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Generalist predators have the capacity to restrict pest population growth, especially early in the season before densities increase. However, their polyphagous feeding habits sometimes translate into reduced pest consumption when they target alternative prey. An order-specific monoclonal antibody was developed to examine the strength of trophic connections between Diptera, a major category of non-pest prey, and linyphiid spiders in alfalfa. We report the development and characterization of a monoclonal antibody with order-level specificity to Diptera. This antibody elicited strong absorbance to 22 Diptera from 13 families, no false-positive reactivity to non-dipteran invertebrates, and antigen detection periods following prey consumption that were comparable between spiders. Over 900 field-collected females of the linyphiid spiders Erigone autumnalis and Bathyphantes pallidus were screened for Diptera antigen. Significantly more B. pallidus screened positive for Diptera (40%) compared to E. autumnalis (16%), indicating differential reliance on these prey. In parallel with the collection of spiders for gut-content analysis, prey availability was estimated at web sites. The two spiders exhibited different feeding responses to prey availability. Consumption of Diptera by B. pallidus was strongly correlated with Diptera abundance whilst the availability of other potential prey did not influence predation rates. Conversely, E. autumnalis did not prey upon Diptera in proportion to availability, but increased Collembola activity-density reduced dipteran consumption. Integration of molecular gut-content analysis with precise sampling of prey demonstrated how two closely related linyphiid spiders exhibit different feeding responses to the availability of prey under natural field conditions. Elucidating the feeding preferences of natural enemies is critical to effective incorporation of biological control by generalist predators in the management of agricultural pests.  相似文献   
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Juvenile loggerhead turtles (Caretta caretta) from West Atlantic nesting beaches occupy oceanic (pelagic) habitats in the eastern Atlantic and Mediterranean, whereas larger juvenile turtles occupy shallow (neritic) habitats along the continental coastline of North America. Hence the switch from oceanic to neritic stage can involve a trans-oceanic migration. Several researchers have suggested that at the end of the oceanic phase, juveniles are homing to feeding habitats in the vicinity of their natal rookery. To test the hypothesis of juvenile homing behaviour, we surveyed 10 juvenile feeding zones across the eastern USA with mitochondrial DNA control region sequences (N = 1437) and compared these samples to potential source (nesting) populations in the Atlantic Ocean and Mediterranean Sea (N = 465). The results indicated a shallow, but significant, population structure of neritic juveniles (PhiST = 0.0088, P = 0.016), and haplotype frequency differences were significantly correlated between coastal feeding populations and adjacent nesting populations (Mantel test R2 = 0.52, P = 0.001). Mixed stock analyses (using a Bayesian algorithm) indicated that juveniles occurred at elevated frequency in the vicinity of their natal rookery. Hence, all lines of evidence supported the hypothesis of juvenile homing in loggerhead turtles. While not as precise as the homing of breeding adults, this behaviour nonetheless places juvenile turtles in the vicinity of their natal nesting colonies. Some of the coastal hazards that affect declining nesting populations may also affect the next generation of turtles feeding in nearby habitats.  相似文献   
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DPC168, a benzylpiperidine-substituted aryl urea CCR3 antagonist evaluated in clinical trials, was a relatively potent inhibitor of the 2D6 isoform of cytochrome P-450 (CYP2D6). Replacement of the cyclohexyl central ring with saturated heterocycles provided potent CCR3 antagonists with improved selectivity against CYP2D6. The favorable preclinical profile of DPC168 was maintained in an acetylpiperidine derivative, BMS-570520.  相似文献   
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This study applies a mental models survey approach to assess public thinking about oil spills and oil spill response. Based on prior interdisciplinary oil spill response research, the study first applies qualitative interview results and a response risk decision model to the design of a survey instrument. The decision model considers controlled burning, public health, and seafood safety. Surveying U.S. coastal residents (36,978 pairs of responses) through Google Insights identifies beliefs and gaps in understanding as well as related values and preferences about oil spills, and oil spill responses. A majority of respondents are concerned about economic impacts of major oil spills, and tend to see ocean ecosystems as fragile. They tend to see information about chemical dispersants as more important than ecological baseline information, and dispersants as toxic, persistent, and less effective than other response options. Although respondents regard laboratory studies as predictive of the effects of oil and of controlled burning, they are less confident that scientists agree on the toxicity and effectiveness of dispersants. The results illustrate opportunities to reframe discussions of oil spill response in terms of tradeoffs between response options, and new possibilities for assessing public opinions and beliefs during events.  相似文献   
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Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.  相似文献   
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