Using Remote Sensing to Map the Risk of Human Monkeypox Virus in the Congo Basin

Although the incidence of human monkeypox has greatly increased in Central Africa over the last decade, resources for surveillance remain extremely limited. We conducted a geospatial analysis using existing data to better inform future surveillance efforts. Using active surveillance data collected between 2005 and 2007, we identified locations in Sankuru district, Democratic Republic of Congo (DRC) where there have been one or more cases of human monkeypox. To assess what taxa constitute the main reservoirs of monkeypox, we tested whether human cases were associated with (i) rope squirrels (Funisciurus sp.), which were implicated in monkeypox outbreaks elsewhere in the DRC in the 1980s, or (ii) terrestrial rodents in the genera Cricetomys and Graphiurus, which are believed to be monkeypox reservoirs in West Africa. Results suggest that the best predictors of human monkeypox cases are proximity to dense forests and associated habitat preferred by rope squirrels. The risk of contracting monkeypox is significantly greater near sites predicted to be habitable for squirrels (OR = 1.32; 95% CI 1.08–1.63). We recommend that semi-deciduous rainforests with oil-palm, the rope squirrel’s main food source, be prioritized for monitoring

Signal processing of the contingent negative variation in schizophrenia using multilayer perceptrons and predictive statistical diagnosis

An event related potential known as the contingent negative variation (CNV) was recorded from two sites from the brains of 20 medicated schizophrenics and 20 normal control subjects. The aim was to apply signal processing, artificial neural networks and statistical techniques to the CNV waveform to improve the understanding of schizophrenia and to develop a neurophysiological technique for its identification and monitoring. CNV recording sites were the vertex and from a point midline approximately 30mm anterior to the vertex (frontal). Three-layer multilayer perceptrons (MLPs) were used to discriminate between the CNV waveforms of the schizophrenics and normal subjects. Although the MLP technique was successful in discrimination, it did not provide a quantitative measure for the analysis. Furthermore, during the test phase it always classified the subjects into one of the two categories and did not provide an output for either type (unknown type). To improve the clinical diagnosis a discrimination technique based on predictive statistical diagnosis (PSD) was developed. The input parameters to the PSD were a time domain feature and three features obtained from the energy spectrum of the CNV waveform. The PSD output indicated the probability and the atypicality index of each subject belonging to one of the two groups. Discrimination accuracy of the PSD was 100% for normal subjects. Three schizophrenics could not be classified into either type, but the rest were identified correctly. T-tests carried out on the recorded CNV waveforms showed that the CNV waveform recorded from the vertex site in normal subjects is significantly different from that recorded from the frontal site; however this was not the case for schizophrenics

Team 2 Process Report: How Team 2 Became Team Velocity

On January 11, 2012 The ETM Communications and Team Building 522/622 class began. After initial introduction of the course, Dr. Dryden instructed class members to form teams of 4 to 6 people that would work together for the entire quarter. This was the beginning of Team Velocity. Most of us knew nothing of our team members when we formed. We formed primarily by chance. Our team consisted of six very diverse people. Mohammad, our unofficial team leader, is from Dubai UAE, three of the girls, Bahareh, Mitra, and Sara are from Iran, and Carl is home grown USA. One member left our team after the first class, however during the second session of class another member joined and became a member of our team; Puniti is from India. Beyond diversity, our team had a generation gap as well as industry experience gap. The age gap is over 20 years between our oldest member and youngest member. Finally, we had very diverse backgrounds in education from Computer Engineering, Civil Engineering, Electrical Engineering, Industrial Engineering, Architectural Engineering, to Biology. One might expect that with this type of diversity, age gap, industry experience, and different education backgrounds, there would be inherent conflict to deal with. This was not the case for our team. We had very little conflict within our team and the conflict we did have was rather insignificant. We worked effectively together and respected each other’s opinions. This does not mean we all played the same role in our team. We had people that were more outspoken and quickly took a leadership role. We had members who were very detailed oriented and others who were bigger picture oriented. We had members that needed encouragement and others who were very self-confident. All of this together made up the complementary skills needed to make an effective team. Through the quarter our team worked together to complete assigned tasks and present material to the class. We all had different ways of looking at things and had different strengths and weaknesses. We learned through the quarter how to capitalize on each member’s strengths and thereby mitigating the weaknesses. We met many times during the quarter to form our presentations. We continually kept in contact over emails in the editing of papers and slides for presentations. Each person in the group was assigned equal amounts of real work to do. We were each accountable to the group to provide summaries of papers that the team would edit. We were also individually accountable to present sections of the class material to the class. The team would meet to compile and edit slides that would be presented. We would then practice the presentations. Each member clearly understood that the team would only succeed if each member did their part. We held ourselves mutually accountable for the work we presented. This is how our team was formed, how we are made up, how we did our work, and how we held ourselves accountable for our success. Through this process we developed a better understanding of each other and found that we really enjoyed working together. We developed a bond and friendship that transcends the individual. This paper describes how team 2 became Team Velocity

Using remote sensing to map the risk of human monkeypox virus in the Congo Basin.

Although the incidence of human monkeypox has greatly increased in Central Africa over the last decade, resources for surveillance remain extremely limited. We conducted a geospatial analysis using existing data to better inform future surveillance efforts. Using active surveillance data collected between 2005 and 2007, we identified locations in Sankuru district, Democratic Republic of Congo (DRC) where there have been one or more cases of human monkeypox. To assess what taxa constitute the main reservoirs of monkeypox, we tested whether human cases were associated with (i) rope squirrels (Funisciurus sp.), which were implicated in monkeypox outbreaks elsewhere in the DRC in the 1980s, or (ii) terrestrial rodents in the genera Cricetomys and Graphiurus, which are believed to be monkeypox reservoirs in West Africa. Results suggest that the best predictors of human monkeypox cases are proximity to dense forests and associated habitat preferred by rope squirrels. The risk of contracting monkeypox is significantly greater near sites predicted to be habitable for squirrels (OR = 1.32; 95% CI 1.08-1.63). We recommend that semi-deciduous rainforests with oil-palm, the rope squirrel's main food source, be prioritized for monitoring

Mapping carbon accumulation potential from global natural forest regrowth

To constrain global warming, we must strongly curtail greenhouse gas emissions and capture excess atmospheric carbon dioxide1,2. Regrowing natural forests is a prominent strategy for capturing additional carbon3, but accurate assessments of its potential are limited by uncertainty and variability in carbon accumulation rates2,3. To assess why and where rates differ, here we compile 13,112 georeferenced measurements of carbon accumulation. Climatic factors explain variation in rates better than land-use history, so we combine the field measurements with 66 environmental covariate layers to create a global, one-kilometre-resolution map of potential aboveground carbon accumulation rates for the first 30 years of natural forest regrowth. This map shows over 100-fold variation in rates across the globe, and indicates that default rates from the Intergovernmental Panel on Climate Change (IPCC)4,5 may underestimate aboveground carbon accumulation rates by 32 per cent on average and do not capture eight-fold variation within ecozones. Conversely, we conclude that maximum climate mitigation potential from natural forest regrowth is 11 per cent lower than previously reported3 owing to the use of overly high rates for the location of potential new forest. Although our data compilation includes more studies and sites than previous efforts, our results depend on data availability, which is concentrated in ten countries, and data quality, which varies across studies. However, the plots cover most of the environmental conditions across the areas for which we predicted carbon accumulation rates (except for northern Africa and northeast Asia). We therefore provide a robust and globally consistent tool for assessing natural forest regrowth as a climate mitigation strategy