Using Poultry to Enhance Food Security in Stann Creek, Belize

Food insecurity is a growing issue in developing and developed countries alike, and in countries like Belize, the prevalence of hunger has soared in recent years. Not having access to diets with sufficient calories and nutrients leads to a host of developmental issues, including stunting and cognitive delays. The purpose of this work was to create an all-encompassing manual for small-scale broiler production, with the intention of another honors student overseeing the implementation of the ideas set forth in the manual, in order to provide nourishment for the students and staff of a school in Belize. After determining that the best way to meet the school’s needs was through dual-purpose birds, background research was conducted to determine small-scale poultry facilities in developing countries. This involved designing the layout for the poultry houses (one for layers and one for broilers) given the space available, as well as sourcing materials both locally and abroad. The manual was written to include background information and step-by-step instructions for constructing the houses and caring for the birds. The researchers anticipate that this farm will positively impact the lives of the students and faculty of the school, in that they will have access to poultry meat and eggs, which will increase physical and cognitive performance, provide hands-on education, and incentivize students to finish their schooling rather than quitting to work to provide for their families

Using Poultry to Enhance Food Security in Stann Creek, Belize

Food insecurity is a growing issue in developing and developed countries alike, and in countries like Belize, the prevalence of hunger has soared in recent years. Not having access to diets with sufficient calories and nutrients leads to a host of developmental issues, including stunting and cognitive delays. The purpose of this work was to create an all-encompassing manual for small-scale poultry production in order to provide nourishment for the students and staff of a school in Belize. After determining that the best way to meet the school’s needs was through dual-purpose birds, background research was conducted to determine small-scale poultry facilities in developing countries. This involved designing the layout for the poultry houses (one for layers and one for broilers) given the space available, as well as sourcing materials both locally and abroad. The manual was written to include background information and step-by-step instructions for constructing the houses and caring for the birds. The researchers anticipate that this farm will positively impact the lives of the students and faculty of the school, in that they will have access to poultry meat and eggs, which will increase physical and cognitive performance, provide hands-on education, and incentivize students to finish their schooling rather than quitting to work to provide for their families

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

In vitro generated antibodies guide thermostable ADDomer nanoparticle design for nasal vaccination and passive immunization against SARS-CoV-2

International audienceAbstract Background Due to COVID-19, pandemic preparedness emerges as a key imperative, necessitating new approaches to accelerate development of reagents against infectious pathogens. Methods Here, we developed an integrated approach combining synthetic, computational and structural methods with in vitro antibody selection and in vivo immunization to design, produce and validate nature-inspired nanoparticle-based reagents against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Results Our approach resulted in two innovations: (i) a thermostable nasal vaccine called ADDoCoV, displaying multiple copies of a SARS-CoV-2 receptor binding motif derived epitope and (ii) a multivalent nanoparticle superbinder, called Gigabody, against SARS-CoV-2 including immune-evasive variants of concern (VOCs). In vitro generated neutralizing nanobodies and electron cryo-microscopy established authenticity and accessibility of epitopes displayed by ADDoCoV. Gigabody comprising multimerized nanobodies prevented SARS-CoV-2 virion attachment with picomolar EC50. Vaccinating mice resulted in antibodies cross-reacting with VOCs including Delta and Omicron. Conclusion Our study elucidates Adenovirus-derived dodecamer (ADDomer)-based nanoparticles for use in active and passive immunization and provides a blueprint for crafting reagents to combat respiratory viral infections