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雌雄同株黄瓜单性结实性主基因+多基因混合遗传分析   总被引:8,自引:2,他引:6  
以雌雄同株黄瓜强单性结实自交系'6457'和非单性结实自交系'6426'为亲本,建立了5世代联合群体(P1、P2、F1、F2、F2∶3),采用植物数量性状主基因+多基因混合遗传模型对群体的单性结实性进行多世代联合分析.结果表明:雌雄同株黄瓜单性结实性表现为不完全显性遗传,符合D-2遗传模型,受1对加性主基因+加性-显性多基因控制.主基因加性效应值为14.7,多基因加性效应值为20.9,多基因显性效应值为25.8.F2的遗传率为56.6%,F2∶3的遗传率为48.7%.因此,对雌雄同株黄瓜单性结实性的遗传改良,可选择强单性结实性材料,通过杂交、回交转移主基因,达到选育强单性结实性材料目的.  相似文献   
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Historically, marine ecologists have lacked efficient tools that are capable of capturing detailed species distribution data over large areas. Emerging technologies such as high‐resolution imaging and associated machine‐learning image‐scoring software are providing new tools to map species over large areas in the ocean. Here, we combine a novel diver propulsion vehicle (DPV) imaging system with free‐to‐use machine‐learning software to semi‐automatically generate dense and widespread abundance records of a habitat‐forming algae over ~5,000 m2 of temperate reef. We employ replicable spatial techniques to test the effectiveness of traditional diver‐based sampling, and better understand the distribution and spatial arrangement of one key algal species. We found that the effectiveness of a traditional survey depended on the level of spatial structuring, and generally 10–20 transects (50 × 1 m) were required to obtain reliable results. This represents 2–20 times greater replication than have been collected in previous studies. Furthermore, we demonstrate the usefulness of fine‐resolution distribution modeling for understanding patterns in canopy algae cover at multiple spatial scales, and discuss applications to other marine habitats. Our analyses demonstrate that semi‐automated methods of data gathering and processing provide more accurate results than traditional methods for describing habitat structure at seascape scales, and therefore represent vastly improved techniques for understanding and managing marine seascapes.  相似文献   
4.
秦岭细鳞鲑栖息地环境特征研究   总被引:3,自引:0,他引:3  
为研究秦岭细鳞鲑(Brachymystax lenok tsinlingensis)的栖息地环境选择偏好,对陕西陇县秦岭细鳞鲑国家级自然保护区和甘肃秦州珍稀水生野生动物国家级自然保护区内25个样点进行了鱼类采样和生境测量。共采集到130尾秦岭细鳞鲑样本,样点的平均分布密度为(0.10±0.07)尾/m,两个保护区内秦岭细鳞鲑的密度存在显著性差异(P < 0.05)。与秦岭细鳞鲑密度分布呈正相关的因子依次为坡度、跌水区密度、海拔、粗糙度、遮蔽度、底栖动物生物量和流速,与密度呈负相关的环境因子依次为河宽、水深和溶氧。冗余性分析(RDA)筛选了坡度、粗糙度、遮蔽度、海拔和跌水区5个关键环境因子。基于关键环境因子的实测值建立了栖息地适合度曲线,秦岭细鳞鲑分布的最适坡度范围为5%-10%,海拔分布范围为1500-2000 m,粗糙度范围0.3-0.4;跌水区密度范围12-18个/100 m,遮蔽度在0.5以上。  相似文献   
5.
We introduce the computer tool “Know Your Samples” (KYSS) for assessment and visualisation of large scale proteomics datasets, obtained by mass spectrometry (MS) experiments. KYSS facilitates the evaluation of sample preparation protocols, LC peptide separation, and MS and MS/MS performance by monitoring the number of missed cleavages, precursor ion charge states, number of protein identifications and peptide mass error in experiments. KYSS generates several different protein profiles based on protein abundances, and allows for comparative analysis of multiple experiments. KYSS was adapted for blood plasma proteomics and provides concentrations of identified plasma proteins. We demonstrate the utility of the KYSS tool for MS based proteome analysis of blood plasma and for assessment of hydrogel particles for depletion of abundant proteins in plasma. The KYSS software is open source and is freely available at http://kyssproject.github.io/.  相似文献   
6.
Data integration and visualization are crucial to obtain meaningful hypotheses from the diversity of ‘omics’ fields and the large volume of heterogeneous and distributed data sets. In this review we focus on network analysis as a key technique to integrate, visualize and extrapolate relevant information from diverse data. We first describe challenges in integrating different types of data and then focus on systematically exploring network properties to gain insight into network function. We also describe the relationship between network structures and function of elements that form it. Next, we highlight the role of the interactome in connecting data derived from different experiments, and we stress the importance of network analysis to recognize interaction context-specific features. Finally, we present an example integration to demonstrate the value of the network approach in cancer research, and highlight the importance of dynamic data in the specific context of signaling pathways.  相似文献   
7.
Promoter analysis typically employs a reporter gene fused to a test promoter combined with a second reporter fused to a control promoter that is used for normalization purposes. However, this approach is not valid when experimental conditions affect the control promoter. We have developed and validated a single secreted luciferase reporter (SSLR) assay for promoter analysis that avoids the use of a control reporter. The approach uses an early level of expression of a secreted luciferase linked to a test promoter as an internal normalization control for subsequent analysis of the same promoter. Comparison of the SSLR assay with the dual luciferase reporter (DLR) assay using HMGCR (3-hydroxy-3-methylglutaryl-coenzyme A reductase) and LDLR (low-density lipoprotein receptor) promoter constructs, which are down-regulated by 25-hydroxycholesterol, show that both assays yield similar results. Comparison of the response of the HMGCR promoter in SSLR transient assays compared very favorably with the response of the same promoter in the stable cell line. Overall, the SSLR assay proved to be a valid alternative to the DLR assay for certain applications and had significant advantages in that measurement of only one luciferase is required and monitoring can be continuous because cell lysis is not necessary.  相似文献   
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棉田非靶标害虫发生丰度与气象因子的关联性分析   总被引:1,自引:0,他引:1       下载免费PDF全文
【背景】近年来,全球气候条件不断变化,其对农田生态系统和病虫草害发生的影响较大。【方法】于2009~2013年在河南安阳田间小区系统调查了转基因棉田和非转基因棉田主要害虫种群丰度,结合5年间本地气象因素,分段分析了影响棉田主要害虫发生的关键气象因子及其关键影响时期。【结果】不同害虫发生危害的关键气象因子及其关键影响时期不同,且不同害虫种群丰度与气象因子及其影响时期有不同的相关性。影响棉蚜的关键性气象因子是1~8月的平均气温,与相对湿度和降雨量相关性较低;影响棉叶蝉的关键气象因子是1~4月的降雨量,其次是1~8月的相对湿度,而平均气温与其相关性很低;5~8月降雨量对烟粉虱有一定的抑制作用,平均气温和相对湿度对其无显著影响;影响棉蓟马的关键气象因子是平均气温,其次是5~8月相对湿度,降雨量与棉蓟马丰度相关性较低;平均气温、相对湿度和降雨量与盲蝽种群丰度的相关性很低,其发生危害可能是多种气象因子协同作用的结果。【结论与意义】本研究结果可为气候变化条件下棉田主要害虫的预测预报和防御提供理论依据。  相似文献   
10.
The production of waste creates both direct and indirect environmental impacts. A range of strategies are available to reduce the generation of waste by industry and households, and to select waste treatment approaches that minimize environmental harm. However, evaluating these strategies requires reliable and detailed data on waste production and treatment. Unfortunately, published Australian waste data are typically highly aggregated, published by a variety of entities in different formats, and do not form a complete time‐series. We demonstrate a technique for constructing a multi‐regional waste supply‐use (MRWSU) framework for Australia using information from numerous waste data sources. This is the first MRWSU framework to be constructed (to the authors' knowledge) and the first sub‐national waste input‐output framework to be constructed for Australia. We construct the framework using the Industrial Ecology Virtual Laboratory (IELab), a cloud‐hosted computational platform for building Australian multi‐regional input‐output tables. The structure of the framework complies with the System of Environmental‐Economic Accounting (SEEA). We demonstrate the use of the MRWSU framework by calculating waste footprints that enumerate the full supply chain waste production for Australian consumers.  相似文献   
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