Multi-Grained Named Entity Recognition

This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested. Different from traditional approaches regarding NER as a sequential labeling task and annotate entities consecutively, MGNER detects and recognizes entities on multiple granularities: it is able to recognize named entities without explicitly assuming non-overlapping or totally nested structures. MGNER consists of a Detector that examines all possible word segments and a Classifier that categorizes entities. In addition, contextual information and a self-attention mechanism are utilized throughout the framework to improve the NER performance. Experimental results show that MGNER outperforms current state-of-the-art baselines up to 4.4% in terms of the F1 score among nested/non-overlapping NER tasks.Comment: In ACL 2019 as a long pape

Digital twin brain: a bridge between biological intelligence and artificial intelligence

In recent years, advances in neuroscience and artificial intelligence have paved the way for unprecedented opportunities for understanding the complexity of the brain and its emulation by computational systems. Cutting-edge advancements in neuroscience research have revealed the intricate relationship between brain structure and function, while the success of artificial neural networks highlights the importance of network architecture. Now is the time to bring them together to better unravel how intelligence emerges from the brain's multiscale repositories. In this review, we propose the Digital Twin Brain (DTB) as a transformative platform that bridges the gap between biological and artificial intelligence. It consists of three core elements: the brain structure that is fundamental to the twinning process, bottom-layer models to generate brain functions, and its wide spectrum of applications. Crucially, brain atlases provide a vital constraint, preserving the brain's network organization within the DTB. Furthermore, we highlight open questions that invite joint efforts from interdisciplinary fields and emphasize the far-reaching implications of the DTB. The DTB can offer unprecedented insights into the emergence of intelligence and neurological disorders, which holds tremendous promise for advancing our understanding of both biological and artificial intelligence, and ultimately propelling the development of artificial general intelligence and facilitating precision mental healthcare

Defining Fatty Acid Changes Linked to Rumen Development, Weaning and Growth in Holstein-Friesian Heifers

After birth, as effectively monogastric animals, calves undergo substantial physiological changes to become ruminants by 3 months of age and reach sexual maturity at approximately 15 months of age. Herein, we assess longitudinal metabolomic changes in Holstein-Friesian (HF) heifers from birth until sexual maturity during this developmental process. Sera from 20 healthy, HF heifers were sampled biweekly from 2 weeks of age until 13 months of age and then monthly until 19 months of age. Sera were assessed using flow infusion electrospray high-resolution mass spectrometry (FIE-HRMS) on a Q Exactive hybrid quadrupole-Orbitrap mass spectrometer for high-throughput, sensitive, non-targeted metabolite fingerprinting. Partial least squares discriminant analysis (PLS-DA) and unsupervised hierarchical clustering analysis (HCA) of the derived metabolomes indicated changes detectable in heifers’ sera over time. Time series analyses identified 30 metabolites that could be related to rumen development and weaning at ~3 months of age. Further time series analysis identified 40 metabolites that could be correlated with growth. These findings highlight the role of acetic acid and 3-phenylpropionate (3-PP) in rumen development and growth, suggest that weaning induces elevated levels of fatty acyls in response to a post-weaning stress-induced innate immune response and demonstrate the utilization of fatty acyls in growth. The identified metabolites offer serum metabolites which could inform the nutrition and healthy development of heifers

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Physiological and Psychological Effects of Nature Experiences in Different Forests on Young People

Many studies have proved that having nature experiences in forests is conducive to human physiological and psychological health. However, currently there is little research focusing on the effects of forest characteristics and the experiential characteristics of nature experiences on changes in health. In the study, three types of forest (mixed forest; deciduous forest; coniferous forest) and an urban site were used to measure the effects of these environments on participants’ physiological and psychological restoration after nature experience activities (sitting and walking activities). The study participants were 30 young adult students from Beijing Forestry University. Restorative effects were measured by physiological indicators (blood pressure and heart rate) and four psychological questionnaires (Profile of Mood States (POMS); Restorative Outcome Scale (ROS); Subjective Vitality Scale (SVS); Warwick-Edinburgh Mental Well-being Scale (WEMWBS)). Results demonstrated that all types of forest were beneficial to lower blood pressure and heart rate as well as to reduce negative feelings while boosting positive emotions. The mixed forest was more effective in lowering blood pressure and heart rate as well as increasing vitality. The levels of restoration and positive mental health increased significantly, while all subscales of the POMS (with the exception of vigor) decreased greatly in the coniferous forest. Relative to the sitting activity, obvious decreases in blood pressure and negative emotions were observed, while significant increases in restoration, vitality and positive mental health were observed after the walking activity. In conclusion, the impact on subjects’ health restoration varied with different forest characteristics, and the experiential characteristics of exposure may be helpful for creating supportive interventions and lifting the benefits of forest therapy as people interact with the forest

Co-activation Probability Estimation (CoPE): An approach for modeling functional co-activation architecture based on neuroimaging coordinates

Recent progress in functional neuroimaging has prompted studies of brain activation during various cognitive tasks. Coordinate-based meta-analysis has been utilized to discover the brain regions that are consistently activated across experiments. However, within-experiment co-activation relationships, which can reflect the underlying functional relationships between different brain regions, have not been widely studied. In particular, voxel-wise co-activation, which may be able to provide a detailed configuration of the co-activation network, still needs to be modeled. To estimate the voxel-wise co-activation pattern and deduce the co-activation network, a Co-activation Probability Estimation (CoPE) method was proposed to model within-experiment activations for the purpose of defining the co-activations. A permutation test was adopted as a significance test. Moreover, the co-activations were automatically separated into local and long-range ones, based on distance. The two types of co-activations describe distinct features: the first reflects convergent activations; the second represents co-activations between different brain regions. The validation of CoPE was based on five simulation tests and one real dataset derived from studies of working memory. Both the simulated and the real data demonstrated that CoPE was not only able to find local convergence but also significant long-range co-activation. In particular, CoPE was able to identify a ‘core’ co-activation network in the working memory dataset. As a data-driven method, the CoPE method can be used to mine underlying co-activation relationships across experiments in future studies

Hierarchy of connectivity-function relationship of the human cortex revealed through predicting activity across functional domains

Many studies showed that anatomical connectivity supports both anatomical and functional hierarchies that span across the primary and association cortices in the cerebral cortex. Even though a structure–function relationship has been indicated to uncouple in the association cortex, it is still unknown whether anatomical connectivity can predict functional activations to the same degree throughout the cortex, and it remains unclear whether a hierarchy of this connectivity–function relationship (CFR) exists across the human cortex. We first addressed whether anatomical connectivity could be used to predict functional activations across different functional domains using multilinear regression models. Then, we characterized the CFR by predicting activity from anatomical connectivity throughout the cortex. We found that there is a hierarchy of CFR between sensory–motor and association cortices. Moreover, this CFR hierarchy was correlated to the functional and anatomical hierarchies, respectively, reflected in functional flexibility and the myelin map. Our results suggest a shared hierarchical mechanism in the cortex, a finding which provides important insights into the anatomical and functional organizations of the human brain

Coastal Wetlands Play an Important Role in the Ecological Security Pattern of the Coastal Zone

The construction of an ecological security pattern can effectively overcome the contradiction between regional human exploitation and ecological protection in the coastal zone. Taking the Xiangshan Bay (XSB) basin as an example, this study identified ecological source areas from three aspects, namely ecosystem services’ importance, ecological sensitivity, and landscape connectivity, and then constructed ecological resistance surfaces, identified ecological corridors, and constructed an ecological security pattern. The results show that the natural reserves in the XSB basin were all located in the identified primary ecological source areas, thus indicating the feasibility and reliability of the “importance–connectivity–sensitivity” ecological source identification mechanism in this study. The ecological corridor in the coastal wetland area accounts for about 40% of the total corridor length, which is the link connecting other ecological sources, revealing the important role of coastal wetlands in the coastal ecosystem. Through the ecological security pattern of the XSB basin and field investigation, we put forward suggestions such as clearing Spartina alterniflora, restoring salt marsh wetland vegetation, and strengthening follow-up monitoring for the restoration of coastal wetlands. This study is expected to provide reference and guidance for the improvement of coastal zone ecological protection and restoration