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Objectives Alzheimer's disease (AD) is the most common form of dementia in the United States. Sleep is one of the lifestyle-related factors that has been shown critical for optimal cognitive function in old age. However, there is a lack of research studying the association between sleep and AD incidence. A major bottleneck for conducting such research is that the traditional way to acquire sleep information is time-consuming, inefficient, non-scalable, and limited to patients' subjective experience. We aim to automate the extraction of specific sleep-related patterns, such as snoring, napping, poor sleep quality, daytime sleepiness, night wakings, other sleep problems, and sleep duration, from clinical notes of AD patients. These sleep patterns are hypothesized to play a role in the incidence of AD, providing insight into the relationship between sleep and AD onset and progression. Materials and Methods A gold standard dataset is created from manual annotation of 570 randomly sampled clinical note documents from the adSLEEP, a corpus of 192 000 de-identified clinical notes of 7266 AD patients retrieved from the University of Pittsburgh Medical Center (UPMC). We developed a rule-based natural language processing (NLP) algorithm, machine learning models, and large language model (LLM)-based NLP algorithms to automate the extraction of sleep-related concepts, including snoring, napping, sleep problem, bad sleep quality, daytime sleepiness, night wakings, and sleep duration, from the gold standard dataset. Results The annotated dataset of 482 patients comprised a predominantly White (89.2%), older adult population with an average age of 84.7 years, where females represented 64.1%, and a vast majority were non-Hispanic or Latino (94.6%). Rule-based NLP algorithm achieved the best performance of F1 across all sleep-related concepts. In terms of positive predictive value (PPV), the rule-based NLP algorithm achieved the highest PPV scores for daytime sleepiness (1.00) and sleep duration (1.00), while the machine learning models had the highest PPV for napping (0.95) and bad sleep quality (0.86), and LLAMA2 with finetuning had the highest PPV for night wakings (0.93) and sleep problem (0.89). Discussion Although sleep information is infrequently documented in the clinical notes, the proposed rule-based NLP algorithm and LLM-based NLP algorithms still achieved promising results. In comparison, the machine learning-based approaches did not achieve good results, which is due to the small size of sleep information in the training data. Conclusion The results show that the rule-based NLP algorithm consistently achieved the best performance for all sleep concepts. This study focused on the clinical notes of patients with AD but could be extended to general sleep information extraction for other diseases. [ABSTRACT FROM AUTHOR]

Ludwig Wittgenstein felt aesthetics was incorrectly treated as a kind of science, that beauty was wrongly used as an adjective, and that aesthetic judgments of beauty were inaccurately applied to the detriment of other aesthetic judgments. Wittgenstein's aesthetic appreciation was closely linked to his sense of the tremendous.

Land plants develop filamentous cells--root hairs, rhizoids, and caulonemata--at the interface with the soil. Members of the group XI basic helix-loop-helix (bHLH) transcription factors encoded by LOTUS JAPONICUS ROOTHAIRLESS1-LIKE (LRL) genes positively regulate the development of root hairs in the angiosperms Lotus japonicus, Arabidopsis thaliana, and rice (Oryza sativa). Here we show that auxin promotes rhizoid and caulonema development by positively regulating the expression of PpLRL1 and PpLRL2, the two LRL genes in the Physcomitrella patens genome. Although the group VIII bHLH proteins, AtROOT HAIR DEFECTIVE6 and AtROOT HAIR DEFECTIVE SIX-LIKE1, promote root-hair development by positively regulating the expression of AtLRL3 in A. thaliana, LRL genes promote rhizoid development independently of PpROOT HAIR DEFECTIVE SIX-LIKE1 and PpROOT HAIR DEFECTIVE SIX-LIKE2 (PpRSL1 and PpRSL2) gene function in P. patens. Together, these data demonstrate that both LRL and RSL genes are components of an ancient auxin-regulated gene network that controls the development of tip-growing cells with rooting functions among most extant land plants. Although this network has diverged in the moss and the angiosperm lineages, our data demonstrate that the core network acted in the last common ancestor of the mosses and angiosperms that existed sometime before 420 million years ago. [ABSTRACT FROM AUTHOR]

With generative artificial intelligence (GenAI), particularly large language models (LLMs), continuing to make inroads in healthcare, assessing LLMs with human evaluations is essential to assuring safety and effectiveness. This study reviews existing literature on human evaluation methodologies for LLMs in healthcare across various medical specialties and addresses factors such as evaluation dimensions, sample types and sizes, selection, and recruitment of evaluators, frameworks and metrics, evaluation process, and statistical analysis type. Our literature review of 142 studies shows gaps in reliability, generalizability, and applicability of current human evaluation practices. To overcome such significant obstacles to healthcare LLM developments and deployments, we propose QUEST, a comprehensive and practical framework for human evaluation of LLMs covering three phases of workflow: Planning, Implementation and Adjudication, and Scoring and Review. QUEST is designed with five proposed evaluation principles: Quality of Information, Understanding and Reasoning, Expression Style and Persona, Safety and Harm, and Trust and Confidence., (© 2024. The Author(s).)