A Coordinated Research Agenda for Nature-Based Learning

Evidence is mounting that nature-based learning (NBL) enhances children’s educational and developmental outcomes, making this an opportune time to identify promising questions to carry research and practice in this field forward. We present the outcomes of a process to set a research agenda for NBL, undertaken by the Science of Nature-Based Learning Collaborative Research Network, with funding from the National Science Foundation. A literature review and several approaches to gathering input from researchers, practitioners, and funders resulted in recommendations for research questions and methodological improvements to increase the relevance and rigor of research in this field. Some questions seek to understand how learning in nature affects what children learn, how they learn, and how it varies based on age, gender, socioeconomic status, ethnic background, special needs, and individual differences. Outcomes of interest cover academic performance, practical skills, personal development, and environmental stewardship. Other questions seek to find causal explanations for observed outcomes. To create optimal conditions for NBL, the research agenda includes practical questions about how to prepare teachers to work successfully in nature and how to support their adoption of this approach. Not least, the research agenda asks whether learning in nature can address major societal issues by moderating the effect of socioeconomic disadvantage on children’s academic achievement, personal development and wellbeing, and how these benefits might be attained at reasonable costs. A deeper understanding of how, why and for whom different forms of nature contact enhance learning and development is needed to guide practice and policy decision-making

Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population

Since December 2019 the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemic COVID-19. Early symptoms overlap with other common conditions such as common cold and Influenza, making early screening and diagnosis are crucial goals for health practitioners. The aim of the study was to use machine learning (ML), an artificial neural network (ANN) and a simple statistical test to identify SARS-CoV-2 positive patients from full blood counts without knowledge of symptoms or history of the individuals. The dataset included in the analysis and training contains anonymized full blood counts results from patients seen at the Hospital Israelita Albert Einstein, at São Paulo, Brazil, and who had samples collected to perform the SARS-CoV-2 rt-PCR test during a visit to the hospital. Patient data was anonymised by the hospital, clinical data was standardized to have a mean of zero and a unit standard deviation. This data was made public with the aim to allow researchers to develop ways to enable the hospital to rapidly predict and potentially identify SARS-CoV-2 positive patients. We find that with full blood counts random forest, shallow learning and a flexible ANN model predict SARS-CoV-2 patients with high accuracy between populations on regular wards (AUC = 94–95%) and those not admitted to hospital or in the community (AUC = 80–86%). Here, AUC is the Area Under the receiver operating characteristics Curve and a measure for model performance. Moreover, a simple linear combination of 4 blood counts can be used to have an AUC of 85% for patients within the community. The normalised data of different blood parameters from SARS-CoV-2 positive patients exhibit a decrease in platelets, leukocytes, eosinophils, basophils and lymphocytes, and an increase in monocytes. SARS-CoV-2 positive patients exhibit a characteristic immune response profile pattern and changes in different parameters measured in the full blood count that are detected from simple and rapid blood tests. While symptoms at an early stage of infection are known to overlap with other common conditions, parameters of the full blood counts can be analysed to distinguish the viral type at an earlier stage than current rt-PCR tests for SARS-CoV-2 allow at present. This new methodology has potential to greatly improve initial screening for patients where PCR based diagnostic tools are limited

Application of a targeted-enrichment methodology for full-genome sequencing of Dengue 1-4, Chikungunya and Zika viruses directly from patient samples.

The frequency of epidemics caused by Dengue viruses 1-4, Zika virus and Chikungunya viruses have been on an upward trend in recent years driven primarily by uncontrolled urbanization, mobility of human populations and geographical spread of their shared vectors, Aedes aegypti and Aedes albopictus. Infections by these viruses present with similar clinical manifestations making them challenging to diagnose; this is especially difficult in regions of the world hyperendemic for these viruses. In this study, we present a targeted-enrichment methodology to simultaneously sequence the complete viral genomes for each of these viruses directly from clinical samples. Additionally, we have also developed a customized computational tool (BaitMaker) to design these enrichment baits. This methodology is robust in its ability to capture diverse sequences and is amenable to large-scale epidemiological studies. We have applied this methodology to two large cohorts: a febrile study based in Colombo, Sri Lanka taken during the 2009-2015 dengue epidemic (n = 170) and another taken during the 2016 outbreak of Zika virus in Singapore (n = 162). Results from these studies indicate that we were able to cover an average of 97.04% ± 0.67% of the full viral genome from samples in these cohorts. We also show detection of one DENV3/ZIKV co-infected patient where we recovered full genomes for both viruses

Energy Injection for Fast Ignition

In the fast ignition concept, assembled fuel is ignited through a separate high intensity laser pulse. Fast Ignition targets facilitate this ignition using a reentrant cone. It provides clear access through the overlaying coronal plasma, and controls the laser plasma interaction to optimize hot-electron production and transport into the compressed plasma. Recent results suggest that the cone does not play any role in guiding light or electrons to its tip, and coupling to electrons can be reduced by a small amount of preplasma. This puts stringent requirements on the ignition laser focusing, pointing, and prepulse

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV

Measurement of the top quark mass using charged particles in pp collisions at root s=8 TeV

Peer reviewe

Measurement of t(t)over-bar normalised multi-differential cross sections in pp collisions at root s=13 TeV, and simultaneous determination of the strong coupling strength, top quark pole mass, and parton distribution functions

Peer reviewe