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".

Ex Input:
Paragraph: To ensure the safety of wastewater reuse for irrigation of food crops and drinking water pathogenic viruses must be reduced to levels that pose no significant risk. To achieve this goal minimum reduction of viruses by treatment trains have been suggested. For use of edible crops a 6-log reduction and for production of potable drinking water a 12-log reduction has been suggested. These reductions were based on assuming infective virus concentrations of 10 5 to 10 6 per liter. Recent application of molecular methods suggests that some pathogenic viruses may be occurring in concentrations of 10 7 to 10 9 per liter. Factors influencing these levels include the development of molecular methods for virus detection, emergence of newly recognized viruses, decrease in per capita water use due to conservation measures, and outbreaks. Since neither cell culture nor molecular methods can assess all the potentially infectious virus in wastewater conservative estimates should be used to assess the virus load in untreated wastewater. This review indicates that an additional 2-to 3-log reduction of viruses above current recommendations may be needed to ensure the safety of recycled water. Information is needed on peak loading of viruses. In addition, more virus groups need to be quantified using better methods of virus quantification, including more accurate methods for measuring viral infectivity in order to better quantify risks from viruses in recycled water. 
 Title: How much reduction of virus is needed for recycled water: A continuous changing need for assessment?

Ex Output:
False


Ex Input:
Paragraph: Objective: Increased hepatic expression of dipeptidyl peptidase 4 (DPP4) is associated with non-alcoholic fatty liver disease (NAFLD). Whether this is causative for the development of NAFLD is not yet clarified. Here we investigate the effect of hepatic DPP4 overexpression on the development of liver steatosis in a mouse model of diet-induced obesity. Methods: Plasma DPP4 activity of subjects with or without NAFLD was analyzed. Wild-type (WT) and liver-specific Dpp4 transgenic mice (Dpp4-Liv-Tg) were fed a high-fat diet and characterized for body weight, body composition, hepatic fat content and insulin sensitivity. In vitro experiments on HepG2 cells and primary mouse hepatocytes were conducted to validate cell autonomous effects of DPP4 on lipid storage and insulin sensitivity. Results: Subjects suffering from insulin resistance and NAFLD show an increased plasma DPP4 activity when compared to healthy controls. Analysis of Dpp4-Liv-Tg mice revealed elevated systemic DPP4 activity and diminished active GLP-1 levels. They furthermore show increased body weight, fat mass, adipose tissue inflammation, hepatic steatosis, liver damage and hypercholesterolemia. These effects were accompanied by increased expression of PPARg and CD36 as well as severe insulin resistance in the liver. In agreement, treatment of HepG2 cells and primary hepatocytes with physiological concentrations of DPP4 resulted in impaired insulin sensitivity independent of lipid content. Conclusions: Our results give evidence that elevated expression of DPP4 in the liver promotes NAFLD and insulin resistance. This is linked to reduced levels of active GLP-1, but also to auto-and paracrine effects of DPP4 on hepatic insulin signaling. (A. Schürmann). Abbreviations: Ad, adenovirus; Akt, Akt serineethreonine protein kinase; ALT, alanine aminotransferase; ApoB, apolipoprotein B; AST, aspartate aminotransferase; BAT, brown adipose tissue; CD36, fatty acid translocase; Cpt1a, carnitine palmitoyltransferase 1a; Dgat2, diacylglycerol O-acyltransferase 2; DPP4, dipeptidyl peptidase 4; Dpp4-Liv-Tg, transgenic mice with hepatocyte-specific Dpp4 overexpression; F4/80, adhesion G protein-coupled receptor E1; Gfp, green fluorescent protein; GGT, gammaglutamyl transpeptidase; GLP-1, glucagon-like peptide 1; gWAT, gonadal white adipose tissue; HFD, high-fat diet; HOMA-IR, homeostatic model assessment for insulin resistance; IL6, interleukin 6; MAPK, mitogen-activated protein kinase; MCP1, chemokine (C-C motif) ligand 2; MOGAT1, monoacylglycerol O-acyltransferase 1; NAFLD, nonalcoholic fatty liver disease; NASH, non-alcoholic steatohepatitis; NFkB, nuclear factor-kB; pAkt, phosphorylated Akt serineethreonine protein kinase; WT, wild-type; PPARg, peroxisome proliferator activated receptor gamma; rhDPP4, recombinant human dipeptidyl peptidase 4; rmDPP4, recombinant mouse dipeptidyl peptidase 4; SM, skeletal muscle; Srebf1, sterol regulatory element binding transcription factor 1; sWAT, subcutaneous white adipose tissue; TNFa, tumor necrosis factor a 
 Title: Elevated hepatic DPP4 activity promotes insulin resistance and non-alcoholic fatty liver disease

Ex Output:
False


Ex Input:
Paragraph: Immunity to respiratory virus infection is governed by complex biological networks that influence disease progression and pathogenesis. Systems biology provides an opportunity to explore and understand these multifaceted interactions based on integration and modeling of multiple biological parameters. In this review, we describe new and refined systems-based approaches used to model, identify, and validate novel targets within complex networks following influenza and coronavirus infection. In addition, we propose avenues for extension and expansion that can revolutionize our understanding of infectious disease processes. Together, we hope to provide a window into the unique and expansive opportunity presented by systems biology to understand complex disease processes within the context of infectious diseases. analysis, and model refinement, thus highlighting the iterative cycles intended by systems biology. In this way, the systems biology paradigm drives understanding of complex systems through identification of novel, important components. The approach is not without criticism. Skeptics of systems biology criticize its high cost, slow pace, and descriptive results (2). While these charges reflect the initial development of any new field, the complaints are not at the root of the disillusionment with systems biology. Rather, subscribers of systems biology also over-emphasized the limitations of reductionist science and the corresponding lack of progress in complex diseases treatments for cancer, human immunodeficiency virus (HIV) infection, and diabetes (9). Systems biology was heralded as the evolution and replacement of these approaches, providing novel insights through hypothesis-neutral and discovery based approaches (4). Yet, it has also been fraught with setbacks and challenges, leading to criticisms on its reliance on reductionist-based approaches for validation, lack of hypothesis-driven direction, and declarations that the entire endeavor as a fishing expedition with a limited, non-specific catch (9). This conflict highlights the great irony of systems biology; an approach designed to integrate biological data has successfully divided the scientific field. 
 Title: Bugs in the System

Ex Output:

False