Toxicopathological evaluation in Wistar rats (Rattus norvegicus) following repeated oral exposure to acephate.

The present study was carried out to evaluate the effects of exposure at different doses of acephate on hematology, blood biochemistry, oxidative stress and immune system of Wistar rats. The experiment was carried out on 40 Wistar rats, which were divided in four groups. Animals of the three treatment groups were given with different sublethal doses (1/40th, 1/20th, 1/10th of lethal dose 50 value) of acephate by oral gavage. The hematology, blood biochemistry, oxidative stress marker, humoral immune response and cell-mediated immunity were evaluated following acephate exposure. Significant alteration in hematological parameters was not observed following different doses of acephate; however, significant alteration in alkaline phosphatase, gamma glutamyl transferase, acetyl cholinesterase, lipid peroxidase and superoxide dismutase was observed in medium- and high-dose group animals. Nonsignificant decrease in antibody titer in animals exposed to high dose has been observed compared with animals of control group. However, significant alteration in cell-mediated immunity was not observed in animals treated with acephate at different doses.

ProbeMatch: rapid alignment of oligonucleotides to genome allowing both gaps and mismatches.

SUMMARY: We have developed a tool, called ProbeMatch, for matching a large set of oligonucleotide sequences against a genome database using gapped alignments. Unlike most of the existing tools such as ELAND which only perform ungapped alignments allowing at most two mismatches, ProbeMatch generates both ungapped and gapped alignments allowing up to three errors including insertion, deletion and mismatch. To speedup sequence alignment, ProbeMatch uses gapped q-grams and q-grams of various patterns to identify target hits to a query sequence. This approach results in fewer initial sequences to examine with no loss in sensitivity. ProbeMatch has been used to align 169,095 Illumina GAII reads against the human genome, which could not be mapped by ELAND, and found alignments for 28,625 reads of the 169,095 reads in less than 3 h. AVAILABILITY: Source code is freely available at (http://www.cs.wisc.edu/~jignesh/probematch/).

Liver MRI at 3 T using a respiratory-triggered time-efficient 3D T2-weighted technique: impact on artifacts and image quality.

OBJECTIVE: The purpose of this retrospective study was to qualitatively and quantitatively compare image quality of a time-efficient 3D T2-weighted sequence-the sampling perfection with application-optimized contrasts using different flip angle evolutions (SPACE) sequence-with a standard 2D T2-weighted turbo spin-echo (TSE) sequence for liver imaging at 3 T. MATERIALS AND METHODS: Twenty patients underwent liver MRI at 3 T using T2-weighted breath-hold 2D TSE and respiratory-triggered SPACE sequences. Two radiologists independently assessed image quality for both sequences during separate sessions, followed by a side-by-side comparison. One reader performed a quantitative analysis of the estimated signal-to-noise ratio (SNR) and the relative contrast between the liver and other tissues. RESULTS: Image quality scores for the SPACE sequence were significantly better than those for the 2D TSE sequence for motion (p < 0.0001) and pulsation (p < 0.0001) artifact, flow signal suppression (p = 0.0015), sharpness of intrahepatic vessels (p < 0.0001), and sharpness of liver edge (p = 0.0015), with motion and pulsation artifacts being nearly eliminated using the SPACE sequence. However, the scores for B(1) inhomogeneity artifact were significantly worse for the SPACE sequence (p = 0.0117). Overall, both readers preferred SPACE sequence, although this difference was significant for only one reader (p = 0.025, p = 0.275). There was no significant difference between the sequences for estimated liver SNR (p = 0.1564), but the SPACE sequence showed significantly higher relative contrast between the liver and the kidney (p < 0.0001), gallbladder (p = 0.0476), and spleen (p < 0.0001). Relative contrast between the liver and parenchymal lesions was higher with the SPACE sequence than with the TSE sequence, although this difference was not statistically significant (p = 0.125). CONCLUSION: For T2-weighted liver imaging at 3 T, the respiratory-triggered SPACE sequence shows better image quality with near elimination of motion and pulsation artifacts and improved tissue contrast than the breath-hold 2D TSE sequence, but suffers from increased B(1) inhomogeneity artifact and longer scanning time.

Fibrillar Collagen Quantification With Curvelet Transform Based Computational Methods.

Quantification of fibrillar collagen organization has given new insight into the possible role of collagen topology in many diseases and has also identified candidate image-based bio-markers in breast cancer and pancreatic cancer. We have been developing collagen quantification tools based on the curvelet transform (CT) algorithm and have demonstrated this to be a powerful multiscale image representation method due to its unique features in collagen image denoising and fiber edge enhancement. In this paper, we present our CT-based collagen quantification software platform with a focus on new features and also giving a detailed description of curvelet-based fiber representation. These new features include C++-based code optimization for fast individual fiber tracking, Java-based synthetic fiber generator module for method validation, automatic tumor boundary generation for fiber relative quantification, parallel computing for large-scale batch mode processing, region-of-interest analysis for user-specified quantification, and pre- and post-processing modules for individual fiber visualization. We present a validation of the tracking of individual fibers and fiber orientations by using synthesized fibers generated by the synthetic fiber generator. In addition, we provide a comparison of the fiber orientation calculation on pancreatic tissue images between our tool and three other quantitative approaches. Lastly, we demonstrate the use of our software tool for the automatic tumor boundary creation and the relative alignment quantification of collagen fibers in human breast cancer pathology images, as well as the alignment quantification of in vivo mouse xenograft breast cancer images.

SAGA: a subgraph matching tool for biological graphs.

MOTIVATION: With the rapid increase in the availability of biological graph datasets, there is a growing need for effective and efficient graph querying methods. Due to the noisy and incomplete characteristics of these datasets, exact graph matching methods have limited use and approximate graph matching methods are required. Unfortunately, existing graph matching methods are too restrictive as they only allow exact or near exact graph matching. This paper presents a novel approximate graph matching technique called SAGA. This technique employs a flexible model for computing graph similarity, which allows for node gaps, node mismatches and graph structural differences. SAGA employs an indexing technique that allows it to efficiently evaluate queries even against large graph datasets. RESULTS: SAGA has been used to query biological pathways and literature datasets, which has revealed interesting similarities between distinct pathways that cannot be found by existing methods. These matches associate seemingly unrelated biological processes, connect studies in different sub-areas of biomedical research and thus pose hypotheses for new discoveries. SAGA is also orders of magnitude faster than existing methods. AVAILABILITY: SAGA can be accessed freely via the web at http://www.eecs.umich.edu/saga. Binaries are also freely available at this website.

A Natural Language Interface for Dissemination of Reproducible Biomedical Data Science.

Computational tools in the form of software packages are burgeoning in the field of medical imaging and biomedical research. These tools enable biomedical researchers to analyze a variety of data using modern machine learning and statistical analysis techniques. While these publicly available software packages are a great step towards a multiplicative increase in the biomedical research productivity, there are still many open issues related to validation and reproducibility of the results. A key gap is that while scientists can validate domain insights that are implicit in the analysis, the analysis itself is coded in a programming language and that domain scientist may not be a programmer. Thus, there is no/limited direct validation of the program that carries out the desired analysis. We propose a novel solution, building upon recent successes in natural language understanding, to address this problem. Our platform allows researchers to perform, share, reproduce and interpret the analysis pipelines and results via natural language. While this approach still requires users to have a conceptual understanding of the techniques, it removes the burden of programming syntax and thus lowers the barriers to advanced and reproducible neuroimaging and biomedical research.

miBLAST: scalable evaluation of a batch of nucleotide sequence queries with BLAST.

A common task in many modern bioinformatics applications is to match a set of nucleotide query sequences against a large sequence dataset. Existing tools, such as BLAST, are designed to evaluate a single query at a time and can be unacceptably slow when the number of sequences in the query set is large. In this paper, we present a new algorithm, called miBLAST, that evaluates such batch workloads efficiently. At the core, miBLAST employs a q-gram filtering and an index join for efficiently detecting similarity between the query sequences and database sequences. This set-oriented technique, which indexes both the query and the database sets, results in substantial performance improvements over existing methods. Our results show that miBLAST is significantly faster than BLAST in many cases. For example, miBLAST aligned 247 965 oligonucleotide sequences in the Affymetrix probe set against the Human UniGene in 1.26 days, compared with 27.27 days with BLAST (an improvement by a factor of 22). The relative performance of miBLAST increases for larger word sizes; however, it decreases for longer queries. miBLAST employs the familiar BLAST statistical model and output format, guaranteeing the same accuracy as BLAST and facilitating a seamless transition for existing BLAST users.

A framework for protein structure classification and identification of novel protein structures.

BACKGROUND: Protein structure classification plays a central role in understanding the function of a protein molecule with respect to all known proteins in a structure database. With the rapid increase in the number of new protein structures, the need for automated and accurate methods for protein classification is increasingly important. RESULTS: In this paper we present a unified framework for protein structure classification and identification of novel protein structures. The framework consists of a set of components for comparing, classifying, and clustering protein structures. These components allow us to accurately classify proteins into known folds, to detect new protein folds, and to provide a way of clustering the new folds. In our evaluation with SCOP 1.69, our method correctly classifies 86.0%, 87.7%, and 90.5% of new domains at family, superfamily, and fold levels. Furthermore, for protein domains that belong to new domain families, our method is able to produce clusters that closely correspond to the new families in SCOP 1.69. As a result, our method can also be used to suggest new classification groups that contain novel folds. CONCLUSION: We have developed a method called proCC for automatically classifying and clustering domains. The method is effective in classifying new domains and suggesting new domain families, and it is also very efficient. A web site offering access to proCC is freely available at http://www.eecs.umich.edu/periscope/procc.

Caprine leptospirosis: Hematobiochemical and urinalyses studies.

AIM: The present study was designed to evaluate clinicopathological alterations in naturally occurring leptospirosis in goats of South Gujarat region, Gujarat. MATERIALS AND METHODS: A total 459 blood/serum and 292 urine samples were collected from different districts of South Gujarat region, India. Blood/serum and urine samples were subjected to hematobiochemical analyses and urinalyses. The serum samples were screened for anti-leptospiral antibodies using the microscopic agglutination test (MAT). On the bases of presence or absence of anti-leptospiral antibodies in serum, seropositive and seronegative groups were made. The results were analyzed using standard statistical methods to know pathological changes in the disease. RESULTS: In MAT, out of 459, 116 goats were seropositive, and 343 were seronegative. In hematobiochemical analyses, statistically significant (p<0.01) decrease in values of packed cell volume, hemoglobin (Hb) concentration, mean corpuscular Hb concentration and total protein and increased activity/level of alanine aminotransferase, aspartate aminotransferase and total bilirubin between seropositive and seronegative goats were noted. Urinalyses did not reveal any specific changes. In the dark field microscopy, urine samples were found to be negative for leptospires. CONCLUSION: Hematobiochemical changes noted in seropositive goats were indicative of hepatic damage, and this knowledge would aid in the therapeutic management of the disease.

Identification of cross-species shared transcriptional networks of diabetic nephropathy in human and mouse glomeruli.

Murine models are valuable instruments in defining the pathogenesis of diabetic nephropathy (DN), but they only partially recapitulate disease manifestations of human DN, limiting their utility. To define the molecular similarities and differences between human and murine DN, we performed a cross-species comparison of glomerular transcriptional networks. Glomerular gene expression was profiled in patients with early type 2 DN and in three mouse models (streptozotocin DBA/2, C57BLKS db/db, and eNOS-deficient C57BLKS db/db mice). Species-specific transcriptional networks were generated and compared with a novel network-matching algorithm. Three shared human-mouse cross-species glomerular transcriptional networks containing 143 (Human-DBA STZ), 97 (Human-BKS db/db), and 162 (Human-BKS eNOS(-/-) db/db) gene nodes were generated. Shared nodes across all networks reflected established pathogenic mechanisms of diabetes complications, such as elements of Janus kinase (JAK)/signal transducer and activator of transcription (STAT) and vascular endothelial growth factor receptor (VEGFR) signaling pathways. In addition, novel pathways not previously associated with DN and cross-species gene nodes and pathways unique to each of the human-mouse networks were discovered. The human-mouse shared glomerular transcriptional networks will assist DN researchers in selecting mouse models most relevant to the human disease process of interest. Moreover, they will allow identification of new pathways shared between mice and humans.

BatchGenAna: a batch platform for large-scale genomic analysis of mammalian small RNAs.

UNLABELLED: An increasing number of small RNAs have been discovered in mammals. However, their primary transcripts and upstream regulatory networks remain largely to be determined. Genomic analysis of small RNAs facilitates identification of their primary transcripts, and hence contributes to researches of their upstream regulatory networks. We here report a batch platform, BatchGenAna, which is specifically designed for large-scale genomic analysis of mammalian small RNAs. It can map and annotate for as many as 1000 small RNAs or 10,000 genomic loci of small RNAs at a time. It provides genomic features including RefSeq genes, mRNAs, ESTs and repeat elements in tabular and graphical results. It also allows extracting flanking sequences of submitted queries, specified genomic regions and host transcripts, which facilitates subsequent analysis such as scanning transcription factor binding sites in upstream sequences and poly(A) signals in downstream sequences. Besides small RNA fields, BatchGenAna can also be applied to other research fields, e.g. in silico analysis of target genes of transcription factors. AVAILABILITY: The The platform is freely available at http://biosrv1.bmi.ac.cn/BatchGenAna.

Exploratory data mining analysis identifying subgroups of patients with depression who are at high risk for suicide.

OBJECTIVE: Although prior research has identified a number of separate risk factors for suicide among patients with depression, little is known about how these factors may interact to modify suicide risk. Using an empirically based decision tree analysis for a large national sample of Veterans Affairs (VA) health system patients treated for depression, we identified subgroups with particularly high or low rates of suicide. METHOD: We identified 887,859 VA patients treated for depression between April 1, 1999, and September 30, 2004. Randomly splitting the data into 2 samples (primary and replication samples), we developed a decision tree for the primary sample using recursive partitioning. We then tested whether the groups developed within the primary sample were associated with increased suicide risk in the replication sample. RESULTS: The exploratory data analysis produced a decision tree with subgroups of patients at differing levels of risk for suicide. These were identified by a combination of factors including a co-occurring substance use disorder diagnosis, male sex, African American race, and psychiatric hospitalization in the past year. The groups developed as part of the decision tree accurately discriminated between those with and without suicide in the replication sample. The patients at highest risk for suicide were those with a substance use disorder who were non-African American and had an inpatient psychiatric stay within the past 12 months. CONCLUSIONS: Study findings suggest that the identification of depressed patients at increased risk for suicide is improved through the examination of higher order interactions between potential risk factors.

Efficient evaluation of radial queries using the target tree.

We propose a novel indexing structure, called the target tree, which is designed to answer a new type of spatial query, called the radial query. A radial query finds all objects in the spatial data set that intersect with line segments emanating from a single target point. Many biomedical applications use radial queries, including neurosurgical planning. A target tree uses a regular hierarchical decomposition of space using wedge shapes that emanate from the target point. We compare the target tree with the R*-tree and quadtree, and show that the target tree is significantly faster than these methods.