Area‐wide monitoring of potato tuberworm (Phthorimaea operculella) by pheromone trapping in Northern Italy: phenology,spatial distribution and relationships between catches and tuber damage

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Molecular characterization of porcine MMP19 and MMP23B genes and its association with immune traits

MMP19 and MMP23B belong to the Matrix metalloproteases (MMPs) family, which are zinc-binding endopeptidases that are capable of degrading various components of the extracellular matrix. They are thought to play important roles in embryonic development, reproduction and tissue remodeling, as well as in cell proliferation, differentiation, migration, angiogenesis, apoptosis and host defense. However, they are poorly understood in pigs. Here, we obtained the full length coding region sequence and genomic sequence of the porcine MMP19 and MMP23B genes and analyzed their genomic structures. The deduced amino acid sequence shares similar precursor protein domains with human and mouse MMP19 and MMP23B protein, respectively. Using IMpRH panel, MMP19 was mapped to SSC5p12-q11 (closely linked to microsatellite DK) and MMP23B was mapped to SSC8q11-q12 (linked to microsatellite Sw2521). Quantitative real-time PCR showed that MMP19 was abundantly expressed in the liver, while MMP23B was strongly expressed in the ovarian and heart. Furthermore, both genes were all expressed increasingly in prenatal skeletal muscle during development. Three SNPs were detected by sequencing and PCR-RFLP methods, and association analysis indicated that C203T at exon 5 of MMP19 has a significant association with the blood parameters WBC (G/L) and IgG2 (mg/mL) (P<0.05), SNP C131T at exon 3 of MMP23B is significantly associated with the blood parameters HGB (g/L) and MCH (P<0.05), and A150G in exon 4 has no significant association with the economic traits in pigs.     

Quantifying change in distributions: a new departure index that detects,measures and describes change in distributions from population structures,size-classes and other ordered data

Many statistics are available to compare distributions. Some are limited to nominal data while others, such as skew, Kullback-Leibler, Kolmogorov-Smirnov and the Gini coefficient, are useful for providing information about ordered distributions. While many of these tests are useful for determining properties of data in histograms, there has not been a test until now that allows for the detection of differences between distributions, describes the difference and is sensitive to the location of the departures. Such a test could be critical for comparing pre-and post-event distributions, such as a change in the distribution of biomass due to fire, for example, or for comparing data from different locations, such as soil size distributions, and even for evaluating economic disparity or examining differences in age demographics. We present a new statistic, a departure index, which allows a test distribution to be compared with any reference distribution. The resulting index contains information about the location, magnitude and direction of departure from the reference distribution to the test distribution. The departure index in turn provides a standardized response range that allows for a comparison of results from different analyses. A case study of actual fire data demonstrates the sensitivity and range of the test.     

A new variant of Autographa californica nuclear polyhedrosis virus

A variant of the baculovirus, Autographa californica nuclear polyhedrosis virus, has been isolated in Idaho during an epizootic disease in a field population of A. californica. Genotypic characterization indicates that the virus is distinct from variants previously characterized. Analysis of five clones, derived by plaque purification in cell culture, indicates relative homogeneity of the original virus isolate. Further exploration of the factors involved in natural genetic variability of baculoviruses is appropriate.     

Cloning and characterization of a novel 3-hydroxy-3-methylglutaryl coenzyme A reductase gene from Salvia miltiorrhiza involved in diterpenoid tanshinone accumulation

The 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) catalyzes the conversion of HMG-CoA to mevalonate (MVA), which is a rate-limiting step in the isoprenoid biosynthesis via the MVA pathway. In this study, the full-length cDNA encoding HMGR (designated as SmHMGR2, GenBank accession no. FJ747636) was isolated from Salvia miltiorrhiza by rapid amplification of cDNA ends (RACE). The cloned gene was then transformed into the hairy root of S. miltiorrhiza, and the enzyme activity and production of diterpenoid tanshinones and squalene were monitored. The full-length cDNA of SmHMGR2 comprises 1959 bp, with a 1653-bp open reading frame encoding a 550-amino-acid protein. Molecular modeling showed that SmHMGR2 is a new HMGR with a spatial structure similar to other plant HMGRs. SmHMGR2 contains two HMG-CoA-binding motifs and two NADP(H)-binding motifs. The SmHMGR2 catalytic domain can form a homodimer. The deduced protein has an isoelectric point of 6.28 and a calculated molecular weight of approximately 58.67 kDa. Sequence comparison analysis showed that SmHMGR2 had the highest homology to HMGR from Atractylodes lancea. As expected, a phylogenetic tree analysis indicates that SmHMGR2 belongs to plant HMGR group. Tissue expression pattern analysis shows that SmHMGR2 is strongly expressed in the leaves, stem, and roots. Functional complementation of SmHMGR2 in HMGR-deficient mutant yeast JRY2394 demonstrates that SmHMGR2 mediates the MVA biosynthesis in yeasts. Overexpression of SmHMGR2 increased enzyme activity and enhanced the production of tanshinones and squalene in cultured hairy roots of S. miltiorrhiza. Our DNA gel blot analysis has confirmed the presence and integration of the associated SmHMGR2 gene. SmHMGR2 is a novel and important enzyme involved in the biosynthesis of diterpenoid tanshinones in S. miltiorrhiza.     

Exclusion effects on vegetation characteristics and their correlation to soil factors in the semi-arid rangeland of Mu Us Sandland,China

Exclusion has been applied as a main measure for re-vegetation all over the world.This paper,by comparing the results of year-round exclusion,seasonal exclusion,and non-exclusion,quantified the vegetation variations under three different exclusion measures and their correlation to soil factors.The analysis results for community species component and plant diversity using multi-response permutation procedures (MRPPs)showed that exclusion did change the species component and increase plant diversity remarkably,while the period of exclusion had no significant influence on these two community features.The indicator species analysis and calculation of similarity indices indicated that community for year-round exclusion were becoming xerophytization and unpalatability,and showed highly spatial heterogeneity of plant species distribution,whereas community for seasonal exclusion was under stable non-equilibrium condition.Detrended correspondence analysis (DCA)and detrend canonical correspondence analysis (DCCA)results of relationship between plant species and soil variables demonstrated that soil moisture was a controlling factor for plant species component,microbiotic soil crust cover,soil organic matter,and soil bulk density had significant effects on soil moisture,among which microbiotic soil crust was a leading factor owing to its limitation to rainfall infiltration on the one hand,and its constraints to entrance of herbaceous seeds into soil or to germination of soil seeds on the other hand.As a result of long-term removal of animal grazing,crust kept intact in year-round exclusion community,which was a main reason of community xerophytization.It was also obvious from ordination results that some important environmental factors,such as tempo-spatial change of rainfall and corresponding tempo-spatial change of soil moisture,were neglected during direct gradient analysis.In addition,biodiversity was close related to soil nutrients as well as to soil moisture condition (soil water content and crust cover),and it had positive relation to available N,and negative relation to available P.Higher soil N had advantage to non-leguminous plants growth on nutrition-poor sand land definitely.The impact of P to community component was unclear and should be studied from plant physiology.Further researches on nonequilibrium theory in semi-arid rangeland will provide a scientific and flexible animal development paradigm for being implementing livestock fen-raising and grazing-forbidden policies in China.     

Predictive margins with survey data

In the analysis of covariance, the display of adjusted treatment means allows one to compare mean (treatment) group outcomes controlling for different covariate distributions in the groups. Predictive margins are a generalization of adjusted treatment means to nonlinear models. The predictive margin for group r represents the average predicted response if everyone in the sample had been in group r. This paper discusses the use of predictive margins with complex survey data, where an important consideration is the choice of covariate distribution used to standardize the predictive margin. It is suggested that the textbook formula for the standard error of an adjusted treatment mean from the analysis of covariance may be inappropriate for applications involving survey data. Applications are given using data from the 1992 National Health Interview Survey (NHIS) and the Epidemiologic Followup Study to the first National Health and Nutrition Examination Survey (NHANES I).     

Data-Mining Discovery of Pattern and Process in Ecological Systems

ABSTRACT Most ecologists use statistical methods as their main analytical tools when analyzing data to identify relationships between a response and a set of predictors; thus, they treat all analyses as hypothesis tests or exercises in parameter estimation. However, little or no prior knowledge about a system can lead to creation of a statistical model or models that do not accurately describe major sources of variation in the response variable. We suggest that under such circumstances data mining is more appropriate for analysis. In this paper we 1) present the distinctions between data-mining (usually exploratory) analyses and parametric statistical (confirmatory) analyses, 2) illustrate 3 strengths of data-mining tools for generating hypotheses from data, and 3) suggest useful ways in which data mining and statistical analyses can be integrated into a thorough analysis of data to facilitate rapid creation of accurate models and to guide further research.     

Integrating frugivory and animal movement: a review of the evidence and implications for scaling seed dispersal

General principles about the consequences of seed dispersal by animals for the structure and dynamics of plant populations and communities remain elusive. This is in part because seed deposition patterns emerge from interactions between frugivore behaviour and the distribution of food resources, both of which can vary over space and time. Here we advocate a frugivore‐centred, process‐based, synthetic approach to seed dispersal research that integrates seed dispersal ecology and animal movement across multiple spatio‐temporal scales. To guide this synthesis, we survey existing literature using paradigms from seed dispersal and animal movement. Specifically, studies are discussed with respect to five criteria: selection of focal organisms (animal or plant); measurement of animal movement; characterization of seed shadow; animal, plant and environmental factors included in the study; and scales of the study. Most studies focused on either frugivores or plants and characterized seed shadows directly by combining gut retention time with animal movement data or indirectly by conducting maternity analysis of seeds. Although organismal traits and environmental factors were often measured, they were seldom used to characterize seed shadows. Multi‐scale analyses were rare, with seed shadows mostly characterized at fine spatial scales, over single fruiting seasons, and for individual dispersers. Novel animal‐ and seed‐tracking technologies, remote environmental monitoring tools, and advances in analytical methods can enable effective implementation of a hierarchical mechanistic approach to the study of seed dispersal. This kind of mechanistic approach will provide novel insights regarding the complex interplay between the factors that modulate animal behaviour and subsequently influence seed dispersal patterns across spatial and temporal scales.