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1.
Production of nisin and pediocin were followed, respectively, in Lactococcus lactis subsp. lactis CECT 539 and Pediococcus acidilactici NRRL B-5627 grown with lactose and four different nitrogen sources. Neither NH4Cl nor glycine improved production of the bacteriocins. Both yeast extract and Casitone increased pediocin production from 55 BU ml–1 to 195 BU ml–1 and 185 BU ml–1, respectively. Nisin increased from 21 BU ml–1 to 74 BU ml–1 and 59 BU ml–1 with these nitrogen sources. 相似文献
2.
Isabella?C.?Felli Roberta?Pierattelli Steffen?J.?Glaser Burkhard?LuyEmail author 《Journal of biomolecular NMR》2009,43(3):187-196
Isotropic mixing sequences are one of the key methods to achieve efficient coherence transfer. Among them, the MOCCA-XY16,
which keeps the magnetization longitudinal for a significant amount of time, is characterised by favourable relaxation properties.
We show here that its adapted version is particularly suited for carbonyl–carbonyl correlations in 13C direct detection NMR experiments. 相似文献
3.
Marcelo dos Santos Guerra Maria Tereza M. Nogueira 《Plant Systematics and Evolution》1990,170(3-4):229-236
Analysis of several populations in a large part of the distribution area of the genusEmilia in Brazil has revealed only two species: the diploidE. sonchifolia and the tetraploidE. fosbergii. The more widely reportedE. coccinea was not found. They show a karyotype constancy in morphology and chromosome number (2n = 10 and 2n = 20, respectively), C-banding pattern and number of secondary constrictions. Some indications were found thatE. fosbergii may be an allopolyploid and that its ancestors had different genome sizes. 相似文献
4.
Roncarati R Latronico MV Musumeci B Aurino S Torella A Bang ML Jotti GS Puca AA Volpe M Nigro V Autore C Condorelli G 《Journal of cellular physiology》2011,226(11):2894-2900
Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiac disease. Fourteen sarcomeric and sarcomere‐related genes have been implicated in HCM etiology, those encoding β‐myosin heavy chain (MYH7) and cardiac myosin binding protein C (MYBPC3) reported as the most frequently mutated: in fact, these account for around 50% of all cases related to sarcomeric gene mutations, which are collectively responsible for approximately 70% of all HCM cases. Here, we used denaturing high‐performance liquid chromatography followed by bidirectional sequencing to screen the coding regions of MYH7 and MYBPC3 in a cohort (n = 125) of Italian patients presenting with HCM. We found 6 MHY7 mutations in 9/125 patients and 18 MYBPC3 mutations in 19/125 patients. Of the three novel MYH7 mutations found, two were missense, and one was a silent mutation; of the eight novel MYBPC3 mutations, one was a substitution, three were stop codons, and four were missense mutations. Thus, our cohort of Italian HCM patients did not harbor the high frequency of mutations usually found in MYH7 and MYBPC3. This finding, coupled to the clinical diversity of our cohort, emphasizes the complexity of HCM and the need for more inclusive investigative approaches in order to fully understand the pathogenesis of this disease. J. Cell. Physiol. 226: 2894–2900, 2011. © 2011 Wiley‐Liss, Inc. 相似文献
5.
Masakazu Takagi Hideyuki Tamaki Yukiko Miyamoto Roberta Leonardi Satoshi Hanada Suzanne Jackowski Shigeru Chohnan 《Journal of bacteriology》2010,192(1):233-241
Pantothenate kinase (CoaA) catalyzes the first step of the coenzyme A (CoA) biosynthetic pathway and controls the intracellular concentrations of CoA through feedback inhibition in bacteria. An alternative enzyme found in archaea, pantoate kinase, is missing in the order Thermoplasmatales. The PTO0232 gene from Picrophilus torridus, a thermoacidophilic euryarchaeon, is shown to be a distant homologue of the prokaryotic type I CoaA. The cloned gene clearly complements the poor growth of the temperature-sensitive Escherichia coli CoaA mutant strain ts9, and the recombinant protein expressed in E. coli cells transfers phosphate to pantothenate at pH 5 and 55°C. In contrast to E. coli CoaA, the P. torridus enzyme is refractory to feedback regulation by CoA, indicating that in P. torridus cells the CoA levels are not regulated by the CoaA step. These data suggest the existence of two subtypes within the class of prokaryotic type I CoaAs.Coenzyme A (CoA) is an essential cofactor synthesized from pantothenate (vitamin B5), cysteine, and ATP (1, 20, 30). The thiol group derived from the cysteine moiety in a CoA molecule forms a thioester bond, which is a high-energy bond, with carboxylates including fatty acids. The resulting compounds are called acyl-CoAs (CoA thioesters) and function as the major acyl group carriers in numerous metabolic and energy-yielding pathways. Since it is thought that the pantetheine moiety in CoA existed when life first came about on Earth (25) and at present, a CoA, acyl-CoA, or 4′-phosphopantethein moiety that is common to CoA and acyl carrier proteins is utilized by about 4% of all enzymes as a substrate (6), these compounds are thought to play a crucial role in the earliest metabolic system.Bacteria, fungi, and plants can produce pantothenate, which is the starting material of CoA biosynthesis, although animals must take it from their diet (41). The canonical CoA biosynthetic pathway consists of five enzymatic steps: i.e., pantothenate kinase (CoaA in prokaryotes and PanK in eukaryotes; EC 2.7.1.33), phosphopantothenoylcysteine synthetase (CoaB; EC 6.3.2.5), phosphopantothenoylcysteine decarboxylase (CoaC: EC 4.1.1.36), phosphopantetheine adenylyltransferase (CoaD; EC 2.7.7.3), and dephospho-CoA kinase (CoaE; EC 2.7.1.24). The organisms belonging to the domains Bacteria and Eukarya have this pathway (20, 30). CoaB, CoaC, CoaD, and CoaE are detectable in the complete genome sequences as orthologs of the counterparts from E. coli and humans (15, 16, 32). However, there is diversity among the CoaAs and PanKs, depending on their primary structures, and to date, three types of CoaA in bacteria and one type of PanK in eukaryotes have been identified. CoaAs and PanK catalyze the phosphorylation of pantothenate to produce 4′-phosphopantothenate at the first step of the pathway. First, the Escherichia coli CoaA (CoaAEc) was cloned as a prokaryotic type I CoaA after characterization of the properties enzymatically (42-44, 48). Thereafter, the eukaryotic PanK isoforms were isolated from Aspergillus nidulans (AnPanK), mice (mPanK), and humans (hPanK) (10, 17, 28, 29, 33, 34, 54-56). These enzyme activities were clearly regulated by end products of the biosynthetic pathway such as CoA, acetyl-CoA, and malonyl-CoA, and the pantothenate kinases governed the intracellular concentrations of CoA and acyl-CoAs (10, 17, 28, 29, 33, 34, 43, 44, 48, 54, 55). However, CoaAs insensitive to CoA and acyl-CoAs were recently identified from Staphylococcus aureus (CoaASa), Pseudomonas aeruginosa (CoaAPa), and Helicobacter pylori (CoaAHp) as prokaryotic type II and III CoaAs (9, 11, 18, 27). The structural and functional diversity among pantothenate kinases suggests that they are key indicators of the regulation of the CoA biosynthesis. In archaea neither CoaA nor pantothenate synthetase (PanC; EC 6.3.2.1), which catalyzes the condensation of pantoate and β-alanine to produce pantothenate, had been identified biochemically until very recently. COG1829 and COG1701 were assigned as the respective candidates based on comparative genomic analysis (15). COG1701 was reported to be PanC (36), and later the enzyme was revised to phosphopantothenate synthetase, which catalyzed the condensation of phosphopantoate and β-alanine (52). Together with the identification of COG1701, COG1829 was found to be pantoate kinase, responsible for the phosphorylation of pantoate (52). Homologues of pantoate kinase and phosphopantothenate synthetase are found in most archaeal genomes, thus establishing a noncanonical CoA biosynthetic pathway involving the two novel enzymes. However, homologues of the two novel enzymes are missing in the order Thermoplasmatales.Hence, we proceeded with a search for the kinase genes of the remaining archaea to elucidate the regulatory mechanism(s) underlying archaeal CoA biosynthesis. The PTO0232 gene in the complete genome sequence of Picrophilus torridus was identified as encoding a distant homologue of CoaAEc by a BLAST search. The recombinant protein phosphorylated pantothenate, but the activity was not inhibited at all by CoA or CoA thioesters despite its classification as prokaryotic type I CoaA. This functional difference between P. torridus CoaA (CoaAPt) and CoaAEc can be accounted for by an amino acid substitution at position 247 which possibly interacts with CoA. Here we describe the existence of a second subtype in the class of prokaryotic type I CoaAs. 相似文献
6.
Silvia Uccella Roberta Cerutti Davide Vigetti Daniela Furlan Rita Oldrini Ileana Carnevali Giuseppe Pelosi Stefano La Rosa Alberto Passi Carlo Capella 《The journal of histochemistry and cytochemistry》2006,54(8):863-875
Histidine decarboxylase (HDC) and vesicular monoamine transporter 2 (v-MAT2) are involved in the biosynthesis and storage of histamine. DOPA decarboxylase (DDC) is involved in the biosynthesis of a variety of amines and shares a high degree of homology with HDC. HDC and v-MAT2 immunoreactivities (IR) have recently been detected in well-differentiated neuroendocrine tumors (WDNETs) and poorly differentiated neuroendocrine carcinomas (PDNECs) of various sites and have been proposed as general endocrine markers. We evaluated HDC and v-MAT2 IR in a series of 117 WDNETs and PDNECs from different sites. Western blotting analysis was performed to verify the specificity of anti-DDC and anti-HDC antibodies. Real-time RT-PCR was performed using specific probes for HDC and DDC on 42 cases, examined also for DDC IR. HDC and v-MAT2 IR were observed in the majority of WDNETs and PDNECs of all sites and HDC-IR cases were always also DDC-IR. In contrast, high levels of HDC mRNA were detected only in the gastroenteropancreatic WDNETs, which did not show increased DDC mRNA levels. On the other hand, bronchial carcinoids and lung PDNECs showed high DDC mRNA levels, but nearly undetectable HDC mRNA levels. Western blotting analysis showed a cross-reaction between anti-HDC and anti-DDC antibodies. HDC should not be considered as a general endocrine marker and HDC IR in bronchial carcinoids and PDNECs of the lung can probably be attributed to a cross-reaction with DDC. 相似文献
7.
Emrah Altindis Roberta Cozzi Benedetta Di Palo Francesca Necchi Ravi P. Mishra Maria Rita Fontana Marco Soriani Fabio Bagnoli Domenico Maione Guido Grandi Sabrina Liberatori 《Molecular & cellular proteomics : MCP》2015,14(2):418-429
New generation vaccines are in demand to include only the key antigens sufficient to confer protective immunity among the plethora of pathogen molecules. In the last decade, large-scale genomics-based technologies have emerged. Among them, the Reverse Vaccinology approach was successfully applied to the development of an innovative vaccine against Neisseria meningitidis serogroup B, now available on the market with the commercial name BEXSERO® (Novartis Vaccines). The limiting step of such approaches is the number of antigens to be tested in in vivo models. Several laboratories have been trying to refine the original approach in order to get to the identification of the relevant antigens straight from the genome. Here we report a new bioinformatics tool that moves a first step in this direction. The tool has been developed by identifying structural/functional features recurring in known bacterial protective antigens, the so called “Protectome space,” and using such “protective signatures” for protective antigen discovery. In particular, we applied this new approach to Staphylococcus aureus and Group B Streptococcus and we show that not only already known protective antigens were re-discovered, but also two new protective antigens were identified.Although vaccines based on attenuated pathogens as pioneered by Luis Pasteur have been shown to be extremely effective, safety and technical reasons recommend that new generation vaccines include few selected pathogen components which, in combination with immunostimulatory molecules, can induce long lasting protective responses. Such approach implies that the key antigens sufficient to confer protective immunity are singled out among the plethora of pathogen molecules. As it turns out, the search for such protective antigens can be extremely complicated.Genomic technologies have opened the way to new strategies in vaccine antigen discovery (1, 2, 3). Among them, Reverse Vaccinology (RV)1 has proved to be highly effective, as demonstrated by the fact that a new Serogroup B Neisseria meningitidis (MenB) vaccine, incorporating antigens selected by RV, is now available to defeat meningococcal meningitis (4, 5). In essence, RV is based on the simple assumption that cloning all annotated proteins/genes and screening them against a robust and reliable surrogate-of-protection assay must lead to the identification of all protective antigens. Because most of the assays available for protective antigen selection involve animal immunization and challenge, the number of antigens to be tested represents a severe bottleneck of the entire process. For this reason, despite the fact that RV is a brute force, inclusive approach (“test-all-to-lose-nothing” type of approach) in their pioneered work of MenB vaccine discovery, Pizza and co-workers did not test the entire collection of MenB proteins but rather restricted their analysis to the ones predicted to be surface-localized. This was based on the evidence that for an anti-MenB vaccine to be protective bactericidal antibodies must be induced, a property that only surface-exposed antigens have. For the selection of surface antigens Pizza and co-workers mainly used PSORT and other available tools like MOTIFS and FINDPATTERNS to find proteins carrying localization-associated features such as transmembrane domains, leader peptides, and lipobox and outer membrane anchoring motifs. At the end, 570 proteins were selected and entered the still very labor intensive screening phase. Over the last few years, our laboratories have been trying to move to more selective strategies. Our ultimate goal, we like to refer to as the “Holy Grail of Vaccinology,” is to identify protective antigens by “simply” scanning the genome sequence of any given pathogen, thus avoiding time consuming “wet science” and “move straight from genome to the clinic” (6).With this objective in mind, we have developed a series of proteomics-based protocols that, in combination with bioinformatics tools, have substantially reduced the number of antigens to be tested in the surrogate-of-protection assays (7, 8). In particular, we have recently described a three-technology strategy that allows to narrow the number of antigens to be tested in the animal models down to less than ten (9). However, this strategy still requires high throughput experimental activities. Therefore, the availability of in silico tools that selectively and accurately single out relevant categories of antigens among the complexity of pathogen components would greatly facilitate the vaccine discovery process.In the present work, we describe a new bioinformatics approach that brings an additional contribution to our “from genome to clinic” goal. The approach has been developed on the basis of the assumption that protective antigens are protective in that they have specific structural/functional features (“protective signatures”) that distinguish them from immunologically irrelevant pathogen components. These features have been identified by using existing databases and prediction tools, such as PFam and SMART. Our approach focuses on protein biological role rather than its localization: it is completely protein localization unbiased, and lead to the identification of both surface-exposed and secreted antigens (which are the majority in extracellular bacteria) as well as cytoplasmic protective antigens (for instance, antigens that elicit interferon γ producing CD4+ T cells, thus potentiating the killing activity of phagocytic cells toward intracellular pathogens). Should these assumptions be valid, PS could be identified if: (1) all known protective antigens are compiled to create what we refer to as “the Protectome space,” and (2) Protectome is subjected to computer-assisted scrutiny using selected tools. Once signatures are identified, novel protective antigens of a pathogen of interest should be identifiable by scanning its genome sequence in search for proteins that carry one or more protective signatures. A similar attempt has been reported (10), where the discrimination of protective antigens versus nonprotective antigens was tried using statistical methods based on amino acid compositional analysis and auto cross-covariance. This model was implemented in a server for the prediction of vaccine candidates, that is, Vaxijen (www.darrenflower.info/Vaxijen); however, the selection criteria applied are still too general leading to a list of candidates that include ca. 30% of the total genome ORFs very similarly to the number of antigens predicted by classical RV based on the presence of localization signals.Here we show that Protectome analysis unravels specific signatures embedded in protective antigens, most of them related to the biological role/function of the proteins. These signatures narrow down the candidate list to ca. 3% of the total ORFs content and can be exploited for protective antigen discovery. Indeed, the strategy was validated by demonstrating that well characterized vaccine components could be identified by scanning the genome sequence of the corresponding pathogens for the presence of the PS. Furthermore, when the approach was applied to Staphylococcus aureus and Streptococcus agalactiae (Group B Streptococcus, GBS) not only already known protective antigens were rediscovered, but also two new protective antigens were identified. 相似文献
8.
Lanford RE Guerra B Lee H Averett DR Pfeiffer B Chavez D Notvall L Bigger C 《Journal of virology》2003,77(2):1092-1104
9.
Faenza I Bavelloni A Fiume R Santi P Martelli AM Maria Billi A Lo Vasco VR Manzoli L Cocco L 《Journal of cellular physiology》2004,200(2):291-296
In the present work, we have analyzed the expression and subcellular localization of all the members of inositide-specific phospholipase C (PLCbeta) family in muscle differentiation, given that nuclear PLCbeta1 has been shown to be related to the differentiative process. Cell cultures of C2C12 myoblasts were induced to differentiate towards the phenotype of myotubes, which are also indicated as differentiated C2C12 cells. By means of immunochemical and immunocytochemical analysis, the expression and subcellular localization of PLCbeta1, beta2, beta3, beta4 have been assessed. As further characterization, we investigated the localization of PLCbeta isoenzymes in C2C12 cells by fusing their cDNA to enhanced green fluorescent protein (GFP). In myoblast culture, PLCbeta4 was the most expressed isoform in the cytoplasm, whereas PLCbeta1 and beta3 exhibited a lesser expression in this cell compartment. In nuclei of differentiated myotube culture, PLCbeta1 isoform was expressed at the highest extent. A marked decrease of PLCbeta4 expression in the cytoplasm of differentiated C2C12 cells was detected as compared to myoblasts. No relevant differences were evidenced as regards the expression of PLCbeta3 at both cytoplasmatic and nuclear level, whilst PLCbeta2 expression was almost undetectable. Therefore, we propose that the different subcellular expression of these PLC isoforms, namely the increase of nuclear PLCbeta1 and the decrease of cytoplasmatic PLCbeta4, during the establishment of myotube differentiation, is related to a spatial-temporal signaling event, involved in myogenic differentiation. Once again the subcellular localization appears to be a key step for the diverse signaling activity of PLCbetas. 相似文献
10.
Caldeira RL Jannotti-Passos LK Lira PM Carvalho OS 《Memórias do Instituto Oswaldo Cruz》2004,99(5):499-502
Freshwater snails belonging to the genus Biomphalaria act as intermediate hosts for the parasite trematode Schistosoma mansoni in Africa and in the neotropical region. Identification of such molluscs is carried out based on morphological characters and the presence of cercariae is verified through squeezing snails between two glass slides or by exposing them to artificial light. However, sometimes, the material collected includes molluscs with decomposed bodies or, yet, only empty shells, which precludes their identification and S. mansoni detection. Due to these difficulties, we have developed a methodology in which DNA may be extracted from traces of organic material from inside shells in order to identify molluscs through polymerase chain reaction and restriction fragment length polymorphism and to detect S. mansoni into these snails, by using low stringency polymerase chain reaction. Species-specific profiles obtained from B. glabrata, B. straminea, and B. tenagophila snails and their shells, maintained in laboratory for ten years, showed the same profiles. S. mansoni profiles showed to be present in shell specimens as far as the eighth week after being removed from aquarium. 相似文献