/*
* -------
* :ATTRIBUTION NOTICE BEGINS
*
* The following JSMsg functions are:
*
* (C) Copyright 2002 - 2004, Creativyst, Inc.
* ALL RIGHTS RESERVED
*
* For more information go to:
* http://www.Creativyst.com/Doc/Std/JSMsg/JSMsg.htm
* or email:
* Support@Creativyst.com
*
* Licensed under the Open Software License version 1.1
*
* END ATTRIBUTION NOTICE:
* -------
*
* The following version token is
* required by aggregators
* -------
* JSMsgVersion:1.0a
* -------
*
* Made by: JSMFeed.pl v1.0e
*/
/* JSCpragma:StartCompress
*/
/* These functions are free software; you can redistribute
* them and/or modify them under the terms of the Open
* Software License (OSL) version 1.1 as published by
* the Open Source Initiative;
*
* This program is distributed in the hope that it will
* be useful, but WITHOUT ANY WARRANTY; without even the
* implied warranty of MERCHANTABILITY or FITNESS FOR A
* PARTICULAR PURPOSE. See the Open Software License
* version 1.1 for more details.
*
* For the complete details of the Open Software License
* please visit the Open Source Initiative website.
*
*
* These functions are part of a shared convention.
* called JSMsg. Please see the JSMsg document
* for more details.
*
* www.creativyst.com/Doc/Std/JSMsg/JSMsg.htm
*
* -------------------------------------------
* Parts of this code were automatically
* generated and may contain copyrighted
* content from other sources as well.
* Such content may not be used without
* first obtaining explicit permissions
* from their sources.
*
*
*/
function nn_JSMsg() {
this.Title = Array([this.ShortListSize]);
this.Desc = Array([this.ShortListSize]);
this.Author = Array([this.ShortListSize]);
this.Link = Array([this.ShortListSize]);
this.DateN = Array([this.ShortListSize]);
this.MetaKeys = Array([5]);
this.MetaVals = Array([5]);
this.ShortListSize = 10;
this.BeenLoaded = 0;
this.dispNumb;
this.Msg = function(msgPart, msgOffset)
{
document.write( this.Get(msgPart, msgOffset) );
}
this.MsgDay = function(msgPart)
{
document.write( this.GetDay(msgPart) );
}
this.MsgN = function(msgPart, msgNumber)
{
document.write( this.GetN(msgPart, msgNumber) );
}
this.Get = function(msgPart, msgOffset)
{
if(!msgOffset) {
msgOffset = 0;
}
if(!this.BeenLoaded) {
this.BeenLoaded = 1;
dispNumb =
this.getRandInt(this.ShortListSize);
}
return( this.GetN(msgPart, (dispNumb + msgOffset)) );
}
this.GetMsgN = function()
{
return(this.ShortListSize);
}
this.GetDay = function(msgPart)
{
var d = new Date();
var x = d.getDate();
return( this.GetN(msgPart, x) );
}
this.GetN = function(msgPart, msgNumber)
{
return( this.GetNCore(msgPart, msgNumber) );
}
this.GetNCore = function(msgPart, msgNumber)
{
var myTmp;
if(!msgPart) {
msgPart = "Body";
}
if(!msgNumber) {
msgNumber = 0;
}
if(msgNumber >= this.ShortListSize) {
msgNumber = msgNumber % this.ShortListSize;
}
if( this.ok(msgPart,"Type") ) {
return(this.Type[msgNumber]);
}
if( this.ok(msgPart,"Title") ) {
return(this.Title[msgNumber]);
}
if( this.ok(msgPart, "Desc Description Content") ) {
return(this.Desc[msgNumber]);
}
if( this.ok(msgPart,"Body") ) {
return(this.Body[msgNumber]);
}
if( this.ok(msgPart, "Attrib Attribute Source") ) {
return(this.Attrib[msgNumber]);
}
if( this.ok(msgPart, "Author") ) {
if(!this.Author[msgNumber]) {
return(this.Attrib[msgNumber]);
}
return(this.Author[msgNumber]);
}
if( this.ok(msgPart,"DayN Day2N Day") ) {
myTmp = this.DateN[msgNumber];
if(myTmp.length) {
myTmp = myTmp.substr(6,2);
if( this.ok(msgPart,"Day2N") ) {
return(myTmp);
}
if(myTmp.charAt(0) == "0") {
return(myTmp.substr(1,1))
}
return(myTmp);
}
}
if( this.ok(msgPart, "MonthN Month2N") ) {
myTmp = this.DateN[msgNumber];
if(myTmp.length) {
myTmp = myTmp.substr(4,2)
if( this.ok(msgPart,"Month2N") ) {
return(myTmp);
}
if(myTmp.charAt(0) == "0") {
return(myTmp.substr(1,1))
}
return(myTmp);
}
}
if( this.ok(msgPart, "Year2N Year4N YearN Year") ) {
if(this.DateN[msgNumber].length) {
if( this.ok(msgPart,"Year2N") ) {
return(this.DateN[msgNumber].substr(2,2));
}
return(this.DateN[msgNumber].substr(0,4));
}
}
if( this.ok(msgPart,"Time") ) {
myTmp = this.DateN[msgNumber];
return(myTmp.substr(8,6));
}
if( this.ok(msgPart,"Hour2N") ) {
myTmp = this.DateN[msgNumber];
return(myTmp.substr(8,2));
}
if( this.ok(msgPart,"Minute2N") ) {
myTmp = this.DateN[msgNumber];
return(myTmp.substr(10,2));
}
if( this.ok(msgPart,"Link") ) {
return(this.Link[msgNumber]);
}
if( this.ok(msgPart,"LinkTitle") ) {
if(this.MetaMsg(msgPart,"Link") && this.MetaMsg(msgPart,"Title") ) {
if( !this.Link[msgNumber] ) {
return(this.Title[msgNumber]);
}
if( !this.Title[msgNumber] ) {
this.Title[msgNumber] = this.Link[msgNumber];
}
myTmp = '\n';
myTmp = myTmp + this.Title[msgNumber] + '\n';
return(myTmp);
}
if( this.MetaMsg(msgPart,"Title") ) {
return(this.Title[msgNumber]);
}
if( this.MetaMsg(msgPart,"Link") ) {
if( this.Link[msgNumber] ) {
myTmp = "\n";
myTmp = myTmp + this.Link[msgNumber] + "\n";
return(myTmp);
}
}
return("");
}
if( this.ok(msgPart,"Cat Category Subject") ) {
return(this.Cat[msgNumber]);
}
if( this.ok(msgPart,"Thumb") ) {
return(this.Thumb[msgNumber]);
}
if( this.ok(msgPart,"Alt") ) {
return(this.Alt[msgNumber]);
}
return("");
}
this.getRandInt = function(RandSz)
{
var t, t2, t3;
t = 0;
while(t < .000001) {
t = Math.random();
t = Math.abs(t);
}
t2 = t * 1000000;
t2 = Math.round(t2);
t3 = t2 % RandSz;
return(t3);
}
this.ok = function(a, b)
{
var t, t2;
if(!this.is(a,b)) {
return(0);
}
return(this.MetaMsg(a));
}
this.is = function(a, b)
{
var t, t2;
t = a + " ";
t2 = b + " ";
t2 = t2.toUpperCase();
t = t.toUpperCase();
if(t2.indexOf(t) == -1) {
return(0);
}
return(1);
}
this.MetaMsg = function(msgCmd)
{
var i,l;
if( this.is(msgCmd, "Title LinkTitle Desc Description Author Link LinkTitle DayN Day2N MonthN Month2N Year2N Year4N YearN Year Time Hour2N Minute2N ") ) {
return(1);
}
else if( this.is(msgCmd, "MetaCount") ) {
return(10);
}
else {
if(this.MetaKeys) {
l = this.MetaKeys.length;
for(i = 0;i < l;i++) {
if(this.MetaKeys[i] == msgCmd) {
return(this.MetaVals[i]);
}
}
}
return(0);
}
}
}
nn = new nn_JSMsg();
/* how slow is the with statement */
with( nn ) {
/* Meta values
*/
MetaKeys[0] = "MetaTitle";
MetaVals[0] = "BioMed Central - Latest Articles";
MetaKeys[1] = "MetaLink";
MetaVals[1] = "http://www.biomedcentral.com/";
MetaKeys[2] = "MetaLinkTitle";
MetaVals[2] = "BioMed Central - Latest Articles";
MetaKeys[3] = "MetaUpdateDate";
MetaVals[3] = "20100209000000";
MetaKeys[4] = "MetaDesc";
MetaVals[4] = "The latest research articles published by BioMed Central";
/* content vaues:
*/
Title[0] = "Parameters for accurate genome alignment";
Desc[0] = "Background:\nGenome sequence alignments form the basis of much research. Genome alignment depends on various mundane but critical choices, such as how to mask repeats and which score parameters to use. Surprisingly, there has been no large-scale assessment of these choices using real genomic data. Moreover, rigorous procedures to control the rate of spurious alignment have not been employed.\nResults:\nWe have assessed 495 combinations of score parameters for alignment of animal, plant, and fungal genomes. As our gold-standard of accuracy, we used genome alignments implied by multiple alignments of proteins and of structural RNAs. We found the HOXD scoring schemes underlying alignments in the UCSC genome database to be far from optimal, and suggest better parameters. Higher values of the X-drop parameter are not always better. E-values accurately indicate the rate of spurious alignment, but only if tandem repeats are masked in a non-standard way. Finally, we show that gamma-centroid (probabilistic) alignment can find highly reliable subsets of aligned bases.\nConclusion:\nThese results enable more accurate genome alignment, with reliability measures for local alignments and for individual aligned bases. This study was made possible by our new software, LAST, which can align vertebrate genomes in a few hours (http://last.cbrc.jp/).";
Author[0] = "Martin Frith";
Link[0] = "http://www.biomedcentral.com/1471-2105/11/80";
DateN[0] = "20100209000000";
Title[1] = "Construct validation of a non-exercise measure of cardiorespiratory fitness in older adults";
Desc[1] = "Background:\nCardiorespiratory fitness (CRF) is associated with a decreased risk of all-cause mortality but is rarely assessed in medical settings due to burdens of time, risk, and resources. The purpose of this study was to test the construct validity of a regression equation developed by Jurca and colleagues (2005) to estimate CRF without exercise testing in community dwelling older adults.\nMethods:\nParticipants (n=172) aged 60 to 80 years with no contraindications to submaximal or maximal exercise testing completed a maximal graded exercise test (GXT) and the submaximal Rockport 1-mile walk test on separate occasions. Data included in the regression equation (age, sex, body mass index, resting heart rate, and physical activity) were obtained via measurement or self-report. Participants also reported presence of cardiovascular conditions.\nResults:\nThe multiple R for the regression equation was .72, p < .001 and CRF estimated from this equation was significantly correlated with the MET value from the GXT (r = 0.66) and with CRF estimated from submaximal field testing (r =0.67). All three CRF indices were significantly and inversely associated with reporting more cardiovascular conditions.\nConclusions:\nThe non-exercise estimate of CRF is at least as valid as field test estimates of CRF and represents a low-risk, cost-effective, and expedient method for estimating fitness in older adults.";
Author[1] = "Emily Mailey";
Link[1] = "http://www.biomedcentral.com/1471-2458/10/59";
DateN[1] = "20100208000000";
Title[2] = "Transcriptional profiling reveals the expression of novel genes in response to various stimuli in the human dermatophyte Trichophyton rubrum";
Desc[2] = "Background:\nCutaneous mycoses are common human infections among healthy and immunocompromised hosts, and the anthropophilic fungus Trichophyton rubrum is the most prevalent microorganism isolated from such clinical cases worldwide. The aim of this study was to determine the transcriptional profile of T. rubrum exposed to various stimuli in order to obtain insights into the responses of this pathogen to different environmental challenges. Therefore, we generated an expressed sequence tag (EST) collection by constructing one cDNA library and nine suppression subtractive hybridization libraries.\nResults:\nThe 1388 unigenes identified in this study were functionally classified based on the Munich Information Center for Protein Sequences (MIPS) categories. The identified proteins were involved in transcriptional regulation, cellular defense and stress, protein degradation, signaling, transport, and secretion, among other functions. Analysis of these unigenes revealed 575 T. rubrum sequences that had not been previously deposited in public databases.\nConclusion:\nIn this study, we identified novel T. rubrum genes that will be useful for ORF prediction in genome sequencing and facilitating functional genome analysis. Annotation of these expressed genes revealed metabolic adaptations of T. rubrum to carbon sources, ambient pH shifts, and various antifungal drugs used in medical practice. Furthermore, challenging T. rubrum with cytotoxic drugs and ambient pH shifts extended our understanding of the molecular events possibly involved in the infectious process and resistance to antifungal drugs.";
Author[2] = "Nalu T A Peres";
Link[2] = "http://www.biomedcentral.com/1471-2180/10/39";
DateN[2] = "20100208000000";
Title[3] = "Decreased NPC1L1 expression in the liver from Chinese female gallstone patients";
Desc[3] = "Background:\nCholesterol gallstone disease is a very common disease in both industrialized and developing countries. Many studies have found that cholesterol gallstones are more common in women than men. The molecular mechanisms underlying the relationship between female gallstone disease and hepatic sterol transporters are still undergoing definition and have not been evaluated in humans. Aims: The aim of this study is to probe for underlying hepatic molecular defects associated with development of gallstones in female. Methods/Results: Fifty-seven nonobese, normolipidemic Chinese female gallstone patients (GS) were investigated with 12 age- and body mass index-matched female gallstone-free controls (GSF). The bile from the female GS had higher cholesterol saturation than that from the female GSF. The hepatic NPC1L1 mRNA levels were lower in female GS, correlated with SREBP2 mRNA. NPC1L1 downregulation was confirmed at protein levels. Consistently, immunohistochemistry showed decreased NPC1L1 expression in female GS. Conclusions: The decreased hepatic NPC1L1 levels in female GS might indicate a downregulated reabsorption of biliary cholesterol in the liver, which, in turn, leads to the cholesterol supersaturation of bile. Our data are consistent with the possibility that hepatic NPC1L1 may be mediated by SREBP2.";
Author[3] = "Wei Cui";
Link[3] = "http://www.lipidworld.com/content/9/1/17";
DateN[3] = "20100208000000";
Title[4] = "The combined positive impact of Lean methodology and Ventana Symphony autostainer on histology lab workflow";
Desc[4] = "Background:\nHistologic samples all funnel through the H&E microtomy staining area. Here manual processes intersect with semi-automated processes creating a bottleneck. We compare alternate work processes in anatomic pathology primarily in the H&E staining work cell.\nMethods:\nWe established a baseline measure of H&E process impact on personnel, information management and sample flow from historical workload and production data and direct observation. We compared this to performance after implementing initial LEAN process modifications, including workstation reorganization, equipment relocation and workflow levelling, and the Ventana Symphony stainer to assess the impact on productivity in the H&E staining work cell.\nResults:\nAverage time from gross station to assembled case decreased by 2.9 hours (12%). Total process turnaround time (TAT) exclusive of processor schedule changes decreased 48 minutes/case (4%). Mean quarterly productivity increased 8.5% with the new methods. Process redesign reduced the number of manual steps from 219 to 182, a 17% reduction. Specimen travel distance was reduced from 773 ft/case to 395 ft/case (49%) overall, and from 92 to 53 ft/case in the H&E cell (42% improvement).\nConclusions:\nImplementation of Lean methods in the H&E work cell of histology can result in improved productivity, improved through-put and case availability parameters including TAT.";
Author[4] = "Lewis Hassell";
Link[4] = "http://www.biomedcentral.com/1472-6890/10/2";
DateN[4] = "20100208000000";
Title[5] = "Using simple artificial intelligence methods for predicting \n\namyloidogenesis in antibodies";
Desc[5] = "Background:\nAll polypeptide backbones have the potential to form amyloid fibrils, which are associated with a number of degenerative disorders. However, the likelihood that amyloidosis would actually occur under physiological conditions depends largely on the amino acid composition of a protein. We explore using a naive Bayesian classier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences.\nResults:\nThe average accuracy based on leave-one-out (LOO) cross validation of a Bayesian classier generated from 143 amyloidogenic sequences is 60.84%. This is consistent with the average accuracy of 61.15 % for a holdout test set comprised of 103 AM and 28 non-amyloidogenic sequences. The LOO cross validation accuracy increases to 81.08% when the training set is augmented by the holdout test set. In comparison, the average classication accuracy for the holdout test set obtained using a decision tree is 78.64%. Non-amyloidogenic sequences are predicted with average LOO cross validation accuracies between 74.05 % and 77.24% using the Bayesian classier, depending on the training set size. The accuracy for the holdout test set was 89 %. For the decision tree, the non-amyloidogenic prediction accuracy is 75.00 %.\nConclusions:\nThis exploratory study indicates that both classication methods may be promising in providing straightforward predictions on the amyloidogenicity of a sequence. Nevertheless, the number of available sequences that satisfy the premises of this study are limited, and are consequently smaller than the ideal training set size. Increasing the size of the training set clearly increases the accuracy, and the expansion of the training set to include not only more derivatives, but more alignments, would make the method more sound. The accuracy of the classiers may also be improved when additional factors, such as structural andphysico-chemical data, are considered. The development of this type of classifier has signicant applications in evaluating engineered antibodies, and may be adapted for evaluating engineered proteins in general.";
Author[5] = "Maria Pamela David";
Link[5] = "http://www.biomedcentral.com/1471-2105/11/79";
DateN[5] = "20100208000000";
Title[6] = "Posttraumatic stress, anxiety and depression symptoms in patients during the first year post intensive care unit discharge ";
Desc[6] = "IntroductionTo study the level and predictors of posttraumatic stress, anxiety and depression symptoms in medical, surgical and trauma patients during the first year post intensive care unit (ICU) discharge.\nMethods:\nOf 255 patients included, 194 participated at 12 months. Patients completed the Impact of Event Scale (IES), Hospital Anxiety and Depression Scale (HADS), Life Orientation Test (LOT) at 4 to 6 weeks, 3 and 12 months and ICU memory tool at the first assessment (baseline). Case level for posttraumatic stress symptoms with high probability of a posttraumatic stress disorder (PTSD) was [greater than or equal to] 35. Case level of HADS-Anxiety or Depression was [greater than or equal to] 11. Memory of pain during ICU stay was measured at baseline on a five-point Likert-scale (0-low to 4-high). Patient demographics and clinical variables were controlled for in logistic regression analyses.\nResults:\nMean IES score one year after ICU treatment was 22.5 (95%CI 20.0 to 25.1) and 27% (48/180) were above case level, IES [greater than or equal to] 35. No significant differences in the IES mean scores across the three time points were found (P = 0.388). In a subgroup, 27/170 (16%), patients IES score increased from 11 to 32, P<0.001. No differences in posttraumatic stress, anxiety or depression between medical, surgical and trauma patients were found. High educational level (OR 0.4, 95%CI 0.2 to 1.0), personality trait (optimism) OR 0.9, 95%CI 0.8 to 1.0), factual recall (OR 6.6, 95%CI 1.4 to 31.0) and memory of pain (OR 1.5, 95%CI 1.1 to 2.0) were independent predictors of posttraumatic stress symptoms at one year. Optimism was a strong predictor for less anxiety (OR 0.8, 0.8 to 0.9) and depression symptoms (OR 0.8, 0.8 to 0.9) after one year.\nConclusions:\nThe mean level of posttraumatic stress symptoms in patients one year following ICU treatment was high and one of four were above case level Predictors of posttraumatic stress symptoms were mainly demographics and experiences during hospital stay whereas clinical injury related variables were insignificant. Pessimism was a predictor of posttraumatic stress, anxiety and depression symptoms. A subgroup of patients developed clinically significant distress symptoms during the follow-up period.";
Author[6] = "Hilde Myhren";
Link[6] = "http://ccforum.com/content/14/1/R14";
DateN[6] = "20100208000000";
Title[7] = "Chromosome 7p linkage and association study for diabetes related traits and type 2 diabetes in an African-American population enriched for nephropathy";
Desc[7] = "Background:\nPreviously we performed a linkage scan of 638 African American affected sibling pairs (ASP) with type 2 diabetes (T2D) enriched for end-stage renal disease (ESRD). Ordered subset linkage analysis (OSA) revealed a linkage peak on chromosome 7p in the subset of families with earlier age of T2D diagnosis.\nMethods:\nWe fine mapped this region by genotyping 11 additional polymorphic markers in the same ASP and investigated a total of 68 single nucleotide polymorphisms (SNPs) in functional candidate genes (GCK1, IL6, IGFBP1 and IGFBP3) for association with age of T2D, age of ESRD diagnosis, duration of T2D to onset of ESRD, body mass index (BMI) in African American cases and T2D-ESRD in an African American case-control cohort. OSA of fine mapping markers supported linkage at 28 cM on 7p (near D7S3051) in early-onset T2D families (max. LOD=3.61, P=0.002). SNPs in candidate genes and 70 ancestry-informative markers (AIMs) were evaluated in 577 African American T2D-ESRD cases and 596 African American controls.\nResults:\nThe most significant association was observed between ESRD age of diagnosis and SNP rs730497, located in intron 1 of the GCK1 gene (recessive T2D age-adjusted P=0.0006). Nominal associations were observed with GCK1 SNPs and T2D age of diagnosis (BMI-adjusted P=0.014 to 0.032). Also, one IGFBP1 and four IGFBP3 SNPs showed nominal genotypic association with T2D-ESRD (P=0.002-0.049). After correcting for multiple tests, only rs730497 remanined significant.\nConclusion:\nVariant rs730947 in the GCK1 gene appears to play a role in early ESRD onset in African Americans.";
Author[7] = "Tennille Leak";
Link[7] = "http://www.biomedcentral.com/1471-2350/11/22";
DateN[7] = "20100208000000";
Title[8] = "Testing the additional predictive value of high-dimensional\n\nmolecular data";
Desc[8] = "Background:\nWhile high-dimensional molecular data such as microarray gene expression data have been used for disease outcome prediction or diagnosis purposes for about ten years in biomedical research, the question of the additional predictive value of such data given that classical predictors are already available has long been under-considered in the bioinformatics literature.\nResults:\nWe suggest an intuitive permutation-based testing procedure for assessing the additional predictive value of high-dimensional molecular data. Our method combines two well-known statistical tools: logistic regression and boosting regression. We give clear advice for the choice of the only method parameter (the number of boosting iterations). In simulations, our novel approach is found to have very good power in different settings, e.g. few strong predictors or many weak predictors. For illustrative purpose, it is applied to the two publicly available cancer data sets.\nConclusions:\nOur simple and computationally efficient approach can be used to globally assess the additional predictive power of a large number of candidate predictors given that a few clinical covariates or a known prognostic index are already available. It is implemented in the R package ''globalboosttest'' which is publicly available from R-forge and will be sent to the CRAN as soon as possible.";
Author[8] = "Anne-Laure Boulesteix";
Link[8] = "http://www.biomedcentral.com/1471-2105/11/78";
DateN[8] = "20100208000000";
Title[9] = "Infantile spasms: review of the literature and personal experience";
Desc[9] = "This epileptic disorder has become a classic topic for neuropediatricians and the interest is documented by the large number of publications on this subject.The relative frequency among the epileptic syndromes is an another reason why not only neuropediatricians but also general pediatricians must be fully informed about diagnostic, clinical, imaging and genetic aspects.Early diagnosis is of paramount importance in order to obtain even complete results in patients with so called idiopathic situations. A number of problems are still to be solved. There is no agreement on the type and the schedule of treatment. A common denominator about this problem is not jet available even if some advances in this regard have been accomplished. Of paramount importance is an accurate clinical and laboratory examination as a prerequisite regarding prognosis and results of therapy in every single case.However, even if more than 170 years have elapsed since the first communication of dr. West on the peculiar syndrome that his child was suffering of, the interest of scientists on this subject has now been enriched and rewarded.";
Author[9] = "Alberto Fois";
Link[9] = "http://www.ijponline.net/content/36/1/15";
DateN[9] = "20100208000000";
} // end with block