Evolution of the Gene Lineage Encoding the Carbon Dioxide Receptor in Insects

A heterodimer of the insect chemoreceptors Gr21a and Gr63a has been shown to be the carbon dioxide receptor in Drosophila melanogaster (Meigen) (Diptera: Drosophilidae). Comparison of the genes encoding these two proteins across the 12 available drosophilid fly genomes allows refined definition of their N-termini. These genes are highly conserved, along with a paralog of Gr21a, in the Anopheles gambiae, Aedes aegypti, and Culex pipiens mosquitoes, as well as in the silk moth Bombyx mori and the red flour beetle Tribolium castaneum. In the latter four species we name these three proteins Gr1, Gr2, and Gr3. Intron evolution within this distinctive three gene lineage is considerable, with at least 13 inferred gains and 39 losses. Surprisingly, this entire ancient gene lineage is absent from all other available more basal insect and related arthropod genomes, specifically the honey bee, parasitoid wasp, human louse, pea aphid, waterflea, and blacklegged tick genomes. At least two of these species can detect carbon dioxide, suggesting that they evolved other means to do so

Do self-reported hearing and visual impairments predict longitudinal dementia in older adults?

Background Sensory impairments have been associated with dementia in older adults. However, the contribution of different impairments and how they interact in the development of dementia is not clear. We examined the independent and interaction effects of hearing impairment (HI) and visual impairment (VI) on incident dementia. Design Multi-centric population-based prospective cohort study. Setting Data were taken from the AgeDifferent.de platform, pooling participants aged 75 and older from the German LEILA75+ and AgeCoDe/AgeQualiDe cohorts. Participants Older adults (N = 3497) with mean age 79.8 years, 67.2% female. Measurements Standardized interviews and questionnaires were used to assess self-reported HI and VI at baseline and all-cause dementia in 9 follow-ups, spanning over 20 years. Methods Competing risk regression models were conducted to test the main and interaction effects of HI and VI on dementia incidence, adjusting for established risk factors of dementia and accumulated mortality. Results HI and VI at baseline were reported by 30.3% and 16.6% of individuals, respectively. Adjusting for baseline information on sociodemographics, substance use, cognitive functioning and morbidity, and controlling for accumulated mortality risk, HI (sHR 1.16, 95% CI 1.04–1.30, p = 0.011) but not VI (sHR 1.07, 95% CI 0.90–1.28, p = 0.462) was significantly associated with incident dementia. There was no interaction between HI and VI (sHR 1.09, 95% CI 0.81–1.46, p = 0.567). Conclusions Hearing impairment is associated with an increased incidence of all-cause dementia in older adults. There is no excess risk or risk compensation through the additional presence or absence of visual impairment. Early prevention measures for hearing impairment might help to reduce the long-term risk of dementia

New Insights into the Genetic Etiology of Alzheimer’s Disease and Related Dementias

Characterization of the genetic landscape of Alzheimer’s disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/‘proxy’ AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele

Modeling Denitrification : Can We Report What We Don't Know?

Funding Information: This study is the products of a workshop funded by the Deutsche Forschungsgemeinschaft through the research unit DFG‐FOR 2337: Denitrification in Agricultural Soils: Integrated Control and Modelling at Various Scales (DASIM), and by the German Federal Ministry of Education and Research (BMBF) under the “Make our Planet Great Again—German Research Initiative”, Grant 306060, implemented by the German Academic Exchange Service (DAAD). This work was supported by the European Union's Horizon 2020 research and innovation programme project VERIFY (grant agreement no. 776810). We would like to thank the contribution of all workshop participants of the II. DASIM Modeler Workshop. Publisher Copyright: © 2023. The Authors.Peer reviewedPublisher PD

Micrometeorological methods for greenhouse gas measurement

Micrometeorological techniques are useful if greenhouse gas (GHG) emissions from larger areas (i.e. entire fields) should be integrated. The theory and the various techniques such as flux-gradient, aerodynamic, and Bowen ratio as well as Eddy correlationmethods are described and discussed. Alternativemethods also used areEddy correlation, mass balance techniques, and tracer-based methods.The analytical techniques with current state-of-the-art approaches as well as the calculation procedures are presented

Greenhouse gases from agriculture

The rapidly changing global climate due to increased emission of anthropogenic greenhouse gases (GHGs) is leading to an increased occurrence of extreme weather events such as droughts, floods, and heatwaves. The three major GHGs are carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). The major natural sources of CO2 include ocean-atmosphere exchange, respiration of animals, soils (microbial respiration) and plants, and volcanic eruption; while the anthropogenic sources include burning of fossil fuel (coal, natural gas, and oil), deforestation, and the cultivation of land that increases the decomposition of soil organic matter and crop and animal residues. Natural sources of CH4 emission include wetlands, termite activities, and oceans. Paddy fields used for rice production, livestock production systems (enteric emission from ruminants), landfills, and the production and use of fossil fuels are the main anthropogenic sources of CH4. Nitrous oxide, in addition to being a major GHG, is also an ozone-depleting gas. N2O is emitted by natural processes from oceans and terrestrial ecosystems. Anthropogenic N2O emissions occur mostly through agricultural and other land-use activities and are associated with the intensification of agricultural and other human activities such as increased use of synthetic fertiliser (119.4 million tonnes of N worldwide in 2019), inefficient use of irrigation water, deposition of animal excreta (urine and dung) from grazing animals, excessive and inefficient application of farm effluents and animal manure to croplands and pastures, and management practices that enhance soil organic N mineralisation and C decomposition. Agriculture could act as a source and a sink of GHGs. Besides direct sources, GHGs also come from various indirect sources, including upstream and downstream emissions in agricultural systems and ammonia (NH3) deposition from fertiliser and animal manure

Dietary patterns are related to cognitive functioning in elderly enriched with individuals at increased risk for Alzheimer's disease

PURPOSE: To investigate cross-sectional associations between dietary patterns and cognitive functioning in elderly free of dementia. METHODS: Data of 389 participants from the German DELCODE study (52% female, 69 ± 6 years, mean Mini Mental State Score 29 ± 1) were included. The sample was enriched with elderly at increased risk for Alzheimer's disease (AD) by including participants with subjective cognitive decline, mild cognitive impairment (MCI) and siblings of AD patients. Mediterranean and MIND diets were derived from 148 Food Frequency Questionnaire items, and data-driven patterns by principal component analysis (PCA) of 39 food groups. Associations between dietary patterns and five cognitive domain scores were analyzed with linear regression analyses adjusted for demographics (model 1), and additionally for energy intake, BMI, other lifestyle variables and APOe4-status (model 2). For PCA-derived dietary components, final model 3 included all other dietary components. RESULTS: In fully adjusted models, adherence to Mediterranean and MIND diet was associated with better memory. The 'alcoholic beverages' PCA component was positively associated with most cognitive domains. Exclusion of MCI subjects (n = 60) revealed that Mediterranean and MIND diet were also related to language functions; associations with the alcoholic beverages component were attenuated, but most remained significant. CONCLUSION: In line with data from elderly population samples, Mediterranean and MIND diet and some data-derived dietary patterns were related to memory and language function. Longitudinal data are needed to draw conclusions on the putative effect of nutrition on the rate of cognitive decline, and on the potential of dietary interventions in groups at increased risk for AD

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution;(B) incorporating demographics & clinical covariates;(C) the impact of the size of the training data set;(D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of sample predictions. The highest performance for A 42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status

Friends and Foes from an Ant Brain's Point of View – Neuronal Correlates of Colony Odors in a Social Insect

Background: Successful cooperation depends on reliable identification of friends and foes. Social insects discriminate colony members (nestmates/friends) from foreign workers (non-nestmates/foes) by colony-specific, multi-component colony odors. Traditionally, complex processing in the brain has been regarded as crucial for colony recognition. Odor information is represented as spatial patterns of activity and processed in the primary olfactory neuropile, the antennal lobe (AL) of insects, which is analogous to the vertebrate olfactory bulb. Correlative evidence indicates that the spatial activity patterns reflect odor-quality, i.e., how an odor is perceived. For colony odors, alternatively, a sensory filter in the peripheral nervous system was suggested, causing specific anosmia to nestmate colony odors. Here, we investigate neuronal correlates of colony odors in the brain of a social insect to directly test whether they are anosmic to nestmate colony odors and whether spatial activity patterns in the AL can predict how odor qualities like ‘‘friend’’ and ‘‘foe’’ are attributed to colony odors. Methodology/Principal Findings: Using ant dummies that mimic natural conditions, we presented colony odors and investigated their neuronal representation in the ant Camponotus floridanus. Nestmate and non-nestmate colony odors elicited neuronal activity: In the periphery, we recorded sensory responses of olfactory receptor neurons (electroantennography), and in the brain, we measured colony odor specific spatial activity patterns in the AL (calcium imaging). Surprisingly, upon repeated stimulation with the same colony odor, spatial activity patterns were variable, and as variable as activity patterns elicited by different colony odors. Conclusions: Ants are not anosmic to nestmate colony odors. However, spatial activity patterns in the AL alone do not provide sufficient information for colony odor discrimination and this finding challenges the current notion of how odor quality is coded. Our result illustrates the enormous challenge for the nervous system to classify multi-component odors and indicates that other neuronal parameters, e.g., precise timing of neuronal activity, are likely necessary for attribution of odor quality to multi-component odors

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution;(B) incorporating demographics & clinical covariates;(C) the impact of the size of the training data set;(D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of sample predictions. The highest performance for A 42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status