Objectives/Goals: Identifying and indexing rare disease studies is labor intensive, especially in research centers with a large number of trials. To address this gap, we applied natural language processing (NLP) and visualization techniques to develop an efficient pipeline and user-friendly web interface. Our goal is to offer the rare disease study identification (RDSI) tool for adoption by other sites. Methods/Study Population: The RDSI retrieves study information (short and long titles, study abstract) from the IRB system. These descriptive fields are then processed by the MetaMap Lite NLP program for identifying disease terms and standardizing them to UMLS concepts. By terminology identifier mapping, the diseases intersecting with concepts in rare disease databases (Genetic and Rare Disease program and Orphanet) are further scored to pinpoint studies that focus on a rare disease. The web interface displays a scatter bubble chart as an overview of all the rare diseases, with each bubble size proportional to the number of studies for that disease. In addition to the visual navigation, users can search studies by disease name, PI, or IRB number. Search results contain detailed study information as well as the evidence used by algorithms of the pipeline. Results/Anticipated Results: The RDSI identification results and functions were verified manually and spot-checked by several study investigators. The web interface is a self-contained solution available to our staff for various use cases like reporting or environment scan. We have built in a versioning mechanism that logs the date of each major result in the process. Therefore, even as the rare disease data sources evolve over time, we will be able to preserve any historical context or perform updates as needed. The RDSI outputs are replicated to Mayo Clinic’s enterprise data warehouse daily, allowing tech-savvy users to leverage any useful intermediate results at the backend. We anticipate the performance of the rare disease identification to be further enhanced by employing the advancements in AI technology. Discussion/Significance of Impact: The RDSI represents an informatics solution that offers efficiency in identifying and navigating rare disease clinical studies. It features the use of public databases and open-source tools, manifesting return on investment from the broad translational science ecosystem. These considerations are informative and adoptable by other institutions.

Supraglacial lakes (SGLs) are widespread across the Greenland ice sheet and cause transient changes in ice flow. Here, we produce the first annual ice-sheet wide database of maximum summer SGL extents spanning 1985 to 2023 using all July and August Landsat images. Lake visibility percentages were calculated to estimate the uncertainty induced by variable image data coverage. SGLs were mainly distributed between 1000 and 1600 m elevation, with large lake area observed in northwestern, northeastern and southwestern basins. Lake area increased at a rate of 50.5 km2 a−1 across the entire Greenland, and lakes advanced to higher elevations at an average rate of 10.2 m a−1 during 1985–2023. We leveraged spatiotemporally matched ICESat-2 and Landsat 8 reflectance data to develop a deep learning model for lake depth inversion for the period 2014–23. This model demonstrates the highest accuracy among all image-based methods, albeit with an underestimation of ~15% when compared to ICESat-2 data. A significant positive correlation between lake volume and area is used to up-scale the approach to the entire time period, indicating a lake volume increase of 221.9 ± 63.6 × 106 m3 a−1. Increasing air/land surface temperature, surface pressure and decreasing snowfall were the most important contributing factors in driving lake variability.

Multiple proposals suggest that xenophobia increases when infectious disease threats are salient. The current longitudinal study tested this hypothesis by examining whether and how anti-immigrant sentiments varied in the Netherlands across four time points during the COVID-19 pandemic (May 2020, February 2021, October 2021 and June 2022 through Flycatcher.eu). The results revealed that (1) anti-immigrant sentiments were no higher in early assessments, when COVID-19 hospitalizations and deaths were high, than in later assessments, when COVID-19 hospitalizations were low, and (2) within-person changes in explicit disease concerns and disgust sensitivity did not relate to anti-immigrant sentiments, although stable individual differences in disgust sensitivity did. These findings suggest that anecdotal accounts of increased xenophobia during the pandemic did not generalize to the population sampled from here. They also suggest that not all increases in ecological pathogen threats and disease salience increase xenophobia.

Glauconite pellets of vermiform and lobate morphology occur together in Eocene geologic formations in Maryland. Morphologically, the vermiform pellets appear to be identical to those that have previously been called “altered biotite”. In thin sections these pellets do show a well-defined micaceous morphology with the layers running across the worm-like pellets. Some zones in these pellets appear to be “crystals” that are up to 30 × 70 μ and nearly rectangular in cross section. However, there are tiny cracks along cleavage planes within these “crystals”. Externally, the lobate pellets have many rounded lobes and are similar to one of the shapes that Burst has called free-form. In thin section under crossed nicols these pellets have a grainy appearance, indicating that the lobate pellets are composed of many small zones, each about 5–20 μ across. Within these zones the mineral glau-conite has a single orientation, but the zones are not lined up with each other to give the gross micaceous appearance that is associated with the vermiform pellets.

Random powder X-ray diffraction patterns (prepared with a large 114·59mm Norelco powder camera) of individual vermiform and lobate pellets are nearly identical. Eight vermiform and 9 lobate pellets gave the same mean 001 (10·2 Å) and 060 (1·518 Å) spacings. The patterns from both kinds of pellets are similar, except for the absence of some weak lines, to Warshaw’s (ASTM) pattern for glauconite. The patterns have lines indicating a 1 M polytype, however, hkl lines with k ≠ 3n are broad indicating some disorder. In addition to X-ray diffraction patterns, the K2O content (6·7 per cent) and CEC (29 me/100 g as Ca replaced by Mg) of the pellets indicate that interstratified expanded layers may be the main source of the disorder.

If the vermiform pellets are altered mica, the alteration has been sufficient to give a product that is definitely identified as glauconite by X-ray methods. The possibility of mica alteration is suggested by the geographic nearness of the Piedmont (a mica source area) and the occurrence of Piedmont-type quartz with the glauconite pellets. Alternatively, the vermiform pellets may form during glauconite crystallization or recrystallization processes. The probability that both kinds of pellets obtained their morphology before or during, rather than after, the time they became glauconite (mineralogically) suggests that the proper environment may form glauconite from a variety of starting materials.

Glauconite from the oxidized and reduced zones of soil-geologic columns at two Coastal Plain sites, one in Maryland and one in New Jersey, was examined by Mössbauer spectroscopy. The data indicate that glauconite in the reduced zones had a higher proportion of its structural iron in the ferrous, as opposed to the ferric state. The Fe2+/Fe3+ ratio ranged from 0 to 0.2 for the glauconite from the oxidized zone and was about 0.35 for the glauconite in the reduced zones. Despite the presence of pyrite in the reduced zone, which might be expected to make ferric iron unstable because of the presence of sulfide S, about 75% of the Fe in the glauconite in the reduced zone was in the ferric state. Thin section analysis showed some glauconite in the reduced zones to be intimately associated with pyrite and some aggregates of fine pyrite crystals were locally present in cracks in glauconite pellets. In the oxidized zones, pyrite was absent and the glauconite was more yellow under plane-polarized light, as opposed to more green for the glauconite in the reduced zones. These data indicate that reports of studies of glauconite should stipulate whether samples are from the oxidized or reduced zone of soil-geologic columns.

A kaolinite-rich bed (tonstein) and an associated bentonite in the upper part of Yegua Formation at College Station, east-central Texas, were formed by in situ weathering processes in a late Eocene swamp. X-ray powder diffraction, infrared spectroscopy, petrographic studies, and scanning and transmission electron microscopy not only show that dioctahedral smectite and coarsely crystalline kaolinite are the dominant minerals in the bentonite and tonstein, respectively, but that cryptocrystalline halloysite and kaolinite are localized along the weathering front (transitional zone) between the tonstein and the bentonite. As weathering progressed, the cryptocrystalline minerals gradually recrystallized to yield the coarse books and vermicular growths of kaolinite characteristic of the tonstein.

Small amounts of cristobalite, sanidine, and euhedral zircon crystals with liquid or gaseous inclusions accord with the formation of the bentonite by alteration of volcanic ash. Clinoptilolite in the lignitic layer and sandstone below the bentonite probably formed from ions that were released during alteration of the volcanic materials to smectite, but clinoptilolite in the tonstein and overlying strata appear to have formed after kaolinization of the bentonite.

Machine vision has been extensively researched in the field of unmanned aerial vehicles (UAV) recently. However, the ability of Sense and Avoid (SAA) largely limited by environmental visibility, which brings hazards to flight safety in low illumination or nighttime conditions. In order to solve this critical problem, an approach of image enhancement is proposed in this paper to improve image qualities in low illumination conditions. Considering the complementarity of visible and infrared images, a visible and infrared image fusion method based on convolutional sparse representation (CSR) is a promising solution to improve the SAA ability of UAVs. Firstly, the source image is decomposed into a texture layer and structure layer since infrared images are good at characterising structural information, and visible images have richer texture information. Both the structure and the texture layers are transformed into the sparse convolutional domain through the CSR mechanism, and then CSR coefficient mapping are fused via activity level assessment. Finally, the image is synthesised through the reconstruction results of the fusion texture and structure layers. In the experimental simulation section, a series of visible and infrared registered images including aerial targets are adopted to evaluate the proposed algorithm. Experimental results demonstrates that the proposed method increases image qualities in low illumination conditions effectively and can enhance the object details, which has better performance than traditional methods.