotic stability of the extinction equilibrium. Using fixed point theory, we establish the existence of positive periodic solutions for 0 < m < m_1^* and m_1^*\leq m < m_2^* . Furthermore, by applying the comparison theorem of monotone systems, we demonstrate that the extinction equilibrium is globally asymptotically stable when $m\geq m_2^*$. Finally, numerical examples are presented to confirm our theoretical results

Editorial: Advances in deep learning methods for medical image analysis

The rapid development of artificial intelligence (AI) technology is leading many innovations in the medical field and is playing a major role in establishing objective, consistent, and efficient medical environments with large-scale data. Deep learning represented by convolutional neural networks has achieved remarkable performance improvement in medical image processing fields such as image segmentation, registration, and enhancement. Furthermore, AI technology with deep learning is pioneering medical applications, such as lesion detection, differential diagnosis, disease prognosis, and surgical planning. More advanced AI technologies, such as transformers with self-attention mechanisms, allowing for learning global dependencies, have been widely applied, which further enhanced the capability of deep learning to analyze medical images. However, despite the remarkable advances in deep learning, many challenges remain. For example, when training data are biased or incomplete, deep learning models may fail to achieve the good generalization capability required to solve real-world problems. In addition, the limitations of deep learning models in interpreting results, and misunderstandings of their intended uses and hypotheses make it difficult for AI to gain trust in healthcare settings. In this regard, disease-specific neural networks, generalized learning methods, high-quality training data, and external evaluation based on testable hypotheses can ensure the reliability of medical AI technologies for humans

YeastWeb: a workset-centric web resource for gene family analysis in yeast

<p>Abstract</p> <p>Background</p> <p>Currently, a number of yeast genomes with different physiological features have been sequenced and annotated, which provides invaluable information to investigate yeast genetics, evolutionary mechanism, structure and function of gene families.</p> <p>Description</p> <p>YeastWeb is a novel database created to provide access to gene families derived from the available yeast genomes by assigning the genes into putative families. It has many useful features that complement existing databases, such as SGD, CYGD and Génolevures: 1) Detailed computational annotation was conducted with each entry with InterProScan, EMBOSS and functional/pathway databases, such as GO, COG and KEGG; 2) A well established user-friendly environment was created to allow users to retrieve the annotated genes and gene families using functional classification browser, keyword search or similarity-based search; 3) Workset offers users many powerful functions to manage the retrieved data efficiently, associate the individual items easily and save the intermediate results conveniently; 4) A series of comparative genomics and molecular evolution analysis tools are neatly implemented to allow users to view multiple sequence alignments and phylogenetic tree of gene families. At present, YeastWeb holds the gene families clustered from various MCL inflation values from a total of 13 available yeast genomes.</p> <p>Conclusions</p> <p>Given the great interest in yeast research, YeastWeb has the potential to become a useful resource for the scientific community of yeast biologists and related researchers investigating the evolutionary relationship of yeast gene families. YeastWeb is available at <url>http://centre.bioinformatics.zj.cn/Yeast/</url>.</p

Editorial: Advances in deep learning methods for medical image analysis

The rapid development of artificial intelligence (AI) technology is leading many innovations in the medical field and is playing a major role in establishing objective, consistent, and efficient medical environments with large-scale data. Deep learning represented by convolutional neural networks has achieved remarkable performance improvement in medical image processing fields such as image segmentation, registration, and enhancement. Furthermore, AI technology with deep learning is pioneering medical applications, such as lesion detection, differential diagnosis, disease prognosis, and surgical planning. More advanced AI technologies, such as transformers with self-attention mechanisms, allowing for learning global dependencies, have been widely applied, which further enhanced the capability of deep learning to analyze medical images. However, despite the remarkable advances in deep learning, many challenges remain. For example, when training data are biased or incomplete, deep learning models may fail to achieve the good generalization capability required to solve real-world problems. In addition, the limitations of deep learning models in interpreting results, and misunderstandings of their intended uses and hypotheses make it difficult for AI to gain trust in healthcare settings. In this regard, disease-specific neural networks, generalized learning methods, high-quality training data, and external evaluation based on testable hypotheses can ensure the reliability of medical AI technologies for humans

Ferroptosis: new insight into the mechanisms of diabetic nephropathy and retinopathy

Diabetic nephropathy (DN) and diabetic retinopathy (DR) are the most serious and common diabetes-associated complications. DN and DR are all highly prevalent and dangerous global diseases, but the underlying mechanism remains to be elucidated. Ferroptosis, a relatively recently described type of cell death, has been confirmed to be involved in the occurrence and development of various diabetic complications. The disturbance of cellular iron metabolism directly triggers ferroptosis, and abnormal iron metabolism is closely related to diabetes. However, the molecular mechanism underlying the role of ferroptosis in DN and DR is still unclear, and needs further study. In this review article, we summarize and evaluate the mechanism of ferroptosis and its role and progress in DN and DR, it provides new ideas for the diagnosis and treatment of DN and DR