Genome-Wide Association Study for Multiple Biotic Stress Resistance in Synthetic Hexaploid Wheat

Genetic resistance against biotic stress is a major goal in many wheat breeding programs. However, modern wheat cultivars have a limited genetic variation for disease and pest resistance and there is always a possibility of the evolution of new diseases and pests to overcome previously identified resistance genes. A total of 125 synthetic hexaploid wheats (SHWs; 2n = 6x = 42, AABBDD, Triticum aestivum L.) were characterized for resistance to fungal pathogens that cause wheat rusts (leaf; Puccinia triticina, stem; P. graminis f.sp. tritici, and stripe; P. striiformis f.sp. tritici) and crown rot (Fusarium spp.); cereal cyst nematode (Heterodera spp.); and Hessian fly (Mayetiola destructor). A wide range of genetic variation was observed among SHWs for multiple (two to five) biotic stresses and 17 SHWs that were resistant to more than two stresses. The genomic regions and potential candidate genes conferring resistance to these biotic stresses were identified from a genome-wide association study (GWAS). This GWAS study identified 124 significant marker-trait associations (MTAs) for multiple biotic stresses and 33 of these were found within genes. Furthermore, 16 of the 33 MTAs present within genes had annotations suggesting their potential role in disease resistance. These results will be valuable for pyramiding novel genes/genomic regions conferring resistance to multiple biotic stresses from SHWs into elite bread wheat cultivars and providing further insights on a wide range of stress resistance in wheat

Unzipping flood vulnerability and functionality loss:tale of struggle for existence of riparian buildings

Floods pose significant risk to riparian buildings as evidenced during many historical events. Although structural resilience to tsunami flooding is well studied in the literature, high-velocity and debris-laden floods in steep terrains are not considered adequately so far. Historical floods in steep terrains necessitate the need for flood vulnerability analysis of buildings. To this end, we report vulnerability of riparian-reinforced concrete buildings using forensic damage interpretations and empirical/analytical vulnerability analyses. Furthermore, we propose the concept and implications of functionality loss due to flooding in residential reinforced concrete (RC) buildings using empirical data. Fragility functions using inundation depth and momentum flux are presented for RC buildings considering a recent flooding event in Nepal. The results show that flow velocity and sediment load, rather than hydrostatic load, govern the damages in riparian RC buildings. However, at larger inundation depth, hydrostatic force alone may collapse some of the RC buildings

The International Natural Product Sciences Taskforce (INPST) and the power of Twitter networking exemplified through #INPST hashtag analysis

Background: The development of digital technologies and the evolution of open innovation approaches have enabled the creation of diverse virtual organizations and enterprises coordinating their activities primarily online. The open innovation platform titled "International Natural Product Sciences Taskforce" (INPST) was established in 2018, to bring together in collaborative environment individuals and organizations interested in natural product scientific research, and to empower their interactions by using digital communication tools. Methods: In this work, we present a general overview of INPST activities and showcase the specific use of Twitter as a powerful networking tool that was used to host a one-week "2021 INPST Twitter Networking Event" (spanning from 31st May 2021 to 6th June 2021) based on the application of the Twitter hashtag #INPST. Results and Conclusion: The use of this hashtag during the networking event period was analyzed with Symplur Signals (https://www.symplur.com/), revealing a total of 6,036 tweets, shared by 686 users, which generated a total of 65,004,773 impressions (views of the respective tweets). This networking event's achieved high visibility and participation rate showcases a convincing example of how this social media platform can be used as a highly effective tool to host virtual Twitter-based international biomedical research events

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

Disease Management and Estimated Effects on DON (Deoxynivalenol) Contamination in \u3ci\u3eFusarium\u3c/i\u3e Infested Barley

Fusarium head blight (FHB or scab) economically devastates barley production. FHB is predominantly caused by Fusarium graminearum and has resulted in major reductions in the quality of barley in the United States. The most common source of economic loss is through development of potent mycotoxins in the grain, the most prominent of which, in the United States, is deoxynivalenol (DON). DON levels can be managed through a variety of techniques. This study presents the estimate of the statistical relationship among DON contamination in barley, FHB incidence and severity, and a variety of disease management techniques. Data from 22 field studies and a survey of barley producers are used to estimate the relationship. Fungicide applications reduce DON in barley in general and via complementary interactions with the barley cultivar. Genetic FHB resistance in barley varieties is an important determinant of DON levels, as well as previous crop and factors related to time and location. Taking care to avoid rotations with FHB host crops immediately prior to barley is also important to reduce DON levels in barley. These become key inputs into barley producer decisions for evaluating the economic value of adopting FHB management techniques

Chest Computed Tomography manifestations of Covid-19 : in relation to duration of symptoms

Purpose: To evaluate the abnormalities on thin-section chest Computed Tomographic (CT) scans in patients with COVID-19 and correlate findings to duration of symptoms.Methods: RT-PCR positive patients were classified according to the time after the onset of the initial symptoms, into stage-1 (0–4 days); stage-2 (5–9 days); stage-3 (10–14 days); stage-4 (15–21 days); stage-5 (22–28 days). Each lung lobe was evaluated for extent affected by ground-glass opacities (GGO), crazy-paving pattern and consolidation, in five categories of percentual severity. Summation of scores from all five lung lobes provided the total CT score (maximal CT score, 25).Results: The predominant patterns of lung abnormalities were ground glass opacities (GGO), crazy-paving pattern, consolidation and curvi-linear opacities. The distribution of pulmonary lesions on CT in COVID-19 pneumonitis patients was mostly peripheral in the stages 1 and 2. With the development of the disease, the lesions gradually spread from the periphery to the center. Most chest CT scans showed bilateral lung involvement during the course of the disease. Conclusion: Thin-section CT could provide semi-quantitative analysis of pulmonary damage severity. This disease changed rapidly at the early stage, then tended to be stable and lasted for a long time

Disease Management and Estimated Effects on DON (Deoxynivalenol) Contamination in Fusarium Infested Barley

Fusarium head blight (FHB or scab) economically devastates barley production. FHB is predominantly caused by Fusarium graminearum and has resulted in major reductions in the quality of barley in the United States. The most common source of economic loss is through development of potent mycotoxins in the grain, the most prominent of which, in the United States, is deoxynivalenol (DON). DON levels can be managed through a variety of techniques. This study presents the estimate of the statistical relationship among DON contamination in barley, FHB incidence and severity, and a variety of disease management techniques. Data from 22 field studies and a survey of barley producers are used to estimate the relationship. Fungicide applications reduce DON in barley in general and via complementary interactions with the barley cultivar. Genetic FHB resistance in barley varieties is an important determinant of DON levels, as well as previous crop and factors related to time and location. Taking care to avoid rotations with FHB host crops immediately prior to barley is also important to reduce DON levels in barley. These become key inputs into barley producer decisions for evaluating the economic value of adopting FHB management techniques

Evaluation of evidence for pharmacokinetics-pharmacodynamics-based dose optimization of antimicrobials for treating Gram-negative infections in neonates

Background & objectives: Neonates present a special subgroup of population in whom optimization of antimicrobial dosing can be particularly challenging. Gram-negative infections are common in neonates, and inpatient treatment along with critical care is needed for the management of these infections. Dosing recommendations are often extrapolated from evidence generated in older patient populations. This systematic review was done to identify the knowledge gaps in the pharmacokinetics-pharmacodynamics (PK-PD)-based optimized dosing schedule for parenteral antimicrobials for Gram-negative neonatal infections. Methods: Relevant research questions were identified. An extensive electronic and manual search methodology was used. Potentially eligible articles were screened for eligibility. The relevant data were extracted independently in a pre-specified data extraction form. Pooling of data was planned. Results: Of the 340 records screened, 24 studies were included for data extraction and incorporation in the review [carbapenems - imipenem and meropenem (n=7); aminoglycosides - amikacin and gentamicin (n=9); piperacillin-tazobactam (n=2); quinolones (n=2); third- and fourth-generation cephalosporins (n=4) and colistin nil]. For each of the drug categories, the information for all the questions that the review sought to answer was incomplete. There was a wide variability in the covariates assessed, and pooling of results could not be undertaken. Interpretation & conclusions: There is a wide knowledge gap for determining the doses of antimicrobials used for Gram-negative infections in neonates. A different profile of newborns in the developing countries could affect the disposition of antimicrobials for Gram negative infections, necessitating the generation of PK-PD data of antimicrobials in neonates from developing countries. Further, guidelines for treatment of neonatal conditions may incorporate the evidence-based PK-PD-guided dosing regimens

A Radical Safety Measure for Identifying Environmental Changes Using Machine Learning Algorithms

Due to air pollution, pollutants that harm humans and other species, as well as the environment and natural resources, can be detected in the atmosphere. In real-world applications, the following impurities that are caused due to smog, nicotine, bacteria, yeast, biogas, and carbon dioxide occur uninterruptedly and give rise to unavoidable pollutants. Weather, transportation, and the combustion of fossil fuels are all factors that contribute to air pollution. Uncontrolled fire in parts of grasslands and unmanaged construction projects are two factors that contribute to air pollution. The challenge of assessing contaminated air is critical. Machine learning algorithms are used to forecast the surroundings if any pollution level exceeds the corresponding limit. As a result, in the proposed method air pollution levels are predicted using a machine learning technique where a computer-aided procedure is employed in the process of developing technological aspects to estimate harmful element levels with 99.99% accuracy. Some of the models used to enhance forecasts are Mean Square Error (MSE), Coefficient of Determination Error (CDE), and R Square Error (RSE)

Prevention of Cyber Security with the Internet of Things Using Particle Swarm Optimization

High security for physical items such as intelligent machinery and residential appliances is provided via the Internet of Things (IoT). The physical objects are given a distinct online address known as the Internet Protocol to communicate with the network’s external foreign entities through the Internet (IP). IoT devices are in danger of security issues due to the surge in hacker attacks during Internet data exchange. If such strong attacks are to create a reliable security system, attack detection is essential. Attacks and abnormalities such as user-to-root (U2R), denial-of-service, and data-type probing could have an impact on an IoT system. This article examines various performance-based AI models to predict attacks and problems with IoT devices with accuracy. Particle Swarm Optimization (PSO), genetic algorithms, and ant colony optimization were used to demonstrate the effectiveness of the suggested technique concerning four different parameters. The results of the proposed method employing PSO outperformed those of the existing systems by roughly 73 percent