Integrated Management of Meloidogyne Incognita and Soilborne Fungi Infecting Cucumber Under Protected Cultivation.

Relative efficacy of various approaches for management of Meloidogyne incognita and the soilborne fungus Fusarium oxysporum f. sp. cucumerinum has been tested in cucumber under protected cultivation conditions for two seasons. Management practices, namely, chemicals (fumigant, nonfumigant, and fungicide), organic amendments (neem cake, leaves, and oil opted as soil and seed treatment), and biocontrol agents (egg-parasitic fungus and Purpureocillium lilacinum), were combined for the management of the disease complex in a randomized block design. Two significant parameters were measured: plant growth parameters (shoot length, dry shoot weight, dry root weight, and yield) and disease parameters (galls per plant, final nematode population, egg masses per plant, and fungal incidence). All treatments significantly improved plant growth parameters and reduced nematode reproduction as compared to untreated check. The integration of formalin and neem oil seed treatment favors the low root galling index compared to all other treatments in both the seasons. Formalin and neem oil seed treatment reduced the nematode population and fungal incidence, and increased the yield of cucumber during both the seasons.

A Lightweight Deep Learning-Based Approach for Jazz Music Generation in MIDI Format.

In today's real-world, estimation of the level of difficulty of the musical is part of very meaningful musical learning. A musical learner cannot learn without a defined precise estimation. This problem is not very basic but it is complicated up to some extent because of the subjectivity of the contents and the scarcity of the data. In this paper, a lightweight model that generates original music content using deep learning along with generating music based on a specific genre is proposed. The paper discusses a lightweight deep learning-based approach for jazz music generation in MIDI format. In this work, the genre of music chosen is Jazz, and the songs selected are classical numbers composed by various artists. All the songs are in MIDI format and there might be differences in the pace or tone of the music. It is prudential to make sure that the chosen datasets that do not have these kinds of differences and are similar to the final output as desired. A model is trained to take in a part of a music file as input and should produce its continuation. The result generated should be similar to the dataset given as the input. Moreover, the proposed model also generates music using a particular instrument.

A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients.

In today's scenario, sepsis is impacting millions of patients in the intensive care unit due to the fact that the mortality rate is increased exponentially and has become a major challenge in the field of healthcare. Such peoples require determinant care which increases the cost of the treatment by using a large number of resources because of the nonavailability of the resources. The treatment of sepsis is available in the early state, but treatment is not started at the right time, and then it converts to the advanced level of sepsis and increases the fatalities. Thus, an intensive analysis is required to detect and identify sepsis at the early stage. There are some models available that work based on the manual score and based on only the biomark features, but these are not fully automated. Some machine learning-based models are also available, which can reduce the mortality rate, but accuracy is not up to date. This paper proposes a machine learning model for early detecting and predicting sepsis in intensive care unit patients. Various models, random forest (RF), linear regression (LR), support vector machine (SVM), naive Bayes (NB), ensemble (of SVM, RF, NB, and LR), XGBoost, and proposed ensemble (of SVM, RF, NB, LR, and XGBoost), are simulated by using the collected data from intensive care unit patient's database that is based on the clinical laboratory values and vital signs. The performance of the models is evaluated by considering the same datasets. The balanced accuracy of RF, LR, SVM, NB, ensemble (of SVM, RF, NB, and LR), XGBoost, and proposed ensemble (of SVM, RF, NB, LR, and XGBoost) is 0.90, 0.73, 0.93, 0.74, 0.94, 0.95, and 0.96, respectively. It is also evident from the experimental results that the proposed ensemble model performs well as compared to the other models.

An Optimized Artificial Intelligence System Using IoT Biosensors Networking for Healthcare Problems.

In today's environment, electronics technology is growing rapidly because of the availability of the numerous and latest devices which can be deployed for monitoring and controlling the various healthcare systems. Due to the limitations of such devices, there is a dire need to optimize the utilization of the devices. In healthcare systems, Internet of things (IoT) based biosensors networking has minimal energy during transmission and collecting data. This paper proposes an optimized artificial intelligence system using IoT biosensors networking for healthcare problems for efficient data collection from the deployed sensor nodes. Here, an optimized tunicate swarm algorithm is used for optimizing the route for data collection and transmission among the patient and doctor. The fitness function of the optimized tunicate swarm algorithm used the distance, proximity, residual, and average energy of nodes parameters. The proposed method is attributed to the optimal CH chosen under TSA operation having a lower energy consumption. The performance of the proposed method is compared to the existing methods in terms of various metrics like stability period, lifetime, throughput, and clusters per round.

Fab Advances in Fabaceae for Abiotic Stress Resilience: From 'Omics' to Artificial Intelligence.

Legumes are a better source of proteins and are richer in diverse micronutrients over the nutritional profile of widely consumed cereals. However, when exposed to a diverse range of abiotic stresses, their overall productivity and quality are hugely impacted. Our limited understanding of genetic determinants and novel variants associated with the abiotic stress response in food legume crops restricts its amelioration. Therefore, it is imperative to understand different molecular approaches in food legume crops that can be utilized in crop improvement programs to minimize the economic loss. 'Omics'-based molecular breeding provides better opportunities over conventional breeding for diversifying the natural germplasm together with improving yield and quality parameters. Due to molecular advancements, the technique is now equipped with novel 'omics' approaches such as ionomics, epigenomics, fluxomics, RNomics, glycomics, glycoproteomics, phosphoproteomics, lipidomics, regulomics, and secretomics. Pan-omics-which utilizes the molecular bases of the stress response to identify genes (genomics), mRNAs (transcriptomics), proteins (proteomics), and biomolecules (metabolomics) associated with stress regulation-has been widely used for abiotic stress amelioration in food legume crops. Integration of pan-omics with novel omics approaches will fast-track legume breeding programs. Moreover, artificial intelligence (AI)-based algorithms can be utilized for simulating crop yield under changing environments, which can help in predicting the genetic gain beforehand. Application of machine learning (ML) in quantitative trait loci (QTL) mining will further help in determining the genetic determinants of abiotic stress tolerance in pulses.

Comparative Inter- and IntraSpecies Transcriptomics Revealed Key Differential Pathways Associated With Aluminium Stress Tolerance in Lentil.

Aluminium stress causes plant growth retardation and engenders productivity loss under acidic soil conditions. This study accentuates morpho-physiological and molecular bases of aluminium (Al) tolerance within and between wild (ILWL-15) and cultivated (L-4602 and BM-4) lentil species. Morpho-physiological studies revealed better cyto-morphology of tolerant genotypes over sensitive under Al3+ stress conditions. Mitotic lesions were observed in root cells under these conditions. Transcriptome analysis under Al3+ stress revealed 30,158 specifically up-regulated genes in different comparison groups showing contigs between 15,305 and 18,861 bp. In tolerant genotypes, top up-regulated differentially expressed genes (DEGs) were found to be involved in organic acid synthesis and exudation, production of antioxidants, callose synthesis, protein degradation, and phytohormone- and calcium-mediated signalling under stress conditions. DEGs associated with epigenetic regulation and Al3+ sequestration inside vacuole were specifically upregulated in wild and cultivars, respectively. Based on assembled unigenes, an average of 6,645.7 simple sequence repeats (SSRs) and 14,953.7 high-quality single nucleotide polymorphisms (SNPs) were spotted. By quantitative real-time polymerase chain reaction (qRT-PCR), 12 selected genes were validated. Gene ontology (GO) annotation revealed a total of 8,757 GO terms in three categories, viz., molecular, biological, and cellular processes. Kyoto Encyclopaedia of Genes and Genomes pathway scanning also revealed another probable pathway pertaining to metacaspase-1,-4, and -9 for programmed cell death under Al-stress conditions. This investigation reveals key inter- and intraspecies metabolic pathways associated with Al-stress tolerance in lentil species that can be utilised in designing future breeding programmes to improve lentil and related species towards Al3+ stress.

Mechanistic Association of Quantitative Trait Locus with Malate Secretion in Lentil (Lens culinaris Medikus) Seedlings under Aluminium Stress.

Aluminium (Al) toxicity acts as a major delimiting factor in the productivity of many crops including lentil. To alleviate its effect, plants have evolved with Al exclusion and inclusion mechanisms. The former involves the exudation of organic acid to restrict the entry of Al3+ to the root cells while latter involves detoxification of entered Al3+ by organic acids. Al-induced secretion of organic acids from roots is a well-documented mechanism that chelates and neutralizes Al3+ toxicity. In this study, F6 recombinant inbred lines (RILs) derived from a cross between L-7903 (Al-resistant) and BM-4 (Al-sensitive) were phenotyped to assess variation in secretion levels of malate and was combined with genotypic data obtained from 10 Al-resistance linked simple sequence repeat (SSRs) markers. A major quantitative trait loci (QTL) was mapped for malate (qAlt_ma) secretion with a logarithm of odd (LOD) value of 7.7 and phenotypic variation of 60.2%.Validated SSRs associated with this major QTL will be useful in marker assisted selection programmes for improving Al resistance in lentil.

Molecular mapping of aluminium resistance loci based on root re-growth and Al-induced fluorescent signals (callose accumulation) in lentil (Lens culinaris Medikus).

Development of aluminium (Al) resistant genotypes through molecular breeding is a major approach for increasing seed yield under acidic conditions. There are no available reports on mapping of Al resistance loci and molecular breeding for Al resistant varieties in lentil. The present study reports a major quantitative trait loci (QTL) for Al resistance using simple sequence repeat (SSR) markers in F2 and F3 mapping populations derived from contrasting parents. Phenotypic response to Al was measured on the bases of root re-growth (RRG), fluorescent signals (callose accumulation) and Al contents in hydroponic assay. After screening 495 SSR markers to search polymorphism between two contrasting parents, 73 polymorphic markers were used for bulk segregation analysis. Two major QTLs were identified using seven trait linked markers, one each for fluorescent signals and RRG mapped on linkage group (LG) 1 under Al stress conditions in F2 mapping population of cross BM-4 × L-4602. One major QTL (qAlt_fs) was localised between PLC_88 and PBA_LC_373, covering 25.9 cM with adjacent marker PLC_88 at a distance of 0.4 cM. Another major QTL (qAlt_rrg) for RRG was in the marker interval of PBA_LC_1247 and PLC_51, covering a distance of 45.7 cM with nearest marker PBA_LC_1247 at a distance of 21.2 cM. Similarly, in F3 families of BM-4 × L-4602 and BM-4 × L-7903, LG-1 was extended to 285.9 and 216.4 cM respectively, having four newly developed genic-SSR markers. These QTLs had a logarithm of odd (LOD) value of 140.5 and 28.8 along with phenotypic variation of 52% and 11% for fluorescent signals and RRG respectively, whereas, qAlt_rrg had LOD of 36 and phenotypic variance of 25% in F3 population of BM-4 × L-4602. Two major QTLs identified in the present study can be further dissected for candidate gene discovery and development of molecular markers for breeding improved varieties with high Al resistance.

Evaluation of cultivated and wild genotypes of Lens species under alkalinity stress and their molecular collocation using microsatellite markers.

In this study, 285 lentil genotypes were phenotyped under hydroponic and alkaline field conditions. Significant genotypic variation for alkalinity stress was observed among the six Lens species screened hydroponically and in the field having pH up to 9.1. The crucial parameters, like whole Na+ and K+ contents and the Na+/K+ ratio at 40 mM NaHCO3 were found significantly correlated with seedling survivability under hydroponics (r = -0.95, r = 0.93 and -0.97). Genotypes, ranked on the bases of seed yield, restricted uptake of Na+ with thick pith area, increased vascular bundles, less H2O2 production and low Na+/K+ ratio, were found important physio-anatomical traits for alkalinity stress tolerance. The proper regulation of Na+ uptake was found for maintaining higher K+. This relationship is probably the main factor responsible for a better mechanism for tolerance to high pH up to 9.1 in tolerant breeding lines PDL-1 and PSL-9 (cultivars) and ILWL-15, ILWL-192 and ILWL-20 (wild accessions). Based on UPGMA dendrogram, all the genotypes were clustered into four diverse groups. DMRT was implied within the group to differentiate genotypes based on phenotypic response under alkalinity stress. These results can be utilized for selecting diverse parents for developing alkalinity tolerant genotypes.

Detection of coronary artery disease by reduced features and extreme learning machine.

OBJECTIVE: Cardiovascular diseases generate the highest mortality in the globe population, mainly due to coronary artery disease (CAD) like arrhythmia, myocardial infarction and heart failure. Therefore, an early identification of CAD and diagnosis is essential. For this, we have proposed a new approach to detect the CAD patients using heart rate variability (HRV) signals. This approach is based on subspaces decomposition of HRV signals using multiscale wavelet packet (MSWP) transform and entropy features extracted from decomposed HRV signals. The detection performance was analyzed using Fisher ranking method, generalized discriminant analysis (GDA) and binary classifier as extreme learning machine (ELM). The ranking strategies designate rank to the available features extracted by entropy methods from decomposed heart rate variability (HRV) signals and organize them according to their clinical importance. The GDA diminishes the dimension of ranked features. In addition, it can enhance the classification accuracy by picking the best discerning of ranked features. The main advantage of ELM is that the hidden layer does not require tuning and it also has a fast rate of detection. METHODOLOGY: For the detection of CAD patients, the HRV data of healthy normal sinus rhythm (NSR) and CAD patients were obtained from a standard database. Self recorded data as normal sinus rhythm (Self_NSR) of healthy subjects were also used in this work. Initially, the HRV time-series was decomposed to 4 levels using MSWP transform. Sixty two features were extracted from decomposed HRV signals by non-linear methods for HRV analysis, fuzzy entropy (FZE) and Kraskov nearest neighbour entropy (K-NNE). Out of sixty-two features, 31 entropy features were extracted by FZE and 31 entropy features were extracted by K-NNE method. These features were selected since every feature has a different physical premise and in this manner concentrates and uses HRV signals information in an assorted technique. Out of 62 features, top ten features were selected, ranked by a ranking method called as Fisher score. The top ten features were applied to the proposed model, GDA with Gaussian or RBF kernal + ELM having hidden node as sigmoid or multiquadric. The GDA method transforms top ten features to only one feature and ELM has been used for classification. RESULTS: Numerical experimentations were performed on the combination of datasets as NSR-CAD and Self_NSR- CAD subjects. The proposed approach has shown better performance using top ten ranked entropy features. The GDA with RBF kernel + ELM having hidden node as multiquadric method and GDA with Gaussian kernel + ELM having hidden node as sigmoid or multiquadric method achieved an approximate detection accuracy of 100% compared to ELM and linear discriminant analysis (LDA)+ELM for both datasets. The subspaces level-4 and level-3 decomposition of HRV signals by MSWP transform can be used for detection and analysis of CAD patients.

Correction: Discerning morpho-anatomical, physiological and molecular multiformity in cultivated and wild genotypes of lentil with reconciliation to salinity stress.

[This corrects the article DOI: 10.1371/journal.pone.0177465.].

Discerning morpho-anatomical, physiological and molecular multiformity in cultivated and wild genotypes of lentil with reconciliation to salinity stress.

One hundred and sixty two genotypes of different Lens species were screened for salinity tolerance in hydroponics at 40, 80 and 120 mM sodium chloride (NaCl) for 30 d. The germination, seedling growth, biomass accumulation, seedling survivability, salinity scores, root and shoot anatomy, sodium ion (Na+), chloride ion (Cl-) and potassium ion (K+) concentrations, proline and antioxidant activities were measured to evaluate the performance of all the genotypes. The results were compared in respect of physiological (Na+, K+ and Cl-) and seed yield components obtained from field trials for salinity stress conducted during two years. Expression of salt tolerance in hydroponics was found to be reliable indicator for similarity in salt tolerance between genotypes and was evident in saline soil based comparisons. Impressive genotypic variation for salinity tolerance was observed among the genotypes screened under hydroponic and saline field conditions. Plant concentrations of Na+ and Cl- at 120 mM NaCl were found significantly correlated with germination, root and shoot length, fresh and dry weight of roots and shoots, seedling survivability, salinity scores and K+ under controlled conditions and ranked the genotypes along with their seed yield in the field. Root and shoot anatomy of tolerant line (PDL-1) and wild accession (ILWL-137) showed restricted uptake of Na+ and Cl- due to thick layer of their epidermis and endodermis as compared to sensitive cultigen (L-4076). All the genotypes were scanned using SSR markers for genetic diversity, which generated high polymorphism. On the basis of cluster analysis and population structure the contrasting genotypes were grouped into different classes. These markers may further be tested to explore their potential in marker-assisted selection.

Transcriptome analysis of lentil (Lens culinaris Medikus) in response to seedling drought stress.

BACKGROUND: Drought stress is one of the most harmful abiotic stresses in crop plants. As a moderately drought tolerant crop, lentil is a major crop in rainfed areas and a suitable candidate for drought stress tolerance research work. Screening for drought tolerance stress under hydroponic conditions at seedling stage with air exposure is an efficient technique to select genotypes with contrasting traits. Transcriptome analysis provides valuable resources, especially for lentil, as here the information on complete genome sequence is not available. Hence, the present studies were carried out. RESULTS: This study was undertaken to understand the biochemical mechanisms and transcriptome changes involved in imparting adaptation to drought stress at seedling stage in drought-tolerant (PDL-2) and drought-sensitive (JL-3) cultivars. Among different physiological and biochemical parameters, a significant increase was recorded in proline, glycine betaine contents and activities of SOD, APX and GPX in PDL-2 compared to JL-3while chlorophyll, RWC and catalase activity decreased significantly in JL-3. Transcriptome changes between the PDL-2 and JL-3 under drought stress were evaluated using Illumina HiSeq 2500 platform. Total number of bases ranged from 5.1 to 6.7 Gb. Sequence analysis of control and drought treated cDNA libraries of PDL-2 and JL-3 produced 74032, 75500, 78328 and 81523 contigs, respectively with respective N50 value of 2011, 2008, 2000 and 1991. Differential gene expression of drought treated genotypes along with their controls revealed a total of 11,435 upregulated and 6,934 downregulated transcripts. For functional classification of DEGs, KEGG pathway annotation analysis extracted a total of 413 GO annotation terms where 176 were within molecular process, 128 in cellular and 109 in biological process groups. CONCLUSION: The transcriptional profiles provide a foundation for deciphering the underlying mechanism for drought tolerance in lentil. Transcriptional regulation, signal transduction and secondary metabolism in two genotypes revealed significant differences at seedling stage under severe drought. Our finding suggests role of candidate genes for improving drought tolerance in lentil.

Molecular Scanning and Morpho-Physiological Dissection of Component Mechanism in Lens Species in Response to Aluminium Stress.

Aluminium (Al) stress was imposed on 285 lentil genotypes at seedling stage under hydroponics to study its effects on morpho-physiological traits where resistant cultigens and wilds showed minimum reduction in root and shoot length and maximum root re-growth (RRG) after staining. Molecular assortment based on 46 simple sequence repeat (SSR) markers clustered the genotypes into 11 groups, where wilds were separated from the cultigens. Genetic diversity and polymorphism information content (PIC) varied between 0.148-0.775 and 0.140-0.739, respectively. Breeding lines which were found to be most resistant (L-7903, L-4602); sensitive cultivars (BM-4, L-4147) and wilds ILWL-185 (resistant), ILWL-436 (sensitive) were grouped into different clusters. These genotypes were also separated on the basis of population structure and Jaccard's similarity index and analysed to study Al resistance mechanism through determination of different attributes like localization of Al and callose, lipid peroxidation, secretion of organic acids and production of antioxidant enzymes. In contrast to sensitive genotypes, in resistant ones most of the Al was localized in the epidermal cells, where its movement to apoplastic region was restricted due to release of citrate and malate. Under acidic field conditions, resistant genotypes produced maximum seed yield/plant as compared to sensitive genotypes at two different locations i.e. Imphal, Manipur, India and Basar, Arunanchal Pradesh, India during 2012-13, 2013-14 and 2014-15. These findings suggest that Al stress adaptation in lentil is through exclusion mechanism and hybridization between the contrasting genotypes from distinct clusters can help in development of resistant varieties.

Molecular and Morpho-Agronomical Characterization of Root Architecture at Seedling and Reproductive Stages for Drought Tolerance in Wheat.

Water availability is a major limiting factor for wheat (Triticum aestivum L.) production in rain-fed agricultural systems worldwide. Root architecture is important for water and nutrition acquisition for all crops, including wheat. A set of 158 diverse wheat genotypes of Australian (72) and Indian (86) origin were studied for morpho-agronomical traits in field under irrigated and drought stress conditions during 2010-11 and 2011-12.Out of these 31 Indian wheat genotypes comprising 28 hexaploid (Triticum aestivum L.) and 3 tetraploid (T. durum) were characterized for root traits at reproductive stage in polyvinyl chloride (PVC) pipes. Roots of drought tolerant genotypes grew upto137cm (C306) as compared to sensitive one of 63cm with a mean value of 94.8cm. Root architecture traits of four drought tolerant (C306, HW2004, HD2888 and NI5439) and drought sensitive (HD2877, HD2012, HD2851 and MACS2496) genotypes were also observed at 6 and 9 days old seedling stage. The genotypes did not show any significant variation for root traits except for longer coleoptiles and shoot and higher absorptive surface area in drought tolerant genotypes. The visible evaluation of root images using WinRhizo Tron root scanner of drought tolerant genotype HW2004 indicated compact root system with longer depth while drought sensitive genotype HD2877 exhibited higher horizontal root spread and less depth at reproductive stage. Thirty SSR markers were used to study genetic variation which ranged from 0.12 to 0.77 with an average value of 0.57. The genotypes were categorized into three subgroups as highly tolerant, sensitive, moderately sensitive and tolerant as intermediate group based on UPGMA cluster, STRUCTURE and principal coordinate analyses. The genotypic clustering was positively correlated to grouping based on root and morpho-agronomical traits. The genetic variability identified in current study demonstrated these traits can be used to improve drought tolerance and association mapping.

Molecular Assortment of Lens Species with Different Adaptations to Drought Conditions Using SSR Markers.

The success of drought tolerance breeding programs can be enhanced through molecular assortment of germplasm. This study was designed to characterize molecular diversity within and between Lens species with different adaptations to drought stress conditions using SSR markers. Drought stress was applied at seedling stage to study the effects on morpho-physiological traits under controlled condition, where tolerant cultivars and wilds showed 12.8-27.6% and 9.5-23.2% reduction in seed yield per plant respectively. When juxtaposed to field conditions, the tolerant cultivars (PDL-1 and PDL-2) and wild (ILWL-314 and ILWL-436) accessions showed 10.5-26.5% and 7.5%-15.6% reduction in seed yield per plant, respectively under rain-fed conditions. The reductions in seed yield in the two tolerant cultivars and wilds under severe drought condition were 48-49% and 30.5-45.3% respectively. A set of 258 alleles were identified among 278 genotypes using 35 SSR markers. Genetic diversity and polymorphism information contents varied between 0.321-0.854 and 0.299-0.836, with mean value of 0.682 and 0.643, respectively. All the genotypes were clustered into 11 groups based on SSR markers. Tolerant genotypes were grouped in cluster 6 while sensitive ones were mainly grouped into cluster 7. Wild accessions were separated from cultivars on the basis of both population structure and cluster analysis. Cluster analysis has further grouped the wild accessions on the basis of species and sub-species into 5 clusters. Physiological and morphological characters under drought stress were significantly (P = 0.05) different among microsatellite clusters. These findings suggest that drought adaptation is variable among wild and cultivated genotypes. Also, genotypes from contrasting clusters can be selected for hybridization which could help in evolution of better segregants for improving drought tolerance in lentil.