scholarly journals Influence of environmental variability on harbour porpoise movement

2020 ◽  
Vol 648 ◽  
pp. 207-219 ◽  
Author(s):  
D Stalder ◽  
FM van Beest ◽  
S Sveegaard ◽  
R Dietz ◽  
J Teilmann ◽  
...  
Keyword(s):  
Environmental Conditions ◽  
Marine Mammals ◽  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Environmental Variability ◽  
Conservation Status ◽  
Movement Patterns ◽  
Environmental Parameters ◽  
Harbour Porpoise

The harbour porpoise Phocoena phocoena is a small marine predator with a high conservation status in Europe and the USA. To protect the species effectively, it is crucial to understand its movement patterns and how the distribution of intensively used foraging areas can be predicted from environmental conditions. Here, we investigated the influence of both static and dynamic environmental conditions on large-scale harbour porpoise movements in the North Sea. We used long-term movement data from 57 individuals tracked during 1999-2017 in a state-space model to estimate the underlying behavioural states, i.e. whether animals used area-restricted or directed movements. Subsequently, we assessed whether the probability of using area-restricted movements was related to environmental conditions using a generalized linear mixed model. Harbour porpoises were more likely to use area-restricted movements in areas with low salinity levels, relatively high chlorophyll a concentrations and low current velocity, and in areas with steep bottom slopes, suggesting that such areas are important foraging grounds for porpoises. Our study identifies environmental parameters of relevance for predicting harbour porpoise foraging hot spots over space and time in a dynamic system. The study illustrates how movement patterns and data on environmental conditions can be combined, which is valuable to the conservation of marine mammals.

scholarly journals Exome-wide association studies in general and long-lived populations identify genetic variants related to human age

2020 ◽  
Author(s):  
Patrick Sin-Chan ◽  
Nehal Gosalia ◽  
Chuan Gao ◽  
Cristopher V. Van Hout ◽  
Bin Ye ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Model Systems ◽  
P Value ◽  
Ashkenazi Jews ◽  
Association Analyses ◽  
Age Related

SUMMARYAging is characterized by degeneration in cellular and organismal functions leading to increased disease susceptibility and death. Although our understanding of aging biology in model systems has increased dramatically, large-scale sequencing studies to understand human aging are now just beginning. We applied exome sequencing and association analyses (ExWAS) to identify age-related variants on 58,470 participants of the DiscovEHR cohort. Linear Mixed Model regression analyses of age at last encounter revealed variants in genes known to be linked with clonal hematopoiesis of indeterminate potential, which are associated with myelodysplastic syndromes, as top signals in our analysis, suggestive of age-related somatic mutation accumulation in hematopoietic cells despite patients lacking clinical diagnoses. In addition to APOE, we identified rare DISP2 rs183775254 (p = 7.40×10−10) and ZYG11A rs74227999 (p = 2.50×10−08) variants that were negatively associated with age in either both sexes combined and females, respectively, which were replicated with directional consistency in two independent cohorts. Epigenetic mapping showed these variants are located within cell-type-specific enhancers, suggestive of important transcriptional regulatory functions. To discover variants associated with extreme age, we performed exome-sequencing on persons of Ashkenazi Jewish descent ascertained for extensive lifespans. Case-Control analyses in 525 Ashkenazi Jews cases (Males ≥ 92 years, Females ≥ 95years) were compared to 482 controls. Our results showed variants in APOE (rs429358, rs6857), and TMTC2 (rs7976168) passed Bonferroni-adjusted p-value, as well as several nominally-associated population-specific variants. Collectively, our Age-ExWAS, the largest performed to date, confirmed and identified previously unreported candidate variants associated with human age.

scholarly journals Children With Developmental Coordination Disorder Play Active Virtual Reality Games Differently Than Children With Typical Development

2015 ◽  
Vol 95 (3) ◽  
pp. 360-368 ◽  
Author(s):  
Leandra Gonsalves ◽  
Amity Campbell ◽  
Lynn Jensen ◽  
Leon Straker
Keyword(s):  
Virtual Reality ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Developmental Coordination Disorder ◽  
Movement Patterns ◽  
Typical Development ◽  
Motor Patterns ◽  
Movement Quality ◽  
Hand Path ◽  
Coordination Disorder

BackgroundActive virtual reality gaming (AVG) may be useful for children with developmental coordination disorder (DCD) to practice motor skills if their movement patterns are of good quality while engaged in AVG.ObjectiveThis study aimed to examine: (1) the quality of motor patterns of children with DCD participating in AVG by comparing them with children with typical development (TD) and (2) whether differences existed in the motor patterns utilized with 2 AVG types: Sony PlayStation 3 Move and Microsoft Xbox 360 Kinect.DesignThis was a quasi-experimental, biomechanical laboratory–based study.MethodsTwenty-one children with DCD, aged 10 to 12 years, and 19 age- and sex-matched children with TD played a match of table tennis on each AVG type. Hand path, wrist angle, and elbow angle were recorded using a motion analysis system. Linear mixed-model analyses were used to determine differences between DCD and TD groups and Move and Kinect AVG type for forehands and backhands.ResultsChildren with DCD utilized a slower hand path speed (backhand mean difference [MD]=1.20 m/s; 95% confidence interval [95% CI]=0.41, 1.98); greater wrist extension (forehand MD=34.3°; 95% CI=22.6, 47.0); and greater elbow flexion (forehand MD=22.3°; 95% CI=7.4, 37.1) compared with children with TD when engaged in AVG. There also were differences in movement patterns utilized between AVG types.LimitationsOnly simple kinematic measures were compared, and no data regarding movement outcome were assessed.ConclusionsIf a therapeutic treatment goal is to promote movement quality in children with DCD, clinical judgment is required to select the most appropriate AVG type and determine whether movement quality is adequate for unsupervised practice.

scholarly journals Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies

2017 ◽  
Author(s):  
Wei Zhou ◽  
Jonas B. Nielsen ◽  
Lars G. Fritsche ◽  
Rounak Dey ◽  
Maiken E. Gabrielsen ◽  
...  
Keyword(s):  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Case Control ◽  
Error Rates ◽  
European Ancestry ◽  
Computational Time ◽  
Type I ◽  
Genome Wide Association Studies

AbstractIn genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, linear mixed model and the recently proposed logistic mixed model, perform poorly – producing large type I error rates – in the analysis of phenotypes with unbalanced case-control ratios. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation (SPA) to calibrate the distribution of score test statistics. This method, SAIGE, provides accurate p-values even when case-control ratios are extremely unbalanced. It utilizes state-of-art optimization strategies to reduce computational time and memory cost of generalized mixed model. The computation cost linearly depends on sample size, and hence can be applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 white British European-ancestry samples for >1400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.

scholarly journals Genome-wide association study of body fat distribution traits in Hispanics/Latinos from the HCHS/SOL Study

2021 ◽  
Author(s):  
Anne E. Justice ◽  
Kristin Young ◽  
Stephanie M. Gogarten ◽  
Tamar Sofer ◽  
Misa Graff ◽  
...  
Keyword(s):  
Central Obesity ◽  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Genome Wide Association Study ◽  
Health Concern ◽  
Health Study ◽  
Central Adiposity ◽  
Combined Analysis ◽  
Genome Wide

AbstractCentral obesity is a leading health concern with a great burden carried by ethnic minority populations, and especially Hispanics/Latinos. Genetic factors contribute to the obesity burden overall and to inter-population differences. We aim to: 1) identify novel loci associated with central adiposity measured as waist-to-hip ratio (WHR), waist circumference (WC), and hip circumference (HIP), all adjusted for body mass index (adjBMI), using the Hispanic Community Health Study/Study of Latinos (HCHS/SOL); 2) determine if differences in genetic associations differ by background group within HCHS/SOL; 3) determine whether previously reported association regions generalize to HCHS/SOL. Our analyses included 7,472 women and 5,200 men of mainland (Mexican, Central and South American) and Caribbean (Puerto Rican, Cuban, and Dominican) background residing in the US, with genome-wide array data imputed to the 1000 genomes Phase I multiethnic reference panel. We analyzed associations stratified by sex in addition to sexes combined using linear mixed-model regression. We identified 16 variants for WHRadjBMI, 22 for WCadjBMI, and 28 for HIPadjBMI that reached suggestive significance (P<1×10−6). Many of the loci exhibited differences in strength of associations by ethnic background and sex. We brought a total of 66 variants forward for validation in nine cohort studies (N=34,161) with participants of Hispanic/Latino, African and European descent. We confirmed four novel loci (ancestry-specific P<0.05 in replication, consistent direction of effect with HCHS/SOL, and P<5×10−8 after meta-analysis with HCHS/SOL), including rs13301996 in the sexes-combined analysis, and rs79478137 for women-only for WHRadjBMI; rs28692724 in women-only for HIPadjBMI; and rs3168072 in the sexes combined analysis for WCadjBMI. Also, a total of eight previously reported WHRadjBMI association regions, 12 for HIPadjBMI, and 10 for WCadjBMI generalized to HCHS/SOL. Our study findings highlight the importance of large-scale genomic studies in ancestrally diverse Hispanic/Latino populations for identifying and characterizing central obesity-susceptibility that may be ancestry-specific.

scholarly journals Effects of dance on gait and dual-task gait in Parkinson’s disease

2019 ◽  
Author(s):  
Nadeesha Kalyani Hewa Haputhanthirige ◽  
Karen Sullivan ◽  
Gene Moyle ◽  
Sandy Brauer ◽  
Erica Rose Jeffrey ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Dual Task ◽  
Verbal Fluency ◽  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Step Length ◽  
Gait Velocity ◽  
Control Group

Abstract Background Gait impairments in Parkinson’s disease (PD) limit independence and quality of life. While dance based interventions could improve gait, further studies are needed to determine if the benefits generalise to different terrains and when dual-tasking. The aim was to perform a feasibility study of the effects of a dance intervention, based on the Dance for PD®(DfPD®) program, on gait under different dual-tasks (verbal fluency, serial subtraction) and surfaces (even, uneven), and to determine if a larger scale follow-up RCT is warranted.Methods A dance group (DG; n = 17; age = 65.8 ± 11.7 years) and a control group (CG: n = 16; age = 67.0 ± 7.7 years) comprised of non-cognitively impaired (Addenbrooke’s score: DG = 93.2 ± 3.6, CG = 92.6 ± 4.3) independently locomoting people with PD (Hoehn & Yahr I-III) participated in the study. The DG undertook a one-hour DfPD®based class, twice weekly for 12 weeks. The CG had treatment as usual. Gait analysis was performed at baseline and post-intervention while walking on two surfaces (even, uneven) under three conditions (regular walking; dual-task: verbal-fluency, serial-subtraction). The data was analysed by means of a linear mixed model. ResultsThe DG improved significantly compared to the CG in gait velocity, cadence, step-length, and stride-length when even surface walking, with and without a dual-task. On the uneven surface the DG walked more cautiously during regular walking but had improved gait velocity, cadence and step-length when performing serial-subtractions. Conclusions DfPD®-based classes produced clinically significant improvement on spatiotemporal gait parameters under dual-task conditions and on uneven surfaces. This could arise from improved movement confidence and coordination; emotional expression; cognitive skills (planning, multitasking), and; utilisation of external movement cues. A large-scale RCT of this program is warranted.Trial registration A protocol for this study has been registered retrospectively at Australian New Zealand Clinical Trials Registry on 12.11.2018. Identifier: ACTRN12618001834246.

scholarly journals Marine mammal sightings around oil and gas installations in the central North Sea

2017 ◽  
Vol 98 (5) ◽  
pp. 993-1001 ◽  
Author(s):  
Matthieu Delefosse ◽  
Malene Louise Rahbek ◽  
Lars Roesen ◽  
Karin Tubbert Clausen
Keyword(s):  
North Sea ◽  
Marine Mammal ◽  
Marine Mammals ◽  
Oil And Gas ◽  
Environmental Parameters ◽  
Minke Whale ◽  
Harbour Porpoise ◽  
Mammal Species ◽  
The North ◽  
Central North Sea

Relatively little is known about the distribution and diversity of marine mammals around offshore anthropogenic structures. We present results obtained from incidental sightings of marine mammals around oil and gas installations located 200 km off the Danish coast. A total of 131 sightings corresponding to about 288 animals were reported between May 2013 and May 2016. A total of seven marine mammal species were identified, five cetaceans: harbour porpoise (Phocoena phocoena), minke whale (Balaenoptera acutorostrata), white-beaked dolphin (Lagenorhynchus albirostris), killer whale (Orcinus orca), pilot whales (Globicephala spp.) and two species of pinnipeds: harbour (Phoca vitulina) and grey seals (Halichoerus grypus). The most sighted species were harbour porpoise (41%) and minke whale (31%). Relative counts and biodiversity of marine mammals observed around installations corresponded well with the expected distribution in the central North Sea. Several taxon-specific correlations were identified between number of sightings and environmental parameters (depth and latitude) or installation characteristics (installation aerial footprint). Furthermore, 85% of sightings were made during spring and summer and it is unclear whether the pattern observed reflected a natural seasonal occurrence of marine mammals in the area or an effect of reduced effort during autumn and winter. Despite the potential caveats, results obtained during this programme provide an insight into the relationship between marine mammals and oil and gas offshore installations in the North Sea.

scholarly journals Multi Task Deep Learning for Genomic Predictions

2021 ◽  
Author(s):  
Baohong Guo
Keyword(s):  
Deep Learning ◽  
Ridge Regression ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Moderate Increase ◽  
Data Set ◽  
Test Set ◽  
Genomic Predictions

ABSTRACTGenomic predictions have been recognized as a new promising technique in animal and plant breeding. Linear mixed model is a widely used statistical technique, but it may not be desirable for large training sets and number of molecular markers, because it is intensive in computation. Deep learning is a subfield of machine learning and it can be used for complex predictions on a large scale. Multi task deep learning (MT-DL) incorporates related tasks(labels or traits) into one learning process to enable the learning model to perform better than single task deep learning (ST-DL). I applied MT-DL to genotype by environment genomic predictions to predict the performances of breeding lines at multiple environments. I compared MT-DL with linear mixed model-based Bayesian genotype × environment method (BGGE) and separate genomic predictions on single environments with widely used rrBLUP, ridge regression and ST-DL using cross validations. Compared with rrBLUP, MT-DL and non-linear BGGE showed a moderate increase of 9.4 and 7.6%, respectively, ST-DL has a small increase of 5.4%, ridge regression had a similar prediction accuracy and linear BGGE had a small decrease of −2.0% for prediction accuracy. I also found that all methods including rrBLUP had an overfitting, this is likely because yield genomic predictions are complex and the data set used in this study are small. rrBLUP, ridge regression, ST-DL and MT-DL has similar overfitting. Difference between training and test set prediction accuracies was between 0.344 and 0. 387. Linear and nonlinear BGGE methods seem to have much worse overfitting than other methods. Difference between training and test set prediction accuracies were 0.429 and 0.472, respectively. I also discussed the potential applications of ST-DL and MT-DL in genomic predictions of hybrid crops such as maize

scholarly journals 2PL Model: Compare Generalized Linear Mixed Model with Latent Variable Model based IRT framework

2021 ◽  
Author(s):  
Jihong Zhang ◽  
Terry Ackerman ◽  
Yurou Wang
Keyword(s):  
Latent Variable ◽  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
R Package ◽  
Estimation Accuracy ◽  
Variable Model ◽  
Large Scale Assessment ◽  
Measurement Models ◽  
Lower Accuracy

Fitting item response theory (IRT) models using the generalized mixed logistic regression model (GLMM) has become more popular in large-scale assessment because GLMM allows combining complicated multilevel structures (i.e., students are nested in classrooms which are nested in schools) with IRT measurement models. However, the estimation accuracy of item parameters between these two models is not well examined. This study aimed to compare the estimation results of the GLMM based 2PL model (using the PLmixed R package) with the traditional IRT model (using flexMIRT software) under different sample sizes (N= 500, 1000, 5000) and test length (J = 15, 21) conditions. The simulation results showed that for both the GLMM-based method and the traditional method, item threshold estimates had lower bias than item discrimination parameters. We also found that according to the simulation study, GLMM estimates via PLmixed had lower accuracy than traditional IRT modeling via flexMIRT for items with high discrimination.

scholarly journals Generalized Linear Mixed Model and Lasso Regularization for Statistical Downscaling

2021 ◽  
Vol 1 (01) ◽  
pp. 36-52
Author(s):  
Ma'rufah - Hayati ◽  
Agus Muslim
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Small Scale ◽  
Climate Data ◽  
Explanatory Variables ◽  
Response Variables

Rainfall is one of the climatic elements in the tropics which is very influential in agriculture, especially in determining the growing season. Thus, proper rainfall modeling is needed to help determine the best time to start cultivating the soil. Rainfall modeling can be done using the Statistical Downscaling (SDS) method. SDS is a statistical model in the field of climatology to analyze the relationship between large-scale and small-scale climate data. This study uses response variables as a small-scale climate data in the form of rainfall and explanatory variables as a large-scale climate data of the General Circulation Model (GCM) output in the form of precipitation. However, the application of SDS modeling is known to cause several problems, including correlated and not stationary response variables, multi-dimensional explanatory variables, multicollinearity, and spatial correlation between grids. Modeling with some of these problems will cause violations of the assumptions of independence and multicollinearity. This research aims to model the rainfall in Indramayu Regency, West Java Province using a combined regression model between the Generalized linear mixed model (GLMM) and Least Absolute Selection and Shrinkage Operator (LASSO) regulation (L1). GLMM was used to deal with the problem of independence and Lasso Regulation (L1) was used to deal with multicollinearity problems or the number of explanatory variables that is greater than the response variable. Several models were formed to find the best model for modeling rainfall. This research used the GLMM-Lasso model with Normal spread compared to the GLMM model with Gamma response (Gamma-GLMM). The results showed that the RMSE and R-square GLMM-Lasso models were smaller than the Gamma-GLMM models. Thus, it can be concluded that GLMM-Lasso model can be used to model statistical downscaling and solve the previously mentioned constraints. Received February 10, 2021Revised March 29, 2021Accepted March 29, 2021

scholarly journals Effects of Temperature, Wetness Duration, and Leaflet Age on Infection of Strawberry Foliage by Phomopsis obscurans

Plant Disease ◽  
2003 ◽  
Vol 87 (5) ◽  
pp. 579-584 ◽  
Author(s):  
M. Nita ◽  
M. A. Ellis ◽  
L. V. Madden
Keyword(s):  
Prediction Model ◽  
Environmental Conditions ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Disease Incidence ◽  
Leaf Wetness ◽  
Controlled Environment ◽  
Mixed Model Analysis ◽  
Time Of Infection ◽  
Effects Of Temperature

Temperature, leaf wetness, and leaflet age effects on infection of strawberry foliage by Phomopsis obscurans were examined in controlled-environment experiments. A mid-season (‘Honeoye’) and early-season (‘Earliglow’) cultivar were used. Tested temperatures were 10, 15, 20, 25, 30, and 35°C, and tested wetness periods were 5, 10, 15, 20, 25, and 35 h. Leaflets were labeled based on age: 0 to 1, 2 to 6, and 7 to 14 days old. Effects of temperature, wetness duration, and leaflet age on the logit of disease incidence and severity were quantified using a linear mixed model analysis of variance (ANOVA). Age, wetness duration, and their interaction significantly affected these measures of disease. Disease intensity decreased dramatically with increasing leaflet age at the time of infection, indicating a decrease in susceptibility with maturation of foliage, and increased with increasing wetness duration. Temperature only affected disease incidence with ‘Honeoye’. A prediction model was developed for leaflet infection based on ANOVA results. Coefficients of determination were approximately 0.8 for both cultivars and measures of disease (incidence and severity), indicating that disease could be described accurately based on environmental conditions and leaflet age.

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