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娱乐百分百Chinese scientists develop ‘brain-reading’ AI model to help predict depression risk, may inspire future emotional-perception humanoids_我的网站
A | 宁夏西吉8月24日电 题：闽宁协作三十载：西吉小菌棒成为群众增收“金棒棒” 记者于晶 24日，记者走进固原市西吉县新营乡甘井村菌菇种植基地，连片大棚整齐排列，棚内菌棒码放整齐，平菇、杏鲍菇蓬勃生长，村民忙着采摘分拣，处处是丰收忙碌的景象。  Chinese scientists have developed a “brain-reading” AI model that could help predict the risk of depression among adolescents up to four years in advance by analyzing how humans respond to facial expressions, a technology expected to inspire future development of embodied intelligent humanoids capable of perceiving human emotion and thoughts through nuanced facial cues. WHO data show that around 332 million people worldwide have depression, about one-third of whom have treatment-resistant forms of the condition. In China, an estimated 95 million people suffer from depression, National Business Daily reported, citing statistics from the China Mental Health Survey. Using data from a population-based longitudinal adolescent cohort recruited across several European countries, the research team led by Lu Han, assistant professor at the School of Artificial Intelligence, Shenzhen University, has built an AI model that predicted which 19-year-olds were more likely to develop depression at the age of 23. The predictions were backed up by an independent clinical cohort of individuals with depression. The team’s paper was published in the journal Science Advances this month. According to Lu, the study used brain scans taken at age 19 to predict depression-related symptoms at age 23. The study focuses on adolescence because the transition from adolescence to early adulthood is a key developmental period when depressive symptoms can increase rapidly. The earlier risks are identified, the greater the opportunity for prevention, Lu told the Global Times on Monday, adding that the findings need to be further validated in middle-aged and older adults and across different ethnic groups in future research. In this study, the researchers analyzed data from adolescents in the IMAGEN, a population-based longitudinal cohort recruited across several European countries. At age 19, participants underwent an fMRI emotional-face task, and their emotional symptoms were assessed using standardized questionnaires. Genetic data obtained from blood samples were also analyzed, and participants were followed up at age 23. The researchers examined whether neural representations of angry faces at age 19 were associated with emotional symptoms and could predict elevated emotional symptoms four years later. According to Lu, people without depression can more easily distinguish emotional changes based on others’ facial expressions and respond accordingly – for example, responding with friendliness to a smiling expression. But people with depression cannot do this, and are more likely to assume people are angry with them. A brain-aligned deep-learning model developed by Lu’s team suggested that those participants whose brains were less able to distinguish between different facial emotions and tended to perceive others as angry were more likely to develop symptoms of depression and anxiety in adulthood. The hypothesis that adolescents at risk of depression may respond differently to other people’s facial expressions than those without such risk based on the negative information processing bias long observed in depression research: people at risk of depression are more likely to notice, interpret, or remember negative social information, Lu said. The researchers focused on angry facial expressions because they signal social threat and rejection, which are closely linked to interpersonal difficulties and negativity bias associated with depression. They hope to further understand how this bias develops within the visual system. Building on this, they created a deep learning model, which mimics how the brain processes visual information, to predict how the brain encodes abstract emotional concepts such as anger. They found that 19-year-olds whose response to facial expressions was skewed in favour of negative emotions or memories were the most likely to develop some form of depression. Based on these findings, Lu’s team then developed a marker that can identify possible warning signs. According to Lu, the study found that the computational biomarker was linked to the depression-related variant rs11123030 and polygenic risk for depression, suggesting that genetic susceptibility may affect emotional perception. It also provided predictive information beyond family stress and socioeconomic factors, complementing rather than replacing environmental risk factors. Therefore, depression is neither purely genetic nor purely psychological, but a complex mental disorder arising from the interplay of genetic susceptibility, brain development, emotional and cognitive processes, and life experiences. According to Lu, the study is also expected to advance AI by aligning deep neural networks with human brain activity and using parameter perturbations to probe neural mechanisms, allowing models to both predict and explain how biases may arise. The findings suggest that future affective computing and embodied AI should go beyond simply labeling facial expressions, incorporating visual details while preventing prior assumptions from overriding real-time sensory input, Lu said, adding that the findings could provide valuable insights for developing more interpretable robotic perception systems that more closely emulate the way humans process emotions. 。依托闽宁协作山海纽带，福建福清企业家周长金带着技术扎根六盘山区，让一根根菌棒成为群众稳岗增收的“金棒棒”。
B | 2007年，周长金初到宁夏中卫，南北气候差异曾让他萌生退意。担任食用菌技术员后，他潜心钻研菌种培育与大棚管护。实践中他发现，西吉冷凉干燥、昼夜温差大，菌菇品质出众，还可与东南产区错峰上市，市场潜力巨大，便决心扎根西北发展菌菇产业。 在中卫、武威积累产业经验后，2024年，经闽宁协作西吉工作组对接，周长金将产业布局落在甘井村，注册成立宁夏闽荣鑫农业科技有限公司。
C | 此前村里150栋温室大棚种植蔬菜收益不高，部分设施闲置。企业引入福建种植团队与标准化管护模式，发展多品类食用菌，配套建设菌棒车间与700吨冷链冷库，年产菌棒超240万棒，年产值达1000万元，鲜菇销往北京、上海、四川等地。
D | 福清市投入390万元建设甘井村菌菇鲜储加工基地，年周转鲜菇2000余吨，带动村集体年增收15万元以上，提供5个固定岗位，助力闽宁示范村建设。
E | 周长金表示，这不是单向帮扶，而是闽宁两地合伙干事，福建输出技术渠道，西吉提供土地人力，资源互补才能让产业行稳致远。 当地推行“村集体+企业+农户”联营模式，村集体闲置大棚对外租赁，年获租金24万余元。企业推出低门槛合作，农户仅需缴纳1元定金，菌棒余款从卖菇收益中抵扣，降低创业门槛。
F | 基地带动群众就近务工，日薪130元，采收高峰单日用工160人，务工群众月入3000余元，年增收近20000元。 产业壮大的同时，废弃菌棒处理成为难题。西吉每年产生千万根废旧菌棒，填埋焚烧存在生态风险。企业引进福建微生物发酵技术，在火石寨乡建成有机肥加工厂，上门免费回收菌渣，经破碎腐熟加工成优质有机肥，预计年产有机肥5万吨，实现农业废弃物变废为宝。 企业还联合西北农林科技大学选育苹果新品种，建成249亩苹果示范园，打造“种菇—菌渣制肥—果园还田”循环链条。有机肥厂吸纳56名村民稳定就业，累计发放薪酬230余万元。菌渣有机肥改良盐碱地效果突出，企业计划对接本地枸杞基地拓展销路。 下一步，新营乡将联动中国商飞帮扶团队与闽籍企业，优化联农带农机制，培育榆黄菇等新品种，完善收益共享，让更多群众共享产业发展红利。 Current article:http://www.lichuorenrenyitankanmeimu.cfd/2v4/qcmfe.pptx Published on:13:16:15 |
