Qualitative and Quantitative Traits Associate Genetic Variability of Soybean (Glycine max) Mutants for Expedited Varietal Improvement Program

Background: Soybean is an excellent source of protein, also richer in oil than most legumes, making them a good source for vegetable oil and biofuels. Among various difficulties the maturity period of existing soybean varieties is the main hindrance of utilizing this for the existing cropping system. The narrow genetic base of cultivated soybean varieties and germplasm limit the scope to utilize directly in the breeding program. Methods: Mutation breeding is one of the techniques that provide large genetic diversity from a single source. To broaden the genetic diversity Binasoybean-3 and Binasoybean-4 were imposed to different doses of gamma radiation. The mutants were selected based on their agronomic performance and grouped at five different clusters at M5 generations. Maximum selection pressure was done during maturity period with protein and oil content. Result: Finally, eight mutants were selected for the advance breeding program, whereas mutants SM-03-15-5 mature within 90 days, containing 38% protein and 18.4% oil content will be considered directly for further steps of varietal release system

Binasoybean-6: A High Yielding Mutant Soybean Variety Developed through Sustainable Mutation Breeding

Background: Soybean is an important source of food, protein and oil and hence more research is essential to increase its yield under different agro-ecological conditions, including stress. In this regard, four popular soybean varieties viz. Shohag, BDS-4, BAU-S/64 and BARI Soybean-5 were irradiated using Co60 gamma rays to create genetic variation for earliness, higher seed yield and other desirable agronomic traits. Methods: The experiments were conducted at Bangladesh Institute of Nuclear Agriculture (BINA) Headquarters farm, Mymensingh during 2006-2009 and 28 elite mutant lines were selected for evaluation. The mutant line, SBM-22 derived from mother variety BARI Soybean-5 irradiated with 300Gy of gamma rays was found to be superior compared to other mutants. Considering the superior performance of mutant SBM-22 including 28 mutants and mother check variety BARI Soybean-5, were evaluated through different trials. The evaluation trials were conducted at different agro-ecological zones of the country during Rabi season (January to April) of 2010-2018. Result: Significant variations were observed both in individual location and over locations for all traits. Reactions to major diseases and insect-pests infestation were also studied. Due to better performance of the mutant SBM-22, Bangladesh Institute of Nuclear Agriculture (BINA) applied to the National Seed Board (NSB) of Bangladesh for registration as an important soybean variety “Binasoybean-6”. Consequently, the NSB of Bangladesh registered SBM-22 as an improved soybean variety in 2019 as Binasoybean6 for commercial cultivation

Clustering of Alzheimer's and Parkinson's disease based on genetic burden of shared molecular mechanisms

One of the visions of precision medicine has been to re-define disease taxonomies based on molecular characteristics rather than on phenotypic evidence. However, achieving this goal is highly challenging, specifically in neurology. Our contribution is a machine-learning based joint molecular subtyping of Alzheimer’s (AD) and Parkinson’s Disease (PD), based on the genetic burden of 15 molecular mechanisms comprising 27 proteins (e.g. APOE) that have been described in both diseases. We demonstrate that our joint AD/PD clustering using a combination of sparse autoencoders and sparse non-negative matrix factorization is reproducible and can be associated with significant differences of AD and PD patient subgroups on a clinical, pathophysiological and molecular level. Hence, clusters are disease-associated. To our knowledge this work is the first demonstration of a mechanism based stratification in the field of neurodegenerative diseases. Overall, we thus see this work as an important step towards a molecular mechanism-based taxonomy of neurological disorders, which could help in developing better targeted therapies in the future by going beyond classical phenotype based disease definitions

Deep learning for clustering of multivariate clinical patient trajectories with missing values

BACKGROUND: Precision medicine requires a stratification of patients by disease presentation that is sufficiently informative to allow for selecting treatments on a per-patient basis. For many diseases, such as neurological disorders, this stratification problem translates into a complex problem of clustering multivariate and relatively short time series because (i) these diseases are multifactorial and not well described by single clinical outcome variables and (ii) disease progression needs to be monitored over time. Additionally, clinical data often additionally are hindered by the presence of many missing values, further complicating any clustering attempts. FINDINGS: The problem of clustering multivariate short time series with many missing values is generally not well addressed in the literature. In this work, we propose a deep learning-based method to address this issue, variational deep embedding with recurrence (VaDER). VaDER relies on a Gaussian mixture variational autoencoder framework, which is further extended to (i) model multivariate time series and (ii) directly deal with missing values. We validated VaDER by accurately recovering clusters from simulated and benchmark data with known ground truth clustering, while varying the degree of missingness. We then used VaDER to successfully stratify patients with Alzheimer disease and patients with Parkinson disease into subgroups characterized by clinically divergent disease progression profiles. Additional analyses demonstrated that these clinical differences reflected known underlying aspects of Alzheimer disease and Parkinson disease. CONCLUSIONS: We believe our results show that VaDER can be of great value for future efforts in patient stratification, and multivariate time-series clustering in general

Differences in cohort study data affect external validation of artificial intelligence models for predictive diagnostics of dementia - lessons for translation into clinical practice

Artificial intelligence (AI) approaches pose a great opportunity for individualized, pre-symptomatic disease diagnosis which plays a key role in the context of personalized, predictive, and finally preventive medicine (PPPM). However, to translate PPPM into clinical practice, it is of utmost importance that AI-based models are carefully validated. The validation process comprises several steps, one of which is testing the model on patient-level data from an independent clinical cohort study. However, recruitment criteria can bias statistical analysis of cohort study data and impede model application beyond the training data. To evaluate whether and how data from independent clinical cohort studies differ from each other, this study systematically compares the datasets collected from two major dementia cohorts, namely, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and AddNeuroMed. The presented comparison was conducted on individual feature level and revealed significant differences among both cohorts. Such systematic deviations can potentially hamper the generalizability of results which were based on a single cohort dataset. Despite identified differences, validation of a previously published, ADNI trained model for prediction of personalized dementia risk scores on 244 AddNeuroMed subjects was successful: External validation resulted in a high prediction performance of above 80% area under receiver operator characteristic curve up to 6 years before dementia diagnosis. Propensity score matching identified a subset of patients from AddNeuroMed, which showed significantly smaller demographic differences to ADNI. For these patients, an even higher prediction performance was achieved, which demonstrates the influence systematic differences between cohorts can have on validation results. In conclusion, this study exposes challenges in external validation of AI models on cohort study data and is one of the rare cases in the neurology field in which such external validation was performed. The presented model represents a proof of concept that reliable models for personalized predictive diagnostics are feasible, which, in turn, could lead to adequate disease prevention and hereby enable the PPPM paradigm in the dementia field