An Ensemble Learning Approach for Fast Disaster Response using Social Media Analytics

Natural disaster happens, as a result of natural hazards that cause financial, environmental or human losses. Natural disasters strike unexpectedly, affecting the lives of tens of thousands of people. During the flood, social media sites were also heavily used to disseminate information about flooded areas, rescue agencies, food and relief centres. This work proposes an ensemble learning strategy for combining and analysing social media data in order to close the gap and progress in catastrophic situation. To enable scalability and broad accessibility of the dynamic streaming of multimodal data namely text, image, audio and video, this work is designed around social media data. A fusion technique was employed at the decision level, based on a database of 15 characteristics for more than 300 disasters around the world (Trained with MNIST dataset 60000 training images and 10000 testing images).  This work allows the collected multimodal social media data to share a common semantic space, making individual variable prediction easier. Each  merged numerical vector(tensors) of text and audio  is sent into the K-CNN algorithm, which is an  unsupervised learning algorithm (K-CNN), and the  image and video data is given to a deep learning  based Progressive Neural Artificial Search (PNAS).  The trained data acts as a predictor for future  incidents, allowing for the estimation of total  deaths, total individuals impacted, and total  damage, as well as specific suggestions for food,  shelter and housing inspections. To make such a prediction, the trained model is presented a satellite image from before the accident as well as the geographic and demographic conditions, which is expected to result in a prediction accuracy of more than 85%

CytoSolve: A Scalable Computational Method for Dynamic Integration of Multiple Molecular Pathway Models

A grand challenge of computational systems biology is to create a molecular pathway model of the whole cell. Current approaches involve merging smaller molecular pathway models’ source codes to create a large monolithic model (computer program) that runs on a single computer. Such a larger model is difficult, if not impossible, to maintain given ongoing updates to the source codes of the smaller models. This paper describes a new system called CytoSolve that dynamically integrates computations of smaller models that can run in parallel across different machines without the need to merge the source codes of the individual models. This approach is demonstrated on the classic Epidermal Growth Factor Receptor (EGFR) model of Kholodenko. The EGFR model is split into four smaller models and each smaller model is distributed on a different machine. Results from four smaller models are dynamically integrated to generate identical results to the monolithic EGFR model running on a single machine. The overhead for parallel and dynamic computation is approximately twice that of a monolithic model running on a single machine. The CytoSolve approach provides a scalable method since smaller models may reside on any computer worldwide, where the source code of each model can be independently maintained and updated

Ambient Stable Quantitative PCR Reagents for the Detection of Yersinia pestis

Plague, caused by Yersinia pestis, is one of the oldest and most dangerous diseases in human history, and has claimed millions of lives in the three major historical pandemics. Although panic caused by the Black Death is fading, the threat of the reemergence of plague pandemics still exists, with the additional potential of misuse in biowarfare or bioterrorism. Rapid on-site detection and identification of the pathogen is of paramount significance for timely implementation of effective countermeasures. TaqMan probe-based real-time PCR assays can give quick and accurate identification; however, the need for cold delivery and storage prevents its potential on-site application. The objective of this study was to develop a stable PCR system for easy delivery and storage under room temperature, which is vital for conventional plague surveillance and for preparedness in public health emergencies. We present a solution to this particular issue, hoping that it is helpful to future applications

Comparative Genomics of 2009 Seasonal Plague (Yersinia pestis) in New Mexico

Plague disease caused by the Gram-negative bacterium Yersinia pestis routinely affects animals and occasionally humans, in the western United States. The strains native to the North American continent are thought to be derived from a single introduction in the late 19th century. The degree to which these isolates have diverged genetically since their introduction is not clear, and new genomic markers to assay the diversity of North American plague are highly desired. To assay genetic diversity of plague isolates within confined geographic areas, draft genome sequences were generated by 454 pyrosequencing from nine environmental and clinical plague isolates. In silico assemblies of Variable Number Tandem Repeat (VNTR) loci were compared to laboratory-generated profiles for seven markers. High-confidence SNPs and small Insertion/Deletions (Indels) were compared to previously sequenced Y. pestis isolates. The resulting panel of mutations allowed clustering of the strains and tracing of the most likely evolutionary trajectory of the plague strains. The sequences also allowed the identification of new putative SNPs that differentiate the 2009 isolates from previously sequenced plague strains and from each other. In addition, new insertion points for the abundant insertion sequences (IS) of Y. pestis are present that allow additional discrimination of strains; several of these new insertions potentially inactivate genes implicated in virulence. These sequences enable whole-genome phylogenetic analysis and allow the unbiased comparison of closely related isolates of a genetically monomorphic pathogen

High Throughput, Multiplexed Pathogen Detection Authenticates Plague Waves in Medieval Venice, Italy

Background: Historical records suggest that multiple burial sites from the 14th-16(th) centuries in Venice, Italy, were used during the Black Death and subsequent plague epidemics.Methodology/Principal Findings: High throughput, multiplexed real-time PCR detected DNA of seven highly transmissible pathogens in 173 dental pulp specimens collected from 46 graves. Bartonella quintana DNA was identified in five (2.9%) samples, including three from the 16th century and two from the 15th century, and Yersinia pestis DNA was detected in three (1.7%) samples, including two from the 14th century and one from the 16th century. Partial glpD gene sequencing indicated that the detected Y. pestis was the Orientalis biotype.Conclusions: These data document for the first time successive plague epidemics in the medieval European city where quarantine was first instituted in the 14th century

Entry of Yersinia pestis into the Viable but Nonculturable State in a Low-Temperature Tap Water Microcosm

Yersinia pestis, the causative agent of plague, has caused several pandemics throughout history and remains endemic in the rodent populations of the western United States. More recently, Y. pestis is one of several bacterial pathogens considered to be a potential agent of bioterrorism. Thus, elucidating potential mechanisms of survival and persistence in the environment would be important in the event of an intentional release of the organism. One such mechanism is entry into the viable but non-culturable (VBNC) state, as has been demonstrated for several other bacterial pathogens. In this study, we showed that Y. pestis became nonculturable by normal laboratory methods after 21 days in a low-temperature tap water microcosm. We further show evidence that, after the loss of culturability, the cells remained viable by using a variety of criteria, including cellular membrane integrity, uptake and incorporation of radiolabeled amino acids, and protection of genomic DNA from DNase I digestion. Additionally, we identified morphological and ultrastructural characteristics of Y. pestis VBNC cells, such as cell rounding and large periplasmic spaces, by electron microscopy, which are consistent with entry into the VBNC state in other bacteria. Finally, we demonstrated resuscitation of a small number of the non-culturable cells. This study provides compelling evidence that Y. pestis persists in a low-temperature tap water microcosm in a viable state yet is unable to be cultured under normal laboratory conditions, which may prove useful in risk assessment and remediation efforts, particularly in the event of an intentional release of this organism

Module-based multiscale simulation of angiogenesis in skeletal muscle

<p>Abstract</p> <p>Background</p> <p>Mathematical modeling of angiogenesis has been gaining momentum as a means to shed new light on the biological complexity underlying blood vessel growth. A variety of computational models have been developed, each focusing on different aspects of the angiogenesis process and occurring at different biological scales, ranging from the molecular to the tissue levels. Integration of models at different scales is a challenging and currently unsolved problem.</p> <p>Results</p> <p>We present an object-oriented module-based computational integration strategy to build a multiscale model of angiogenesis that links currently available models. As an example case, we use this approach to integrate modules representing microvascular blood flow, oxygen transport, vascular endothelial growth factor transport and endothelial cell behavior (sensing, migration and proliferation). Modeling methodologies in these modules include algebraic equations, partial differential equations and agent-based models with complex logical rules. We apply this integrated model to simulate exercise-induced angiogenesis in skeletal muscle. The simulation results compare capillary growth patterns between different exercise conditions for a single bout of exercise. Results demonstrate how the computational infrastructure can effectively integrate multiple modules by coordinating their connectivity and data exchange. Model parameterization offers simulation flexibility and a platform for performing sensitivity analysis.</p> <p>Conclusions</p> <p>This systems biology strategy can be applied to larger scale integration of computational models of angiogenesis in skeletal muscle, or other complex processes in other tissues under physiological and pathological conditions.</p

Human plague: An old scourge that needs new answers

Yersinia pestis, the bacterial causative agent of plague, remains an important threat to human health. Plague is a rodent-borne disease that has historically shown an outstanding ability to colonize and persist across different species, habitats, and environments while provoking sporadic cases, outbreaks, and deadly global epidemics among humans. Between September and November 2017, an outbreak of urban pneumonic plague was declared in Madagascar, which refocused the attention of the scientific community on this ancient human scourge. Given recent trends and plague’s resilience to control in the wild, its high fatality rate in humans without early treatment, and its capacity to disrupt social and healthcare systems, human plague should be considered as a neglected threat. A workshop was held in Paris in July 2018 to review current knowledge about plague and to identify the scientific research priorities to eradicate plague as a human threat. It was concluded that an urgent commitment is needed to develop and fund a strong research agenda aiming to fill the current knowledge gaps structured around 4 main axes: (i) an improved understanding of the ecological interactions among the reservoir, vector, pathogen, and environment; (ii) human and societal responses; (iii) improved diagnostic tools and case management; and (iv) vaccine development. These axes should be cross-cutting, translational, and focused on delivering context-specific strategies. Results of this research should feed a global control and prevention strategy within a “One Health” approach

Leptospira species and serovars identified by MALDI-TOF mass spectrometry after database implementation

Background: Leptospirosis, a spirochaetal zoonotic disease of worldwide distribution, endemic in Europe, has been recognized as an important emerging infectious disease, though yet it is mostly a neglected disease which imparts its greatest burden on impoverished populations from developing countries. Leptospirosis is caused by the infection with any of the more than 230 serovars of pathogenic Leptospira sp. In this study we aimed to implement the MALDI-TOF mass spectrometry (MS) database currently available in our laboratory with Leptospira reference pathogenic (L. interrogans, L. borgpetersenii, L. kirschneri, L. noguchii), intermediate (L. fainei) and saprophytic (L. biflexa) strains of our collection in order to evaluate its possible application to the diagnosis of leptospirosis and to the typing of strains. This was done with the goal of understanding whether this methodology could be used as a tool for the identification of Leptospira strains, not only at species level for diagnostic purposes, but also at serovar level for epidemiological purposes, overcoming the limits of serological and molecular conventional methods. Twenty Leptospira reference strains were analysed by MALDI-TOF MS. Statistical analysis of the protein spectra was performed by ClinProTools software. Results: The spectra obtained by the analysis of the reference strains tested were grouped into 6 main classes corresponding to the species analysed, highlighting species-specific protein profiles. Moreover, the statistical analysis of the spectra identified discriminatory peaks to recognize Leptospira strains also at serovar level extending previously published data. Conclusions: In conclusion, we confirmed that MALDI-TOF MS could be a powerful tool for research and diagnostic in the field of leptospirosis with broad applications ranging from the detection and identification of pathogenic leptospires for diagnostic purposes to the typing of pathogenic and non-pathogenic leptospires for epidemiological purposes in order to enrich our knowledge about the epidemiology of the infection in different areas and generate control strategies