Molecular characterization, structural analysis and determination of host range of a novel bacteriophage LSB-1

<p>Abstract</p> <p>Background</p> <p>Bacteriophages (phages) are widespread in the environment and play a crucial role in the evolution of their bacterial hosts and the emergence of new pathogens.</p> <p>Results</p> <p>LSB-1, a reference coliphage strain, was classified as a member of the Podoviridae family with a cystic form (50 ± 5 nm diameter) and short tail (60 ± 5 nm long). The double stranded DNA was about 30 kilobase pairs in length. We identified its host range and determined the gp17 sequences and protein structure using shotgun analysis and bioinformatics technology.</p> <p>Conclusions</p> <p>Coliphage LSB-1 possesses a tailspike protein with endosialidase activity which is probably responsible for its specific enteroinvasive <it>E.coli </it>host range within the laboratory.</p

Lifetime Prediction of DC-link Capacitors in Multiple Drives System Based on Simplified Analytical Modeling

Lifetime prediction of dc-link capacitors in a single drive has been discussed before, which indicates that the capacitor in a standard drive meets serious reliability challenges and in a slim drive does not. However, in most of the applications, drives are connected in parallel with the power grid. The large amount of harmonic distortion produced by nonlinearity drives may transmit and couple between grid and drives, which changes the stresses of devices as well as the dc-link filters. Therefore, the estimated results in a single drive cannot be extended to multiple drives any more. This article investigates the lifetime of dc-link capacitors in multiple drives system. First, by decoupling the interactions among grid-connected drives, a simplified equivalent circuit model and its analytical model to obtain the dc-link continuous current in multiple drives is proposed, which releases the designers from configuring the large simulation for multiple drives. Then, applying the lifetime prediction method, the lifetime of dc-link capacitors in multiple drives is investigated, in terms of types of drives, numbers of drives, and grid conditions. The results show that the lifetime of the standard drives extends in the multidrive systems and the lifetime of the slim drives decreases in the multidrive systems, which break the previous mind. Finally, based on the proposed analytical model and lifetime estimation method, the capacitor sizing from reliability aspect for multiple slim drives is given. The outcomes of the lifetime investigation could be a guideline for the design of the capacitive dc link in multidrive systems

Object tracking using incremental 2D-LDA learning and Bayes inference

The appearances of the tracked object and its surrounding background usually change during tracking. As for tracking methods using subspace analysis, fixed subspace basis tends to cause tracking failure. In this paper, a novel tracking method is proposed by using incremental 2D-LDA learning and Bayes inference. Incremental 2D-LDA formulates object tracking as online classification between foreground and background. It updates the row- or/and column-projected matrix efficiently. Based on the current object location and the prior knowledge, the possible locations of the object (candidates) in the next frame are predicted using simple sampling method. Applying 2D-LDA projection matrix and Bayes inference, candidate that maximizes the posterior probability is selected as the target object. Moreover, informative background samples are selected to update the subspace basis. Experiments are performed on image sequences with the object’s appearance variations due to pose, lighting, etc. We also make comparison to incremental 2D-PCA and incremental FDA. The experimental results demonstrate that the proposed method is efficient and outperforms both the compared methods. Index Terms—object tracking, incremental 2D-LDA, Bayes inferenc

A generative probability model of joint label fusion for multi-atlas based brain segmentation

Automated labeling of anatomical structures in medical images is very important in many neuroscience studies. Recently, patch-based labeling has been widely investigated to alleviate the possible mis-alignment when registering atlases to the target image. However, the weights used for label fusion from the registered atlases are generally computed independently and thus lack the capability of preventing the ambiguous atlas patches from contributing to the label fusion. More critically, these weights are often calculated based only on the simple patch similarity, thus not necessarily providing optimal solution for label fusion. To address these limitations, we propose a generative probability model to describe the procedure of label fusion in a multi-atlas scenario, for the goal of labeling each point in the target image by the best representative atlas patches that also have the largest labeling unanimity in labeling the underlying point correctly. Specifically, sparsity constraint is imposed upon label fusion weights, in order to select a small number of atlas patches that best represent the underlying target patch, thus reducing the risks of including the misleading atlas patches. The labeling unanimity among atlas patches is achieved by exploring their dependencies, where we model these dependencies as the joint probability of each pair of atlas patches in correctly predicting the labels, by analyzing the correlation of their morphological error patterns and also the labeling consensus among atlases. The patch dependencies will be further recursively updated based on the latest labeling results to correct the possible labeling errors, which falls to the Expectation Maximization (EM) framework. To demonstrate the labeling performance, we have comprehensively evaluated our patch-based labeling method on the whole brain parcellation and hippocampus segmentation. Promising labeling results have been achieved with comparison to the conventional patch-based labeling method, indicating the potential application of the proposed method in the future clinical studies

A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.

Although cancer genomes are replete with noncoding mutations, the effects of these mutations remain poorly characterized. Here we perform an integrative analysis of 930 tumor whole genomes and matched transcriptomes, identifying a network of 193 noncoding loci in which mutations disrupt target gene expression. These 'somatic eQTLs' (expression quantitative trait loci) are frequently mutated in specific cancer tissues, and the majority can be validated in an independent cohort of 3,382 tumors. Among these, we find that the effects of noncoding mutations on DAAM1, MTG2 and HYI transcription are recapitulated in multiple cancer cell lines and that increasing DAAM1 expression leads to invasive cell migration. Collectively, the noncoding loci converge on a set of core pathways, permitting a classification of tumors into pathway-based subtypes. The somatic eQTL network is disrupted in 88% of tumors, suggesting widespread impact of noncoding mutations in cancer

Characterization of ultrasound and postnatal pathology in fetuses with heterotaxy syndrome

BackgroundTo explore the diagnostic clues and abnormality spectrum of heterotaxy syndrome by prenatal ultrasonography and postnatal verification.MethodsThe prenatal ultrasonic data of 88 heterotaxy syndrome fetuses were analyzed retrospectively as left isomerism (LI) and right isomerism (RI). Prenatal ultrasound compared with the anatomical casting of the fetal body after labor induction, and the confirmatory postnatal diagnosis after delivery.ResultsFetal LI showed typical malformations of gastric vesicles on different sides from the heart, absence of hepatic segment of the inferior vena cava (IVC), abdominal aorta (AO) parallel with the azygos vein (AV), bilateral left bronchus, bilateral left atrial appendages, and polysplenia; intracardiac malformations of AV septal defects (AVSD), single atrium (SA), left ventricular outflow tract obstruction (LVOTO), and double-outlet right ventricle (DORV); and cardiac conduction abnormalities of sinus bradycardia and AV blockage. Fetal RI reported typical malformations of gastric vesicles on different sides from the heart, juxtaposition of the IVC with AO, anomalous pulmonary venous connection (APVC), asplenia, and bilateral right atrial appendages; intracardiac malformations of AVSD, SA, single ventricle, pulmonary atresia and stenosis, and DORV. The postnatal verification revealed 3 malformations misdiagnoses and 4 malformations missed diagnoses in LI fetuses and 10 misdiagnoses and 8 missed diagnoses in RI fetuses.ConclusionsThe proposed five-step prenatal ultrasonography has an important diagnostic value for the identification and classification of heterotaxy syndrome. The different sides of gastric vesicles and cardiac apex are important diagnostic clues for heterotaxy syndrome, featuring disconnected or hypoplastic IVC, typical complex cardiac malformation, and atrioventricular block in fetal LI, and shown APVC, juxtaposition of IVC and AO, and intracardiac malformations such as AVSD, DORV, and LVOTO in fetal RI

Amino acid Formula induces Microbiota Dysbiosis and Depressive-Like Behavior in Mice

Amino acid formula (AAF) is increasingly consumed in infants with cow\u27s milk protein allergy; however, the long-term influences on health are less described. In this study, we established a mouse model by subjecting neonatal mice to an amino acid diet (AAD) to mimic the feeding regimen of infants on AAF. Surprisingly, AAD-fed mice exhibited dysbiotic microbiota and increased neuronal activity in both the intestine and brain, as well as gastrointestinal peristalsis disorders and depressive-like behavior. Furthermore, fecal microbiota transplantation from AAD-fed mice or AAF-fed infants to recipient mice led to elevated neuronal activations and exacerbated depressive-like behaviors compared to that from normal chow-fed mice or cow\u27s-milk-formula-fed infants, respectively. Our findings highlight the necessity to avoid the excessive use of AAF, which may influence the neuronal development and mental health of children