In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

Paragraph: Accumulating evidence suggests that human γδ T cells act as non-classical T cells and contribute to both innate and adaptive immune responses in infections. Vγ2 Vδ2 T (also termed Vγ9 Vδ2 T) cells exist only in primates, and in humans represent a dominant circulating γδ T-cell subset. Primate Vγ2 Vδ2 T cells are the only γδ T cell subset capable of recognizing microbial phosphoantigen. Since nonhuman primate Vγ2 Vδ2 T cells resemble their human counterparts, indepth studies have been undertaken in macaques to understand the biology and function of human Vγ2 Vδ2 T cells. This article reviews the recent progress for immune biology of Vγ2 Vδ2 T cells in infections. 
 Title: Immune biology of Ag-specific γδ T cells in infections
True

Paragraph: Among the causes for pregnancy loss, viruses and non-infectious factors are among the most important. In both dogs and cats, research and clinical evidence provide proof that there is an increasing incidence of pregnancy loss associated with infectious diseases like herpesvirus, as well as the presence of toxicants or chemicals in the animal's diet and environment. Endocrine causes must be taken into consideration when dealing with pregnancy loss. This review will cover the most recent knowledge regarding viral and non-infectious of pregnancy losses in the dog and cat. # 
 Title: Canine and feline pregnancy loss due to viral and non-infectious causes: A review
True

Paragraph: Novel infectious diseases continue to emerge within human populations. Predictive studies have begun to identify pathogen traits associated with emergence. However, emerging pathogens vary widely in virulence, a key determinant of their ultimate risk to public health. Here, we use structured literature searches to review the virulence of each of the 214 known human-infective RNA virus species. We then use a machine learning framework to determine whether viral virulence can be predicted by ecological traits, including human-tohuman transmissibility, transmission routes, tissue tropisms, and host range. Using severity of clinical disease as a measurement of virulence, we identified potential risk factors using predictive classification tree and random forest ensemble models. The random forest approach predicted literature-assigned disease severity of test data with mean accuracy of 89.4% compared to a null accuracy of 74.2%. In addition to viral taxonomy, the ability to cause systemic infection was the strongest predictor of severe disease. Further notable predictors of severe disease included having neural and/or renal tropism, direct contact or respiratory transmission, and limited (0 < R 0 � 1) human-to-human transmissibility. We present a novel, to our knowledge, comparative perspective on the virulence of all currently known human RNA virus species. The risk factors identified may provide novel perspectives in understanding the evolution of virulence and elucidating molecular virulence mechanisms. These risk factors could also improve planning and preparedness in public health strategies as part of a predictive framework for novel human infections. 
 Title: Tissue tropism and transmission ecology predict virulence of human RNA viruses

True