人类指掌皮肤嵴纹与智力发育的相关性研究

本工作对120例遗传型智残者和60例非遗传型智残者的指、掌皮肤嵴纹数进行分析,并分别与相同例数的对照组同类资料进行比较,找出与智力发育相关的指端、指间区、指基部皮纹参数,据此将遗传型智残组按智商值的不同分为6个组,将不同智商组对应的各区嵴纹数进行分析处理。结果表明,指端皮肤嵴纹数与智商呈正相关,相关系数r＝0.8319,各指间区和指基部嵴纹数与智商值呈负相关,相关系数r＝-0.7392。 Abstract　Digital and palmar of 120 cases of hereditary mental deficiency and 60 cases of non-hereditary mental deficiency were analysed, and compared with the control group using the same cases and identical data, and then skin vein parameter interrelated with intelligence development for digital end, interdigital area, digital root were found. In the light of this we divided the hereditary mental deficiency group into 6 accorcding to IQ value, and analysed statistically every area ridge count corresponding to different IQ group. The result showed that ridge count of digital end was positively correlated with IQ value correlative coefficientr=0.8319; Whereas ridge count of interdigital area and digital root were negatively correlated with IQ value correlative cofficientr=0.7392.     

Learning, working memory, and intelligence revisited

Based on early findings showing low correlations between intelligence test scores and learning on laboratory tasks, psychologists typically have dismissed the role of learning in intelligence and emphasized the role of working memory instead. In 2006, however, B.A. Williams developed a verbal learning task inspired by three-term reinforcement contingencies and reported unexpectedly high correlations between this task and Raven's Advanced Progressive Matrices (RAPM) scores [Williams, B.A., Pearlberg, S.L., 2006. Learning of three-term contingencies correlates with Raven scores, but not with measures of cognitive processing. Intelligence 34, 177-191]. The present study replicated this finding: Performance on the three-term learning task explained almost 25% of the variance in RAPM scores. Adding complex verbal working memory span, measured using the operation span task, did not improve prediction. Notably, this was not due to a lack of correlation between complex working memory span and RAPM scores. Rather, it occurred because most of the variance captured by the complex working memory span was already accounted for by the three-term learning task. Taken together with the findings of Williams and Pearlberg, the present results make a strong case for the role of learning in performance on intelligence tests.     

Toward optimizing vision and cognition in term infants by dietary docosahexaenoic and arachidonic acid supplementation: A review of randomized controlled trials

The question of whether a dietary supply of docosahexaenoic acid (DHA) and arachidonic acid (ARA) imparts advantages to visual or cognitive development in term infants has been debated for many years. DHA and ARA are present in human milk, and nursing infants consume these fatty acids needed for rapid synthesis of cell membranes, particularly neural cells. The reported mean DHA and ARA levels of human milk worldwide are 0.32% and 0.47% of total fatty acids, respectively. Prior to 2002 in the US, formula-fed infants did not receive these fatty acids and relied solely on endogenous conversion of the dietary essential omega-3 (n-3) and omega-6 (n-6) fatty acids, α-linolenic and linoleic acids, to DHA and ARA, respectively. Formula-fed infants were found to have significantly less accretion of DHA in brain cortex after death than breastfed infants. Numerous studies have found positive correlations between blood DHA levels and improvements in cognitive or visual function outcomes of breastfed and formula-fed infants. Results of randomized controlled clinical trials of term formula-fed infants evaluating functional benefits of dietary DHA and ARA have been mixed, likely due to study design heterogeneity. A comparison of visual and cognitive outcomes in these trials suggests that dietary DHA level is particularly relevant. Trials with formulas providing close to the worldwide human milk mean of 0.32% DHA were more likely to yield functional benefits attributable to DHA. We agree with several expert groups in recommending that infants receive at least 0.3% DHA, with at least 0.3% ARA, in infant feedings; in addition, some clinical evidence suggests that an ARA:DHA ratio greater than 1:1 is associated with improved cognitive outcomes.     

智力低下患者的手纹分析

本文对安徽合肥和芜湖地区284例（男145人,女139人）2－59岁智力低下患者的手纹进行了分析。结果表明：患者的皮纹参数与正常对照组有明显的统计学差异,其中尺箕、箕／箕组合、帐弓、皮纹密度、atd角、t距比、掌褶通贯型、悉尼型、过渡Ⅰ型和小指短小类型等项参数均高于对照组（P＜0.01);而简斗、斗／斗组合、a-dRC、t-dRC低于对照组（P＜0.01),a-bRC亦低于对照组（P＜0.05)。这些皮纹参数可以作为智力低下患者的辅助诊断指标。     

脑梗死后智能障碍影响因素分析

目的 探讨影响脑梗死后智能障碍的因素。方法 应用长谷川痴呆量表对249例经CT证实的脑梗死患者进行智能测评,评分0～21．5为智能障碍组,22～32．5为正常组,探讨智能障碍的发生与性别、年龄、文化程度、卒中主要危险因素(高血压和糖尿病)、梗死灶数量及体积,以及脑萎缩和脑白质疏松之间的相互关系．结果 249例脑梗死患者中有106例(42．6％)出现智能障碍．并发现年龄、性别、文化程度、高血压病、糖尿病与智能障碍的发生有关;梗死灶的数量越多、体积越大其智能障碍发生率越高;脑萎缩及脑白质疏松与智能障碍关系密切．结论 脑梗死后智能障碍是多因素相互作用的结果,与梗死灶数量、体积、脑萎缩及脑白质疏松等多种因素有关．     

Current applications of deep-learning in neuro-oncological MRI

PurposeMagnetic Resonance Imaging (MRI) provides an essential contribution in the screening, detection, diagnosis, staging, treatment and follow-up in patients with a neurological neoplasm. Deep learning (DL), a subdomain of artificial intelligence has the potential to enhance the characterization, processing and interpretation of MRI images. The aim of this review paper is to give an overview of the current state-of-art usage of DL in MRI for neuro-oncology.MethodsWe reviewed the Pubmed database by applying a specific search strategy including the combination of MRI, DL, neuro-oncology and its corresponding search terminologies, by focussing on Medical Subject Headings (Mesh) or title/abstract appearance. The original research papers were classified based on its application, into three categories: technological innovation, diagnosis and follow-up.ResultsForty-one publications were eligible for review, all were published after the year 2016. The majority (N = 22) was assigned to technological innovation, twelve had a focus on diagnosis and seven were related to patient follow-up. Applications ranged from improving the acquisition, synthetic CT generation, auto-segmentation, tumor classification, outcome prediction and response assessment. The majority of publications made use of standard (T1w, cT1w, T2w and FLAIR imaging), with only a few exceptions using more advanced MRI technologies. The majority of studies used a variation on convolution neural network (CNN) architectures.ConclusionDeep learning in MRI for neuro-oncology is a novel field of research; it has potential in a broad range of applications. Remaining challenges include the accessibility of large imaging datasets, the applicability across institutes/vendors and the validation and implementation of these technologies in clinical practise.     

Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives

Over the last years, technological innovation in Radiotherapy (RT) led to the introduction of Magnetic Resonance-guided RT (MRgRT) systems.Due to the higher soft tissue contrast compared to on-board CT-based systems, MRgRT is expected to significantly improve the treatment in many situations. MRgRT systems may extend the management of inter- and intra-fraction anatomical changes, offering the possibility of online adaptation of the dose distribution according to daily patient anatomy and to directly monitor tumor motion during treatment delivery by means of a continuous cine MR acquisition.Online adaptive treatments require a multidisciplinary and well-trained team, able to perform a series of operations in a safe, precise and fast manner while the patient is waiting on the treatment couch.Artificial Intelligence (AI) is expected to rapidly contribute to MRgRT, primarily by safely and efficiently automatising the various manual operations characterizing online adaptive treatments. Furthermore, AI is finding relevant applications in MRgRT in the fields of image segmentation, synthetic CT reconstruction, automatic (on-line) planning and the development of predictive models based on daily MRI.This review provides a comprehensive overview of the current AI integration in MRgRT from a medical physicist’s perspective. Medical physicists are expected to be major actors in solving new tasks and in taking new responsibilities: their traditional role of guardians of the new technology implementation will change with increasing emphasis on the managing of AI tools, processes and advanced systems for imaging and data analysis, gradually replacing many repetitive manual tasks.     

脑机接口专利情报研究

脑机接口是在无外周神经系统和肌肉组织参与的条件下,通过计算机等电子设备输出控制信号,进而与外界环境进行交流的全新通讯和控制技术。它的发展依赖于神经科学、心理学、工程学、康复医学和计算机等学科专家间的密切合作,具有非常重要的学术价值、科学意义和广阔的应用前景,是当今世界研究的热点。本文利用新一代专利分析平台和工具Innography,结合专利情报分析理论,在对世界范围内脑机接口技术的专利进行申请趋势分析、区域分析、IPC分析、专利权人分析、诉讼专利和核心专利分析以及重点技术文本聚类分析的基础上,了解国内该技术发展态势,并尝试为脑机接口领域的发展提供有用的竞争情报参考。     

On the Use of Factor Scores for Prediction

There are two types of indeterminacy problems in factor analysis: the indeterminacy of the parameters, which is also known as the problem of identification, and the indeterminacy of the factor scores. The second problem, which will be treated here, is of great importance to the social sciences, given that the factor analysis model was historically developed with the theory of measuring intelligence, where the main interest was to determine one person's intelligence numerically. As a consequence of the indeterminacy of factor scores we will show, by means of a numerical example, that any criterion, including the dates of easter sunday, can be perfectly predicted by the intelligence factors.     

Does perceived trainability of dog (Canis lupus familiaris) breeds reflect differences in learning or differences in physical ability?

Researchers have reported perceived differences in trainability between different dog breeds. These reports could either be the result of underlying differences in learning or differences in physical capabilities. Four studies were conducted to investigate this issue. In Study 1 the level of agility metal-winners amongst those breeds perceived to be high and low in trainability did not deviate significantly from their respective levels of participation in the sport. In Study 2 the level of precision amongst those dogs perceived to be high and low in trainability did not deviate significantly in a real agility competition (P > 0.05), but these dogs did differ in speed (P < 0.05). In Study 3 the amount of training time necessary to achieve agility precision mastery did not significantly differ amongst dogs from breeds perceived to be high and low in trainability (P > 0.05), but there was a significant difference in speed. Finally, in Study 4 breeds considered to be high in trainability were found to be relatively physically homogenous in respects to height, in comparison to breeds considered to be low in trainability. Overall, the results of these studies are more supportive of a physical capability interpretation of perceived breed differences in trainability, than a more cognitive interpretation.