A New Wireless Device for Real-Time Mechanical Impact Evaluation in a Citrus Packing Line

Postharvest handling of fresh fruit is a potential source of bruising and damage, with significant consequences for fruit quality and marketability. In the last 30 years, different types of impact-recording devices (also called electronic fruits or pseudo-fruits) have been developed with the aim of measuring the impacts experienced by fruits during postharvest operations. The aim of this study was to develop and test a novel wireless instrumented sphere to study the critical points in a citrus packing line by measuring the impacts experienced by fruits in real-time. The non-commercial device was based on a MEMS (micro-electro-mechanical system) sensor node with a sensing range from ±1×g to ±400×g (g = 9.8 m s-2), a ferroelectric RAM (FRAM) memory, a radio frequency (RF) transmitter, a microcontroller, and a 75 mAh lithium battery. The sensor node was placed inside a plastic ellipsoid case with a total weight of 100 g to represent a ‘Tardivo di Ciaculli’ mandarin. An FR receiver allowed real-time transmission of the measured data. Tests were performed in the Consorzio del Mandarino Tardivo di Ciaculli packing line (Palermo, Italy). Total acceleration values, representing the stresses experienced by fruit in the packing line, were studied using a variance component model. The results showed that total acceleration remained below 20×g in most of the measurements, but considerably higher values, up to 80×g, were obtained between the brushing and waxing machines. In particular, waxing was identified as the most critical operation based on the impact transmitted to the fruit. Our system proved to be effective for immediate on-line assessment of the accelerations experienced by fruits, allowing prompt intervention to guarantee fruit quality in postharvest operations.HighlightsA novel, wirelessly instrumented sphere was developed and tested to study the critical points in a fruit packing line.The total acceleration experienced by the fruits was studied using a variance component model.The system was proven effective in online assessment of the accelerations experienced by fruits. Keywords: Acceleration, Damage, Instrumented sphere, Mandarin, Postharvest.

Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior

Global signal regression (GSR) is one of the most debated preprocessing strategies for resting-state functional MRI. GSR effectively removes global artifacts driven by motion and respiration, but also discards globally distributed neural information and introduces negative correlations between certain brain regions. The vast majority of previous studies have focused on the effectiveness of GSR in removing imaging artifacts, as well as its potential biases. Given the growing interest in functional connectivity fingerprinting, here we considered the utilitarian question of whether GSR strengthens or weakens associations between resting-state functional connectivity (RSFC) and multiple behavioral measures across cognition, personality and emotion.By applying the variance component model to the Brain Genomics Superstruct Project (GSP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 47% across 23 behavioral measures after GSR. In the Human Connectome Project (HCP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 40% across 58 behavioral measures, when GSR was applied after ICA-FIX de-noising. To ensure generalizability, we repeated our analyses using kernel regression. GSR improved behavioral prediction accuracies by an average of 64% and 12% in the GSP and HCP datasets respectively. Importantly, the results were consistent across methods. A behavioral measure with greater RSFC-explained variance (using the variance component model) also exhibited greater prediction accuracy (using kernel regression). A behavioral measure with greater improvement in behavioral variance explained after GSR (using the variance component model) also enjoyed greater improvement in prediction accuracy after GSR (using kernel regression). Furthermore, GSR appeared to benefit task performance measures more than self-reported measures.Since GSR was more effective at removing motion-related and respiratory-related artifacts, GSR-related increases in variance explained and prediction accuracies were unlikely the result of motion-related or respiratory-related artifacts. However, it is worth emphasizing that the current study focused on whole-brain RSFC, so it remains unclear whether GSR improves RSFC-behavioral associations for specific connections or networks. Overall, our results suggest that at least in the case for young healthy adults, GSR strengthens the associations between RSFC and most (although not all) behavioral measures. Code for the variance component model and ridge regression can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/preprocessing/Li2019_GSR.HighlightsGlobal signal regression improves RSFC-behavior associationsGlobal signal regression improves RSFC-based behavioral prediction accuraciesImprovements replicated across two large-scale datasets and methodsTask-performance measures enjoyed greater improvements than self-reported onesGSR beneficial even after ICA-FIX

Log-transformed B-type natriuretic peptide as a prognostic predictor in patients undergoing cardiovascular surgery

Objective This study was performed to explore the association between circulating B-type natriuretic peptide (BNP) and other mortality-related factors in patients undergoing cardiovascular surgery. Methods In this observational study, multilevel linear regression analysis and multilevel survival analysis were performed to measure the log-transformed BNP (lnBNP) value at four time points in 197 patients with 788 repeated data measurements. Effects of the interaction between the time points and the two intervention groups (cardiac surgery and vascular surgery) were also investigated. Six models were evaluated to identify the best fit for the data. Stata/MP® version 14.2 (Stata Corp., College Station, TX, USA) was used to analyze the two-level variance component model fitting. Results There were significant differences in the fixed-effect parameters of lnBNP, such as the time point, age, body mass index, emergency operation, prognostic nutritional index, and estimated glomerular filtration rate. According to the multilevel survival analysis for all-cause death and vascular death, lnBNP significantly differed and was a common prognostic marker. Conclusion As lnBNP increased by 1 point, all-cause death increased 2.07 times and vascular death increased 3.10 times. lnBNP is an important prognostic predictor and quantitative biochemical marker in patients undergoing cardiovascular surgery.

Refining fine-mapping: effect sizes and regional heritability

Recent statistical approaches have shown that the set of all available genetic variants explains considerably more phenotypic variance of complex traits and diseases than the individual variants that are robustly associated with these phenotypes. However, rapidly increasing sample sizes constantly improve detection and prioritization of individual variants driving the associations between genomic regions and phenotypes. Therefore, it is useful to routinely estimate how much phenotypic variance the detected variants explain for each region by taking into account the correlation structure of variants and the uncertainty in their causal status. Here we extend the FINEMAP software to estimate the effect sizes and regional heritability under the probabilistic model that assumes a handful of causal variants per each region. Using the UK Biobank data to simulate GWAS regions with only a few causal variants, we demonstrate that FINEMAP provides higher precision and enables more detailed decomposition of regional heritability into individual variants than the variance component model implemented in BOLT or the fixed-effect model implemented in HESS. Using data from 51 serum biomarkers and four lipid traits from the FINRISK study, we estimate that FINEMAP captures on average 24% more regional heritability than the variant with the lowest P-value alone and 20% less than BOLT. Our simulations suggest how an upward bias of BOLT and a downward bias of FINEMAP could together explain the observed difference between the methods. We conclude that FINEMAP enables computationally efficient estimation of effect sizes and regional heritability in the era of biobank scale data.

Two variance component model improves genetic prediction in family data sets

Genetic prediction based on either identity by state (IBS) sharing or pedigree information has been investigated extensively using Best Linear Unbiased Prediction (BLUP) methods. Such methods were pioneered in the plant and animal breeding literature and have since been applied to predict human traits with the aim of eventual clinical utility. However, methods to combine IBS sharing and pedigree information for genetic prediction in humans have not been explored. We introduce a two variance component model for genetic prediction: one component for IBS sharing and one for approximate pedigree structure, both estimated using genetic markers. In simulations using real genotypes from CARe and FHS family cohorts, we demonstrate that the two variance component model achieves gains in prediction r2 over standard BLUP at current sample sizes, and we project based on simulations that these gains will continue to hold at larger sample sizes. Accordingly, in analyses of four quantitative phenotypes from CARe and two quantitative phenotypes from FHS, the two variance component model significantly improves prediction r2 in each case, with up to a 20% relative improvement. We also find that standard mixed model association tests can produce inflated test statistics in data sets with related individuals, whereas the two variance component model corrects for inflation.

Comparison of granddaughter design and general pedigree design analysis of QTL in dairy cattle: a simulation study

Traditional methods for detection and mapping of quantitative trait loci (QTL) in dairy cattle populations are usually based on daughter design (DD) or granddaughter design (GDD). Although these designs are well established and usually successful in detecting QTL, they consider sire families independently of each other, thereby ignoring relationships among other animals in the population and consequently, reducing the power of QTL detection. In this study we compared a traditional GDD with a general pedigree design (GPD) and assessed the precision and power of both methods for detecting and locating QTL in a simulated complex pedigree. QTL analyses were performed under the variance component model containing a random QTL and a random polygenic effect. The covariance matrix of the polygenic effect was a standard additive relationship matrix. The (co)variance matrix of the random QTL effect contained probabilities that QTL alleles shared by two individuals were identical by descent (IBD). In the GDD analysis, IBD probabilities were calculated using sires’ and daughters’ marker genotypes. In the GPD analysis, IBD probabilities were obtained using a deterministic approach. The estimation of QTL position and variance components was conducted using REML algorithm. Although both methods were able to locate the region of the QTL properly, the GPD method showed better precision of QTL position estimates in most cases and significantly higher power than the GDD method.  

On the Best Quadratic Minimum Bias Non-Negative Estimator of a Two-Variance Component Model

On the Best Quadratic Minimum Bias Non-Negative Estimator of a Two-Variance Component ModelVariance components (VCs) in linear adjustment models are usually successfully computed by unbiased estimators. However, for many unbiased VC techniques estimated variance components might be negative, a result that cannot be tolerated by the user. This is, for example, the case with the simple additive VC model aσ2/1 + bσ2/2 with known coefficients a and b, where either of the unbiasedly estimated variance components σ2/1 + σ2/2 may frequently come out negative. This fact calls for so-called non-negative VC estimators. Here the Best Quadratic Minimum Bias Non-negative Estimator (BQMBNE) of a two-variance component model is derived. A special case with independent observations is explicitly presented.