Short-Term Memory in Orthogonal Neural Networks

We study the ability of linear recurrent networks obeying discrete time dynamics to store long temporal sequences that are retrievable from the instantaneous state of the network. We calculate this temporal memory capacity for both distributed shift register and random orthogonal connectivity matrices. We show that the memory capacity of these networks scales with system size.Comment: 4 pages, 4 figures, to be published in Phys. Rev. Let

Genotype vs. phenotype and the rise of non-communicable diseases: the importance of lifestyle behaviors during childhood

Despite continued research and growing public awareness, the incidence of non-communicable diseases (NCD) continues to accelerate. While a person may have a genetic predisposition to certain NCDs, the rapidly changing epidemiology of NCDs points to the importance of environmental, social, and behavioural determinants of health. Specifically, three lifestyle behaviours expose children to important environmental cues and stressors: physical activity, nutritional intake, and sleep behaviour. Failure to expose children to proper gene-environment interactions, through the aforementioned lifestyle behaviours, can and will predispose children to the development of NCDs. Reengineering the environments of children can induce a paradigm shift, from a predominantly biomedical health model of treating symptomology, to a more holistic model based on encouraging appropriate behavioral decisions and optimal health

Incident disease associations with mosaic chromosomal alterations on autosomes, X and Y chromosomes: insights from a phenome-wide association study in the UK Biobank.

BackgroundMosaic chromosomal alterations (mCAs) are large chromosomal gains, losses and copy-neutral losses of heterozygosity (LOH) in peripheral leukocytes. While many individuals with detectable mCAs have no notable adverse outcomes, mCA-associated gene dosage alterations as well as clonal expansion of mutated leukocyte clones could increase susceptibility to disease.ResultsWe performed a phenome-wide association study (PheWAS) using existing data from 482,396 UK Biobank (UKBB) participants to investigate potential associations between mCAs and incident disease. Of the 1290 ICD codes we examined, our adjusted analysis identified a total of 50 incident disease outcomes associated with mCAs at PheWAS significance levels. We observed striking differences in the diseases associated with each type of alteration, with autosomal mCAs most associated with increased hematologic malignancies, incident infections and possibly cancer therapy-related conditions. Alterations of chromosome X were associated with increased lymphoid leukemia risk and, mCAs of chromosome Y were linked to potential reduced metabolic disease risk.ConclusionsOur findings demonstrate that a wide range of diseases are potential sequelae of mCAs and highlight the critical importance of careful covariate adjustment in mCA disease association studies

Using intervention mapping and behavior change techniques to develop a digital intervention for self-management in stroke: Development study

BACKGROUND: Digital therapeutics, such as interventions provided via smartphones or the internet, have been proposed as promising solutions to support self-management in persons with chronic conditions. However, the evidence supporting self-management interventions through technology in stroke is scarce, and the intervention development processes are often not well described, creating challenges in explaining why and how the intervention would work. OBJECTIVE: This study describes a specific use case of using intervention mapping (IM) and the taxonomy of behavior change techniques (BCTs) in designing a digital intervention to manage chronic symptoms and support daily life participation in people after stroke. IM is an implementation science framework used to bridge the gap between theories and practice to ensure that the intervention can be implemented in real-world settings. The taxonomy of BCTs consists of a set of active ingredients designed to change self-management behaviors. METHODS: We used the first 4 steps of the IM process to develop a technology-supported self-management intervention, interactive Self-Management Augmented by Rehabilitation Technologies (iSMART), adapted from a face-to-face stroke-focused psychoeducation program. Planning group members were involved in adapting the intervention. They also completed 3 implementation measures to assess the acceptability, appropriateness, and feasibility of iSMART. RESULTS: In step 1, we completed a needs assessment consisting of assembling a planning group to codevelop the intervention, conducting telephone surveys of people after stroke (n=125) to identify service needs, and performing a systematic review of randomized controlled trials to examine evidence of the effectiveness of digital self-management interventions to improve patient outcomes. We identified activity scheduling, symptom management, stroke prevention, access to care resources, and cognitive enhancement training as key service needs after a stroke. The review suggested that digital self-management interventions, especially those using cognitive behavioral theory, effectively reduce depression, anxiety, and fatigue and enhance self-efficacy in neurological disorders. Step 2 identified key determinants, objectives, and strategies for self-management in iSMART, including knowledge, behavioral regulation, skills, self-efficacy, motivation, negative and positive affect, and social and environmental support. In step 3, we generated the intervention components underpinned by appropriate BCTs. In step 4, we developed iSMART with the planning group members. Especially, iSMART simplified the original psychoeducation program and added 2 new components: SMS text messaging and behavioral coaching, intending to increase the uptake by people after stroke. iSMART was found to be acceptable (mean score 4.63, SD 0.38 out of 5), appropriate (mean score 4.63, SD 0.38 out of 5), and feasible (mean score 4.58, SD 0.34 out of 5). CONCLUSIONS: We describe a detailed example of using IM and the taxonomy of BCTs for designing and developing a digital intervention to support people after stroke in managing chronic symptoms and maintaining active participation in daily life

A 5-Enolpyruvylshikimate 3-Phosphate Synthase Functions as a Transcriptional Repressor in Populus.

Long-lived perennial plants, with distinctive habits of inter-annual growth, defense, and physiology, are of great economic and ecological importance. However, some biological mechanisms resulting from genome duplication and functional divergence of genes in these systems remain poorly studied. Here, we discovered an association between a poplar (Populus trichocarpa) 5-enolpyruvylshikimate 3-phosphate synthase gene (PtrEPSP) and lignin biosynthesis. Functional characterization of PtrEPSP revealed that this isoform possesses a helix-turn-helix motif in the N terminus and can function as a transcriptional repressor that regulates expression of genes in the phenylpropanoid pathway in addition to performing its canonical biosynthesis function in the shikimate pathway. We demonstrated that this isoform can localize in the nucleus and specifically binds to the promoter and represses the expression of a SLEEPER-like transcriptional regulator, which itself specifically binds to the promoter and represses the expression of PtrMYB021 (known as MYB46 in Arabidopsis thaliana), a master regulator of the phenylpropanoid pathway and lignin biosynthesis. Analyses of overexpression and RNAi lines targeting PtrEPSP confirmed the predicted changes in PtrMYB021 expression patterns. These results demonstrate that PtrEPSP in its regulatory form and PtrhAT form a transcriptional hierarchy regulating phenylpropanoid pathway and lignin biosynthesis in Populus

Plate size and food consumption: a pre-registered experimental study in a general population sample

Abstract: Background: There is considerable uncertainty regarding the impact of tableware size on food consumption. Most existing studies have used small and unrepresentative samples and have not followed recommended procedures for randomised controlled trials, leading to increased risk of bias. In the first pre-registered study to date, we examined the impact on consumption of using larger versus smaller plates for self-served food. We also assessed impact on the underlying meal micro-structure, such as number of servings and eating rate, which has not previously been studied. Methods: The setting was a purpose-built naturalistic eating behaviour laboratory. A general population sample of 134 adult participants (aged 18–61 years) was randomly allocated to one of two groups varying in the size of plate used for self-serving lunch: large or small. The primary outcome was amount of food energy (kcal) consumed during a meal. Additionally, we assessed impact on meal micro-structure, and examined potential modifying effects of executive function, socio-economic position, and sensitivity to perceptual cues. Results: There was no clear evidence of a difference in consumption between the two groups: Cohen’s d = 0.07 (95% CI [− 0.27, 0.41]), with participants in the large plate group consuming on average 19.2 (95% CI [− 76.5, 115.0]) more calories (3%) compared to the small plate group (large: mean (SD) = 644.1 (265.0) kcal, versus small: 624.9 (292.3) kcal). The difference between the groups was not modified by individual characteristics. There was no evidence of impact on meal micro-structure, with the exception of more food being left on the plate when larger plates were used. Conclusions: This study suggests that previous meta-analyses of a low-quality body of evidence may have considerably overestimated the effects of plate size on consumption. However, the possibility of a clinically significant effect – in either direction – cannot be excluded. Well-conducted trials of tableware size in real-world field settings are now needed to determine whether changing the size of tableware has potential to contribute to efforts to reduce consumption at population-level. Trial registration: The study protocol (https://osf.io/e3dfh/) and data analysis plan (https://osf.io/sh5u7/) were pre-registered on the Open Science Framework

Identifying Farming Strategies Associated With Achieving Global Agricultural Sustainability

Sustainable agroecosystems provide adequate food while supporting environmental and human wellbeing and are a key part of the United Nations Sustainable Development Goals (SDGs). Some strategies to promote sustainability include reducing inputs, substituting conventional crops with genetically modified (GM) alternatives, and using organic production. Here, we leveraged global databases covering 121 countries to determine which farming strategies—the amount of inputs per area (fertilizers, pesticides, and irrigation), GM crops, and percent agriculture in organic production—are most correlated with 12 sustainability metrics recognized by the United Nations Food and Agriculture Organization. Using quantile regression, we found that countries with higher Human Development Indices (HDI) (including education, income, and lifespan), higher-income equality, lower food insecurity, and higher cereal yields had the most organic production and inputs. However, input-intensive strategies were associated with greater agricultural greenhouse gas emissions. In contrast, countries with more GM crops were last on track to meeting the SDG of reduced inequalities. Using a longitudinal analysis spanning 2004–2018, we found that countries were generally decreasing inputs and increasing their share of agriculture in organic production. Also, in disentangling correlation vs. causation, we hypothesize that a country's development is more likely to drive changes in agricultural strategies than vice versa. Altogether, our correlative analyses suggest that countries with greater progress toward the SDGs of no poverty, zero hunger, good health and wellbeing, quality education, decent work, economic growth, and reduced inequalities had the highest production of organic agriculture and, to a lesser extent, intensive use of inputs

Melanoma cells break down LPA to establish local gradients that drive chemotactic dispersal.

The high mortality of melanoma is caused by rapid spread of cancer cells, which occurs unusually early in tumour evolution. Unlike most solid tumours, thickness rather than cytological markers or differentiation is the best guide to metastatic potential. Multiple stimuli that drive melanoma cell migration have been described, but it is not clear which are responsible for invasion, nor if chemotactic gradients exist in real tumours. In a chamber-based assay for melanoma dispersal, we find that cells migrate efficiently away from one another, even in initially homogeneous medium. This dispersal is driven by positive chemotaxis rather than chemorepulsion or contact inhibition. The principal chemoattractant, unexpectedly active across all tumour stages, is the lipid agonist lysophosphatidic acid (LPA) acting through the LPA receptor LPAR1. LPA induces chemotaxis of remarkable accuracy, and is both necessary and sufficient for chemotaxis and invasion in 2-D and 3-D assays. Growth factors, often described as tumour attractants, cause negligible chemotaxis themselves, but potentiate chemotaxis to LPA. Cells rapidly break down LPA present at substantial levels in culture medium and normal skin to generate outward-facing gradients. We measure LPA gradients across the margins of melanomas in vivo, confirming the physiological importance of our results. We conclude that LPA chemotaxis provides a strong drive for melanoma cells to invade outwards. Cells create their own gradients by acting as a sink, breaking down locally present LPA, and thus forming a gradient that is low in the tumour and high in the surrounding areas. The key step is not acquisition of sensitivity to the chemoattractant, but rather the tumour growing to break down enough LPA to form a gradient. Thus the stimulus that drives cell dispersal is not the presence of LPA itself, but the self-generated, outward-directed gradient

Federated Learning on Heterogenous Data using Chest CT

Large data have accelerated advances in AI. While it is well known that population differences from genetics, sex, race, diet, and various environmental factors contribute significantly to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources. Such limitation stems from barriers to large-scale data share in medicine and ethical concerns over data privacy. Federated learning (FL) is one potential pathway for AI development that enables learning across hospitals without data share. In this study, we show the results of various FL strategies on one of the largest and most diverse COVID-19 chest CT datasets: 21 participating hospitals across five continents that comprise >10,000 patients with >1 million images. We present three techniques: Fed Averaging (FedAvg), Incremental Institutional Learning (IIL), and Cyclical Incremental Institutional Learning (CIIL). We also propose an FL strategy that leverages synthetically generated data to overcome class imbalances and data size disparities across centers. We show that FL can achieve comparable performance to Centralized Data Sharing (CDS) while maintaining high performance across sites with small, underrepresented data. We investigate the strengths and weaknesses for all technical approaches on this heterogeneous dataset including the robustness to non-Independent and identically distributed (non-IID) diversity of data. We also describe the sources of data heterogeneity such as age, sex, and site locations in the context of FL and show how even among the correctly labeled populations, disparities can arise due to these biases