Molekularna diferencijacija izolata bolesti kvrgave kože na terenu i cjepnih sojeva virusa Capripox u Egiptu 2018.

Lumpy skin disease virus is a member of the Capripoxvirus genus of the Poxviridae family, which affects cattle and causes a notifiable disease with significant economic losses. It is controlled by vaccination with capripox live attenuated vaccines. The aim of the study was the isolation and identification of the lumpy skin virus field virus strain during 2018. Nodular skin lesions were collected from clinically infected lumpy skin disease cattle that were used for the virus isolation on the chorioallantoic membrane of specific pathogen free embryonated chicken eggs and Madin Darby Bovine Kidney tissue culture. Polymerase chain reaction targeting the Capripoxvirus CaPV ORF103 gene was applied on the isolated virus and three Capripoxvirus vaccinal strains (Kenyan sheep pox virus, Held goat pox virus and Ismailia lumpy skin disease virus). The amplicons of the four strains of Capripoxvirus (one isolated and three vaccinal strains) were used for sequencing. Reference capripox viruses were obtained from GenBank to create the phylogenetic tree. The virus isolated from the collected nodular skin samples on chicken eggs showed clear typical pock lesions on the chorioallantoic membrane and on tissue cultures and showed a characteristic cytopathic effect. Positive samples of the isolated strain were identified by PCR for the CaPV ORF103 gene that yielded expected amplicon sizes of 570 bp. This was confirmed through gene sequence and analysed by BLAST, and submitted to GenBank under accession number MW 546997_LSD_Aziz_LSD. The phylogenetic tree revealed that the field isolate strain of LSDV had different identity percentages ranging from 98.2–99.8% with the tested vaccinal Capripoxvirus strains in Egypt. The amino acid sequence showed only different amino acid found in the field isolate strain and not in other tested vaccinal strains, and a maximum homology (100%) of the isolated strain nucleotide sequence was with two GenBank recorded strains. We recommend maintaining the routine lumpy skin disease vaccination programme in Egypt, frequent eradication of the insect population, and further genetic studies on the genomes of this virus strain and the Capripoxvirus vaccinal strains to reach the most related and homologous vaccinal strain given the massive genome of this disease.Virus bolesti kvrgave kože pripada rodu Capripox virusa porodice Poxviridae, koji pogađa stoku i uzrokuje bolest sa značajnim ekonomskih gubitcima, a koja se kontrolira cijepljenjem živim oslabljenim capripox cjepivima. Cilj studije bio je izolirati i identificirati soj virusa BKK na terenu tijekom 2018. godine. Prikupljene kvrge na koži u stoke klinički inficirane bolešću kvrgave kože (BKK) rabljene su za izoliranje virusa na korioalantoičnoj membrani (CAM) embrioniranog kokošjeg jaja (ECE) bez specifičnog patogena (SPF) i staničnoj kulturi bubrega goveda Madin Darby Bovine Kidney (MDBK stanična linija). Lančane reakcije polimerazom (PCR) usmjerene na ORF103 gen virusa Capripox (CaPV) primijenjene su na izolirani BKK virus i tri cjepna soja virusa Capripox (kenijski virus ovčjih boginja, Held virus kozjih boginja i Ismailia virus bolesti kvrgave kože). PCR amplikoni četiriju sojeva CaPV (jednog izoliranog i tri cjepna soja) rabljeni su za sekvenciranje i dobivanje pristupnog broja u banci gena te ilustriranje filogenetskog stabla u usporedbi s drugim referentnim virusima Capripox dobivenima iz banke gena. BKK virus izoliran iz prikupljenih uzoraka kvrga na koži na embrioniranom kokošjem jaju pokazao je jasne tipične pustula lezije na korioalantoičnoj membrani, a na staničnim kulturama (MDBK stanična linija) pokazao je karakterističan citopatski učinak. Pozitivni uzorci izoliranog soja BKK identificirani su PCR-om za CaPV ORF103 gen koji je dao očekivane veličine amplikona od 570 bp te potvrđeni sekvenciranjem gena uz analizu putem BLAST-a i su dostavljeni banci gena pod pristupnim brojem MW 546997_LSD_Aziz_LSD. Dizajnirano je filogenetsko stablo i otkriveno je da je soj izolata virusa BKK na terenu imao postotke različitog identiteta koji su se kretali od 98,2-99,8 % s testiranim cjepnim sojevima virusa Capripox u Egiptu. Sekvenca aminokiselina pokazala je samo jednu posebnu aminokiselinu koja je pronađena u soju izolata s terena, ali ne u ostalim testiranim cjepnim sojevima. Maksimalna podudarnost (100%) nukleotidne sekvence izoliranog BKK soja bila je s dva soja zabilježena u banci gena (MK342935_LSD-CPD/Menofiya1/18) i (MN792930_LSD/AHRI/_Wadi Elgdid/18). Zaključeno je da je izolirani soj virusa BKK imao velike postotke identiteta (98,2-99,8 %) s testiranim cjepnim sojevima virusa Capripox u Egiptu. Da bi se postigao najpovezaniji i homologni cjepni soj, jer je genom BKK virusa velik preporučujemo nastavak programa rutinskog cijepljenja za BKK u Egiptu, često uništavanje populacije insekata i provedbu dodatnih genetskih studija na genomima soja BKK virusa i cjepnih sojeva virusa Capripox da bi se postigao najpovezaniji i homologni cjepni soj, jer je genom BKK virusa golem

Effect of rapid Solidification on mechanical properties of free machining lead free aluminum alloys for improved machinability

There have been very few reports describing the free machining lead free aluminum alloys containing minimal amounts of tin and indium melt spun process. Our paper describes the effect of fundamental factors on the machinability of free machining lead free aluminum alloys rapidly solidified from melt. Structural and thermal properties have been investigated by x-ray diffraction (XRD) and differential scanning calorimetry (DSC) techniques. Tensile test machine used in studies the mechanical properties such as ultimate tensile strength, elastic constants, yield strength and critical shear stress for Al-Zn-Sn-In alloys. It is noticed that mechanical and thermal properties attributed to fine grained structure, reduced levels of segregation and presence of new intermetallic compounds (IMC) such as AlZn and SnZn due to high casting rate by rapid solidification processes. The determination of mechanical properties was suggested to be attributed to the gradual increase of α-Al crystals. We evaluated the tensile properties using tensile test machine of the melt-spun ribbons at varied stress-strain rates to determine the underlying deformation mechanisms .Critical shear stress (CSS) was also calculated .It was found that it is equal to 9.29 GPa for annealed ribbons at 262 0C for 9 hrs. The results showed that several combination of tensile strength ,yield strength ,elastic moduli can be generated from Al- 0.1wt% Zn-1.5 wt% Sn- 1.63 wt% In alloys before and after heat treatment at (262 0C for 3,6,9 hrs) to meet the needs of free machining aluminum alloy applications.                                                                       &nbsp

Effect of rapid solidification and calcium additions on Sn-38 wt.%Pb-6 wt.%Sb melt-spun alloys

The effect of calcium additions on the structure and physical properties of melt-spun process Sn-38Pb-6Sb alloys have been experimentally investigated at a solidification rate of ~105 K/s. Structure, internal friction, elastic moduli, microhardness and electrical resistivity of the Sn-38%Pb , Sn-38%Pb -6%Sb , Sn-38%Pb -6%Sb-0.5%Ca , Sn-38%Pb -6%Sb -1%Ca , Sn-38%Pb -6%Sb -1.5%Ca , Sn-38%Pb -6%Sb -2%Ca , Sn-38%Pb -6%Sb -2.5%Ca (in wt%) rapidly solidified alloys are investigated. The results showed that the mechanical and electrical properties values are enhanced for ternary Sn-38%Pb -6%Sb alloy. The examined mechanical and electrical conductivity decreased by addition of calcium content in the studied alloys. It also leads to with increasing Ca content the SnSb inter-metallic compound (IMC) precipitates are increased in the Sn matrix. The results were explained in terms of the dislocation theory, effect of quenching rate on the produced density fluctuations in composition and the modes of interaction of crystal lattice defects

Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning

Brain segmentation is a fundamental first step in neuroimage analysis. In the case of fetal MRI, it is particularly challenging and important due to the arbitrary orientation of the fetus, organs that surround the fetal head, and intermittent fetal motion. Several promising methods have been proposed but are limited in their performance in challenging cases and in real-time segmentation. We aimed to develop a fully automatic segmentation method that independently segments sections of the fetal brain in 2D fetal MRI slices in real-time. To this end, we developed and evaluated a deep fully convolutional neural network based on 2D U-net and autocontext, and compared it to two alternative fast methods based on 1) a voxelwise fully convolutional network and 2) a method based on SIFT features, random forest and conditional random field. We trained the networks with manual brain masks on 250 stacks of training images, and tested on 17 stacks of normal fetal brain images as well as 18 stacks of extremely challenging cases based on extreme motion, noise, and severely abnormal brain shape. Experimental results show that our U-net approach outperformed the other methods and achieved average Dice metrics of 96.52% and 78.83% in the normal and challenging test sets, respectively. With an unprecedented performance and a test run time of about 1 second, our network can be used to segment the fetal brain in real-time while fetal MRI slices are being acquired. This can enable real-time motion tracking, motion detection, and 3D reconstruction of fetal brain MRI.Comment: This work has been submitted to ISBI 201

Learning to segment fetal brain tissue from noisy annotations

Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain development at this critical stage. Deep learning methods represent the state of the art in medical image segmentation and have also achieved impressive results in brain segmentation. However, effective training of a deep learning model to perform this task requires a large number of training images to represent the rapid development of the transient fetal brain structures. On the other hand, manual multi-label segmentation of a large number of 3D images is prohibitive. To address this challenge, we segmented 272 training images, covering 19-39 gestational weeks, using an automatic multi-atlas segmentation strategy based on deformable registration and probabilistic atlas fusion, and manually corrected large errors in those segmentations. Since this process generated a large training dataset with noisy segmentations, we developed a novel label smoothing procedure and a loss function to train a deep learning model with smoothed noisy segmentations. Our proposed methods properly account for the uncertainty in tissue boundaries. We evaluated our method on 23 manually-segmented test images of a separate set of fetuses. Results show that our method achieves an average Dice similarity coefficient of 0.893 and 0.916 for the transient structures of younger and older fetuses, respectively. Our method generated results that were significantly more accurate than several state-of-the-art methods including nnU-Net that achieved the closest results to our method. Our trained model can serve as a valuable tool to enhance the accuracy and reproducibility of fetal brain analysis in MRI

The Impact of Big Data Analytics on Investment Efficiency and Financial Performance: Evidence from Saudi Stock Market

The paper investigates the determinants of adopting big data analytics (BDA) in the Saudi environment according to vision 2030, in addition to the impact of BDA on both investment efficiency and financial performance, we analyzed the academic accounting literature and provide a theoretical framework for each variable. Furthermore, to achieve our research objectives we developed three hypotheses and tested them through an empirical study based on three statistical models; our sample consisted of the largest 50 companies operating in the Saudi stock market for five years (2017-2021). Going further, our findings illustrated that (1) Firm Size, Cash Flows, Growth Opportunities, and Dividend Policy have a positive significant impact on the adoption of big data analytics (2) Leverage and Working Capital have a positive significant impact on the adoption of big data analytics (3) There is a positive impact of the adoptions of big data analytics on investment efficiency (4) There is a positive impact of the adoptions of big data analytics on the financial performance

Adaptive Reference Trajectory for Power Quality Enhancement in Three-Phase Four-Wire Standalone Power Supply Systems with Nonlinear and Unbalanced Loads

International audienc

Stability analysis and robust damping of multiresonances in distributed-generation-based islanded microgrids

International audienc

Disaggregation of SMOS soil moisture to 100m resolution using MODIS optical/thermal and sentinel-1 radar data: evaluation over a bare soil site in morocco

The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (σ°). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of σ° and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of σ° ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of σ° where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD = 0.032 m3 m−3).This work is a contribution to the REC project funded by the European Commission Horizon 2020 Programme for Research and Innovation (H2020) in the context of the Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) action under grant agreement no: 645642. In addition, this work has been partially funded by a public grant of Ministerio de Economía y Competitividad (DI-14-06587) and AGAUR-Generalitat de Catalunya (DI-2015-058)

A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth.

Longitudinal characterization of early brain growth in-utero has been limited by a number of challenges in fetal imaging, the rapid change in size, shape and volume of the developing brain, and the consequent lack of suitable algorithms for fetal brain image analysis. There is a need for an improved digital brain atlas of the spatiotemporal maturation of the fetal brain extending over the key developmental periods. We have developed an algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age. We applied this new algorithm to construct a spatiotemporal atlas from MRI of 81 normal fetuses scanned between 19 and 39 weeks of gestation and labeled the structures of the developing brain. We evaluated the use of this atlas and additional individual fetal brain MRI atlases for completely automatic multi-atlas segmentation of fetal brain MRI. The atlas is available online as a reference for anatomy and for registration and segmentation, to aid in connectivity analysis, and for groupwise and longitudinal analysis of early brain growth