Semantic similarity is not enough: A novel NLP-based semantic similarity measure in geospatial context.

In this study, we addressed two primary challenges: firstly, the issue of domain shift, which pertains to changes in data characteristics or context that can impact model performance, and secondly, the discrepancy between semantic similarity and geographical distance. We employed topic modeling in conjunction with the BERT architecture. Our model was crafted to enhance similarity computations applied to geospatial text, aiming to integrate both semantic similarity and geographical proximity. We tested the model on two datasets, Persian Wikipedia articles and rental property advertisements. The findings demonstrate that the model effectively improved the correlation between semantic similarity and geographical distance. Furthermore, evaluation by real-world users within a recommender system context revealed a notable increase in user satisfaction by approximately 22% for Wikipedia articles and 56% for advertisements.

Investigating the temporal dynamics of motor vehicle collision density patterns in urban road networks - A case study of New York.

INTRODUCTION: Motor vehicle collisions are a leading source of mortality and injury on urban highways. From a temporal perspective, the determination of a road segment as being collision-prone over time can fluctuate dramatically, making it difficult for transportation agencies to propose traffic interventions. However, there has been limited research to identify and characterize collision-prone road segments with varying collision density patterns over time. METHOD: This study proposes an identification and characterization framework that profiles collision-prone roads with various collision density variations. We first employ the spatio-temporal network kernel density estimation (STNKDE) method and time-series clustering to identify road segments with different collision density patterns. Next, we characterize collision-prone road segments based on spatio-temporal information, consequences, vehicle types, and contributing factors to collisions. The proposed method is applied to two-year motor vehicle collision records for New York City. RESULTS: Seven clusters of road segments with different collision density patterns were identified. Road segments frequently determined as collision-prone were primarily found in Lower Manhattan and the center of the Bronx borough. Furthermore, collisions near road segments that exhibit greater collision densities over time result in more fatalities and injuries, many of which are caused by both human and vehicle factors. CONCLUSIONS: Collision-prone road segments with various collision density patterns over time have distinct differences in the spatio-temporal domain and the collisions that occur on them. PRACTICAL APPLICATIONS: The proposed method can help policymakers understand how collision-prone road segments change over time, and can serve as a reference for more targeted traffic treatment.

Geospatial Modeling Methods in Epidemiological Kidney Research: An Overview and Practical Example.

Geospatial modeling methods in population-level kidney research have not been used to full potential because few studies have completed associative spatial analyses between risk factors and exposures and kidney conditions and outcomes. Spatial modeling has several advantages over traditional modeling, including improved estimation of statistical variation and more accurate and unbiased estimation of coefficient effect direction or magnitudes by accounting for spatial data structure. Because most population-level kidney research data are geographically referenced, there is a need for better understanding of geospatial modeling for evaluating associations of individual geolocation with processes of care and clinical outcomes. In this review, we describe common spatial models, provide details to execute these analyses, and perform a case-study to display how results differ when integrating geographic structure. In our case-study, we used U.S. nationwide 2019 chronic kidney disease (CKD) data from Centers for Disease Control and Prevention's Kidney Disease Surveillance System and 2006 to 2010 U.S. Environmental Protection Agency environmental quality index (EQI) data and fit a nonspatial count model along with global spatial models (spatially lagged model [SLM]/pseudo-spatial error model [PSEM]) and a local spatial model (geographically weighted quasi-Poisson regression [GWQPR]). We found the SLM, PSEM, and GWQPR improved model fit in comparison to the nonspatial regression, and the PSEM model decreased the positive relationship between EQI and CKD prevalence. The GWQPR also revealed spatial heterogeneity in the EQI-CKD relationship. To summarize, spatial modeling has promise as a clinical and public health translational tool, and our case-study example is an exhibition of how these analyses may be performed to improve the accuracy and utility of findings.

Comparative analysis of Cochrane and non-Cochrane reviews over three decades.

BACKGROUND: Systematic reviews are viewed as the best study design to guide clinical decision-making as they are the least biased publications assuming they are well-conducted and include well-designed studies. Cochrane was initiated in 1993 with an aim of conducting high-quality systematic reviews. We aimed to examine the publication rates of non-Cochrane systematic reviews (henceforth referred to simply as "systematic reviews") and Cochrane reviews produced throughout Cochrane's existence and characterize changes throughout the period. METHODS: This observational study collected data on systematic reviews published between 1993 and 2022 in PubMed. Identified Cochrane reviews were linked to data from the Cochrane Database of Systematic Reviews via their Digital Object Identifier. Systematic reviews and Cochrane reviews were analyzed separately. Two authors screened a random sample of records to validate the overall sample, providing a precision of 98%. RESULTS: We identified 231,602 (94%) systematic reviews and 15,038 (6%) Cochrane reviews. Publication of systematic reviews has continuously increased with a median yearly increase rate of 26%, while publication of Cochrane reviews has decreased since 2015. From 1993 to 2002, Cochrane reviews constituted 35% of all systematic reviews in PubMed compared with 3.5% in 2013-2022. Systematic reviews consistently had fewer authors than Cochrane reviews, but the number of authors increased over time for both. Chinese first authors conducted 15% and 4% of systematic reviews published from 2013-2022 and 2003-2012, respectively. Most Cochrane reviews had first authors from the UK (36%). The native English-speaking countries the USA, the UK, Canada, and Australia produced a large share of systematic reviews (42%) and Cochrane reviews (62%). The largest publishers of systematic reviews in the last 10 years were gold open access journals. CONCLUSIONS: Publication of systematic reviews is increasing rapidly, while fewer Cochrane reviews have been published through the last decade. Native English-speaking countries produced a large proportion of both types of systematic reviews. Gold open access journals and Chinese first authors dominated the publication of systematic reviews for the past 10 years. More research is warranted examining why fewer Cochrane reviews are being published. Additionally, examining these systematic reviews for research waste metrics may provide a clearer picture of their utility.

Building a house without foundations? A 24-country qualitative interview study on artificial intelligence in intensive care medicine.

OBJECTIVES: To explore the views of intensive care professionals in high-income countries (HICs) and lower-to-middle-income countries (LMICs) regarding the use and implementation of artificial intelligence (AI) technologies in intensive care units (ICUs). METHODS: Individual semi-structured qualitative interviews were conducted between December 2021 and August 2022 with 59 intensive care professionals from 24 countries. Transcripts were analysed using conventional content analysis. RESULTS: Participants had generally positive views about the potential use of AI in ICUs but also reported some well-known concerns about the use of AI in clinical practice and important technical and non-technical barriers to the implementation of AI. Important differences existed between ICUs regarding their current readiness to implement AI. However, these differences were not primarily between HICs and LMICs, but between a small number of ICUs in large tertiary hospitals in HICs, which were reported to have the necessary digital infrastructure for AI, and nearly all other ICUs in both HICs and LMICs, which were reported to neither have the technical capability to capture the necessary data or use AI, nor the staff with the right knowledge and skills to use the technology. CONCLUSION: Pouring massive amounts of resources into developing AI without first building the necessary digital infrastructure foundation needed for AI is unethical. Real-world implementation and routine use of AI in the vast majority of ICUs in both HICs and LMICs included in our study is unlikely to occur any time soon. ICUs should not be using AI until certain preconditions are met.

Spatiotemporal Analysis of Malaria Transmission in the Autonomous Indigenous Regions of Panama, Central America, 2015-2022.

Despite ongoing efforts for elimination, malaria continues to be a major public health problem in the Republic of Panama. For effective elimination, it is key that malaria foci and areas of high transmission are identified in a timely manner. Here, we study malaria transmission records for the 2015-2022 period, a time when cases have increased by a factor of ten. Using several methods to study spatial and spatiotemporal malaria confirmed case clusters at the level of localities, including LISA and scan, we found that cases are clustered across indigenous villages located within the autonomous indigenous regions of Ngäbe-Buglé, Guna Yala, and Embera, with the latter on the eastern border of Panama (with Colombia). We discuss the different factors that might be shaping the marked increase in malaria transmission associated with these clusters, which include an inflow of malaria-exposed migrating populations hoping to reach the USA, insufficient health services, and the lack of culturally sensitive actionable tools to reduce malaria exposure among the ethnically diverse and impoverished indigenous populations of Panama.

[Artificial intelligence in intensive care medicine]. / Künstliche Intelligenz in der Intensivmedizin.

The integration of artificial intelligence (AI) into intensive care medicine has made considerable progress in recent studies, particularly in the areas of predictive analytics, early detection of complications, and the development of decision support systems. The main challenges remain availability and quality of data, reduction of bias and the need for explainable results from algorithms and models. Methods to explain these systems are essential to increase trust, understanding, and ethical considerations among healthcare professionals and patients. Proper training of healthcare professionals in AI principles, terminology, ethical considerations, and practical application is crucial for the successful use of AI. Careful assessment of the impact of AI on patient autonomy and data protection is essential for its responsible use in intensive care medicine. A balance between ethical and practical considerations must be maintained to ensure patient-centered care while complying with data protection regulations. Synergistic collaboration between clinicians, AI engineers, and regulators is critical to realizing the full potential of AI in intensive care medicine and maximizing its positive impact on patient care. Future research and development efforts should focus on improving AI models for real-time predictions, increasing the accuracy and utility of AI-based closed-loop systems, and overcoming ethical, technical, and regulatory challenges, especially in generative AI systems.

YouTube as a Source of Information on Public Health Ethics.

BACKGROUND: Public health ethics (PHE) is a dynamic area within bioethics that addresses the complex moral implications of public health measures in the face of growing health threats. YouTube is a powerful and widely used platform for disseminating health-related information. The primary objective of this study is to assess videos related to PHE on YouTube. The aim is to gauge the extent of misinformation in collecting PHE videos on the platform. METHODS: On October 25, 2023, a thorough investigation on YouTube was undertaken, employing pre-determined search phrases involving 'public health,' 'healthcare,' 'health services administration,' and 'health policy and ethics.' The research encompassed a total of 137 videos that were selected according to strict inclusion and exclusion criteria. The videos were evaluated using the Global Quality Scale to measure quality and the modified DISCERN tool to evaluate reliability. The researchers identified video sources and compared several video attributes across different quality groups. RESULTS: A total of 137 videos were analyzed, and 65 (47.45%) were classified as high quality, 52 (37.23%) as moderate quality, and 21 (15.32%) as low quality. In high-quality videos, academic, government, physician, and university-hospital sources predominated, whereas Internet users and news sources were connected with low-quality videos. Significant differences in DISCERN score, per day views, likes, and comments were seen across the quality groups (P = 0.001 for views per day and P = 0.001 for other characteristics). According to the findings, low-quality videos had higher median values for daily views, likes, and comments. CONCLUSION: Although nearly half of the videos were high-quality, low-quality videos attracted greater attention. Critical contributors to high-quality videos included academic, government, physician, and university-hospital sources. The findings highlight the importance of quality control methods on social media platforms and strategies to direct users to trustworthy health information. Authors should prioritize appropriate citations and evaluate YouTube and other comparable platforms for potential promotional low-quality information.

Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review.

BACKGROUND: Breast cancer is the most common disease in women. Recently, explainable artificial intelligence (XAI) approaches have been dedicated to investigate breast cancer. An overwhelming study has been done on XAI for breast cancer. Therefore, this study aims to review an XAI for breast cancer diagnosis from mammography and ultrasound (US) images. We investigated how XAI methods for breast cancer diagnosis have been evaluated, the existing ethical challenges, research gaps, the XAI used and the relation between the accuracy and explainability of algorithms. METHODS: In this work, Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and diagram were used. Peer-reviewed articles and conference proceedings from PubMed, IEEE Explore, ScienceDirect, Scopus and Google Scholar databases were searched. There is no stated date limit to filter the papers. The papers were searched on 19 September 2023, using various combinations of the search terms 'breast cancer', 'explainable', 'interpretable', 'machine learning', 'artificial intelligence' and 'XAI'. Rayyan online platform detected duplicates, inclusion and exclusion of papers. RESULTS: This study identified 14 primary studies employing XAI for breast cancer diagnosis from mammography and US images. Out of the selected 14 studies, only 1 research evaluated humans' confidence in using the XAI system-additionally, 92.86% of identified papers identified dataset and dataset-related issues as research gaps and future direction. The result showed that further research and evaluation are needed to determine the most effective XAI method for breast cancer. CONCLUSION: XAI is not conceded to increase users' and doctors' trust in the system. For the real-world application, effective and systematic evaluation of its trustworthiness in this scenario is lacking. PROSPERO REGISTRATION NUMBER: CRD42023458665.

Ciencia perdida: un debate latinoamericano

La presente contribución analiza las formas en que se construye la ciencia en América Latina y el Caribe. Su objetivo principal es destacar las particularidades de esta región y cómo estas diferencias afectan el desarrollo científico en comparación con otras áreas del mundo, desde la perspectiva de la ciencia perdida e invisible. Se argumenta que la ciencia en América Latina y el Caribe se ha desarrollado principalmente a través de la colaboración y la cooperación entre países, pero sus revistas científicas se encuentran en desventaja a partir de la ciencia globalizada que limita el acceso a temáticas locales. Este enfoque se ha impulsado por la necesidad de superar las limitaciones económicas y tecnológicas presentes en la región. Como resultado, se han creado iniciativas propias que destacan la importancia de las aportaciones y el conocimiento local en la construcción de la ciencia. Tener una perspectiva propia y contextualizada es fundamental para abordar los desafíos y necesidades específicas de la región. Esto se refleja en la diversidad de temas de investigación y enfoques científicos. El artículo también menciona algunos de los desafíos y obstáculos que enfrenta la ciencia en América Latina y el Caribe, a pesar los cuales se destaca la resiliencia y creatividad de los científicos latinoamericanos y caribeños, quienes han logrado hacer contribuciones significativas a nivel mundial.

ABTRASCT This contribution analyzes the ways in which science is constructed in Latin America and the Caribbean. Its main objective is to highlight the particularities of this region and how these differences affect scientific development compared to other areas of the world, from the lost and invisible science's perspective. It is argued that science in Latin America and the Caribbean has developed mainly through collaboration and cooperation between countries, but its scientific journals are at a disadvantage due to globalized science that limits access to local topics. This approach has been driven by the need to overcome the economic and technological limitations present in the region. As a result, own initiatives have been created that highlight the importance of contributions and local knowledge in the construction of science. Having its own and contextualized perspective is essential to address the specific challenges and needs of the region. This is reflected in the diversity of research topics and scientific approaches. The article also mentions some of the challenges and obstacles faced by science in Latin America and the Caribbean, despite which the resilience and creativity of Latin American and Caribbean scientists stands out, who have managed to make significant contributions worldwide.

Sub-Diffraction Correlation of Quantum Emitters and Local Strain Fields in Strain-Engineered WSe2 Monolayers.

Strain-engineering in atomically thin metal dichalcogenides is a useful method for realizing single-photon emitters (SPEs) for quantum technologies. Correlating SPE position with local strain topography is challenging due to localization inaccuracies from the diffraction limit. Currently, SPEs are assumed to be positioned at the highest strained location and are typically identified by randomly screening narrow-linewidth emitters, of which only a few are spectrally pure. In this work, hyperspectral quantum emitter localization microscopy is used to locate 33 SPEs in nanoparticle-strained WSe2 monolayers with sub-diffraction-limit resolution (≈30 nm) and correlate their positions with the underlying strain field via image registration. In this system, spectrally pure emitters are not concentrated at the highest strain location due to spectral contamination; instead, isolable SPEs are distributed away from points of peak strain with an average displacement of 240 nm. These observations point toward a need for a change in the design rules for strain-engineered SPEs and constitute a key step toward realizing next-generation quantum optical architectures.

"When somebody says this is a program for adults, it kind of smacks of pornography": Older Adults' Opinions on Age Labels in the Public Library.

BACKGROUND AND OBJECTIVES: Chronological age is invariably used as a categorizing tool for spaces, collections, and programs in public libraries. With age-based conceptions of human experience in library practice, education, and scholarship primarily focused on those under the age 18, little is known how age-based classifications are implemented in public libraries, and with what impacts, for older library patrons. RESEARCH DESIGN AND METHODS: Stemming from a larger project that seeks to bring attention to the ways in which public libraries engage with community-dwelling older adults, this paper explores 51 older patrons' perspectives on the numbers and language (e.g., 55+, older adult, seniors, adult) assigned to older adults in library programs and which label best (or least) suits their sense of identity and, in turn, what language encourages or deters their engagement with library programs. RESULTS: Findings illustrate that age-based language describing older adult library programs is often at odds with patrons' perceptions of how library programming relevant to them ought to be labeled. Common to all participants was a clear dislike for the term "elderly." While most participants preferred "older adult" to "senior," others voiced no preference, as long as they felt heard and valued. Many participants linked the use of language used to describe library programs to being excluded from and treated differently from other (younger) library patrons. DISCUSSION AND IMPLICATIONS: The language used to group and describe different library populations directly shapes feelings of belonging (or exclusion) in library programs. Insights from this research contribute to our evolving understandings of the ways in which language connected to age can shape one's sense of identity. Results also serve to cultivate a more sensitive and critical approach to the question of age within library science, and, by extension, the experiences of older adults who frequent the library.

Does spatial resolution matter in the estimation of average annual soil loss by using RUSLE?-a study of the Urmodi River Watershed (Maharashtra), India.

The study investigates the influence of multispectral satellite data's spatial resolution on land degradation in the Urmodi River Watershed in which Kaas Plateau, a UNESCO World Heritage site, is located. Specifically, the research focuses on soil erosion and its risk zonation. The study employs Landsat 8 (30-m resolution) and Sentinel-2 (10-m resolution) data to assess soil erosion risk. The Revised Universal Soil Loss Equation (RUSLE) is used to quantify the average annual soil erosion output denoted by (A), by using its factors such as rainfall (R), soil erodibility (K), slope-length (LS), cover management (C), and support practices (P). R-factor was computed from MERRA-2 rainfall data, K-factor was derived from field soil sample-based analysis, LS factor was from Cartosat Digital Elevation Model-based data. The C factor was derived from NDVI of Landsat 8 and Sentinel-2, and the P factor was prepared from LULC derived from Landsat 8, and Sentinel-2 was incorporated in the final integration. The soil erosion hazard map ranged from slight to extremely severe. Remote sensing (RS)-based parameters like Land Use Land Cover (LULC) are derived from the Landsat 8 and Sentine-2 satellite data and used to compute the difference in the final outcome of the integration. The study found similarities in average annual soil loss (A) in plain areas, but differences in final soil erosion risk zone (A) were influenced by LULC map variations due to different cell sizes, P factor, and slope gradient. Notable differences were observed in soil erosion risk categories, particularly in high to very severe zones, with a cumulative difference of 73.85 km2. In addition to this, a scatterplot between the final outputs was computed and found the moderate (R2 = 42.08%) correlation between Landsat 8 and Sentinel-2 imagery-based final average annual soil erosion (A) of RUSLE. The study area encompasses various landforms ranging from the plateau to pediplain, and in such situation, the water-led soil erosion categories vary depending on terrain condition along with its biophysical factors and, hence, need to analyze the need of such factors on the average annual soil erosion quantification. Different spatial resolution has an effect on the final output, and hence, there is a need to track this change at various spatial resolutions. This analysis highlights the significant impact of spatial resolution on land degradation assessment, providing precise identification of surface features and enhancing soil erosion risk zoning accuracy.

Defining the Boundaries of Psychiatric and Medical Knowledge: Applying Cartographic Principles to Self-Organising Maps.

Biases in selection, training, and continuing professional development of medical specialists arise in part from reliance upon expert judgement for the design, implementation, and management of medical education. Reducing bias in curriculum development has primarily relied upon consensus processes modelled on the Delphi technique. The application of machine learning algorithms to databases indexing peer-reviewed medical literature can extract objective evidence about the novelty, relevance, and relative importance of different areas of medical knowledge. This study reports the construction of a map of medical knowledge based on the entire corpus of the MEDLINE database indexing more than 30 million articles published in medical journals since the 19th century. Techniques used in cartography to maximise the visually intelligible differentiation between regions are applied to knowledge clusters identified by a self-organising map to show the structure of published psychiatric evidence and its relationship to non-psychiatric medical domains.

The Case for a Defect Genome Initiative.

The Materials Genome Initiative (MGI) has streamlined the materials discovery effort by leveraging generic traits of materials, with focus largely on perfect solids. Defects such as impurities and perturbations, however, drive many attractive functional properties of materials. The rich tapestry of charge, spin, and bonding states hosted by defects are not accessible to elements and perfect crystals, and defects can thus be viewed as another class of "elements" that lie beyond the periodic table. Accordingly, a Defect Genome Initiative (DGI) to accelerate functional defect discovery for energy, quantum information, and other applications is proposed. First, major advances made under the MGI are highlighted, followed by a delineation of pathways for accelerating the discovery and design of functional defects under the DGI. Near-term goals for the DGI are suggested. The construction of open defect platforms and design of data-driven functional defects, along with approaches for fabrication and characterization of defects, are discussed. The associated challenges and opportunities are considered and recent advances towards controlled introduction of functional defects at the atomic scale are reviewed. It is hoped this perspective will spur a community-wide interest in undertaking a DGI effort in recognition of the importance of defects in enabling unique functionalities in materials.