Oral cancer is a complex disorder. Its creation and spreading are due to the interaction of several proteins and genes in different biological thoroughfares. To study biological pathways, many high-yield methods have been used. Efforts to merge several data found at separate levels related to biological thoroughfares and interlinkage networks remain elusive. In our research work, we have proposed a technique known as protein-protein interaction network for analysis and exploring the genes involved in oral cancer disorders. The previous studies have not fully analyzed the proteins or genes involved in oral cancer. Our proposed technique is fully interactive and analyzes the data of oral cancer disorder more accurately and efficiently. The methods used here enabled us to observe the wide network consists of one mighty network comprising of 208 nodes 1572 edges which connect these nodes and various detached small networks. In our study, TP53 is a gene that occupied an important position in the network. TP53 has a 113-degree value and 0.03881821 BC value, indicating that TP53 is centrally localized in the network and is a significant bottleneck protein in the oral cancer protein-protein interaction network. These findings suggested that the pathogenesis of oral cancer variation was organized by means of an integrated PPI network, which is centered on TP53. Furthermore, our identification shows that TP53 is the key role-playing protein in the oral cancer network, and its significance in the cellular networks in the body is determined as well. As TP53 (tumor protein 53) is a vital player in the cell division process, the cells may not grow or divide disorderly; it fulfills the function of at least one of the gene groups in oral cancer. However, the latter progression in the area is any measure; the intention of developing these networks is to transfigure sketch of core disease development, prognosis, and treatment. Verbal cancer, mouth cancer, and head and neck cancer are all related to each other , which aOral cancer is the most common cancer in the world with low overall survival rates , where oVarious biological procedures are enunciated as systems. Networks are demonstrated from the field of atomic sciences such as metabolic networks, protein interaction networks, and gene regulatory networks. Network analysis, sculpting, and visualization are imperative stride about a framework's natural perception of living beings. The visual interpretation of such networks gives the basic idea about the structure of network and also can create an idea about newly generated complex biological data .Genetically, oral cancer has still not been fully analyzed and explored even after several years of research. Many casual or susceptible genes associated with oral cancer have been reported in the research studies. Thus, the focus of our research work is to obtain results that give us useful data related to genes involved in oral cancer and enable us to draw conclusions about gene-gene and related protein-protein interactions.Latest disease genes identificationStudy of their network valuablesSpotting disease-linked subnetworksNetwork-based disease sortingThe role of proteins networks in syndromes identification and analyses is very significant. Throughout a period, study in miniature creatures and protein networks plays a vital role in molecular development study enhancement to increase vision in the strength of cells to disruption and also for allotment of new protein occupations. Succeeding those investigations and the present increase of protein interactivity capabilities in mammals, protein networks are progressively helping as toolkit to unknot the molecular foundation of disease. The authors of this paper review favorable uses of protein networks in disease in four main parts:Proteins networks can also be used in areas like infectious disease, self-medication, and pharmacology .Due to interaction detection methods, millions of interactions between proteins have been discovered. Focus of the paper is based on the graph theory, and the authors derived a new method from graph theory known as spectral method. Spectral method was used to disclose unseen networked structures of complex protein-protein collaboration networks. They come up with the idea that these concealed topological layouts comprise organically germane serviceable clusters. This idea stirs a novel approach to forecast the function of unknown proteins based on the classification of known proteins within network structures. With the use of this procedure, 48 quasi-cliques and six quasi-bipartite were separated from a network consisting of 11855 interactions among 2617 proteins in budding yeast, and 76 uncharacterized proteins were allocated functions .All biological processes were considered important and specifically achieved through protein-protein interactions. They use spectral analysis method as a technique to disclose high-level structures using colossal and multifaceted associations. the structurae of the image radius that the authours obtained by the speculative analysis is used in the nalysis for the topology making is used in the analysis for the topology making for the disclosure of unseen topological structures of a complicated interface network .Proteome functional organization can understand better by using human PPI for the identification of interrelating sets of human proteins scientifically; a protein matrix of 4456 baits and 5632 victims was separated by predetermined yeast two-hybrid (Y2H) interaction coupling. They acknowledged 3186 frequently original relations among 1705 proteins, which produce a huge, highly connected network, following Y2H technique. The Y2H scheme is an influential PPIs identification tool, applicable to high-throughput method in detection of complete proteome of organism interactions. To describe high-level confidence relation among proteins, different techniques were used, for example, topological, GO criteria and scoring system. The network was also used to locate genes without any character assigned to humans' disease proteins players in regulating the cellar trials. Screening human proteins relations systematically can enhance the understanding of protein functionality and cellular procedures . They prThere are a huge number of collaboration networks; studying them, finding functional nodes and links, and investigating the inner function of cells are the aims of this research. The authors performed a precise chart theory-based examination of this PPI arranged to build computational models for relating and anticipating the properties of deadly changes and proteins contributing in hereditary contacts, utilitarian bunches, protein complexes, and signaling pathways. Their examination suggests that deadly modifications are not as it were exceedingly related inside the organize structure, but they moreover fulfill an extra property. Their evacuation causes a diversion in organized structure. Creators moreover give proof for the nearness of diverse ways that maintain a strategic distance from practical proteins in PPI systems, whereas such ways do not exist for lethal change .The PPI network includes a minor quantity of exceedingly associated protein hubs  and various ineffectively associated hubs. Genome-wide consideration shows that cancellation of a center protein is more likely to be hazardous than cancellation of a nonhub protein; a wonder known as the centrality-lethality runs the show. This run of the show is commonly accumulated to reflect the unprecedented centrality of centers in organizing the courses of action of qualities, which in turn suggests the normal importance of organize plans, a key conviction of frameworks of science. Concurring to creators, for the notoriety of this clarification, the fundamental cause of the centrality-lethality running the show has never been basically inspected. They propose the concept of basic PPIs to discover out PPIs that are vital for the presence or propagation of a life form. As anticipated, basic PPIs are developmentally more moderated than insignificant PPIs. Considering the part of basic PPIs in deciding quality essentiality, they discover the yeast PPI organize practically more energetic than arbitrary systems, however with less distant sound than the potential ideal. These and other discoveries give unused viewpoints on the organic centrality of arranged structure and vigor .Genomic consideration shows that erasing an exceedingly associated protein hub (center) is more likely to be deadly to a living being than erasing a humble associated hub (nonhub), a strategy recognized as the centrality-lethality running the show. As center points are more imperative than nonhubs in organizing the worldwide arranged structure, the centrality-lethality running the show is broadly accepted to reflect the worth of organized engineering in characterizing organized work, a key idea of frameworks science. Consequently, the centrality-lethality running the show is clarified without the association of organized engineering. Utilizing yeast information, the creators gave down to earth confirmation supporting their speculation .In paper , ARIYA eIn paper  by amiriProtein-protein interaction (PPI) network analysis of cancer has gained focus of medical and biological scientists. Through this approach, examination of the interaction between genes that cause cancer could lead us to improve the diagnosis and treatment of patients , 16. In The methodology of protein-protein interaction of oral cancer consists of seven steps. The first step is the attainment of the candidate genes of oral cancer, which was done in two ways: Firstly, PolySearch text-mining system is used, which is a web-based tool exploiting various techniques to highlight and align informative text. PolySearch results gave us a large list of genes and proteins. A lot of data are available on cancer. As our specific target is to collect and analyze data on oral cancer, PolySearch tool is used for obtaining the specific data on oral cancer. Secondly, we manually confirmed from reviewing the literature that the obtained genes and proteins were indeed of oral cancer. The second step of the methodology involved the use of STRING database for scanning the protein interactions. Construction of PPI networks, from which an extended network was derived, was used to extract the giant component making up the third step. The fourth step involved the analysis of the PPI network based on its topology. The fifth step was the creation of a backbone network from the giant network based on the highest betweenness centrality (BC) value. The sixth step involved the construction of a subnetwork from the giant network that consisted of all shortest routes among the aspirant genes. The seventh step concluded with TSPO as central protein.Candidate genes associated with oral cancer were searched using PolySearch text-mining system, which is a useful tool in producing a list of ideas based on the query of the user. PudMed, OMIM, Drug Bank, and Swiss-Prot are among the few sources of information from which data relevant to the query can be extracted. PolySearch contributes a great deal to extracting information on important biomedical concepts such as genes/proteins, disease, SNPs, drugs, metabolites, pathways, and tissues, which could help in finding the relevant genes of oral cancer . This meOral cancer candidate genes listed in A system, which consisted of the seed proteins and has also direct association with their PPI's neighbors, was developed. This system was built using Cytoscape , which i  Average degree (<K>): mean of total degree values of nodes in a network  Mean shortest path length (mspl): represents connecting average of the steps involved to every pair of nodes using their shortest path  Diameter (D): longest paths among all shortest paths, characteristics of nodes, and measurements used to characterize the network were calculated by Cytoscape software in this paperFor accessing the hubs in the system, nodes properties such as betweenness centrality (BC), closeness centrality (CC), and degree (K) were noted and used, especially K and BC values. Quantity of adjacent joints which is based on the determination of the number of connections of one protein to its neighbors determines the most important factor, that is, the degree (K). The number of shortest routes that pass through each node, thus measuring the frequent occurrence of the node which has the greatest restricted routes between other nodes, determines the betweenness centrality. Shortest path or the most limited way is found by measuring the length of every last one of the geodesics to or from the network. The flow in the network is characterized by a high BC node value to great extent. BC may assume an integral part as a global property, since it is a valuable pointer to distinguish bottlenecks in a system. The converse of the normal length of the briefest paths among all the other nodes in the graph, which let us know the topological focus of the network, constitutes a closeness centrality (CC). Estimates of networks based on global topology include average degree, mean most brief path length, and distances utilized in the network.This study involved observing the PPI supporting the oral cancer-related gene network.Consequently, the proteins with high BC ought to have vigorously utilized crossing points as the backbone actually consisted of these proteins and their connections within the network. The critical point with high BC value was set at top 15 genes of the total number of nodes within the network , 10. BC Some of the candidate genes in the network are not directly connected and that includes the giant network as well. For constructing a subnetwork, genes connected with least number of nodes, in link with oral cancer, are required. For this, each combination of candidate qualities and their most limited way was calculated. These ways were found using Cytoscape software, coming about within the subnetwork comprising all nodes in these ways.Validation was essential for checking the healthiness of the backbone network. For this, test networks were developed as they were employing a little component of 208 genes as initial seeds. For this, once more Cytoscape computer program was utilized. BC value was determined in these test networks. Segments having nodes with top 15 BC in the test networks agreed with the nodes in the backbone network as per description in the fifth step to determine the accuracy of the backbone. Furthermore, the healthiness of backbone network of oral cancer proteins was determined by calculation of the frequency of the nodes having the largest BC value which gives the accuracy of the backbone nodes.In this research work, we have obtained protein-protein interaction network through applying various steps discussed in the Methodology section. Then, we have obtained the key nodes involved in protein-protein interaction network and perform the analysis for obtaining important protein or genes involved in oral cancer.A huge network with several collapsed smaller networks, respectively, found from the seed proteins TSPO and TP53, was also a part of the extended network . Giant nKey nodes were viewed by using nodes having large degree or high betweenness centrality values, and the critical point was set as 10% of the total number of nodes for both the degree and BC in the network in this study. Among the total 208 nodes, those that had high BC were 59 in number, and 109 nodes had a large degree. 10 nodes had a large degree and high betweenness centrality , nodes aGenetically oral cancer has not been fully explored; even after several years of research it still remains unexplored. Many casual or susceptible genes associated with oral cancer have been reported. The multiple factors contributing to hepatocarcinogenesis include ecological, transmissible, sustaining, metabolic, and endocrine structures, which are involved specifically or indirectly in the development of oral cancer. The oral cancer pathogenesis indicates the involvement of several genes and can broadly be classified into four majors groups: regulatory genes involved in response to DNA damage, cell cycle control players, those intricate growth inhibition, apoptosis, and also the genesis involved in cell-cell collaboration and signal transduction. Analyzing the contribution of genes and proteins pertaining to oral cancer pathogenesis in addition to the involvement of other key proteins in the PPI networks by analyzing their topology was the goal of this study. The results obtained gave us useful data related to genes involved in oral cancer and enabled us to draw conclusions about gene-gene and related protein-protein interactions. It was observed that most of the seed proteins (208) connected to giant network in terms of oral cancer and their PPI neighbors. This giant oral cancer network consisted of 1572 edges. Backbone network topology, however, resulted in different small network topologies. As it is not possible to draw conclusion from the giant network, we further split down the backbone network into small subnetworks for obtaining clear results on oral cancer. The splitting of backbone network is performed according to properties of nodes, that is, BC, CC, and degree values. One backbone network consists of 29 nodes and 322 edges. Another subnetwork consists of 27 nodes 87 edges. There is one small subnetwork of 4 nodes and 5 edges. Other subnetworks also exist but they are very small. Literature was analyzed to find further techniques and methods supporting the importance of different genes in oral cancer. TP53 is a gene that occupied important position in the network. TP53 has degree value of 113 and BC value of 0.03881821. Literature was analyzed to find further techniques and methods supporting the importance of TP53 in cancer. In this regard, researchers have clarified TP53 changes in different types of cancers. Moreover, it has been discovered that TP53 reaction pathway is regularly flawed in human diseases and the recurrence of TP53 fluctuates greatly. In another study conducted by Ding and colleagues, polymorphisms were affiliated with this disorder. The study was focused around metainvestigation to better comprehend the relationship between polymorphism of Condon 72 and the tumor protein p53 (TP53) gene, which results in a missense transformation of arginine (R) to proline (p) and causes vulnerability to hepatocellular carcinoma . TSPO isThe mainstay network shows clearly all the essentials genes of oral cancer, their associated regulatory pathways, and the interactions between them. This eventually brought us to the conclusion that TP53 is the protein with the highest degree, TSPO is the protein with largest BC, and EGFR is the protein with highest CC values, but TP53 obtained the important position in the network due to its degree, BC, and CC values, indicating that TP53 is centrally localized in the network and is a significant bottleneck protein in oral cancer protein-protein interaction network. These findings suggested that pathogenesis of oral cancer variation was organized by means of an integrated PPI network, which is centered on TP53. Furthermore, our identification of TP53 as the bottleneck protein in oral cancer network determined its significance in cellular network in the body as well, as P53.Tumor protein 53 is important in the regulation of cell division; by restricting the cells from growing or dividing uncontrollably, it fulfills the function of at least one of the gene groups in oral cancer.


Burkholderia pseudomallei by its dominant phage. B. pseudomallei is the causative agent of melioidosis, which is among the most fatal diseases in Southeast Asia and across the world. We assess the spatial aspect of B. pseudomallei-phage interactions in soil, which has been so far overlooked in the literature, using the reaction-diffusion PDE-based framework with external forcing through daily and seasonal parameter variation. Through extensive computer simulations for realistic biological parameters, we obtain results suggesting that phages may regulate B. pseudomallei numbers across seasons in endemic areas, and that the abundance of highly pathogenic phage-free bacteria shows a clear annual cycle. The model predicts particularly dangerous soil layers characterised by high pathogen densities. Our findings can potentially help refine melioidosis prevention and monitoring practices.We apply mathematical modelling to explore bacteria-phage interaction mediated by condition-dependent lysogeny, where the type of the phage infection cycle (lytic or lysogenic) is determined by the ambient temperature. In a natural environment, daily and seasonal variations of the temperature cause a frequent switch between the two infection scenarios, making the bacteria-phage interaction with condition-dependent lysogeny highly complex. As a case study, we explore the natural control of the pathogenic bacteria  Targeting of undesirable bacteria using phages has been successfully used to overcome bacterial resistance to antibiotics in both food safety and medical applications4, and phages have be used to control the spread of deadly pathogens, such as Vibrio cholerae5. However, the role of phages in regulating the number of bacteria in complex heterogeneous environments is still poorly understood. Mathematical modelling backed up by empirical data is a powerful tool for quantifying and forecasting the dynamics of bacterial hosts and their phages under changing environmental conditions6. In this paper, we use modelling to explore the regulation of the pathogenic bacterium, Burkholderia pseudomallei, by its dominant phage with a temperature-dependent life cycle switch in complex spatio-temporal environments.Among major factors controlling bacterial numbers both in the wild and in artificial environments are natural enemies known as bacteriophages or phages. Phages are viruses that can specifically infect their host by attaching to particular bacterial receptors, injecting their genomic DNA (or RNA) into the host cell cytoplasm, and triggering a process that can lead to phage replication or integration of phage genome into the host chromosome. Viruses are considered to be the most abundant biological entity on our planet, and are known to be the key factor regulating the length and amplitude of algal and bacterial blooms in the oceanB. pseudomallei causes melioidosis, which is a serious human illness. It is a significant problem for the rice growing industry as the organism abounds in rice paddies, infecting agricultural workers, and killing around  45% of those infected. The overall number of deaths caused by the pathogen is estimated to be as high as 90,000 per year10. The US Centers for Disease Control and Prevention have identified B. pseudomallei as a potential biothreat agent11. B. pseudomallei is highly abundant in the natural environment and agricultural fields across the tropics, especially in Southeast Asia, particularly in Thailand and Laos, and northern Australia12. Unlike plankton blooms in the ocean, which are highly visible due to changes of the natural water colour, bacterial outbreaks in the soil are invisible. Due to the absence of coloration, these ‘invisible blooms’ can be easily overlooked by the general public and health control organisations. Until recently, the potential impact of phages on B. pseudomallei and its infectivity in the environment have been largely overlooked in the literature, but available empirical data suggest that phages can potentially control B. pseudomallei numbers in water or soil in a similar way to control of cyanobacterial blooms by marine viruses14. High phage numbers have been observed in soil containing B. pseudomallei in Thailand, Laos and Vietnam15, indicating that they possibly interact with and impact the bacteria.The soil- and water-borne pathogen B. pseudomallei by its natural phages stem from the high complexity of the underlying biological system. In particular, B. pseudomallei-phage interaction is affected by the recently discovered ‘condition-dependent lysogeny’ that is characteristic for a clade of highly abundant phages15. Condition-dependent lysogeny is schematically depicted in Fig. 15 and infected bacteria become phage-associated. Lysogenic (i.e. phage-associated) B. pseudomallei which enter a warm-blooded host would experience lysis of the pathogen, and would not be able to cause disease. In warm conditions most bacteria in the environment are in the ‘phage-free’ form and can cause disease when entering the human body. The switch between the lytic and the lysogenic infection cycles occurs at temperatures of around 35 °C16 which has important consequences for bacteria-phage interactions: daily and seasonal variation of the temperature in the main endemic areas of Southeast Asia and Australia should cause a transition between the two infection cycles. In this paper, we are interested in predicting natural control of B. pseudomallei by the phage by means of computational modelling in order to provide important knowledge to facilitate the natural bacterial control by the phage via adjusting the existing agricultural practices.Difficulties in understanding and predicting the efficiency of control of 17 and later by Lenski et al.18. The amount of theoretical studies has increased dramatically within recent years (for a brief review see6), but surprisingly, systems involving temperature-dependent lysogeny have not received much attention until very recently. Egilmez et al. modelled B. pseudomallei-phage interactions under varying temperature using historic data from two endemic provinces in Thailand16. Their modelling study predicts that B. pseudomallei can exhibit annual blooms (outbreaks) during warm seasons, which positively correlates with the reported increased cases of melioidosis during these periods. The model, however, considered bacteria-phage dynamics in a well-mixed environment in the surface water of agricultural fields. As such, the model neglected the spatial aspect of the problem, while the fact that the key drivers of interactions, the temperature and the carrying capacity of bacteria in soil, have a pronounced spatial gradient in the vertical direction. Adding space might have a substantial effect on previous conclusions about disease prevention and monitoring practices from the well-mixed model.Mathematical and computational modelling of interactions between bacteria and their natural viruses has a long history starting from the seminal work of Campbell16 by adding a spatial dimension. We explore the spatiotemporal dynamics of bacteria-phage interactions in the upper layer of soil under daily and seasonal variation of the temperature. The dependence of model parameters on the temperature and the depth is taken from the available empirical literature and the temperature variation is taken from historical records for two endemic provinces of Thailand (Sa Kaeo and Nakhon). Using extensive computer simulations we explore spatiotemporal dynamics within a large range of biologically realistic model parameters. The spatial model is based on the classical reaction-diffusion framework (PDEs), and despite the overall complexity of the system, the model can be still considered to be parsimonious compared to highly complex soil models. The reaction-diffusion approach can be applied since the considered non-homogeneous system is under a strong external forcing .Here we extend the model of temperature-dependent lysogeny developed by Egilmez et al.B. pseudomallei are observed at some intermediate depths, which can have important consequences for the safety of agricultural workers. Interestingly, enrichment of the environment in the model  can cause irregular spikes in phage-free bacteria numbers, signifying a higher risk of disease. This theoretical study re-enforces the early hypothesis that temperature-responsive phages could play a key role in regulating bacterial numbers both daily and across seasons16. It provides possible ways to improve the existing monitoring of B. pseudomallei in soil using information on the vertical distribution of the pathogen.Our model predicts that the system undergoes clear seasonal cycling behaviour with outbreaks of phage-free pathogenic bacteria occurring at the onset of the hot season in both provinces considered. We find that the vertical profiles of phage-free and infected bacterial cells (in both lysogenic and lytic states) show a non-monotonous pattern: bacterial blooms with a high density of phage-free H  susceptible to infection by the phage, bacteria infected by the phage in its lysogenic (P). The total density of the host bacterial populations N is defined as 16. The local species interactions are described based on the classical modelling approach19. Our spatiotemporal model is of reaction-diffusion type and is described by the following equationsWe introduce a spatiotemporal model to describe the bacteria-phage interaction in the upper part of the soil with the depth 6, where C is the carrying capacity of the environment; we assume that C(h) varies with depth. Infection of S by phages P at low temperatures results in lysogeny which is described by a mass action term S; however, with a different maximal growth rate b new phages, the the burst size6. In the equation for P, KN(t)P(t) stands for the loss of phage due to binding to any type of bacteria . Finally, In the above model, we parameterise the growth of susceptible bacteria via a standard logistic growth functionT across the soil is described by the heat equationAccording to this framework, the vertical displacement of the phage and bacteria are modelled by a diffusion term (first term in each equation), where mentclass2pt{minim16, we assume that the maximal bacterial growth rates T20.Next we describe the functional forms of the dependence of model parameters on the temperature and the depth. Following the previous studyB. pseudomallei under such conditions are yet to be studied. For simplicity they are assumed to be the same as under aerobic conditions. Realistic values of the above parameters are listed in Table In the equation for mentclass2pt{minimK is estimated as 16. The adsorption constants 16:16. The lysis rate of bacteria 13) and the burst size 16. The temperature dependence of The overall adsorption rate of the phage entclass1pt{minimaC of the bacteria varies with the depth of the soil, according to empirical observations23. This can be explained by the fact that the humus, oxygen, nitrogen contents, or/and water content in the soil generally decrease with depth24. We use a combined approach to parameterise C(h) based on the available empirical data. We assume that in the absence of phages, the bacteria achieve numbers close to the carrying capacity at a given depth. Firstly, we parameterise the dependence of the overall bacterial load on depth in paddy soils in Southern Asia using the existing data22. Then we re-scale the obtained curve based on the available observations of B. pseudomallei at a depth 26. We approximate C(h) using the following simple Gaussian-type curveh), B determines how fast the bacterial abundance decreases with depth, C(h) has a maximum at the surface and monotonically decreases with depth. We assume that the carrying capacity of the environment is not influenced by seasonal variation.The carrying capacity 27 which gives 16. The bottom boundary temperature The coefficient 28, but the diffusivity in soil should be smaller than this. As such, these values should be considered as upper limits for The paddy fields in which we model the bacteria-phage interactions are flooded lands, where the soil is either mud or muddy water. Many factors can affect vertical dispersal of bacteria and phages in such soil. For instance, rain water can carry bacteria and phage up or down in the soil, which can be mathematically modelled by adding an advection term; however, for simplicity we ignore such effects in this paper. We also assume the phage and bacteria vertical diffusion coefficients to be constant; however, it is rather hard to provide accurate estimates for T(t) with a smaller time resolution and then apply the temperature obtained to model bacteria-phage interactions for a larger time resolution . The unit of the densities of bacteria and phages are cells/ml. The summary of model parameters as well their values are provided in Table In our numerical simulations, we use both explicit and implicit numerical schemes. We take a 0.1 cm spatial step size to get a proper resolution. We separately compute the heat equation to define T as well as dynamics across seasons. Figure Using Eq.  and hist29, and found a good overall agreement between theoretical and empirical values which allows us to substitute the theoretical values of T into model equations  is characterised by two pronounced maxima: a narrow peak in the upper soil layers, and a lower one at a depth of approximately S achieves its deep maximum. The reason that S can outcompete Figure S near the surface with the lowest and the highest abundances being at mid-day and late evening, respectively. These oscillations are strongly correlated with those of the phage density P. The overall pattern of the vertical distribution of microorganisms remains similar across seasons with only minor alterations. For example, in April–May the locations characterised by a high density of S extend from the surface to deeper depths. This results in peaks of the overall amount of pathogenic bacteria shown in Fig. S remains constant throughout all seasons. Our simulations demonstrate a similar pattern of daily and annual dynamics of bacterial and phage numbers for Sa Kaeo province . This mechanism is similar to the classical paradox of enrichment in predator-prey systems33. High amplitude oscillations of S would signify a higher risk of disease acquisition.We varied key model parameters to assess their influence on system dynamics. Increasing the carrying capacity of bacteria S near the surface shrinks with an increase of S which presents potential danger for agricultural workers . Analysis of the model for a fixed temperature shows that the existence of these sharp peaks is related to a Hopf bifurcation which occurs when the carrying capacity is varied.Enrichment of the soil largely alters the vertical distribution of bacteria and phages shown in Fig. S become altered. This can be explained by a strong external forcing on the system by the temperature variation: the system essentially becomes a set of independent oscillators synchronised by external forcing . This holds true for any ratio of Other important parameters of the model are the diffusion coefficients of microorganisms in the soil. We find that, surprisingly, variation of b and the adsorption constant K. The results are summarised in the two following diagrams shown in Fig. Finally, we investigated the role of three other key model parameters describing the efficiency of phages: the phage mortality 15 in a heterogeneous environment such as soil. The practical importance of the problem comes from the fact that melioidosis—the disease caused by infection of the pathogenic bacteria Burkholderia pseudomallei—is now estimated to be the third most fatal in Southeast Asian countries12. Despite available empirical data indicating high abundance of phages in the soil of endemic areas in Thailand , the natural control of the pathogen by phages has been largely overlooked in the literature so far and this study is intended to partly bridge the existing gap. Note that more complex mathematical models exist, describing microbial transport in the soil as a series of attachment and detachment processes or complicated random walks35. Other approaches include integro-differential equations describing the spatial effects of replication delay of the phage36. Here, however we address the problem using a parsimonious model based on the reaction-diffusion approach which is well-known in mathematical ecology37. We argue that implementation of this approach is justified because of the low sensitivity of the model prediction to variation in the diffusion coefficients . Note that in the non-spatial model—considering interactions in the surface water of agricultural fields—the strength and variation of solar UV radiation was found to be a major regulator of bacteria-phage interactions, but it actually does not play a role in the spatial system with soil. In the spatial model, the external seasonal forcing of the soil temperature gradient plays a dominant role in causing the density of S to peak in the warmest season.We find that generally the densities of bacteria and phage in soil show a seasonal trend both in terms of their vertical distribution and the total numbers. In particular, the model predicts that during the warmest period of the year (April–July), the densities of phage-free bacteria and phages increase while the density of lysogenic bacteria decreases. This would signify a higher risk of infection during warm periods of the year since phage-free bacteria can readily infect warm-blooded hosts, while lysogenised bacteria are likely to be killed by the induced phage upon entry. The spatial model overall confirms the seasonal pattern reported earlier based on the non-spatial model16 and in top soil, the B. pseudomallei population is predicted to be dominated by the more virulent phage-free bacteria during warmer seasons of the year, whereas during colder winter season phage-associated bacteria are more prevalent. Since surface water and top soil are presumably the main sources of the infection, this is likely to be a major contributing factor to the increase of melioidosis cases during warm seasons in Thailand that is reported in the literature38.For both provinces of Thailand we considered, the spatial model predicts the existence of a pronounced peak of highly virulent phage-free bacteria both near the surface and, even more strikingly, at the depth of approximately S is a consequence of phage-free bacteria having a higher per-capita effective population growth rate  than of S eventually outcompetes The model predicts that the high-density maximum of phage-free bacteria located at around B. pseudomallei is more frequently found in soil samples taken at 30 cm or deeper, and that bacteria could be found at these depths throughout the year, irrespective of the season24. The existence of a non-seasonal permanent peak of phage-free bacteria at or near B. pseudomallei in the environment should be done at the depth of 30 cm9. For the safety of agricultural workers, the position of the peak of phage-free B. pseudomallei in the soil specifies a layer of soil whose disturbance would increase the risk of infection (note that neither lysogenic nor lytic bacteria can cause disease). Coincidentally, 30 cm is the approximate traditional depth of ploughing rice paddy fields. Our study suggests that considering more shallow tillage for rice farming in areas of high endemicity of melioidosis may reduce the risk of infection.Significantly, this prediction is in agreement with environmental sampling data which indicate that B. pseudomallei in the upper soil layers, which would be invisible to the human eye. Such blooms occur in narrow soil layers and they can be hard to monitor by standard course sampling, but the density of phage-free bacteria in these layers can be extremely high: a whole order of magnitude higher than at nearby depths. Such layers would present an extra risk of infection for agricultural workers.Our model also predicts that enrichment of the environment (e.g. by adding fertiliser) may result in sudden irregular bursts of phage-free B. pseudomallei.From the diagram in Fig. B. pseudomallei can control the dynamics of the bacteria and their spatial distribution in the soil environment. Taking into account our modelling outcomes can potentially help to improve current melioidosis prevention efforts in Southeast Asia and across the world.In conclusion, our modelling findings reveal that a dominant temperature-responsive clade of phages that is capable of infecting Supplementary Information.


We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number  As foreseen in 2017 by the global risk report of the World Economic forum, global risks are interconnected. In particular, the case of the COVID-19 epidemic (the infectious disease caused by the most recently discovered human coronavirus) is showing the critical role of information diffusion in a disintermediated news cycle2.The World Health Organization (WHO) defined the SARS-CoV-2 virus outbreak as a severe global threatinfodemic4 has been coined to outline the perils of misinformation phenomena during the management of disease outbreaks7, since it could even speed up the epidemic process by influencing and fragmenting social response8. As an example, CNN has recently anticipated a rumor about the possible lock-down of Lombardy  to prevent pandemics9, publishing the news hours before the official communication from the Italian Prime Minister. As a result, people overcrowded trains and airports to escape from Lombardy toward the southern regions before the lock-down was put in place, disrupting the government initiative aimed to contain the epidemics and potentially increasing contagion. Thus, an important research challenge is to determine how people seek or avoid information and how those decisions affect their behavior10, particularly when the news cycle—dominated by the disintermediated diffusion of information—alters the way information is consumed and reported on.The term 12.The case of the COVID-19 epidemic shows the critical impact of this new information environment. The information spreading can strongly influence people’s behavior and alter the effectiveness of the countermeasures deployed by governments. To this respect, models to forecast virus spreading are starting to account for the behavioral response of the population with respect to public health interventions and the communication dynamics behind content consumption13. This shift from the traditional news paradigm profoundly impacts the construction of social perceptions14 and the framing of narratives; it influences policy-making, political communication, as well as the evolution of public debate16, especially when issues are controversial17. Users online tend to acquire information adhering to their worldviews19, to ignore dissenting information21 and to form polarized groups around shared narratives23. Furthermore, when polarization is high, misinformation might easily proliferate25. Some studies pointed out that fake news and inaccurate information may spread faster and wider than fact-based news26. However, this might be platform-specific effect. The definition of “Fake News” may indeed be inadequate since political debate often resorts to labelling opposite news as unreliable or fake27. Studying the effect of the social media environment on the perception of polarizing topics is being addressed also in the case of COVID-19. The issues related to the current infodemics are indeed being tackled by the scientific literature from multiple perspectives including the dynamics of hatespeech and conspiracy theories29, the effect of bots and automated accounts30, and the threats of misinformation in terms of diffusion and opinions formation32.Social media platforms such as YouTube and Twitter provide direct access to an unprecedented amount of content and may amplify rumors and questionable information. Taking into account users’ preferences and attitudes, algorithms mediate and facilitate content promotion and thus information spreading33, the dynamics behind information consumption might be particular to the environment in which they spread on. Consequently, in this paper we perform a comparative analysis on five social media platforms  during the COVID-19 outbreak. The dataset includes more than 8 million comments and posts over a time span of 45 days. We analyze user engagement and interest about the COVID-19 topic, providing an assessment of the discourse evolution over time on a global scale for each platform. Furthermore, we model the spread of information with epidemic models, characterizing for each platform its basic reproduction number  reliable or (mostly) questionable contents and we characterize the spreading of information regarding COVID-19 relying on this classification. We find that users in mainstream platforms are less susceptible to the diffusion of information from questionable sources and that information deriving from news outlets marked either as reliable or questionable do not present significant difference in the way it spreads.Finally, coherently with the classification provided by the fact-checking organization Media Bias/Fact CheckOur findings suggest that the interaction patterns of each social media combined with the peculiarity of the audience of each platform play a pivotal role in information and misinformation spreading. We conclude the paper by measuring rumor’s amplification parameters for COVID-19 on each social media platform.We analyze mainstream platforms such as Twitter, Instagram and YouTube as well as less regulated social media platforms such as Gab and Reddit. Gab is a crowdfunded social media whose structure and features are Twitter-inspired. It performs very little control on content posted; in the political spectrum, its user base is considered to be far-right. Reddit is an American social news aggregation, web content rating, and discussion website based on collective filtering of information.coronavirus, coronavirusoutbreak, imnotavirus, ncov, ncov-19, pandemic, wuhan. The deriving dataset is then composed of 1,342,103 posts and 7,465,721 comments produced by 3,734,815 users. For more details regarding the data collection refer to Methods.We perform a comparative analysis of information spreading dynamics around the same argument in different environments having different interaction settings and audiences. We collect all pieces of content related to COVID-19 from the 1st of January to the 14th of February. Data have been collected filtering contents accordingly to a selected sample of Google Trends’ COVID-19 related queries such as: 35. The highest volume of interactions in terms of posting and commenting can be observed on mainstream platforms such as YouTube and Twitter.First, we analyze the interactions  that users have with COVID-19 topics on each platform. The upper panel of Fig. Then, to provide an overview of the debate concerning the disease outbreak, we extract and analyze the topics related to the COVID-19 content by means of Natural Language Processing techniques. We build word embedding for the text corpus of each platform, i.e. a word vector representation in which words sharing common contexts are in close proximity. Moreover, by running clustering procedures on these vector representations, we separate groups of words and topics that are perceived as more relevant for the COVID-19 debate. For further details refer to Methods. The results  among platforms.Finally, to characterize user engagement with the COVID-19 on the five platforms, we compute the cumulative number of new posts each day  and with the standard SIR  compartmental model44. Further details on the modeling approach can be found in Methods.Most of the epidemiological models focus on the basic reproduction number As shown in Fig. mentclass2pt{minim47. For instance, in the case of Instagram, we observe an abrupt jump in the number of new users that cannot be explained with continuous models like the standard epidemic ones; accordingly, the SIR model estimates a value of While 34. In order to clarify the limits of an approach that is based on labelling news outlets rather than single articles, as for instance performed in48, we report the definitions used in this paper for questionable and reliable information sources. In accordance with the criteria established by MBFC, by questionable information source we mean a news outlet systematically showing one or more of the following characteristics: extreme bias, consistent promotion of propaganda/conspiracies, poor or no sourcing to credible information, information not supported by evidence or unverifiable, a complete lack of transparency and/or fake news. By reliable information sources we mean news outlets that do not show any of the aforementioned characteristics. Such outlets can anyway produce contents potentially displaying a bias towards liberal/conservative opinion, but this does not compromise the overall reliability of the source.We conclude our analysis by comparing the diffusion of information from questionable and reliable sources on each platform. We tag links as reliable or questionable according to the data reported by the independent fact-checking organization Media Bias/Fact CheckFigure In particular, we observe that in mainstream social media the number of posts produced by questionable sources represents a small fraction of posts produced by reliable ones; the same thing happens in Reddit. Among less regulated social media, a peculiar effect is observed in Gab: while the volume of posts from questionable sources is just the To further investigate this issue, we define the amplification factor entclass1pt{minimaTherefore, we conclude that the main drivers of information spreading are related to specific peculiarities of each platform and depends upon the group dynamics of individuals engaged with the topic.In this work we perform a comparative analysis of users’ activity on five different social media platforms during the COVID-19 health emergency. Such a timeframe is a good benchmark for studying content consumption dynamics around critical events in a times when the accuracy of information is threatened. We assess user engagement and interest about the COVID-19 topic and characterize the evolution of the discourse over time.Furthermore, we model the spread of information using epidemic models and provide basic growth parameters for each social media platform. We then analyze the diffusion of questionable information for all channels, finding that Gab is the environment more susceptible to misinformation dissemination. However, information deriving from sources marked either as reliable or questionable do not present significant differences in their its spreading patterns. Our analysis suggests that information spreading is driven by the interaction paradigm imposed by the specific social media or/and by the specific interaction patterns of groups of users engaged with the topic. We conclude the paper by computing rumor’s amplification parameters for social media platforms.We believe that the understanding of social dynamics between content consumption and social media platforms is an important research subject, since it may help to design more efficient epidemic models accounting for social behavior and to design more effective and tailored communication strategies in time of crisis.Table Pushift.io archive, exploiting the related API. In order to filter contents linked to COVID-19, we used our set of keywords.The Reddit dataset was downloaded from the In Gab, although no official guides are available, there is an API service that given a certain keyword, returns a list of users, hashtags and groups related to it. We queried all the keywords we selected based on Google Trends and we downloaded all hashtags linked to them. We then manually browsed the results and selected a set of hashtags based on their meaning. For each hashtag in our list, we downloaded all the posts and comments linked to it.For YouTube, we collected videos by using the YouTube Data API by searching for videos that matched our keywords. Then an in depth search was done by crawling the network of videos by searching for more related videos as established by the YouTube algorithm. From the gathered set, we filtered the videos that matched coronavirus, nCov, corona virus, corona-virus, corvid, covid or SARS-CoV in the title or description. We then collected all the comments received by those videos.For Twitter, we collect tweets related to the topic coronavirus by using both the search and stream endpoint of the Twitter API. The data derived from the stream API represent only 1% of the total volume of tweets, further filtered by the selected keywords. The data derived from the search API represent a random sample of the tweets containing the selected keywords up to a maximum rate limit of 18000 tweets every 10 minutes.Since no official API are available for Instagram data, we built our own process to collect public contents related to our keywords. We manually took notes of posts, comments and populated the Instagram Dataset.We consider all the posts in our dataset that contain at least one URL linking to a website outside the related social media platfrom . We separate URLs in two main categories obtained using the classification provided by MediaBias/FactCheck (MBFC). MBFC provides a classification determined by ranking bias in four different categories, one of them being Factual/Sourcing. In that category, each news outlet is associated to a label that refers to its reliability as expressed in three labels, namely Conspiracy-Pseudoscience, Pro-Science or Questionable. Noticeably, also the Questionable set include a wide range of political bias, from Extreme Left to Extreme Right.Using such a classification, we assign to each of these outlets a binary label that partially stems from the labelling provided by MBFC. We divide the news outlets into Questionable and Reliable. All the outlets already classified as Questionable or belonging to the category Conspiracy-Pseudoscience are labelled as Questionable, the rest is labelled as Reliable. Thus, by questionable information source we mean a news outlet systematically showing one or more of the following characteristics: extreme bias, consistent promotion of propaganda/conspiracies, poor or no sourcing to credible information, information not supported by evidence or unverifiable, a complete lack of transparency and/or fake news. By reliable information sources we mean news outlets that do not show any of the aforementioned characteristics. Such outlets can anyway produce contents potentially displaying a bias towards liberal/conservative opinion, but this does not compromise the overall reliability of the source.Considering all the 2637 news outlets that we retrieve from the list provided by MBFC we end up with 800 outlets classified as Questionable 1837 outlets classified as Reliable. Using such a classification we quantify our overall ability to match and label domains of posts containing URLs, as reported in Table To provide an overview of the debate concerning the virus outbreak on the various platforms, we extract and analyze all topics related to COVID-19 by applying Natural Language Processing techniques to the written content of each social media platform. We first build word embedding for the text corpus of each platform, then, to assess the topics around which the perception of the COVID-19 debate is concentrated, we cluster words by running the Partitioning Around Medoids (PAM) algorithm on their vector representations.49. In this paper we use the Skip-gram model50 to construct word embedding of each social media corpus. More formally, given a content represented by the sequence of words 51 for maximizing the average log probabilityk is the size of the training window. Therefore, during training the vector representations of closely related words are pushed to be close to each other.Word embeddings, i.e., distributed representations of words learned by neural networks, represent words as vectors in w is associated with its input and output vectors, V is the number of words in the corpus vocabulary. Two major parameters affect the training quality: the dimensionality of word vectors, and the size of the surrounding words window. We choose 200 as vector dimension—that is typical value for training large dataset—and 6 words for the window.In the Skip-gram model, every word cld3. Then we cleaned the corpora by removing HTML code, URLs and email addresses, user mentions, hashtags, stop-words, and all the special characters including digits. Finally, we dropped words composed by less than three characters, words occurring less than five times in all the corpus, and contents with less than three words.Before applying the tool, we reduced the contents to those written in English as detected with k, we calculate the average silhouette width for each value of k. Moreover, for evaluating the cluster stability, we calculate the average pairwise Jaccard similarity between clusters based on 90% sub-samples of the data. Lastly, we produce word clouds to identify the topic of each cluster. To provide a view about the debate around the virus outbreak, we define the distribution over topics c as the distribution of its words among the word clusters. Thus, to quantify the relevance of each topic within a corpus, we restrict to contents c with To analyze the topics related to COVID-19, we cluster words by PAM and using as proximity metric the cosine distance matrix of words in their vector representations. In order to select the number of clusters, 41.Several mathematical models can be used to analyse potential mechanisms that underline epidemiological data. Generally, we can distinguish among phenomenological models that emphasize the reproducibility of empirical data without insights in the mechanisms of growth, and more insightful mechanistic models that try to incorporate such mechanisms43 since it naturally provides an estimate of the basic reproduction number 43.To fit our cumulative curves, we first use the adjusted exponential model ofI is incidence, t is the number of days, d is a damping factor accounting for the reduction in transmissibility over time. In our case, we interpret I as the number The model is defined by the following single equation:44. In such a model, a susceptible population can be infected with a rate S is the number of susceptible, I is the number of infected and R is the number of recovered. In our case, we interpret the number As a mechanistic model, we employ the classical SIR modelIn the SIR model, the basic reproduction number 42. The range of parameters is estimated via bootstrapping52.To estimate the basic reproduction numbers Table


The pandemic of Coronavirus Disease-19 (COVID-19) has spread around the world, causing an existential health crisis. Automated detection of COVID-19 infections in the lungs from Computed Tomography (CT) images offers huge potential in tackling the problem of slow detection and augments the conventional diagnostic procedures. However, segmenting COVID-19 from CT Scans is problematic, due to high variations in the types of infections and low contrast between healthy and infected tissues. While segmenting Lung CT Scans for COVID-19, fast and accurate results are required and furthermore, due to the pandemic, most of the research community has opted for various cloud based servers such as Google Colab, etc. to develop their algorithms. High accuracy can be achieved using Deep Networks but the prediction time would vary as the resources are shared amongst many thus requiring the need to compare different lightweight segmentation model. To address this issue, we aim to analyze the segmentation of COVID-19 using four Convolutional Neural Networks (CNN). The images in our dataset are preprocessed where the motion artifacts are removed. The four networks are UNet, Segmentation Network (Seg Net), High-Resolution Network (HR Net) and VGG UNet. Trained on our dataset of more than 3,000 images, HR Net was found to be the best performing network achieving an accuracy of 96.24% and a Dice score of 0.9127. The analysis shows that lightweight CNN models perform better than other neural net models when to segment infectious tissue due to COVID-19 from CT slices. During the winter months December 2019, a highly contagious disease out broke in Wuhan, China . High grThe successful control of the outbreak depends on the rapid and accurate detection and identification of the symptoms isolating the patient from the community, so that the spread of the disease can be stopped. Currently, the method used for the detection is Real-time reverse transcriptase polymerase chain reaction (RT-PCR) . It is tAlso, to accurately and efficiently control the virus, studies have been conducted to implement forecasting models to predict the spread of COVID-19 . Due to The common manifestations of SARS-CoV-2 in chest CT scan are ground glass opacities, consolidation, crazy paving, dilation of vessel width in some cases and round shape lesions in few cases . The effThe main objective of the research work is, comparing the segmentation performance of computationally non-intensive models deep learning model when subjected to Lung CT Scans for that are affected by COVID-19. The models utilized for the research belong to the U-Net variants models which are the most popular models of choice for segmentation of Medical Images. Here we compare the traditional U-Net model as proposed by Ronneberger with other variants such as Seg Net, U-Net based on VGG16 and High Resolution Net (HR-Net) and present both qualitative and quantitative results.The block diagram describing the entire methodology is shown below in The used dataset consists of 3,770 images and their corresponding ground truths. A total of 3,020 training images are used and 750 testing images are used. The CT scans of 50 patients were taken from mosmed.ai  were opeThe images in the dataset are riddled with motion artifacts and noise. Motion artifacts are caused due to improper imaging techniques and are a specific kind of noise relevant to CT Scans. Therefore, removing this noise is important or else it will cause the algorithms to learn improperly. MATLAB is used to remove the noise and motion artifacts. The original image is converted to grayscale from RGB and then, the image properties are extracted. Area and Solidity are used and then, the image is thresholded after selecting the max area and highest solidity. Once a mask is ready, the mask is multiplied with the original image to get the pre-processed image. A comparison is given below in HRNet is developed at Microsoft and has signified state of art presentation in the areas of semantic segmentation, image classification, facial detection, object detection and pose estimation . Its attHRNet is able of uphold high-resolution representation throughout the process as repeated multi-scale combinations are conducted by switching the information through the multi-resolution parallel subnetworks repeatedly throughout the process . The arcExisting models works by linking high to low resolution convolutions subnetwork in series, where each individual subnetwork form a platform, collection of an arrangement of convolutions furthermore, there is a down sample layer through end-to-end subnetworks to split the resolution into halves.sth and resolution index r. First subnet resolution is given by S phases/stages  can be indicated as:Let Starting from first phase/stage begin with high resolution subnet, slowly enhance high to low resolution subnet, generating new phases/stages, and associate multi-resolution subnet in parallel. Eventually, the parallel subnet resolution of a later phase/stage comprises of the resolution from an earlier stage and below one stage. The network shown below contains 4 parallel subnets.In this network exchange units were introduced throughout parallel subnet in such a way that an individual subnet continuously collects information from parallel subnets. How information is exchanged lets understand this process through an example here third stage is subdivided into multiple exchange blocks and every block consists of three parallel convolution modules, having exchange units followed by parallel units which is shown below:where:s is the stage, r is the resolution and b is the blockand Explanation of exchange units is show in HRNet experimental results (when tested with different datasets) show remarkable results for the applications like facial detection, semantic segmentation, and object detection.At the university of Cambridge, UK, team of the robotics group researched and developed that SegNet is a deep encoder decoder architecture for multiclass pixel-wise segmentation . The fraSegNet is composed of encoder and decoder after a last pixel-wise classification layer. The architecture is shown in SegNet a deep network was used for semantic segmentation. Basically, It was designed because the motivation behindhand was to propose an architecture for roads, outdoor and indoor sites which is proficient together in terms of computational time and memory. Feature map’s maxpooling indices are only stored in SegNet and to attain better performance it uses them in its decoder network.The UNet design is based upon the fully convolution network and adjusted such that it produces better segmentation results in medical imaging. UNet consists of two paths named as contracting and and expansive. In the contracting path it captures the context whereas in expansive path it enables exact localization. While contracting path is a classical architecture of UNet. It includes two 3 × 3 convolutions, max pooling operation with repeating application. The Each stage in the expansive path involves upsampling of the feature channel followed by (2 × 2) convolution that splits the number of feature channels into halves. In contracting path, it crops the feature map because of loss in border pixel in each convolution. Final layer is mapped by 1 × 1 convolutions which is used to map all 64 units feature vector. The network contains total 23 convolutional layers. UNet performs well on image segmentation .While training the UNet model, the cross-entropy loss function united with the last feature map and by applying a pixel-wise softmax over it, the softmax is denoted as:kK: No. of classIn the training data set, to counterbalance the diverse frequency of pixels from a specific class the weight map is pre calculated for ground-truth segmentation, and enforcing the network to study the minor separation borders amid touching cells introduce by us.The morphological operation used to calculate separation borders, the weight map calculated using:w: denotes the weight mapImage segmentation, which is performed pixel wise is most preferable task in the field of computer vision. Encoders-decoders when combined they form UNET architectures, which are very famous for image segmentation in medical imaging and satellite images etc. The weights of the pre-trained models (like ImageNet) are used to initialize the weights of the neural network  as it gives better performance major than those models, which are trained on small dataset from scratch. Models accuracy is very important in some applications like traffic safety and medicine pre-trained encoder can enhance the architecture and performance of UNET. Applications like Object detection, image classification and scene understanding have improved their performance after the introduction of convolutional neural network (CNN). Nowadays, CNN has outperformed in several fields over human experts.Image segmentation plays vital role in the field of medical imaging to enhance the diagnostic capabilities. Fully connected network (FCN) is amongst the most popular state-of-the-art machine learning technique . Segmentm denotes the no. of samplesCommon loss function that is, binary cross entropy can be used for classification problem where To validate the performance of the models presented above, Sensitivity, Specificity, Jaccard Index, Dice Coefficient, Accuracy and Precision are used. To measure the accuracy of the segmented image, accuracy and precision are used and to measure the quality of segmentation, sensitivity and specificity are used. The various performance measures are described below:Accuracy and Precision are used to calculate the accuracy of the segmentation model itself. Accuracy is as the ratio of correct predictions to the total number of predictions and Precision is defined as the ratio of correctly predicted positive observations to the total number of correctly predicted observations.In the case of segmentation, accuracy and precision are used to measure the binary segmentation of each pixel of the image by the model. Although precision and accuracy may seem to be enough to describe the performance of the model, other factors are also important to describe the quality of segmentation.Sensitivity and Specificity are used to measure the quality of segmentation between the classes. In this case, the models are performing binary segmentation. So, Sensitivity, or the True Positive Rate, measures the quality of segmentation of one class and Specificity, or the True Negative Rate, measures the quality of segmentation of the other class. Sensitivity and Specificity can be defined as:The Jaccard Index and Dice Coefficient are used to quantify the similarity between the original image and the segmented image. Jaccard Index and Dice Coefficient are similar to the Intersection over Union (IoU) used to evaluate Object detection models. Jaccard Index and Dice Coefficient ranges from 0 to 1 where 0 means no overlap and 1 mean full similarity. Jaccard Index and Dice Coefficient can be defined as:The experiments were conducted in the Google Colab platform. As shown in As can be seen from In this article, we analyzed four models for segmenting COVID-19 from Lung CT Images. With the growing number of cases worldwide, quick and accurate testing is needed. To solve this problem, we approached the problem by reviewing four lightweight models that do not take a long time for training or testing. First, we remove motion artifacts from the tomographic images through Thresholding and use the pre-processed images for training the models. The four models trained are Seg Net, UNet, VGG UNet and HR-Net. We evaluate the models on their performance using accuracy, dice, Jaccard index and precision. We also used Specificity and Sensitivity as secondary evaluation characteristics. The results obtained demonstrate that lightweight convolutional networks have high latent ability to segment COVID-19 from CT images with HRNet being the best network out of the four models analyzed. Our work can be used in real-time environments to deploy on low-power devices. Low-Power devices require less computation time and have many constraints. When we consider these constraints, then using our lightweight models is very efficient as the user can accurately segment COVID-19 from CT Images. This system can be used in the field where electricity is constrained and fast and accurate predictions are required. The proposed light weight model can be implemented a simpler hardware that requires less area and power requirements. Due to the lower power usage, the prototype can be used as standalone systems in power constrained conditions but require accurate predictions. The results in this study can be improved by collecting more data from hospitals and clinics to improve the accuracy of the segmentation. We can also improve the work by changing the architecture of the proposed models to extract more features without increasing the inference time significantly.


This paper presents a control allocation method for enhancing the attitude following performance and the energy efficiency of a variable-pitch propeller (VPP) system on quadrotor-based unmanned aerial vehicles. The VPP system was modeled according to the blade element momentum (BEM) theory, and an actuator allocation method was developed with the aim of enhancing the attitude control and energy performance. A simulation environment was built to validate the VPP system by creating a thrust and moment database from the experiments. A four-motor variable-pitch quadrotor was built for verifying the proposed method. The control allocation method was firstly verified in a simulation environment, and was then implemented in a flight controller for indoor flight experiments. The simulation results show the proposed control allocation method greatly improves the yaw following performance. The experimental results demonstrate a difference in the energy consumption through various pitch angles, as well as a reduction in energy consumption, by applying this VPP system. With the emerging Microelectromechanical Systems (MEMS) technology, the weight, volume, and price of microsensors and actuators are significantly reducing . The manQuadrotor-based UAVs include the commonly seen quadrotor and the VTOL type, both with four propulsion systems and wings. A series of novel designs for VTOL UAVs, such as tail-sitters and tilt-rotors, merge the quadrotor-type structure into a general fixed-wing design ,8. This Generally, quadrotor-based VTOL UAVs use the same propulsion system in both vertical and horizontal flying modes; however, the energy efficiency of the propulsion system needs to be addressed. The large flight envelope that this type of UAV experiences constitutes a great challenge in the design of its propulsion system, as it requires the generation of precise thrust across a large range of airspeed. Another common problem of quadrotor-based VTOL UAVs is their large wing area, which may encounter a significant crosswind and thus generate an enormous force on the fuselage of the UAV, making it difficult to maintain the vehicle’s position during a maneuver in the vertical stage . GeneralVariable-pitch propeller (VPP) systems, which are broadly used in manned aircrafts, were originally designed to generate varied thrust and to optimize the energy efficiency of combustion engines . The maiVPP systems have already been implemented by researchers in various kinds of small UAV systems. For easy classification, they are sorted by the number of main propellers; those using a single set of main propellers are more akin to traditional helicopters. The cyclic pitch is engaged for generating moments in the roll and pitch axes to maintain their attitude in the hovering mode. As for the yaw axis, smaller auxiliary propellers are used only for generating the thrust to counter a large amount of moment from the main rotor ,15. The VPP systems are also widely used in typical quadrotor UAVs, which can be divided into two categories by the number of powerplants. The first is those with a single powerplant driving four propellers with a belt or a transmission shaft. This setup, combined with an internal combustion engine with a high energy density, can guarantee the long endurance of the UAV ,19. SomeDespite the previous works regarding the application of VPP systems to small UAVs, no efforts have been made to increase the performance of the yaw stability and energy consumption of quadrotor-based UAVs. Due to the dual-signal input characteristic of the VPP system, a single actuator set gains the ability to provide more than a single combination of thrust and moment. This characteristic consequently provides a broader range of outputted force and moment, yielding a better flight performance of the whole UAV system. However, the added actuators and the over-actuated characteristic of the VPP system bring the complexity to the controller and actuator allocation comparing to other small UAVs. In particular, the yaw moment generated by VPP systems has not been emphasized in previous works. Stability in the yaw axis, which is crucial during the take-off and landing stages of a quadrotor-based VTOL UAV, influences the initial state of the transition stage of the aircraft and affects the safety of transition into the horizontal flying mode. VPP systems can also generate a larger amount of moment to provide a considerably greater control margin for UAVs than can fixed-pitch propulsion systems.The main contribution of this work is to introduce an effective control allocation method for implementing a VPP system into a quadrotor-based UAV. The proposed approach aims to improve the energy efficiency, attitude and position control of all kinds of quadrotor-based UAV systems during hover flight. A simulation testbed is built with data sets collected from static thrust experiments for validating and testing the proposed method. At last, flight tests are also accomplished with self-designed VPP systems mounted on a small quadrotor-type UAV.The rest of this paper is organized as follows. The modeling of the VPP system was inspired by both traditional helicopter rotors and small multi-rotor UAV systems. The lift and drag generated by the system were evaluated separately using the blade element momentum (BEM) theory. In this section, the numeric modeling of the VPP system is described. Considering the general setup of a quadrotor-based UAV, which does not have an overlapping section on its blade surface, complete analysis of the whole UAV was not necessary. Hence, the following modeling process considers each VPP unit individually.In this proposed UAV system, the incoming airflow toward the system is relatively slow. As a result, air is assumed to be incompressible and inviscid, and the inflow angle for the propeller blade of the system is considered very close to 0°. All stall conditions are ignored in the evaluation process.The more specific airfoil geometry, velocity, and forces are defined in The aerodynamic lift generated by the propeller can then be rewritten as:By integrating the lift along the whole length of the rotor blade The total thrust generated by the system is then determined by multiplying Equation (3) by the total number of blades After evaluating the lift of the propeller, the torque generated by the propeller is also calculated. Drag needs to be considered in two parts, namely lift-induced drag and aerodynamic drag. The aerodynamic drag is the part of force generated by the airfoil:For small UAVs, the airspeed toward the rotational plane  with The coordinate system used for our modeling is shown in The position and attitude controllers of this work were adopted from the PX4 flight stack . The outLet the thrust generated by each motor be i = 1–4) is connected. With four control outputs but a total of eight actuator setpoints, the over-actuated system provides a certain amount of redundancy for the flight controller. The objective of the proposed allocation method is to increase the attitude tracking performance  of quadrotor-based UAVs, together with minimizing the energy consumption.In this moment, the relationship between the four control outputs and the eight actuator setpoints , which was set at a fixed voltage (12.6 V). During each measurement, a fixed rotational speed (RPM) was set and the pitch angle was changed and maintained for 20 s. Force and torque data were collected through a NI-9220 DAQ system , and the power consumption information was provided directly from the DC power supply. Finally, the actuators’ control signals and rotational speed during the test period were collected through the Arduino.A simulation testbed was set up for verifying and testing the proposed allocation algorithm. The proposed method was applied to several test scenarios to compare its performance with that of a traditional fixed-pitch propulsion system.The simulation environment was built up within the MATLAB Simulink environment. The built-in Aerospace Blockset was used to represent the equations of motion of the UAV system. In the simulation environment, an X-type quadrotor was used to demonstrate the proposed allocation method. The quadrotor was set to use the same propulsion system from the static thrust experiment, with an arm length of 25 cm and the total weight of 2 kg.,, and The simulation environment can be pre-scheduled with a series of commands, which are the desired three-dimensional position setpoints  of the yaw direction for the three simulations, the RMSE results of these three simulation scenarios are shown in Following the simulations, a variable-pitch quadrotor UAV was built. The detailed configuration of the UAV platform is illustrated below. The design of the quadrotor with the VPP system is introduced first, followed by the avionics and test environment. Finally, the flight test results are presented.As shown in The avionics of the UAV system included a flight controller and an on-board computer. Pixhawk 2 (Hex Cube)  with open-source PX4 firmware (v1.11.0dev)  was usedThe flight experiments were conducted in an indoor laboratory equipped with the VICON  motion capture system. The VICON motion capture system provides a real-time position and attitude information through the Wi-Fi signal to the avionics. The on-board computer was the communication center of the whole experimental setup. It communicated with the ground control station and the VICON system through the ROS network using the Wi-Fi and passed the information using the MAVROS  node to Preplanned waypoints and attitude commands were programmed as ROS nodes on the on-board computer. The tracking commands were generated and input into the flight controller in a fixed schedule, providing a controllable quantitative result for the flight tests. The network structure is shown in This section outlines the flight experiments that were carried out to test the proposed method in a realistic flight environment. Similar scenarios to that in the simulation section were adopted in the flight tests, including the three scenarios: (1) Step yaw command, (2) step position command, and (3) waypoint mission tracking. Furthermore, power consumption during each flight was recorded for comparison.In the first flight test, the aggressive yaw command scenario (In the second test shown in , a step The total energy usage in the above step yaw and position tracking tests is shown in The last test scenario involved testing the power and energy consumptions at different initial pitch angles init=12° . The totIn this work, the objective was to increase the attitude following performance and reduce the energy consumption of a quadrotor-based UAV by implementing the VPP system. During this research, the VPP model was firstly evaluated using the BEM theory, followed by the introduction of the VPP actuator allocation method. The actuator allocation method was validated and tested in the simulation testbed and further implemented in the Pixhawk flight controller for indoor flight tests. From the results of both the simulation and flight tests, the allocation method was shown to be effective and can withstand high-intensity step maneuvers. Furthermore, the proposed method demonstrates an enhancement in the UAV performance of the yaw control with less effect on its altitude. The flight test results also showed energy consumption reduction by both varying the initial pitch angle and enabling the VPP controller during hovering status of the quadrotor-based UAV.To obtain further improvements in the VPP system, attention will be focused on its application to a VTOL UAV system such as a tail-sitter. Wind tunnel experiments will be carried out to simulate the level flight cruising stage of the UAV. Then, a global energy optimization method for variable-pitch quadrotor-based VTOL aircrafts can be developed.


This work presents a novel platform conceived as an interconnect box (ICB) that brings high-frequency signals from microwave instruments to consumable lab-on-a-chip devices. The ICB can be connected to instruments with a standard coaxial connector and to consumable chips by introducing a spring-levered interface with elastomer conductive pins. With the spring-system, microwave-microfluidic chips can be mounted reliably on the setup in a couple of seconds. The high-frequency interface within the ICB is protected from the environment by an enclosure having a single slit for mounting the chip. The stability and repeatability of the contact between the ICB and inserted consumable chips are investigated to prove the reliability of the proposed ICB. Given the rapid interconnecting of chips using the proposed ICB, five different interdigital capacitor (IDC) designs having the same sensing area were investigated for dielectric permittivity extraction of liquids. The designed IDCs, embedded in a polydimethylsiloxane (PDMS) channel, were fabricated with a lift-off gold patterning technology on a quartz substrate. Water–Isopropanol (IPA) mixtures with different volume fractions were flushed through the channel over IDCs and sensed based on the measured reflection coefficients. Dielectric permittivity was extracted using permittivity extraction techniques, and fitted permittivity data shows good agreement with literature from 100 to 25 GHz. In the past two decades, microfluidic technology  has provAll these applications were enabled by novel manufacturing technology, which has brought together planar microfluidic devices, planar microwave devices, and standard electrical signal interfacing. This interfacing of high-frequency signals to the lab-on-chip systems is usually done in two ways. One of them is based on probes ,7 while All above drawbacks are preventing the broadcasting of microwave-microfluidic technology to nonmicrowave researchers or even for commercialization. This encourages us to search for a novel interface between the expensive lab equipment and the relatively cheaper or even consumable microwave-microfluidic chips. In other words, to make widely usable microwave-microfluidic technology, a novel interface is needed. This interface should be cheap, easy to manufacture, and should offer high mobility. Furthermore, it should provide instant and stable contact between instruments and microwave-microfluidic chips.In this paper, a novel idea of interconnecting microwave-microfluidic chips and microwave instruments is proposed. Firstly, the novel interface is described and validated with microwave measurement. Then, the stability and repeatability of the contacting between the interface is investigated. Finally, given the possibility to rapidly interconnect microwave-microfluidic devices using the novel interface, five different interdigital capacitor (IDC) sensor designs having the same sensing area were investigated for dielectric permittivity extraction. The designed IDCs, embedded in a microfluidic channel built in a polydimethylsiloxane (PDMS) layer, were fabricated with a lift-off gold patterning technology on a quartz substrate. Broadband dielectric permittivity extraction shows a huge potential for the proposed interface to make microwave-microfluidic measurements much more accessible to a broader scientific community. The interconnect box (ICB) is presented in The electric part of the system consists of a microwave transmission line a inside The mechanical part of the system consists of a spring-levered system with a handle, used for fixing the consumable fluidic chip to the Invisipins on the PCB, and also for providing enough pressure to compress the Invisipins to the conducting state  b. DuringAside from the main purpose of the PMMA enclosure to house electric–mechanic system, its secondary function is to protect the microwave transmission line from environmental influences such as moisture in the incubator, proximity of metal wires that influence the signal propagation over the line, and so on. Therefore, the PMMA enclosure has only two openings, one for the SMA connector and the other slit for loading the chip, as shown in b.The ICB adds an extra transmission line to the measurement setup, which adds an extra insertion loss and phase delay to the complete setup. The reflection coefficient of the ICB without loading a chip was measured to estimate the insertion loss of the ICB. The measured reflection coefficient of an ideal ICB should be 1 or 0 dB, since it acts as an open-ended transmission line that reflects back all input power. The measurement setup, presented in The proposed ICB is used as the interface between the VNA and IDC-based microwave-microfluidic sensors—with the duty to bring the microwave signal from the VNA through a coaxial cable to the CPW transmission line on the quartz chip. This proves its potential to be used as a reliable, instantly reconnectable interface to bring the microwave signals to the lab-on-a-chip system. Traditionally, those measurements are performed with either a probe-based on-wafer approach or a connector-based approach, which have certain drawbacks as discussed in the previous section of this paper. The IDC-based microwave-microfluidic sensor consists of a patterned gold layer to achieve an IDC embedded in a microfluidic channel fed by a CPW transmission line.The stability of the measurement is important for microwave-microfluidic applications. It directly decides the minimum distinguishable difference of S-parameters in sensing applications and the uncertainty of the extract electrical permittivity in broadband spectroscopy applications. The stability of measurements depends on several facts, such as the noise of the measurement instrument, the stability of the contacting, and the stability of the measurement environment—such as the temperature of the environment and the mechanical stability of the measurement setup. The stability and repeatability of the contact between the Invisipins on PCB in ICB and the quartz chip is investigated by microwave measurements. An IDC sensor chip was inserted into the ICB of the measurement setup shown in The stability and repeatability of the contact between the Invisipins and gold on the quartz chip was evaluated with the following steps. First, a quartz chip was inserted and a contact between the Invisipins and gold on the quartz chip was obtained. A set of ten measurements of reflection coefficient was performed while maintaining the chip position. The mean value and standard deviation of these ten measurement results—defined as measurement set one—were calculated. The standard deviation in this case reflects the stability of the contact between the Invisipins and gold on the quartz chip. In the second step, the chip was taken out of the ICB and inserted back into the ICB. Once the contact between the Invisipins and gold was obtained, a new set of ten measurements was performed. Once again, the mean value and standard deviation were calculated for measurement set two. These procedures were repeated six times to get six sets of measurement results. Finally, the standard deviation between the mean value of the measured reflection coefficient of the six measurement sets was calculated and used to investigate the contact repeatability over different measurement campaigns. The mean magnitudes of measured SIDC sensors for microwave-microfluidic applications were designed and fabricated on a 4-inch quartz wafer. The fabrication process of the IDC sensor chips consists of three main steps . First, The capacitance of on-chip IDCs depends on the physical dimensions of its electrodes and the dielectric permittivity of both the substrate and material covering the electrodes. By guiding the liquid under test on top of the electrodes through microfluidic channel in a PDMS layer, the capacitance of the IDC is changed, and this change will be detected by a microwave measurement instrument. A method has been proposed to extract electrical permittivity of the liquid under test with an on-wafer measurement setup to achieve a broadband spectroscopy . This meCapacitance of on-chip IDC built with interdigital electrodes ,25 or coWith the proposed ICB, the fabricated chips can be easily and rapidly used in measurements. As shown in The magnitude of the reflection coefficient is measured from 100 MHz to 25 GHz for different IDC sensors, as shown in By using Equation (1), the dielectric permittivity of mixture with 80% IPA volume fraction was extracted using the measured reflection coefficients and modeled dielectric permittivity (from ) of the eference . As showeference . In summA novel microwave platform for biosensing conceived around the interconnect box is proposed to bring microwave signals to microwave-microfluidic chips with a verified stable contact. In addition to the microwave platform, a set of consumable microwave-microfluidic chips was designed based on the interdigital electrode array topology to test the microwave platform. The IDCs having different capacitances over the identical sensing area were loaded onto the proposed microwave platform and measured with VNA. The IDC sensor designed with the largest capacitance offers broadband spectroscopy in the frequency range from 100 MHz to 25 GHz. Further research will focus on increasing the permittivity extraction accuracy by improving alignment procedures for consumable chips and incorporating temperature stability. By avoiding the usage of the bulky, immobile, and expensive probe station—and extra costs related to connectorization of disposal chips—microwave-microfluidics sensing and heating devices are getting an opportunity to enter life science laboratories and make an impact in interdisciplinary biosensing domains.