Mechanism for controlling the monomer-dimer conversion of SARS coronavirus main protease

[[incitationindex]]SCI[[booktype]]紙

Multi-step tail biting outbreak intervention protocols for pigs housed on slatted floors

Solutions are needed to keep pigs under commercial conditions without tail biting outbreaks (TBOs). However, as TBOs are inevitable even in well managed farms, it is crucial to know how to manage TBOs when they occur. We evaluated the effectiveness of multi-step intervention protocols to control TBOs. Across 96 pens (1,248 undocked pigs) managed on fully-slatted floors, 40 TBOs were recorded (≥3 out of 12–14 pigs, with fresh tail wounds). When an outbreak was identified, either the biters or victims were removed or enrichment (3 ropes) was added. If the intervention failed, another intervention was randomly used until all 3 interventions had been deployed once. Fifty percent of TBOs were controlled after one intervention, 30% after 2–3 interventions, and 20% remained uncontrolled. A high proportion of biters/victims per pen reduced intervention success, more so than the type of intervention. When only one intervention was used, adding ropes was the fastest method to overcome TBOs. Removed biters and victims were successfully reintroduced within 14 days back to their home pens. In conclusion, 80% of TBO were successfully controlled, within on average 18.4 ± 1.7 days, using one or multiple cost-effective intervention strategies

Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites

It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using Gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches

'Unite and conquer': enhanced prediction of protein subcellular localization by integrating multiple specialized tools

<p>Abstract</p> <p>Background</p> <p>Knowing the subcellular location of proteins provides clues to their function as well as the interconnectivity of biological processes. Dozens of tools are available for predicting protein location in the eukaryotic cell. Each tool performs well on certain data sets, but their predictions often disagree for a given protein. Since the individual tools each have particular strengths, we set out to integrate them in a way that optimally exploits their potential. The method we present here is applicable to various subcellular locations, but tailored for predicting whether or not a protein is localized in mitochondria. Knowledge of the mitochondrial proteome is relevant to understanding the role of this organelle in global cellular processes.</p> <p>Results</p> <p>In order to develop a method for enhanced prediction of subcellular localization, we integrated the outputs of available localization prediction tools by several strategies, and tested the performance of each strategy with known mitochondrial proteins. The accuracy obtained (up to 92%) surpasses by far the individual tools. The method of integration proved crucial to the performance. For the prediction of mitochondrion-located proteins, integration via a two-layer decision tree clearly outperforms simpler methods, as it allows emphasis of biologically relevant features such as the mitochondrial targeting peptide and transmembrane domains.</p> <p>Conclusion</p> <p>We developed an approach that enhances the prediction accuracy of mitochondrial proteins by uniting the strength of specialized tools. The combination of machine-learning based integration with biological expert knowledge leads to improved performance. This approach also alleviates the conundrum of how to choose between conflicting predictions. Our approach is easy to implement, and applicable to predicting subcellular locations other than mitochondria, as well as other biological features. For a trial of our approach, we provide a webservice for mitochondrial protein prediction (named YimLOC), which can be accessed through the AnaBench suite at http://anabench.bcm.umontreal.ca/anabench/. The source code is provided in the Additional File <supplr sid="S2">2</supplr>.</p> <suppl id="S2"> <title> <p>Additional file 2</p> </title> <text> <p>This file contains scripts for the online server YimLOC. Please note that there scripts only codes for the ready-to-use STACK-mem-DT described in the main text. The scripts do not provide the training process.</p> </text> <file name="1471-2105-8-420-S2.pdf"> <p>Click here for file</p> </file> </suppl

Methionine Adenosyltransferase I/III Deficiency in Portugal: High Frequency of a Dominantly Inherited Form in a Small Area of Douro High Lands

Methionine adenosyltransferase deficienc(MAT I/III deficiency) is an inborn error of metabolism resulting in isolated hypermethioninemia, and usually inherited as an autosomal recessive trait, although a dominant form has been reported in several families. During the last 6 years, approximately 520,000 newborns were screened in the Portuguese Newborn Screening Laboratory by MS/MS, and 21 cases of persistent hypermethioninemia were found. One case was confirmed to be a deficiency of cystathionine b-synthase and 20 cases were confirmed by MAT1A gene analysis to have an elevation of methionine due to MAT I/III deficiency, which indicates an incidence for this condition of 1/26,000. Twelve of the MAT I/III deficient newborns, belonging to 11 families, were identified in the northern region of Portugal and sent to the same treatment center, where they are under follow-up. Clinical, biochemical, and genetic characteristics of individuals from these 11 families are presented. Plasma methionine and homocysteine concentrations were found to be moderately increased in all newborns, and molecular analysis revealed that they all were heterozygous for R264H mutation. Normal growth,development, and neurological examination were observed in all cases, and cerebral MRI performed in six cases revealed myelination abnormalities in one case. Plasma methionine concentration for all 12 cases was always below 300 mM, and they are all on a normal diet for their age

SLC37A1 and SLC37A2 Are Phosphate-Linked, Glucose-6-Phosphate Antiporters

Blood glucose homeostasis between meals depends upon production of glucose within the endoplasmic reticulum (ER) of the liver and kidney by hydrolysis of glucose-6-phosphate (G6P) into glucose and phosphate (Pi). This reaction depends on coupling the G6P transporter (G6PT) with glucose-6-phosphatase-α (G6Pase-α). Only one G6PT, also known as SLC37A4, has been characterized, and it acts as a Pi-linked G6P antiporter. The other three SLC37 family members, predicted to be sugar-phosphate:Pi exchangers, have not been characterized functionally. Using reconstituted proteoliposomes, we examine the antiporter activity of the other SLC37 members along with their ability to couple with G6Pase-α. G6PT- and mock-proteoliposomes are used as positive and negative controls, respectively. We show that SLC37A1 and SLC37A2 are ER-associated, Pi-linked antiporters, that can transport G6P. Unlike G6PT, neither is sensitive to chlorogenic acid, a competitive inhibitor of physiological ER G6P transport, and neither couples to G6Pase-α. We conclude that three of the four SLC37 family members are functional sugar-phosphate antiporters. However, only G6PT/SLC37A4 matches the characteristics of the physiological ER G6P transporter, suggesting the other SLC37 proteins have roles independent of blood glucose homeostasis

Accurate Prediction of Protein Structural Class

Because of the increasing gap between the data from sequencing and structural genomics, the accurate prediction of the structural class of a protein domain solely from the primary sequence has remained a challenging problem in structural biology. Traditional sequence-based predictors generally select several sequence features and then feed them directly into a classification program to identify the structural class. The current best sequence-based predictor achieved an overall accuracy of 74.1% when tested on a widely used, non-homologous benchmark dataset 25PDB. In the present work, we built a multiple linear regression (MLR) model to convert the 440-dimensional (440D) sequence feature vector extracted from the Position Specific Scoring Matrix (PSSM) of a protein domain to a 4-dimensinal (4D) structural feature vector, which could then be used to predict the four major structural classes. We performed 10-fold cross-validation and jackknife tests of the method on a large non-homologous dataset containing 8,244 domains distributed among the four major classes. The performance of our approach outperformed all of the existing sequence-based methods and had an overall accuracy of 83.1%, which is even higher than the results of those predicted secondary structure-based methods

Patterns of ambulatory care utilization in Taiwan

BACKGROUND: We used the insurance claims of a representative cohort to quantify the patterns of ambulatory care visits, especially the doctor-shopping phenomenon, in Taiwan. METHODS: The ambulatory visit files of the 200,000-person cohort datasets from the National Health Insurance Research Database in 2002 were analyzed. Only a visit with physician consultation would be considered. We computed the visit patterns both by visit count and by patient count. RESULTS: In 2002, there were 182,474 eligible people with 2,443,003 physician consultations. During the year, 87.4% of the cohort had visited physician clinics and 57.5% had visited hospital-based outpatient or emergency departments. On average, a person had 13.4 physician consultations and consulted 3.4 specialties, 5.2 physicians, and 3.9 healthcare facilities in a year. In 2002, 17.3% of the cohort had ever visited different healthcare facilities on the same day; 23.5% had ever visited physicians of the same specialty at different healthcare facilities within 7 days and the percentage of second visits was 3.8% of all visits. Besides, 7.6% of the cohort had visited two or more specialties at the same facility on the same day, and such visits make up 2.5% of all visits. CONCLUSION: The people in Taiwan did visit the physicians and outpatient departments frequently. Many patients not only consulted several physicians of different specialties and at different healthcare facilities during the year, but also switched the physicians and facilities quickly. An effective referral system with efficient data exchange between facilities might be the solution

Prediction of protein structural classes for low-homology sequences based on predicted secondary structure

<p>Abstract</p> <p>Background</p> <p>Prediction of protein structural classes (<it>α</it>, <it>β</it>, <it>α </it>+ <it>β </it>and <it>α</it>/<it>β</it>) from amino acid sequences is of great importance, as it is beneficial to study protein function, regulation and interactions. Many methods have been developed for high-homology protein sequences, and the prediction accuracies can achieve up to 90%. However, for low-homology sequences whose average pairwise sequence identity lies between 20% and 40%, they perform relatively poorly, yielding the prediction accuracy often below 60%.</p> <p>Results</p> <p>We propose a new method to predict protein structural classes on the basis of features extracted from the predicted secondary structures of proteins rather than directly from their amino acid sequences. It first uses PSIPRED to predict the secondary structure for each protein sequence. Then, the <it>chaos game representation </it>is employed to represent the predicted secondary structure as two time series, from which we generate a comprehensive set of 24 features using <it>recurrence quantification analysis</it>, <it>K-string based information entropy </it>and <it>segment-based analysis</it>. The resulting feature vectors are finally fed into a simple yet powerful Fisher's discriminant algorithm for the prediction of protein structural classes. We tested the proposed method on three benchmark datasets in low homology and achieved the overall prediction accuracies of 82.9%, 83.1% and 81.3%, respectively. Comparisons with ten existing methods showed that our method consistently performs better for all the tested datasets and the overall accuracy improvements range from 2.3% to 27.5%. A web server that implements the proposed method is freely available at <url>http://www1.spms.ntu.edu.sg/~chenxin/RKS_PPSC/</url>.</p> <p>Conclusion</p> <p>The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the predicted secondary structure sequences, which is capable of characterizing the sequence order information, local interactions of the secondary structural elements, and spacial arrangements of <it>α </it>helices and <it>β </it>strands. Thus, it is a valuable method to predict protein structural classes particularly for low-homology amino acid sequences.</p