An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning

As a noninvasive and "task-free" technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients

Single cell atlas for 11 non-model mammals, reptiles and birds.

The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving

Insights into the role of N6-methyladenosine in ferroptosis

N6-methyladenosine (m6A) methylation modification is one of the most prevalent epigenetic modifications of eukaryotic RNA. m6A methylation is widely associated with many biological processes through the modification of RNA metabolism and is associated with multiple disease states. As a newly discovered regulatory cell death in recent years, ferroptosis is an iron-dependent cell death characterized by excessive lipid peroxidation. Emerging evidence supports that ferroptosis has a significant role in the progression of diverse diseases. Besides, the key regulators of ferroptosis exhibit aberrant m6A levels under different pathological conditions. However, the correlation between m6A-modified ferroptosis and multiple diseases has not been well elucidated. In this review, we summarized the functions of m6A in ferroptosis, which are associated with the initiation and progression of multiple diseases. Investigating the role of m6A in ferroptosis might both facilitate a better understanding of the pathogenesis of these diseases and provide new opportunities for targeted treatment

The Role of Autophagy and NLRP3 Inflammasome in Liver Fibrosis

Liver fibrosis is an intrinsic repair process of chronic injury with excessive deposition of extracellular matrix. As an early stage of various liver diseases, liver fibrosis is a reversible pathological process. Therefore, if not being controlled in time, liver fibrosis will evolve into cirrhosis, liver failure, and liver cancer. It has been demonstrated that hepatic stellate cells (HSCs) play a crucial role in the formation of liver fibrosis. In particular, the activation of HSCs is a key step for liver fibrosis. Recent researches have suggested that autophagy and inflammasome have biological effect on HSC activation. Herein, we review current studies about the impact of autophagy and NOD-like receptors containing pyrin domain 3 (NLRP3) inflammasome on liver fibrosis and the underlying mechanisms