MidCog study: a prospective, observational cohort study investigating health literacy, self-management skills and cognitive function in middle-aged adults.

INTRODUCTION: The lack of definitive means to prevent or treat cognitive impairment or dementia is driving intense efforts to identify causal mechanisms. Recent evidence suggests clinically meaningful declines in cognition might present as early as middle age. Studying cognitive changes in middle adulthood could elucidate modifiable factors affecting later cognitive and health outcomes, yet few cognitive ageing studies include this age group. The purpose of the MidCog study is to begin investigations of less-studied and potentially modifiable midlife determinants of later life cognitive outcomes. METHODS AND ANALYSIS: MidCog is a prospective cohort study of adults ages 35-64, with two in-person interviews 2.5 years apart. Data will be collected from interviews, electronic health records and pharmacy fill data. Measurements will include health literacy, self-management skills, cognitive function, lifestyle and health behaviours, healthcare use, health status and chronic disease outcomes. Associations of health literacy and self-management skills with health behaviours and cognitive/health outcomes will be examined in a series of regression models, and moderating effects of modifiable psychosocial factors.Finally, MidCog data will be linked to an ongoing, parallel cohort study of older adults recruited at ages 55-74 in 2008 ('LitCog'; ages 70-90 in 2023), to explore associations between age, health literacy, self-management skills, chronic diseases, health status and cognitive function among adults ages 35-90. ETHICS AND DISSEMINATION: The Institutional Review Board at Northwestern University has approved the MidCog study protocol (STU00214736). Results will be published in peer-reviewed journals and summaries will be provided to the funders of the study as well as patients.

Perceived Adequacy of Tangible Support during Stay-at-Home Orders in Chicago and New York.

OBJECTIVES: Physical distancing precautions during the COVID-19 pandemic may challenge the provision of tangible support many middle age and older adults receive in managing their health. We examined the association between unmet tangible support needs and self-management behaviors and mental health status during the stay-at-home orders in Chicago and New York. METHODS: We used data from the COVID-19 & Chronic Conditions study collected between May 1st and May 22nd, 2020. A total of 801 middle age and older adults with ≥1 chronic condition in Chicago and New York City completed the telephone interview. Adequacy of tangible social support was measured using a brief, validated scale that determined whether an individual needed assistance managing his or her health, and if yes, whether this need was met. Participants reported their level of difficulty engaging in self-management behaviors using 2 discrete items; they also self-reported medication adherence using the ASK-12 medication adherence scale. Mental health status was measured using the depression and anxiety PROMIS short-form instruments. RESULTS: Participants' mean age was 63 years; 30% identified as Black, 26% identified as Latino, and 12% identified unmet support needs. Inadequacy of tangible support was associated with greater difficulty managing one's health and accessing medications due to COVID-19, as well as poorer medication adherence, increased anxiety and depressive symptoms, and poorer overall well-being (P's < .05). CONCLUSIONS: Perceived unmet support needs during stay-at-home orders were associated with greater difficulty engaging in self-management behaviors and poorer overall well-being. Two brief items quickly identified individuals with unmet support needs.

Self-organizing maps with variable neighborhoods facilitate learning of chromatin accessibility signal shapes associated with regulatory elements.

BACKGROUND: Assigning chromatin states genome-wide (e.g. promoters, enhancers, etc.) is commonly performed to improve functional interpretation of these states. However, computational methods to assign chromatin state suffer from the following drawbacks: they typically require data from multiple assays, which may not be practically feasible to obtain, and they depend on peak calling algorithms, which require careful parameterization and often exclude the majority of the genome. To address these drawbacks, we propose a novel learning technique built upon the Self-Organizing Map (SOM), Self-Organizing Map with Variable Neighborhoods (SOM-VN), to learn a set of representative shapes from a single, genome-wide, chromatin accessibility dataset to associate with a chromatin state assignment in which a particular RE is prevalent. These shapes can then be used to assign chromatin state using our workflow. RESULTS: We validate the performance of the SOM-VN workflow on 14 different samples of varying quality, namely one assay each of A549 and GM12878 cell lines and two each of H1 and HeLa cell lines, primary B-cells, and brain, heart, and stomach tissue. We show that SOM-VN learns shapes that are (1) non-random, (2) associated with known chromatin states, (3) generalizable across sets of chromosomes, and (4) associated with magnitude and multimodality. We compare the accuracy of SOM-VN chromatin states against the Clustering Aggregation Tool (CAGT), an unsupervised method that learns chromatin accessibility signal shapes but does not associate these shapes with REs, and we show that overall precision and recall is increased when learning shapes using SOM-VN as compared to CAGT. We further compare enhancer state assignments from SOM-VN in signals above a set threshold to enhancer state assignments from Predicting Enhancers from ATAC-seq Data (PEAS), a deep learning method that assigns enhancer chromatin states to peaks. We show that the precision-recall area under the curve for the assignment of enhancer states is comparable to PEAS. CONCLUSIONS: Our work shows that the SOM-VN workflow can learn relationships between REs and chromatin accessibility signal shape, which is an important step toward the goal of assigning and comparing enhancer state across multiple experiments and phenotypic states.

Metagenomic insights into lignocellulose-degrading genes through Illumina-based de novo sequencing of the microbiome in Vietnamese native goats' rumen.

The scarcity of enzymes having an optimal activity in lignocellulose deconstruction is an obstacle for industrial-scale conversion of cellulosic biomass into biofuels. With the aim of mining novel lignocellulolytic enzymes, a ~9 Gb metagenome of bacteria in Vietnamese native goats' rumen was sequenced by Illumina platform. From the data, 821 ORFs encoding carbohydrate esterases (CEs) and polysaccharide lyases (PLs) serving for lignocellulose pre-treatment, 816 ORFs encoding 11 glycoside hydrolase families (GHs) of cellulases, and 2252 ORFs encoding 22 GHs of hemicellulases, were mined. The carbohydrate binding module (CBM) was also abundant with 763 ORFs, of which 480 ORFs are located with lignocellulolytic enzymes. The enzyme modularity analysis showed that CBMs are usually present in endoglucanase, endo 1,3-beta-D-glucosidase, and endoxylanase, whereas fibronectin 3-like module (FN3) mainly represents in GH3 and immunoglobulin-like domain (Ig) was located in GH9 only. Every domain located in each ORF was analyzed in detail to contribute enzymes' modularity which is valuable for modelling, to study the structure, and for recombinant production. With the aim of confirming the annotated results, a mined ORF encoding CBM63 was highly expressed in E. coli in soluble form. The purified recombinant CBM63 exhibited no cellulase activity, but enhanced a commercial cellulase activity in the destruction of a paper filter.