Identification of plankton habitats in the North Sea.

The definition of an ecological niche makes it possible to anticipate the responses of a species to changing environmental conditions. Broad tolerance limits and a paucity of readily observable niches in the pelagic zone make it difficult to anticipate responses of the plankton community related to anthropogenic or environmental changes. Plankton distributions are closely linked to climate change and shape the seascape for higher trophic levels, so monitoring plankton distributions and defining ecological niches will help to understand and predict ecosystem responses. Here we apply a machine learning autoencoder and a density-based clustering algorithm to high-frequency datasets sampled with a ROTV Triaxus in the North Sea. The results indicate that in this highly dynamic environment, local hydrography prevents niche-based separation of plankton species at the sub-mesoscale, despite the availability of different habitats. Plankton patches were associated with naturally occurring frontal systems and anthropogenically induced upwelling-downwelling dipoles in the vicinity of offshore wind farms (OWFs).

On the Development of Smart Framework for Printability Maps in Additive Manufacturing of AISI 316L Stainless Steel.

In this work, we propose a methodology to develop printability maps for the laser powder bed fusion of AISI 316L stainless steel. Regions in the process space associated with different defect types, including lack of fusion, balling, and keyhole formation, have been considered as a melt pool geometry function, determined using a finite element method model containing temperature-dependent thermophysical properties. Experiments were performed to validate the printability maps, showing a reliable correlation between experiments and simulations. The validated simulation model was then applied to collect the data by varying laser scanning speed, laser power, powder layer thickness, and powder bed preheating temperature. Following this, the collected data were used to train and test the adaptive neuro-fuzzy interference system (ANFIS)-based machine learning model. The validated ANFIS model was used to develop printability maps by correlating the melt pool characteristics to the defect types. The smart printability maps produced by the proposed methodology can be used to identify the processing window to attain defects-free components, thus attaining dense parts.

Human and hamster sera correlate well in identifying antigenic drift among SARS-CoV-2 variants, including JN.1.

Antigenic assessments of SARS-CoV-2 variants inform decisions to update COVID-19 vaccines. Primary infection sera are often used for assessments, but such sera are rare due to population immunity from SARS-CoV-2 infections and COVID-19 vaccinations. Here, we show that neutralization titers and breadth of matched human and hamster pre-Omicron variant primary infection sera correlate well and generate similar antigenic maps. The hamster antigenic map shows modest antigenic drift among XBB sub-lineage variants, with JN.1 and BA.4/BA.5 variants within the XBB cluster, but with fivefold to sixfold antigenic differences between these variants and XBB.1.5. Compared to sera following only ancestral or bivalent COVID-19 vaccinations, or with post-vaccination infections, XBB.1.5 booster sera had the broadest neutralization against XBB sub-lineage variants, although a fivefold titer difference was still observed between JN.1 and XBB.1.5 variants. These findings suggest that antibody coverage of antigenically divergent JN.1 could be improved with a matched vaccine antigen.IMPORTANCEUpdates to COVID-19 vaccine antigens depend on assessing how much vaccine antigens differ antigenically from newer SARS-CoV-2 variants. Human sera from single variant infections are ideal for discriminating antigenic differences among variants, but such primary infection sera are now rare due to high population immunity. It remains unclear whether sera from experimentally infected animals could substitute for human sera for antigenic assessments. This report shows that neutralization titers of variant-matched human and hamster primary infection sera correlate well and recognize variants similarly, indicating that hamster sera can be a proxy for human sera for antigenic assessments. We further show that human sera following an XBB.1.5 booster vaccine broadly neutralized XBB sub-lineage variants but titers were fivefold lower against the more recent JN.1 variant. These findings support updating the current COVID-19 vaccine variant composition and developing a framework for assessing antigenic differences in future variants using hamster primary infection sera.

Exploring Journey Maps as Products From Qualitative Research: Application Through Food Insecure Veterans' Experiences.

Journey maps are graphic representations of participant, user, customer, or patient experiences or "journeys" with a particular phenomenon, product, business, or organization. Journey maps help visualize complex pathways and phases in accessible, digestible ways. They also capture emotions, reactions, and values associated with the processes participants undergo, complemented by images or quotes from participants. Here, we outline the foundations of journey maps in research and in practice settings. Our goal is to describe journey maps to researchers new to the product and emphasize the novelty and utility of journey maps as visual products from qualitative research particularly in a health setting. To explore journey maps-including their benefits, drawbacks, and relevance-we discuss examples including our own process for designing a journey map of food insecure Veterans' experiences using qualitative, in-depth interviews and supported by member checking. Our journey map depicts food insecurity as a repetitive process, a unique contribution given that many journey maps are designed with discrete starting and stopping points. We conclude by discussing the novelty of journey maps as innovative products that researchers can use to identify opportunities for process improvements and innovation using multiple data sources or methods.

Latent Archetypes of the Spatial Patterns of Cancer.

The cancer atlas edited by several countries is the main resource for the analysis of the geographic variation of cancer risk. Correlating the observed spatial patterns with known or hypothesized risk factors is time-consuming work for epidemiologists who need to deal with each cancer separately, breaking down the patterns according to sex and race. The recent literature has proposed to study more than one cancer simultaneously looking for common spatial risk factors. However, this previous work has two constraints: they consider only a very small (2-4) number of cancers previously known to share risk factors. In this article, we propose an exploratory method to search for latent spatial risk factors of a large number of supposedly unrelated cancers. The method is based on the singular value decomposition and nonnegative matrix factorization, it is computationally efficient, scaling easily with the number of regions and cancers. We carried out a simulation study to evaluate the method's performance and apply it to cancer atlas from the USA, England, France, Australia, Spain, and Brazil. We conclude that with very few latent maps, which can represent a reduction of up to 90% of atlas maps, most of the spatial variability is conserved. By concentrating on the epidemiological analysis of these few latent maps a substantial amount of work is saved and, at the same time, high-level explanations affecting many cancers simultaneously can be reached.

High-resolution spatio-temporal risk mapping for malaria in Namibia: a comprehensive analysis.

BACKGROUND: Namibia, a low malaria transmission country targeting elimination, has made substantial progress in reducing malaria burden through improved case management, widespread indoor residual spraying and distribution of insecticidal nets. The country's diverse landscape includes regions with varying population densities and geographical niches, with the north of the country prone to periodic outbreaks. As Namibia approaches elimination, malaria transmission has clustered into distinct foci, the identification of which is essential for deployment of targeted interventions to attain the southern Africa Elimination Eight Initiative targets by 2030. Geospatial modelling provides an effective mechanism to identify these foci, synthesizing aggregate routinely collected case counts with gridded environmental covariates to downscale case data into high-resolution risk maps. METHODS: This study introduces innovative infectious disease mapping techniques to generate high-resolution spatio-temporal risk maps for malaria in Namibia. A two-stage approach is employed to create maps using statistical Bayesian modelling to combine environmental covariates, population data, and clinical malaria case counts gathered from the routine surveillance system between 2018 and 2021. RESULTS: A fine-scale spatial endemicity surface was produced for annual average incidence, followed by a spatio-temporal modelling of seasonal fluctuations in weekly incidence and aggregated further to district level. A seasonal profile was inferred across most districts of the country, where cases rose from late December/early January to a peak around early April and then declined rapidly to a low level from July to December. There was a high degree of spatial heterogeneity in incidence, with much higher rates observed in the northern part and some local epidemic occurrence in specific districts sporadically. CONCLUSIONS: While the study acknowledges certain limitations, such as population mobility and incomplete clinical case reporting, it underscores the importance of continuously refining geostatistical techniques to provide timely and accurate support for malaria elimination efforts. The high-resolution spatial risk maps presented in this study have been instrumental in guiding the Namibian Ministry of Health and Social Services in prioritizing and targeting malaria prevention efforts. This two-stage spatio-temporal approach offers a valuable tool for identifying hotspots and monitoring malaria risk patterns, ultimately contributing to the achievement of national and sub-national elimination goals.

QSAR modelling of enzyme inhibition toxicity of ionic liquid based on chaotic spotted hyena optimization algorithm.

Ionic liquids (ILs) have attracted considerable interest due to their unique properties and prospective uses in various industries. However, their potential toxicity, particularly regarding enzyme inhibition, has become a growing concern. In this study, a QSAR model was proposed to predict the enzyme inhibition toxicity of ILs. A dataset of diverse ILs with corresponding toxicity data against three enzymes was compiled. Molecular descriptors that capture the physicochemical, structural, and topological properties of the ILs were calculated. To optimize the selection of descriptors and develop a robust QSAR model, the chaotic spotted hyena optimization algorithm, a novel nature-inspired metaheuristic, was employed. The proposed algorithm efficiently searches for an optimal subset of descriptors and model parameters, enhancing the predictive performance and interpretability of the QSAR model. The developed model exhibits excellent predictive capability, with high classification accuracy and low computation time. Sensitivity analysis and molecular interpretation of the selected descriptors provide insights into the critical structural features influencing the toxicity of ILs. This study showcases the successful application of the chaotic spotted hyena optimization algorithm in QSAR modelling and contributes to a better understanding of the toxicity mechanisms of ILs, aiding in the design of safer alternatives for industrial applications.

Crossover evaluation of time-of-flight-based attenuation correction in brain 18F-FDG and 18F-flutemetamol PET.

BACKGROUND: Brain-dedicated positron emission tomography (PET) systems offer high spatial resolution and sensitivity for accurate clinical assessments. Attenuation correction (AC) is important in PET imaging, particularly in brain studies. This study assessed the reproducibility of attenuation maps (µ-maps) generated by a specialized time-of-flight (TOF) brain-dedicated PET system for imaging using different PET tracers. METHODS: Twelve subjects underwent both 18F-fluorodeoxyglucose (FDG)-PET and 18F-flutemetamol (FMM) amyloid-PET scans. Images were reconstructed with µ-maps obtained by a maximum likelihood-based AC method. Voxel-based and region-based analyses were used to compare µ-maps obtained with FDG-PET versus FMM-PET; FDG-PET images reconstructed using an FDG-PET µ-map (FDG × FDG) versus those reconstructed with an FMM-PET µ-map (FDG × FMM); and FMM-PET images reconstructed using an FDG-PET µ-map (FMM × FDG) versus those reconstructed with an FMM-PET µ-map (FMM × FMM). RESULTS: Small but significant differences in µ-maps were observed between tracers, primarily in bone regions. In the comparison between the µ-maps obtained with FDG-PET and FMM-PET, the µ-maps obtained with FDG-PET had higher µ-values than those obtained with FMM-PET in the parietal regions of the head and skull, in a portion of the cerebellar dentate nucleus and on the surface of the frontal lobe. The comparison between FDG and FDG × FMM values in different regions yielded findings similar to those of the µ-maps comparison. FDG × FMM values were significantly higher than FDG values in the bilateral temporal bones and a small part of the temporal lobe. Similarly, FMM values were significantly higher than FMM × FDG values in the bilateral temporal bones. FMM × FDG values were significantly higher than FMM values in a small area of the right cerebellar hemisphere. However, the relative errors in these µ-maps were within ± 4%, suggesting that they are clinically insignificant. In PET images reconstructed with the original and swapped µ-maps, the relative errors were within ± 7% and the quality was nearly equivalent. CONCLUSION: These findings suggest the clinical reliability of the AC method without an external radiation source in TOF brain-dedicated PET systems.

Photoplethysmography-Based Angiography of Skin Tumors in Arbitrary Areas of Human Body.

Noninvasive, rapid, and robust diagnostic techniques for clinical screening of tumors located in arbitrary areas of the human body are in demand. To address this challenge, we analyzed the feasibility of photoplethysmography-based angiography for assessing vascular structures within malignant and benign tumors. The proposed hardware and software were approved in a clinical study involving 30 patients with tumors located in the legs, torso, arms, and head. High-contrast and detailed vessel maps within both benign and malignant tumors were obtained. We demonstrated that capillary maps are consistent and can be interpreted using well-established dermoscopic criteria for vascular morphology. Vessel mapping provides valuable details, which may not be available in dermoscopic images and can aid in determining whether a tumor is benign or malignant. We believe that the proposed approach may become a valuable tool in the preliminary cancer diagnosis and is suitable for large-scale screening.

Standardization of Dual-Energy CT Iodine Uptake of the Abdomen and Pelvis: Defining Reference Values in a Big Data Cohort.

Background: To establish dual-energy-derived iodine density reference values in abdominopelvic organs in a large cohort of healthy subjects. Methods: 597 patients who underwent portal venous phase dual-energy CT scans of the abdomen were retrospectively enrolled. Iodine distribution maps were reconstructed, and regions of interest measurements were placed in abdominal and pelvic structures to obtain absolute iodine values. Subsequently, normalization of the abdominal aorta was conducted to obtain normalized iodine ratios. The values obtained were subsequently analyzed and differences were investigated in subgroups defined by sex, age and BMI. Results: Overall mean iodine uptake values and normalized iodine ratios ranged between 0.31 and 6.08 mg/mL and 0.06 and 1.20, respectively. Women exhibited higher absolute iodine concentration across all organs. With increasing age, normalized iodine ratios mostly tend to decrease, being most significant in the uterus, prostate, and kidneys (p < 0.015). BMI was the parameter less responsible for variations in iodine concentrations; normal weighted patients demonstrated higher values of both absolute and normalized iodine. Conclusions: Iodine concentration values and normalized iodine ratios of abdominal and pelvic organs reveal significant gender-, age-, and BMI-related differences, underscoring the necessity to integrate these variables into clinical practice.

Using Geovisualizations to Educate the Public About Environmental Health Hazards: What Works and Why.

PURPOSE OF REVIEW: Informing the public about environmental risks to health is crucial for raising awareness around hazards, and promoting actions that minimize exposures. Geographic visualizations-geovisualizations-have become an increasingly common way to disseminate web-based information about environmental hazards, displaying spatial variations in exposures and health outcomes using a map. Unfortunately, ineffective geovisualizations can result in inaccurate inferences about a hazard, leading to misguided actions or policies. In this narrative review, we discuss key considerations for the use of geovisualizations to promote environmental health literacy. RECENT FINDINGS: Many conventional geovisualizations used for hazard education and risk communication fail to consider how people process visual information. Design choices that prompt viewers to think and feel, leveraging processes such as individual attention, memory, and emotion, could promote improved comprehension and decision making around environmental health risks using geovisualizations. Based on the studies reviewed, we recommend six strategies for designing effective, evidence-based geovisualizations, synthesizing evidence from the cognitive sciences, cartography, and environmental health. These strategies include: Displaying only key data, tailoring and testing geovisualizations with the desired audience, using salient cues, leveraging emotion, aiding pattern recognition, and limiting visual distractions. Geovisualizations offer a promising avenue for advancing public awareness and fostering proactive measures in addressing complex environmental health challenges. This review highlights how incorporating evidence-based design principles into geovisualizations could promote environmental health literacy. More experimental research evaluating geovisualizations, using interdisciplinary approaches, is needed.

Non-invasive ultrasound localization microscopy (ULM) in azoospermia: connecting testicular microcirculation to spermatogenic functions.

Rationale: Azoospermia is a significant reproductive challenge. Differentiating between non-obstructive azoospermia (NOA) and obstructive azoospermia (OA) is crucial as each type requires distinct management strategies. Testicular microcirculation plays a profound role in spermatogenic functions. However, current diagnostic methods are limited in their ability to effectively elucidate this crucial connection. Methods: We employed ultrasound localization microscopy (ULM) to visualize testicular microcirculation in NOA and OA patients and quantified the testicular hemodynamic parameters. Pearson correlation analysis was conducted to investigate the inner connection between parameters of testicular microcirculation and clinical spermatogenic functions. We conducted multiple logistic regression analysis to establish a new diagnostic model that integrates follicle-stimulating hormone (FSH) and mean vascular diameter to distinguish NOA from OA. Results: Our findings demonstrated significant differences in vascular parameters between NOA and OA, with NOA characterized by lower mean vascular diameter (p < 0.001), vessel density (p < 0.001), and fractal number (p < 0.001). Testicular volume showed a moderate positive correlation with mean vascular diameter (r = 0.419, p < 0.01) and vessel density (r = 0.415, p < 0.01); Mean vascular diameter exhibited negative correlations with both FSH (r = -0.214, p < 0.05) and age (r = -0.240, p < 0.05); FSH (r = -0.202, p < 0.05) and luteinizing hormone (LH) (r = -0.235, p < 0.05) were negatively correlated with mean blood flow velocity. The diagnostic model demonstrated an area under the curve (AUC) of 0.968. We also reported a method to map the vascular pressure distribution derived from the blood flow velocity generated by ULM. Conclusions: ULM provides a non-invasive and detailed assessment of testicular microvascular dynamics. The ULM-derived vascular parameters are able to connect testicular microcirculation to spermatogenic functions. The combination of FSH and mean vascular diameter enhances diagnostic precision and holds potential for distinguishing NOA from OA.

Machine learning misclassification networks reveal a citation advantage of interdisciplinary publications only in high-impact journals.

Given a large enough volume of data and precise, meaningful categories, training a statistical model to solve a classification problem is straightforward and has become a standard application of machine learning (ML). If the categories are not precise, but rather fuzzy, as in the case of scientific disciplines, the systematic failures of ML classification can be informative about properties of the underlying categories. Here we classify a large volume of academic publications using only the abstract as information. From the publications that are classified differently by journal categories and ML categories (i.e., misclassified publications, when using the journal assignment as ground truth) we construct a network among disciplines. Analysis of these misclassifications provides insight in two topics at the core of the science of science: (1) Mapping out the interplay of disciplines. We show that this misclassification network is informative about the interplay of academic disciplines and it is similar to, but distinct from, a citation-based map of science, where nodes are scientific disciplines and an edge indicates a strong co-citation count between publications in these disciplines. (2) Analyzing the success of interdisciplinarity. By evaluating the citation patterns of publications, we show that misclassification can be linked to interdisciplinarity and, furthermore, that misclassified articles have different citation frequencies than correctly classified articles: In the highest 10 percent of journals in each discipline, these misclassified articles are on average cited more frequently, while in the rest of the journals they are cited less frequently.

Driving factor, source identification, and health risk of PFAS contamination in groundwater based on the self-organizing map.

The complex interactions between groundwater chemical environments and PFAS present challenges for data analysis and factor assessment of the spatial distribution and source attribution of PFAS in groundwater. This study employed spatial response analysis combining self-organizing maps (SOM), K-means clustering, Spearman correlation, positive matrix factorization (PMF) and risk quotient (RQ), to uncover the spatial characteristics, driving factors, sources, and human health risks of groundwater PFAS in the Pearl River Basin. The results indicated that the characteristics of PFAS in groundwater were classified into 16 neurons, which were further divided into 6 clusters (I-VI). This division was due to the contribution of industrial pollution (33.2 %) and domestic pollution (31.5 %) to the composition of PFAS in groundwater. In addition, the hydrochemical indicators such as pH, dissolved organic carbon (DOC), chloride (Cl-), and calcium ions (Ca2+) might also affect the distribution pattern of PFAS. The potential human health risk in the area was minimal, with cluster Ⅱ presenting the highest risk (RQ value 0.25) which is closely related to PFOA emissions from fluoropolymer industry. This study provides a theoretical basis and data support for applying of SOM to the visualization and control of PFAS contamination in groundwater.

The Utility of Apparent Water Diffusion Coefficient Maps for Evaluating the Presence of Myometrial Invasion in Patients with Endometrial Cancer.

PURPOSE: To assess the utility of apparent diffusion coefficient maps (ADC) for diagnosing myometrial invasion (MI) in endometrial cancer (EC). METHODS: This retrospective study included 164 patients (mean age, 56 years; range, 25-89 years) who underwent preoperative MRI for EC with <1/2 MI or no MI between April 2016 and July 2023. Five sequences were evaluated: T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), ADC, dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI), and contrast-enhanced T1WI (CE-T1WI). Three experienced radiologists independently assessed the sequences for MI. For ADC, MI was determined if the endometrial-myometrial junction-tumor boundary had disappeared. Additionally, the assessment of MI was performed using the combination of T2WI, DWI, and ADC, as well as T2WI, DCE-T1WI, and CE-T1WI. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) for the presence of MI were calculated and compared between the sequences and combinations. Inter-reader agreement was assessed using kappa (κ) statistics. RESULTS: The sensitivity of ADC was significantly higher than T2WI (P < 0.001) and DCE-T1WI (P = 0.018) for one reader and significantly higher than CE-T1WI (P = 0.045 and 0.043) for two readers. The specificity of ADC was significantly lower than T2WI (P = 0.015 and < 0.001) and CE-T1WI (P = 0.031 and 0.01) for two readers and significantly lower than DCE-T1WI (P = 0.031) for one reader. The AUC of ADC was significantly higher than T2WI (P = 0.048) and DCE-T1WI (P = 0.049) for one reader. The combination including ADC showed higher positive predictive value for all three readers compared to any sequence or combination including contrast enhancement. Additionally, ADC demonstrated the highest agreement rates. CONCLUSION: ADC had high sensitivity for MI and the highest agreement rate among all sequences. Thus, this sequence, combined with other sequences, can be crucial for a comprehensive evaluation of MI.

Examining how customers perceive community pharmacies based on Google maps reviews: Multivariable and sentiment analysis.

Objective: This study aims to understand customer perceptions of community pharmacies utilizing publicly available data from Google Maps platform. Materials and methods: Python was used to scrape data with Google Maps APIs. As a result, 17,237 reviews were collected from 512 pharmacies distributed over Riyadh city, Saudi Arabia. Logistic regression was conducted to test the relationships between multiple variables and the given score. In addition, sentiment analysis using VADER (Valence Aware Dictionary for Sentiment Reasoning) model was conducted on written reviews, followed by cross-tabulation and chi-square tests. Results: The Logistic regression model implies that a unit increase in the Pharmacy score enhances the odds of attaining a higher score by approximately 3.734 times. The Mann-Whitney U test showed that a notable and statistically significant difference between "written reviews" and "unwritten reviews" (U = 39,928,072.5, p < 0.001). The Pearson chi-square test generated a value of 2991.315 with 8 degrees of freedom, leading to a p value of 0.000. Discussion: Our study found that the willingness of reviewers to write reviews depends on their perception. This study provides a descriptive analysis of conducted sentiment analysis using VADAR. The chi-square test indicates a significant relationship between rating scores and review sentiments. Conclusion: This study offers valuable findings on customer perception of community pharmacies using a new source of data.

Siamese based deep neural network for ADHD detection using EEG signal.

BACKGROUND: Detecting Attention-Deficit/Hyperactivity Disorder (ADHD) in children is crucial for timely intervention and personalized treatment. OBJECTIVE: This study aims to utilize deep learning techniques to analyze brain maps derived from Power Spectral Density (PSD) of Electroencephalography (EEG) signals in pediatric subjects for ADHD detection. METHODS: We employed a Siamese-based Convolutional Neural Network (CNN) to analyze EEG-based brain maps. Gradient-weighted class activation mapping (Grad-CAM) was used as an explainable AI (XAI) visualization method to identify significant features. RESULTS: The CNN model achieved a high classification accuracy of 99.17 %. Grad-CAM analysis revealed that PSD features from the theta band of the frontal and occipital lobes are effective discriminators for distinguishing children with ADHD from healthy controls. CONCLUSION: This study demonstrates the effectiveness of deep learning in ADHD detection and highlights the importance of regional PSD metrics in accurate classification. By utilizing Grad-CAM, we elucidate the discriminative power of specific brain regions and frequency bands, thereby enhancing the understanding of ADHD neurobiology for improved diagnostic precision in pediatric populations.

Clinical usability of deep learning-based saliency maps for occlusion myocardial infarction identification from the prehospital 12-Lead electrocardiogram.

INTRODUCTION: Deep learning (DL) models offer improved performance in electrocardiogram (ECG)-based classification over rule-based methods. However, for widespread adoption by clinicians, explainability methods, like saliency maps, are essential. METHODS: On a subset of 100 ECGs from patients with chest pain, we generated saliency maps using a previously validated convolutional neural network for occlusion myocardial infarction (OMI) classification. Three clinicians reviewed ECG-saliency map dyads, first assessing the likelihood of OMI from standard ECGs and then evaluating clinical relevance and helpfulness of the saliency maps, as well as their confidence in the model's predictions. Questions were answered on a Likert scale ranging from +3 (most useful/relevant) to -3 (least useful/relevant). RESULTS: The adjudicated accuracy of the three clinicians matched the DL model when considering area under the receiver operating characteristics curve (AUC) and F1 score (AUC 0.855 vs. 0.872, F1 score = 0.789 vs. 0.747). On average, clinicians found saliency maps slightly clinically relevant (0.96 ± 0.92) and slightly helpful (0.66 ± 0.98) in identifying or ruling out OMI but had higher confidence in the model's predictions (1.71 ± 0.56). Clinicians noted that leads I and aVL were often emphasized, even when obvious ST changes were present in other leads. CONCLUSION: In this clinical usability study, clinicians deemed saliency maps somewhat helpful in enhancing explainability of DL-based ECG models. The spatial convolutional layers across the 12 leads in these models appear to contribute to the discrepancy between ECG segments considered most relevant by clinicians and segments that drove DL model predictions.

Virtual Assessment of Physical Activity-Related Built Environment in Soweto, South Africa: What Is the Role of Contextual Familiarity?

Understanding how urban environments shape physical activity is critical in rapidly urbanizing countries such as South Africa. We assessed the reliability of virtual audits for characterizing urban features related to physical activity in Soweto, South Africa. We used the Microscale Audit of Pedestrian Streetscapes Global tool to characterize pedestrian-related features from Google Street View images in four neighborhoods of Soweto. Neighborhoods were selected to represent different levels of deprivation. Inter-rater reliability was analyzed according to the rater's familiarity with the local area. The results show a higher inter-rater reliability was observed among auditors with greater contextual familiarity. Many measurements however generated inconclusive results due to either low variability in the raters' responses or the absence of the features in the streets. It is evident from our findings that virtual audits are efficient tools that can be used to assess the built environment. However, to ensure meaningful use of these tools in diverse settings, we recommend that auditors comprise of people with contextual familiarity.

Cognitive profiles across the psychosis continuum.

Cognitive impairments are core features in individuals across the psychosis continuum and predict functional outcomes. Nevertheless, substantial variability in cognitive functioning within diagnostic groups, along with considerable overlap with healthy controls, hampers the translation of research findings into personalized treatment planning. Aligned with precision medicine, we employed a data driven machine learning method, self-organizing maps, to conduct transdiagnostic clustering based on cognitive functions in a sample comprising 228 healthy controls, 200 individuals at ultra-high risk for psychosis, and 98 antipsychotic-naïve patients with first-episode psychosis. The self-organizing maps revealed six clinically distinct cognitive profiles that significantly predicted baseline functional level and changes in functional level after one year. Cognitive flexibility in particular, as well as specific executive functions emerged as cardinal in differentiating the profiles. The application of self-organizing maps appears to be a promising approach to inform clinical decision-making based on individualized cognitive profiles, including patient allocation to different interventions. Moreover, this method has the potential to enable cross-diagnostic stratification in research trials, utilizing data-driven subgrouping informed by categories from underlying dimensions of cognition rather than from clinical diagnoses. Finally, the method enables cross-diagnostic profiling across other data modalities, such as brain networks or metabolic subtypes.