Retrospective analysis of necropsy findings in patients of H1N1 and their correlation to clinical features

India reported its first case of H1N1 in July 2009 in Pune and since then, the number of reported cases and deaths exploded in India. Since very little data is available about histopathological findings in patients of H1N1 fatal cases in India, a retrospective chart analysis of necropsy findings of 15 cases of 2009 H1N1 fatal cases was performed. Common clinical features were fever, cough , and breathlessness followed by sore throat and rhinorrhea. Common lung findings were mononuclear cell infiltration, thick alveolar septae, intraalveolar hemorrhage . The other findings were congested pulmonary blood vessels, pulmonary edema, cytomegaly, fibrin accumulation and formation of eosinophilic membrane. These findings are suggestive of diffuse alveolar damage ( DAD) and DAD with hemorrhage. All patients who underwent necropsy had radiographic findings suggestive of unilobar or multilobar pneumonia. This clinical finding can be correlated pathologically in these patients as all of them had either polymorphonuclear or mononuclear infiltrate. Furthermore, necrotizing pneumonitis pattern seen on these patients is the likely cause of mortality in these patients. Although clinical ARDS pattern was noted in all these patients, it was well correlated in lung pathology in all these cases

The Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX): overview and preliminary results

While the demand for enhancing rainfall through cloud seeding is strong and persistent in the country, considerable uncertainty exists on the success of such an endeavour at a given location. To understand the pathways of aerosol-cloud interaction through which this might be achieved, a national experiment named Cloud Aerosol Interaction and Precipitation Enhancement EXperiment (CAIPEEX) in two phases, was carried out. The rationale of CAIPEEX, the strategy for conducting the experiment, data quality and potential for path-breaking science are described in this article. Pending completion of quality control and calibration of the CAIPEEX phase-II data, here we present some initial results of CAIPEEX phase-I aimed at documenting the prevailing microphysical characteristics of aerosols and clouds and associated environmental conditions over different regions of the country and under different monsoon conditions with the help of an instrumented research aircraft. First-time simultaneous observations of aerosol, cloud condensation nuclei (CCN) and cloud droplet number concentration (CDNC) over the Ganges Valley during monsoon season show very high concentrations (> 1000 cm-3) of CCN at elevated layers. Observations of elevated layers with high aerosol concentration over the Gangetic valley extending up to 6 km and relatively less aerosol concentration in the boundary layer are also documented. We also present evidence of strong cloud- aerosol interaction in the moist environments with an increase in the cloud droplet effective radius. Our observations also show that pollution increases CDNC and the warm rain depth, and delays its initiation. The critical effective radius for warm rain initiation is found to be between 10 and 12 µm in the polluted clouds and it is between 12 and 14 µm in cleaner monsoon clouds

Application of Machine Learning Techniques to Parameter Selection for Flight Risk Identification

In recent years, the use of data mining and machine learning techniques for safety analysis, incident and accident investigation, and fault detection has gained traction among the aviation community. Flight data collected from recording devices contains a large number of heterogeneous parameters, sometimes reaching up to thousands on modern commercial aircraft. More data is being collected continuously which adds to the ever-increasing pool of data available for safety analysis. However, among the data collected, not all parameters are important from a risk and safety analysis perspective. Similarly, in order to be useful for modern analysis techniques such as machine learning, using thousands of parameters collected at a high frequency might not be computationally tractable. As such, an intelligent and repeatable methodology to select a reduced set of significant parameters is required to allow safety analysts to focus on the right parameters for risk identification. In this paper, a step-by-step methodology is proposed to down-select a reduced set of parameters that can be used for safety analysis. First, correlation analysis is conducted to remove highly correlated, duplicate, or redundant parameters from the data set. Second, a pre-processing step removes metadata and empty parameters. This step also considers requirements imposed by regulatory bodies such as the Federal Aviation Administration and subject matter experts to further trim the list of parameters. Third, a clustering algorithm is used to group similar flights and identify abnormal operations and anomalies. A retrospective analysis is conducted on the clusters to identify their characteristics and impact on flight safety. Finally, analysis of variance techniques are used to identify which parameters were significant in the formation of the clusters. Visualization dashboards were created to analyze the cluster characteristics and parameter significance. This methodology is employed on data from the approach phase of a representative single-aisle aircraft to demonstrate its application and robustness across heterogeneous data sets. It is envisioned that this methodology can be further extended to other phases of flight and aircraft

Draft genome sequence of Sclerospora graminicola, the pearl millet downy mildew pathogen:Genome sequence of pearl millet downy mildew pathogen

Sclerospora graminicola pathogen is one of the most important biotic production constraints of pearl millet worldwide. We report a de novo whole genome assembly and analysis of pathotype 1. The draft genome assembly contained 299,901,251 bp with 65,404 genes. Pearl millet [Pennisetum glaucum (L.) R. Br.], is an important crop of the semi-arid and arid regions of the world. It is capable of growing in harsh and marginal environments with highest degree of tolerance to drought and heat among cereals (1). Downy mildew is the most devastating disease of pearl millet caused by Sclerospora graminicola (sacc. Schroet), particularly on genetically uniform hybrids. Estimated annual grain yield loss due to downy mildew is approximately 10?80 % (2-7). Pathotype 1 has been reported to be the highly virulent pathotype of Sclerospora graminicola in India (8). We report a de novo whole genome assembly and analysis of Sclerospora graminicola pathotype 1 from India. A susceptible pearl millet genotype Tift 23D2B1P1-P5 was used for obtaining single-zoospore isolates from the original oosporic sample. The library for whole genome sequencing was prepared according to the instructions by NEB ultra DNA library kit for Illumina (New England Biolabs, USA). The libraries were normalised, pooled and sequenced on Illumina HiSeq 2500 (Illumina Inc., San Diego, CA, USA) platform at 2 x100 bp length. Mate pair (MP) libraries were prepared using the Nextera mate pair library preparation kit (Illumina Inc., USA). 1 ?g of Genomic DNA was subject to tagmentation and was followed by strand displacement. Size selection tagmented/strand displaced DNA was carried out using AmpureXP beads. The libraries were validated using an Agilent Bioanalyser using DNA HS chip. The libraries were normalised, pooled and sequenced on Illumina MiSeq (Illumina Inc., USA) platform at 2 x300 bp length. The whole genome sequencing was performed by sequencing of 7.38 Gb with 73,889,924 paired end reads from paired end library, and 1.15 Gb with 3,851,788 reads from mate pair library generated from Illumina HiSeq2500 and Illumina MiSeq, respectively. The sequences were assembled using various assemblers like ABySS, MaSuRCA, Velvet, SOAPdenovo2, and ALLPATHS-LG. The assembly generated by MaSuRCA (9) algorithm was observed superior over other algorithms and hence used for scaffolding using SSPACE. Assembled draft genome sequence of S. graminicola pathotype 1 was 299,901,251 bp long, with a 47.2 % GC content consisting of 26,786 scaffolds with N50 of 17,909 bp with longest scaffold size of 238,843 bp. The overall coverage was 40X. The draft genome sequence was used for gene prediction using AUGUSTUS. The completeness of the assembly was investigated using CEGMA and revealed 92.74% proteins completely present and 95.56% proteins partially present, while BUSCO fungal dataset indicated 64.9% complete, 12.4% fragmented, 22.7% missing out of 290 BUSCO groups. A total of 52,285 predicted genes were annotated using BLASTX and 38,120 genes were observed with significant BLASTX match. Repetitive element analysis in the assembly revealed 8,196 simple repeats, 1,058 low complexity repeats and 5,562 dinucleotide to hexanucleotide microsatellite repeats.publishersversionPeer reviewe

Evaluating the Dimensionality of First-Grade Written Composition

Purpose—We examined dimensions of written composition using multiple evaluative approaches such as an adapted 6+1 trait scoring, syntactic complexity measures, and productivity measures. We further examined unique relations of oral language and literacy skills to the identified dimensions of written composition. Method—A large sample of first grade students (N = 527) was assessed on their language, reading, spelling, letter writing automaticity, and writing in the spring. Data were analyzed using a latent variable approach including confirmatory factor analysis and structural equation modeling. Results—The seven traits in the 6+1 trait system were best described as two constructs: substantive quality, and spelling and writing conventions. When the other evaluation procedures such as productivity and syntactic complexity indicators were included, four dimensions emerged: substantive quality, productivity, syntactic complexity, and spelling and writing conventions. Language and literacy predictors were differentially related to each dimension in written composition. Conclusions—These four dimensions may be a useful guideline for evaluating developing beginning writer’s compositions

An RxLR effector from phytophthora infestans prevents re-localisation of two plant NAC transcription factors from the endoplasmic reticulum to the nucleus

The plant immune system is activated following the perception of exposed, essential and invariant microbial molecules that are recognised as non-self. A major component of plant immunity is the transcriptional induction of genes involved in a wide array of defence responses. In turn, adapted pathogens deliver effector proteins that act either inside or outside plant cells to manipulate host processes, often through their direct action on plant protein targets. To date, few effectors have been shown to directly manipulate transcriptional regulators of plant defence. Moreover, little is known generally about the modes of action of effectors from filamentous (fungal and oomycete) plant pathogens. We describe an effector, called Pi03192, from the late blight pathogen Phytophthora infestans, which interacts with a pair of host transcription factors at the endoplasmic reticulum (ER) inside plant cells. We show that these transcription factors are released from the ER to enter the nucleus, following pathogen perception, and are important in restricting disease. Pi03192 prevents the plant transcription factors from accumulating in the host nucleus, revealing a novel means of enhancing host susceptibility