Comparing cadence-based and machine learning based estimates for physical activity intensity classification: The UK Biobank.

Objectives: Cadence thresholds have been widely used to categorize physical activity intensity in health-related research. We examined the convergent validity of two cadence-based intensity classification approaches against a machine-learning-based intensity schema in 84,315 participants (≥40 years) with wrist-worn accelerometers.

Design: Validity study.

Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK Biobank.

Purpose: Step count is an intuitive measure of physical activity frequently quantified in health-related studies; however, accurate step counting is difficult in the free-living environment, with error routinely above 20% in wrist-worn devices against camera-annotated ground truth. This study aimed to describe the development and validation of step count derived from a wrist-worn accelerometer and assess its association with cardiovascular and all-cause mortality in a large prospective cohort.

Methods: We developed and externally validated a self-supervised machine learning step detection model, trained on an open-source and step-annotated free-living dataset.

Cephalopod Sex Determination and its Ancient Evolutionary Origin.

Octopuses, squids, and cuttlefishes - the coleoid cephalopods - are a remarkable branch in the tree of life whose members exhibit a repertoire of sophisticated behaviors (Hanlon and Messenger, 2018). As a clade, coleoids harbor an incredible variety of novel traits including the most complex nervous system among invertebrates, derived camera-type eyes, and rapid adaptive camouflage abilities (Young, 1971; Hanlon, 2007). The burst of evolutionary novelty that distinguishes cephalopods is even more striking in a phylogenetic context; cephalopods are a deeply diverged lineage that last share a common ancestor with other extant molluscs in the Cambrian period, roughly 550 million years ago (Ponder and Lindberg, 2008; Huang et al.

Retromer-dependent lysosomal stress in Parkinson's disease.

While causative mutations in complex disorders are rare, they can be used to extract a biological pathway whose pathogenicity can generalize to common forms of the disease. Here we begin by relying on the biological consequences of mutations in LRRK2 and VPS35, genetic causes of autosomal-dominant Parkinson's disease, to hypothesize that 'Retromer-dependent lysosomal stress' represents a pathway that can generalize to idiopathic Parkinson's disease. Next, we outline a series of studies that can test this hypothesis, including the development of biomarkers of pathway dysfunction.

Amount and intensity of physical activity and risk of incident cancer in the UK Biobank.

Importance: The influence of total daily and light intensity activity on cancer risk remains unclear, as most existing knowledge is drawn from studies relying on self-reported leisure-time activities of moderate-vigorous intensity.

Objective: To investigate associations between total daily activity, including step counts, and activity intensity on incident cancer risk.

Design Setting And Participants: Prospective analysis of cancer-free UK Biobank participants who wore accelerometers for 7-days (between 2013-2015), followed for cancer incidence through national registries (mean follow-up 5.

Device-Measured Physical Activity in 3506 Individuals with Knee or Hip Arthroplasty.

Purpose: Hip and knee arthroplasty aims to reduce joint pain and increase functional mobility in patients with osteoarthritis; however, the degree to which arthroplasty is associated with higher physical activity is unclear. The current study sought to assess the association of hip and knee arthroplasty with objectively measured physical activity.

Methods: This cross-sectional study analyzed wrist-worn accelerometer data collected in 2013-2016 from UK Biobank participants (aged 43-78 yr).

Dose-response of accelerometer-measured physical activity, step count, and cancer risk in the UK Biobank: a prospective cohort analysis.

Background: Cancer is an age-related condition, but changes to modifiable lifestyle-related behaviours, including physical activity, could impact risk. While step count is an accessible metric of activity for older adults, its association with cancer risk remains poorly understood. We investigated the association between accelerometer-measured total activity, step count, and cancer risk.

On the causal role of retromer-dependent endosomal recycling in Alzheimer's disease.

Recent findings ranging from genetics to structural biology, together with studies in human neurons, animal models and patient brains, implicate the retromer-dependent endosomal recycling pathway as both causal and common in Alzheimer’s disease.

Detecting schizophrenia with 3D structural brain MRI using deep learning.

Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis using a single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3D whole-brain structure using standard post-processing methods.

What Role Does Patello-Femoral Joint Degeneration Have on Pain and Function After Unicompartmental Knee Arthroplasty? A Prospective Observational Cohort Study.

Background: Influences on anterior knee pain, stair climbing limitations, and function such as rising from sitting are poorly understood in unicompartmental knee arthroplasty (UKA). Original indications for UKA excluded patients who had patello-femoral disease, while more recent studies have expanded the indications to include these patients. This study examined the influence of the patello-femoral joint degeneration on patient-reported outcome measures relating to anterior knee pain and function after UKA.

Magnetic Resonance Spectroscopy Spectral Registration Using Deep Learning.

Background: Deep learning-based methods have been successfully applied to MRI image registration. However, there is a lack of deep learning-based registration methods for magnetic resonance spectroscopy (MRS) spectral registration (SR).

Purpose: To investigate a convolutional neural network-based SR (CNN-SR) approach for simultaneous frequency-and-phase correction (FPC) of single-voxel Meshcher-Garwood point-resolved spectroscopy (MEGA-PRESS) MRS data.

Incidence of Anterior Knee Pain and Functional Difficulty in a Prospectively Recruited Cohort Following Total Knee Arthroplasty With Selective Patellar Resurfacing.

Background: The influence of patellar resurfacing on anterior knee pain, stair climbing, and functional activity outcomes following total knee arthroplasty (TKA) are incompletely understood. This study examined the influence of patellar resurfacing on patient-reported outcome measures (PROMs) relating to anterior knee pain and function.

Methods: The Knee Injury and Osteoarthritis Outcome Score of Joint Replacement (KOOS, JR.

Development and Validation of a Machine Learning Wrist-worn Step Detection Algorithm with Deployment in the UK Biobank.

Background: Step count is an intuitive measure of physical activity frequently quantified in a range of health-related studies; however, accurate quantification of step count can be difficult in the free-living environment, with step counting error routinely above 20% in both consumer and research-grade wrist-worn devices. This study aims to describe the development and validation of step count derived from a wrist-worn accelerometer and to assess its association with cardiovascular and all-cause mortality in a large prospective cohort study.

Methods: We developed and externally validated a hybrid step detection model that involves self-supervised machine learning, trained on a new ground truth annotated, free-living step count dataset (OxWalk, n=39, aged 19-81) and tested against other open-source step counting algorithms.

Increased parietal and occipital lobe gyrification predicts conversion to syndromal psychosis in a clinical high-risk cohort.

Background: Local gyrification index (lGI), indicative of the degree of cortical folding is a proxy marker for early cortical neurodevelopmental abnormalities. We studied the difference in lGI between those who do and do not convert to psychosis (non-converters) in a clinical high-risk (CHR) cohort, and whether lGI predicts conversion to psychosis.

Methods: Seventy-two CHR participants with attenuated positive symptom syndrome were followed up for two years.

Standing genetic variation and chromosome differences drove rapid ecotype formation in a major malaria mosquito.

Species distributed across heterogeneous environments often evolve locally adapted ecotypes, but understanding of the genetic mechanisms involved in their formation and maintenance in the face of gene flow is incomplete. In Burkina Faso, the major African malaria mosquito comprises two strictly sympatric and morphologically indistinguishable yet karyotypically differentiated forms reported to differ in ecology and behavior. However, knowledge of the genetic basis and environmental determinants of diversification was impeded by lack of modern genomic resources.

Dimerization of the Alzheimer's disease pathogenic receptor SORLA regulates its association with retromer.

, the gene encoding the large multidomain SORLA protein, has emerged as only the fourth gene that when mutated can by itself cause Alzheimer's disease (AD), and as a gene reliably linked to both the early- and late-onset forms of the disease. SORLA is known to interact with the endosomal trafficking regulatory complex called retromer in regulating the recycling of endosomal cargo, including the amyloid precursor protein (APP) and the glutamate receptor GluA1. Nevertheless, SORLA's precise structural-functional relationship in endosomal recycling tubules remains unknown.

Sequential fear generalization and network connectivity in trauma exposed humans with and without psychopathology.

While impaired fear generalization is known to underlie a wide range of psychopathology, the extent to which exposure to trauma by itself results in deficient fear generalization and its neural abnormalities is yet to be studied. Similarly, the neural function of intact fear generalization in people who endured trauma and did not develop significant psychopathology is yet to be characterized. Here, we utilize a generalization fMRI task, and a network connectivity approach to clarify putative behavioral and neural markers of trauma and resilience.

Anatomical biology guides a search for nutrients for the aging brain.

Considerable evidence has established the importance of specific nutrients that have been found vital for the developing brain. We hypothesize that in a similar manner there should be nutrients vital to the aging brain and that based on aging's distinct pathophysiology they should be different than those essential to development. Specific brain networks that govern cognition are particularly vulnerable to the aging process, resulting in what is referred to as 'cognitive aging'.

A genetically modified minipig model for Alzheimer's disease with SORL1 haploinsufficiency.

The established causal genes in Alzheimer's disease (AD), APP, PSEN1, and PSEN2, are functionally characterized using biomarkers, capturing an in vivo profile reflecting the disease's initial preclinical phase. Mutations in SORL1, encoding the endosome recycling receptor SORLA, are found in 2%-3% of individuals with early-onset AD, and SORL1 haploinsufficiency appears to be causal for AD. To test whether SORL1 can function as an AD causal gene, we use CRISPR-Cas9-based gene editing to develop a model of SORL1 haploinsufficiency in Göttingen minipigs, taking advantage of porcine models for biomarker investigations.

Deep learning of MRI contrast enhancement for mapping cerebral blood volume from single-modal non-contrast scans of aging and Alzheimer's disease brains.

While MRI contrast agents such as those based on Gadolinium are needed for high-resolution mapping of brain metabolism, these contrast agents require intravenous administration, and there are rising concerns over their safety and invasiveness. Furthermore, non-contrast MRI scans are more commonly performed than those with contrast agents and are readily available for analysis in public databases such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). In this article, we hypothesize that a deep learning model, trained using quantitative steady-state contrast-enhanced structural MRI datasets, in mice and humans, can generate contrast-equivalent information from a single non-contrast MRI scan.