Assessment of the genetic diversity of groundnut ( Arachis hypogaea L.) genotypes for kernel yield, oil and fodder quantity and quality under drought conditions

The objective of this study was to determine drought tolerance, kernel (KY) and fodder yield and quality amongst diverse groundnut genotypes for direct production or breeding. Hundred genotypes were evaluated at ICRISAT, India during 2018–2019 and 2019–2020 under drought-stressed (DS) and non stressed (NS) conditions. Data were collected on KY; oil content (OC); oil yield (OY); protein content; palmitic, stearic, oleic, and linoleic acid contents; haulm yield (HY); and fodder quality parameters such as the contents of dry matter, ash, nitrogen (NC), neutral detergent fiber (NDFDM), acid detergent fiber (ADFDM), acid detergent lignin (ADLDM), in vitro digestibility, and metabolizable energy. Data were subjected to parametric and nonparametric statistical analyses. Combined analysis of variance revealed significant (P < .05) genotype differences for all assessed traits. Genotype × water regime interaction effects were significant for KY, OC, ash, NC, NDFDM, and ADLDM. Kernel yield positively and significantly (P < .05) correlated with OY (r = .99), LAC (r = .13), ash (r = .32), and NDFDM (r = .54) under DS condition. Haulm yield was positively and significantly (P < .05) correlated with OC (r = .24),NDFDM(r = .19), ADFDM (r = .18), and ADLDM (r = .17) under DS condition. The study identified four genotypes with high kernel and haulm yields, and six genotypes with high oleic acid content. Further, 10 genotypes were selected with relatively better drought tolerance. The selected genotypes are recommended for further breeding and variety release adapted to drought conditions

Exploring the characteristics and most bothersome symptoms in MECP2 duplication syndrome to pave the path toward developing parent-oriented outcome measures

BACKGROUND: MECP2 Duplication Syndrome (MDS), resulting from the duplication of Xq28 region, including MECP2, is a rare disorder with a nascent understanding in clinical features and severity. Studies using antisense oligonucleotides revealed a broad phenotypic rescue in transgenic mice. With human clinical trials on the horizon, there is a need to develop clinical outcome measures for MDS. METHODS: We surveyed caregivers of MDS individuals to explore the frequency and severity of MDS clinical features, and identify the most meaningful symptoms/domains that need to be included in the outcome measure scales. RESULTS: A total of 101 responses were eligible for the survey. The top six most meaningful symptoms to caregivers in descending order included epilepsy, gross motor, fine motor, communication, infection, and constipation problems. Epilepsy was present in 58.4% of the subjects and 75% were drug‐resistant, Furthermore, ~12% required intensive care unit (ICU) admission. Infections were present in 55% of the subjects, and one‐fourth of them required ICU admission. Constipation was present in ~85% of the subjects and one‐third required enemas/suppositories. CONCLUSION: Our study is one of the largest cohorts conducted on MDS individuals characterizing the frequency and severity of MDS symptoms. Additionally, these study results will contribute to establishing a foundation to develop parent‐reported outcomes in MDS

Less Engagement in Pleasure Activities is associated with poorer quality of life for Veterans with Comorbid Post-Deployment Conditions

Objective: The presence of multiple comorbid conditions is common after combat deployment and complicates treatment. A potential treatment approach is to target shared mechanisms across conditions that maintain poorer health-related quality of life (HRQOL). One such mechanism may be decrements in pleasurable activities. Impairment in pleasurable activities frequently occurs after deployment and may be associated with poorer HRQOL. Method: In this brief report, we surveyed 126 Veterans who had previously sought an assessment at a Veterans Affairs post-deployment health clinic and assessed pleasurable activities, HRQOL, and post-deployment health symptoms. Results: Forty-three percent of Veterans met our criteria for all three post-deployment conditions (PTSD, depression and chronic wide-spread physical symptoms). Greater engagement in pleasurable activities was associated with better HRQOL for all Veterans regardless of type or level of post-deployment health symptoms. Conclusion: Future research should study if interventions that encourage Veterans with post-deployment health conditions to engage in pleasurable activities are effective rehabilitation strategies

Hydrodynamic modeling for identifying flood vulnerability zones in lower Damodar river of eastern India

The identification of flood vulnerability zone is very essential to minimize the damage associated with the flood. The present study adopted the Hydrodynamic modeling technique for the identification of flood vulnerability zones in lower Damodar river of eastern India. Preprocessing of data and preparation of various input geometry data (river network, bank line) for the hydrodynamic model were done in an ArcGIS environment with the help of high resolution satellite imagery and field survey. Model was calibrated for the Manning’s coefficient of roughness (n) and validated with ground data and field photographs highresolution, the efficiency of the model was estimated by the index of agreement ‘‘d” which clearly shows good agreement between model data and observed data. Based on the model output flooding hotspots were demarcated. It was observed that areas downstream to the bifurcation point of the Damodar river are more vulnerable to flooding

Status of Adoption of Improved Groundnut Technologies in Odisha State

Paddy is the dominant staple crop in the state of Odisha in India. Groundnut, sesame, mustard and niger are the major oilseed crops cultivated in the state. Groundnut occupied about 34% of total oilseed area and contributed more than 68% of total oilseeds production during the triennium ending (TE) 2017-18. The area under groundnut declined from 318,000 ha to 210,000 ha between TE 1995-96 and 2017-18 (25 years) (Behura et al 2014, Odisha Agricultural Statistics - various issues. https://agriodisha.nic.in/Home/staticstics). It has registered a negative growth rate (cropped area) of 0.93% per annum. Production too declined from 466,000 tons to 374,000 tons during the same period. However, groundnut productivity saw an uptrend from 1465 kg/ha to 1783 kg/ha due to the introduction of modern high-yielding varieties. Figure 1 shows the trends in area, production and productivity of groundnut in the state

Highly Selective End-Tagged Antimicrobial Peptides Derived from PRELP

Background: Antimicrobial peptides (AMPs) are receiving increasing attention due to resistance development against conventional antibiotics. Pseudomonas aeruginosa and Staphylococcus aureus are two major pathogens involved in an array of infections such as ocular infections, cystic fibrosis, wound and post-surgery infections, and sepsis. The goal of the study was to design novel AMPs against these pathogens. Methodology and Principal Findings: Antibacterial activity was determined by radial diffusion, viable count, and minimal inhibitory concentration assays, while toxicity was evaluated by hemolysis and effects on human epithelial cells. Liposome and fluorescence studies provided mechanistic information. Protease sensitivity was evaluated after subjection to human leukocyte elastase, staphylococcal aureolysin and V8 proteinase, as well as P. aeruginosa elastase. Highly active peptides were evaluated in ex vivo skin infection models. C-terminal end-tagging by W and F amino acid residues increased antimicrobial potency of the peptide sequences GRRPRPRPRP and RRPRPRPRP, derived from proline arginine-rich and leucine-rich repeat protein (PRELP). The optimized peptides were antimicrobial against a range of Gram-positive S. aureus and Gram-negative P. aeruginosa clinical isolates, also in the presence of human plasma and blood. Simultaneously, they showed low toxicity against mammalian cells. Particularly W-tagged peptides displayed stability against P. aeruginosa elastase, and S. aureus V8 proteinase and aureolysin, and the peptide RRPRPRPRPWWWW-NH2 was effective against various "superbugs'' including vancomycin-resistant enterococci, multi-drug resistant P. aeruginosa, and methicillin-resistant S. aureus, as well as demonstrated efficiency in an ex vivo skin wound model of S. aureus and P. aeruginosa infection. Conclusions/Significance: Hydrophobic C-terminal end-tagging of the cationic sequence RRPRPRPRP generates highly selective AMPs with potent activity against multiresistant bacteria and efficiency in ex vivo wound infection models. A precise "tuning'' of toxicity and proteolytic stability may be achieved by changing tag-length and adding W-or F-amino acid tags

PHYTOCHEMICALS OF CHRISTIA VESPERTILIONIS LEAF EXTRACT: ANTIOXIDANT, ANTIDIABETIC AND TOXICITY CAPABILITIES

Phytochemicals of Christia vespertilionis plant is known for medicinal properties and used to treat various health problems. The present study revealed medicinal properties of the leaf extract of Christia vespertilionis plant as its total phenolic content derived is screened for their antioxidant, antidiabetic and toxicity properties by Folin-Ciocalteu method, DPPH assay with butylated hydroxytoluene standard, α-amylase inhibition assay with metformin standard, brine shrimp lethality bioassay respectively

A genetic algorithm

Castelli, M., Dondi, R., Manzoni, S., Mauri, G., & Zoppis, I. (2019). Top k 2-clubs in a network: A genetic algorithm. In J. J. Dongarra, J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, R. Lam, V. V. Krzhizhanovskaya, M. H. Lees, ... P. M. A. Sloot (Eds.), Computational Science. ICCS 2019: 19th International Conference, 2019, Proceedings (Vol. 5, pp. 656-663). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11540 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22750-0_63The identification of cohesive communities (dense sub-graphs) is a typical task applied to the analysis of social and biological networks. Different definitions of communities have been adopted for particular occurrences. One of these, the 2-club (dense subgraphs with diameter value at most of length 2) has been revealed of interest for applications and theoretical studies. Unfortunately, the identification of 2-clubs is a computationally intractable problem, and the search of approximate solutions (at a reasonable time) is therefore fundamental in many practical areas. In this article, we present a genetic algorithm based heuristic to compute a collection of Top k 2-clubs, i.e., a set composed by the largest k 2-clubs which cover an input graph. In particular, we discuss some preliminary results for synthetic data obtained by sampling Erdös-Rényi random graphs.authorsversionpublishe

Genotype × Environment Studies on Resistance to Late Leaf Spot and Rust in Genomic Selection Training Population of Peanut (Arachis hypogaea L.)

Foliar fungal diseases especially late leaf spot (LLS) and rust are the important production constraints across the peanut growing regions of the world. A set of 340 diverse peanut genotypes that includes accessions from gene bank of International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), elite breeding lines from the breeding program, and popular cultivars were screened for LLS and rust resistance and yield traits across three locations in India under natural and artificial disease epiphytotic conditions. The study revealed significant variation among the genotypes for LLS and rust resistance at different environments. Combined analysis of variance revealed significant environment (E) and genotype × environment (G×E) interactions for both the diseases indicating differential response of genotypes in different environments. The present study reported 31 genotypes as resistant to LLS and 66 to rust across the locations at 90 DAS with maturity duration 103 to 128 days. Twenty-eight genotypes showed resistance to both the diseases across the locations, of which 19 derived from A. cardenasii, five from A. hypogaea, and four from A. villosa. Site regression and Genotype by Genotype x Environment (GGE) biplot analysis identified eight genotypes as stable for LLS, 24 for rust and 14 for pod yield under disease pressure across the environments. Best performing environment specific genotypes were also identified. Nine genotypes resistant to LLS and rust showed 77% to 120% increase in pod yield over control under disease pressure with acceptable pod and kernel features that can be used as potential parents in LLS and rust resistance breeding. Pod yield increase as a consequence of resistance offered to foliar fungal diseases suggests the possibility of considering ‘foliar fungal disease resistance’ as a must-have trait in all the peanut cultivars that will be released for cultivation in rain fed ecologies in Asia and Africa. The phenotypic data of the present study will be used for designing genomic selection prediction models in peanut

COVID-19 patient health prediction using boosted random forest algorithm

© 2020 Iwendi, Bashir, Peshkar, Sujatha, Chatterjee, Pasupuleti, Mishra, Pillai and Jo. Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature. The Coronavirus disease 2019 (COVID-19) pandemic, which originated in Wuhan China, has had disastrous effects on the global community and has overburdened advanced healthcare systems throughout the world. Globally; over 4,063,525 confirmed cases and 282,244 deaths have been recorded as of 11th May 2020, according to the European Centre for Disease Prevention and Control agency. However, the current rapid and exponential rise in the number of patients has necessitated efficient and quick prediction of the possible outcome of an infected patient for appropriate treatment using AI techniques. This paper proposes a fine-tuned Random Forest model boosted by the AdaBoost algorithm. The model uses the COVID-19 patient’s geographical, travel, health, and demographic data to predict the severity of the case and the possible outcome, recovery, or death. The model has an accuracy of 94% and a F1 Score of 0.86 on the dataset used. The data analysis reveals a positive correlation between patients’ gender and deaths, and also indicates that the majority of patients are aged between 20 and 70 years