High-Dimensional Linear and Functional Analysis of Multivariate Grapevine Data

Variable selection plays a major role in multivariate high-dimensional statistical modeling. Hence, we need to select a consistent model, which avoids overfitting in prediction, enhances model interpretability and identifies relevant variables. We explore various continuous, nearly unbiased, sparse and accurate technique of linear model using coefficients paths like penalized maximum likelihood and nonconvex penalties, and iterative Sure Independence Screening (SIS). The convex penalized (pseudo-) likelihood approach based on the elastic net uses a mixture of the ℓ1 (Lasso) and ℓ2 (ridge regression) simultaneously achieve automatic variable selection, continuous shrinkage, and selection of the groups of correlated variables. Variable selection using coefficients paths for minimax concave penalty (MCP), starts applying penalization at the same rate as Lasso, and then smoothly relaxes the rate down to zero as the absolute value of the coefficient increases. The sure screening method is based on correlation learning, which computes component wise estimators using AIC for tuning the regularization parameter of the penalized likelihood Lasso. To reflect the eternal nature of spectral data, we use the Functional Data approach by approximating the finite linear combination of basis functions using B-splines. MCP, SIS and Functional regression are based on the intuition that the predictors are independent. However, high-dimensional grapevine dataset suffers from ill-conditioning of the covariance matrix due to multicollinearity. Under collinearity, the Elastic-Net Regularization path via Coordinate Descent yields the best result to control the sparsity of the model and cross-validation to reduce bias in variable selection. Iterative stepwise multiple linear regression reduces complexity and enhances the predictability of the model by selecting only significant predictors

Synthesis of 2-azetidinones substituted coumarin derivative

α-Naphthol is converted into 4-methylbenzo[h]chromen-2-one by reacting with ethyl acetoacetate in the presence of bismuth trichloride which is then oxidized to 2-oxo-2H-benzo[h]chromene-4-carbaldehyde and then condensed with aromatic primary amines to give Schiff bases (3a-3d). These Schiff bases are then reacted with acid chlorides in the presence of base in toluene to give 1, 3, 4-substituted 2-azetidinones

Identification of simple sequence repeat markers linked to heat tolerance in rice using bulked segregant analysis in F2 population of NERICA-L 44 × Uma

The damage caused by high temperature is one of the most important abiotic stress affecting rice production. Reproductive stage of rice is highly susceptible to high temperature. The present investigation was undertaken to identify polymorphic microsatellite markers (SSR) associated with heat tolerance. The rice cultivars NERICA– L 44 (heat tolerant) and Uma (heat susceptible) were crossed to generate F1 and F2 populations. The F2 population was subjected to heat stress at >38°C and the 144 F2 plants were evaluated for their tolerance. The results note that the mean of the F2 population was influenced by the tolerant parent with regards to the traits of plant height, membrane stability index, photosynthetic rate, stomatal conductance, evapotranspiration rate, pollen viability, spikelet fertility and 1000 grain weight. Ten each of the extremely susceptible and tolerant plants were selected based on the spikelet fertility percentage. Their DNA was pooled into tolerant and susceptible bulks and Bulked Segregant Analysis (BSA) was carried out using 100 SSR markers to check for polymorphism. The survey revealed a polymorphism of 18% between the parents. RM337, RM10793, RM242, RM5749, RM6100, RM490, RM470, RM473, RM222 and RM556 are some of the prominent markers that were found to be polymorphic between the parents and the bulks. We performed gene annotation and enrichment analysis of identified polymorphic markers. Result revealed that the sequence specific site of that chromosome mostly enriched with biological processes like metabolic pathway, molecular mechanism, and subcellular function. Among that RM337 was newly reported marker for heat tolerance. Expression analysis of two genes corresponds to RM337 revealed that LOP1 (LOC_Os08g01330) was linked to high temperature tolerance in rice. The results demonstrate that BSA using SSR markers is useful in identifying genomic regions that contribute to thermotolerance

Genomics-assisted breeding in four major pulse crops of developing countries: present status and prospects

The global population is continuously increasing and is expected to reach nine billion by 2050. This huge population pressure will lead to severe shortage of food, natural resources and arable land. Such an alarming situation is most likely to arise in developing countries due to increase in the proportion of people suffering from protein and micronutrient malnutrition. Pulses being a primary and affordable source of proteins and minerals play a key role in alleviating the protein calorie malnutrition, micronutrient deficiencies and other undernourishment-related issues. Additionally, pulses are a vital source of livelihood generation for millions of resource-poor farmers practising agriculture in the semi-arid and sub-tropical regions. Limited success achieved through conventional breeding so far in most of the pulse crops will not be enough to feed the ever increasing population. In this context, genomics-assisted breeding (GAB) holds promise in enhancing the genetic gains. Though pulses have long been considered as orphan crops, recent advances in the area of pulse genomics are noteworthy, e.g. discovery of genome-wide genetic markers, high-throughput genotyping and sequencing platforms, high-density genetic linkage/QTL maps and, more importantly, the availability of whole-genome sequence. With genome sequence in hand, there is a great scope to apply genome-wide methods for trait mapping using association studies and to choose desirable genotypes via genomic selection. It is anticipated that GAB will speed up the progress of genetic improvement of pulses, leading to the rapid development of cultivars with higher yield, enhanced stress tolerance and wider adaptability

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Breeding and Genomics Interventions for Developing Ascochyta Blight Resistant Grain Legumes

Grain legumes are a key food source for ensuring global food security and sustaining agriculture. However, grain legume production is challenged by growing disease incidence due to global climate change. Ascochyta blight (AB) is a major disease, causing substantial yield losses in grain legumes worldwide. Harnessing the untapped reserve of global grain legume germplasm, landraces, and crop wild relatives (CWRs) could help minimize yield losses caused by AB infection in grain legumes. Several genetic determinants controlling AB resistance in various grain legumes have been identified following classical genetic and conventional breeding approaches. However, the advent of molecular markers, biparental quantitative trait loci (QTL) mapping, genome-wide association studies, genomic resources developed from various genome sequence assemblies, and whole-genome resequencing of global germplasm has revealed AB-resistant gene(s)/QTL/genomic regions/haplotypes on various linkage groups. These genomics resources allow plant breeders to embrace genomics-assisted selection for developing/transferring AB-resistant genomic regions to elite cultivars with great precision. Likewise, advances in functional genomics, especially transcriptomics and proteomics, have assisted in discovering possible candidate gene(s) and proteins and the underlying molecular mechanisms of AB resistance in various grain legumes. We discuss how emerging cutting-edge next-generation breeding tools, such as rapid generation advancement, field-based high-throughput phenotyping tools, genomic selection, and CRISPR/Cas9, could be used for fast-tracking AB-resistant grain legumes to meet the increasing demand for grain legume-based protein diets and thus ensuring global food security

An Improved Generalized Method for Evaluation of Parameters, Modeling, and Simulation of Photovoltaic Modules

This paper proposes an improved generalized method for evaluation of parameters, modeling, and simulation of photovoltaic modules. A new concept “Level of Improvement” has been proposed for evaluating unknown parameters of the nonlinear I-V equation of the single-diode model of PV module at any environmental condition, taking the manufacturer-specified data at Standard Test Conditions as inputs. The main contribution of the new concept is the improvement in the accuracy of values of evaluated parameters up to various levels and is based on mathematical equations of PV modules. The proposed evaluating method is implemented by MATLAB programming and, for demonstration, by using the values of parameters of the I-V equation obtained from programming results, a PV module model is build with MATLAB. The parameters evaluated by the proposed technique are validated with the datasheet values of six different commercially available PV modules (thin film, monocrystalline, and polycrystalline) at Standard Test Conditions and Nominal Operating Cell Temperature Conditions. The module output characteristics generated by the proposed method are validated with experimental data of FS-270 PV module. The effects of variation of ideality factor and resistances on output characteristics are also studied. The superiority of the proposed technique is proved