Spatial-temporal data mining procedure: LASR

This paper is concerned with the statistical development of our spatial-temporal data mining procedure, LASR (pronounced ``laser''). LASR is the abbreviation for Longitudinal Analysis with Self-Registration of large-$p$-small-$n$ data. It was motivated by a study of ``Neuromuscular Electrical Stimulation'' experiments, where the data are noisy and heterogeneous, might not align from one session to another, and involve a large number of multiple comparisons. The three main components of LASR are: (1) data segmentation for separating heterogeneous data and for distinguishing outliers, (2) automatic approaches for spatial and temporal data registration, and (3) statistical smoothing mapping for identifying ``activated'' regions based on false-discovery-rate controlled $p$-maps and movies. Each of the components is of interest in its own right. As a statistical ensemble, the idea of LASR is applicable to other types of spatial-temporal data sets beyond those from the NMES experiments.Comment: Published at http://dx.doi.org/10.1214/074921706000000707 in the IMS Lecture Notes--Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation

The field of protein folding research has been greatly advanced by deep learning methods, with AlphaFold2 (AF2) demonstrating exceptional performance and atomic-level precision. As co-evolution is integral to protein structure prediction, AF2's accuracy is significantly influenced by the depth of multiple sequence alignment (MSA), which requires extensive exploration of a large protein database for similar sequences. However, not all protein sequences possess abundant homologous families, and consequently, AF2's performance can degrade on such queries, at times failing to produce meaningful results. To address this, we introduce a novel generative language model, MSA-Augmenter, which leverages protein-specific attention mechanisms and large-scale MSAs to generate useful, novel protein sequences not currently found in databases. These sequences supplement shallow MSAs, enhancing the accuracy of structural property predictions. Our experiments on CASP14 demonstrate that MSA-Augmenter can generate de novo sequences that retain co-evolutionary information from inferior MSAs, thereby improving protein structure prediction quality on top of strong AF2

Multimorbidity: constellations of conditions across subgroups of midlife and older individuals, and related Medicare expenditures

Introduction: The Department of Health and Human Services’ 2010 Strategic Framework on Multiple Chronic Conditions called for the identification of common constellations of conditions in older adults. Objectives: To analyze patterns of conditions constituting multimorbidity (CCMM) and expenditures in a US representative sample of midlife and older adults (50–64 and ≥65 years of age, respectively). Design: A cross-sectional study of the 2010 Health and Retirement Study (HRS; n=17,912). The following measures were used: (1) count and combinations of CCMM, including (i) chronic conditions (hypertension, arthritis, heart disease, lung disease, stroke, diabetes, cancer, and psychiatric conditions), (ii) functional limitations (upper body limitations, lower body limitations, strength limitations, limitations in activities of daily living, and limitations in instrumental activities of daily living), and (iii) geriatric syndromes (cognitive impairment, depressive symptoms, incontinence, visual impairment, hearing impairment, severe pain, and dizziness); and (2) annualized 2011 Medicare expenditures for HRS participants who were Medicare fee-for-service beneficiaries (n=5,677). Medicaid beneficiaries were also identified based on their self-reported insurance status. Results: No large representations of participants within specific CCMM categories were observed; however, functional limitations and geriatric syndromes were prominently present with higher CCMM counts. Among fee-for-service Medicare beneficiaries aged 50–64 years, 26.7% of the participants presented with ≥10 CCMM, but incurred 48% of the expenditure. In those aged ≥65 years, these percentages were 16.9% and 34.4%, respectively. Conclusion: Functional limitations and geriatric syndromes considerably add to the MM burden in midlife and older adults. This burden is much higher than previously reported. Keywords: comorbidity, functional limitations, geriatric syndromes, multimorbidity, healthcare expenditure

Application of micro/nanorobot in medicine

The development of micro/nanorobots and their application in medical treatment holds the promise of revolutionizing disease diagnosis and treatment. In comparison to conventional diagnostic and treatment methods, micro/nanorobots exhibit immense potential due to their small size and the ability to penetrate deep tissues. However, the transition of this technology from the laboratory to clinical applications presents significant challenges. This paper provides a comprehensive review of the research progress in micro/nanorobotics, encompassing biosensors, diagnostics, targeted drug delivery, and minimally invasive surgery. It also addresses the key issues and challenges facing this technology. The fusion of micro/nanorobots with medical treatments is poised to have a profound impact on the future of medicine

The Influence of Multimorbidity on Leading Causes of Death in Older Adults With Cognitive Impairment

Objective: The aim of this study is to evaluate the relationship of leading causes of death with gradients of cognitive impairment and multimorbidity. Method: This is a population-based study using data from the linked 1992- 2010 Health and Retirement Study and National Death Index (n = 9,691). Multimorbidity is defined as a combination of chronic conditions, functional limitations, and geriatric syndromes. Regression trees and Random Forest identified which combinations of multimorbidity associated with causes of death. Results: Multimorbidity is common in the study population. Heart disease is the leading cause in all groups, but with a larger percentage of deaths in the mild and moderate/severe cognitively impaired groups than among the noncognitively impaired. The different “paths” down the regression trees show that the distribution of causes of death changes with different combinations of multimorbidity. Discussion: Understanding the considerable heterogeneity in chronic conditions, functional limitations, geriatric syndromes, and causes of death among people with cognitive impairment can target care management and resource allocation

Multimorbidity: constellations of conditions across subgroups of midlife and older individuals, and related Medicare expenditures

Introduction: The Department of Health and Human Services’ 2010 Strategic Framework on Multiple Chronic Conditions called for the identification of common constellations of conditions in older adults. Objectives: To analyze patterns of conditions constituting multimorbidity (CCMM) and expenditures in a US representative sample of midlife and older adults (50–64 and ≥65 years of age, respectively). Design: A cross-sectional study of the 2010 Health and Retirement Study (HRS; n=17,912). The following measures were used: (1) count and combinations of CCMM, including (i) chronic conditions (hypertension, arthritis, heart disease, lung disease, stroke, diabetes, cancer, and psychiatric conditions), (ii) functional limitations (upper body limitations, lower body limitations, strength limitations, limitations in activities of daily living, and limitations in instrumental activities of daily living), and (iii) geriatric syndromes (cognitive impairment, depressive symptoms, incontinence, visual impairment, hearing impairment, severe pain, and dizziness); and (2) annualized 2011 Medicare expenditures for HRS participants who were Medicare fee-for-service beneficiaries (n=5,677). Medicaid beneficiaries were also identified based on their self-reported insurance status. Results: No large representations of participants within specific CCMM categories were observed; however, functional limitations and geriatric syndromes were prominently present with higher CCMM counts. Among fee-for-service Medicare beneficiaries aged 50–64 years, 26.7% of the participants presented with ≥10 CCMM, but incurred 48% of the expenditure. In those aged ≥65 years, these percentages were 16.9% and 34.4%, respectively. Conclusion: Functional limitations and geriatric syndromes considerably add to the MM burden in midlife and older adults. This burden is much higher than previously reported