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".

Example Input: Paragraph: Coronaviruses (CoVs), enveloped positive-sense RNA viruses, are characterized by club-like spikes that project from their surface, an unusually large RNA genome, and a unique replication strategy. Coronaviruses cause a variety of diseases in mammals and birds ranging from enteritis in cows and pigs and upper respiratory disease chickens to potentially lethal human respiratory infections. Here we provide a brief introduction to coronaviruses discussing their replication and pathogenicity, and current prevention and treatment strategies. We will also discuss the outbreaks of the highly pathogenic Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and the recently identified Middle Eastern Respiratory Syndrome Coronavirus (MERS-CoV). Coronaviruses (CoVs) are the largest group of viruses belonging to the Nidovirales order, which includes Coronaviridae, Arteriviridae, and Roniviridae families. The Coronavirinae comprise one of two subfamilies in the Coronaviridae family, with the other being the Torovirinae. The Coronavirinae are further subdivided into four groups, the alpha, beta, gamma and delta coronaviruses. The viruses were initially sorted into these groups based on serology but are now divided by phylogenetic clustering. All viruses in the Nidovirales order are enveloped, non-segmented positive-sense RNA viruses. They all contain very large genomes for RNA viruses, with Coronavirinae having the largest identified RNA genomes, containing approximately 30 kilobase (kb) genomes. Other common features within the Nidovirales order include: i) a highly conserved genomic organization, with a large replicase gene preceding structural and accessory genes; ii) expression of many nonstructural genes by ribosomal frameshifting; iii) several unique or unusual enzymatic activities encoded within the large replicase-transcriptase polyprotein; and iv) expression of downstream genes by synthesis of 3′ nested sub-genomic mRNAs. In fact, the Nidovirales order name is derived from these nested 3′ mRNAs as nido is Latin for "nest". The major differences within the Nidovirus families are in the number, type, and Coronavirus virions are spherical with diameters of approximately 125 nm as depicted in recent studies by cryo-electron tomography and cryo-electron microscopy [2,3]. The most prominent feature of coronaviruses is the club-shape spike projections emanating from the surface of the virion. These spikes are a defining feature of the virion and give them the appearance of a solar corona, prompting the name, coronaviruses. Within the envelope of the virion is the nucleocapsid. Coronaviruses have helically symmetrical nucleocapsids, which is uncommon among positive-sense RNA viruses, but far more common for negativesense RNA viruses. Coronavirus virus particles contain four main structural proteins. These are the spike (S), membrane (M), envelope (E), and nucleocapsid (N) proteins, all of which are encoded within the 3′ end of the viral genome. The S protein (∼150 kDa), utilizes an N-terminal signal sequence to gain access to the ER, and is heavily N-linked glycosylated. Homotrimers of the virus encoded S protein make up the distinctive spike structure on the surface of the virus [4, 5] . The trimeric S glycoprotein is a class I fusion protein [6] and mediates attachment to the host receptor [7] . In most, but not all, coronaviruses, S is cleaved by a host cell furin-like protease into two separate polypeptides noted S1 and S2 [8, 9] . S1 makes up the large receptor-binding domain of the S protein while S2 forms the stalk of the spike molecule [10] . The M protein is the most abundant structural protein in the virion. It is a small (∼25-30 kDa) protein with 3 transmembrane domains [11] and is thought to give the virion its shape. It has a small N-terminal glycosylated ectodomain and a much larger C-terminal Fehr and Perlman The initial attachment of the virion to the host cell is initiated by interactions between the S protein and its receptor. The sites of receptor binding domains (RBD) within the S1 region of a coronavirus S protein vary depending on the virus, with some having the RBD at the Nterminus of S1 (MHV) while others (SARS-CoV) have the RBD at the C-terminus of S1 [29, 30] . The S-protein/receptor interaction is the primary determinant for a coronavirus to Fehr and Perlman  
 Title: Coronaviruses: An Overview of Their Replication and Pathogenesis HHS Public Access
Example Output: False

Example Input: Paragraph: Mechanistic hypotheses about airborne infectious disease transmission have traditionally emphasized the role of coughing and sneezing, which are dramatic expiratory events that yield both easily visible droplets and large quantities of particles too small to see by eye. Nonetheless, it has long been known that normal speech also yields large quantities of particles that are too small to see by eye, but are large enough to carry a variety of communicable respiratory pathogens. Here we show that the rate of particle emission during normal human speech is positively correlated with the loudness (amplitude) of vocalization, ranging from approximately 1 to 50 particles per second (0.06 to 3 particles per cm 3 ) for low to high amplitudes, regardless of the language spoken (english, spanish, Mandarin, or Arabic). Furthermore, a small fraction of individuals behaves as "speech superemitters," consistently releasing an order of magnitude more particles than their peers. our data demonstrate that the phenomenon of speech superemission cannot be fully explained either by the phonic structures or the amplitude of the speech. these results suggest that other unknown physiological factors, varying dramatically among individuals, could affect the probability of respiratory infectious disease transmission, and also help explain the existence of superspreaders who are disproportionately responsible for outbreaks of airborne infectious disease. It has long been recognized that particles expelled during human expiratory events, such as sneezing, coughing, talking, and breathing, serve as vehicles for respiratory pathogen transmission 1-6 . The relative contribution of each expiratory activity in transmitting infectious microorganisms, however, remains unclear 4 . Much previous research has focused on coughing 7-12 and sneezing 11,13,14 activities that yield relatively large droplets (approximately 50 μm or larger) easily visible to the naked eye. Less noticeable, but arguably more infectious for some diseases, are the smaller particles emitted during sneezing and coughing as well as during breathing 15-17 and talking 16, 18, 19 . These small particles are believed to be generated during breathing and talking from the mucosal layers coating the respiratory tract via a combination of a "fluid-film burst" mechanism within the bronchioles and from vocal folds adduction and vibration within the larynx 6,20,21 . The particles emitted during breathing and typical speech predominantly average only 1 μm in diameter 15-17 and are thus too small to see without specialized equipment; most people outside of the community of bioaerosol researchers are less aware of them. Despite their small size, however, these micron-scale particles are sufficiently large to carry a variety of respiratory pathogens such as measles virus (50-500 nm) 22 , influenza virus (100 nm-1 µm) 23 , and Mycobacterium tuberculosis (1-3 µm) 24 . Indeed, recent work by Yan et al. has confirmed that significant amounts of influenza viral RNA are present in small particles (<5 μm) emitted by influenza-infected individuals during natural breathing, without coughing or sneezing 25 . These small particles are potentially more infectious than larger sneeze-or 
 Title: Aerosol emission and superemission during human speech increase with voice loudness
Example Output: False

Example Input: Paragraph: Inhomogeneous percolation, for its closer relationship with real-life, can be more useful and reasonable than homogeneous percolation to illustrate the critical phenomena and dynamical behaviour of complex networks. However, due to its intricacy, the theoretical framework of inhomogeneous percolation is far from being complete and many challenging problems are still open. In this paper, we first investigate inhomogeneous site percolation on Bethe Lattices with two occupation probabilities, and then extend the result to percolation with m occupation probabilities. The critical behaviour of this inhomogeneous percolation is shown clearly by formulating the percolation probability P p ( ) ∞ with given occupation probability p, the critical occupation probability p p P p = sup{ ( ) = 0} c ∞ , and the average cluster size χ p ( ) where p is subject to P p ( ) = 0 ∞ . Moreover, using the above theory, we discuss in detail the diffusion behaviour of an infectious disease (SARS) and present specific disease-control strategies in consideration of groups with different infection probabilities. Percolation (for reviews see Stauffer 1979 1 , Essam 1980 2 ) is the random occupation of sites or bonds on lattices or networks, named as site percolation or bond percolation, respectively. Site percolation is more general than bond percolation because every bond model may be reformulated as a site model (on a different graph) and the converse is in general not true 3 . The appeal of percolation is the occurrence of a critical phenomenon, which has attracted attention for a wide range of applications: liquid flows in porous media 4,5 , epidemic spread 6-8 , granular and composite materials 9-12 , forest fires 13-15 and fracture patterns and earthquakes in rocks 16 . The research originated from homogeneous percolation, i.e., percolation with a single occupation probability. For example, Fisher and Essam (1961) solved homogeneous percolation problems on Bethe lattices 17 ; Sykes and Essam (1964) studied the exact critical occupation probabilities in two dimensions 18 ; Gerald (et al., 2001) derived the value of critical occupation probabilities for a four-dimensional percolation problem on hyper-cubic lattices 19 . Building on those results, researchers began to study inhomogeneous percolation 20-26 , in which sites (or bonds) may have different occupation probabilities. In 1982, Kesten 20 obtained a critical surface of occupation probabilities for inhomogeneous percolation on square lattices. In 2013, Grimmett 21 extended the result of inhomogeneous bond percolation to triangular and hexagonal lattices utilizing Russo-Seymour-Welsh (RSW) theory of box-crossings. Recently, Radicchi 27 studied percolation on not necessarily infinite graphs and used graph decomposition methods to identify abrupt and continuous changes in percolation. In the current paper, we focus on inhomogeneous percolation on Bethe lattices. The interest in a good understanding of this inhomogeneous percolation process is twofold. Firstly, Bethe lattices might hold fractal structures, which allow more complex behaviour than square or triangular lattices. The results of this inhomogeneous percolation on Bethe lattices have more extensive applications, e.g., the spreading problem of social networks or infectious diseases (see section III). Secondly, as is well known, most results on percolation are obtained using the heuristic approximation or numerical approaches 27-29 and it is difficult to get the exact solutions for average cluster sizes and the percolation probability even for homogeneous percolation. In this paper, we investigate inhomogeneous site percolation on Bethe lattices from two occupation probabilities to m occupation probabilities: for the case of two occupation probabilities, we present the explicit formula of the critical occupation probability and the exact solution of average cluster size χ p ( ); and for the case of m occupation probabilities, because of computational complexity, we formulate and obtain the numerical solutions of average cluster size and percolation probability, which might shed light on deriving the results of inhomogeneous percolation. Besides, we analyse in detail 
 Title: How Inhomogeneous Site Percolation Works on Bethe Lattices: Theory and Application OPEN
Example Output:
False