High-throughput diagnostic markers for foliar fungal disease resistance and high oleic acid content in groundnut.

BACKGROUND: Foliar diseases namely late leaf spot (LLS) and leaf rust (LR) reduce yield and deteriorate fodder quality in groundnut. Also the high oleic acid content has emerged as one of the most important traits for industries and consumers due to its increased shelf life and health benefits. RESULTS: Genetic mapping combined with pooled sequencing approaches identified candidate resistance genes (LLSR1 and LLSR2 for LLS and LR1 for LR) for both foliar fungal diseases. The LLS-A02 locus housed LLSR1 gene for LLS resistance, while, LLS-A03 housed LLSR2 and LR1 genes for LLS and LR resistance, respectively. A total of 49 KASPs markers were developed from the genomic regions of important disease resistance genes, such as NBS-LRR, purple acid phosphatase, pentatricopeptide repeat-containing protein, and serine/threonine-protein phosphatase. Among the 49 KASP markers, 41 KASPs were validated successfully on a validation panel of contrasting germplasm and breeding lines. Of the 41 validated KASPs, 39 KASPs were designed for rust and LLS resistance, while two KASPs were developed using fatty acid desaturase (FAD) genes to control high oleic acid levels. These validated KASP markers have been extensively used by various groundnut breeding programs across the world which led to development of thousands of advanced breeding lines and few of them also released for commercial cultivation. CONCLUSION: In this study, high-throughput and cost-effective KASP assays were developed, validated and successfully deployed to improve the resistance against foliar fungal diseases and oleic acid in groundnut. So far deployment of allele-specific and KASP diagnostic markers facilitated development and release of two rust- and LLS-resistant varieties and five high-oleic acid groundnut varieties in India. These validated markers provide opportunities for routine deployment in groundnut breeding programs.

Aspergillus flavus pangenome (AflaPan) uncovers novel aflatoxin and secondary metabolite associated gene clusters.

BACKGROUND: Aspergillus flavus is an important agricultural and food safety threat due to its production of carcinogenic aflatoxins. It has high level of genetic diversity that is adapted to various environments. Recently, we reported two reference genomes of A. flavus isolates, AF13 (MAT1-2 and highly aflatoxigenic isolate) and NRRL3357 (MAT1-1 and moderate aflatoxin producer). Where, an insertion of 310 kb in AF13 included an aflatoxin producing gene bZIP transcription factor, named atfC. Observations of significant genomic variants between these isolates of contrasting phenotypes prompted an investigation into variation among other agricultural isolates of A. flavus with the goal of discovering novel genes potentially associated with aflatoxin production regulation. Present study was designed with three main objectives: (1) collection of large number of A. flavus isolates from diverse sources including maize plants and field soils; (2) whole genome sequencing of collected isolates and development of a pangenome; and (3) pangenome-wide association study (Pan-GWAS) to identify novel secondary metabolite cluster genes. RESULTS: Pangenome analysis of 346 A. flavus isolates identified a total of 17,855 unique orthologous gene clusters, with mere 41% (7,315) core genes and 59% (10,540) accessory genes indicating accumulation of high genomic diversity during domestication. 5,994 orthologous gene clusters in accessory genome not annotated in either the A. flavus AF13 or NRRL3357 reference genomes. Pan-genome wide association analysis of the genomic variations identified 391 significant associated pan-genes associated with aflatoxin production. Interestingly, most of the significantly associated pan-genes (94%; 369 associations) belonged to accessory genome indicating that genome expansion has resulted in the incorporation of new genes associated with aflatoxin and other secondary metabolites. CONCLUSION: In summary, this study provides complete pangenome framework for the species of Aspergillus flavus along with associated genes for pathogen survival and aflatoxin production. The large accessory genome indicated large genome diversity in the species A. flavus, however AflaPan is a closed pangenome represents optimum diversity of species A. flavus. Most importantly, the newly identified aflatoxin producing gene clusters will be a new source for seeking aflatoxin mitigation strategies and needs new attention in research.

Celebrating Professor Rajeev K. Varshney's transformative research odyssey from genomics to the field on his induction as Fellow of the Royal Society.

Professor Rajeev K. Varshney's transformative impact on crop genomics, genetics, and agriculture is the result of his passion, dedication, and unyielding commitment to harnessing the potential of genomics to address the most pressing challenges faced by the global agricultural community. Starting from a small town in India and reaching the global stage, Professor Varshney's academic and professional trajectory has inspired many scientists active in research today. His ground-breaking work, especially his effort to list orphan tropical crops to genomic resource-rich entities, has been transformative. Beyond his scientific achievements, Professor Varshney is recognized by his colleagues as an exemplary mentor, fostering the growth of future researchers, building institutional capacity, and strengthening scientific capability. His focus on translational genomics and strengthening seed system in developing countries for the improvement of agriculture has made a tangible impact on farmers' lives. His skills have been best utilized in roles at leading research centres where he has applied his expertise to deliver a new vision for crop improvement. These efforts have now been recognized by the Royal Society with the award of the Fellowship (FRS). As we mark this significant milestone in his career, we not only celebrate Professor Varshney's accomplishments but also his wider contributions that continue to transform the agricultural landscape.

Designing future peanut: the power of genomics-assisted breeding.

KEY MESSAGE: Integrating GAB methods with high-throughput phenotyping, genome editing, and speed breeding hold great potential in designing future smart peanut cultivars to meet market and food supply demands. Cultivated peanut (Arachis hypogaea L.), a legume crop greatly valued for its nourishing food, cooking oil, and fodder, is extensively grown worldwide. Despite decades of classical breeding efforts, the actual on-farm yield of peanut remains below its potential productivity due to the complicated interplay of genotype, environment, and management factors, as well as their intricate interactions. Integrating modern genomics tools into crop breeding is necessary to fast-track breeding efficiency and rapid progress. When combined with speed breeding methods, this integration can substantially accelerate the breeding process, leading to faster access of improved varieties to farmers. Availability of high-quality reference genomes for wild diploid progenitors and cultivated peanuts has accelerated the process of gene/quantitative locus discovery, developing markers and genotyping assays as well as a few molecular breeding products with improved resistance and oil quality. The use of new breeding tools, e.g., genomic selection, haplotype-based breeding, speed breeding, high-throughput phenotyping, and genome editing, is probable to boost genetic gains in peanut. Moreover, renewed attention to efficient selection and exploitation of targeted genetic resources is also needed to design high-quality and high-yielding peanut cultivars with main adaptation attributes. In this context, the combination of genomics-assisted breeding (GAB), genome editing, and speed breeding hold great potential in designing future improved peanut cultivars to meet market and food supply demands.

High-density bin-based genetic map reveals a 530-kb chromosome segment derived from wild peanut contributing to late leaf spot resistance.

KEY MESSAGE: Twenty-eight QTLs for LLS disease resistance were identified using an amphidiploid constructed mapping population, a favorable 530-kb chromosome segment derived from wild species contributes to the LLS resistance. Late leaf spot (LLS) is one of the major foliar diseases of peanut, causing serious yield loss and affecting the quality of kernel and forage. Some wild Arachis species possess higher resistance to LLS as compared with cultivated peanut; however, ploidy level differences restrict utilization of wild species. In this study, a synthetic amphidiploid (Ipadur) of wild peanuts with high LLS resistance was used to cross with Tifrunner to construct TI population. In total, 200 recombinant inbred lines were collected for whole-genome resequencing. A high-density bin-based genetic linkage map was constructed, which includes 4,809 bin markers with an average inter-bin distance of 0.43 cM. The recombination across cultivated and wild species was unevenly distributed, providing a novel recombination landscape for cultivated-wild Arachis species. Using phenotyping data collected across three environments, 28 QTLs for LLS disease resistance were identified, explaining 4.35-20.42% of phenotypic variation. The major QTL located on chromosome 14, qLLS14.1, could be consistently detected in 2021 Jiyang and 2022 Henan with 20.42% and 12.12% PVE, respectively. A favorable 530-kb chromosome segment derived from Ipadur was identified in the region of qLLS14.1, in which 23 disease resistance proteins were located and six of them showed significant sequence variations between Tifrunner and Ipadur. Allelic variation analysis indicating the 530-kb segment of wild species might contribute to the disease resistance of LLS. These associate genomic regions and candidate resistance genes are of great significance for peanut breeding programs for bringing durable resistance through pyramiding such multiple LLS resistance loci into peanut cultivars.

Linkage Mapping and Genome-Wide Association Study Identified Two Peanut Late Leaf Spot Resistance Loci, PLLSR-1 and PLLSR-2, Using Nested Association Mapping.

Identification of candidate genes and molecular markers for late leaf spot (LLS) disease resistance in peanut (Arachis hypogaea) has been a focus of molecular breeding for the U.S. industry-funded peanut genome project. Efforts have been hindered by limited mapping resolution due to low levels of genetic recombination and marker density available in traditional biparental mapping populations. To address this, a multi-parental nested association mapping population has been genotyped with the peanut 58K single-nucleotide polymorphism (SNP) array and phenotyped for LLS severity in the field for 3 years. Joint linkage-based quantitative trait locus (QTL) mapping identified nine QTLs for LLS resistance with significant phenotypic variance explained up to 47.7%. A genome-wide association study identified 13 SNPs consistently associated with LLS resistance. Two genomic regions harboring the consistent QTLs and SNPs were identified from 1,336 to 1,520 kb (184 kb) on chromosome B02 and from 1,026.9 to 1,793.2 kb (767 kb) on chromosome B03, designated as peanut LLS resistance loci, PLLSR-1 and PLLSR-2, respectively. PLLSR-1 contains 10 nucleotide-binding site leucine-rich repeat disease resistance genes. A nucleotide-binding site leucine-rich repeat disease resistance gene, Arahy.VKVT6A, was also identified on homoeologous chromosome A02. PLLSR-2 contains five significant SNPs associated with five different genes encoding callose synthase, pollen defective in guidance protein, pentatricopeptide repeat, acyl-activating enzyme, and C2 GRAM domains-containing protein. This study highlights the power of multi-parent populations such as nested association mapping for genetic mapping and marker-trait association studies in peanuts. Validation of these two LLS resistance loci will be needed for marker-assisted breeding.

Flood hazard potential evaluation using decision tree state-of-the-art models.

Floods occur frequently in Romania and throughout the world and are one of the most devastating natural disasters that impact people's lives. Therefore, in order to reduce the potential damages, an accurate identification of surfaces susceptible to flood phenomena is mandatory. In this regard, the quantitative calculation of flood susceptibility has become a very popular practice in the scientific research. With the development of modern computerized methods such as geographic information system and machine learning models, and as a result of the possibility of combining them, the determination of areas susceptible to floods has become increasingly accurate, and the algorithms used are increasingly varied. Some of the most used and highly accurate machine learning algorithms are the decision tree models. Therefore, in the present study focusing on flood susceptibility zonation mapping in the Trotus River basin, the following algorithms were applied: forest by penalizing attribute-weights of evidence (forest-PA-WOE), best first decision tree-WOE, alternating decision tree-WOE, and logistic regression-WOE. The best performant, characterized by a maximum accuracy of 0.981, proved to be forest-PA-WOE, whereas in terms of flood exposure, an area of over 16.22% of the Trotus basin is exposed to high and very high floods susceptibility. The performances applied models in the present work are higher than the models applied in the previous studies in the same study area. Moreover, it should be noted that the accuracy of the models is similar with the accuracies of the decision tree models achieved in the studies focused on other areas across the world. Therefore, we can state that the models applied in the present research can be successfully used in by the researchers in other case studies. The findings of this research may substantially map the flood risk areas and further aid watershed managers in limiting and remediating flood damage in the data-scarce regions. Moreover, the results of this study can be a very useful for the hazard management and planning authorities.

Crop type discrimination using Geo-Stat Endmember Extraction and machine learning algorithms.

The identification of crop diversity in today's world is very crucial to ensure adaptation of the crop with changing climate for better productivity as well as food security. Towards this, Hyperspectral Remote Sensing (HRS) is an efficient technique based on imaging spectroscopy that offers the opportunity to discriminate crop types based on morphological as well as physiological features due to availability of contiguous spectral bands. The current work utilized the benefits of Airborne Visible Infrared Imaging spectrometer- New Generation (AVIRIS-NG) data and explored the techniques for classification and identification of crop types. The endmembers were identified using the Geo-Stat Endmember Extraction (GSEE) algorithm for pure pixels identification and to generate the spectral library of the different crop types. Spectral feature comparison was done among AVIRIS-NG, Analytical Spectral Device (ASD)-Spectroradiometer and Continuum Removed (CR) spectra. The best-fit spectra obtained with the Reference ASD-Spectroradiometer and Pure Pixel spectral library were then used for crop discrimination using the ten supervised classifiers namely Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), Support Vector Machine (SVM), Minimum Distance Classifier (MDC), Binary Encoding, deep learning-based Convolution Neural Network (CNN) and different algorithms of Ensemble learning such as Tree Bag, AdaBoost (Adaptive Boosting), Discriminant and RUSBoost (Random Under Sampling). In total, nine crop types were identified, namely, wheat, maize, tobacco, sorghum, linseed, castor, pigeon pea, fennel and chickpea. The performance evaluation of the classifiers was made using various metrics like Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score. The classifier 2D-CNN was found to be the best with Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score values of 89.065 %, 0.871,87.565%, 89.541% and 88.678% respectively. The output of this work can be utilized for large scale mapping of crop types at the species level in a short interval of time of a large area with high accuracy.

Impact of climate change on future flood susceptibility projections under shared socioeconomic pathway scenarios in South Asia using artificial intelligence algorithms.

This study investigated the impact of climate change on flood susceptibility in six South Asian countries Afghanistan, Bangladesh, Bhutan, Bharat (India), Nepal, and Pakistan-under two distinct Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6 and SSP5-5.8, for 2041-2060 and 2081-2100. To predict flood susceptibility, we employed three artificial intelligence (AI) algorithms: the K-nearest neighbor (KNN), conditional inference random forest (CIRF), and regularized random forest (RRF). Predictions were based on data from 2452 historical flood events, alongside climatic variables measured over monthly, seasonal, and annual timeframes. The innovative aspect of this research is the emphasis on using climatic variables across these progressively condensed timeframes, specifically addressing eight precipitation factors. The performance evaluation, employing the area under the receiver operating characteristic curve (AUC) metric, identified the RRF model as the most accurate, with the highest AUC of 0.94 during the testing phase, followed by the CIRF (AUC = 0.91) and the KNN (AUC = 0.86). An analysis of variable importance highlighted the substantial role of certain climatic factors, namely precipitation in the warmest quarter, annual precipitation, and precipitation during the wettest month, in the modeling of flood susceptibility in South Asia. The resultant flood susceptibility maps demonstrated the influence of climate change scenarios on susceptibility classifications, signalling a dynamic landscape of flood-prone areas over time. The findings revealed variable trends under different climate change scenarios and periods, with marked differences in the percentage of areas classified as having high and very high flood susceptibility. Overall, this study advances our understanding of how climate change affects flood susceptibility in South Asia and offers an essential tool for assessing and managing flood risks in the region.

Seeing from space makes sense: Novel earth observation variables accurately map species distributions over Himalaya.

Topical advances in earth observation have enabled spatially explicit mapping of species' fundamental niche limits that can be used for nature conservation and management applications. This study investigates the possibility of applying functional variables of ecosystem retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard sensor data to map the species distribution of two alpine treeline species, namely Betula utilis D.Don and Rhododendron campanulatum D.Don over the Himalayan biodiversity hotspot. In this study, we have developed forty-nine Novel Earth Observation Variables (NEOVs) from MODIS products, an asset to the present investigation. To determine the effectiveness and ecological significance of NEOVs combinations, we built and compared four different models, namely, a bioclimatic model (BCM) with bioclimatic predictor variables, a phenology model (PhenoM) with earth observation derived phenological predictor variables, a biophysical model (BiophyM) with earth observation derived biophysical predictor variables, and a hybrid model (HM) with a combination of selected predictor variables from BCM, PhenoM, and BiophyM. All models utilized topographical variables by default. Models that include NEOVs were competitive for focal species, and models without NEOVs had considerably poor model performance and explanatory strength. To ascertain the accurate predictions, we assessed the congruence of predictions by pairwise comparisons of their performance. Among the three machine learning algorithms tested (artificial neural networks, generalised boosting model, and maximum entropy), maximum entropy produced the most promising predictions for BCM, PhenoM, BiophyM, and HM. Area under curve (AUC) and true skill statistic (TSS) scores for the BCM, PhenoM, BiophyM, and HM models derived from maximum entropy were AUC ≥0.9 and TSS ≥0.6 for the focal species. The overall investigation revealed the competency of NEOVs in the accurate prediction of species' fundamental niches, but conventional bioclimatic variables were unable to achieve such a level of precision. A principal component analysis of environmental spaces disclosed that niches of focal species substantially overlapped each other. We demonstrate that the use of satellite onboard sensors' biotic and abiotic variables with species occurrence data can provide precision and resolution for species distribution mapping at a scale that is relevant ecologically and at the operational scale of most conservation and management actions.

Nitrogen dioxide as proxy indicator of air pollution from fossil fuel burning in New Delhi during lockdown phases of COVID-19 pandemic period: impact on weather as revealed by Sentinel-5 precursor (5p) spectrometer sensor.

There has been a long-lasting impact of the lockdown imposed due to COVID-19 on several fronts. One such front is climate which has seen several implications. The consequences of climate change owing to this lockdown need to be explored taking into consideration various climatic indicators. Further impact on a local and global level would help the policymakers in drafting effective rules for handling challenges of climate change. For in-depth understanding, a temporal study is being conducted in a phased manner in the New Delhi region taking NO2 concentration and utilizing statistical methods to elaborate the quality of air during the lockdown and compared with a pre-lockdown period. In situ mean values of the NO2 concentration were taken for four different dates, viz. 4th February, 4th March, 4th April, and 25th April 2020. These concentrations were then compared with the Sentinel (5p) data across 36 locations in New Delhi which are found to be promising. The results indicated that the air quality has been improved maximum in Eastern Delhi and the NO2 concentrations were reduced by one-fourth than the pre-lockdown period, and thus, reduced activities due to lockdown have had a significant impact. The result also indicates the preciseness of Sentinel (5p) for NO2 concentrations.

Mechanically Ventilated Patients Shed High-Titer Live Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) for Extended Periods From Both the Upper and Lower Respiratory Tract.

BACKGROUND: SARS-CoV-2 infection can lead to severe acute respiratory distress syndrome needing intensive care admission and may lead to death. As a virus that transmits by respiratory droplets and aerosols, determining the duration of viable virus shedding from the respiratory tract is critical for patient prognosis, and informs infection-control measures both within healthcare settings and the public domain. METHODS: We prospectively examined upper and lower airway respiratory secretions for both viral RNA and infectious virions in mechanically ventilated patients admitted to the intensive care unit (ICU) of the University Hospital of Wales. Samples were taken from the oral cavity (saliva), oropharynx (subglottic aspirate), or lower respiratory tract (nondirected bronchoalveolar lavage [NBAL] or bronchoalveolar lavage [BAL]) and analyzed by both quantitative PCR (qPCR) and plaque assay. RESULTS: 117 samples were obtained from 25 patients. qPCR showed extremely high rates of positivity across all sample types; however, live virus was far more common in saliva (68%) than in BAL/NBAL (32%). Average titers of live virus were higher in subglottic aspirates (4.5 × 107) than in saliva (2.2 × 106) or BAL/NBAL (8.5 × 106) and reached >108 PFU/mL in some samples. The longest duration of shedding was 98 days, while most patients (14/25) shed live virus for ≥20 days. CONCLUSIONS: ICU patients infected with SARS-CoV-2 can shed high titers of virus both in the upper and lower respiratory tract and tend to be prolonged shedders. This information is important for decision making around cohorting patients, de-escalation of personal protective equipment, and undertaking potential aerosol-generating procedures.

Comprehensive evaluation of Chinese peanut mini-mini core collection and QTL mapping for aflatoxin resistance.

BACKGROUND: Aflatoxin contamination caused by Aspergillus fungi has been a serious factor affecting food safety of peanut (Arachis hypogaea L.) because aflatoxins are highly harmful for human and animal health. As three mechanisms of resistance to aflatoxin in peanut including shell infection resistance, seed infection resistance and aflatoxin production resistance exist among naturally evolved germplasm stocks, it is highly crucial to pyramid these three resistances for promoting peanut industry development and protecting consumers' health. However, less research effort has been made yet to investigate the differentiation and genetic relationship among the three resistances in diversified peanut germplasm collections. RESULTS: In this study, the Chinese peanut mini-mini core collection selected from a large basic collection was systematically evaluated for the three resistances against A. flavus for the first time. The research revealed a wide variation among the diversified peanut accessions for all the three resistances. Totally, 14 resistant accessions were identified, including three with shell infection resistance, seven with seed infection resistance and five with aflatoxin production resistance. A special accession, Zh.h1312, was identified with both seed infection and aflatoxin production resistance. Among the five botanic types of A. hypogaea, the var. vulgaris (Spanish type) belonging to subspecies fastigiata is the only one which possessed all the three resistances. There was no close correlation between shell infection resistance and other two resistances, while there was a significant positive correlation between seed infection and toxin production resistance. All the three resistances had a significant negative correlation with pod or seed size. A total of 16 SNPs/InDels associated with the three resistances were identified through genome-wide association study (GWAS). Through comparative analysis, Zh.h1312 with seed infection resistance and aflatoxin production resistance was also revealed to possess all the resistance alleles of associated loci for seed infection index and aflatoxin content. CONCLUSIONS: This study provided the first comprehensive understanding of differentiation of aflatoxin resistance in diversified peanut germplasm collection, and would further contribute to the genetic enhancement for resistance to aflatoxin contamination.

BSA­seq and genetic mapping identified candidate genes for branching habit in peanut.

KEY MESSAGE: The candidate gene AhLBA1 controlling lateral branch angel of peanut was fine-mapped to a 136.65-kb physical region on chromosome 15 using the BSA-seq and QTL mapping. Lateral branch angel (LBA) is an important plant architecture trait of peanut, which plays key role in lodging, peg soil penetration and pod yield. However, there are few reports of fine mapping and quantitative trait loci (QTLs)/cloned genes for LBA in peanut. In this project, a mapping population was constructed using a spreading variety Tifrunner and the erect variety Fuhuasheng. Through bulked segregant analysis sequencing (BSA-seq), a major gene related to LBA, named as AhLBA1, was preliminarily mapped at the region of Chr.15: 150-160 Mb. Then, using traditional QTL approach, AhLBA1 was narrowed to a 1.12 cM region, corresponding to a 136.65-kb physical interval of the reference genome. Of the nine genes housed in this region, three of them were involved in hormone metabolism and regulation, including one "F-box protein" and two "2-oxoglutarate (2OG) and Fe(II)-dependent oxygenase (2OG oxygenase)" encoding genes. In addition, we found that the level of some classes of cytokinin (CK), auxin and ethylene showed significant differences between spreading and erect peanuts at the junction of main stem and lateral branch. These findings will aid further elucidation of the genetic mechanism of LBA in peanut and facilitating marker-assisted selection (MAS) in the future breeding program.

Comparative incidence of early and late bloodstream and respiratory tract co-infection in patients admitted to ICU with COVID-19 pneumonia versus Influenza A or B pneumonia versus no viral pneumonia: wales multicentre ICU cohort study.

OBJECTIVE: The aim is to characterise early and late respiratory and bloodstream co-infection in patients admitted to intensive care units (ICUs) with SARS-CoV-2-related acute hypoxemic respiratory failure (AHRF) needing respiratory support in seven ICUs within Wales, during the first wave of the COVID-19 pandemic. We compare the rate of positivity of different secondary pathogens and their antimicrobial sensitivity in three different patient groups: patients admitted to ICU with COVID-19 pneumonia, Influenza A or B pneumonia, and patients without viral pneumonia. DESIGN: Multicentre, retrospective, observational cohort study with rapid microbiology data from Public Health Wales, sharing of clinical and demographic data from seven participating ICUs. SETTING: Seven Welsh ICUs participated between 10 March and 31 July 2020. Clinical and demographic data for COVID-19 disease were shared by each participating centres, and microbiology data were extracted from a data repository within Public Health Wales. Comparative data were taken from a cohort of patients without viral pneumonia admitted to ICU during the same period as the COVID-19 cohort (referred to as no viral pneumonia or 'no viral' group), and to a retrospective non-matched cohort of consecutive patients with Influenza A or B admitted to ICUs from 20 November 2017. The comparative data for Influenza pneumonia and no viral pneumonia were taken from one of the seven participating ICUs. PARTICIPANTS: A total of 299 consecutive patients admitted to ICUs with COVID-19 pneumonia were compared with 173 and 48 patients admitted with no viral pneumonia or Influenza A or B pneumonia, respectively. MAIN OUTCOME MEASURES: Primary outcome was to calculate comparative incidence of early and late co-infection in patients admitted to ICU with COVID-19, Influenza A or B pneumonia and no viral pneumonia. Secondary outcome was to calculate the individual group of early and late co-infection rate on a per-patient and per-sample basis, with their antimicrobial susceptibility and thirdly to ascertain any statistical correlation between clinical and demographic variables with rate of acquiring co-infection following ICU admission. RESULTS: A total of 299 adults (median age 57, M/F 2:1) were included in the COVID-19 ICU cohort. The incidence of respiratory and bloodstream co-infection was 40.5% and 15.1%, respectively. Staphylococcus aureus was the predominant bacterial pathogen within the first 48 h. Gram-negative organisms from Enterobacterales group were predominantly seen after 48 h in COVID-19 cohort. Comparative no viral pneumonia cohort had lower rates of respiratory tract infection and bloodstream infection. The influenza cohort had similar rates respiratory tract infection and bloodstream infection. Mortality in all three groups was similar, and no clinical or demographic variables were found to increase the rate of co-infection and ICU mortality. CONCLUSIONS: Higher incidence of bacterial co-infection was found in COVID-19 cohort as compared to the no viral pneumonia cohort admitted to ICUs for respiratory support.

De novo full length transcriptome analysis of Arachis glabrata provides insights into gene expression dynamics in response to biotic and abiotic stresses.

The perennial ornamental peanut Arachis glabrata represents one of the most adaptable wild Arachis species. This study used PacBio combined with BGISEQ-500 RNA-seq technology to study the transcriptome and gene expression dynamics of A. glabrata. Of the total 109,747 unique transcripts obtained, >90,566 transcripts showed significant homology to known proteins and contained the complete coding sequence (CDS). RNA-seq revealed that 1229, 1039, 1671, 3923, 1521 and 1799 transcripts expressed specifically in the root, stem, leaf, flower, peg and pod, respectively. We also identified thousands of differentially expressed transcripts in response to drought, salt, cold and leaf spot disease. Furthermore, we identified 30 polyphenol oxidase encoding genes associated with the quality of forage, making A. glabrata suitable as a forage crop. Our findings presented the first transcriptome study of A. glabrata which will facilitate genetic and genomics studies and lays the groundwork for a deeper understanding of the A. glabrata genome.

A National Strategy to Diagnose Coronavirus Disease 2019-Associated Invasive Fungal Disease in the Intensive Care Unit.

BACKGROUND: Fungal coinfection is a recognized complication of respiratory virus infections, increasing morbidity and mortality, but can be readily treated if diagnosed early. An increasing number of small studies describing aspergillosis in coronavirus disease 2019 (COVID-19) patients with severe respiratory distress are being reported, but comprehensive data are lacking. The aim of this study was to determine the incidence, risk factors, and impact of invasive fungal disease in adult COVID-19 patients with severe respiratory distress. METHODS: An evaluation of a national, multicenter, prospective cohort evaluation of an enhanced testing strategy to diagnose invasive fungal disease in COVID-19 intensive care patients. Results were used to generate a mechanism to define aspergillosis in future COVID-19 patients. RESULTS: One-hundred and thirty-five adults (median age: 57, M/F: 2.2/1) were screened. The incidence was 26.7% (14.1% aspergillosis, 12.6% yeast infections). The overall mortality rate was 38%; 53% and 31% in patients with and without fungal disease, respectively (P = .0387). The mortality rate was reduced by the use of antifungal therapy (mortality: 38.5% in patients receiving therapy vs 90% in patients not receiving therapy (P = .008). The use of corticosteroids (P = .007) and history of chronic respiratory disease (P = .05) increased the likelihood of aspergillosis. CONCLUSIONS: Fungal disease occurs frequently in critically ill, mechanically ventilated COVID-19 patients. The survival benefit observed in patients receiving antifungal therapy implies that the proposed diagnostic and defining criteria are appropriate. Screening using a strategic diagnostic approach and antifungal prophylaxis of patients with risk factors will likely enhance the management of COVID-19 patients.

Extracorporeal membrane oxygenation in a patient with newly diagnosed acute myeloblastic leukaemia presenting with severe respiratory failure.

Veno-venous extracorporeal membrane oxygenation (ECMO) is typically instituted in severe respiratory failure, defined by Lung Injury Score, and caused either by pulmonary or extra-pulmonary reversible disease processes. These processes will have led to acute worsening of oxygenation and/or respiratory acidosis together with an inability to provide safe, lung protective, mechanical ventilation. Patients with underlying chronic immunosuppression or haematological malignancies treated with ECMO for severe respiratory failure have poor short- and long-term functional and survival outcomes. Consequently, in many centres, a diagnosis of haematological malignancy is considered a contraindication to provision of ECMO support for severe respiratory failure. We present a case of a 51-year-old female who attended her local hospital with symptoms suggestive of community-acquired pneumonia. Within a few days, there was progression to severe respiratory failure, initially managed with invasive mechanical ventilation but rapidly deteriorating respiratory failure triggered referral for ECMO support. Initial investigations on ECMO demonstrated features of acute myeloblastic leukaemia with a superimposed community-acquired pneumonia. This was successfully managed with supportive treatment alongside mechanical respiratory therapy and targeted chemotherapy, achieving complete remission and full functional recovery.

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors.

There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network-Frequency Ratio (DLNN-FR), Deep Learning Neural Network-Weights of Evidence (DLNN-WOE), Alternating Decision Trees-Frequency Ratio (ADT-FR) and Alternating Decision Trees-Weights of Evidence (ADT-WOE). The model's performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPIDLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.

Comparative Transcriptome Analysis Identified Candidate Genes for Late Leaf Spot Resistance and Cause of Defoliation in Groundnut.

Late leaf spot (LLS) caused by fungus Nothopassalora personata in groundnut is responsible for up to 50% yield loss. To dissect the complex nature of LLS resistance, comparative transcriptome analysis was performed using resistant (GPBD 4), susceptible (TAG 24) and a resistant introgression line (ICGV 13208) and identified a total of 12,164 and 9954 DEGs (differentially expressed genes) respectively in A- and B-subgenomes of tetraploid groundnut. There were 135 and 136 unique pathways triggered in A- and B-subgenomes, respectively, upon N. personata infection. Highly upregulated putative disease resistance genes, an RPP-13 like (Aradu.P20JR) and a NBS-LRR (Aradu.Z87JB) were identified on chromosome A02 and A03, respectively, for LLS resistance. Mildew resistance Locus (MLOs)-like proteins, heavy metal transport proteins, and ubiquitin protein ligase showed trend of upregulation in susceptible genotypes, while tetratricopeptide repeats (TPR), pentatricopeptide repeat (PPR), chitinases, glutathione S-transferases, purple acid phosphatases showed upregulation in resistant genotypes. However, the highly expressed ethylene responsive factor (ERF) and ethylene responsive nuclear protein (ERF2), and early responsive dehydration gene (ERD) might be related to the possible causes of defoliation in susceptible genotypes. The identified disease resistance genes can be deployed in genomics-assisted breeding for development of LLS resistant cultivars to reduce the yield loss in groundnut.