Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executives

A combination of federal and state-level decision making has shaped the response to COVID-19 in the United States. In this paper, we analyze the Twitter narratives around this decision making by applying a dynamic topic model to COVID-19 related tweets by U.S. Governors and Presidential cabinet members. We use a network Hawkes binomial topic model to track evolving sub-topics around risk, testing, and treatment. We also construct influence networks amongst government officials using Granger causality inferred from the network Hawkes process

Predicting Virality on Networks Using Local Graphlet Frequency Distribution

The task of predicting virality has far-reaching consequences, from the world of advertising to more recent attempts to reduce the spread of fake news. Previous work has shown that graphlet distribution is an effective feature for predicting virality. Here, we investigate the use of aggregated edge-centric local graphlets around source nodes as features for virality prediction. These prediction features are used to predict expected virality for both a time-independent Hawkes model and an independent cascade model of virality. In the Hawkes model, we use linear regression to predict the number of Hawkes events and node ranking, while in the independent cascade model we use logistic regression to predict whether a k-size cascade will multiply by a factor X in size. Our study indicates that local graphlet frequency distribution can effectively capture the variances of the viral processes simulated by Hawkes process and independent-cascade process. Furthermore, we identify a group of local graphlets which might be significant in the viral processes. We compare the effectiveness of our methods with eigenvector centrality-based node choice

Source detection on networks using spatial temporal graph convolutional networks

Detecting the source of an outbreak cluster during a pandemic like COVID-19 can provide insights into the transmission process, associated risk factors, and help contain the spread. In this work we study the problem of source detection from multiple snapshots of spreading on an arbitrary network structure. We use a spatial temporal graph convolutional network based model (SD-STGCN) to produce a source probability distribution, by fusing information from temporal and topological spaces. We perform extensive experiments using popular compartmental simulation models over synthetic networks and empirical contact networks. We also demonstrate the applicability of our approach with real COVID-19 case data

Learning network event sequences using long short-term memory and second-order statistic loss

Modeling temporal event sequences on the vertices of a network is an important problem with widespread applications; examples include modeling influences in social networks, preventing crimes by modeling their space–time occurrences, and forecasting earthquakes. Existing solutions for this problem use a parametric approach, whose applicability is limited to event sequences following some well-known distributions, which is not true for many real life event datasets. To overcome this limitation, in this work, we propose a composite recurrent neural network model for learning events occurring in the vertices of a network over time. Our proposed model combines two long short-term memory units to capture base intensity and conditional intensity of an event sequence. We also introduce a second-order statistic loss that penalizes higher divergence between the generated and the target sequence's distribution of hop count distance of consecutive events. Given a sequence of vertices of a network in which an event has occurred, the proposed model predicts the vertex where the next event would most likely occur. Experimental results on synthetic and real-world datasets validate the superiority of our proposed model in comparison to various baseline methods

Comparison of immune response generated against Japanese encephalitis virus envelope protein expressed by DNA vaccines under macrophage associated versus ubiquitous expression promoters

<p>Abstract</p> <p>Background</p> <p>Japanese encephalitis virus (JEV) is the leading cause of viral encephalitis, with ~50,000 cases reported annually worldwide. Vaccination is the only measure for prevention. Recombinant vaccines are an efficient and safe alternative for formalin inactivated or live attenuated vaccines. Nowadays, incorporation of molecular adjuvants has been the main strategy for melioration of vaccines. Our attempt of immunomodulation is based on targeting antigen presenting cells (APC) "majorly macrophages" by using macrosialin promoter. We have compared the immune response of the constructed plasmids expressing JEV envelope (E) protein under the control of aforesaid promoter and cytomegalovirus (CMV) immediate early promoter in mouse model. Protection of immunized mice from lethal challenge with JEV was also studied.</p> <p>Results</p> <p>The E protein was successfully expressed in the macrophage cell line and was detected using immunofluorescence assay (IFA) and Western blotting. APC expressing promoter showed comparable expression to CMV promoter. Immunization of mice with either of the plasmids exhibited induction of variable JEV neutralizing antibody titres and provided protection from challenge with a lethal dose of JEV. Immune splenocytes showed proliferative response after stimulation with the JEV antigen (Ag), however, it was higher for CMV promoter. The magnitude of immunity provided by APC dominant promoter was non-significantly lower in comparison to CMV promoter. More importantly, immune response directed by APC promoter was skewed towards Th1 type in comparison to CMV promoter, this was evaluated by cytokine secretion profile of immune splenocytes stimulated with JEV Ag.</p> <p>Conclusions</p> <p>Thus, our APC-expressing DNA vaccination approach induces comparable immunity in comparison to ubiquitous promoter construct. The predominant Th1 type immune responses provide opportunities to further test its potency suitable for response in antiviral or anticancer vaccines.</p

Correlating nano-scale surface replication accuracy and cavity temperature in micro-injection moulding using in-line process control and high-speed thermal imaging

YesMicro-injection moulding (μIM) stands out as preferable technology to enable the mass production of polymeric components with micro- and nano-structured surfaces. One of the major challenges of these processes is related to the quality assurance of the manufactured surfaces: the time needed to perform accurate 3D surface acquisitions is typically much longer than a single moulding cycle, thus making impossible to integrate in-line measurements in the process chain. In this work, the authors proposed a novel solution to this problem by defining a process monitoring strategy aiming at linking sensitive in-line monitored process variables with the replication quality. A nano-structured surface for antibacterial applications was manufactured on a metal insert by laser structuring and replicated using two different polymers, polyoxymethylene (POM) and polycarbonate (PC). The replication accuracy was determined using a laser scanning confocal microscope and its dependence on the variation of the main μIM parameters was studied using a Design of Experiments (DoE) experimental approach. During each process cycle, the temperature distribution of the polymer inside the cavity was measured using a high-speed infrared camera by means of a sapphire window mounted in the movable plate of the mould. The temperature measurements showed a high level of correlation with the replication performance of the μIM process, thus providing a fast and effective way to control the quality of the moulded surfaces in-line.MICROMAN project (“Process Fingerprint for Zero-defect Net-shape MICRO MANufacturing”, http://www.microman.mek.dtu.dk/) - H2020 (Project ID: 674801), H2020 agreement No. 766871 (HIMALAIA), H2020 ITN Laser4Fun (agreement No. 675063

Global, regional and national burden of bladder cancer and its attributable risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease study 2019

Introduction The current study determined the level and trends associated with the incidence, death and disability rates for bladder cancer and its attributable risk factors in 204 countries and territories, from 1990 to 2019, by age, sex and sociodemographic index (SDI; a composite measure of sociodemographic factors). Methods Various data sources from different countries, including vital registration and cancer registries were used to generate estimates. Mortality data and incidence data transformed to mortality estimates using the mortality to incidence ratio (MIR) were used in a cause of death ensemble model to estimate mortality. Mortality estimates were divided by the MIR to produce incidence estimates. Prevalence was calculated using incidence and MIR-based survival estimates. Age-specific mortality and standardised life expectancy were used to estimate years of life lost (YLLs). Prevalence was multiplied by disability weights to estimate years lived with disability (YLDs), while disability-adjusted life years (DALYs) are the sum of the YLLs and YLDs. All estimates were presented as counts and age-standardised rates per 100 000 population. Results Globally, there were 524 000 bladder cancer incident cases (95% uncertainty interval 476 000 to 569 000) and 229 000 bladder cancer deaths (211 000 to 243 000) in 2019. Age-standardised death rate decreased by 15.7% (8.6 to 21.0), during the period 1990–2019. Bladder cancer accounted for 4.39 million (4.09 to 4.70) DALYs in 2019, and the age-standardised DALY rate decreased significantly by 18.6% (11.2 to 24.3) during the period 1990–2019. In 2019, Monaco had the highest age-standardised incidence rate (31.9 cases (23.3 to 56.9) per 100 000), while Lebanon had the highest age-standardised death rate (10.4 (8.1 to 13.7)). Cabo Verde had the highest increase in age-standardised incidence (284.2% (214.1 to 362.8)) and death rates (190.3% (139.3 to 251.1)) between 1990 and 2019. In 2019, the global age-standardised incidence and death rates were higher among males than females, across all age groups and peaked in the 95+ age group. Globally, 36.8% (28.5 to 44.0) of bladder cancer DALYs were attributable to smoking, more so in males than females (43.7% (34.0 to 51.8) vs 15.2% (10.9 to 19.4)). In addition, 9.1% (1.9 to 19.6) of the DALYs were attributable to elevated fasting plasma glucose (FPG) (males 9.3% (1.6 to 20.9); females 8.4% (1.6 to 19.1)). Conclusions There was considerable variation in the burden of bladder cancer between countries during the period 1990–2019. Although there was a clear global decrease in the age-standardised death, and DALY rates, some countries experienced an increase in these rates. National policy makers should learn from these differences, and allocate resources for preventative measures, based on their country-specific estimates. In addition, smoking and elevated FPG play an important role in the burden of bladder cancer and need to be addressed with prevention programmes.publishedVersio

Global injury morbidity and mortality from 1990 to 2017 : results from the Global Burden of Disease Study 2017

Correction:Background Past research in population health trends has shown that injuries form a substantial burden of population health loss. Regular updates to injury burden assessments are critical. We report Global Burden of Disease (GBD) 2017 Study estimates on morbidity and mortality for all injuries. Methods We reviewed results for injuries from the GBD 2017 study. GBD 2017 measured injury-specific mortality and years of life lost (YLLs) using the Cause of Death Ensemble model. To measure non-fatal injuries, GBD 2017 modelled injury-specific incidence and converted this to prevalence and years lived with disability (YLDs). YLLs and YLDs were summed to calculate disability-adjusted life years (DALYs). Findings In 1990, there were 4 260 493 (4 085 700 to 4 396 138) injury deaths, which increased to 4 484 722 (4 332 010 to 4 585 554) deaths in 2017, while age-standardised mortality decreased from 1079 (1073 to 1086) to 738 (730 to 745) per 100 000. In 1990, there were 354 064 302 (95% uncertainty interval: 338 174 876 to 371 610 802) new cases of injury globally, which increased to 520 710 288 (493 430 247 to 547 988 635) new cases in 2017. During this time, age-standardised incidence decreased non-significantly from 6824 (6534 to 7147) to 6763 (6412 to 7118) per 100 000. Between 1990 and 2017, age-standardised DALYs decreased from 4947 (4655 to 5233) per 100 000 to 3267 (3058 to 3505). Interpretation Injuries are an important cause of health loss globally, though mortality has declined between 1990 and 2017. Future research in injury burden should focus on prevention in high-burden populations, improving data collection and ensuring access to medical care.Peer reviewe

Global, regional, and national burden of colorectal cancer and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Funding: F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia, I.P. (FCT), in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy i4HB; FCT/MCTES through the project UIDB/50006/2020. J Conde acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325). V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006.proofepub_ahead_of_prin