Existing Data Sets Research Articles

Application of a computer vision algorithm to identify foci of demyelination in multiple sclerosis on MRI images

The research was aimed at analyzing modern algorithms for diagnosing lesions in multiple sclerosis on MRI images. Multiple sclerosis is a severe disease of the central nervous system and ranks first among the causes of disability in patients of young working age. In connection with the development of computer vision and machine learning technologies, the relevance of using these technologies for medical diagnostics is growing. Such approaches are necessary for the effective development and implementation of diagnostic systems using artificial intelligence. Modern algorithms and models for lesion segmentation were selected and implemented. Material and methods. The paper presents CV features of diagnosing multiple sclerosis on MRI images, existing data sets: ISBI-2015, MSSEG-2016, MSSEG-2021; existing algorithms and models for lesion segmentation: U-Net, nnU-Net, TransUnet, TransBTS, UNETR, Swin UNETR. Results and discussion. The architectures and models of nnU-Net, UNETR, Swin UNETR were trained and compared at ISBI2015 with various parameters and loss functions. Four MRI sequences were used: T2-WI, T2-FLAIR, PD, MPRAGE. Lesion segmentation was approved by certified experienced neuroradiologists. Conclusions. The approaches described in the paper including data processing, model training, and results analysis, focused on the selection and development of high-quality computer vision algorithms for identifying multiple sclerosis lesions in MRI images. Identification and segmentation of demyelination foci is a necessary step for diagnosing the disease, as well as for calculating and interpreting more meaningful indicators of disease severity and progression.

Abstract A081: Chromobox 2 (CBX2) drives an immunosuppressive tumor microenvironment in high grade serous carcinoma (HGSC)

Abstract Introduction: Epigenetic reader and component of Polycomb Repressor Complex 1 (PRC1), Chromobox 2 (CBX2) is expressed in greater than 75% of high-grade serous carcinoma (HGSC) tumors. Increased CBX2 expression conveys a poorer disease-free and overall survival. CBX2 knockdown leads to increased chemotherapy sensitivity and attenuates stem-like properties, while CBX2 overexpression is sufficient to promote chemoresistance. In an HGSC immune competent murine model, knockdown of CBX2 decreased tumor progression. We sought to explore the impact of modulation of CBX2 on the tumor immune microenvironment (TIME), understanding that TIME plays a critical role in disease progression and development of therapy resistance. Methods: We performed an exploration of existing data sets, including The Cancer Genome Atlas (TCGA), TIMERv2, and Carcinoma EcoTyper. We used HGSC cell lines (OVCAR4 and PEO1) with knockdown of CBX2 and RNA-sequencing. KEGG pathway analysis performed. A syngeneic ID8 mouse model was completed with shCBX2 compared to shControl. Nanostring Immuno-Oncology Panel and Multispectral Immunohistochemistry (mIHC) were completed to evaluate the TIME. Cell lines with genetic CBX2 modulation were utilized to create spheroids to recapitulate HGSC growth and confocal microscopy was employed to evaluate macrophage recruitment. Monocyte recruitment and macrophage polarization were evaluated using flow cytometry. Results: We found that existing data sets demonstrate a shift in TIME dependent on up or downregulation of CBX2. For instance, high CBX2 is associated with decreased CD8+ T cells (14.69% vs. 4.89%, p=0.0032) and increased undifferentiated macrophages (14.77 vs. 29.93%, p=0.006). Also, CBX2 is a defining feature of the Cell State Type 6 Carcinoma EcoType, which is associated with poor prognosis. RNA sequencing identified an association between CBX2 and CCL2, a cytokine known to recruit immunosuppressive macrophages. Pathway analysis of differentially expressed genes demonstrates an enrichment of several immune signatures. CBX2 knockdown in the ID8 syngeneic mouse model led to decreased tumor progression compared to control (11.50 mg. vs 51.63 mg, p=0.0001). NanoString analysis of the specimens suggest a shift in immune cell composition, with an increase in macrophages. mIHC defined the spatial relationships in this composition and confirmed increase in macrophage infiltration (28.64% vs. 34.60%, p=0.0231). Confocal microscopy and co-culture experiments to explore monocyte recruitment, found that both knockdown and overexpression of CBX2 lead to increased recruitment and infiltration of macrophages. Flow cytometry of macrophages cultured with CBX2 overexpressing cells showed an increase in M2-like (CD206+) macrophages and decreased phagocytosis activity. Conclusion: Increased CBX2 expression leads to increased recruitment and polarization of M2-like macrophages leading to an immunosuppressive TIME. Targeting of CBX2 may serve to modulate the TIME to enhance the efficacy of immune therapies. Citation Format: Lindsay W. Brubaker, Ritsuko Iwanaga, Tomomi M Yamamoto, Elizabeth R. Woodruff, Nicole Marjon, Siddhartha Mitra, Benjamin G. Bitler. Chromobox 2 (CBX2) drives an immunosuppressive tumor microenvironment in high grade serous carcinoma (HGSC) [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A081.

The universal law of generalization holds for naturalistic stimuli.

Shepard's universal law of generalization is a remarkable hypothesis about how intelligent organisms should perceive similarity. In its broadest form, the universal law states that the level of perceived similarity between a pair of stimuli should decay as a concave function of their distance when embedded in an appropriate psychological space. While extensively studied, evidence in support of the universal law has relied on low-dimensional stimuli and small stimulus sets that are very different from their real-world counterparts. This is largely because pairwise comparisons-as required for similarity judgments-scale quadratically in the number of stimuli. We provide strong evidence for the universal law in a naturalistic high-dimensional regime by analyzing an existing data set of 214,200 human similarity judgments and a newly collected data set of 390,819 human generalization judgments (N = 2,406 U.S. participants) across three sets of natural images. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Open Dataset for Testing of Visual SLAM Algorithms under Different Weather Conditions

Existing datasets for testing SLAM algorithms in outdoor environments are not suitable for assessing the influence of weather conditions on localization accuracy. Obtaining a suitable dataset from the real world is difficult due to the long data collection period and the inability to exclude dynamic environmental factors. Artificially generated datasets make it possible to bypass the described limitations, but up to date, researchers have not identified testing SLAM algorithms under different weather conditions as a stand-alone task, despite the fact that it is one of the main aspects of the difference between outdoor and indoor environments. This work presents a new open dataset that consists of 36 sequences of robot movement in an urban environment or rough terrain, in the form of images from a stereo camera and the ground truth position of the robot, collected at a frequency of 30 Hz. Movement within one area occurs along a fixed route; the sequences are distinguished only by whether conditions, which can make it possible to correctly assess the influence of weather phenomena on the accuracy of localization.

Combined benthic and stream edge sampling better represent macroinvertebrate assemblages than benthic sampling alone along an aridity gradient

AbstractStudies of stream macroinvertebrates traditionally use sampling methods that target benthic habitats. These methods could underestimate biodiversity if important assemblage components exist outside of the benthic zone. To test the efficacy of different sampling methods, we collected paired reach‐wide benthic and edge samples from up to 10 study reaches in nine basins spanning an aridity gradient across the United States. Edge sampling targeted riparian‐adjacent microhabitats not typically sampled, including submerged vegetation, roots, and overhanging banks. We compared observed richness, asymptotic richness, and assemblage dissimilarity between benthic samples alone and different combinations of benthic and edge samples to determine the magnitude of increased diversity and assemblage dissimilarity values with the addition of edge sampling. We also examined how differences in richness and assemblage composition varied across an aridity gradient. The addition of edge sampling significantly increased observed richness (median increase = 29%) and asymptotic richness (median increase = 173%). Similarly, median Bray–Curtis dissimilarity values increased by as much as 0.178 when benthic and edge samples were combined. Differences in richness metrics were generally higher in arid basins, but assemblage dissimilarity either increased or decreased across the aridity gradient depending on how benthic and edge samples were combined. Our results suggest that studies that do not sample stream edges may significantly underestimate reach diversity and misrepresent assemblage compositions, with effects that can vary across climates. We urge researchers to carefully consider sampling methods in field studies spanning climatic zones and the comparability of existing data sets when conducting data synthesis studies.

Sensory perception is a holistic inference process.

Sensory perception is widely considered an inference process that reflects the best guess of a stimulus feature based on uncertain sensory information. Here we challenge this reductionist view and propose that perception is rather a holistic inference process that operates not only at the feature but jointly across all levels of the representational hierarchy. We test this hypothesis in the context of a commonly used psychophysical matching task in which subjects are asked to report their perceived orientation of a test stimulus by adjusting a probe stimulus (method-of-adjustment). We introduce a holistic matching model that assumes that subjects' reports reflect an optimal match between the test and probe stimulus, both in terms of their inferred feature (orientation) and also their higher level representation (orientation category). Validation against several existing data sets demonstrates that the model accurately and comprehensively predicts subjects' response behavior and outperforms previous models both qualitatively and quantitatively. Moreover, the model generalizes to other feature domains and offers an alternative account for categorical color perception. Our results suggest that categorical effects in sensory perception are ubiquitous and can be parsimoniously explained as optimal behavior based on holistic sensory representations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Hubble Deep Hydrogen Alpha (HDHα) Project. I. Catalog of Emission-line Galaxies

We present the first results of the Hubble Deep Hydrogen Alpha (HDHα) project, which analyzes the space-borne deep Hα narrowband imaging data in the GOODS-S region. The HDHα data comprises 72 orbits’ images taken with the Hubble Space Telescope (HST) Advanced Camera for Surveys/Wide Field Channel F658N filter. The exposure time varies across a total area of ∼76.1 arcmin2, adding up to a total exposure time of 195.7 ks, among which 68.8 ks are spent in the deepest region. These images are aligned, reprojected, and combined to have the same pixel grid as the Hubble Legacy Fields. The scientific goals of the HDHα include establishing a sample of emission-line galaxies (ELGs) including [O iii] emitters at z ∼ 0.3, [O ii] emitters at z ∼ 0.8, and Lyα emitters (LAEs) at z ∼ 4.4, studying the line morphology of ELGs with high resolution imaging data, and statistically analyzing the line luminosity functions and line equivalent-width distributions of ELGs selected with HST. Furthermore, the HDHα project enhances the legacy value of the GOODS-S field by contributing the first HST-based narrowband image to the existing data sets, which includes the HST broadband data and other ancillary data from X-ray to radio taken by other facilities. In this paper, we describe the data reduction process of the HDHα, select ELGs based on HST's F658N and broadband data, validate the redshifts of the selected candidates by crossmatching with the public spectroscopic catalogs in the GOODS-S, and present a final catalog of the confirmed [O iii] emitters at z ∼ 0.3, [O ii] emitters at z ∼ 0.8, and LAEs at z ∼ 4.4.

An introduction to Criteria for Reporting and Evaluating Exposure Datasets (CREED) for use in environmental assessments.

Risks posed by environmental exposure to chemicals are routinely assessed to inform activities ranging from environmental status reporting to authorization and registration of chemicals for commercial uses. Environmental risk assessment generally relies on two key values generated from exposure data and ecotoxicity data. Data sets of measured concentrations of chemicals in environmental matrices, referred to here as exposure data, are widely used to support environmental risk management, decision-making, and reporting, such as for chemical screening, ecological or human health risk assessments, and establishment of guidelines. Practitioners have developed schemes to determine the suitability of ecotoxicity data for specific purposes, focused on evaluating reliability and relevance, but analogous schemes are not available for exposure data. Moreover, regulatory guidance arguably provides less resolution on reporting and evaluating exposure data sets compared to ecotoxicity data. The evaluation of exposure data sets is subject to limitations from variable or unreported data quality objectives and/or from differences in expert judgments, potentially introducing bias and leading to decisions based on flawed and/or inconsistent information. Exposure data sets should be evaluated for reliability and relevance prior to use in environmental assessments. This paper is the first of a four-paper series detailing the outcomes of a Society of Environmental Toxicology and Chemistry technical workshop that has developed Criteria for Reporting and Evaluating Exposure Datasets (CREED). The workshop participants developed practical, systematic criteria for consistent and transparent evaluation of the reliability (quality) and relevance (fitness for purpose) of exposure data. This guidance should apply to many different (unspecified) purposes of assessment. CREED can be used to evaluate existing data sets, but can also inform data generators interested in improving their data collection and reporting to maximize data utility to other users. This first paper details existing frameworks for the evaluation of exposure data sets and demonstrates the need for CREED, drawing from different regulatory assessments, and describes the technical workshop. Integr Environ Assess Manag 2024;00:1-6. © 2024 SETAC.

Gross primary production of Mediterranean watersheds: Using isotope mass balance approach to improve estimations

AbstractGlobal‐scale estimates of carbon fluxes from satellite data‐driven models are constrained by considerable uncertainties regarding Gross Primary Production (GPP) and the lack of the watershed‐scale measurements required for model calibration. Recently conducted global modelling efforts indicate that semiarid ecosystems dominate the increasing trends and inter‐annual variation of net CO2 exchange with the atmosphere, but semi‐arid regions have received little attention with regard to GPP estimation. In this study, we used the distinct isotope effect of transpiration and evaporation to calculate transpiration losses and subsequently CO2 uptake by terrestrial vegetation through the water and carbon cycle using the water use efficiency of plants. By studying two Mediterranean watersheds with contrasted environmental conditions over several hydrological years, we found a strong dependence of GPP on annual and seasonal water availability. The results demonstrated that when compared to GPP values obtained in worldwide biomes using biological methods, our isotope approach was validated, highlighting the limitations of satellite‐data‐driven models like MODIS in capturing the impact of water stress on photosynthesis and GPP estimates. These results encourage investigation of GPP by the isotope mass balance approach where direct carbon flux measurements are rare or absent in order to help to substantiate, modify or shed doubt on interpolated GPP for those regions and achieve consensus on global GPP estimates. Given the relevant role of semi‐arid ecosystems in the global carbon balance as well as the limitation of existing data sets, our improved method based on the isotope mass balance approach helps to obtain rapid and affordable estimates of GPP for semi‐arid ecosystems.

The American Heart Association Emergency Cardiovascular Care 2030 Impact Goals and Call to Action to Improve Cardiac Arrest Outcomes: A Scientific Statement From the American Heart Association.

Every 10 years, the American Heart Association (AHA) Emergency Cardiovascular Care Committee establishes goals to improve survival from cardiac arrest. These goals align with broader AHA Impact Goals and support the AHA's advocacy efforts and strategic investments in research, education, clinical care, and quality improvement programs. This scientific statement focuses on 2030 AHA emergency cardiovascular care priorities, with a specific focus on bystander cardiopulmonary resuscitation, early defibrillation, and neurologically intact survival. This scientific statement also includes aspirational goals, such as establishing cardiac arrest as a reportable disease and mandating reporting of standardized outcomes from different sources; advancing recognition of and knowledge about cardiac arrest; improving dispatch system response, availability, and access to resuscitation training in multiple settings and at multiple time points; improving availability, access, and affordability of defibrillators; providing a focus on early defibrillation, in-hospital programs, and establishing champions for debriefing and review of cardiac arrest events; and expanding measures to track outcomes beyond survival. The ability to track and report data from these broader aspirational targets will potentially require expansion of existing data sets, development of new data sets, and enhanced integration of technology to collect process and outcome data, as well as partnerships of the AHA with national, state, and local organizations. The COVID-19 (coronavirus disease 2019) pandemic, disparities in COVID-19 outcomes for historically excluded racial and ethnic groups, and the longstanding disparities in cardiac arrest treatment and outcomes for Black and Hispanic or Latino populations also contributed to an explicit focus and target on equity for the AHA Emergency Cardiovascular Care 2030 Impact Goals.

Absolute radiance calibration in the UV and visible spectral range using atmospheric observations during twilight

Abstract. We present an improved radiance calibration method for UV–Vis spectroscopic instruments with a narrow field of view (up to a few degrees) based on the calibration method by Wagner et al. (2015). The updated method uses only measurements during the twilight period instead of several hours as for the original method. The calibration is based on the comparison of measurements and simulations of the radiance of zenith-scattered sunlight. The main advantage of our method compared to radiance calibration methods in the laboratory is that the calibration can be directly applied in the field. This allows routine radiance calibrations whenever the sky is clear during twilight. The calibration can also be performed retrospectively and will thus be applicable for the large number of existing data sets. Also, potential changes in the instrument properties during transport from the laboratory to the field are avoided. The new version of the calibration method presented here has two main advantages. First, the required measurement period can be rather short (only a few minutes during twilight for cloud-free conditions). Second, even without knowledge of the aerosol optical depth (AOD), the errors in the calibration method are rather small, especially in the UV spectral range where they range from about 4 % at 340 nm to 8 % at 420 nm. If the AOD is known, the uncertainties are even smaller (about 3 % at 340 nm to 4 % at 420 nm). For visible wavelengths, good accuracy is only obtained if the AOD is approximately known with uncertainties from about 4 % at 420 nm to 10 % between about 550 and 700 nm (generally the AOD is nevertheless smaller in the visible than in the UV spectral range). One shortcoming of the method is that it is not possible to determine the AOD exactly at the time of the (twilight) measurements because AOD observations from sun photometer measurements or the MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) measurements are usually not meaningful for such high solar zenith angle (SZA). But the related uncertainty can be minimised by repeating the radiance calibrations during the twilight periods of several days.

Pitfalls in evaluating permeability experiments with Caco-2/MDCK cell monolayers

When studying the transport of molecules across biological membranes, intrinsic membrane permeability (P0) is more informative than apparent permeability (Papp), because it eliminates external (setup-specific) factors, provides consistency across experiments and mechanistic insight. It is thus an important building block for modeling the total permeability in any given scenario. However, extracting P0 is often difficult, if not impossible, when the membrane is not the dominant transport resistance. In this work, we set out to analyze Papp values measured with Caco-2/MDCK cell monolayers of 69 literature references. We checked the Papp values for a total of 318 different compounds for the extractability of P0, considering possible limitations by aqueous boundary layers, paracellular transport, recovery issues, active transport, a possible proton flux limitation, and sink conditions. Overall, we were able to extract 77 reliable P0 values, which corresponds to about one quarter of the total compounds analyzed, while about half were limited by the diffusion through the aqueous layers. Compared to an existing data set of P0 values published by Avdeef, our approach resulted in a much higher exclusion of compounds. This is a consequence of stricter compound- and reference-specific exclusion criteria, but also because we considered possible concentration-shift effects due to different pH values in the aqueous layers, an effect only recently described in literature. We thus provide a consistent and reliable set of P0, e.g. as a basis for future modeling.

Comprehensive Integration of Multiple Single-Cell Transcriptomic Data Sets Defines Distinct Cell Populations and Their Phenotypic Changes in Murine Atherosclerosis.

The application of single-cell transcriptomic (single-cell RNA sequencing) analysis to the study of atherosclerosis has provided unique insights into the molecular and genetic mechanisms that mediate disease risk and pathophysiology. However, nonstandardized methodologies and relatively high costs associated with the technique have limited the size and replication of existing data sets and created disparate or contradictory findings that have fostered misunderstanding and controversy. To address these uncertainties, we have performed a conservative integration of multiple published single-cell RNA sequencing data sets into a single meta-analysis, performed extended analysis of native resident vascular cells, and used in situ hybridization to map the disease anatomic location of the identified cluster cells. To investigate the transdifferentiation of smooth muscle cells to macrophage phenotype, we have developed a classifying algorithm based on the quantification of reporter transgene expression. The reporter gene expression tool indicates that within the experimental limits of the examined studies, transdifferentiation of smooth muscle cell to the macrophage lineage is extremely rare. Validated transition smooth muscle cell phenotypes were defined by clustering, and the location of these cells was mapped to lesion anatomy with in situ hybridization. We have also characterized 5 endothelial cell phenotypes and linked these cellular species to different vascular structures and functions. Finally, we have identified a transcriptomically unique cellular phenotype that constitutes the aortic valve. Taken together, these analyses resolve a number of outstanding issues related to differing results reported with vascular disease single-cell RNA sequencing studies, and significantly extend our understanding of the role of resident vascular cells in anatomy and disease.

Engaging the Regulatory Community to Aid Environmental Consenting/Permitting Processes for Marine Renewable Energy

Regulators involved in consenting/permitting marine renewable energy (MRE) have faced multiple challenges due to relatively new, unfamiliar technologies and uncertainty surrounding potential environmental impacts. This has resulted in slow progress for the MRE industry, including long consenting timeframes and extensive and expensive monitoring requirements, which increase financial risk for investors. OES-Environmental has surveyed regulators internationally to understand their key knowledge gaps and perspectives to support the development of the MRE industry. From the results of these surveys a data transferability process and a risk retirement pathway have been developed to assess consenting and monitoring requirements in proportion to risk. A tool for discovering existing data sets by using an online matrix has been developed, along with training materials, regulatory guidance documents, and a strategic outreach plan to engage regulators and advisers. his engagement and the application of these products should lead to a better understanding of the environmental effects of marine energy, and more efficient consenting processes.

CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland

Abstract. We present CAMELS-CH (Catchment Attributes and MEteorology for Large-sample Studies – Switzerland), a large-sample hydro-meteorological data set for hydrologic Switzerland in central Europe. This domain covers 331 basins within Switzerland and neighboring countries. About one-third of the catchments are located in Austria, France, Germany and Italy. As an Alpine country, Switzerland covers a vast diversity of landscapes, including mountainous environments, karstic regions, and several strongly cultivated regions, along with a wide range of hydrological regimes, i.e., catchments that are glacier-, snow- or rain dominated. Similar to existing data sets, CAMELS-CH comprises dynamic hydro-meteorological variables and static catchment attributes. CAMELS-CH (Höge et al., 2023; available at https://doi.org/10.5281/zenodo.7784632) encompasses 40 years of data between 1 January 1981 and 31 December 2020, including daily time series of stream flow and water levels, and of meteorological data such as precipitation and air temperature. It also includes daily snow water equivalent data for each catchment starting from 2 September 1998. Additionally, we provide annual time series of land cover change and glacier evolution per catchment. The static catchment attributes cover location and topography, climate, hydrology, soil, hydrogeology, geology, land use, human impact and glaciers. This Swiss data set complements comparable publicly accessible data sets, providing data from the “water tower of Europe”.

An extension of the shifted Wald model of human response times: Capturing the time dynamic properties of human cognition : Trial-varying Wald model.

Despite the ubiquitous nature of evidence accumulation models in cognitive and experimental psychology, there has been a comparatively limited uptake of such techniques in the applied literature. While quantifying latent cognitive processing properties has significant potential for applied domains such as adaptive work systems, accumulator models often fall short in practical applications. Two primary reasons for these shortcomings are the complexities and time needed for the application of cognitive models, and the failure of current models to capture systematic trial-to-trial variability in parameters. In this manuscript, we develop a novel, trial-varying extension of the shifted Wald model to address these concerns. By leveraging conjugate properties of the Wald distribution, we derive computationally efficient solutions for threshold and drift parameters which can be updated instantaneously with new data. The resulting model allows the quantification of systematic variation in latent cognitive parameters across trials and we demonstrate the utility of such analyses through simulations and an exemplar application to an existing data set. The analytic nature of our solutions opens the door for real-world applications, significantly extending the reach of computational models of behavioral responses.

Abstract C063: Frequency and spectrum of KRAS mutations in lung adenocarcinoma from Black/African American and White/European patients

Abstract Lung cancer is the most common cause of cancer death in the United States and a major source of cancer health disparity. Lung cancer incidence and mortality rates are higher in Black/African American (B/AA) men than in men from other racial/ethnic groups. Lung adenocarcinoma, arising in the alveoli or air sacs of the lung, is the most common subtype of lung cancer in all ethnic/racial groups. Genetic driver mutations can differ among racial/ethnic groups, in the identity of the mutated genes, their mutation frequencies, and the spectrum of mutations for each given gene. In addition, the same mutation could have different effects depending on an individual patient’s genetic background, resulting in differential responses to therapies. Therefore, it is important to identify the frequency and spectrum of driver mutations in different population groups. Protooncogenes from the RAS family, KRAS, NRAS, and HRAS, play a crucial role in cell growth regulation, cell proliferation, and survival. KRAS is frequently mutated in numerous cancer types, including lung adenocarcinoma, and has been reported to be the driver gene in ~30% of lung adenocarcinomas from White/European (W/E) patients. Due to the much lower number of lung cancer samples analyzed from B/AA patients, it has been unclear if the KRAS mutational frequency and spectrum in B/AA lung adenocarcinoma patients differs from that in W/E patients. Here we collected available existing data sets with mutational information on KRAS muations in lung adenocarcinoma from B/AA and W/E patients. We obtained data for 1555 B/AA and 13840 White lung adenocarcinoma patients. While B/AA lung adenocarcinoma patients had a lower prevalence of KRAS mutations (27% vs. 35%) than W/E patients, (χ2 p-value= 5.877e-10), KRAS was still the most frequently mutated cancer driver gene in both groups. The most common KRAS mutations in both B/AA and W/E patients were alterations of glycine 12 to cysteine (G12C), valine (G12V), aspartic acid (G12D), and alanine (G12A), as well as glutamine 61 to histidine (Q61H) and glycine 13 to cysteine (G13C). Given that a substantial proportion of lung adenocarcinoma patients carry a mutated KRAS gene, investigating new potential KRAS-targeting therapies remains especially urgent. To this end, we are investigating polyisoprenylated cysteinyl amide inhibitors (PCAIs), a group of potential cancer therapy agents designed to specifically disrupt and suppress hyperactive G-protein signaling, as caused by mutated RAS proteins. We are testing PCAI efficiacy on B/AA and W/E lung adenocarcinoma cell lines carrying various KRAS mutations. Citation Format: Daniel J. Mullen, Pablo E. Puente, Christopher Leon, Diana I. Romero, Kweku Ofosu-Asante, Yong Huang, Nazarius S. Lamango, Ite A. Offringa. Frequency and spectrum of KRAS mutations in lung adenocarcinoma from Black/African American and White/European patients [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C063.

Artificial intelligence in remote sensing geomorphology—a critical study

Planetary geomorphological maps over a wide range of spatial and temporal scales provide important information on landforms and their evolution. The process of producing a geomorphological map is extremely time-consuming and maps are often difficult to reproduce. The success of deep learning and machine learning promises to drastically reduce the cost of producing these maps and also to increase their reproducibility. However, deep learning methods strongly rely on having sufficient ground truth data to recognize the wanted surface features. In this study, we investigate the results from an artificial intelligence (AI)–based workflow to recognize lunar boulders on images taken from a lunar orbiter to produce a global lunar map showing all boulders that have left a track in the lunar regolith. We compare the findings from the AI study with the results found by a human analyst (HA) who was handed an identical database of images to identify boulders with tracks on the images. The comparison involved 181 lunar craters from all over the lunar surface. Our results show that the AI workflow used grossly underestimates the number of identified boulders on the images that were used. The AI approach found less than one fifth of all boulders identified by the HA. The purpose of this work is not to quantify the absolute sensitivities of the two approaches but to identify the cause and origin for the differences that the two approaches deliver and make recommendations as to how the machine learning approach under the given constraints can be improved. Our research makes the case that despite the increasing ease with which deep learning methods can be applied to existing data sets, a more thorough and critical assessment of the AI results is required to ensure that future network architectures can produce the reliable geomorphological maps that these methods are capable of delivering.

Assessing risks for bovine and zoonotic tuberculosis through spatial analysis and a questionnaire survey in Fiji – A pilot study

Mycobacterium bovis causes tuberculosis in cattle and when transmitted to humans typically causes extra-pulmonary tuberculosis (EPTB). Bovine tuberculosis (bTB) has a global distribution and is controlled in most countries to protect animal and public health. Recent studies revealed that bTB is established on dairy farms in Fiji where EPTB cases have been reported in people. The aims of this pilot investigation were to look for putative zoonotic TB (EPTB) cases in people and to evaluate practices that might contribute to the persistence and transmission of M. bovis between cattle and to humans. Existing data sets were shared between the Fiji Ministry of Agriculture and Ministry of Health and a questionnaire-based survey was implemented using One Health principles. Statistically significant co-location and close proximity of EPTB cases and bovine TB affected farms were identified. The bTB infection status of farms was significantly associated with unfenced water sources where cattle grazed. Of 247 households, 65 % shared drinking water sources with cattle and 36 % consumed raw milk without boiling, while 62 % of participants reported backyard slaughter of cattle. Several participants reported current symptoms potentially suggestive of TB (chronic cough) but the impact of smoking and history of previous TB treatment could not be evaluated. Farmers had limited understanding of the practices required to prevent bTB at farm level. Further study is recommended and should include an assessment of lifetime EPTB diagnoses, classification of farms based on more recent bTB test data and molecular typing of mycobacterial isolates from humans, cattle and the environment. A targeted awareness and education approach is required to reduce the future risk of zoonotic TB and to help ensure uptake of recommendations and practices aimed at controlling and preventing bTB.

Effects of variations in atmospheric temperature and humidity on the estimation of exclusive breastfeeding status using the deuterium oxide dose-to-mother technique.

The deuterium dose-to-mother (DTM) method measures the human milk intake of breastfed children. Recently, the use of this method has been expanded to classify babies objectively as exclusively breast fed (EBF) or not (non-EBF) based on quantification of non-milk oral water intake (NMOI). However, the calculation of NMOI estimates involves atmospheric temperature and humidity. To evaluate the effects of atmospheric temperature and humidity on NMOI calculation and the classification of exclusive breastfeeding. The effect of indoor temperature and relative humidity on NMOI and the estimated prevalence of non-EBF were examined in two existing data sets of DTM in children by varying temperature in the range of 15 to 35°C and relative humidity in the range of 20 to 80% representing the maximum span of indoor conditions expected. Population-level estimates of NMOI distributions were derived using the rstan package for R v2.21.2. The NMOI decreased at a rate of -1.15 g/day per °C increase and at a rate of -1.01 g/day per percent increase in relative humidity; this was due to variations in non-oral water intake from the atmosphere, a component of the calculation of NMOI, which is dependent on temperature and humidity. For the various locations considered, the mean calculated NMOI varied between 24.6 and 53.3 g/day using the same input data. In the mixed-fed sample of babies, the prevalence of non-EBF based on the earlier defined NMOI cut-off of 86.6 g/day was reduced by 19% when relative humidity was increased by 60%. Atmospheric conditions are essential factors in the computation of NMOI, used in the objective classification of babies as exclusively breast fed or not, and should be considered when the DTM method is used to classify exclusive breastfeeding.