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Clinical discrimination of posterior uveitis entities remains a challenge. This exploratory, cross-sectional study investigated the green (GEFC) and red emission fluorescent components (REFC) of retinal and choroidal lesions in posterior uveitis to facilitate discrimination of the different entities. Eyes were imaged by color fundus photography, spectrally resolved fundus autofluorescence (Color-FAF) and optical coherence tomography. Retinal/choroidal lesions’ intensities of GEFC (500–560 nm) and REFC (560–700 nm) were determined, and intensity-normalized Color-FAF images were compared for birdshot chorioretinopathy, ocular sarcoidosis, acute posterior multifocal placoid pigment epitheliopathy (APMPPE), and punctate inner choroidopathy (PIC). Multivariable regression analyses were performed to reveal possible confounders. 76 eyes of 45 patients were included with a total of 845 lesions. Mean GEFC/REFC ratios were 0.82 ± 0.10, 0.92 ± 0.11, 0.86 ± 0.10, and 1.09 ± 0.19 for birdshot chorioretinopathy, sarcoidosis, APMPPE, and PIC lesions, respectively, and were significantly different in repeated measures ANOVA ( p  < 0.0001). Non-pigmented retinal/choroidal lesions, macular neovascularizations, and fundus areas of choroidal thinning featured predominantly GEFC, and pigmented retinal lesions predominantly REFC. Color-FAF imaging revealed involvement of both, short- and long-wavelength emission fluorophores in posterior uveitis. The GEFC/REFC ratio of retinal and choroidal lesions was significantly different between distinct subgroups. Hence, this novel imaging biomarker could aid diagnosis and differentiation of posterior uveitis entities.

The human face represents a combined set of highly heritable phenotypes, but knowledge on its genetic architecture remains limited, despite the relevance for various fields. A series of genome-wide association studies on 78 facial shape phenotypes quantified from 3-dimensional facial images of 10,115 Europeans identified 24 genetic loci reaching study-wide suggestive association (p < 5 × 10−8), among which 17 were previously unreported. A follow-up multi-ethnic study in additional 7917 individuals confirmed 10 loci including six unreported ones (padjusted < 2.1 × 10−3). A global map of derived polygenic face scores assembled facial features in major continental groups consistent with anthropological knowledge. Analyses of epigenomic datasets from cranial neural crest cells revealed abundant cis-regulatory activities at the face-associated genetic loci. Luciferase reporter assays in neural crest progenitor cells highlighted enhancer activities of several face-associated DNA variants. These results substantially advance our understanding of the genetic basis underlying human facial variation and provide candidates for future in-vivo functional studies.

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ‐GUESS, ORCHIDEE, SiB‐3, and SiB‐CASA. All models were wrapped in a software framework driven with common forcing data, spin‐up, and run protocols specified by the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901–2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open‐source, cloud‐based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges. Plain Language Summary Comparing models is fundamental to our understanding of how the land surface responds to changes in climate. However, these comparisons are challenging to conduct, requiring the organization of multiple, decentralized teams throughout the world. We explored centralizing these models on a single supercomputing system. The models were all run the same way. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) the centralized system takes a lot of burden off individual teams; (b) running models the same way helps to identify differences in how the world is represented in the models; and (c) the system allows us to run many model experiments relatively quickly. The challenges are: (a) lots of models require lots of data storage and transfer needs; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. Another system, called PEcAn, which has a lot of tools that can help overcome these challenges, can potentially be used in future work. Key Points We ran nine terrestrial biosphere models centralized on a common computing framework The Farm allows multiple MIP experiments to be run relatively quickly and uniformly Challenges included technological demand, model versioning, and interpretation of results

BackgroundWest Nile virus (WNV) is the most common cause of mosquito-borne disease in the continental USA, with an average of ~1200 severe, neuroinvasive cases reported annually from 2005 to 2021 (range 386–2873). Despite this burden, efforts to forecast WNV disease to inform public health measures to reduce disease incidence have had limited success. Here, we analyze forecasts submitted to the 2022 WNV Forecasting Challenge, a follow-up to the 2020 WNV Forecasting Challenge.MethodsForecasting teams submitted probabilistic forecasts of annual West Nile virus neuroinvasive disease (WNND) cases for each county in the continental USA for the 2022 WNV season. We assessed the skill of team-specific forecasts, baseline forecasts, and an ensemble created from team-specific forecasts. We then characterized the impact of model characteristics and county-specific contextual factors (e.g., population) on forecast skill.ResultsEnsemble forecasts for 2022 anticipated a season at or below median long-term WNND incidence for nearly all (> 99%) counties. More counties reported higher case numbers than anticipated by the ensemble forecast median, but national caseload (826) was well below the 10-year median (1386). Forecast skill was highest for the ensemble forecast, though the historical negative binomial baseline model and several team-submitted forecasts had similar forecast skill. Forecasts utilizing regression-based frameworks tended to have more skill than those that did not and models using climate, mosquito surveillance, demographic, or avian data had less skill than those that did not, potentially due to overfitting. County-contextual analysis showed strong relationships with the number of years that WNND had been reported and permutation entropy (historical variability). Evaluations based on weighted interval score and logarithmic scoring metrics produced similar results.ConclusionsThe relative success of the ensemble forecast, the best forecast for 2022, suggests potential gains in community ability to forecast WNV, an improvement from the 2020 Challenge. Similar to the previous challenge, however, our results indicate that skill was still limited with general underprediction despite a relative low incidence year. Potential opportunities for improvement include refining mechanistic approaches, integrating additional data sources, and considering different approaches for areas with and without previous cases.

Evidence regarding gender-related differences in response to transcatheter aortic valve implantation according to the valve type is lacking. This study aimed to evaluate the impact of gender on the treatment effect of Evolut PRO/PRO+ (PRO) or SAPIEN 3 Ultra (ULTRA) devices on clinical outcomes. The Comparative Analysis of Evolut PRO vs SAPIEN 3 Ultra Valves for Transfemoral Transcatheter Aortic Valve Implantation (OPERA-TAVI) is a multicenter, multinational registry including patients who underwent the latest-iteration PRO or ULTRA implantation. Overall, 1,174 of 1,897 patients were matched based on valve type and compared according to gender, whereas 470 men and 630 women were matched and compared according to valve type. The 30-day and 1-year outcomes were evaluated. In the PRO and ULTRA groups, men had a higher co-morbidity burden, whereas women had smaller aortic root. The 30-day (device success [DS], early safety outcome, permanent pacemaker implantation, patient-prosthesis mismatch, paravalvular regurgitation, bleedings, vascular complications, and all-cause death) and 1-year outcomes (all-cause death, stroke, and heart failure hospitalization) did not differ according to gender in both valve groups. However, the male gender decreased the likelihood of 30-day DS with ULTRA versus PRO (p for interaction = 0.047). A higher risk of 30-day permanent pacemaker implantation and 1-year stroke and a lower risk of patient-prosthesis mismatch was observed in PRO versus ULTRA, regardless of gender. In conclusion, gender did not modify the treatment effect of PRO versus ULTRA on clinical outcomes, except for 30-day DS, which was decreased in men (vs women) who received ULTRA (vs PRO).

The cycle of baryons through the circumgalactic medium (CGM) is important to understand in the context of galaxy formation and evolution. In this study we forecast constraints on the feedback processes heating the CGM with current and future Sunyaev-Zeldovich (SZ) observations. To constrain these processes, we use a suite of cosmological simulations, the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS), that varies four different feedback parameters of two previously existing hydrodynamical simulations, IllustrisTNG and SIMBA. We capture the dependencies of SZ radial profiles on these feedback parameters with an emulator, calculate their derivatives, and forecast future constraints on these feedback parameters from upcoming experiments. We find that for a DESI-like (Dark Energy Spectroscopic Instrument) galaxy sample observed by the Simons Observatory all four feedback parameters are able to be constrained (some within the \\(10\\%\\) level), indicating that future observations will be able to further restrict the parameter space for these sub-grid models. Given the modeled galaxy sample and forecasted errors in this work, we find that the inner SZ profiles contribute more to the constraining power than the outer profiles. Finally, we find that, despite the wide range of AGN feedback parameter variation in the CAMELS simulation suite, we cannot reproduce the tSZ signal of galaxies selected by the Baryon Oscillation Spectroscopic Survey as measured by the Atacama Cosmology Telescope.

We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from 2,000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span \\(\\sim\\)100 million light years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N-body simulations from the CAMELS project. Designed to train machine learning models, CMD is the largest dataset of its kind containing more than 70 Terabytes of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community. We release all data and provide further technical details at https://camels-multifield-dataset.readthedocs.io.

The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4,233 cosmological simulations, 2,049 N-body and 2,184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogues, power spectra, bispectra, Lyman-\\(\\alpha\\) spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over one thousand catalogues that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz Semi-Analytic Model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies and summary statistics. We provide further technical details on how to access, download, read, and process the data at \\url{https://camels.readthedocs.io}.