Mechanistic insights into genomic structure and functions of a novel oncogene YEATS4

As a novel oncogene, the role of YEATS domain-containing protein 4 (YEATS4) in the occurrence, development, and treatment of tumors is now beginning to be appreciated. YEATS4 plays an important role in regulating DNA repair during replication. The upregulation of YEAST4 promotes DNA damage repair and prevents cell death, whereas its downregulation inhibits DNA replication and induces apoptosis. Additionally, accumulating evidence indicates that the aberrant activation of YEATS4 leads to changes in drug resistance, epithelial-mesenchymal transition and also in the migration and invasion capacity of tumor cells. Therefore, specific inhibition of the expression or activity of YEATS4 protein may be an effective strategy for inhibiting the proliferation, motility, differentiation, and/or survival of tumor cells. Taken together, YEATS4 has emerged as a potential target for multiple cancers and is an attractive protein for the development of small-molecule inhibitors. However, research on YEAST4 in tumor-related fields is limited and its biological functions, metabolism, and the regulatory mechanism of YEATS4 in numerous cancers remain undetermined. This review comprehensively and extensively summarizes the functions, structure and oncogenic roles of YEATS4 in cancer progression and aims to further contribute to the study of its underlying molecular mechanism and targeted drugs

The robust shortest path problem for multimodal transportation considering timetable with interval data

In the multimodal transport network, due to various uncertain factors such as weather and traffic conditions, the transport time will become uncertain accordingly, besides, railway transport and water transport are usually limited by timetable. These factors will inevitably affect the path selection. The purpose of this study is to seek an optimal transport scheme that considers both the uncertainty of the multimodal transport network and the timetable limit. In view of the uncertainties of the multimodal transport network, interval data are used to represent the uncertainty of network weights, and robust optimization method is then adopted to process the interval data. An optimal model of robust shortest path considering timetable limit is established and genetic algorithm (GA) is designed to solve the problem. The GA designed provides an encoding method for variable-length chromosomes applicable to shortest path problem solving in the multimodal transport. And the handling methods for loops and inaccessible paths due to chromosome crossover and mutation are also suggested. And finally, numerical examples are provided to verify the validity of the model and algorithm

Motor Learning Improves the Stability of Large-Scale Brain Connectivity Pattern

Repeated practice is fundamental to the acquisition of skills, which is typically accompanied by increasing reliability of neural representations that manifested as more stable activation patterns for the trained stimuli. However, large-scale neural pattern induced by learning has been rarely studied. Here, we investigated whether global connectivity patterns became more reliable as a result of motor learning using a novel analysis of the multivariate pattern of functional connectivity (MVPC). Human participants were trained with a finger-tapping motor task for five consecutive days and went through Functional magnetic resonance imaging (fMRI) scanning before and after training. We found that motor learning increased the whole-brain MVPC stability of the primary motor cortex (M1) when participants performed the trained sequence, while no similar effects were observed for the untrained sequence. Moreover, the increase of MVPC stability correlated with participants’ improvement in behavioral performance. These findings suggested that the acquisition of motor skills was supported by the increased connectivity pattern stability between the M1 and the rest of the brain. In summary, our study not only suggests global neural pattern stabilization as a neural signature for effective learning but also advocates applying the MVPC analysis to reveal mechanisms of distributed network reorganization supporting various types of learning

Developmental reorganization of the core and extended face networks revealed by global functional connectivity

Prior studies on development of functional specialization in human brain mainly focus on age-related increases in regional activation and connectivity among regions. However, a few recent studies on the face network demonstrate age-related decrease in face-specialized activation in the extended face network (EFN), in addition to increase in activation in the core face network (CFN). Here we used a voxel-based global brain connectivity approach to investigate whether development of the face network exhibited both increase and decrease in network connectivity. We found the voxel-wise resting-state functional connectivity (FC) within the CFN increased with age in bilateral posterior superior temporal sulcus, suggesting the integration of the CFN during development. Interestingly, the FC of the voxels in the EFN to the right fusiform face area and occipital face area decreased with age, suggesting that the CFN segregated from the EFN during development. Moreover, the age-related connectivity in the CFN was related to behavioral performance in face processing. Overall, our study demonstrated developmental reorganization of the face network achieved by both integration within the CFN and segregation of the CFN from the EFN, which may account for the simultaneous increases and decreases in neural activation during the development of the face network

Motor Training Increases the Stability of Activation Patterns in the Primary Motor Cortex

Learning to be skillful is an endowed talent of humans, but neural mechanisms underlying behavioral improvement remain largely unknown. Some studies have reported that the mean magnitude of neural activation is increased after learning, whereas others have instead shown decreased activation. In this study, we used functional magnetic resonance imaging (fMRI) to investigate learning-induced changes in the neural activation in the human brain with a classic motor training task. Specifically, instead of comparing the mean magnitudes of activation before and after training, we analyzed the learning-induced changes in multi-voxel spatial patterns of neural activation. We observed that the stability of the activation patterns, or the similarity of the activation patterns between the even and odd runs of the fMRI scans, was significantly increased in the primary motor cortex (M1) after training. By contrast, the mean magnitude of neural activation remained unchanged. Therefore, our study suggests that learning shapes the brain by increasing the stability of the activation patterns, therefore providing a new perspective in understanding the neural mechanisms underlying learning

Typical and Atypical Development of Functional Connectivity in the Face Network

Extensive studies have demonstrated that face recognition performance does not reach adult levels until adolescence. However, there is no consensus on whether such prolonged improvement stems from development of general cognitive factors or face-specific mechanisms. Here, we used behavioral experiments and functional magnetic resonance imaging (fMRI) to evaluate these two hypotheses. With a large cohort of children (n = 379), we found that the ability of face-specific recognition in humans increased with age throughout childhood and into late adolescence in both face memory and face perception. Neurally, to circumvent the potential problem of age differences in task performance, attention, or cognitive strategies in task-state fMRI studies, we measured the resting-state functional connectivity (RSFC) between the occipital face area (OFA) and fusiform face area (FFA) in human brain and found that the OFA-FFA RSFC increased until 11-13 years of age. Moreover, the OFA-FFA RSFC was selectively impaired in adults with developmental prosopagnosia (DP). In contrast, no age-related changes or differences between DP and normal adults were observed for RSFCs in the object system. Finally, the OFA-FFA RSFC matured earlier than face selectivity in either the OFA or FFA. These results suggest the critical role of the OFA-FFA RSFC in the development of face recognition. Together, our findings support the hypothesis that prolonged development of face recognition is face specific, not domain general.status: publishe

The hippocampus underlies the association between self-esteem and physical health

Abstract Self-esteem refers to the extent to which we feel positive or negative about ourselves, and reflects an individual’s subjective evaluation of personal worth and attitudes about the self. As one kind of positive psychosocial resources, high self-esteem has been found to buffer the effects of stress on physical health. However, little is known about the possible neural basis underlying the association between physical health and self-esteem. In the present study, we investigated whether the hippocampus served as a neuroanatomical basis for the association between self-esteem and physical health in a large population of healthy young adults. We examined self-esteem and self-reported physical health with the Rosenberg Self Esteem Scale (RSES) and the Chinese Constitution Questionnaire (CCQ) respectively, and gray matter volume of the hippocampus was measured using magnetic resonance imaging. As expected, we found that individuals with higher levels of self-esteem had better self-reported physical health. Importantly, the mediation analysis showed that the gray matter volume of the hippocampus mediated the link between self-esteem and physical health, suggesting its critical role in the neural circuitry through which self-esteem is related to physical health