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This resource is about post-Pleistocene adaptive change among the aboriginal cultures of the mountains and deserts of Arizona and New Mexico.

Competency-based medical education (CBME) in India emphasizes early competency formation, higher-order cognitive processing, and self-directed learning. Although e-learning is widely adopted, there is limited evidence on structured e-modules explicitly designed using Bloom taxonomy and the Miller pyramid for Indian MBBS students. This study aims to design, validate, implement, and evaluate CBME-aligned e-modules for first-year MBBS foundational subjects (anatomy, physiology, and biochemistry) and to compare their effectiveness with traditional teaching on cognitive, psychomotor, and affective learning outcomes using Kirkpatrick levels 1 and 2. A randomized controlled study was conducted among 690 first-year medical undergraduates (control: n=370; intervention: n=320). The intervention group received Sharable Content Object Reference Model-based interactive e-modules through Moodle (Modular Object-Oriented Dynamic Learning Environment) in addition to standard lectures, while the control group received lectures only. e-Modules were designed using Bloom taxonomy and the Miller pyramid, validated by internal and external experts, and implemented following Kern 6-step approach. Learning outcomes were assessed using structured feedback (Kirkpatrick level 1) and end-of-block internal assessments comprising multiple choice questions, short notes, and objective structured clinical examinations (Kirkpatrick level 2). Students in the intervention group performed significantly better across all cognitive levels compared with the control group: remember (mean 6.52, SD 2.24 vs mean 5.26, SD 2.89; P<.001), understand (mean 2.92, SD 1.45 vs mean 2.55, SD 1.13; P<.001), apply (mean 3.43, SD 1.06 vs mean 2.56, SD 1.08; P<.001), and analyze (mean 3.01, SD 0.83 vs mean 2.53, SD 1.11; P<.001). Psychomotor scores (objective structured clinical examination manipulation: mean 4.55, SD 1.12 vs mean 4.10, SD 1.42; P<.001) and affective domain scores (mean 3.02, SD 0.81 vs mean 2.03, SD 0.81; P<.001) were also significantly higher in the intervention group. Subgroup analysis showed the largest gains among medium achievers across domains. CBME-aligned e-modules significantly enhanced student performance in cognitive, psychomotor, and affective domains compared with traditional teaching alone, with particularly pronounced benefits for medium achievers. Well-designed e-modules represent a scalable, adaptable strategy to support CBME implementation across diverse medical education settings in India.

Frequent, sustained stress is linked to poor health and requires monitoring for early intervention. Electrocardiograms (ECG) are promising biomarkers because they can be recorded noninvasively and continuously using wearable devices. However, tracking stress with ECG is challenging because daily activities elicit responses similar to mental stress (MS), and various mental stimuli that individuals encounter complicate the use of machine learning (ML) models trained on a limited set of stressors. We (1) evaluated the ability of ML models to distinguish MS episodes from a composite \"no-stress\" background, including rest and low- to moderate-intensity activities; (2) assessed their generalizability to new stressors and participants; and (3) tested robustness to lower sampling rates and fewer features, to explore their suitability for lightweight wearables. We used a comprehensive ECG dataset sampled at 1000 hertz from 127 participants who underwent various mental stressors and engaged in diverse physical activities. A 30-second window was used to extract 55 features from time, frequency, nonlinear, and morphological domains. We trained a logistic regression (LR) model and an extreme gradient boosting (XGBoost) model, splitting the data into 60/20/20 for training, validation, and testing. Shapley additive explanation values were computed to explain model predictions. Additional analyses included leave-one-stressor-out; downsampling to 500, 250, and 125 hertz; a time-window sensitivity analysis; and reducing the number of features to as few as 5. XGBoost achieved an area under the receiver operating characteristic curve (AUROC) of 0.741 (95% CI 0.701-0.783) and an area under the precision-recall curve (AUPRC) of 0.706 (95% CI 0.658-0.753), compared with 0.724 (95% CI 0.678-0.772) and 0.691 (95% CI 0.639-0.742) for LR. The mean performance difference between XGBoost and LR was 0.017 for AUROC (95% CI 0.001-0.032) and 0.015 for AUPRC (95% CI -0.001 to 0.037; clustered bootstrap analysis using 2000 participant-level resamples), suggesting that LR performs comparably to the nonlinear XGBoost model. Both models were robust to downsampling and feature reduction (10 features retained >93% of performance). Extending the analysis window to 60 seconds improved model performance across all sampling rates, highlighting a trade-off between rapid detection and overall performance. When evaluating discrimination from physical activity, models achieved acceptable specificity for light physical activity (XGBoost: 0.787; LR: 0.794) but poor specificity for moderate physical activity (XGBoost: 0.418; LR: 0.444). Both models generalized to most unseen stressors, although performance varied across stressors, with limited transfer to the social-evaluative stressor. Feature importance analysis revealed fuzzy entropy and frequency-based features as key predictors. ML models can detect MS with high sensitivity and remain robust to lower sampling rates and fewer features. Generalization to novel stressors was stressor-dependent. Importantly, our results highlight challenges in distinguishing stress-related cardiac responses from those caused by physical exertion, revealing critical limitations of single-sensor ECG approaches for MS detection.

Annotated bibliographies summarize literature, but training, experience, and time are needed to create concise yet accurate annotations. Summaries generated by artificial intelligence (AI) can save human resources, but AI-generated content can also contain serious errors. To determine the feasibility of using AI as an alternative to human annotators, we explored whether ChatGPT can generate annotations with characteristics that are comparable to those written by humans. We had 2 humans and 3 versions of ChatGPT (3.5, 4, and 5) independently write annotations on the same set of 15 publications. We collected data on word count and Flesch Reading Ease (FRE). In this study, 2 assessors who were masked to the source of the annotations independently evaluated (1) capture of main points, (2) presence of errors, and (3) whether the annotation included a discussion of both the quality and context of the article within the broader literature. We evaluated agreement and disagreement between the assessors and used descriptive statistics and assessor-stratified binary and cumulative mixed-effects logit models to compare annotations written by ChatGPT and humans. On average, humans wrote shorter annotations (mean 90.20, SD 36.8 words) than ChatGPT (mean 113, SD 16 words) which were easier to interpret (human FRE score, mean 15.3, SD 12.4; ChatGPT FRE score, mean 5.76, SD 7.32). Our assessments of agreement and disagreement revealed that one assessor was consistently stricter than the other. However, assessor-stratified models of main points, errors, and quality/context showed similar qualitative conclusions. There was no statistically significant difference in the odds of presenting a better summary of main points between ChatGPT- and human-generated annotations for either assessor (Assessor 1: OR 0.96, 95% CI 0.12-7.71; Assessor 2: OR 1.64, 95% CI 0.67-4.06). However, both assessors observed that human annotations had lower odds of having one or more types of errors compared to ChatGPT (Assessor 1: OR 0.31, 95% CI 0.09-1.02; Assessor 2: OR 0.10, 95% CI 0.03-0.33). On the other hand, human annotations also had lower odds of summarizing the paper's quality and context when compared to ChatGPT (Assessor 1: OR 0.11, 95% CI 0.03-0.33; Assessor 2: OR 0.03, 95% CI 0.01-0.10). That said, ChatGPT's summaries of quality and context were sometimes inaccurate. Rapidly learning a body of scientific literature is a vital yet daunting task that may be made more efficient by AI tools. In our study, ChatGPT quickly generated concise summaries of academic literature and also provided quality and context more consistently than humans. However, ChatGPT's discussion of the quality and context was not always accurate, and ChatGPT annotations included more errors. Annotated bibliographies that are AI-generated and carefully verified by humans may thus be an efficient way to provide a rapid overview of literature. More research is needed to determine the extent that prompt engineering can reduce errors and improve chatbot performance.

Measuring the optic nerve sheath diameter (ONSD) with ultrasound is a promising, noninvasive way to estimate intracranial pressure (ICP). While magnetic resonance imaging (MRI) provides high-resolution imaging, it is less accessible in urgent or perioperative settings. Comparing ONSD measurements between ultrasound and MRI may help confirm the use of ultrasound in neurosurgical patients. The aim of this study is to evaluate how closely ultrasound and MRI measurements of ONSD align in patients undergoing surgery for supratentorial brain tumors. This prospective, single-center observational study included 50 adult patients scheduled for elective supratentorial tumor resection. ONSD was measured preoperatively using both transorbital ultrasound and MRI. Measurements were compared using Pearson and Spearman correlation coefficients, the intraclass correlation coefficient, and Bland-Altman analysis. The average ONSD measured by ultrasound was 5.94 (0.99) mm, compared to 5.75 (SD 1.08) mm via MRI. The two methods showed a strong correlation (Pearson r=0.88, P<.001) and good agreement (intraclass correlation coefficient=0.86). Bland-Altman analysis showed a mean bias of 0.19 mm (95% limits of agreement: -0.62 to 1.00 mm). Ultrasound-based ONSD measurements closely matched those obtained by MRI in this patient group. These findings support the use of ultrasound as a practical tool for noninvasive ICP assessment in the perioperative care of patients with intracranial tumors.

Novel technologies, such as ecological momentary assessment (EMA) and wearable biosensor wristwatches, are increasingly being used to assess outcomes and mechanisms of change in psychological treatments. However, there is still a dearth of information on the feasibility and acceptability of these technologies and whether they can be reliably used to measure variables of interest. Our objectives were to assess the feasibility and acceptability of incorporating these technologies into dialectical behavior therapy and conduct a pilot evaluation of whether these technologies can be used to assess emotion regulation processes and associated problems over the course of treatment. A total of 20 adults with borderline personality disorder were enrolled in a 6-month course of dialectical behavior therapy. For 1 week out of every treatment month, participants were asked to complete EMA 6 times a day and to wear a biosensor watch. Each EMA assessment included measures of several negative affect and suicidal thinking, among other items. We used multilevel correlations to assess the contemporaneous association between electrodermal activity and 11 negative emotional states reported via EMA. A multilevel regression was conducted in which changes in composite ratings of suicidal thinking were regressed onto changes in negative affect. On average, participants completed 54.39% (SD 33.1%) of all EMA (range 4.7%-92.4%). They also wore the device for an average of 9.52 (SD 6.47) hours per day and for 92.6% of all days. Importantly, no associations were found between emotional state and electrodermal activity, whether examining a composite of all high-arousal negative emotions or individual emotional states (within-person r ranged from -0.026 to -0.109). Smaller changes in negative affect composite scores were associated with greater suicidal thinking ratings at the subsequent timepoint, beyond the effect of suicidal thinking at the initial timepoint. Results indicated moderate overall compliance with EMA and wearing the watch; however, there was no concurrence between EMA and wristwatch data on emotions. This pilot study raises questions about the reliability and validity of these technologies incorporated into treatment studies to evaluate emotion regulation mechanisms.

Chlorella ellipsoidea is a single-celled eukaryotic green microalgae with high nutritional value. Its value may be further increased if a simple, reliable and cost-effective transformation method for C. ellipsoidea can be developed. In this paper, we describe a novel transformation method for C. ellipsoidea . This system is based on treatment of C. ellipsoidea cells with cellulolytic enzymes to weaken their cell walls, making them become competent to take up foreign DNA. To demonstrate the usefulness and effectiveness of this method, we treated C. ellipsoidea cells with a cell wall-degrading enzyme, cellulase, followed by transformation with plasmid pSP-Ubi-GUS harbouring both the zeocin resistance gene and the beta-glucuronidase (GUS) reporter gene that serve as selective makers for transformation. Transformants were readily obtained on zeocin selection medium, reaching transformation efficiency of 2.25 × 10³ transformants/μg of plasmid DNA. PCR analysis has also demonstrated the presence of the GUS reporter gene in the zeocin-resistant transformants. Histochemical assays further showed the expression of the GUS activity in both primary transformants and transformants after long-term growth (10 months) with antibiotic selection on and off. Availability of a simple and efficient transformation system for C. ellipsoidea will accelerate the exploration of this microalga for a broader range of biotechnological applications, including its use as a biologic factory for the production of high-value human therapeutic proteins.