Pesticides and natural enemies (particularly ground beetles) of aphids on potato

The arthropod fauna of fields of ware potatoes in eastern Scotland was assessed to determine the species composition and relative abundance of the natural enemies of aphids on potato. Aphids and aphid-specific predators and parasitoids were surveyed by visual searches of foliage; epigeal arthropods were assessed by pitfall trapping.Aphid-specific natural enemies were generally uncommon but may have been underestimated. Approximately 11,000 animals were caught in pitfall traps and most were of the ground beetle genus Pterostichus (Coleoptera : Carabidae). Gut dissections showed that 14.4 and 30.5 per cent of Pterostichus melanarius and Pterostichus madidus respectively, contained aphid remnants. In the laboratory, demeton-S-methyl applied directly to these beetles had little apparent effect but 19.1 per cent died after consumption of treated aphids. Field experiments indicated that demeton-S-methyl influenced the trap catch of Pterostichus spp by altering their predatory activity. Caution should thus be exercised when interpreting such data.It is suggested that certain species of Carabidae may be important in the control of aphids on potato. Their potential is discussed and suggestions given for further research

Identifying key multi-modal predictors of incipient dementia in Parkinson’s disease: a machine learning analysis and Tree SHAP interpretation

BackgroundPersons with Parkinson’s disease (PD) differentially progress to cognitive impairment and dementia. With a 3-year longitudinal sample of initially non-demented PD patients measured on multiple dementia risk factors, we demonstrate that machine learning classifier algorithms can be combined with explainable artificial intelligence methods to identify and interpret leading predictors that discriminate those who later converted to dementia from those who did not.MethodParticipants were 48 well-characterized PD patients (Mbaseline age = 71.6; SD = 4.8; 44% female). We tested 38 multi-modal predictors from 10 domains (e.g., motor, cognitive) in a computationally competitive context to identify those that best discriminated two unobserved baseline groups, PD No Dementia (PDND), and PD Incipient Dementia (PDID). We used Random Forest (RF) classifier models for the discrimination goal and Tree SHapley Additive exPlanation (Tree SHAP) values for deep interpretation.ResultsAn excellent RF model discriminated baseline PDID from PDND (AUC = 0.84; normalized Matthews Correlation Coefficient = 0.76). Tree SHAP showed that ten leading predictors of PDID accounted for 62.5% of the model, as well as their relative importance, direction, and magnitude (risk threshold). These predictors represented the motor (e.g., poorer gait), cognitive (e.g., slower Trail A), molecular (up-regulated metabolite panel), demographic (age), imaging (ventricular volume), and lifestyle (activities of daily living) domains.ConclusionOur data-driven protocol integrated RF classifier models and Tree SHAP applications to selectively identify and interpret early dementia risk factors in a well-characterized sample of initially non-demented persons with PD. Results indicate that leading dementia predictors derive from multiple complementary risk domains

Net primary productivity of forest stands in New Hampshire estimated from Landsat and MODIS satellite data

© 2007 Potter et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Expanded encyclopaedias of DNA elements in the human and mouse genomes

All data are available on the ENCODE data portal: www.encodeproject. org. All code is available on GitHub from the links provided in the methods section. Code related to the Registry of cCREs can be found at https:// github.com/weng-lab/ENCODE-cCREs. Code related to SCREEN can be found at https://github.com/weng-lab/SCREEN.© The Author(s) 2020. The human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal (https://www.encodeproject.org), including phase II ENCODE1 and Roadmap Epigenomics2 data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.This work was supported by grants from the NIH under U01HG007019, U01HG007033, U01HG007036, U01HG007037, U41HG006992, U41HG006993, U41HG006994, U41HG006995, U41HG006996, U41HG006997, U41HG006998, U41HG006999, U41HG007000, U41HG007001, U41HG007002, U41HG007003, U54HG006991, U54HG006997, U54HG006998, U54HG007004, U54HG007005, U54HG007010 and UM1HG009442

Genome-wide identification and phenotypic characterization of seizure-associated copy number variations in 741,075 individuals

Copy number variants (CNV) are established risk factors for neurodevelopmental disorders with seizures or epilepsy. With the hypothesis that seizure disorders share genetic risk factors, we pooled CNV data from 10,590 individuals with seizure disorders, 16,109 individuals with clinically validated epilepsy, and 492,324 population controls and identified 25 genome-wide significant loci, 22 of which are novel for seizure disorders, such as deletions at 1p36.33, 1q44, 2p21-p16.3, 3q29, 8p23.3-p23.2, 9p24.3, 10q26.3, 15q11.2, 15q12-q13.1, 16p12.2, 17q21.31, duplications at 2q13, 9q34.3, 16p13.3, 17q12, 19p13.3, 20q13.33, and reciprocal CNVs at 16p11.2, and 22q11.21. Using genetic data from additional 248,751 individuals with 23 neuropsychiatric phenotypes, we explored the pleiotropy of these 25 loci. Finally, in a subset of individuals with epilepsy and detailed clinical data available, we performed phenome-wide association analyses between individual CNVs and clinical annotations categorized through the Human Phenotype Ontology (HPO). For six CNVs, we identified 19 significant associations with specific HPO terms and generated, for all CNVs, phenotype signatures across 17 clinical categories relevant for epileptologists. This is the most comprehensive investigation of CNVs in epilepsy and related seizure disorders, with potential implications for clinical practice

Genetic factors moderate everyday physical activity effects on executive functions in aging: Evidence from the Victoria Longitudinal Study

Objective: Everyday physical activity (EPA) is an important modifiable contributor to age-related variability in executive functioning (EF). However, its role may be moderated by nonmodifiable genetic factors. We tested independent and interactive effects of brain-derived neurotrophic factor (BDNF rs6265) and insulin degrading enzyme (IDE rs6583817) on EF and EPA–EF relationships. Method: The sample consisted of genotyped older adults (N = 577; M age = 70.47 years) over 3 waves (∼9 years) of the Victoria Longitudinal Study. Analyses included (a) confirmatory factor analysis establishing a single latent EF factor from 4 standard EF tasks, (b) latent growth modeling over a 40-year band of aging (ages 53 to 95), and (c) structural regression to investigate the independent and interactive effects of BDNF, IDE, and EPA. Results: First, higher levels of EPA were associated with better EF performance at the centering age (75 years) and less EF decline. Second, IDE G+ (protective) carriers exhibited better EF performance at Age 75 than their G− (nonprotective) peers. Third, within the IDE G+ carrier group, those with higher EPA exhibited better EF performance and slower decline over time than those with lower EPA. Fourth, for the BDNF homozygote Val group, higher EPA was associated with better EF performance and more gradual EF change; however, this beneficial effect was not seen for Met carriers. Conclusion: The effect of modifiable physical health factors on EF is moderated by biological mechanisms associated with risk-protection genetic polymorphisms

Memory Resilience to Alzheimer's Genetic Risk: Sex Effects in Predictor Profiles

Objectives: Apolipoprotein E (APOE) ɛ4 and Clusterin (CLU) C alleles are risk factors for Alzheimer’s disease (AD) and episodic memory (EM) decline. Memory resilience occurs when genetically at-risk adults perform at high and sustained levels. We investigated whether (a) memory resilience to AD genetic risk is predicted by biological and other risk markers and (b) the prediction profiles vary by sex and AD risk variant. Method: Using a longitudinal sample of nondemented adults (n = 642, aged 53–95) we focused on memory resilience (over 9 years) to 2 AD risk variants (APOE, CLU). Growth mixture models classified resilience. Random forest analysis, stratified by sex, tested the predictive importance of 22 nongenetic risk factors from 5 domains (n = 24–112). Results: For both sexes, younger age, higher education, stronger grip, and everyday novel cognitive activity predicted memory resilience. For women, 9 factors from functional, health, mobility, and lifestyle domains were also predictive. For men, only fewer depressive symptoms was an additional important predictor. The prediction profiles were similar for APOE and CLU. Discussion: Although several factors predicted resilience in both sexes, a greater number applied only to women. Sexspecific mechanisms and intervention targets are implied.This work was supported by the National Institutes of Health (National Institute on Aging; grant number R01 AG008235); the Canada Research Chairs program; and the Canadian Consortium on Neurodegeneration in Aging (with funding from Canadian Institutes of Health Research and partners, including SANOFI-ADVENTIS R&D) to Roger Dixon. The National Health and Medical Research Council (Research Fellowship #1102694 and Grant #1100579) supported Kaarin Anstey’s involvement

Alzheimer's Environmental and Genetic Risk Scores are Differentially Associated With General Cognitive Ability and Dementia Severity

Purpose: We investigated the association of the Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI) and an Alzheimer disease (AD) genetic risk score (GRS) with cognitive performance. Methods: The ANU-ADRI (composed of 12 risk factors for AD) and GRS (composed of 25 AD risk loci) were computed in 1061 community-dwelling older adults. Participants were assessed on 11 cognitive tests and activities of daily living. Structural equation modeling was used to evaluate the association of the ANU-ADRI and GRS with: (1) general cognitive ability (g), (2) dementia-related variance in cognitive performance (δ), and (3) verbal ability (VA), episodic memory (EM), executive function (EF), and processing speed (PS). Results: A worse ANU-ADRI score was associated with poorer performance in “g” [β (SE)=−0.40 (0.02), P<0.001], δ [−0.40 (0.04), P<0.001], and each cognitive domain [VA=−0.29 (0.04), P<0.001; EM=−0.34 (0.03), P<0.001; EF=−0.38 (0.03), P<0.001; and PS=−0.40 (0.03), P<0.001]. A worse GRS was associated with poorer performance in δ [−0.08 (0.03), P=0.041] and EM [−0.10 (0.03), P=0.035]. Conclusions: The ANU-ADRI was broadly associated with worse cognitive performance, including general ability and dementia severity, validating its further use in early dementia risk assessment