Single port/incision laparoscopic surgery compared with standard three-port laparoscopic surgery for appendicectomy : a randomised controlled trial

PMID: 23111090 [PubMed - indexed for MEDLINE] PMCID: PMC3543288 Free PMC ArticlePeer reviewedPublisher PD

Regulatory network of secondary metabolism in Brassica rapa:insight into the glucosinolate pathway

Brassica rapa studies towards metabolic variation have largely been focused on the profiling of the diversity of metabolic compounds in specific crop types or regional varieties, but none aimed to identify genes with regulatory function in metabolite composition. Here we followed a genetical genomics approach to identify regulatory genes for six biosynthetic pathways of health-related phytochemicals, i.e carotenoids, tocopherols, folates, glucosinolates, flavonoids and phenylpropanoids. Leaves from six weeks-old plants of a Brassica rapa doubled haploid population, consisting of 92 genotypes, were profiled for their secondary metabolite composition, using both targeted and LC-MS-based untargeted metabolomics approaches. Furthermore, the same population was profiled for transcript variation using a microarray containing EST sequences mainly derived from three Brassica species: B. napus, B. rapa and B. oleracea. The biochemical pathway analysis was based on the network analyses of both metabolite QTLs (mQTLs) and transcript QTLs (eQTLs). Co-localization of mQTLs and eQTLs lead to the identification of candidate regulatory genes involved in the biosynthesis of carotenoids, tocopherols and glucosinolates. We subsequently focused on the well-characterized glucosinolate pathway and revealed two hotspots of co-localization of eQTLs with mQTLs in linkage groups A03 and A09. Our results indicate that such a large-scale genetical genomics approach combining transcriptomics and metabolomics data can provide new insights into the genetic regulation of metabolite composition of Brassica vegetables

Multiplicity Distributions and Charged-neutral Fluctuations

Results from the multiplicity distributions of inclusive photons and charged particles, scaling of particle multiplicities, event-by-event multiplicity fluctuations, and charged-neutral fluctuations in 158$\cdot A$ GeV Pb+Pb collisions are presented and discussed. A scaling of charged particle multiplicity as $N_{part}^{1.07\pm 0.05}$ and photons as $N_{part}^{1.12\pm 0.03}$ have been observed, indicating violation of naive wounded nucleon model. The analysis of localized charged-neutral fluctuation indicates a model-independent demonstration of non-statistical fluctuations in both charged particles and photons in limited azimuthal regions. However, no correlated charged-neutral fluctuations are observed.Comment: Talk given at the International Symposium on Nuclear Physics (ISNP-2000), Mumbai, India, 18-22 Dec 2000, Proceedings to be published in Pramana, Journal of Physic

A multimodal approach for tracing lateralization along the olfactory pathway in the honeybee through electrophysiological recordings, morpho-functional imaging, and behavioural studies

Recent studies have revealed asymmetries between the left and right sides of the brain in invertebrate species. Here we present a review of a series of recent studies from our labs, aimed at tracing asymmetries at different stages along the honeybee's (Apis mellifera) olfactory pathway. These include estimates of the number of sensilla present on the two antennae, obtained by scanning electron microscopy, as well as electroantennography recordings of the left and right antennal responses to odorants. We describe investigative studies of the antennal lobes, where multi-photon microscopy is used to search for possible morphological asymmetries between the two brain sides. Moreover, we report on recently published results obtained by two-photon calcium imaging for functional mapping of the antennal lobe aimed at comparing patterns of activity evoked by different odours. Finally, possible links to the results of behavioural tests, measuring asymmetries in single-sided olfactory memory recall, are discussed.Comment: 28 pages, 8 figure

Functional Analysis: Evaluation of Response Intensities - Tailoring ANOVA for Lists of Expression Subsets

Background: Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria (metabolic pathway, pathology signature, co-regulation by a common factor, etc.) at the same time and the cost of these methods allows for the use of more values to help discover the underlying biological mechanisms. Results: As several methods assume different null hypotheses, we propose to reformulate the main question that biologists seek to answer. To determine which genesets are associated with expression values that differ between two experiments, we focused on three ad hoc criteria: expression levels, the direction of individual gene expression changes (up or down regulation), and correlations between genes. We introduce the FAERI methodology, tailored from a two-way ANOVA to examine these criteria. The significance of the results was evaluated according to the self-contained null hypothesis, using label sampling or by inferring the null distribution from normally distributed random data. Evaluations performed on simulated data revealed that FAERI outperforms currently available methods for each type of set tested. We then applied the FAERI method to analyze three real-world datasets on hypoxia response. FAERI was able to detect more genesets than other methodologies, and the genesets selected were coherent with current knowledge of cellular response to hypoxia. Moreover, the genesets selected by FAERI were confirmed when the analysis was repeated on two additional related datasets. Conclusions: The expression values of genesets are associated with several biological effects. The underlying mathematical structure of the genesets allows for analysis of data from several genes at the same time. Focusing on expression levels, the direction of the expression changes, and correlations, we showed that two-step data reduction allowed us to significantly improve the performance of geneset analysis using a modified two-way ANOVA procedure, and to detect genesets that current methods fail to detect

Effects of non-steroidal anti-inflammatory drugs on cancer sites other than the colon and rectum: a meta-analysis

BACKGROUND: Observational studies have consistently shown that aspirin and non-steroidal anti-inflammatory drug (NSAID) use is associated with a close to 50% reduced risk of colorectal cancer. Studies assessing the effects of NSAIDs on other cancers have shown conflicting results. Therefore, we conducted a meta-analysis to evaluate the relationship between NSAID use and cancer other than colorectal. METHODS: We performed a search in Medline (from 1966 to 2002) and identified a total of 47 articles (13 cohort and 34 case-control studies). Overall estimates of the relative risk (RR) were calculated for each cancer site using random effects models. RESULTS: Aspirin use was associated with a reduced risk of cancer of the esophagus and the stomach (RR, 0.51; 95%CI (0.38–0.69), and 0.73; 95%CI (0.63–0.84)). Use of NSAIDs was similarly associated with a lower risk of esophageal and gastric cancers (RR,0.65; 95% CI(0.46–0.92) and RR,0.54; 95%CI (0.39–0.75)). Among other cancers, only the results obtained for breast cancer were fairly consistent in showing a slight reduced risk among NSAID and aspirin users (RR, 0.77; 95%CI (0.66–0.88), and RR, 0.77; 95%CI (0.69–0.86) respectively)). CONCLUSIONS: The results of this meta-analysis show that the potential chemopreventive role of NSAIDs in colorectal cancer might be extended to other gastrointestinal cancers such as esophagus and stomach. Further research is required to evaluate the role of NSAIDs at other cancers sites

SIMS: A Hybrid Method for Rapid Conformational Analysis

Proteins are at the root of many biological functions, often performing complex tasks as the result of large changes in their structure. Describing the exact details of these conformational changes, however, remains a central challenge for computational biology due the enormous computational requirements of the problem. This has engendered the development of a rich variety of useful methods designed to answer specific questions at different levels of spatial, temporal, and energetic resolution. These methods fall largely into two classes: physically accurate, but computationally demanding methods and fast, approximate methods. We introduce here a new hybrid modeling tool, the Structured Intuitive Move Selector (SIMS), designed to bridge the divide between these two classes, while allowing the benefits of both to be seamlessly integrated into a single framework. This is achieved by applying a modern motion planning algorithm, borrowed from the field of robotics, in tandem with a well-established protein modeling library. SIMS can combine precise energy calculations with approximate or specialized conformational sampling routines to produce rapid, yet accurate, analysis of the large-scale conformational variability of protein systems. Several key advancements are shown, including the abstract use of generically defined moves (conformational sampling methods) and an expansive probabilistic conformational exploration. We present three example problems that SIMS is applied to and demonstrate a rapid solution for each. These include the automatic determination of ﾑﾑactiveﾒﾒ residues for the hinge-based system Cyanovirin-N, exploring conformational changes involving long-range coordinated motion between non-sequential residues in Ribose- Binding Protein, and the rapid discovery of a transient conformational state of Maltose-Binding Protein, previously only determined by Molecular Dynamics. For all cases we provide energetic validations using well-established energy fields, demonstrating this framework as a fast and accurate tool for the analysis of a wide range of protein flexibility problems

Whole-body tissue stabilization and selective extractions via tissue-hydrogel hybrids for high-resolution intact circuit mapping and phenotyping

To facilitate fine-scale phenotyping of whole specimens, we describe here a set of tissue fixation-embedding, detergent-clearing and staining protocols that can be used to transform excised organs and whole organisms into optically transparent samples within 1–2 weeks without compromising their cellular architecture or endogenous fluorescence. PACT (passive CLARITY technique) and PARS (perfusion-assisted agent release in situ) use tissue-hydrogel hybrids to stabilize tissue biomolecules during selective lipid extraction, resulting in enhanced clearing efficiency and sample integrity. Furthermore, the macromolecule permeability of PACT- and PARS-processed tissue hybrids supports the diffusion of immunolabels throughout intact tissue, whereas RIMS (refractive index matching solution) grants high-resolution imaging at depth by further reducing light scattering in cleared and uncleared samples alike. These methods are adaptable to difficult-to-image tissues, such as bone (PACT-deCAL), and to magnified single-cell visualization (ePACT). Together, these protocols and solutions enable phenotyping of subcellular components and tracing cellular connectivity in intact biological networks

Airborne Signals from a Wounded Leaf Facilitate Viral Spreading and Induce Antibacterial Resistance in Neighboring Plants

Many plants release airborne volatile compounds in response to wounding due to pathogenic assault. These compounds serve as plant defenses and are involved in plant signaling. Here, we study the effects of pectin methylesterase (PME)-generated methanol release from wounded plants (“emitters”) on the defensive reactions of neighboring “receiver” plants. Plant leaf wounding resulted in the synthesis of PME and a spike in methanol released into the air. Gaseous methanol or vapors from wounded PME-transgenic plants induced resistance to the bacterial pathogen Ralstonia solanacearum in the leaves of non-wounded neighboring “receiver” plants. In experiments with different volatile organic compounds, gaseous methanol was the only airborne factor that could induce antibacterial resistance in neighboring plants. In an effort to understand the mechanisms by which methanol stimulates the antibacterial resistance of “receiver” plants, we constructed forward and reverse suppression subtractive hybridization cDNA libraries from Nicotiana benthamiana plants exposed to methanol. We identified multiple methanol-inducible genes (MIGs), most of which are involved in defense or cell-to-cell trafficking. We then isolated the most affected genes for further analysis: β-1,3-glucanase (BG), a previously unidentified gene (MIG-21), and non-cell-autonomous pathway protein (NCAPP). Experiments with Tobacco mosaic virus (TMV) and a vector encoding two tandem copies of green fluorescent protein as a tracer of cell-to-cell movement showed the increased gating capacity of plasmodesmata in the presence of BG, MIG-21, and NCAPP. The increased gating capacity is accompanied by enhanced TMV reproduction in the “receivers”. Overall, our data indicate that methanol emitted by a wounded plant acts as a signal that enhances antibacterial resistance and facilitates viral spread in neighboring plants