Genome-wide identification and characterization of DIRIGENT gene family (CcDIR) in pigeonpea (Cajanus cajan L.) provide insights on their spatial expression pattern and relevance to stress response.

This study is a thorough characterization of pigeonpea dirigent gene (CcDIR) family, an important component of the lignin biosynthesis pathway. Genome-wide analysis identified 25 CcDIR genes followed by a range of analytical approaches employed to unravel their structural and functional characteristics. Structural examination revealed a classic single exon and no intron arrangement in CcDIRs contributing to our understanding on evolutionary dynamics. Phylogenetic analysis elucidated evolutionary relationships among CcDIR genes with six DIR sub-families, while motif distribution analysis displayed and highlighted ten conserved protein motifs in CcDIRs. Promoter analyses of all the dirigent genes detected 18 stress responsive cis-acting elements offering insights into transcriptional regulation. While spatial expression analyses across six plant tissues showed preferential expression of CcDIR genes, exposure to salt (CcDIR2 and CcDIR9) and herbivory (CcDIR1, CcDIR2, CcDIR3 and CcDIR11), demonstrated potential roles of specific DIRs in plant defense. Interestingly, increased gene expression during herbivory, also correlated with increased lignin content authenticating the specific response. Furthermore, exogenous application of stress hormones, SA and MeJA on leaves significantly induced the expression of CcDIRs that responded to herbivory. Taken together, these findings contribute to a comprehensive understanding of CcDIR genes impacting development and stress response in the important legume pigeonpea.

Deep volcanic residual U-Net for nodal metastasis (Nmet) identification from lung cancer.

Lymph node metastasis detections are more clinically significant task associated with the presence and reappearance of lung cancer. The development of the computer-assisted diagnostic approach has greatly supported the diagnosis of human disorders in the field of medicine including lung cancer. Lung cancer treatment is possible if it is detected at the initial stage. Radiologists have great difficulty identifying and categorizing lung cancers in the initial phase. So, several methods were used to predict the lung cancer but does not provide accurate solutions with increased error rate. To overcome these issues, a Deep Volcanic Residual U-Net (DVR U-Net) for nodal metastasis is proposed in this manuscript which identifies the LC accurately in the early stage. Initially, the input images are taken from two datasets. After that, these input data are pre-processed using Anisotropic Diffusion Filter with a Fuzzy based Contrast-Limited Adaptive Histogram Equalization (ADFFCLAHE) method. Then the pre-processed images are given to the DVR U-Net to segment and extract the volume of interest for estimating the nodal stage of each volume of interest. Finally, DVR U-Net effectively detects and classifies the N + (nodal metastasis) or N- (non-nodal metastasis). The introduced method attains 99.9% higher accuracy as compared with the existing methods. Also, the statistical analysis of the Shapiro-Wilk test, Friedman test and Wilcoxon Signed-Rank test are executed to prove the statistical effectiveness of the implemented method.

Identifying cancer risks using spectral subset feature selection based on multi-layer perception neural network for premature treatment.

Recently, human beings have been affected mainly by dreadful cancer diseases. Predicting cancer risk levels is a major challenge in biomedical research for feature selection and classification at the margins. To resolve this problem, we propose a Subset Clustering-Based Feature Selection using a Multi-Layer Perception Neural Network (SCFS-MLPNN). Initially, pre-processing is carried out with Intensive Mutual Disease Influence Rate (IMDIR) to identify the relational features. In addition, the Successive Disease Pattern Stimulus Rate (SDPSR) is carried out to create relative feature patterns. Based on the patterns, the features are selected and grouped into clustering. Inter-Class Sub-Space Clustering (ICSSC) is applied to split the features by class labels depending on the marginal rate. From the class labels, marginal features are obtained using spectral subset feature selection (SSFS). The selected features are then trained in a Multi-Layer Perception Neural Network (MLPNN) classifier to classify the patient features by risk. Its contribution is to exploit subset features to improve classification accuracy by clustering relational features. The proposed classifier yields higher classification accuracy than previous methods and observes cancer detection for early detection. Therefore, the proposed method achieved a risk analysis accuracy of 91.8% and an F-measure of 91.3% for early detection, which is recommended for early diagnosis.

Integration of miRNA dynamics and drought tolerant QTLs in rice reveals the role of miR2919 in drought stress response.

To combat drought stress in rice, a major threat to global food security, three major quantitative trait loci for 'yield under drought stress' (qDTYs) were successfully exploited in the last decade. However, their molecular basis still remains unknown. To understand the role of secondary regulation by miRNA in drought stress response and their relation, if any, with the three qDTYs, the miRNA dynamics under drought stress was studied at booting stage in two drought tolerant (Sahbaghi Dhan and Vandana) and one drought sensitive (IR 20) cultivars. In total, 53 known and 40 novel differentially expressed (DE) miRNAs were identified. The primary drought responsive miRNAs were Osa-MIR2919, Osa-MIR3979, Osa-MIR159f, Osa-MIR156k, Osa-MIR528, Osa-MIR530, Osa-MIR2091, Osa-MIR531a, Osa-MIR531b as well as three novel ones. Sixty-one target genes that corresponded to 11 known and 4 novel DE miRNAs were found to be co-localized with the three qDTYs, out of the 1746 target genes identified. We could validate miRNA-mRNA expression under drought for nine known and three novel miRNAs in eight different rice genotypes showing varying degree of tolerance. From our study, Osa-MIR2919, Osa-MIR3979, Osa-MIR528, Osa-MIR2091-5p and Chr01_11911S14Astr and their target genes LOC_Os01g72000, LOC_Os01g66890, LOC_Os01g57990, LOC_Os01g56780, LOC_Os01g72834, LOC_Os01g61880 and LOC_Os01g72780 were identified as the most promising candidates for drought tolerance at booting stage. Of these, Osa-MIR2919 with 19 target genes in the qDTYs is being reported for the first time. It acts as a negative regulator of drought stress tolerance by modulating the cytokinin and brassinosteroid signalling pathway.

Geochemical fractionation, mobility of elements and environmental significance of surface sediments in a Tropical River, Borneo.

Miri River is a tropical river in Borneo that drains on flat terrain and urbanised area and debauches into the South China Sea. This paper documents the environmental status of this river, and provides an insight into the provenance using bulk chemistry of the sediments, and brings out the geochemical mobility, bioavailability, and potential toxicity of some critical elements based on BCR sequential extraction. The sediments are intense to moderately weathered and recycled products of Neogene sedimentary rocks. The hydrodynamic characteristics of the river favoured an upstream section dominated by fine sand, while the downstream sediments are medium silt. Based on the bulk geochemistry, the Miri River sediments are moderate to considerably contaminated by Cu, Mo, and As in the upstream and by Sb, As and Cu in the downstream. The potential ecological risk values are low except Cu and a significant biological impact is expected in downstream due to Cu, As, Zn and Cr. The mobility, bioavailability and Risk Assessment Code values for Zn and Mn are higher and thus may pose moderate to very high risk to aquatic organisms. Though a high bulk concentration of Cu is observed, the association of Cu with the bioavailable fraction is low.

Ensemble classifier fostered detection of arrhythmia using ECG data.

Electrocardiogram (ECG) is a non-invasive medical tool that divulges the rhythm and function of the human heart. This is broadly employed in heart disease detection including arrhythmia. Arrhythmia is a general term for abnormal heart rhythms that can be identified and classified into many categories. Automatic ECG analysis is provided by arrhythmia categorization in cardiac patient monitoring systems. It aids cardiologists to diagnose the ECG signal. In this work, an Ensemble classifier is proposed for accurate arrhythmia detection using ECG Signal. Input data are taken from the MIT-BIH arrhythmia dataset. Then the input data was pre-processed using Python in Jupyter Notebook which run the code in an isolated manner and was able to keep code, formula, comments, and images. Then, Residual Exemplars Local Binary Pattern is applied for extracting statistical features. The extracted features are given to ensemble classifiers, like Support vector machines (SVM), Naive Bayes (NB), and random forest (RF) for classifying the arrhythmia as normal (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion beat (F), and unknown beat (Q). The proposed AD-Ensemble SVM-NB-RF method is implemented in Python. The proposed AD-Ensemble SVM-NB-RF method is 44.57%, 52.41%, and 29.49% higher accuracy; 2.01%, 3.33%, and 3.19% higher area under the curve (AUC); and 21.52%, 23.05%, and 12.68% better F-Measure compared with existing models, like multi-model depending on the ensemble of deep learning for ECG heartbeats arrhythmia categorization (AD-Ensemble CNN-LSTM-RRHOS), ECG signal categorization utilizing VGGNet: a neural network based classification method (AD-Ensemble CNN-LSTM) and higher performance arrhythmic heartbeat categorization utilizing ensemble learning along PSD based feature extraction method (AD-Ensemble MLP-NB-RF).

Meta-Analysis of Microarray Data and Their Utility in Dissecting the Mapped QTLs for Heat Acclimation in Rice.

In the current global warming scenario, it is imperative to develop crops with improved heat tolerance or acclimation, for which knowledge of major heat stress-tolerant genes or genomic regions is a prerequisite. Though several quantitative trait loci (QTLs) for heat tolerance have been mapped in rice, candidate genes from these QTLs have not been reported yet. The meta-analysis of microarray datasets for heat stress in rice can give us a better genomic resource for the dissection of QTLs and the identification of major candidate genes for heat stress tolerance. In the present study, a database, RiceMetaSys-H, comprising 4227 heat stress-responsive genes (HRGs), was created using seven publicly available microarray datasets. This included in-house-generated microarray datasets of Nagina 22 (N22) and IR64 subjected to 8 days of heat stress. The database has provisions for searching the HRGs through genotypes, growth stages, tissues, and physical intervals in the genome, as well as Locus IDs, which provide complete information on the HRGs with their annotations and fold changes, along with the experimental material used for the analysis. The up-regulation of genes involved in hormone biosynthesis and signalling, sugar metabolism, carbon fixation, and the ROS pathway were found to be the key mechanisms of enhanced heat tolerance. Integrating variant and expression analysis, the database was used for the dissection of the major effect of QTLs on chromosomes 4, 5, and 9 from the IR64/N22 mapping population. Out of the 18, 54, and 62 genes in these three QTLs, 5, 15, and 12 genes harboured non-synonymous substitutions. Fifty-seven interacting genes of the selected QTLs were identified by a network analysis of the HRGs in the QTL regions. Variant analysis revealed that the proportion of unique amino acid substitutions (between N22/IR64) in the QTL-specific genes was much higher than the common substitutions, i.e., 2.58:0.88 (2.93-fold), compared to the network genes at a 0.88:0.67 (1.313-fold) ratio. An expression analysis of these 89 genes showed 43 DEGs between IR64/N22. By integrating the expression profiles, allelic variations, and the database, four robust candidates (LOC_Os05g43870, LOC_Os09g27830, LOC_Os09g27650, andLOC_Os09g28000) for enhanced heat stress tolerance were identified. The database thus developed in rice can be used in breeding to combat high-temperature stress.

Risk assessment in supply chains: a state-of-the-art review of methodologies and their applications.

The year 2020 can be earmarked as the year of global supply chain disruption owing to the outbreak of the coronavirus (COVID-19). It is however not only because of the pandemic that supply chain risk assessment (SCRA) has become more critical today than it has ever been. With the number of supply chain risks having increased significantly over the last decade, particularly during the last 5 years, there has been a flurry of literature on supply chain risk management (SCRM), illustrating the need for further classification so as to guide researchers to the most promising avenues and opportunities. We therefore conduct a bibliometric and network analysis of SCRA publications to identify research areas and underlying themes, leading to the identification of three major research clusters for which we provide interpretation and guidance for future work. In doing so we focus in particular on the variety of parameters, analytical approaches, and characteristics of multi-criteria decision-making techniques for assessing supply chain risks. This offers an invaluable synthesis of the SCRA literature, providing recommendations for future research opportunities. As such, this paper is a formidable starting point for operations researchers delving into this domain, which is expected to increase significantly also due to the current pandemic.

Identification of major candidate genes for multiple abiotic stress tolerance at seedling stage by network analysis and their validation by expression profiling in rice (Oryza sativa L.).

A wealth of microarray and RNA-seq data for studying abiotic stress tolerance in rice exists but only limited studies have been carried out on multiple stress-tolerance responses and mechanisms. In this study, we identified 6657 abiotic stress-responsive genes pertaining to drought, salinity and heat stresses from the seedling stage microarray data of 83 samples and used them to perform unweighted network analysis and to identify key hub genes or master regulators for multiple abiotic stress tolerance. Of the total 55 modules identified from the analysis, the top 10 modules with 8-61 nodes comprised 239 genes. From these 10 modules, 10 genes common to all the three stresses were selected. Further, based on the centrality properties and highly dense interactions, we identified 7 intra-modular hub genes leading to a total of 17 potential candidate genes. Out of these 17 genes, 15 were validated by expression analysis using a panel of 4 test genotypes and a pair of standard check genotypes for each abiotic stress response. Interestingly, all the 15 genes showed upregulation under all stresses and in all the genotypes, suggesting that they could be representing some of the core abiotic stress-responsive genes. More pertinently, eight of the genes were found to be co-localized with the stress-tolerance QTL regions. Thus, in conclusion, our study not only provided an effective approach for studying abiotic stress tolerance in rice, but also identified major candidate genes which could be further validated by functional genomics for abiotic stress tolerance. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-022-03182-7.

Healthcare Biclustering-Based Prediction on Gene Expression Dataset.

In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the duplication of broad gene information components. Moreover, because of its adequacy in the problem where populations of potential solutions allow exploration of a greater portion of the research area, machine learning or heuristic algorithm has become extensively used for healthcare biclustering in the field of healthcare. The study is evaluated in terms of average match score for nonoverlapping modules, overlapping modules through the influence of noise for constant bicluster and additive bicluster, and the run time. The results show that proposed FCM blustering method has higher average match score, and reduced run time proposed FCM than the existing PSO-SA and fuzzy logic healthcare biclustering methods.

Convalescent plasma bank facility location-allocation problem for COVID-19.

With convalescent plasma being recognized as an eminent treatment option for COVID-19, this paper addresses the location-allocation problem for convalescent plasma bank facilities. This is a critical topic, since limited supply and overtly increasing cases demand a well-established supply chain. We present a novel plasma supply chain model considering stochastic parameters affecting plasma demand and the unique features of the plasma supply chain. The primary objective is to first determine the optimal location of the plasma banks and to then allocate the plasma collection facilities so as to maintain proper plasma flow within the network. In addition, recognizing the perishable nature of plasma, we integrate a deteriorating rate with the objective that as little plasma as possible is lost. We formulate a robust mixed-integer linear programming (MILP) model by considering two conflicting objective functions, namely the minimization of overall plasma transportation time and total plasma supply chain network cost, with the latter also capturing inventory costs to reduce wastage. We then propose a CPLEX-based optimization approach for solving the MILP functions. The feasibility of our results is validated by a comparison study using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and a proposed modified NSGA-III. The application of the proposed model is evaluated by implementing it in a real-world case study within the context of India. The optimized numerical results, together with their sensitivity analysis, provide valuable decision support for policymakers.

A multi-echelon dynamic cold chain for managing vaccine distribution.

While cold chain management has been part of healthcare systems, enabling the efficient administration of vaccines in both urban and rural areas, the COVID-19 virus has created entirely new challenges for vaccine distributions. With virtually every individual worldwide being impacted, strategies are needed to devise best vaccine distribution scenarios, ensuring proper storage, transportation and cost considerations. Current models do not consider the magnitude of distribution efforts needed in our current pandemic, in particular the objective that entire populations need to be vaccinated. We expand on existing models and devise an approach that considers the needed extensive distribution capabilities and special storage requirements of vaccines, while at the same time being cognizant of costs. As such, we provide decision support on how to distribute the vaccine to an entire population based on priority. We do so by conducting predictive analysis for three different scenarios and dividing the distribution chain into three phases. As the available vaccine doses are limited in quantity at first, we apply decision tree analysis to find the best vaccination scenario, followed by a synthetic control analysis to predict the impact of the vaccination programme to forecast future vaccine production. We then formulate a mixed-integer linear programming (MILP) model for locating and allocating cold storage facilities for bulk vaccine production, followed by the proposition of a heuristic algorithm to solve the associated objective functions. The application of the proposed model is evaluated by implementing it in a real-world case study. The optimized numerical results provide valuable decision support for healthcare authorities.

Allele mining for a drought responsive gene DRO1 determining root growth angle in donors of drought tolerance in rice (Oryza sativa L.).

Deeper Rooting 1 (DRO1) gene identified from a major QTL on chromosome 9 increases the root growth angle (RGA) and thus facilitates survival under drought and hence is an excellent candidate for rice improvement. Twenty-four major Indian upland and lowland genotypes including the 'yield under drought' (DTY) QTL donors were subjected to allele mining of DRO1 (3058 bp) using four pairs of overlapping primers. A total of 216 and 52 SNPs were identified across all genotypes in the gene and coding region (756 bp) respectively with transversions 3.6 fold more common than transitions in the gene and 2.5 times in the CDS. In 251 amino acid long protein, substitutions were found in 19 positions, wherein change in position 92 was the most frequent. Based on allele mining, the 24 genotypes can be classified into 16 primary structure variants ranging from complete functional allele (Satti, IR36 and DTY 3.1 donor, IR81896-B-B-195) to truncated non-functional alleles in PMK2, IR64, IR20 and Swarna. All the DTY donors, other than IR81896-B-B-195, and most of the upland drought tolerant cultivars (Nagina 22, Vandana and Dhagaddeshi) had accumulated 6-19 SNPs and 4-8 amino acid substitutions resulting in substantial differences in their protein structure. The expression analysis revealed that all the genotypes showed upregulation under drought stress though the degree of upregulation varied among genotypes. The information on structural variations in DRO1 gene will be very useful for the breeders, especially in the light of recent breeding programmes on improving drought tolerance using several DTY donors and upland accessions. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s12298-021-00950-2).

Selection of the best healthcare waste disposal techniques during and post COVID-19 pandemic era.

In recent years, municipal authorities especially in the developing nations are battling to select the best health care waste (HCW) disposal technique for the effective treatment of the medical wastes during and post COVID-19 era. As evaluation of various disposal alternatives of HCW and selection of the best technique requires considering various tangible and intangible criteria, this can be framed as multi-criteria decision-making (MCDM) problem. In this paper, we propose an assessment framework for the selection of the best HCW disposal technique based on socio-technical and triple bottom line perspectives. We have identified 10 criteria on which the best HCW disposal techniques to be selected based on extant literature review. Next, we use Fuzzy VIKOR method to evaluate 9 HCW disposal alternatives. The effectiveness of the proposed framework has been demonstrated with a real-life case study in Indian context. To check the robustness of the proposed methodology, we have compared the results obtained with Fuzzy TOPSIS (Technique of Order Preference Similarity to the Ideal Solution). The results help the municipal authorities to establish a methodical approach to choose the best HCW disposal techniques. Our findings indicate that incineration is the best waste disposal technique among the available alternatives. Even if the dataset indicates 'incineration' is the best method, we must not forget about the environmental concerns arising from this method. In COVID time, incineration may be the best method as indicated by the data analysis, but "COVID" should not be an excuse for causing "Environmental Pollution".

Effect of surgical delay on survival outcomes in patients undergoing curative resection for primary hepatocellular carcinoma: Inverse probability of treatment weighting using propensity scores and propensity score adjustment.

BACKGROUND: The evidence is conflicting regarding the effect of delays from the time of diagnosis to surgery on the survival in patients with hepatocellular carcinoma. We sought to investigate the impact of time to surgery on overall survival for patients who underwent curative resection for primary hepatocellular carcinoma. METHODS: We performed a retrospective review of all patients who underwent liver resection for primary hepatocellular carcinoma between the years 2000 and 2015. Using 30-, 60-, and 90-day cutoffs, we investigated the effect of time to surgery on survival outcomes by dichotomizing the patients and using inverse probability of treatment weighting to ensure comparability. We also investigated time to surgery in prognostic subgroups by modeling the statistical interaction between time to surgery and the relevant prognostic variable in multivariable Cox models. RESULTS: A total of 863 patients underwent liver resection for primary hepatocellular carcinoma during the study period. Using 30-, 60-, and 90-day cutoffs, time to surgery did not have a significant bearing on overall survival. For elderly patients (>70 years), patients with Child-Pugh B liver disease, American Society of Anesthesiologists status 2/3, tumor size >5cm, tumor size ≥10cm and presence of extrahepatic invasion, hazard ratio decreased and overall survival improved as time to surgery increased. However, for patients with liver cirrhosis or portal hypertension, increasing time to surgery was found to portend higher risks of death. CONCLUSION: Time to surgery does not have a significant bearing on overall survival, and modest delays even appear to be associated with improved survival in specific subsets of patients. The importance of these findings is that patients with hepatocellular carcinoma should be fully optimized before and not rushed to surgery because of concerns of tumor progression and a diminished survival.

Non-equilibrium atmospheric pressure plasma assisted degradation of the pharmaceutical drug valsartan: influence of catalyst and degradation environment.

This paper investigated the degradation of the pharmaceutical drug Valsartan (VS) using non-equilibrium atmospheric pressure plasma (NEAPP) with various operating conditions. The heterogeneous photocatalyst ZnO nanoparticles (NP's) were synthesized using a hydrothermal process. The morphology, chemical composition and structure of as-synthesized ZnO NPs were examined by Field Emission Scanning Electron Microscopy (FE-SEM), Fourier Transform Infrared Spectroscopy (FTIR) and X-ray diffraction (XRD) analysis. Then, VS degradation was examined in three subsequent treatment conditions including plasma treatment alone, the combination of plasma with as-prepared ZnO NPs and various environments (air, oxygen and hydrogen peroxide) at fixed plasma operating potential and treatment time. The degradation efficiency of plasma-treated VS by various conditions was observed using UV-visible spectroscopy. Optical Emission Spectrometry (OES) was used to characterize the distribution and emission intensity of various reactive species (OH˙, N2-SPS and O) during the degradation processes which plays a vital role in the degradation of VS. The role of OH˙ and H2O2 during the degradation process was further examined by chemical dosimetry and spectroscopic techniques. Furthermore, pH, conductivity and TOC of the untreated and plasma-treated VS were also investigated. The results on the degradation of VS showed that plasma treatment combined with ZnO NP's has a significant effect on degradation of molecules of VS than degradation processes carried out by other experimental conditions due to the formation of higher concentrations of various reactive oxygen and nitrogen species during the degradation processes.

A Novel Stay-Green Mutant of Rice with Delayed Leaf Senescence and Better Harvest Index Confers Drought Tolerance.

Three Ethyl methansulphonate (EMS)-induced stay-green mutants (SGM-1, SGM-2 and SGM-3) and their wild-type (WT), were tested for their Stay-Green (SG) and drought tolerance nature as the relation between these two attributes is not yet established in rice. In the dark induced senescence assay, SGM-3 showed delayed senescence while SGM-1 and SGM-2 showed complete lack of senescence. Mutants showed stable transcript abundance over time, for 15 candidate genes (CGs) associated with senescence, compared to the WT. SGM-3 however showed moderately increasing transcript abundance over time for ATG6a, ATG4a, NYC1, NOL and NYC3. Only SGM-3 performed better than the WT for yield and harvest index under well irrigated as well as drought conditions, though all the mutants showed better performance for other agronomic traits under both the conditions and ascorbate peroxidase activity under drought. Thus, SG trait showed positive correlation with drought tolerance though only SGM-3 could convert this into higher harvest index. Sequence analysis of 80 senescence-associated genes including the 15 CGs showed non-synonymous mutations in four and six genes in SGM-1 and SGM-2 respectively, while no SNPs were found in SGM-3. Analysis of the earlier reported Quantitative Trait Loci (QTL) regions in SGM-3 revealed negligible variations from WT, suggesting it to be a novel SG mutant.

Evaluation of surface properties of low density polyethylene (LDPE) films tailored by atmospheric pressure non-thermal plasma (APNTP) assisted co-polymerization and immobilization of chitosan for improvement of antifouling properties.

This work describes the development of antifouling functional coatings on the surface of low density polyethylene (LDPE) films by means of atmospheric pressure non-thermal plasma (APNTP) assisted copolymerization using a mixture of acrylic acid and poly (ethylene glycol). The aim of the study was to investigate the antifouling properties of the plasma copolymerized LDPE films and the same was carried out as a function of deposition time with fixed applied potential of 14 kV. In a second stage, the plasma copolymerized LDPE films were functionalized with chitosan (CHT) to further enhance its antifouling properties. The surface hydrophilicity, structural, topographical and chemistry of the plasma copolymerized LDPE films were examined by contact angle (CA), X-ray diffraction (XRD), atomic force microscopy (AFM), Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). Coating stability was also studied in detail over a storage time of 15 days by storing in water and air. The antifouling properties of the plasma copolymerized LDPE films were examined via protein adsorption and platelet adhesion studies. CA study showed significant changes in surface wettability after the coating process. XPS and FTIR analysis proved the presence of a dense multifunctional coating and an efficient immobilization of CHT. Substantial amendments in surface topography were observed, positively enhancing the overall surface hydrophilicity. Finally, in-vitro analysis showed excellent antifouling behavior of the surface modified LDPE films.

Whole Genome Characterization of a Few EMS-Induced Mutants of Upland Rice Variety Nagina 22 Reveals a Staggeringly High Frequency of SNPs Which Show High Phenotypic Plasticity Towards the Wild-Type.

The Indian initiative, in creating mutant resources for the functional genomics in rice, has been instrumental in the development of 87,000 ethylmethanesulfonate (EMS)-induced mutants, of which 7,000 are in advanced generations. The mutants have been created in the background of Nagina 22, a popular drought- and heat-tolerant upland cultivar. As it is a pregreen revolution cultivar, as many as 573 dwarf mutants identified from this resource could be useful as an alternate source of dwarfing. A total of 541 mutants, including the macromutants and the trait-specific ones, obtained after appropriate screening, are being maintained in the mutant garden. Here, we report on the detailed characterizations of the 541 mutants based on the distinctness, uniformity, and stability (DUS) descriptors at two different locations. About 90% of the mutants were found to be similar to the wild type (WT) with high similarity index (>0.6) at both the locations. All 541 mutants were characterized for chlorophyll and epicuticular wax contents, while a subset of 84 mutants were characterized for their ionomes, namely, phosphorous, silicon, and chloride contents. Genotyping of these mutants with 54 genomewide simple sequence repeat (SSR) markers revealed 93% of the mutants to be either completely identical to WT or nearly identical with just one polymorphic locus. Whole genome resequencing (WGS) of four mutants, which have minimal differences in the SSR fingerprint pattern and DUS characters from the WT, revealed a staggeringly high number of single nucleotide polymorphisms (SNPs) on an average (16,453 per mutant) in the genic sequences. Of these, nearly 50% of the SNPs led to non-synonymous codons, while 30% resulted in synonymous codons. The number of insertions and deletions (InDels) varied from 898 to 2,595, with more than 80% of them being 1-2 bp long. Such a high number of SNPs could pose a serious challenge in identifying gene(s) governing the mutant phenotype by next generation sequencing-based mapping approaches such as Mutmap. From the WGS data of the WT and the mutants, we developed a genic resource of the WT with a novel analysis pipeline. The entire information about this resource along with the panicle architecture of the 493 mutants is made available in a mutant database EMSgardeN22 (http://14.139.229.201/EMSgardeN22).

Development and Genetic Characterization of A Novel Herbicide (Imazethapyr) Tolerant Mutant in Rice (Oryza sativa L.).

BACKGROUND: Increased water and labour scarcity in major rice growing areas warrants a shift towards direct seeded rice cultivation under which management of weeds is a major issue. Use of broad spectrum non-selective herbicides is an efficient means to manage weeds. Availability of rice genotypes with complete tolerance against broad-spectrum non-selective herbicides is a pre-requisite for advocating use of such herbicides. In the present study, we developed an EMS induced rice mutant, 'HTM-N22', exhibiting tolerance to a broad spectrum herbicide, 'Imazethapyr', and identified the mutations imparting tolerance to the herbicide. RESULTS: We identified a stable and true breeding rice mutant, HTM-N22 (HTM), tolerant to herbicide, Imazethapyr, from an EMS-mutagenized population of approximately 100,000 M2 plants of an upland rice variety, Nagina 22 (N22). Analysis of inheritance of herbicide tolerance in a cross between Pusa 1656-10-61/HTM showed that this trait is governed by a single dominant gene. To identify the causal gene for Imazethapyr tolerance, bulked segregant analysis (BSA) was followed using microsatellite markers flanking the three putative candidate genes viz., an Acetolactate Synthase (ALS) on chromosome 6 and two Acetohydroxy Acid Synthase (AHAS) genes, one on chromosomes 2 and another on chromosome 4. RM 6844 on chromosome 2 located 0.16 Mbp upstream of AHAS (LOC_Os02g30630) was found to co-segregate with herbicide tolerance. Cloning and sequencing of AHAS (LOC_Os02g30630) from the wild type, N22 and the mutant HTM and their comparison with reference Nipponbare sequence revealed several Single Nucleotide Polymorphisms (SNPs) in the mutant, of which eight resulted in non-synonymous mutations. Three of the eight amino acid substitutions were identical to Nipponbare and hence were not considered as causal changes. Of the five putative candidate SNPs, four were novel (at positions 30, 50, 81 and 152) while the remaining one, S627D was a previously reported mutant, known to result in Imidazolinone tolerance in rice. Of the novel ones, G152E was found to alter the hydrophobicty and abolish an N myristoylation site in the HTM compared to the WT, from reference based modeling and motif prediction studies. CONCLUSIONS: A novel mutant tolerant to the herbicide "Imazethapyr" was developed and characterized for genetic, sequence and protein level variations. This is a HTM in rice without any IPR (Intellectual Property Rights) infringements and hence can be used in rice breeding as a novel genetic stock by the public funded organizations in the country and elsewhere.