Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping.

Gathering information about forest variables is an expensive and arduous activity. As such, directly collecting the data required to produce high-resolution maps over large spatial domains is infeasible. Next generation collection initiatives of remotely sensed Light Detection and Ranging (LiDAR) data are specifically aimed at producing complete-coverage maps over large spatial domains. Given that LiDAR data and forest characteristics are often strongly correlated, it is possible to make use of the former to model, predict, and map forest variables over regions of interest. This entails dealing with the high-dimensional (~102) spatially dependent LiDAR outcomes over a large number of locations (~105-106). With this in mind, we develop the Spatial Factor Nearest Neighbor Gaussian Process (SF-NNGP) model, and embed it in a two-stage approach that connects the spatial structure found in LiDAR signals with forest variables. We provide a simulation experiment that demonstrates inferential and predictive performance of the SF-NNGP, and use the two-stage modeling strategy to generate complete-coverage maps of forest variables with associated uncertainty over a large region of boreal forests in interior Alaska.

Revealing the hidden carbon in forested wetland soils.

Inland wetlands are critical carbon reservoirs storing 30% of global soil organic carbon (SOC) within 6% of the land surface. However, forested regions contain SOC-rich wetlands that are not included in current maps, which we refer to as 'cryptic carbon'. Here, to demonstrate the magnitude and distribution of cryptic carbon, we measure and map SOC stocks as a function of a continuous, upland-to-wetland gradient across the Hoh River Watershed (HRW) in the Pacific Northwest of the U.S., comprising 68,145 ha. Total catchment SOC at 30 cm depth (5.0 TgC) is between estimates from global SOC maps (GSOC: 3.9 TgC; SoilGrids: 7.8 TgC). For wetland SOC, our 1 m stock estimates are substantially higher (Mean: 259 MgC ha-1; Total: 1.7 TgC) compared to current wetland-specific SOC maps derived from a combination of U.S. national datasets (Mean: 184 MgC ha-1; Total: 0.3 TgC). We show that total unmapped or cryptic carbon is 1.5 TgC and when added to current estimates, increases the estimated wetland SOC stock to 1.8 TgC or by 482%, which highlights the vast stores of SOC that are not mapped and contained in unprotected and vulnerable wetlands.

Wildfires correlate with reductions in aboveground tree carbon stocks and sequestration capacity on forest land in the Western United States.

In the United States (US), forest ecosystems are the largest terrestrial carbon sink, offsetting the equivalent of >12 % of economy-wide greenhouse gas (GHG) emissions annually. In the Western US, wildfires have shaped much of the landscape by changing forest structure and composition, increasing tree mortality, impacting forest regeneration, and influencing forest carbon storage and sequestration capacity. Here, we used remeasurements of >25,000 plots from the US Department of Agriculture, Forest Service Forest Inventory and Analysis (FIA) program and auxiliary information (e.g., Monitoring Trends in Burn Severity) to characterize the role of fire along with other natural and anthropogenic drivers on estimates of carbon stocks, stock changes, and sequestration capacity on forest land in the Western US. Several biotic (e.g., tree size, species, and forest structure) and abiotic factors (e.g., warm climate, severe drought, compound disturbances, and anthropogenic interventions) influenced post-fire tree mortality and regeneration and had concomitant impacts on carbon stocks and sequestration capacity. Forest ecosystems in a high severity and low frequency wildfire regime had greater reductions in aboveground biomass carbon stocks and sequestration capacity compared to forests in a low severity and high frequency fire regime. Results from this study can improve our understanding of the role of wildfire along with other biotic and abiotic drivers on carbon dynamics in forest ecosystems in the Western US.

A Phase II Open-Label Study of Bermekimab in Patients with Hidradenitis Suppurativa Shows Resolution of Inflammatory Lesions and Pain.

The objective of this study was to evaluate the safety and efficacy of bermekimab, an IL-1α inhibitor, in the treatment of hidradenitis suppurativa (HS). This study was a phase II, multicenter, open-label study of two dose cohorts of bermekimab in patients with moderate-to-severe HS who are naïve to or have failed prior anti-TNF therapy. Patients with HS (n = 42) were divided into groups A and B based on whether or not they had previously failed an anti-TNF therapy. In group A (n = 24), bermekimab was administered subcutaneously at a dose of 400 mg weekly (13 doses) in patients who had previously failed anti-TNF therapy; in group B (n = 18), bermekimab was administered subcutaneously at a dose of 400 mg weekly (13 doses) in patients who were anti-TNF naïve. Bermekimab, previously found to be effective in treating HS, was evaluated using a subcutaneous formulation in patients with HS naïve to or having failed anti-TNF therapy. There were no bermekimab-related adverse events with the exception of injection site reactions. Bermekimab was effective despite treatment history, with 61% and 63% of patients naïve to and having failed anti-TNF therapy, respectively, achieving HS clinical response after 12 weeks of treatment. A significant reduction in abscesses and inflammatory nodules of 60% (P < 0.004) and 46% (P < 0.001) was seen in anti-TNF naïve and anti-TNF failure groups, respectively. Clinically and statistically significant reduction was seen in patients experiencing pain, with the Visual Analogue Scale pain score reducing by 64% (P < 0.001) and 54% (P < 0.001) in the anti-TNF naïve and anti-TNF failure groups, respectively. IL-1α is emerging as an important clinical target for skin disease, and bermekimab may represent a new therapeutic option for treating moderate-to-severe HS.