Titlebook: Viral Infections of Humans; Epidemiology and Con Alfred S. Evans,Richard A. Kaslow Book Jun 19974th edition Springer-Verlag US 1997 Epidemi

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書目名稱Viral Infections of Humans影響因子(影響力)




書目名稱Viral Infections of Humans影響因子(影響力)學(xué)科排名




書目名稱Viral Infections of Humans網(wǎng)絡(luò)公開度




書目名稱Viral Infections of Humans網(wǎng)絡(luò)公開度學(xué)科排名




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書目名稱Viral Infections of Humans讀者反饋學(xué)科排名

秘傳

https://doi.org/10.1007/978-1-4899-0036-4Epidemiology; Public Health; arbovirus; arenaviruses; infection; infections; viral infection; virus; infecti

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剛毅

Discrete

CytomegalovirusOnly a small proportion of people throughout the world escape infection with cytomegalovirus (CMV). The age at acquisition, the presence and type of clinical manifestations, and the sites and extent of virus excretion vary, but serological surveys conducted on all continents confirm the ubiquitous distribution of human CMV.

忘川河

rly the mediolateral oblique and mediolateral projections, play a vital role in traditional mammography, offering comprehensive views that include difficult-to-visualize areas. However, infrared imaging, which measures surface temperature distribution, provides a complementary approach by highlighti

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減少

Richard A. Kaslow,Alfred S. Evans-tailed excitatory postsynaptic potentials (EPSPs), involving a minority of extra-large (XL) EPSPs, are currently garnering much attention, which strongly relates to cognitive functions. In addition to physiological studies, mathematical modeling approaches are effective in neuroscience because they

雪白

Robert L. Atmar,Janet A. Englundynamically over time, creating high dimensional representations. This has been exploited in recurrent networks with fixed connections, which avoid the complexity of temporal credit assignment in modifying fixed connections. The processing of such reservoir networks can be further refined and constra

Anecdote

Richard A. Kaslow,Alfred S. Evansility in local feature extraction and feature scales. Most existing variants of Vision Transformers handling these challenges introduce additional computational overhead. Therefore, we present a .ynamic Kernel with .aussian .usion Transformer named ., tailored for dense prediction tasks, to facilita