Novel Tools for Adjusting Spatial Variability in the Early Sugarcane Breeding Stage

The detection of spatial variability in field trials has great potential for accelerating plant breeding progress due to the possibility of better controlling non-genetic variation. Therefore, we aimed to evaluate a digital soil mapping approach and a high-density soil sampling procedure for identifying and adjusting spatial dependence in the early sugarcane breeding stage. Two experiments were conducted in regions with different soil classifications. High-density sampling of soil physical and chemical properties was performed in a regular grid to investigate the structure of spatial variability. Soil apparent electrical conductivity (ECa) was measured in both experimental areas with an EM38-MK2® sensor. In addition, principal component analysis (PCA) was employed to reduce the dimensionality of the physical and chemical soil data sets. After conducting the PCA and obtaining different thematic maps, we determined each experimental plot’s exact position within the field. Tons of cane per hectare (TCH) data for each experiment were obtained and analyzed using mixed linear models. When environmental covariates were considered, a previous forward model selection step was applied to incorporate the variables. The PCA based on high-density soil sampling data captured part of the total variability in the data for Experimental Area 1 and was suggested to be an efficient index to be incorporated as a covariate in the statistical model, reducing the experimental error (residual variation coefficient, CVe). When incorporated into the different statistical models, the ECa information increased the selection accuracy of the experimental genotypes. Therefore, we demonstrate that the genetic parameter increased when both approaches (spatial analysis and environmental covariates) were employed.

Quantifying the prevalence of (non)-response to fertilizers in sub-Saharan Africa using on-farm trial data

Poor and variable crop responses to fertilizer applications constitute a production risk and may pose a barrier to fertilizer adoption in sub-Saharan Africa (SSA). Attempts to measure response variability and quantify the prevalence of non-response empirically are complicated by the fact that data from on-farm fertilizer trials generally include diverse nutrients and do not include on-site replications. The first aspect limits the extent to which different studies can be combined and compared, while the second does not allow to distinguish actual field-level response variability from experimental error and other residual variations. In this study, we assembled datasets from 41 on-farm fertilizer response trials on cereals and legumes across 11 countries, representing different nutrient applications, to assess response variability and quantify the frequency of occurrence of non-response to fertilizers. Using two approaches to account for residual variation, we estimated non-response, defined here as a zero agronomic response to fertilizer in a given year, to be relatively rare, affecting 0–1 and 7–16% of fields on average for cereals and legumes respectively. The magnitude of response could not be explained by climatic and selected topsoil variables, suggesting that much of the observed variation may relate to unpredictable seasonal and/or local conditions. This implies that, despite demonstrable spatial bias in our sample of trials, the estimated proportion of non-response may be representative for other agro-ecologies across SSA. Under the latter assumption, we estimated that roughly 260,000 ha of cereals and 3,240,000 ha of legumes could be expected to be non-responsive in any particular year.

Multiyear trend in reproduction underpins interannual variation in gametogenic development of an Antarctic urchin

Ecosystems and their biota operate on cyclic rhythms, often entrained by predictable, small-scale changes in their natural environment. Recording and understanding these rhythms can detangle the effect of human induced shifts in the climate state from natural fluctuations. In this study, we assess long-term patterns of reproductive investment in the Antarctic sea urchin, Sterechinus neumayeri, in relation to changes in the environment to identify drivers of reproductive processes. Polar marine biota are sensitive to small changes in their environment and so serve as a barometer whose responses likely mirror effects that will be seen on a wider global scale in future climate change scenarios. Our results indicate that seasonal reproductive periodicity in the urchin is underpinned by a multiyear trend in reproductive investment beyond and in addition to, the previously reported 18–24 month gametogenic cycle. Our model provides evidence that annual reproductive investment could be regulated by an endogenous rhythm since environmental factors only accounted for a small proportion of the residual variation in gonad index. This research highlights a need for multiyear datasets and the combination of biological time series data with large-scale climate metrics that encapsulate multi-factorial climate state shifts, rather than using single explanatory variables to inform changes in biological processes.

A metabolomic aging clock using human CSF

Quantifying the physiology of aging is essential for improving our understanding of age-related disease and the heterogeneity of healthy aging. Recent studies have shown that in regression models using “-omic” platforms to predict chronological age, residual variation in predicted age is correlated with health outcomes, and suggest that these “omic clocks” provide measures of biological age. This paper presents predictive models for age using metabolomic profiles of cerebrospinal fluid from healthy human subjects, and finds that metabolite and lipid data are generally able to predict chronological age within 10 years. We use these models to predict the age of a cohort of subjects with Alzheimer’s and Parkinson’s disease and find an increase in prediction error, potentially indicating that the relationship between the metabolome and chronological age differs with these diseases. However, evidence is not found to support the hypothesis that our models will consistently over-predict the age of these subjects. In our analysis of control subjects, we find the carnitine shuttle, sucrose, biopterin, vitamin E metabolism, tryptophan, and tyrosine to be the most associated with age. We showcase the potential usefulness of age prediction models in a small dataset (n = 85), and discuss techniques for drift correction, missing data imputation, and regularized regression, which can be used to help mitigate the statistical challenges that commonly arise in this setting. To our knowledge, this work presents the first multivariate predictive metabolomic and lipidomic models for age using mass spectrometry analysis of cerebrospinal fluid.

A latitudinal signal in the relationship between species geographic range size and climatic niche area

Species with broader niches may have the opportunity to occupy larger geographic areas, assuming no limitations on dispersal and a relatively homogeneous environmental space. While there is general support for positive \textit{geographic range size – climatic niche area} relationships, a great deal of variation exists across taxonomic and spatial gradients. Here, we use data on a large set of mammal ($n$ = 1225), bird ($n$ = 1829), and tree ($n$ = 341) species distributed across the Americas to examine the \textbf{1}) relationship between geographic range size and climatic niche area, \textbf{2}) influence of species traits on species departures from the best fit geographic range size – climatic niche area relationship, and \textbf{3}) how detection of these relationships is sensitive to how species range size and climatic niche area are estimated. We find positive \textit{geographic range size – climatic niche area} relationships for all taxa. Residual variation in this relationship contained a strong latitudinal signal. Subsampling the occurrence data to create a null expectation, we found that residual variation did not strongly deviate from the null expectation. Together, we provide support for the generality of \textit{geographic range size – climatic niche area} relationships, which may be constrained by latitude but are agnostic to species identity, suggesting that species traits are far less responsible than geographic barriers and the distribution of land area and available environmental space.

Exploring polygenic-environment and residual-environment interactions for depressive symptoms within the UK Biobank

Substantial advances have been made in identifying genetic contributions to depression, but little is known about how the effect of genes can be modulated by the environment, creating a gene-environment interaction. Using multivariate reaction norm models (MRNMs) within the UK Biobank (N=61294-91644), we investigate whether the polygenic and residual variation of depressive symptoms are modulated by 25 a-priori selected covariate traits: 12 environmental variables, 5 biomarkers and polygenic risk scores for 8 mental health disorders. MRNMs provide unbiased polygenic-covariate interaction estimates for a quantitative trait by controlling for outcome-covariate correlations and residual-covariate interactions. Of the 25 selected covariates, 11 significantly modulate depressive symptoms, but no single interaction explains a large proportion of phenotypic variation. Results are dominated by residual-covariate interactions, suggesting that covariate traits (including neuroticism, childhood trauma and BMI) typically interact with unmodelled variables, rather than a genome-wide polygenic score, to influence depressive symptoms. Only average sleep duration has a polygenic-covariate interaction explaining a demonstrably non-zero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% CI [0.54,1.89]) of variation. The presence of an interaction highlights a specific focus for intervention, but the negative results here indicate a limited contribution from polygenic-environment interactions.

Away from arbitrary thresholds: using robust statistics to improve artifact rejection in ERP

Traditionally, artifacts are handled one of two ways in ERP studies: (1) rejection of affected segments and (2) correction via e.g. ICA. Threshold-based rejection is problematic because of the arbitrariness of the chosen limits and particular threshold criterion (e.g. peak-to-peak, absolute, slope, etc.), resulting in large researcher degrees of freedom. Manual rejection may suffer from low inter-rater reliability and is often done without appropriate blinding. Additionally, rejections are typically done for an entire trial, even if the ERP measure of interest isn't impacted by the artifact in question (e.g. motion artifact at the end of the trial). Additionally, fixed thresholds cannot distinguish between non-artifactual extreme values (i.e. those arising from brain activity and which have some 'signal' and some 'noise') and truly artifactual values (e.g. those arising from muscle activity or the electrical environment and which are essentially pure 'noise'). These aspects all become particularly problematic when analyzing EEG recorded under more naturalistic conditions, such as free dialogue in hyperscanning or virtual reality. By using modern, robust statistical methods, we can avoid setting arbitrary thresholds and allow the statistical model to extract the signal from the noise. To demonstrate this, we re-analyzed data from a multimodal virtual-reality N400 paradigm. We created two versions of the dataset, one using traditional threshold-based peak-to-peak artifact rejection (150µV), and one without artifact rejection, and examined the mean voltage at 250-350ms after stimulus onset. We then analyzed the data with both robust and traditional techniques from both a frequentist and Bayesian perspective. The non-robust models yielded different effect estimates when fit to dirty data than when fit to cleaned data, as well as different estimates of the residual variation. The robust models meanwhile estimated similar effect sizes for the dirty and cleaned data, with slightly different estimates of the residual variation. In other words, the robust model worked equally well with or without artifact rejection and did not require setting any arbitrary thresholds. Conversely, the standard, non-robust model was sensitive to the degree of data cleaning. This suggests that robust methods should become the standard in ERP analysis, regardless of data cleaning procedure.

Construction of Accurate Crack Identification on Concrete Structure using Hybrid Deep Learning Approach

In general, several conservative techniques are available for detecting cracks in concrete bridges but they have significant limitations, including low accuracy and efficiency. Due to the expansion of the neural network method, the performance of digital image processing based crack identification has recently diminished. Many single classifier approaches are used to detect the cracks with high accuracy. The classifiers are not concentrating on random fluctuation in the training dataset and also it reflects in the final output as an over-fitting phenomenon. Though this model contains many parameters to justify the training data, it fails in the residual variation. These residual variations are frequent in UAV recorded photos as well as many camera images. To reduce this challenge, a noise reduction technique is utilized along with an SVM classifier to reduce classification error. The proposed technique is more resourceful by performing classification via SVM approach, and further the feature extraction and network training has been implemented by using the CNN method. The captured digital images are processed by incorporating the bending test through reinforced concrete beams. Moreover, the proposed method is determining the widths of the crack by employing binary conversion in the captured images. The proposed model outperforms conservative techniques, single type classifiers, and image segmentation type process methods in terms of accuracy. The obtained results have proved that, the proposed hybrid method is more accurate and suitable for crack detection in concrete bridges especially in the unmanned environment.

Taking stock of COVID-19 policy measures to protect Europe’s elderly living in long-term care facilities

The current COVID-19 pandemic has come to impact all areas of life involving the health, psycho-social and economic wellbeing of individuals, as well as all stages of life from childhood to old age. Particularly, the frail elderly have had to face the gravest consequences of the disease; while reporting measures tend to differ between countries making direct comparisons difficult, national statistics worldwide point to a disproportionate and staggering share of COVID-19 related mortality coming from residential long-term care facilities (LTCFs). Still, the severity of the impact on the institutionalized elderly has not been uniform across countries. In an effort to better understand the disparities in impact on Europe’s elderly living in LTCFs, we review data on mortality outcomes seen during the first wave of the pandemic (months March to June 2020). We then set out to understand the role played by the following two factors: (1) the infection rate in the general population and (2) member state adherence to policy recommendations put forth by the European Centre for Disease Prevention and Control (ECDC) targeting the LTC sector. Regarding the latter, we compare the content of national policy measures in six countries – Austria, Denmark, Germany, Ireland, Spain and Sweden – with those of the ECDC. Our findings establish that infection rates in the general population accounted for most of the variation in mortality among member states, however adherence to EU policy helped to explain the residual variation between cases. This suggests that in order to best protect the institutionalized elderly from infectious disease of this kind, countries need to adopt a two-pronged approach to developing measures: one that aims at reducing transmission within the general population and one that specifically targets LTCFs.

Including distorted specimens in allometric studies: linear mixed models account for deformation

Allometry—patterns of relative change in body parts—is a staple for examining how clades exhibit scaling patterns representative of evolutionary constraint on phenotype, or quantifying patterns of ontogenetic growth within a species. Reconstructing allometries from ontogenetic series is one of the few methods available to reconstruct growth in fossil specimens. However, many fossil specimens are deformed (twisted, flattened, displaced bones) during fossilization, changing their original morphology in unpredictable and sometimes undecipherable ways. To mitigate against post burial changes, paleontologists typically remove clearly distorted measurements from analyses. However, this can potentially remove evidence of individual variation and limits the number of samples amenable to study, which can negatively impact allometric reconstructions. Ordinary least squares regression (OLS) and major axis regression are common methods for estimating allometry, but they assume constant levels of residual variation across specimens, which is unlikely to be true when including both distorted and undistorted specimens. Alternatively, a generalized linear mixed model (GLMM) can attribute additional variation in a model (e.g., fixed or random effects). We performed a simulation study based on a empirical analysis of the extinct cynodont, Exaeretodon argentinus, to test the efficacy of a GLMM on allometric data. We found that GLMMs estimate the allometry using a full dataset better than simply using only non-distorted data. We apply our approach on two empirical datasets, cranial measurements of actual specimens of E. argentinus (n = 16) and femoral measurements of the dinosaur Tawa hallae (n = 26). Taken together, our study suggests that a GLMM is better able to reconstruct patterns of allometry over an OLS in datasets comprised of extinct forms and should be standard protocol for anyone using distorted specimens.