Reduced FDG-PET brain metabolism and executive function predict clinical progression in elderly healthy subjects

Brain changes reminiscent of Alzheimer disease (AD) have been previously reported in a substantial portion of elderly cognitive healthy (HC) subjects. The major aim was to evaluate the accuracy of MRI assessed regional gray matter (GM) volume, 18F-fluorodeoxyglucose positron emission tomography (FDG-PET),and neuropsychological test scores to identify those HC subjects who subsequently convert to mild cognitive impairment (MCI) or AD dementia. We obtained in 54 healthy control (HC) subjects a priori defined region of interest (ROI) values of medial temporal and parietal FDG-PET and medial temporal GM volume. In logistic regression analyses, these ROI values were tested together with neuropsychological test scores (free recall, trail making test B (TMT-B)) as predictors of HC conversion during a clinical follow-up between 3 and 4 years. In voxelbased analyses, FDG-PET and MRI GM maps were compared between HC converters and HC non-converters. Out of the 54 HC subjects, 11 subjects converted to MCI or AD dementia. Lower FDG-PET ROI values were associated with higher likelihood of conversion (p = 0.004),with the area under the curve (AUC) yielding 82.0% (95% CI = (95.5%,68.5%)). The GM volume ROI was not a significant predictor (p = 0.07). TMT-B but not the free recall tests were a significant predictor (AUC = 71% (95% CI = 50.4%,91.7%)). For the combination of FDG-PET and TMT-B, the AUC was 93.4% (sensitivity = 82%,specificity = 93%). Voxel-based group comparison showed reduced FDG-PET metabolism within the temporo-parietal and prefrontal cortex in HC converters. In conclusion, medial temporal and-parietal FDG-PET and executive function show a clinically acceptable accuracy for predicting clinical progression in elderly HC subjects. (C) 2013 The Authors. Published by Elsevier Inc. All rights reserved

DataShare: Empowering Researcher Data Curation

Researchers are increasingly being asked to ensure that all products of research activity – not just traditional publications – are preserved and made widely available for study and reuse as a precondition for publication or grant funding, or to conform to disciplinary best practices. In order to conform to these requirements, scholars need effective, easy-to-use tools and services for the long-term curation of their research data. The DataShare service, developed at the University of California, is being used by researchers to: (1) prepare for curation by reviewing best practice recommendations for the acquisition or creation of digital research data; (2) select datasets using intuitive file browsing and drag-and-drop interfaces; (3) describe their data for enhanced discoverability in terms of the DataCite metadata schema; (4) preserve their data by uploading to a public access collection in the UC3 Merritt curation repository; (5) cite their data in terms of persistent and globally-resolvable DOI identifiers; (6) expose their data through registration with well-known abstracting and indexing services and major internet search engines; (7) control the dissemination of their data through enforceable data use agreements; and (8) discover and retrieve datasets of interest through a faceted search and browse environment. Since the widespread adoption of effective data management practices is highly dependent on ease of use and integration into existing individual, institutional, and disciplinary workflows, the emphasis throughout the design and implementation of DataShare is to provide the highest level of curation service with the lowest possible technical barriers to entry by individual researchers. By enabling intuitive, self-service access to data curation functions, DataShare helps to contribute to more widespread adoption of good data curation practices that are critical to open scientific inquiry, discourse, and advancement

Gas turbulence modulation in a two-fluid model for gas-solid flows

Recent rapid progress in the theoretical and experimental study of turbulence modulation has led to greater understanding of the physics of particle-gas turbulence interactions. A new two-fluid model incorporating these advances for relatively dilute gas-solid flows containing high-inertia particles is established. The effect of aerodynamic forces upon the particulate stresses is considered in this kinetic theory-based model, and the influence of the particles on the turbulent gas is addressed: the work associated with drag forces contributes to the gas turbulent energy, and the space occupied by particles restricts the turbulent length scale. The interparticle length scale, which is usually ignored, has been incorporated into a new model for determining the turbulent length scale. This model also considers the transport effect on the turbulent length scale. Simulation results for fully developed steady flows in vertical pipes are compared with a wide range of published experimental data and, generally, good agreement is shown. This comprehensive and validated model accounts for many of the interphase interactions that have been shown to be important

Biomedical knowledge graph-enhanced prompt generation for large language models

Large Language Models (LLMs) have been driving progress in AI at an unprecedented rate, yet still face challenges in knowledge-intensive domains like biomedicine. Solutions such as pre-training and domain-specific fine-tuning add substantial computational overhead, and the latter require domain-expertise. External knowledge infusion is task-specific and requires model training. Here, we introduce a task-agnostic Knowledge Graph-based Retrieval Augmented Generation (KG-RAG) framework by leveraging the massive biomedical KG SPOKE with LLMs such as Llama-2-13b, GPT-3.5-Turbo and GPT-4, to generate meaningful biomedical text rooted in established knowledge. KG-RAG consistently enhanced the performance of LLMs across various prompt types, including one-hop and two-hop prompts, drug repurposing queries, biomedical true/false questions, and multiple-choice questions (MCQ). Notably, KG-RAG provides a remarkable 71% boost in the performance of the Llama-2 model on the challenging MCQ dataset, demonstrating the framework's capacity to empower open-source models with fewer parameters for domain-specific questions. Furthermore, KG-RAG enhanced the performance of proprietary GPT models, such as GPT-3.5 which exhibited improvement over GPT-4 in context utilization on MCQ data. Our approach was also able to address drug repurposing questions, returning meaningful repurposing suggestions. In summary, the proposed framework combines explicit and implicit knowledge of KG and LLM, respectively, in an optimized fashion, thus enhancing the adaptability of general-purpose LLMs to tackle domain-specific questions in a unified framework.Comment: 28 pages, 5 figures, 2 tables, 1 supplementary fil

Biomedical knowledge graph-optimized prompt generation for large language models

MotivationLarge Language Models (LLMs) are being adopted at an unprecedented rate, yet still face challenges in knowledge-intensive domains like biomedicine. Solutions such as pre-training and domain-specific fine-tuning add substantial computational overhead, requiring further domain-expertise. Here, we introduce a token-optimized and robust Knowledge Graph-based Retrieval Augmented Generation (KG-RAG) framework by leveraging a massive biomedical KG (SPOKE) with LLMs such as Llama-2-13b, GPT-3.5-Turbo and GPT-4, to generate meaningful biomedical text rooted in established knowledge.ResultsCompared to the existing RAG technique for Knowledge Graphs, the proposed method utilizes minimal graph schema for context extraction and uses embedding methods for context pruning. This optimization in context extraction results in more than 50% reduction in token consumption without compromising the accuracy, making a cost-effective and robust RAG implementation on proprietary LLMs. KG-RAG consistently enhanced the performance of LLMs across diverse biomedical prompts by generating responses rooted in established knowledge, accompanied by accurate provenance and statistical evidence (if available) to substantiate the claims. Further benchmarking on human curated datasets, such as biomedical true/false and multiple-choice questions (MCQ), showed a remarkable 71% boost in the performance of the Llama-2 model on the challenging MCQ dataset, demonstrating the framework's capacity to empower open-source models with fewer parameters for domain-specific questions. Furthermore, KG-RAG enhanced the performance of proprietary GPT models, such as GPT-3.5 and GPT-4. In summary, the proposed framework combines explicit and implicit knowledge of KG and LLM in a token optimized fashion, thus enhancing the adaptability of general-purpose LLMs to tackle domain-specific questions in a cost-effective fashion.Availability and implementationSPOKE KG can be accessed at https://spoke.rbvi.ucsf.edu/neighborhood.html. It can also be accessed using REST-API (https://spoke.rbvi.ucsf.edu/swagger/). KG-RAG code is made available at https://github.com/BaranziniLab/KG_RAG. Biomedical benchmark datasets used in this study are made available to the research community in the same GitHub repository.Supplementary informationSupplementary data are available at Bioinformatics online

The Effects of Dietary Mobile Apps on Nutritional Outcomes in Adults with Chronic Diseases : A Systematic Review and Meta-Analysis

Abstract Background Dietary interventions are effective prevention and treatment strategies for chronic diseases; however, they require extensive commitment, time, and resources. Dietary mobile applications (apps) have gained popularity and are thus being incorporated into dietary management. Objective The aim of this review is to assess the effects of the use of dietary mobile apps on nutritional outcomes in adults with chronic diseases. Methods A systematic review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines using MEDLINE, PubMed, Embase, and CINAHL databases. The protocol was registered on PROSPERO. Intervention studies evaluating the nutritional outcomes of dietary apps, published in English between January 1, 2007 and November 15, 2017 were included. The methodological quality of included articles was assessed via the Academy of Nutrition and Dietetics\u27 Quality Criteria Checklist: Primary Research. Heterogeneity was confirmed using the I2 index and a random-effects meta-analysis was performed for randomized controlled trials. Estimates of the pooled mean difference were calculated for app usage compared to no app usage. Main outcomes measure Nutritional outcomes, categorized as food-/nutrition-related, anthropometric measurements, pertinent clinical/biochemical data, and nutrition-focused physical findings, were extracted from the included intervention studies. Results Upon completion of the searches, 18,649 articles were identified, and data were extracted from 22 articles. Pooled estimates showed a significantly greater decrease in weight ( “2.45 kg, 95% CI “3.33 to “1.58 kg; P Conclusions The findings of this systematic review and meta-analysis indicate that dietary mobile apps are effective self-monitoring tools, and that their use results in positive effects on measured nutritional outcomes in chronic diseases, especially weight loss

Spatial navigation, episodic memory, episodic future thinking, and theory of mind in children with autism spectrum disorder: evidence for impairments in mental simulation?

This study explored spatial navigation alongside several other cognitive abilities that are thought to share common underlying neurocognitive mechanisms (e.g., the capacity for self-projection, scene construction, or mental simulation), and which we hypothesized may be impaired in autism spectrum disorder (ASD). Twenty intellectually high-functioning children with ASD (with a mean age of ~8 years) were compared to 20 sex, age, IQ, and language ability matched typically developing children on a series of tasks to assess spatial navigation, episodic memory, episodic future thinking (also known as episodic foresight or prospection), theory of mind (ToM), relational memory, and central coherence. This is the first study to explore these abilities concurrently within the same sample. Spatial navigation was assessed using the “memory island” task, which involves finding objects within a realistic, computer simulated, three-dimensional environment. Episodic memory and episodic future thinking were assessed using a past and future event description task. ToM was assessed using the “animations” task, in which children were asked to describe the interactions between two animated triangles. Relational memory was assessed using a recognition task involving memory for items (line drawings), patterned backgrounds, or combinations of items and backgrounds. Central coherence was assessed by exploring differences in performance across segmented and unsegmented versions of block design. Children with ASD were found to show impairments in spatial navigation, episodic memory, episodic future thinking, and central coherence, but not ToM or relational memory. Among children with ASD, spatial navigation was found to be significantly negatively related to the number of repetitive behaviors. In other words, children who showed more repetitive behaviors showed poorer spatial navigation. The theoretical and practical implications of the results are discussed

Project Brainstorm: Using Neuroscience to Connect College Students with Local Schools

Neuroscience can be used as a tool to inspire an interest in science in school children as well as to provide teaching experience to college students