The possible significance of trisomy 8 in acute myeloid leukemia

Background: Acute myeloid leukemia (AML) is a heterogeneous disorder that results from a block in the differentiation of haematopoietic progenitor cells along with uncontrolled proliferation. Trisomy 8 is the most common recurring numerical chromosomal aberrations in acute myeloid leukemia (AML). It occurs either as a sole anomaly or together with other additional chromosomal aberrations. The prognostic significance of trisomy 8 in presence of other additional chromosomal abnormality depends on clonal cytogenetic changes. The patients with trisomy 8 had shorter survival with significantly increased risk with other chromosomal abnormality.Methods: Total 139 patients were screened between January 2016 to November 2016 who were suspected of AML cases. Bone marrow cultures were set up using conventional cytogenetic methods. Chromosomal preparation was made and subjected to GTG banding technique. Banded metaphases were analysed and karyotyped for further analysis.Results: Cytogenetic evaluation of karyotyped of 139 suspected AML patients showed 52 with t(8;21)(q22;q22), 36 with t(15;17)(q22;q12), and 11 with inv(16)(p13;q22). The rest 40 cases found with additional chromosomal abnormalities, of which 16 were sole trisomy 8 and 24 cases were found with other chromosomal abnormalities In addition, only one person found with t(8;21) and trisomy 8, while  three person having t(15;17) with trisomy 8.Conclusions: AML is considered to be one of the most important cytogenetic prognostic determinants. Recurrent chromosomal translocation with trisomy 8 varying 1.9% for t(8;21) and 8.3% for t(15;17). In the present study trisomy 8 in AML with known favourable anomalies is very small. Therefore, it cannot be taken as a prognostic marker

Canonicalizing Knowledge Base Literals

Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability is limited by various quality issues. One such issue is the use of string literals instead of semantically typed entities. In this paper we study the automated canonicalization of such literals, i.e., replacing the literal with an existing entity from the KB or with a new entity that is typed using classes from the KB. We propose a framework that combines both reasoning and machine learning in order to predict the relevant entities and types, and we evaluate this framework against state-of-the-art baselines for both semantic typing and entity matching

Modeling regional aerosol variability over California and its sensitivity to emissions and long-range transport during the 2010 CalNex and CARES campaigns

Abstract. The performance of the Weather Research and Forecasting regional model with chemistry (WRF-Chem) in simulating the spatial and temporal variations in aerosol mass, composition, and size over California is quantified using measurements collected during the California Nexus of Air Quality and Climate Experiment (CalNex) and the Carbonaceous Aerosol and Radiative Effects Study (CARES) conducted during May and June of 2010. The extensive meteorological, trace gas, and aerosol measurements collected at surface sites and along aircraft and ship transects during CalNex and CARES were combined with operational monitoring network measurements to create a single dataset that was used to evaluate the one configuration of the model. Simulations were performed that examined the sensitivity of regional variations in aerosol concentrations to anthropogenic emissions and to long-range transport of aerosols into the domain obtained from a global model. The configuration of WRF-Chem used in this study is shown to reproduce the overall synoptic conditions, thermally-driven circulations, and boundary layer structure observed in region that controls the transport and mixing of trace gases and aerosols. However, sub-grid scale variability in the meteorology and emissions as well as uncertainties in the treatment of secondary organic aerosol chemistry likely contribute to errors at a primary surface sampling site located at the edge of the Los Angeles basin. Differences among the sensitivity simulations demonstrate that the aerosol layers over the central valley detected by lidar measurements likely resulted from lofting and recirculation of local anthropogenic emissions along the Sierra Nevada. Reducing the default emissions inventory by 50% led to an overall improvement in many simulated trace gases and black carbon aerosol at most sites and along most aircraft flight paths; however, simulated organic aerosol was closer to observed when there were no adjustments to the primary organic aerosol emissions. The model performance for some aerosol species was not uniform over the region, and we found that sulfate was better simulated over northern California whereas nitrate was better simulated over southern California. While the overall spatial and temporal variability of aerosols and their precursors were simulated reasonably well, we show cases where the local transport of some aerosol plumes were either too slow or too fast, which adversely affects the statistics regarding the differences between observed and simulated quantities. Comparisons with lidar and in-situ measurements indicate that long-range transport of aerosols from the global model was likely too high in the free troposphere even though their concentrations were relatively low. This bias led to an over-prediction in aerosol optical depth by as much as a factor of two that offset the under-predictions of boundary-layer extinction resulting primarily from local emissions. Lowering the boundary conditions of aerosol concentrations by 50% greatly reduced the bias in simulated aerosol optical depth for all regions of California. This study shows that quantifying regional-scale variations in aerosol radiative forcing and determining the relative role of emissions from local and distant sources is challenging during "clean" conditions and that a wide array of measurements are needed to ensure model predictions are correct for the right reasons. In this regard, the combined CalNex and CARES datasets are an ideal testbed that can be used to evaluate aerosol models in great detail and develop improved treatments for aerosol processes

Quarkonium and hydrogen spectra with spin dependent relativistic wave equation

A non-linear non-perturbative relativistic atomic theory introduces spin in the dynamics of particle motion. The resulting energy levels of Hydrogen atom are exactly same as the Dirac theory. The theory accounts for the energy due to spin-orbit interaction and for the additional potential energy due to spin and spin-orbit coupling. Spin angular momentum operator is integrated into the equation of motion. This requires modification to classical Laplacian operator. Consequently the Dirac matrices and the k operator of Dirac's theory are dispensed with. The theory points out that the curvature of the orbit draws on certain amount of kinetic and potential energies affecting the momentum of electron and the spin-orbit interaction energy constitutes a part of this energy. The theory is developed for spin 1/2 bound state single electron in Coulomb potential and then further extended to quarkonium physics by introducing the linear confining potential. The unique feature of this quarkonium model is that the radial distance can be exactly determined and does not have a statistical interpretation. The established radial distance is then used to determine the wave function. The observed energy levels are used as the input parameters and the radial distance and the string tension are predicted. This ensures 100% conformance to all observed energy levels for the heavy quarkonium.Comment: 14 pages, v7: Journal reference adde

Copy number variations in 375 patients with oesophageal atresia and/or tracheoesophageal fistula

Oesophageal atresia (OA) with or without tracheoesophageal fistula (TOF) are rare anatomical congenital malformations whose cause is unknown in over 90% of patients. A genetic background is suggested, and among the reported genetic defects are copy number variations (CNVs). We hypothesized that CNVs contribute to OA/TOF development. Quantifying their prevalence could aid in genetic diagnosis and clinical care strategies. Therefore, we profiled 375 patients in a combined Dutch, American and German cohort via genomic microarray and compared the CNV profiles with their unaffected parents and published control cohorts. We identified 167 rare CNVs containing genes (frequency<0.0005 in our in-house cohort). Eight rare CNVs - in six patients - were de novo, including one CNV previously associated with oesophageal disease. (hg19 chr7:g.(143820444-143839360)-(159119486-159138663)del) 1.55% of isolated OA/TOF patients and 1.62% of patients with additional congenital anomalies had de novo CNVs. Furthermore, three (15q13.3, 16p13.3 and 22q11.2) susceptibility loci were identified based on their overlap with known OA/TOF-associated CNV syndromes and overlap with loci in published CNV association case-control studies in developmental delay. Our study suggests that CNVs contribute to OA/TOF development. In addition to the identified likely deleterious de novo CNVs, we detected 167 rare CNVs. Although not directly disease-causing, these CNVs might be of interest, as they can act as a modifier in a multiple hit model, or as the second hit in a recessive condition

Simulating organic aerosol in Delhi with WRF-Chem using the volatility-basis-set approach: exploring model uncertainty with a Gaussian process emulator

The nature and origin of organic aerosol in the atmosphere remain unclear. The gas–particle partitioning of semi-volatile organic compounds (SVOCs) that constitute primary organic aerosols (POAs) and the multigenerational chemical aging of SVOCs are particularly poorly understood. The volatility basis set (VBS) approach, implemented in air quality models such as WRF-Chem (Weather Research and Forecasting model with Chemistry), can be a useful tool to describe emissions of POA and its chemical evolution. However, the evaluation of model uncertainty and the optimal model parameterization may be expensive to probe using only WRF-Chem simulations. Gaussian process emulators, trained on simulations from relatively few WRF-Chem simulations, are capable of reproducing model results and estimating the sources of model uncertainty within a defined range of model parameters. In this study, a WRF-Chem VBS parameterization is proposed; we then generate a perturbed parameter ensemble of 111 model runs, perturbing 10 parameters of the WRF-Chem model relating to organic aerosol emissions and the VBS oxidation reactions. This allowed us to cover the model's uncertainty space and to compare outputs from each run to aerosol mass spectrometer observations of organic aerosol concentrations and O:C ratios measured in New Delhi, India. The simulations spanned the organic aerosol concentrations measured with the aerosol mass spectrometer (AMS). However, they also highlighted potential structural errors in the model that may be related to unsuitable diurnal cycles in the emissions and/or failure to adequately represent the dynamics of the planetary boundary layer. While the structural errors prevented us from clearly identifying an optimized VBS approach in WRF-Chem, we were able to apply the emulator in the following two periods: the full period (1–29 May) and a subperiod period of 14:00–16:00 h LT (local time) on 1–29 May. The combination of emulator analysis and model evaluation metrics allowed us to identify plausible parameter combinations for the analyzed periods. We demonstrate that the methodology presented in this study can be used to determine the model uncertainty and to identify the appropriate parameter combination for the VBS approach and hence to provide valuable information to improve our understanding of OA production

Evolutionary Sequence Modeling for Discovery of Peptide Hormones

There are currently a large number of “orphan” G-protein-coupled receptors (GPCRs) whose endogenous ligands (peptide hormones) are unknown. Identification of these peptide hormones is a difficult and important problem. We describe a computational framework that models spatial structure along the genomic sequence simultaneously with the temporal evolutionary path structure across species and show how such models can be used to discover new functional molecules, in particular peptide hormones, via cross-genomic sequence comparisons. The computational framework incorporates a priori high-level knowledge of structural and evolutionary constraints into a hierarchical grammar of evolutionary probabilistic models. This computational method was used for identifying novel prohormones and the processed peptide sites by producing sequence alignments across many species at the functional-element level. Experimental results with an initial implementation of the algorithm were used to identify potential prohormones by comparing the human and non-human proteins in the Swiss-Prot database of known annotated proteins. In this proof of concept, we identified 45 out of 54 prohormones with only 44 false positives. The comparison of known and hypothetical human and mouse proteins resulted in the identification of a novel putative prohormone with at least four potential neuropeptides. Finally, in order to validate the computational methodology, we present the basic molecular biological characterization of the novel putative peptide hormone, including its identification and regional localization in the brain. This species comparison, HMM-based computational approach succeeded in identifying a previously undiscovered neuropeptide from whole genome protein sequences. This novel putative peptide hormone is found in discreet brain regions as well as other organs. The success of this approach will have a great impact on our understanding of GPCRs and associated pathways and help to identify new targets for drug development

Seizure prediction : ready for a new era

Acknowledgements: The authors acknowledge colleagues in the international seizure prediction group for valuable discussions. L.K. acknowledges funding support from the National Health and Medical Research Council (APP1130468) and the James S. McDonnell Foundation (220020419) and acknowledges the contribution of Dean R. Freestone at the University of Melbourne, Australia, to the creation of Fig. 3.Peer reviewedPostprin