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1.
One of the challenges in oceanography is to understand the influence of environmental factors on the abundances of prokaryotes and viruses. Generally, conventional statistical methods resolve trends well, but more complex relationships are difficult to explore. In such cases, Artificial Neural Networks (ANNs) offer an alternative way for data analysis. Here, we developed ANN-based models of prokaryotic and viral abundances in the Arctic Ocean. The models were used to identify the best predictors for prokaryotic and viral abundances including cytometrically-distinguishable populations of prokaryotes (high and low nucleic acid cells) and viruses (high- and low-fluorescent viruses) among salinity, temperature, depth, day length, and the concentration of Chlorophyll-a. The best performing ANNs to model the abundances of high and low nucleic acid cells used temperature and Chl-a as input parameters, while the abundances of high- and low-fluorescent viruses used depth, Chl-a, and day length as input parameters. Decreasing viral abundance with increasing depth and decreasing system productivity was captured well by the ANNs. Despite identifying the same predictors for the two populations of prokaryotes and viruses, respectively, the structure of the best performing ANNs differed between high and low nucleic acid cells and between high- and low-fluorescent viruses. Also, the two prokaryotic and viral groups responded differently to changes in the predictor parameters; hence, the cytometric distinction between these populations is ecologically relevant. The models imply that temperature is the main factor explaining most of the variation in the abundances of high nucleic acid cells and total prokaryotes and that the mechanisms governing the reaction to changes in the environment are distinctly different among the prokaryotic and viral populations. 相似文献
2.
Three different pennation angle assumptions are compared to experimental data from Huijing and Woittiez (Neth. J. Zool. 34, 21-32, 1984) that relate fibre length to angle of pennation changes. The assumptions tested are: (1) neglecting pennation; (2) assuming a fixed pennation; and (3) assuming a constant muscle volume and thickness resulting in pennation angle being dependent on fibre length. Each assumption is compared by transforming fibre force/length and force/velocity characteristics to muscle properties. In general, the fixed pennation assumption provides the worst estimate of muscle force output with a peak error of 0.31 Fo during isometric contractions at small muscle lengths. A better estimate of muscle force output was provided by neglecting pennation entirely. The assumption that the pennation angle changed with fibre length maintained an error of less than 0.05 Fo for most lengths and velocities tested and provided the best estimate of muscle force output. 相似文献
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Jan G. de Winter 《BMJ (Clinical research ed.)》1953,2(4836):604-605
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Alfred Buschinger Werner Ehrhardt Ursula Winter 《Ethology : formerly Zeitschrift fur Tierpsychologie》1980,53(3):245-264
Slavemaking occurs in 8 genera or ants belonging to 2 subfamilies. Their raiding behaviour is described and compared. The basic organisation of a raid comprises scouting, recruitment, fight, and brood transportation. These elements can ordinarily be derived from the behaviour of the independent host species. Certain trends in the evolution of recruitment and fighting behaviour are discussed. “Intraspecific slavery”, which has been described by various authors, probably belongs to territorial behaviour. We doubt whether it represents an evolutionary precursor of true, interspecific slavery. 相似文献
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P Raeymaekers P De Jonghe L Swerts G De Winter J Gheuens J J Martin A Vandenberghe C Van Broeckhoven 《Cytogenetics and cell genetics》1989,50(2-3):178-180
We previously described a large five-generation family with autosomal dominant inheritance of hereditary motor and sensory neuropathy type I, or Charcot-Marie-Tooth disease (CMT1). The genetic defect in this family was not linked to the Duffy blood group. We investigated the possibility of a disease locus on the short arm of chromosome 1 using 12 anonymous DNA markers. Two markers, D1S2 and D1S22, showed positive linkage, suggesting the existence of a CMT1 locus on 1p. D1S2 and D1S22 are clustered in the 1p31----p22 region. However, multipoint linkage analysis, including additional DNA markers from this chromosome region, excluded a possible CMT1 locus in this part of chromosome 1. 相似文献
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