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Bill & Melinda Gates Foundation

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Grand Challenges is a family of initiatives fostering innovation to solve key global health and development problems. Each initiative is an experiment in the use of challenges to focus innovation on making an impact. Individual challenges address some of the same problems, but from differing perspectives.

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Combining Epitope-Based Vaccine Design with Informatics-Based Evaluation to Obtain a Universal Influenza Vaccine

Rebeca SalmeronFoundation for the National Institutes of Health IncNorth Bethesda, Maryland, United States
Grand Challenges
Influenza Vaccine
28 Aug 2019

Turning Influenza into Measles via Mosaic Natural Selective Targeting of Immune Responses (MONSTIR)

Patrick WilsonUniversity of ChicagoChicago, Illinois, United States
Grand Challenges
Influenza Vaccine
28 Aug 2019

An Unconventionally MHC-Restricted T Cell Vaccine for Influenza

Jonah SachaOregon Health and Science UniversityPortland, Oregon, United States
Grand Challenges
Influenza Vaccine
20 Aug 2019

New Safer Contraceptives That Block Ovulation

Darryl RussellUniversity of AdelaideAdelaide, South Australia, Australia
Grand Challenges Explorations
Contraceptive Discovery
18 Aug 2019

Darryl Russell of the University of Adelaide in Australia is seeking safer contraceptives that block ovulation without altering hormone levels and cause fewer side effects using an automated in-vitro screening platform that measures cell adhesion in the cumulus-oocyte complex, which is required to release the oocyte from the ovary. In Phase I, they built the screening platform by isolating cumulus-oocyte complexes from mice, culturing them in fibronectin-coated multi-well plates, and quantifying adhesion in a 96-well plate format using an automated assay. In a first run, they screened a library of 129 FDA-approved chemical compounds over four months and identified seven candidate contraceptives with known protein targets, one of which showed a strong reduction in ovulation when tested in mice. In Phase II, they will study whether one of the main target proteins identified in their first screen is a key target for blocking ovulation. They will also test whether the other candidates from their first screen can block ovulation in mice and screen larger and more diverse libraries to identify new candidate contraceptives and prioritize them for further drug development and testing.

Digital Immune Optimized and Selected Universal Influenza Vaccine Antigens (DIOS-UIVA)

Jonathan HeeneyUniversity of CambridgeCambridge, United Kingdom
Grand Challenges
Influenza Vaccine
13 Aug 2019

Predictive Supply Chain for Vaccines

Benjamin FelsMacro-Eyes, Inc.Fall City, Washington, United States
Grand Challenges Explorations
Health Supply Chains
11 Aug 2019

Drew Arenth, Benjamin Fels, and Suvrit Sra of Macro-Eyes in the U.S. are applying a statistical machine learning approach to the immunization supply chains of health facilities in Tanzania that accurately and continuously predicts demand to ensure the right vaccines and levels are being stocked. Currently, vaccine supply is largely fixed or driven by depleted stocks. This leaves children unable to be vaccinated due to stock outs at clinics, as well as often high levels of waste, which could both be overcome by better forecasting vaccination needs for individual clinics. In Phase I, they worked with an NGO and the Ministry of Health to access routinely-collected daily vaccination data from 710 health facilities spread across Tanzania. These data were then used to train algorithms to identify predictive patterns that were tested on independent datasets. This led to a model that could accurately forecast future vaccine consumption. In Phase II, they will work out how best to integrate their approach as an automated component within the existing supply chain infrastructure in Tanzania and develop tools and train advocates to demonstrate its value and encourage implementation and adoption.

Rational Design of a Universal Flu Vaccine Using Recombinant Neuraminidase

Alice McHardyThe Helmholtz Centre for Infection ResearchBraunschweig, Germany
Grand Challenges
Influenza Vaccine
23 Jul 2019

DEtection Technology for Evaluating Crop Threats (DETECT)

Peter WagstaffSelf Help AfricaDublin, Ireland
Grand Challenges Explorations
Crop Disease Surveillance
1 Jul 2019

Peter Wagstaff of Self Help Africa in Ireland will build an advanced machine learning algorithm that automatically analyzes high-resolution satellite images for near real-time, low-cost detection of crop pests and diseases across wide, varied landscapes. Current detection methods are either resource- or cost-intensive and limited in their ability to provide up-to-date information across large and complex geographic areas. Crop pests and diseases can alter leaf color and expose soil, which can be detected by very high-resolution satellite imaging. They will combine satellite images provided by their partner with field data on the fall armyworm crop pest collected by their project team over 18 months on smallholder plots in the Balaka district in Malawi. These data will be used to train an algorithm to detect pests and diseases. They will use cloud-based workflows to enable computationally intensive processing of large quantities of high-resolution images in near real-time. The accuracy of the algorithm will be evaluated by an independent field survey. Note: This grant is funded by the Foundation for Food and Agriculture Research (FFAR).

Low-Cost Real-Time Sensor Network for Large-Area Pest and Disease Surveillance of Crop Plants

Hanseup KimUniversity of UtahSalt Lake City, Utah, United States
Grand Challenges Explorations
Crop Disease Surveillance
1 Jun 2019

Hanseup Kim of the University of Utah in the U.S. will develop small, ultra-low power, chemical sensors that can be distributed around farms to help detect crop diseases in low-resource settings. Plants under attack from pests and diseases release low levels of volatile organic compounds that could be used as an early warning system to reduce crop losses, which can be substantial. They will design chemical sensors that trigger a change in electrical conductivity when they bind a target compound to minimize energy consumption so that they can be operated over the eight-month farming season in low-resource settings. The sensors will first be developed to bind trace levels of hexenol, hexenal, or indole, which are released from damaged maize and sorghum. They will optimize sensitivity by testing different sensor materials and correlate compound detection with different types and stages of crop damage. They will also evaluate wireless monitoring of multiple sensors distributed around a small plot of crops ready for scaling up to future in-field testing on these and other crops. Note: This grant is funded by the Foundation for Food and Agriculture Research (FFAR).

Real-Time Genomic Epidemiology and Improved Data Sharing to Control Middle East Respiratory Syndrome (MERS-CoV)

David AanensenUniversity of OxfordOxford, United Kingdom
Grand Challenges
Annual Meeting Call-to-Action
23 May 2019

David Aanensen from the University of Oxford and the Wellcome Sanger Institute in the United Kingdom and Maria van Kerkhove of the World Health Organization in Switzerland will combine next generation DNA sequencing technology with a simple, web-based data collection, processing, and distribution platform to better track the global spread of deadly infectious diseases including Middle East Respiratory Syndrome (MERS-CoV). MERS - also known as camel flu - is a viral disease that causes fever, cough, diarrhea, and shortness of breath, and is transmitted from camels to humans. One third of people diagnosed with the disease die. Next generation sequencing (NGS) technology allows rapid, inexpensive detection of pathogens as they spread. However, laboratories in different member states use different formats for sequencing data, and there is no mechanism for sharing it in real time. This limits the value of the technology for stopping outbreaks. To address this, they will establish routine sequencing protocols for both human and camel samples, and develop an interactive web platform on which the sequencing and epidemiological data can be shared. This will help develop more effective, real-time medical and non-medical interventions at local, national, and international levels. Once established, the protocols developed here may be applied to outbreaks of other diseases.

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