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Significance: The INFORM-Africa Consortium, a research hub of the NIH-funded DS-I Africa, will leverage the Data Management and Analysis Core (DMAC) and Next Generation Sequencing (NGS) Core to ensure effective data management and analysis. The DMAC will capture and analyse data, making it accessible to collaborators across multiple African countries and future research hubs. The aim is to increase access to high-quality, reproducible data that can be used to engage policymakers and better prepare for future pandemics, while also removing barriers to data sharing and integration across institutions. Ultimately, this goal will facilitate data-driven decision-making and advance public health initiatives.

“Earth” is an integrated whole, a relationship of ecosystems, providing the habitat for life. However, the “world” is composed of disparate nations, each with distinct cultures and economies. The peoples of these nations celebrate nature in their poetry and song, in their parks and protected areas, and share aim to “protect, restore and promote sustainable use of terrestrial ecosystems, forests, combat desertification, and halt biodiversity loss.” 1 Paradoxically, these same nation States also actively degrade Earth's habitat, imperiling human civilization. The paper seeks to examine the scope and broad contours for affixing state responsibility for causing harm to the Earth's climate system that impinges upon our planetary future.

Estimates of biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC) are a fundamental requirement for effectively monitoring and managing forest environments. With its red-edge bands and high spatial resolution, the Multispectral Instrument (MSI) on board the Sentinel-2 missions is particularly well-suited to LAI and CCC retrieval. Using field data collected throughout the growing season at a deciduous broadleaf forest site in Southern England, we evaluated the performance of two hybrid retrieval algorithms for estimating LAI and CCC from MSI data: the Scattering by Arbitrarily Inclined Leaves (SAIL)-based L2B retrieval algorithm made available to users in the Sentinel Application Platform (SNAP), and an alternative retrieval algorithm optimised for forest environments, trained using the Invertible Forest Reflectance Model (INFORM). Moderate performance was associated with the SNAP L2B retrieval algorithm for both LAI (r2 = 0.54, RMSE = 1.55, NRMSE = 43%) and CCC (r2 = 0.52, RMSE = 0.79 g m−2, NRMSE = 45%), while improvements were obtained using the INFORM-based retrieval algorithm, particularly in the case of LAI (r2 = 0.79, RMSE = 0.47, NRMSE = 13%), but also in the case of CCC (r2 = 0.69, RMSE = 0.52 g m−2, NRMSE = 29%). Forward modelling experiments confirmed INFORM was better able to reproduce observed MSI spectra than SAIL. Based on our results, for forest-related applications using MSI data, we recommend users seek retrieval algorithms optimised for forest environments.

Digital pathology can efficiently assess immunohistochemistry (IHC) data on tissue microarrays (TMAs). Yet, it remains important to evaluate the comparability of the data acquired by different software applications and validate it against pathologist manual interpretation. In this study, we compared the IHC quantification of 5 clinical breast cancer biomarkers—estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), and cytokeratin 5/6 (CK5/6)—across 3 software applications (Definiens Tissue Studio, inForm, and QuPath) and benchmarked the results to pathologist manual scores. IHC expression for each marker was evaluated across 4 TMAs consisting of 935 breast tumor tissue cores from 367 women within the Nurses’ Health Studies; each women contributing three 0.6-mm cores. The correlation and agreement between manual and software-derived results were primarily assessed using Spearman’s ρ, percentage agreement, and area under the curve (AUC). At the TMA core-level, the correlations between manual and software-derived scores were the highest for HER2 (ρ ranging from 0.75 to 0.79), followed by ER (0.69–0.71), PR (0.67–0.72), CK5/6 (0.43–0.47), and EGFR (0.38–0.45). At the case-level, there were good correlations between manual and software-derived scores for all 5 markers (ρ ranging from 0.43 to 0.82), where QuPath had the highest correlations. Software-derived scores were highly comparable to each other (ρ ranging from 0.80 to 0.99). The average percentage agreements between manual and software-derived scores were excellent for ER (90.8%–94.5%) and PR (78.2%–85.2%), moderate for HER2 (65.4%–77.0%), highly variable for EGFR (48.2%–82.8%), and poor for CK5/6 (22.4%–45.0%). All AUCs across markers and software applications were ≥0.83. The 3 software applications were highly comparable to each other and to manual scores in quantifying these 5 markers. QuPath consistently produced the best performance, indicating this open-source software is an excellent alternative for future use. •It is important to evaluate the comparability of the data acquired by different software applications and validate it against pathologist manual interpretation.•Definiens Tissue Studio®, inForm®, and QuPath were highly comparable to each other and to pathologist manual scores in quantifying 5 clinical breast cancer biomarkers—ER, PR, HER2, EGFR, and CK5/6—in tissue microarrays.•QuPath had the highest correlation and %agreement with manual scores.•QuPath is a reliable open-source tool to automate IHC expression quantification of breast tumor biomarkers.

Heterogeneous Systems Architecture - a new compute platform infrastructure presents a next-generation hardware platform, and associated software, that allows processors of different types to work efficiently and cooperatively in shared memory from a single source program.

Social vulnerability assessments play a crucial role in guiding the allocation of budgets and resources for effective disaster preparedness and humanitarian response. Climate change, escalating conflicts, and the climate finance and humanitarian funding gap make social vulnerability assessments essential. Despite advances in data collection, availability, and analysis, there remains a lack of consensus regarding the most suitable method to assess social vulnerability. This study sheds light on the consequences of methodological choices on social vulnerability assessments by comparing two commonly used methods in space and over time: the inductive principal component approach and the hierarchical INFORM approach. Our analysis focuses on a case study of the 351 communes in Burkina Faso from 2015 to 2022, a period marked by conflicts and extreme weather events. By comparing the two methods, we find important differences in the rankings of the communes’ social vulnerability. By investigating the spatial and temporal results, we offer insights into the potential consequences of using different methodological choices. Our findings underscore the need for contextualized approaches.

The ability of health systems to cope with coronavirus disease (COVID-19) cases is of major concern. In preparation, we used clinical pathway models to estimate healthcare requirements for COVID-19 patients in the context of broader public health measures in Australia. An age- and risk-stratified transmission model of COVID-19 demonstrated that an unmitigated epidemic would dramatically exceed the capacity of the health system of Australia over a prolonged period. Case isolation and contact quarantine alone are insufficient to constrain healthcare needs within feasible levels of expansion of health sector capacity. Overlaid social restrictions must be applied over the course of the epidemic to ensure systems do not become overwhelmed and essential health sector functions, including care of COVID-19 patients, can be maintained. Attention to the full pathway of clinical care is needed, along with ongoing strengthening of capacity.

Vegetation indices (VIs) are widely used in optical remote sensing to estimate biophysical variables of vegetated surfaces. With the advent of spectroscopy technology, spectral bands can be combined in numerous ways to extract the desired information. This resulted in a plethora of proposed indices, designed for a diversity of applications and research purposes. However, it is not always clear whether they are sensitive to the variable of interest while at the same time, responding insensitive to confounding factors. Hence, to be able to quantify the robustness of VIs, a systematic evaluation is needed, thereby introducing a widest possible variety of biochemical and structural heterogeneity. Such exercise can be achieved with coupled leaf and canopy radiative transfer models (RTMs), whereby input variables can virtually simulate any vegetation scenario. With the intention of evaluating multiple VIs in an efficient way, this led us to the development of a global sensitivity analysis (GSA) toolbox dedicated to the analysis of VIs on their sensitivity towards RTM input variables. We identified VIs that are designed to be sensitive towards leaf chlorophyll content (LCC), leaf water content (LWC) and leaf area index (LAI) for common sensors of terrestrial Earth observation satellites: Landsat 8, MODIS, Sentinel-2, Sentinel-3 and the upcoming imaging spectrometer mission EnMAP. The coupled RTMs PROSAIL and PROINFORM were used for simulations of homogeneous and forest canopies respectively. GSA total sensitivity results suggest that LCC-sensitive indices respond most robust: for the great majority of scenarios, chlorophyll a + b content (Cab) drives between 75% and 82% of the indices’ variability. LWC-sensitive indices were most affected by confounding variables such as Cab and LAI, although the equivalent water thickness (Cw) can drive between 25% and 50% of the indices’ variability. Conversely, the majority of LAI-sensitive indices are not only sensitive to LAI but rather to a mixture of structural and biochemical variables.

Yaws is a neglected tropical disease targeted for eradication by 2030. To achieve eradication, finding and treating asymptomatic infections as well as clinical cases is crucial. The proposed plan, the Morges strategy, involves rounds of total community treatment (i.e., treating the whole population) and total targeted treatment (TTT) (i.e., treating clinical cases and contacts). However, modeling and empirical work suggests asymptomatic infections often are not found in the same households as clinical cases, reducing the utility of household-based contact tracing for a TTT strategy. We use a model fitted to data from the Solomon Islands to predict the likelihood of elimination of transmission under different intervention schemes and levels of systematic nontreatment resulting from the intervention. Our results indicate that implementing additional treatment rounds through total community treatment is more effective than conducting additional rounds of treatment of at-risk persons through TTT.

As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m−2, NRMSD = 64–84%, bias = −0.17–0.89 g m−2). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m−2, NRMSD = 52–73%, bias = 0.03–0.42 g m−2) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation.