An adaptive genetic algorithm for selection of blood-based biomarkers for prediction of Alzheimer's disease progression

Background: Alzheimer's disease is a multifactorial disorder that may be diagnosed earlier using a combination of tests rather than any single test. Search algorithms and optimization techniques in combination with model evaluation techniques have been used previously to perform the selection of suitable feature sets. Previously we successfully applied GA with LR to neuropsychological data contained within the The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, to select cognitive tests for prediction of progression of AD. This research addresses an Adaptive Genetic Algorithm (AGA) in combination with LR for identifying the best biomarker combination for prediction of the progression to AD. Results: The model has been explored in terms of parameter optimization to predict conversion from healthy stage to AD with high accuracy. Several feature sets were selected - the resulting prediction moddels showed higher area under the ROC values (0.83-0.89). The results has shown consistency with some of the medical research reported in literature. Conclusion: The AGA has proven useful in selecting the best combination of biomarkers for prediction of AD progression. The algorithm presented here is generic and can be extended to other data sets generated in projects that seek to identify combination of biomarkers or other features that are predictive of disease onset or progression

Ownership characteristics and crop selection in California cropland

Land ownership is one of the primary determinants of how agricultural land is used, and property size has been shown to drive many land use decisions. Land ownership information is also key to understanding food production systems and land fragmentation, and in targeting outreach materials to improve agricultural production and conservation practices. Using a parcel dataset containing all 58 California counties, we describe the characteristics of cropland ownership across California. The largest 5% of properties — with “property” defined as all parcels owned by a given landowner — account for 50.6% of California cropland, while the smallest 84% of properties account for 25% of cropland. Cropland ownership inequality (few large properties, many small properties) was greatest in Kings, Kern and Contra Costa counties and lowest in Mendocino, Napa and Santa Clara counties. Of crop types, rice properties had the largest median size, while properties with orchard trees had the smallest median sizes. Cluster analysis of crop mixes revealed that properties with grapes, rice, almonds and alfalfa/hay tended to be planted to individual crops, while crops such as grains, tomatoes and vegetables were more likely to be mixed within a single property. Analyses of cropland ownership patterns can help researchers prioritize outreach efforts and tailor research to stakeholders' needs

Livestock Protection Tools for California Ranchers

Conflicts between livestock and predators are perhaps inevitable, especially on extensively managed rangelands This publication helps producers evaluate livestock lethal and non-lethal protection tools that may fit their site-specific needs

Downregulation of Barley Regulator of Telomere Elongation Helicase 1 Alters the Distribution of Meiotic Crossovers

Programmed meiotic DNA double-strand breaks (DSBs), necessary for proper chromosomal segregation and viable gamete formation, are repaired by homologous recombination (HR) as crossovers (COs) or non-crossovers (NCOs). The mechanisms regulating the number and distribution of COs are still poorly understood. The regulator of telomere elongation helicase 1 (RTEL1) DNA helicase was previously shown to enforce the number of meiotic COs in Caenorhabditis elegans but its function in plants has been studied only in the vegetative phase. Here, we characterised barley RTEL1 gene structure and expression using RNA-seq data previously obtained from vegetative and reproductive organs and tissues. Using RNAi, we downregulated RTEL1 expression specifically in reproductive tissues and analysed its impact on recombination using a barley 50k iSelect SNP Array. Unlike in C. elegans, in a population segregating for RTEL1 downregulated by RNAi, high resolution genome-wide genetic analysis revealed a significant increase of COs at distal chromosomal regions of barley without a change in their total number. Our data reveal the important role of RTEL1 helicase in plant meiosis and control of recombination

Genetic algorithm with logistic regression for prediction of progression to Alzheimer\u27s disease

Assessment of risk and early diagnosis of Alzheimer\u27s disease (AD) is a key to its prevention or slowing the progression of the disease. Previous research on risk factors for AD typically utilizes statistical comparison tests or stepwise selection with regression models. Outcomes of these methods tend to emphasize single risk factors rather than a combination of risk factors. However, a combination of factors, rather than any one alone, is likely to affect disease development. Genetic algorithms (GA) can be useful and efficient for searching a combination of variables for the best achievement (eg. accuracy of diagnosis), especially when the search space is large, complex or poorly understood, as in the case in prediction of AD development. This study showed the potential of GA application in the neural science area. It demonstrated that the combination of a small set of variables is superior in performance than the use of all the single significant variables in the model for prediction of progression of disease. Variables more frequently selected by GA might be more important as part of the algorithm for prediction of disease development

Unlocking the genetic diversity and population structure of the newly introduced two-row spring European HerItage Barley collecTion (ExHIBiT)

In the last century, breeding programs have traditionally favoured yield-related traits, grown under high-input conditions, resulting in a loss of genetic diversity and an increased susceptibility to stresses in crops. Thus, exploiting understudied genetic resources, that potentially harbour tolerance genes, is vital for sustainable agriculture. Northern European barley germplasm has been relatively understudied despite its key role within the malting industry. The European Heritage Barley collection (ExHIBiT) was assembled to explore the genetic diversity in European barley focusing on Northern European accessions and further address environmental pressures. ExHIBiT consists of 363 spring-barley accessions, focusing on two-row type. The collection consists of landraces (~14%), old cultivars (~18%), elite cultivars (~67%) and accessions with unknown breeding history (~1%), with 70% of the collection from Northern Europe. The population structure of the ExHIBiT collection was subdivided into three main clusters primarily based on the accession's year of release using 26,585 informative SNPs based on 50k iSelect single nucleotide polymorphism (SNP) array data. Power analysis established a representative core collection of 230 genotypically and phenotypically diverse accessions. The effectiveness of this core collection for conducting statistical and association analysis was explored by undertaking genome-wide association studies (GWAS) using 24,876 SNPs for nine phenotypic traits, four of which were associated with SNPs. Genomic regions overlapping with previously characterised flowering genes (HvZTLb) were identified, demonstrating the utility of the ExHIBiT core collection for locating genetic regions that determine important traits. Overall, the ExHIBiT core collection represents the high level of untapped diversity within Northern European barley, providing a powerful resource for researchers and breeders to address future climate scenarios.</p

Unlocking the genetic diversity and population structure of the newly introduced two-row spring European HerItage Barley collecTion (ExHIBiT)

In the last century, breeding programs have traditionally favoured yield-related traits, grown under high-input conditions, resulting in a loss of genetic diversity and an increased susceptibility to stresses in crops. Thus, exploiting understudied genetic resources, that potentially harbour tolerance genes, is vital for sustainable agriculture. Northern European barley germplasm has been relatively understudied despite its key role within the malting industry. The European Heritage Barley collection (ExHIBiT) was assembled to explore the genetic diversity in European barley focusing on Northern European accessions and further address environmental pressures. ExHIBiT consists of 363 spring-barley accessions, focusing on two-row type. The collection consists of landraces (~14%), old cultivars (~18%), elite cultivars (~67%) and accessions with unknown breeding history (~1%), with 70% of the collection from Northern Europe. The population structure of the ExHIBiT collection was subdivided into three main clusters primarily based on the accession's year of release using 26,585 informative SNPs based on 50k iSelect single nucleotide polymorphism (SNP) array data. Power analysis established a representative core collection of 230 genotypically and phenotypically diverse accessions. The effectiveness of this core collection for conducting statistical and association analysis was explored by undertaking genome-wide association studies (GWAS) using 24,876 SNPs for nine phenotypic traits, four of which were associated with SNPs. Genomic regions overlapping with previously characterised flowering genes (HvZTLb) were identified, demonstrating the utility of the ExHIBiT core collection for locating genetic regions that determine important traits. Overall, the ExHIBiT core collection represents the high level of untapped diversity within Northern European barley, providing a powerful resource for researchers and breeders to address future climate scenarios.</p