Ulnar dimelia variant: a case report

We report a case of ulnar dimelia, commonly called mirror hand, in a 2-month-old female child who had restriction of elbow flexion and forearm rotation. There was no facial or other internal organ malformation. Radiographs revealed seven triphalangeal digits with double ulnae (one following the other) and absent radius. To the best of the authors’ knowledge, this is the first report of this mirror hand deformity in which fingers are symmetrical while duplicated ulnae are not

Genetic variation of ten Iraqi wheat genotypes using (SSR) markers and morphological characterization

The current study based on using morphological traits and simple sequence repeat(SSRs) markers to study variation among ten Iraqi wheat genotypes.  Primers  wmc596 and  wmc603 produced  three alleles distributed between one in wmc596 and two in wmc603 with an average number of 1.50 allele  per locus . Primer wmc603 was more  informative  than wmc596 as produced PIC reached  0.3750. Morphological traits including whole plant ( dry weight , height ,leaf number ,leaf area and branches number) , spike( dry weight ,length and number) and weight of 100 grain used for cluster analysis .Cluster analysis depending on  morphological traits grouped wheat genotypes among  two major groups , the first included only Faris genotype while the other large one included the rest genotypes which further divided in to two sub clusters. Genotypes  identification  and studying genetic variation produce an efficient tool for genotypes selecting in breeding programs

Drought stress-induced modification of morpho-anatomical and yield attributes of mung bean associated with the application of silicon and Moringa leaf extract

Mung bean (Vigna radiata) is the rich source of fiber and essential nutrients. They play a vital role in sustainable agriculture due to their ability to fix nitrogen in the soil and enhance soil fertility. Drought is characterized by limited water resources and severe arid climatic conditions, notably impair crop growth and yield. In the current experiment, two genotypes, Azri-M 2006 and NM-92, were studied against drought stress that was applied as 2 days and 4 days irrigation gap per week. Foliar application of magnesium-silicate (20 ppm and 30 ppm concentrations) and Moringa leaf extract (30% v/v solution) was applied as treatments. The results from the experiment morphology anatomical and yield components were recorded according to the prescribed methods. The result revealed that drought stress reduced the growth of plant. Foliar application of 30 ppm silicon against drought stress showed a highly significant (p<0.001) result compared with control group. Morphology parameters, including shoot and root length, shoot and root fresh weight, root dry weight, leaf area, leaf number, the anatomical structure included (stem epidermis, cortex, and stem vascular bundles,) and also yield components (pod length, and seed numbers). In contrast, MLE (30%) showed a significant impact (p<0.01) on leaf lamina thickness (Leaf anatomical parameters; midrib xylem and phloem, number of stomata on the adaxial and abaxial surface) and yield components included (100-grain weight, grains weight per plant, and numbers of pods,). The overall impact of 30 ppm Si was 39.9% more positive on Azri-M2006 than the NM-92 against the drought stress. The 30-ppm silicon and 30% MLE showed 90% similar results in all studied parameters. This study confirms that 30% MLE could be recommended to farmers to improve productivity under arid conditions than the silicon

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

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Kinetics of oxidation of para- and meta-substituted benzaldehydes by pyridinium chlorochromate

46-48The oxidation of para- and meta-substituted ben-zaldehydes by pyridinium chlorochromate (PCC) is first order each in substrate, [PCC] and [H+J. Electron-releasing substituents retard and the electron-withdrawing groups enhance the rate. Addition of monomer to the reaction mixture gives a polymer. Activation parameters have been computed and a suitable mechanism has been postulated

Predicting physical properties of oxygenated gasoline and diesel range fuels using machine learning

Understanding the physical properties of distillate petroleum fuels like gasoline and diesel is very critical to ensure the normal operation of internal combustion (IC) engines with regards to processes like spray atomization, heating, evaporation etc. Two of most important physical properties are density and viscosity. Many factors such as molecular structure, molecular weight, temperature etc. effect the physical properties of the fuel. The present work deals with the development of a machine learning model for predicting the density and viscosity of petroleum fuels containing oxygenated chemical classes such as alcohols, esters, ketones and aldehydes. The model was developed using the molecular structure of the compounds expressed in the form of functional groups as inputs. The density and viscosity of 164 pure compounds spanning various chemical families and 14 blends of known compositions was collected from the literature. An artificial neural network model (ANN) for predicting density and viscosity was developed using the neural network tool in Matlab. Each of the ANN model was tested against 15% of the data and the results show that the models were able to successfully predict the density and viscosity of the unseen data points to a good accuracy. A regression coefficient of 0.99 (for density) and 0.98 (for viscosity) was obtained for the test set. The developed models can be used to predict and screen the density and viscosity of real petroleum fuels containing drop in oxygenated bio-fuels