Influence of structural characteristics on the binding of synthetic cannabinoids from the JWH family to the CB1 receptor: A computational study.

Synthetic cannabinoids, including some from the John W. Huffman (JWH) family, emerged on the drug scene around 2004 as "alternative marijuana," despite being considerably more potent than marijuana. Like Δ9-tetrahydrocannabinol (THC), the principal psychoactive ingredient in marijuana, synthetic cannabinoids have also been found to interact with cannabinoid receptors CB1 and CB2, found in the brain, immune system, and peripheral organs. The JWH compounds and other synthetic cannabinoids have become important subjects of research in the forensic science community due to their drug-abuse potential, undetectability under routine drug screening, and unpredictable toxicity. In this study, an active-state CB1 receptor model was used to assess the receptor-ligand interactions between the CB1 receptor and ligands from the JWH synthetic cannabinoid family, as well as some newly designed JWH-like virtual compounds, labeled as MGCS compounds, using docking, binding free-energy calculations (ΔG), and molecular dynamics simulations (MDs). The calculated ΔG revealed that the carbonyl group between the naphthalene and the indole, characteristic of the JWH family, and the length of the N-linked alkyl chain were two important structural characteristics that influenced the predicted CB1 binding affinity, especially as increasing the length of the alkyl chain led to better predicted binding affinity. MDs and per-residue-breakdown results showed that the designed MGCS compounds with a pentyl chain attached to the naphthalene moiety and selected JWH compounds formed stable and strong hydrophobic interactions with the key residues Phe170, Phe174, Phe177, Phe200, Phe268, and Trp279 of the CB1 receptor. Comprehension of these critical interactions can help forensic chemists predict the structure of undiscovered families of synthetic cannabinoids.

Metabolomics of early blight (Alternaria solani) susceptible tomato (Solanum lycopersicum) unfolds key biomarker metabolites and involved metabolic pathways.

Tomato (Solanum lycopersicum) is among the most important commercial horticultural crops worldwide. The crop quality and production is largely hampered due to the fungal pathogen Alternaria solani causing necrotrophic foliage early blight disease. Crop plants usually respond to the biotic challenges with altered metabolic composition and physiological perturbations. We have deciphered altered metabolite composition, modulated metabolic pathways and identified metabolite biomarkers in A. solani-challenged susceptible tomato variety Kashi Aman using Liquid Chromatography-Mass Spectrometry (LC-MS) based metabolomics. Alteration in the metabolite feature composition of pathogen-challenged (m/z 9405) and non-challenged (m/z 9667) plant leaves including 8487 infection-exclusive and 8742 non-infection exclusive features was observed. Functional annotation revealed putatively annotated metabolites and pathway mapping indicated their enrichment in metabolic pathways, biosynthesis of secondary metabolites, ubiquinone and terpenoid-quinones, brassinosteroids, steroids, terpenoids, phenylpropanoids, carotenoids, oxy/sphingolipids and metabolism of biotin and porphyrin. PCA, multivariate PLS-DA and OPLS-DA analysis showed sample discrimination. Significantly up regulated 481 and down regulated 548 metabolite features were identified based on the fold change (threshold ≥ 2.0). OPLS-DA model based on variable importance in projection (VIP scores) and FC threshold (> 2.0) revealed 41 up regulated discriminant metabolite features annotated as sphingosine, fecosterol, melatonin, serotonin, glucose 6-phosphate, zeatin, dihydrozeatin and zeatin-ß-D-glucoside. Similarly, 23 down regulated discriminant metabolites included histidinol, 4-aminobutyraldehyde, propanoate, tyramine and linalool. Melatonin and serotonin in the leaves were the two indoleamines being reported for the first time in tomato in response to the early blight pathogen. Receiver operating characteristic (ROC)-based biomarker analysis identified apigenin-7-glucoside, uridine, adenosyl-homocysteine, cGMP, tyrosine, pantothenic acid, riboflavin (as up regulated) and adenosine, homocyctine and azmaline (as down regulated) biomarkers. These results could aid in the development of metabolite-quantitative trait loci (mQTL). Furthermore, stress-induced biosynthetic pathways may be the potential targets for modifications through breeding programs or genetic engineering for improving crop performance in the fields.

Annual (2023) taxonomic update of RNA-directed RNA polymerase-encoding negative-sense RNA viruses (realm Riboviria: kingdom Orthornavirae: phylum Negarnaviricota).

In April 2023, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by one new family, 14 new genera, and 140 new species. Two genera and 538 species were renamed. One species was moved, and four were abolished. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV.

Genome-wide identification and characterization of Puccinia striiformis-responsive lncRNAs in Triticum aestivum.

Wheat stripe rust (yellow rust) caused by Puccinia striiformis f. sp. tritici (Pst) is a serious biotic stress factor limiting wheat production worldwide. Emerging evidence demonstrates that long non-coding RNAs (lncRNAs) participate in various developmental processes in plants via post-transcription regulation. In this study, RNA sequencing (RNA-seq) was performed on a pair of near-isogenic lines-rust resistance line FLW29 and rust susceptible line PBW343-which differed only in the rust susceptibility trait. A total of 6,807 lncRNA transcripts were identified using bioinformatics analyses, among which 10 lncRNAs were found to be differentially expressed between resistance and susceptible lines. In order to find the target genes of the identified lncRNAs, their interactions with wheat microRNA (miRNAs) were predicted. A total of 199 lncRNAs showed interactions with 65 miRNAs, which further target 757 distinct mRNA transcripts. Moreover, detailed functional annotations of the target genes were used to identify the candidate genes, pathways, domains, families, and transcription factors that may be related to stripe rust resistance response in wheat plants. The NAC domain protein, disease resistance proteins RPP13 and RPM1, At1g58400, monodehydroascorbate reductase, NBS-LRR-like protein, rust resistance kinase Lr10-like, LRR receptor, serine/threonine-protein kinase, and cysteine proteinase are among the identified targets that are crucial for wheat stripe rust resistance. Semiquantitative PCR analysis of some of the differentially expressed lncRNAs revealed variations in expression profiles of two lncRNAs between the Pst-resistant and Pst-susceptible genotypes at least under one condition. Additionally, simple sequence repeats (SSRs) were also identified from wheat lncRNA sequences, which may be very useful for conducting targeted gene mapping studies of stripe rust resistance in wheat. These findings improved our understanding of the molecular mechanism responsible for the stripe rust disease that can be further utilized to develop wheat varieties with durable resistance to this disease.

Untargeted Metabolomics of Alternaria solani-Challenged Wild Tomato Species Solanum cheesmaniae Revealed Key Metabolite Biomarkers and Insight into Altered Metabolic Pathways.

Untargeted metabolomics of moderately resistant wild tomato species Solanum cheesmaniae revealed an altered metabolite profile in plant leaves in response to Alternaria solani pathogen. Leaf metabolites were significantly differentiated in non-stressed versus stressed plants. The samples were discriminated not only by the presence/absence of specific metabolites as distinguished markers of infection, but also on the basis of their relative abundance as important concluding factors. Annotation of metabolite features using the Arabidopsis thaliana (KEGG) database revealed 3371 compounds with KEGG identifiers belonging to biosynthetic pathways including secondary metabolites, cofactors, steroids, brassinosteroids, terpernoids, and fatty acids. Annotation using the Solanum lycopersicum database in PLANTCYC PMN revealed significantly upregulated (541) and downregulated (485) features distributed in metabolite classes that appeared to play a crucial role in defense, infection prevention, signaling, plant growth, and plant homeostasis to survive under stress conditions. The orthogonal partial least squares discriminant analysis (OPLS-DA), comprising a significant fold change (≥2.0) with VIP score (≥1.0), showed 34 upregulated biomarker metabolites including 5-phosphoribosylamine, kaur-16-en-18-oic acid, pantothenate, and O-acetyl-L-homoserine, along with 41 downregulated biomarkers. Downregulated metabolite biomarkers were mapped with pathways specifically known for plant defense, suggesting their prominent role in pathogen resistance. These results hold promise for identifying key biomarker metabolites that contribute to disease resistive metabolic traits/biosynthetic routes. This approach can assist in mQTL development for the stress breeding program in tomato against pathogen interactions.

Comparative expression analysis of water buffalo (Bubalus bubalis) to identify genes associated with economically important traits.

The milk, meat, skins, and draft power of domestic water buffalo (Bubalus bubalis) provide substantial contributions to the global agricultural economy. The world's water buffalo population is primarily found in Asia, and the buffalo supports more people per capita than any other livestock species. For evaluating the workflow, output rate, and completeness of transcriptome assemblies within and between reference-free (RF) de novo transcriptome and reference-based (RB) datasets, abundant bioinformatics studies have been carried out to date. However, comprehensive documentation of the degree of consistency and variability of the data produced by comparing gene expression levels using these two separate techniques is lacking. In the present study, we assessed the variations in the number of differentially expressed genes (DEGs) attained with RF and RB approaches. In light of this, we conducted a study to identify, annotate, and analyze the genes associated with four economically important traits of buffalo, viz., milk volume, age at first calving, post-partum cyclicity, and feed conversion efficiency. A total of 14,201 and 279 DEGs were identified in RF and RB assemblies. Gene ontology (GO) terms associated with the identified genes were allocated to traits under study. Identified genes improve the knowledge of the underlying mechanism of trait expression in water buffalo which may support improved breeding plans for higher productivity. The empirical findings of this study using RNA-seq data-based assembly may improve the understanding of genetic diversity in relation to buffalo productivity and provide important contributions to answer biological issues regarding the transcriptome of non-model organisms.

Identification of the Putative Binding Site of a Benzimidazole Opioid (Etazene) and Its Metabolites at µ-Opioid Receptor: A Human Liver Microsomal Assay and Systematic Computational Study.

The synthetic benzimidazole opioid etazene (which has a 70-times higher analgesic activity than morphine), a recreational drug, has gained popularity as a novel psychoactive substance (NPS) on the illegal/darknet market; however, no experimental information is available at the molecular level on the binding mechanism and putative binding site of etazene and its metabolites at the µ-opioid receptor (MOR). In the present study, we investigated the metabolism of etazene in human liver microsomes using ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). We also explored the possibilities of MOR activation by etazene and its metabolites by studying their binding mechanisms and interaction profiles at an active-state MOR model via molecular docking, binding free energy calculations, and all-atom molecular dynamics (MD) simulations. The putative metabolites of etazene were also predicted using the ADMET Predictor 10.1. The molecular docking studies and free energy calculations showed that etazene and its metabolites (M1, M2, and M5-M7) exhibited strong predicted binding affinity at MOR and showed overlapped binding orientation with MOR-bound agonist BU72, which was co-crystallized in the MOR X-ray crystal structure (PDB ID: 5C1M). MD also confirmed the stability of the MOR-etazene and MOR-M6 complexes. These results suggest that etazene and its metabolites may act as strong MOR agonists, highlighting the necessity of experimental validation. The insights from this study, such as key interactions between etazene and its metabolites and the MOR, will allow authorities to predict potential analogs and clarify the target-protein interactions associated with this illicit substance, granting advanced or rapid reactions to confiscating or banning potential emerging drugs.

In Vitro and In Silico Studies of Neolignans from Magnolia grandiflora L. Seeds against Human Cannabinoids and Opioid Receptors.

Magnolia grandiflora L. (Magnoliaceae) is a plant of considerable medicinal significance; its flowers and seeds have been used in various traditional remedies. Radioligand binding assays of n-hexane seeds extract showed displacement of radioligand for cannabinoid (CB1 and CB2) and opioid δ (delta), κ (kappa), and µ (mu) receptors. Bioactivity-guided fractionation afforded 4-O-methylhonokiol (1), magnolol (2), and honokiol (3), which showed higher binding to cannabinoid rather than opioid receptors in radioligand binding assays. Compounds 1-3, together with the dihydro analog of 2 (4), displayed selective affinity towards CB2R (Ki values of 0.29, 1.4, 1.94, and 0.99 µM, respectively), compared to CB1R (Ki 3.85, 17.82, 14.55, and 19.08 µM, respectively). An equal mixture of 2 and 3 (1:1 ratio) showed additive displacement activity towards the tested receptors compared to either 2 or 3 alone, which in turn provides an explanation for the strong displacement activity of the n-hexane extract. Due to the unavailability of an NMR or X-ray crystal structure of bound neolignans with the CB1 and CB2 receptors, a docking study was performed to predict ligand-protein interactions at a molecular level and to delineate structure-activity relationships (SAR) of the neolignan analogs with the CB1 and CB2 receptors. The putative binding modes of neolignans 1-3 and previously reported related analogs (4, 4a, 5, 5a, 6, 6a, and 6b) into the active site of the CB1 and CB2 receptors were assessed for the first time via molecular docking and binding free-energy (∆G) calculations. The docking and ∆G results revealed the importance of a hydroxyl moiety in the molecules that forms strong H-bonding with Ser383 and Ser285 within CB1R and CB2R, respectively. The impact of a shift from a hydroxyl to the methoxy group on experimental binding affinity to CB1R versus CB2R was explained through ∆G data and the orientation of the alkyl chain within the CB1R. This comprehensive SAR, influenced by the computational study and the observed in vitro displacement binding affinities, has indicated the potential of magnolia neolignans for developing new CB agonists for potential use as analgesics, anti-inflammatory agents, or anxiolytics.

Diffusion of CO2 in single-step silica nanofluid for subsurface utilization: an experimental study.

This study investigates the role of single-step silica nanofluids as additives to increase CO2 absorption in polymeric solutions for proposed oilfield applications. Using pressure decay approach, the study investigates the applicability of single-step silica nanofluids for CO2 absorption in a high pressure-high temperature (HPHT) cell. Various parameters like nanoparticle size (30-120 nm) and concentration (0.1-1 wt%) were investigated to ascertain the absorption performance of the nanofluids and optimization their application in subsurface applications as carrier fluids for CO2. The solutions under observation (deionized water and silica nanofluids) were pressurized under the desired pressure and temperature inside a stirring pot and the decline in pressure was continuously noted. To comprehensively cover the near-reservoir field conditions, the CO2 absorption was investigated in the pressure range of 5-10 MPa and at temperatures of 30-90 °C. While increasing the nanoparticle concentration (from 0.1 to 1 wt%) increased the CO2 absorption (evident by the sharper decline in pressure), increasing the nanoparticle size reduced the absorption capacity of the nanofluids as a lesser volume of decline in pressure was noted. Furthermore, increasing the temperature of the experimental investigation caused a major reduction (12-19%) in the pressure decay. However, it was also observed that higher pressure (> 7.5 MPa) was detrimental for CO2 absorption (due to its supercritical nature). Adding salt (sodium chloride, NaCl) was found to massively lower (up to 33%) while adding surfactant (sodium dodecyl sulfate, SDS) slightly increased the amount of CO2 absorption (in presence of salinity). Based on the observations of this study, the use of single-step silica nanofluids as CO2 carrier fluids is recommended for oilfield conditions where salinity is less than 4 wt%.

Metabolomics-Driven Mining of Metabolite Resources: Applications and Prospects for Improving Vegetable Crops.

Vegetable crops possess a prominent nutri-metabolite pool that not only contributes to the crop performance in the fields, but also offers nutritional security for humans. In the pursuit of identifying, quantifying and functionally characterizing the cellular metabolome pool, biomolecule separation technologies, data acquisition platforms, chemical libraries, bioinformatics tools, databases and visualization techniques have come to play significant role. High-throughput metabolomics unravels structurally diverse nutrition-rich metabolites and their entangled interactions in vegetable plants. It has helped to link identified phytometabolites with unique phenotypic traits, nutri-functional characters, defense mechanisms and crop productivity. In this study, we explore mining diverse metabolites, localizing cellular metabolic pathways, classifying functional biomolecules and establishing linkages between metabolic fluxes and genomic regulations, using comprehensive metabolomics deciphers of the plant's performance in the environment. We discuss exemplary reports covering the implications of metabolomics, addressing metabolic changes in vegetable plants during crop domestication, stage-dependent growth, fruit development, nutri-metabolic capabilities, climatic impacts, plant-microbe-pest interactions and anthropogenic activities. Efforts leading to identify biomarker metabolites, candidate proteins and the genes responsible for plant health, defense mechanisms and nutri-rich crop produce are documented. With the insights on metabolite-QTL (mQTL) driven genetic architecture, molecular breeding in vegetable crops can be revolutionized for developing better nutritional capabilities, improved tolerance against diseases/pests and enhanced climate resilience in plants.

CerealESTDb: A Comprehensive Resource for Abiotic Stress-Responsive Annotated ESTs With Predicted Genes, Gene Ontology, and Metabolic Pathways in Major Cereal Crops.

Cereals are the most important food crops and are considered key contributors to global food security. Loss due to abiotic stresses in cereal crops is limiting potential productivity in a significant manner. The primary reasons for abiotic stresses are abrupt temperature, variable rainfall, and declining nutrient status of the soil. Varietal development is the key to sustaining productivity under influence of multiple abiotic stresses and must be studied in context with genomics and molecular breeding. Recently, advances in a plethora of Next Generation Sequencing (NGS) based methods have accelerated the enormous genomic data generation associated with stress-induced transcripts such as microarray, RNAseq, Expressed Sequenced Tag (ESTs), etc. Many databases related to microarray and RNA-seq based transcripts have been developed and profusely utilized. However, an abundant amount of transcripts related to abiotic stresses in various cereal crops arising from EST technology are available but still remain underutilized in absence of a consolidated database. In this study, an attempt has been made with a primary goal to integrate, analyse, and characterise the available resources of ESTs responsive to abiotic stresses in major cereals. The developed CerealESTdb presents a customisable search in two different ways in the form of searchable content for easy access and potential use. This database comprises ESTs from four major cereal crops, namely rice (Oryza sativa L.), wheat (Triticum aestivum L.), sorghum (Sorghum bicolour L.), and maize (Zea mays L.), under a set of abiotic stresses. The current statistics of this cohesive database consists of 55,826 assembled EST sequences, 51,791 predicted genes models, and their 254,609 gene ontology terms including extensive information on 1,746 associated metabolic pathways. We anticipate that developed CerealESTdb will be helpful in deciphering the knowledge of complex biological phenomena under abiotic stresses to accelerate the molecular breeding programs towards the development of crop cultivars resilient to abiotic stresses. The CerealESTdb is publically available with the URL http://cabgrid.res.in/CerealESTDb.

Modified smart water flooding for promoting carbon dioxide utilization in shale enriched heterogeneous sandstone under surface conditions for oil recovery and storage prospects.

Modern oil reservoirs exhibit high macro-scale heterogeneity, i.e., presence of shales and clays, which complicate the implementation of conventional enhanced oil recovery (EOR) practices. Hence, there is a need to investigate new class of EOR methods which not only improve recovery of oil from reservoir but also reduce formation damage. Thus, in this study, synthetic smart brines of varying salinity were formulated to investigate carbon utilization in shaly-sandstone for oil recovery and sequestration applications. To prepare shaly-sandstone samples, shale content in sand varied between 0 and 25 wt%. The addition of shale reduced porosity and permeability of sand-packs, and porosity ~ 25 and permeability < 10 md were measured for a combination of 75% sand + 25% shale which were originally 38% and 692 md for 100% sand + 0% shale. The oil recovery experiments were performed at temperature ≈ 40 °C and ambient pressure. The impact of shale content was insignificant on CO2-based oil recovery resulting its value remained nearly constant (5-7%). Smart saline water (SSW) solutions were prepared through the dilution of formation water (FW) of typical oilfield salinity and used these SSW solutions in investigating shale swelling and interfacial tension with CO2. Compared to other SSW solutions, SSW-2 (1 part FW/9 part water: 1/10th of FW) demonstrated superior control on mitigating shale swelling (by 67%) and reduce interfacial tension (by 30%) when compared to FW. Moreover, it helped to mobilize higher amount of oil (50% OOIP) from sand-pack (80% sand + 20% shale) in which conventional water flood failed to perform, indicating its viability for EOR from heterogeneous reservoir. In addition, SSW solutions promoted use of carbonated (CO2-enriched) water injection for oil recovery from sandstone exhibiting high shale content of 20% as over 5-8% higher oil recovery was obtained compared to conventional water flooding. Comparative performance of water flooding, salinity water-alternating CO2 flooding and carbonated smart water injection in heterogeneous sandstone.

Machine learning in the estimation of CRISPR-Cas9 cleavage sites for plant system.

CRISPR-Cas9 system is one of the recent most used genome editing techniques. Despite having a high capacity to alter the precise target genes and genomic regions that the planned guide RNA (or sgRNA) complements, the off-target effect still exists. But there are already machine learning algorithms for people, animals, and a few plant species. In this paper, an effort has been made to create models based on three machine learning-based techniques [namely, artificial neural networks (ANN), support vector machines (SVM), and random forests (RF)] for the prediction of the CRISPR-Cas9 cleavage sites that will be cleaved by a particular sgRNA. The plant dataset was the sole source of inspiration for all of these machine learning-based algorithms. 70% of the on-target and off-target dataset of various plant species that was gathered was used to train the models. The remaining 30% of the data set was used to evaluate the model's performance using a variety of evaluation metrics, including specificity, sensitivity, accuracy, precision, F1 score, F2 score, and AUC. Based on the aforementioned machine learning techniques, eleven models in all were developed. Comparative analysis of these produced models suggests that the model based on the random forest technique performs better. The accuracy of the Random Forest model is 96.27%, while the AUC value was found to be 99.21%. The SVM-Linear, SVM-Polynomial, SVM-Gaussian, and SVM-Sigmoid models were trained, making a total of six ANN-based models (ANN1-Logistic, ANN1-Tanh, ANN1-ReLU, ANN2-Logistic, ANN2-Tanh, and ANN-ReLU) and Support Vector Machine models (SVM-Linear, SVM-Polynomial, SVM-Gaussian However, the overall performance of Random Forest is better among all other ML techniques. ANN1-ReLU and SVM-Linear model performance were shown to be better among Artificial Neural Network and Support Vector Machine-based models, respectively.

MetaConClust - Unsupervised Binning of Metagenomics Data using Consensus Clustering.

Background: Binning of metagenomic reads is an active area of research, and many unsupervised machine learning-based techniques have been used for taxonomic independent binning of metagenomic reads. Objective: It is important to find the optimum number of the cluster as well as develop an efficient pipeline for deciphering the complexity of the microbial genome. Methods: Applying unsupervised clustering techniques for binning requires finding the optimal number of clusters beforehand and is observed to be a difficult task. This paper describes a novel method, MetaConClust, using coverage information for grouping of contigs and automatically finding the optimal number of clusters for binning of metagenomics data using a consensus-based clustering approach. The coverage of contigs in a metagenomics sample has been observed to be directly proportional to the abundance of species in the sample and is used for grouping of data in the first phase by MetaConClust. The Partitioning Around Medoid (PAM) method is used for clustering in the second phase for generating bins with the initial number of clusters determined automatically through a consensus-based method. Results: Finally, the quality of the obtained bins is tested using silhouette index, rand Index, recall, precision, and accuracy. Performance of MetaConClust is compared with recent methods and tools using benchmarked low complexity simulated and real metagenomic datasets and is found better for unsupervised and comparable for hybrid methods. Conclusion: This is suggestive of the proposition that the consensus-based clustering approach is a promising method for automatically finding the number of bins for metagenomics data.

Effect of nanoparticle on rheological properties of surfactant-based nanofluid for effective carbon utilization: capturing and storage prospects.

Previous studies have shown insufficient dispersion and thermal stability of nanofluids for high-temperature carbon capture and storage applications. Compared to the other NPs, TiO2 nanofluids exhibit superior stability due to their high zeta potential. In previous studies, TiO2 nanofluids have shown superior performance in heat transfer and cooling applications along with importing the stability of other nanofluids like SiO2 in form of nanocomposites. Therefore, in this study, a nanofluid formulation consisting of titania nanofluid in a base solution of ethylene glycol (EG) with different co-stabilizers such as surfactants was synthesized for better dispersion stability, enhanced electrical, and rheological properties especially for the use in high-temperature industrial applications which include carbon capture and storage along with enhanced oil recovery. The formulated nanofluid was investigated for stability using dynamic light scattering (DLS) study and electrical conductivity. Additionally, the formulated nanofluid was also examined for thermal stability at high temperatures using an electrical conductivity study followed by rheological measurements at 30 and 90 °C. At a high temperature, the shear-thinning behavior of EG was found highly affected by shear rate; however, this deformation was controlled using TiO2 nanoparticles (NPs). Furthermore, the role of surfactant was also investigated on dispersion stability, electrical conductivity followed by viscosity results, and it was found that the nanofluid is superior in presence of anionic surfactant sodium dodecyl sulfate (SDS) as compared to nonionic surfactant Triton X-100 (TX-100). The inclusion of ionic surfactant provides a charged layer of micelles surrounding the core of a NP and it produced additional surface potential. Consequently, it increases the repulsive force between two adjacent NPs and renders a greater stability to nanofluid while nonionic surfactant allowed monomers to adsorb on the surface of NP via hydrophobic interaction and enhances the short-range interparticle repulsion, to stabilize nanofluid. This makes titania nanofluid suitable for widespread high-temperature applications where conventional nanofluids face limitations. Finally, the application of the synthesized titania nanofluids was explored for the capture and transport of CO2 where the inclusion of the anionic surfactant was found to increase the CO2 capturing ability of titania nanofluids by 140-220% (over the conventional nanofluid) while also showing superior retention at both investigated temperatures. Thus, the study promotes the role of novel surfactant-treated titania nanofluids for carbon removal and storage and recommends their applications involving carbonated fluid injection (CFI) to carbon utilization in oilfield applications.

Understanding Rice-Magnaporthe Oryzae Interaction in Resistant and Susceptible Cultivars of Rice under Panicle Blast Infection Using a Time-Course Transcriptome Analysis.

Rice blast is a global threat to food security with up to 50% yield losses. Panicle blast is a more severe form of rice blast and the response of rice plant to leaf and panicle blast is distinct in different genotypes. To understand the specific response of rice in panicle blast, transcriptome analysis of blast resistant cultivar Tetep, and susceptible cultivar HP2216 was carried out using RNA-Seq approach after 48, 72 and 96 h of infection with Magnaporthe oryzae along with mock inoculation. Transcriptome data analysis of infected panicle tissues revealed that 3553 genes differentially expressed in HP2216 and 2491 genes in Tetep, which must be the responsible factor behind the differential disease response. The defense responsive genes are involved mainly in defense pathways namely, hormonal regulation, synthesis of reactive oxygen species, secondary metabolites and cell wall modification. The common differentially expressed genes in both the cultivars were defense responsive transcription factors, NBS-LRR genes, kinases, pathogenesis related genes and peroxidases. In Tetep, cell wall strengthening pathway represented by PMR5, dirigent, tubulin, cell wall proteins, chitinases, and proteases was found to be specifically enriched. Additionally, many novel genes having DOMON, VWF, and PCaP1 domains which are specific to cell membrane were highly expressed only in Tetep post infection, suggesting their role in panicle blast resistance. Thus, our study shows that panicle blast resistance is a complex phenomenon contributed by early defense response through ROS production and detoxification, MAPK and LRR signaling, accumulation of antimicrobial compounds and secondary metabolites, and cell wall strengthening to prevent the entry and spread of the fungi. The present investigation provided valuable candidate genes that can unravel the mechanisms of panicle blast resistance and help in the rice blast breeding program.

Characterizing the putative mitogen-activated protein kinase (MAPK) and their protective role in oxidative stress tolerance and carbon assimilation in wheat under terminal heat stress.

Wheat, being sensitive to terminal heat, causes drastic reduction in grain quality and yield. MAPK cascade regulates the network of defense mechanism operated inside plant system. Here, we have identified 21 novel MAPKs through gel-based proteomics and RNA-seq data analysis. Based on digital gene expression, two transcripts (transcript_2834 and transcript_8242) showing homology with MAPK were cloned and characterized from wheat (acc. nos. MK854806 and KT835664). Transcript_2834 was cloned in pET28a vector and recombinant MAPK protein of ∼40.3 kDa was isolated and characterized to have very high in-vitro kinase activity under HS. Native MAPK showed positive correlation with the expression of TFs, HSPs, genes linked with antioxidant enzyme (SOD, CAT, GPX), photosynthesis and starch biosynthesis pathways in wheat under HS. Wheat cv. HD3086 (thermotolerant) having higher expression and activity of MAPK under HS showed significant increase in accumulation of proline, H2O2, starch, and granule integrity, compared with BT-Schomburgk (thermosusceptible).

A Novel Way of Comparing Protein 3D Structure Using Graph Partitioning Approach.

Alignment and comparison of protein 3D structures is an important and fundamental task in structural biology to study evolutionary, functional and structural relatedness among proteins. Since two decades, the research on protein structure alignment has been taken up on priority and numbers of research articles are being published. There are incremental advances over previous efforts, and still these methods continue to improve over the time and still this is an open problem in structural biology. A novel methodology has been developed for comparing protein 3D structure by employing conversion of pair of protein 3D structures into 2D graphs (undirected weighted graph), partitioning of 2D graphs into sub-graphs, matching sub-graphs with main graphs and finally these sub-graphs matches calculates similarity between the pair of proteins. The proposed method has been implemented in MATLAB and R Package. The performance of the developed methodology is tested with four existing best methods such as CE, jFATCAT, TM_Align and Dali on 100 proteins benchmark dataset with SCOP database. The proposed method is efficient in terms of time complexity, accuracy, grouping of proteins in relevant structural groups and provides additional information towards non-bonded interactions and sub-graphs indicates the dominance of secondary structure.

Inferring Relationship of Blood Metabolic Changes and Average Daily Gain With Feed Conversion Efficiency in Murrah Heifers: Machine Learning Approach.

Machine learning algorithms were employed for predicting the feed conversion efficiency (FCE), using the blood parameters and average daily gain (ADG) as predictor variables in buffalo heifers. It was observed that isotonic regression outperformed other machine learning algorithms used in study. Further, we also achieved the best performance evaluation metrics model with additive regression as the meta learner and isotonic regression as the base learner on 10-fold cross-validation and leaving-one-out cross-validation tests. Further, we created three separate partial least square regression (PLSR) models using all 14 parameters of blood and ADG as independent (explanatory) variables and FCE as the dependent variable, to understand the interactions of blood parameters, ADG with FCE each by inclusion of all FCE values (i), only higher FCE values (negative RFI) (ii), and inclusion of only lower FCE (positive RFI) values (iii). The PLSR model including only the higher FCE values was concluded the best, based on performance evaluation metrics as compared to PLSR models developed by inclusion of the lower FCE values and all types of FCE values. IGF1 and its interactions with the other blood parameters were found highly influential for higher FCE measures. The strength of the estimated interaction effects of the blood parameter in relation to FCE may facilitate understanding of intricate dynamics of blood parameters for growth.

A Comparative Study of Single-Trait and Multi-Trait Genomic Selection.

In recent years of animal and plant breeding research, genomic selection (GS) became a choice for selection of appropriate candidate for breeding as it significantly contributes to enhance the genetic gain. Various studies related to GS have been carried out in the recent past. These studies were mostly confined to single trait. Although GS methods based on single trait have not performed very well in cases like pleiotropy, missing data and when the trait under study has low heritability. Gradually, some studies were carried out to explore the possibility of methods for GS based on multiple traits in the view of overcoming the above-mentioned problems in the method of single-trait GS (STGS). Currently, multi-trait-based GS methods are getting importance as it exploits the information of correlated structure among response. In this study, we have compared various methods related to STGS, such as stepwise regression, ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian, best linear unbiased prediction, and support vector machine, and multi-trait-based GS methods, such as multivariate regression with covariance estimation, conditional Gaussian graphical models, mixed model, and LASSO. In almost all cases, multi-trait-based methods are found to be more accurate. Based on the results of this study, it may be concluded that multi-trait-based methods have great potential to increase genetic gain as they utilize the correlation among the response variable as extra information, which contributes to estimate breeding value more precisely. This study is a comprehensive review of the methods of GS right from single trait to multiple traits and comparisons among these two classes.