Cluster-based Epidemic Control Through Smartphone-based Body Area Networks

Increasing population density, closer social contact, and interactions make epidemic control difficult. Traditional offline epidemic control methods (e.g., using medical survey or medical records) or model-based approach are not effective due to its inability to gather health data and social contact information simultaneously or impractical statistical assumption about the dynamics of social contact networks, respectively. In addition, it is challenging to find optimal sets of people to be isolated to contain the spread of epidemics for large populations due to high computational complexity. Unlike these approaches, in this paper, a novel cluster-based epidemic control scheme is proposed based on Smartphonebased body area networks. The proposed scheme divides the populations into multiple clusters based on their physical location and social contact information. The proposed control schemes are applied within the cluster or between clusters. Further, we develop a computational efficient approach called UGP to enable an effective cluster-based quarantine strategy using graph theory for large scale networks (i.e., populations). The effectiveness of the proposed methods is demonstrated through both simulations and experiments on real social contact networks

Comparative model for classification of forest degradation

The challenges of forest degradation together with its related effects have attracted research from diverse disciplines, resulting in different definitions of the concept. However, according to a number of researchers, the central element of this issue is human intrusion that destroys the state of the environment. Therefore, the focus of this research is to develop a comparative model using a large amount of multi-spectral remote sensing data, such as IKONOS, QUICKBIRD, SPOT, WORLDVIEW-1, Terra-SARX, and fused data to detect forest degradation in Cameron Highlands. The output of this method in line with the performance measurement model. In order to identify the best data, fused data and technique to be employed. Eleven techniques have been used to develop a Comparative technique by applying them on fifteen sets of data. The output of the Comparative technique was used to feed the performance measurement model in order to enhance the accuracy of each classification technique. Moreover, a Performance Measurement Model has been used to verify the results of the Comparative technique; and, these outputs have been validated using the reflectance library. In addition, the conceptual hybrid model proposed in this research will give the opportunity for researchers to establish a fully automatic intelligent model for future work. The results of this research have demonstrated the Neural Network (NN) to be the best Intelligent Technique (IT) with a 0.912 of the Kappa coefficient and 96% of the overall accuracy, Mahalanobis had a 0.795 of the Kappa coefficient and 88% of the overall accuracy and the Maximum likelihood (ML) had a 0.598 of the Kappa coefficient and 72% of the overall accuracy from the best fused image used in this research, which was represented by fusing the IKONOS image with the QUICKBIRD image as finally employed in the Comparative technique for improving the detectability of forest change

Characterization of the Hemagglutinin Cleaving Transmembrane Serine Proteases Matriptase and TMPRSS2

Influenza is one of the commonest infectious diseases affecting millions of people every year including 290,000 – 650,000 heavy casualties. Influenza viruses undergo constant genetic changes and every 10 – 50 years new influenza virus strains emerge that potentially cause a severe pandemic. In this modern interconnected world, experts believe the next influenza pandemic will be a “devastating global health event with far-reaching consequences” [1]. Novel effective anti-influenza drugs are in need. One strategy of influenza research is to focus on host-specific proteases that are essential for virus activation and spread. Trypsin-like serine proteases are crucial for influenza activation by mediating the cleavage of the viral surface glycoprotein HA and hence promoting the fusion potential of the virus. Therefore, their inhibition provides a promising therapeutic approach. The present work focused on the characterization of two relevant HA cleaving type-II transmembrane serine proteases matriptase and TMPRSS2. Chapter 3 and chapter 4 of this thesis engaged with the recombinant production of matriptase (chapter 3) in order to obtain a pure functional enzyme of high quality for a SAR study with novel monobasic (hence potentially bioavailable) matriptase inhibitors of the 3-amidinophenylalanine type (chapter 4). Adequate amounts of high-quality matriptase enzymes were isolated using a new expression system and in total 5 matriptase crystals were available at the end of this thesis for structural analysis. The matriptase inhibitor design in this thesis focused on matriptase-affine compounds with a fair selectivity profile against the blood coagulation enzymes thrombin and fXa. In total, 18 new monobasic and potentially bioavailable, as well as four new dibasic compounds of the 3-amidinophenylalanine types were tested. Based on the last published crystal structure of this inhibitor type in complex with matriptase from 2006 (PDB code 2GV6) docking was used as a structure-based virtual screening method for lead optimization of the compounds N-terminus. Selected compounds were suggested to interact with the carbonyl side chain of Gln175 of matriptase to achieve a higher affinity of matriptase compared to fXa. The 4-tert-butylureido-piperidine could be identified as suitable C-terminus in combination with 3-fluoro-4-hydroxymethyl biphenylsulphonyl N-terminally in order to obtain excellent selectivity over thrombin. The binding mode of this compound (compound 55) was crystallographically determined in complex with matriptase as well as trypsin. Trypsin proved as a suitable alternative to matriptase for detailed binding mode analysis of the compounds N-terminus. However, different preferences were detected for the C-terminus. Dibasic compounds showed higher matriptase affinity and selectivity in comparison with the monobasic analogues. However, the tested monobasic compounds were still decent matriptase inhibitors that are additionally suitable for cell culture and animal studies in their benzamidine prodrug forms, which are well established from related inhibitors of thrombin. In addition, selected monobasic as well as dibasic compounds demonstrated strong suppression of the replication of certain H9N2 influenza viruses in a matriptase-expressing MDCK II cell model. These matriptase inhibitors could be potential lead structures for the development of new drugs against H9 strains for influenza. TMPRSS2 is widely discussed for its role in influenza activation. With a TMPRSS2 dependancy of HA-activation of certain subtypes, the characterization of this protease is an important prerequisite for being available as a target for influenza drug design. However, only little is known about the physiological function of TMPRSS2 and no experimental structure data are available at the moment to enable a structure-based drug development. Therefore, chapter 5 of this thesis focused on the characterization of TMPRSS2 in order to develop a strategy for the isolation of proteolytically active TMPRSS2 from cell culture. Even though, no functional TMPRSS2 could be recovered at the end of this work some new structural characteristics of TMPRSS2 were identified as crucial for functionality insight the cell. In general, TMPRSS2 without the cytosolic part, the transmembrane domain and the LDLRA domain is able to undergo autocatalytically activation if an artificial signal peptide was added N-terminal to enable entry into the endoplasmic reticulum. The presence of the cysteine-rich SRCR domain and the presence of the disulfide chain that connects the SPD and the stem region after activation cleavage have been identified as crucial for activity. N-terminal truncation of TMPRSS2 did not result in obvious dislocation within the cell: as the full-length positive control truncated TMPRSS2 was exclusively found in cell compartments surrounding the nucleus in immunofluorescence experiments. However, a reduced proteolytic cleavage activity towards H3-HA in co-expression experiments has been observed and might be a result of dislocation, since truncated TMPRSS2 is not bound to the biomembrane anymore. In addition, TMPRSS2 has been identified as a potential substrate of matriptase in vitro, which suggests possible participation in several zymogen cascades

Social Contagion of Violence

Since 1968, violence and other crimes in New York City have followed a pattern of recurring epidemics. There have been three consecutive and contiguous cycles characterized by sharp increases in homicides and assaults to an elevated rate followed by equally steep declines to levels near the previous starting point. The most recent epidemic, from 1985-96, had the sharpest rise and steepest decline of the three epidemics. Popular explanations of the current epidemic fail to account for both the rise and fall of the decline, or for the repetitive pattern of these epidemics. In this article, we use public health data to identify factors associated with the cyclical rise and fall of homicides and non-lethal injury violence in this most recent of the three epidemics. Homicides in this period were concentrated among minority males, ages 15-24, while victimization rates for females of all ages remained stable. Gun homicides and non-lethal gun assaults accounted for all the increase and decline in interpersonal violence from 1985-95; intentional injuries caused by other means also were stable or declining over this period. Next, we use hierarchical linear regression models, with a rich set of time-varying covariates and controls for both temporal and spatial autocorrelation, to identify whether the rise and fall of homicide are explained by processes of diffusion across adjacent neighborhoods. We estimate the probabilities of homicides and assaults in a neighborhood controlling for rates of homicide or assault in the surrounding neighborhoods in the preceding year, and find that gun violence diffuses across neighborhoods over time. Epidemic patterns of violence disproportionately affected African Americans as victims of gun violence, both homicides and non-lethal gun assaults. Diffusion was strongest in neighborhoods where social control was compromised by extreme poverty and concentrated racial segregation. Concentrations of immigrant households in neighborhoods were a protective factor in suppressing the violence epidemic. We then use social contagion theories to link contagion of violence across neighborhoods to individual data on social interactions that may animate the transmission and diffusion of violence. Analyses of the social contexts and interpersonal dynamics of violent events reveals how street interactions in interpersonal disputes link individuals within and across social networks in a competition for status that is skewed by the presence of firearms. Decisions to carry, show and use weapons are based on perceptions of threat and danger, which are shaped by the presence of firearms and the potential stigma associated with non-action. These events occur across multiple contexts - bars, streetcorners, drug markets - to reinforce perceptions of risk and expectations of the threat or reality of lethal violence

Perspectives on adaptive dynamical systems

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches

Programming frameworks for mobile sensing

The proliferation of smart mobile devices in people’s daily lives is making context-aware computing a reality. A plethora of sensors available in these devices can be utilized to understand users’ context better. Apps can provide more relevant data or services to the user based on improved understanding of user’s context. With the advent of cloud-assisted mobile platforms, apps can also perform collaborative computation over the sensing data collected from a group of users. However, there are still two main issues: (1) A lack of simple and effective personal sensing frameworks: existing frameworks do not provide support for real-time fusing of data from motion and visual sensors in a simple manner, and no existing framework collectively utilizes sensors from multiple personal devices and personal IoT sensors, and (2) a lack of collaborative/distributed computing frameworks for mobile users. This dissertation presents solutions for these two issues. The first issue is addressed by TagPix and Sentio, two frameworks for mobile sensing. The second issue is addressed by Moitree, a middleware for mobile distributed computing, and CASINO, a collaborative sensor-driven offloading system. TagPix is a real-time, privacy preserving photo tagging framework, which works locally on the phones and consumes little resources (e.g., battery). It generates relevant tags for landscape photos by utilizing sensors of a mobile device and it does not require any previous training or indexing. When a user aims the mobile camera to a particular landmark, the framework uses accelerometer and geomagnetic field sensor to identify in which direction the user is aiming the camera at. It then uses a landmark database and employs a smart distance estimation algorithm to identify which landmark(s) is targeted by the user. The framework then generates relevant tags for the captured photo using these information. A more versatile sensing framework can be developed using sensors from multiple devices possessed by a user. Sentio is such a framework which enables apps to seamlessly utilize the collective sensing capabilities of the user’s personal devices and of the IoT sensors located in the proximity of the user. With Sentio, an app running on any personal mobile/wearable device can access any sensor of the user in real-time using the same API, can selectively switch to the most suitable sensor of a particular type when multiple sensors of this type are available at different devices, and can build composite sensors. Sentio offers seamless connectivity to sensors even if the sensor-accessing code is offloaded to the cloud. Sentio provides these functionalities with a high-level API and a distributed middleware that handles all low-level communication and sensor management tasks. This dissertation also proposes Moitree, a middleware for the mobile cloud platforms where each mobile device is augmented by an avatar, a per-user always-on software entity that resides in the cloud. Mobile-avatar pairs participate in distributed computing as a unified computing entity. Moitree provides a common programming and execution framework for mobile distributed apps. Moitree allows the components of a distributed app to execute seamlessly over a set of mobile/avatar pairs, with the provision of offloading computation and communication to the cloud. The programming framework has two key features: user collaborations are modeled using group semantics - groups are created dynamically based on context and are hierarchical; data communication among group members is offloaded to the cloud through high-level communication channels. Finally, this dissertation presents and discusses CASINO, a collaborative sensor-driven computation offloading framework which can be used alongside Moitree. This framework includes a new scheduling algorithm which minimizes the total completion time of a collaborative computation that executes over a set of mobile/avatar pairs. Using the CASINO API, the programmers can mark their classes and functions as ”offloadable”. The framework collects profiling information (network, CPU, battery, etc.) from participating users’ mobile devices and avatars, and then schedules ”offloadable” tasks in mobiles and avatars in a way that reduces the total completion time. The scheduling problem is proven to be NP-Hard and there is no polynomial time optimization algorithm for it. The proposed algorithm can generate a schedule in polynomial time using a topological sorting and greedy technique

The Case of Ebola in West Africa - Swedish Healthcare Workers' Experiences of Navigating Global Health Interventions

In this time of global health interventions, preventing borderless diseases, such as Ebola, is a question of implementing global health policies in different cultural contexts. Although these interventions are mediated by international organs, the healthcare workers on the ground are actually those who implement policies. From this starting point, this thesis investigates the extent to which Swedish healthcare workers, when combating the Ebola virus disease in West Africa, take into consideration the local context in their application of global health preventive measures. It does so by exploring healthcare workers’ experiences of navigating global health interventions, while negotiating culture. Qualitative semi-structured interviews were conducted with participants in the Swedish Civil Contingencies Agency’s medical mission, which aimed to combat Ebola in Liberia and Sierra Leone. To understand their experiences, Michael Lipsky’s ‘bottom-up’ theory and conceptualization of the street-level bureaucrat inspired this study’s theoretical foundation. Three themes were prevalent in the interview material: Navigating the field and establishing trust, Consolidating objectives and Negotiating culture. This thesis argues that constant flexibility and adjustment to the pre-existing challenges in the field are vital in the adaptation of health policies. Moreover, flexibility is dependent on the information transferred from the field. Without rapid information transferal, bureaucracies and their employees have false perceptions of the field, on which they articulate their objectives for partaking in the health interventions. It is further argued that these actors continuously have an internal negotiation of ‘Self’ in relation to ‘Other’ and the bureaucracy that they work for, while trying to navigate health interventions in a foreign context. In conclusion, the ‘one size fits all’ approach does not work and this mindset (re-)produces a dichotomy between ‘us’ and ‘them’ where a certain way of doing things is seen as predominant