Unlocking the potential of sensors for our environment:A call to action from a NERC writing retreat

Funded by NERC Constructed for the Digital Environment, the report is a culmination of an intensive co-creation process and writing retreat that brought together experts in the field of environmental sensing to explore how to accelerate advancements in environmental sensing and sensor networks that acknowledge and respond to the interconnections between people, places, and ethics. The report emerges as a foundational document aimed at guiding future funding calls, stimulating innovation, and advocating for interdisciplinary research approaches

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

Droplet behaviour in microfluidic devices

This work is a study to understand the various aspects of a microfluidic device. In the first half we take the role of an end user, experimenting to learn how best to use the device efficiently. In the second half we are the manufacturer, trying to fabricate a user friendly, and fully functioning microfluidic device. As the end user, we have three different T-junction droplet generator devices, with similar geometries. We start investigating by generating water droplets in an oil medium. They self-organise into various flow patterns: single-profile, double-helix profile and triple-helix profile. We document how, with increasing flow rate ratio and capillary number, we observe more densely packed droplet flow patterns. The device with the deeper expansion channel provides more space for the droplets and they self-organise the triple-helix pattern in 3-dimension. We then use the same devices to generate droplets for which we can calculate the volume. The fluid flow in a microchannel happens in four different regimes: ballooning, squeezing, dripping and jetting regimes. In single-cell and single-molecule analysis devices, the ability to create droplets on demand and of a certain volume is a desired capability. This can be achieved by understanding and learning how to use the fluid flow characteristics accurately. We experiment with the three different sized microfluidic devices, to measure the droplet volume throughout the squeezing to dripping regimes. This is achieved by manipulating the capillary number and the flow rate ratio. We observe a similar result as with the flow patterns: that the capillary number has an impact on the droplet volume. As the capillary number increases the droplet diameter decreases. Further, for a set capillary number we can fine tune the droplet diameter by changing the flow rate ratio. As the flow rate ratio increases the volume of water droplets increases, despite the fact the capillary number is set. These coincide with our flow pattern results. Our results fit to the scaling law to predict the droplet size introduced by Tanet al. in 2008 [51]. Unlike some other authors in the literature, we did not observe a critical capillary number where the droplet volume changes suddenly. However, we did observe a transition area where we cannot define the regime of the fluid flow. As the manufacturer we designed and fabricated our own planar free standing microfluidic devices using a polymer called SU-8. After looking into the weaknesses and the strengths of using SU-8, we describe how we successfully fabricated working devices and developeda new procedure in adhesive low temperature bonding. We finish by considering the challenges of connecting micro sized structures to a macro sized syringe pump, and fabricated a chip-holder inspired by applications in industry.This work is a study to understand the various aspects of a microfluidic device. In the first half we take the role of an end user, experimenting to learn how best to use the device efficiently. In the second half we are the manufacturer, trying to fabricate a user friendly, and fully functioning microfluidic device. As the end user, we have three different T-junction droplet generator devices, with similar geometries. We start investigating by generating water droplets in an oil medium. They self-organise into various flow patterns: single-profile, double-helix profile and triple-helix profile. We document how, with increasing flow rate ratio and capillary number, we observe more densely packed droplet flow patterns. The device with the deeper expansion channel provides more space for the droplets and they self-organise the triple-helix pattern in 3-dimension. We then use the same devices to generate droplets for which we can calculate the volume. The fluid flow in a microchannel happens in four different regimes: ballooning, squeezing, dripping and jetting regimes. In single-cell and single-molecule analysis devices, the ability to create droplets on demand and of a certain volume is a desired capability. This can be achieved by understanding and learning how to use the fluid flow characteristics accurately. We experiment with the three different sized microfluidic devices, to measure the droplet volume throughout the squeezing to dripping regimes. This is achieved by manipulating the capillary number and the flow rate ratio. We observe a similar result as with the flow patterns: that the capillary number has an impact on the droplet volume. As the capillary number increases the droplet diameter decreases. Further, for a set capillary number we can fine tune the droplet diameter by changing the flow rate ratio. As the flow rate ratio increases the volume of water droplets increases, despite the fact the capillary number is set. These coincide with our flow pattern results. Our results fit to the scaling law to predict the droplet size introduced by Tanet al. in 2008 [51]. Unlike some other authors in the literature, we did not observe a critical capillary number where the droplet volume changes suddenly. However, we did observe a transition area where we cannot define the regime of the fluid flow. As the manufacturer we designed and fabricated our own planar free standing microfluidic devices using a polymer called SU-8. After looking into the weaknesses and the strengths of using SU-8, we describe how we successfully fabricated working devices and developeda new procedure in adhesive low temperature bonding. We finish by considering the challenges of connecting micro sized structures to a macro sized syringe pump, and fabricated a chip-holder inspired by applications in industry

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

HNRNPC haploinsufficiency affects alternative splicing of intellectual disability-associated genes and causes a neurodevelopmental disorder

Heterogeneous nuclear ribonucleoprotein C (HNRNPC) is an essential, ubiquitously abundant protein involved in mRNA processing. Genetic variants in other members of the HNRNP family have been associated with neurodevelopmental disorders. Here, we describe 13 individuals with global developmental delay, intellectual disability, behavioral abnormalities, and subtle facial dysmorphology with heterozygous HNRNPC germline variants. Five of them bear an identical in-frame deletion of nine amino acids in the extreme C terminus. To study the effect of this recurrent variant as well as HNRNPC haploinsufficiency, we used induced pluripotent stem cells (iPSCs) and fibroblasts obtained from affected individuals. While protein localization and oligomerization were unaffected by the recurrent C-terminal deletion variant, total HNRNPC levels were decreased. Previously, reduced HNRNPC levels have been associated with changes in alternative splicing. Therefore, we performed a meta-analysis on published RNA-seq datasets of three different cell lines to identify a ubiquitous HNRNPC-dependent signature of alternative spliced exons. The identified signature was not only confirmed in fibroblasts obtained from an affected individual but also showed a significant enrichment for genes associated with intellectual disability. Hence, we assessed the effect of decreased and increased levels of HNRNPC on neuronal arborization and neuronal migration and found that either condition affects neuronal function. Taken together, our data indicate that HNRNPC haploinsufficiency affects alternative splicing of multiple intellectual disability-associated genes and that the developing brain is sensitive to aberrant levels of HNRNPC. Hence, our data strongly support the inclusion of HNRNPC to the family of HNRNP-related neurodevelopmental disorders. [Display omitted] We identified genetic variants of HNRNPC in 13 individuals with intellectual disability and global developmental delay. Through a meta-analysis of multiple cell types, we found that loss of HNRNPC affects alternative splicing, in particular of intellectual disability-associated genes. In vivo assays confirmed that neurodevelopment was affected by aberrant HNRNPC levels