Structured Sequence Learning: Animal Abilities,Cognitive Operations,and Language Evolution

Human language is a salient example of a neurocognitive system that is specialized to process complex dependencies between sensory events distributed in time, yet how this system evolved and specialized remains unclear. Artificial Grammar Learning (AGL) studies have generated a wealth of insights into how human adults and infants process different types of sequencing dependencies of varying complexity. The AGL paradigm has also been adopted to examine the sequence processing abilities of nonhuman animals. We critically evaluate this growing literature in species ranging from mammals (primates and rats) to birds (pigeons, songbirds, and parrots) considering also cross‐species comparisons. The findings are contrasted with seminal studies in human infants that motivated the work in nonhuman animals. This synopsis identifies advances in knowledge and where uncertainty remains regarding the various strategies that nonhuman animals can adopt for processing sequencing dependencies. The paucity of evidence in the few species studied to date and the need for follow‐up experiments indicate that we do not yet understand the limits of animal sequence processing capacities and thereby the evolutionary pattern. This vibrant, yet still budding, field of research carries substantial promise for advancing knowledge on animal abilities, cognitive substrates, and language evolution.     

经鼻导管高流量吸氧与经鼻气道正压通气在重症毛细支气管炎呼吸支持中的应用价值比较

目的：比较经鼻导管高流量吸氧（HFNC）与经鼻气道正压通气（nCPAP）在重症毛细支气管炎呼吸支持中的应用价值，为临床治疗方案的选择提供参考。方法：选取2016年12月至2018年12月我院儿科收治的重症毛细支气管炎患儿90例，采用随机数字表法分为观察组和对照组各45例。两组患儿入院后均给予常规综合治疗以保证呼吸道通畅，在此基础上观察组采用HFNC治疗，对照组采用nCPAP治疗，比较两组患儿治疗前和治疗24 h后呼吸频率、经皮血氧饱和度（TcSO2）、呼吸窘迫评分体系（CSS）评分、动脉血氧分压（PaO2）等呼吸相关指标及治疗前后临床症状体征改善情况。结果：两组患儿治疗24 h后呼吸频率、CSS评分均降低，且观察组降低程度更大，TcSO2、PaO2于治疗24 h后升高，观察组升高幅度较对照组明显；治疗后两组患儿咳嗽及肺部湿啰音、肺部炎症情况均改善，观察组症状体征消失时间早于对照组，差异均有统计学意义（P<0.05）。结论：重症毛细支气管炎患儿采用HFNC治疗可明显改善通气功能和临床症状，治疗效果优于nCPAP治疗，可扩大样本量进一步观察。     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

Registries,Databases and Repositories for Developing Artificial Intelligence in Cancer Care

Modern artificial intelligence techniques have solved some previously intractable problems and produced impressive results in selected medical domains. One of their drawbacks is that they often need very large amounts of data. Pre-existing datasets in the form of national cancer registries, image/genetic depositories and clinical datasets already exist and have been used for research. In theory, the combination of healthcare Big Data with modern, data-hungry artificial intelligence techniques should offer significant opportunities for artificial intelligence development, but this has not yet happened. Here we discuss some of the structural reasons for this, barriers preventing artificial intelligence from making full use of existing datasets, and make suggestions as to enable progress. To do this, we use the framework of the 6Vs of Big Data and the FAIR criteria for data sharing and availability (Findability, Accessibility, Interoperability, and Reuse). We share our experience in navigating these barriers through The Brain Tumour Data Accelerator, a Brain Tumour Charity-supported initiative to integrate fragmented patient data into an enriched dataset. We conclude with some comments as to the limits of such approaches.     

“人工智能+X”背景下医学信息管理专业研究生培养模式

阐述当前“人工智能+X”背景下市场对医学信息管理专业人才能力的需求，分析医学信息管理专业人才培养现状，提出从重塑学科人才培养目标、优化课程内容与课程设置、建设“双师型”导师队伍、搭建多方协同共建共享在线平台及设立“政用产学研”联合培养基地等方面探索医学信息管理专业研究生培养模式，以期培养适应人工智能时代发展，具备学科优势特色的高层次、高水平、高质量的复合型、应用型、创新型人才。     

Could non-HDL-cholesterol be a better marker of atherogenic dyslipidemia in obstructive sleep apnea?

Background/objectiveObstructive sleep apnea (OSA) is independently associated with dyslipidemia, a surrogate marker of atherosclerosis. Low-density lipoprotein (LDL)-cholesterol is accepted as a major independent risk factor for cardiovascular disease. However, non-high-density lipoprotein (HDL)-cholesterol is a better marker of atherogenic dyslipidemia and recommended as a target of lipid lowering therapy. We aimed to assess the prevalence of atherogenic dyslipidemia, and relationship between OSA severity and serum LDL-cholesterol and non-HDL cholesterol levels in OSA patients.MethodsWe retrospectively evaluated treatment naïve 2361 subjects admitted to the sleep laboratory of a university hospital for polysomnography. All subjects’ lipid profile including total cholesterol, LDL-cholesterol, HDL-cholesterol, triglycerides, and non-HDL-cholesterol were measured.ResultsOut of 2361 patients (mean age 49.6 ± 11.9 years; 68.9% male, apnea-hypopnea index 36.6 ± 28.4/h), 185 (7.8%) had no OSA and 2176 (92.2%) had OSA. Atherogenic dyslipidemia prevalence was high (57–66%) in OSA patients, and especially increased in severe OSA compared to other groups (p < 0.05). Though total and LDL-cholesterol did not differ between those with and without OSA, non-HDL-cholesterol (p = 0.020), and triglycerides (p = 0.001) were higher and HDL-cholesterol levels (p = 0.018) were lower in OSA patients than non-OSA. Non-HDL-cholesterol was significantly correlated with OSA severity (p < 0.001) and hypoxia parameters (p < 0.01), whereas LDL-cholesterol showed no correlation.ConclusionsAtherogenic dyslipidemia is highly prevalent and non-HDL-cholesterol levels are significantly increased, predominantly in severe OSA patients. Non-HDL-cholesterol but not LDL-cholesterol, is significantly correlated with OSA severity and hypoxia parameters. Therefore, it could be better to use non-HDL-cholesterol, which is a guideline recommended target of lipid therapy, as a marker of atherosclerotic cardiovascular risk in OSA patients.