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The traditional approach to formulating building codes often is slow and labor-intensive, and may struggle to keep pace with the rapid evolution of technology and domain findings. Overcoming such challenges necessitates a methodology that streamlines the modernization of codal provisions. This paper proposes a machine learning (ML) approach to append a variety of codal provisions, including those of empirical, statistical, and theoretical natures. In this approach, a codal provision (i.e., equation) is analyzed to trace its properties (e.g., engineering intuition and causal logic). Then a ML model is tailored to preserve the same properties and satisfy a collection of similarity and performance measures until declared equivalent to the provision at hand. The resulting ML model harnesses the predictive capabilities of ML while arriving at predictions similar to the codal provision used to train the ML model, and hence it becomes possible to use in lieu of the codal expression. This approach was examined successfully for seven structural engineering phenomena contained within various building codes, including those in North America and Australia. The findings suggest that the proposed approach could lay the groundwork for implementing ML in the development of future building codes. [ABSTRACT FROM AUTHOR]

We present a method, including tool support, for bibliometric mining of trends in large and dynamic research areas. The method is applied to the machine learning research area for the years 2013 to 2022. A total number of 398,782 documents from Scopus were analyzed. A taxonomy containing 26 research directions within machine learning was defined by four experts with the help of a Python program and existing taxonomies. The trends in terms of productivity, growth rate, and citations were analyzed for the research directions in the taxonomy. Our results show that the two directions, Applications and Algorithms, are the largest, and that the direction Convolutional Neural Networks is the one that grows the fastest and has the highest average number of citations per document. It also turns out that there is a clear correlation between the growth rate and the average number of citations per document, i.e., documents in fast-growing research directions have more citations. The trends for machine learning research in four geographic regions (North America, Europe, the BRICS countries, and The Rest of the World) were also analyzed. The number of documents during the time period considered is approximately the same for all regions. BRICS has the highest growth rate, and, on average, North America has the highest number of citations per document. Using our tool and method, we expect that one could perform a similar study in some other large and dynamic research area in a relatively short time. [ABSTRACT FROM AUTHOR]

Introduction: Degenerative Cervical Myelopathy (DCM) is a common and disabling condition, with a relatively modest research capacity. In order to accelerate knowledge discovery, the AO Spine RECODE-DCM project has recently established the top priorities for DCM research. Uptake of these priorities within the research community will require their effective dissemination, which can be supported by identifying key opinion leaders (KOLs). In this paper, we aim to identify KOLs using artificial intelligence. We produce and explore a DCM co-authorship network, to characterise researchers' impact within the research field., Methods: Through a bibliometric analysis of 1674 scientific papers in the DCM field, a co-authorship network was created. For each author, statistics about their connections to the co-authorship network (and so the nature of their collaboration) were generated. Using these connectedness statistics, a neural network was used to predict H-Index for each author (as a proxy for research impact). The neural network was retrospectively validated on an unseen author set., Results: DCM research is regionally clustered, with strong collaboration across some international borders (e.g., North America) but not others (e.g., Western Europe). In retrospective validation, the neural network achieves a correlation coefficient of 0.86 (p<0.0001) between the true and predicted H-Index of each author. Thus, author impact can be accurately predicted using only the nature of an author's collaborations., Discussion: Analysis of the neural network shows that the nature of collaboration strongly impacts an author's research visibility, and therefore suitability as a KOL. This also suggests greater collaboration within the DCM field could help to improve both individual research visibility and global synergy., Competing Interests: Have read the journal’s policy and the authors of this manuscript have the following competing interests: BMD is a National Institute for Health Research (NIHR) Clinical Doctoral Research Fellow. This report is independent research arising from a NIHR doctoral research fellowship. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Problem definition: Our objective is to design a defibrillator-enabled drone network that augments the existing emergency medical services (EMS) system to rapidly respond to out-of-hospital cardiac arrest (OHCA).Academic/practical relevance: OHCA claims more than 400,000 lives each year in North America and is one of the most time-sensitive medical emergencies. Drone-delivered automated external defibrillators (AEDs) have the potential to be a transformative innovation in the provision of emergency care for OHCA.Methodology: We develop an integrated location-queuing model that incorporates existing EMS response times and is based on the p-median architecture, where each base constitutes an explicit M / M / d / d queue (i.e., Erlang loss). We then develop a reformulation technique that exploits the existing EMS response times, allowing us to solve real-world instances to optimality using an off-the-shelf solver. We evaluate our solutions using a tactical simulation model that accounts for the effects of congestion and dispatching, and we use a machine-learning model to translate our response-time reductions into survival estimates.Results: Using real data from an area covering 26,000 square kilometers around Toronto, Canada, we find that a modest number of drones are required to significantly reduce response times in all regions. An objective function that minimizes average response time results in drone resources concentrated in cities, with little impact on the tail of the distribution. In contrast, optimizing for the tail of the response-time distribution produces larger and more geographically dispersed drone networks that improve response-time equity across the regions. We estimate that the response-time reductions achieved by the drone network are associated with between a 42% and 76% higher survival rate and up to 144 additional lives saved each year across the geographical region we consider.Managerial implications: Overall, this paper provides a realistic framework that can be leveraged by system designers and/or EMS personnel seeking to investigate design questions associated with a drone network. An objective function focused on improving the tail of the response-time distribution is well-suited for use in practice because the model provides equitable solutions that reduce the entire response-time distribution and corresponds to the real-world metrics, on which EMS systems are most commonly evaluated. [ABSTRACT FROM AUTHOR]

Vegetation gross primary productivity (GPP) is the single largest carbon flux of the terrestrial biosphere which, in turn, is responsible for sequestering 25%–30% of anthropogenic carbon dioxide emissions. The ability to model GPP is therefore critical for calculating carbon budgets as well as understanding climate feedbacks. Earth system models (ESMs) have the capability to simulate GPP but vary greatly in their individual estimates, resulting in large uncertainties. We describe a machine learning (ML) approach to investigate two key factors responsible for differences in simulated GPP quantities from ESMs: the relative importance of different atmospheric drivers and differences in the representation of land surface processes. We describe the different steps in the development of our interpretable ML framework including the choice of algorithms, parameter tuning, training and evaluation. Our results show that ESMs largely agree on the physical climate drivers responsible for GPP as seen in the literature, for instance drought variables in the Mediterranean region or radiation and temperature in the Arctic region. However differences do exist since models don't necessarily agree on which individual variable is most relevant for GPP. We also explore a distance measure to attribute GPP differences to climate influences versus process differences and provide examples for where our methods work (South Asia, Mediterranean) and where they are inconclusive (Eastern North America). Plain Language Summary: Gross primary productivity (GPP) is the rate at which plants remove carbon dioxide from the atmosphere during photosynthesis. Carbon dioxide is a greenhouse gas and excess in the atmosphere causes global warming and climate change. Changes in the amounts of atmospheric carbon dioxide will impact the entire Earth System. We therefore need the ability to accurately calculate GPP, especially for different possible carbon usage pathways in the future. Earth system models or ESMs allow us to simulate various processes happening in the earth's atmosphere and biosphere including photosynthesis and can help us estimate GPP changes for such different pathways. However, ESMs can vary significantly in their simulated GPP estimates making it difficult to have confidence in using these estimates. We describe a ML framework to better understand where ESMs differ in calculating GPP so that we can address knowledge gaps in models. This approach allows us to understand the processes involved without having to run computationally expensive simulations. With improved models, we can also improve our ability to predict climate change outcomes for the future. Key Points: A machine learning framework to advance our understanding of the terrestrial carbon cycle in earth system models or ESMs is proposedDifferences in the relative importance of atmospheric drivers of gross primary productivity highlights differences across modelsA method to attribute differences in productivity estimates from ESMs due to process representation versus atmospheric forcing is demonstrated [ABSTRACT FROM AUTHOR]

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management. [ABSTRACT FROM AUTHOR]

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Introduction: Spinal cord injury (SCI) is a significant and transforming event, with an estimated annual incidence of 40 cases per million individuals in North America. Considering the significance of accurate diagnosis and effective therapy in managing SCI, Machine Learning (ML) and Robot-Assisted Gait Training (RAGT) technologies hold promise for enhancing optimal practices and elevating the quality of care. This study aims to determine the impact of the ML and RAGT techniques employed on the outcome results of SCI. Methods: We reviewed four databases, including PubMed, Scopus, ScienceDirect, and the Cochrane Central Register of Controlled Trials (CENTRAL), until 20 August 2023. The keywords used in this study encompassed the following: a comprehensive search was executed on research exclusively published in the English language: machine learning, robotics, and spinal cord injury. Results: A comprehensive search was conducted across four databases, identifying 2367 articles following rigorous data filtering. The results of the odd ratio (OR) and confidence interval (CI) of 95% for the ASIA Impairment Scale, or AIS grade A, were 0.093 (0.011–0.754, p = 0.026), for AIS grade B, 0.875 (0.395–1.939, p = 0.743), for AIS grade C, 3.626 (1.556–8.449, p = 0.003), and for AIS grade D, 8.496 (1.394–51.768, p = 0.020). The robotic group exhibited a notable reduction in AS (95% CI = −0.239 to −0.045, p = 0.004) and MAS (95% CI = −3.657 to −1.066, p ≤ 0.001) measures. This study also investigated spasticity and walking ability, which are significant. Conclusions: The ML approach exhibited enhanced precision in forecasting AIS result scores. Implementing RAGT has been shown to impact spasticity reduction and improve walking ability. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the associations between periodontitis and metabolic syndrome (MetS) components and related conditions while controlling for sociodemographics, health behaviors, and caries levels among young and middle-aged adults. We analyzed data from the Dental, Oral, and Medical Epidemiological (DOME) record-based cross-sectional study that combines comprehensive sociodemographic, medical, and dental databases of a nationally representative sample of military personnel. The research consisted of 57,496 records of patients, and the prevalence of periodontitis was 9.79% (5630/57,496). The following parameters retained a significant positive association with subsequent periodontitis multivariate analysis (from the highest to the lowest OR (odds ratio)): brushing teeth (OR = 2.985 (2.739–3.257)), obstructive sleep apnea (OSA) (OR = 2.188 (1.545–3.105)), cariogenic diet consumption (OR = 1.652 (1.536–1.776)), non-alcoholic fatty liver disease (NAFLD) (OR = 1.483 (1.171–1.879)), smoking (OR = 1.176 (1.047–1.322)), and age (OR = 1.040 (1.035–1.046)). The following parameters retained a significant negative association (protective effect) with periodontitis in the multivariate analysis (from the highest to the lowest OR): the mean number of decayed teeth (OR = 0.980 (0.970–0.991)); North America as the birth country compared to native Israelis (OR = 0.775 (0.608–0.988)); urban non-Jewish (OR = 0.442 (0.280–0.698)); and urban Jewish (OR = 0.395 (0.251–0.620)) compared to the rural locality of residence. Feature importance analysis using the eXtreme Gradient Boosting (XGBoost) machine learning algorithm with periodontitis as the target variable ranked obesity, OSA, and NAFLD as the most important systemic conditions in the model. We identified a profile of the "patient vulnerable to periodontitis" characterized by older age, rural residency, smoking, brushing teeth, cariogenic diet, comorbidities of obesity, OSA and NAFLD, and fewer untreated decayed teeth. North American-born individuals had a lower prevalence of periodontitis than native Israelis. This study emphasizes the holistic view of the MetS cluster and explores less-investigated MetS-related conditions in the context of periodontitis. A comprehensive assessment of disease risk factors is crucial to target high-risk populations for periodontitis and MetS. [ABSTRACT FROM AUTHOR]

Land surface phenology (LSP) products are currently of large uncertainties due to cloud contaminations and other impacts in temporal satellite observations and they have been poorly validated because of the lack of spatially comparable ground measurements. This study provided a reference dataset of gap-free time series and phenological dates by fusing the Harmonized Landsat 8 and Sentinel-2 (HLS) observations with near-surface PhenoCam time series for 78 regions of 10 × 10 km2 across ecosystems in North America during 2019 and 2020. The HLS-PhenoCam LSP (HP-LSP) reference dataset at 30 m pixels is composed of: (1) 3-day synthetic gap-free EVI2 (two-band Enhanced Vegetation Index) time series that are physically meaningful to monitor the vegetation development across heterogeneous levels, train models (e.g., machine learning) for land surface mapping, and extract phenometrics from various methods; and (2) four key phenological dates (accuracy ≤5 days) that are spatially continuous and scalable, which are applicable to validate various satellite-based phenology products (e.g., global MODIS/VIIRS LSP), develop phenological models, and analyze climate impacts on terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

The Great Lakes are critical freshwater sources, supporting millions of people, agriculture, and ecosystems. However, climate change has worsened droughts, leading to significant economic and social consequences. Accurate multi-month drought forecasting is, therefore, essential for effective water management and mitigating these impacts. This study introduces the Multivariate Standardized Lake Water Level Index (MSWI), a modified drought index that utilizes water level data collected from 1920 to 2020. Four hybrid models are developed: Support Vector Regression with Beluga whale optimization (SVR-BWO), Random Forest with Beluga whale optimization (RF-BWO), Extreme Learning Machine with Beluga whale optimization (ELM-BWO), and Regularized ELM with Beluga whale optimization (RELM-BWO). The models forecast droughts up to six months ahead for Lake Superior and Lake Michigan-Huron. The best-performing model is then selected to forecast droughts for the remaining three lakes, which have not experienced severe droughts in the past 50 years. The results show that incorporating the BWO improves the accuracy of all classical models, particularly in forecasting drought turning and critical points. Among the hybrid models, the RELM-BWO model achieves the highest level of accuracy, surpassing both classical and hybrid models by a significant margin (7.21 to 76.74%). Furthermore, Monte-Carlo simulation is employed to analyze uncertainties and ensure the reliability of the forecasts. Accordingly, the RELM-BWO model reliably forecasts droughts for all lakes, with a lead time ranging from 2 to 6 months. The study's findings offer valuable insights for policymakers, water managers, and other stakeholders to better prepare drought mitigation strategies. [ABSTRACT FROM AUTHOR]

Artificial intelligence and machine learning (AI/ML) are playing increasingly important roles, permeating the field of medical devices (MDs). This rapid progress has not yet been matched by the Health Technology Assessment (HTA) process, which still needs to define a common methodology for assessing AI/ML-based MDs. To collect existing evidence from the literature about the methods used to assess AI-based MDs, with a specific focus on those used for the management of heart failure (HF), the International Federation of Medical and Biological Engineering (IFMBE) conducted a scoping meta-review. This manuscript presents the results of this search, which covered the period from January 1974 to October 2022. After careful independent screening, 21 reviews, mainly conducted in North America and Europe, were retained and included. Among the findings were that deep learning is the most commonly utilised method and that electronic health records and registries are among the most prevalent sources of data for AI/ML algorithms. Out of the 21 included reviews, 19 focused on risk prediction and/or the early diagnosis of HF. Furthermore, 10 reviews provided evidence of the impact on the incidence/progression of HF, and 13 on the length of stay. From an HTA perspective, the main areas requiring improvement are the quality assessment of studies on AI/ML (included in 11 out of 21 reviews) and their data sources, as well as the definition of the criteria used to assess the selection of the most appropriate AI/ML algorithm. [ABSTRACT FROM AUTHOR]

Nitrogen oxides (NOx=NO+NO2) emissions are estimated in three regions in the Northern Hemisphere, generally located in North America, Europe, and East Asia, by calculating the directional derivatives of NO2 column amounts observed by the TROPOspheric Monitoring Instrument (TROPOMI) with respect to the horizontal wind vectors. We present monthly averaged emissions from 1 May 2018 to 31 January 2023 to capture variations before and after the COVID-19 pandemic. We focus on a diverse collection of 54 cities, 18 in each region. A spatial resolution of 0.04 ∘ resolves intracity emission variations and reveals NOx emission hotspots at city cores, industrial areas, and sea ports. For each selected city, post-COVID-19 changes in NOx emissions are estimated by comparing monthly and annually averaged values to the pre-COVID-19 year of 2019. While emission reductions are initially found during the first outbreak of COVID-19 in early 2020 in most cities, the cities' paths diverge afterwards. We group the selected cities into four clusters according to their normalized annual NOx emissions in 2019–2022 using an unsupervised learning algorithm. All but one of the selected North American cities fall into cluster 1 characterized by weak emission reduction in 2020 (- 7 % relative to 2019) and an increase in 2022 by + 5 %. Cluster 2 contains mostly European cities and is characterized by the largest reduction in 2020 (- 31 %), whereas the selected East Asian cities generally fall into clusters 3 and 4, with the largest impacts in 2022 (- 25 % and - 37 %). This directional derivative approach has been implemented in object-oriented, open-source Python and is available publicly for high-resolution and low-latency emission estimation for different regions, atmospheric species, and satellite instruments. [ABSTRACT FROM AUTHOR]

Myriophyllum spicatum, more commonly known as Eurasian watermilfoil (EWM), is one of the most invasive aquatic plants in North America, causing negative ecological and economic impacts in ecosystems where it proliferates. Many control strategies have been developed and implemented to mitigate EWM growth and spread, although the results are mixed and there is no consensus on lake‐specific strategies. Here, we describe the development of a predictive model using a support vector technique, that predicts the success of biological pest control using Euhrychiopsis lecontei (the milfoil weevil), a milfoil specialist, to reduce EWM in lakes. Such a model is informed by lake characteristics (limnological and landscape) and augmentation strategies. To develop our predictive model, we performed a metadata analysis from 133 published peer‐reviewed literature and professional reports of milfoil weevil augmentation field experiments that contained information on lake characteristics. The predictive model's algorithm uses a support vector machine (SMV) to learn patterns among lake characteristics, along with the recorded augmentation strategy and the reported success of each study, where success is a measure of EWM change over a season and is recorded in a variety of ways (e.g., EWM biomass change, EWM percent change, EWM visual change, etc.,). Overall, the model results suggests that shallower lakes, more frequent weevil augmentations, and larger weevil overwintering habitat are the most important predictors for EWM reduction success by weevil augmentation. Although watermilfoil weevil augmentation is a promising mitigation strategy, it may not work for all lakes. However, in terms of suggesting weevil augmentation, our model is a valuable tool for lake stakeholders and resource managers, who can use it to determine whether milfoil weevil augmentation, which can be very costly due to the difficulties in finding and raising milfoil weevils, will be a useful and sustainable approach to control EWM in their lake community. [ABSTRACT FROM AUTHOR]

We present the first application of a fast super resolution convolutional neural network (FSRCNN) based approach for downscaling earth system model (ESM) simulations. Unlike other SR approaches, FSRCNN uses the same input feature dimensions as the low resolution input. This allows it to have smaller convolution layers, avoiding over‐smoothing, and reducing computational costs. We adapt the FSRCNN to improve reconstruction on ESM data, we term the FSRCNN‐ESM. We use high‐resolution (∼0.25°) monthly averaged model output of five surface variables over North America from the US Department of Energy's Energy Exascale Earth System Model's control simulation. These high‐resolution and corresponding coarsened low‐resolution (∼1°) pairs of images are used to train the FSRCNN‐ESM and evaluate its use as a downscaling approach. We find that FSRCNN‐ESM outperforms FSRCNN and other super‐resolution methods in reconstructing high resolution images producing finer spatial scale features with better accuracy for surface temperature, surface radiative fluxes, and precipitation. Plain Language Summary: High resolution global climate data is computationally expensive to run but crucial for assessing climate change effects at local and regional scales. Here, we adapt a new deep learning technique, called fast super‐resolution convolutional neural network, to remap climate data from low resolution to high resolution grids. This approach is faster and more accurate for statistical downscaling climate data compared to other prevalent methods. Key Points: We present a fast super resolution convolutional neural network (FSRCNN) based approach for downscaling gridded earth system model (ESM) dataFSRCNN‐ESM's reconstruction of high resolution spatial patterns improves upon both traditional and machine learning downscaling methodsThe FSRCNN is computationally less expensive to train than other machine learning downscaling methods [ABSTRACT FROM AUTHOR]

Even though culture has been found to play some role in negative emotion expression, affective computing research primarily takes on a basic emotion approach when analyzing social signals for automatic emotion recognition technologies. Furthermore, automatic negative emotion recognition systems still train data that originates primarily from North America and contains a majority of Caucasian training samples. As such, the current study aims to address this problem by analyzing what the differences are of the underlying social signals by leveraging machine learning models to classify 3 negative emotions, contempt, anger and disgust (CAD) amongst 3 different cultures: North American, Persian, and Filipino. Using a curated data set compiled from YouTube videos, a support vector machine (SVM) was used to predict negative emotions amongst differing cultures. In addition a one-way ANOVA was used to analyse the differences that exist between each culture group in-terms of level of activation of underlying social signal. Our results not only highlighted the significant differences in the associated social signals that were activated for each culture, but also indicated the specific underlying social signals that differ in our cross-cultural data sets. Furthermore, the automatic classification methods showed North American expressions of CAD to be well-recognized, while Filipino and Persian expressions were recognized at near chance levels. [ABSTRACT FROM AUTHOR]

Stations are an important source of meteorological data, but often suffer from missing values and short observation periods. Gap filling is widely used to generate serially complete datasets (SCDs), which are subsequently used to produce gridded meteorological estimates. However, the value of SCDs in spatial interpolation is scarcely studied. Based on our recent efforts to develop a SCD over North America (SCDNA), we explore the extent to which gap filling improves gridded precipitation and temperature estimates. We address two specific questions: 1) Can SCDNA improve the statistical accuracy of gridded estimates in North America? 2) Can SCDNA improve estimates of trends on gridded data? In addressing these questions, we also evaluate the extent to which results depend on the spatial density of the station network and the spatial interpolation methods used. Results show that the improvement in statistical interpolation due to gap filling is more obvious for precipitation, followed by minimum temperature and maximum temperature. The improvement is larger when the station network is sparse and when simpler interpolation methods are used. SCDs can also notably reduce the uncertainties in spatial interpolation. Our evaluation across North America from 1979 to 2018 demonstrates that SCDs improve the accuracy of interpolated estimates for most stations and days. SCDNA-based interpolation also obtains better trend estimation than observation-based interpolation. This occurs because stations used for interpolation could change during a specific period, causing changepoints in interpolated temperature estimates and affect the long-term trends of observation-based interpolation, which can be avoided using SCDNA. Overall, SCDs improve the performance of gridded precipitation and temperature estimates. [ABSTRACT FROM AUTHOR]

Species are being lost at an unprecedented rate during the Anthropocene. Progress has been made in clarifying how species traits influence their propensity to go extinct, but the role historical demography plays in species loss or persistence is unclear. In eastern North America, five charismatic landbirds went extinct last century, and the causes of their extinctions have been heavily debated. Although these extinctions are most often attributed to post-colonial human activity, other factors such as declining ancestral populations prior to European colonization could have made these species particularly susceptible. We used population genomic data from these extinct birds and compared them with those from four codistributed extant species. We found extinct species harboured lower genetic diversity and effective population sizes than extant species, but both extinct and non-extinct birds had similar demographic histories of population expansion. These demographic patterns are consistent with population size changes associated with glacial–interglacial cycles. The lack of support for overall population declines during the Pleistocene corroborates the view that, although species that went extinct may have been vulnerable due to low diversity or small population size, their disappearance was driven by human activities in the Anthropocene. [ABSTRACT FROM AUTHOR]

Background: With the growing momentum for the adoption of machine learning (ML) in medical field, it is likely that reliance on ML for imaging will become routine over the next few years. We have developed a software named BAAD, which uses ML algorithms for the diagnosis of Alzheimer's disease (AD) and prediction of mild cognitive impairment (MCI) progression. Methods: We constructed an algorithm by combining a support vector machine (SVM) to classify and a voxel-based morphometry (VBM) to reduce concerned variables. We grouped progressive MCI and AD as an AD spectrum and trained SVM according to this classification. We randomly selected half from the total 1,314 subjects of AD neuroimaging Initiative (ADNI) from North America for SVM training, and the remaining half were used for validation to fine-tune the model hyperparameters. We created two types of SVMs, one based solely on the brain structure (SVMst), and the other based on both the brain structure and Mini-Mental State Examination score (SVMcog). We compared the model performance with two expert neuroradiologists, and further evaluated it in test datasets involving 519, 592, 69, and 128 subjects from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Aging (AIBL), Japanese ADNI, the Minimal Interval Resonance Imaging in AD (MIDIAD) and the Open Access Series of Imaging Studies (OASIS), respectively. Results: BAAD's SVMs outperformed radiologists for AD diagnosis in a structural magnetic resonance imaging review. The accuracy of the two radiologists was 57.5 and 70.0%, respectively, whereas, that of the SVMst was 90.5%. The diagnostic accuracy of the SVMst and SVMcog in the test datasets ranged from 88.0 to 97.1% and 92.5 to 100%, respectively. The prediction accuracy for MCI progression was 83.0% in SVMst and 85.0% in SVMcog. In the AD spectrum classified by SVMst, 87.1% of the subjects were Aβ positive according to an AV-45 positron emission tomography. Similarly, among MCI patients classified for the AD spectrum, 89.5% of the subjects progressed to AD. Conclusion: Our ML has shown high performance in AD diagnosis and prediction of MCI progression. It outperformed expert radiologists, and is expected to provide support in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Our objective was to determine the impacts of artificial intelligence (AI) on public health practice.Methods: We used a fundamental qualitative descriptive study design, enrolling 15 experts in public health and AI from June 2018 until July 2019 who worked in North America and Asia. We conducted in-depth semi-structured interviews, iteratively coded the resulting transcripts, and analyzed the results thematically.Results: We developed 137 codes, from which nine themes emerged. The themes included opportunities such as leveraging big data and improving interventions; barriers to adoption such as confusion regarding AI's applicability, limited capacity, and poor data quality; and risks such as propagation of bias, exacerbation of inequity, hype, and poor regulation.Conclusions: Experts are cautiously optimistic about AI's impacts on public health practice, particularly for improving disease surveillance. However, they perceived substantial barriers, such as a lack of available expertise, and risks, including inadequate regulation. Therefore, investment and research into AI for public health practice would likely be beneficial. However, increased access to high-quality data, research and education regarding the limitations of AI, and development of rigorous regulation are necessary to realize these benefits. [ABSTRACT FROM AUTHOR]

Station-based serially complete datasets (SCDs) of precipitation and temperature observations are important for hydrometeorological studies. Motivated by the lack of serially complete station observations for North America, this study seeks to develop an SCD from 1979 to 2018 from station data. The new SCD for North America (SCDNA) includes daily precipitation, minimum temperature (Tmin⁡), and maximum temperature (Tmax⁡) data for 27 276 stations. Raw meteorological station data were obtained from the Global Historical Climate Network Daily (GHCN-D), the Global Surface Summary of the Day (GSOD), Environment and Climate Change Canada (ECCC), and a compiled station database in Mexico. Stations with at least 8-year-long records were selected, which underwent location correction and were subjected to strict quality control. Outputs from three reanalysis products (ERA5, JRA-55, and MERRA-2) provided auxiliary information to estimate station records. Infilling during the observation period and reconstruction beyond the observation period were accomplished by combining estimates from 16 strategies (variants of quantile mapping, spatial interpolation, and machine learning). A sensitivity experiment was conducted by assuming that 30 % of observations from stations were missing – this enabled independent validation and provided a reference for reconstruction. Quantile mapping and mean value corrections were applied to the final estimates. The median Kling–Gupta efficiency (KGE ′) values of the final SCDNA for all stations are 0.90, 0.98, and 0.99 for precipitation, Tmin⁡ , and Tmax⁡ , respectively. The SCDNA is closer to station observations than the four benchmark gridded products and can be used in applications that require either quality-controlled meteorological station observations or reconstructed long-term estimates for analysis and modeling. The dataset is available at 10.5281/zenodo.3735533 (Tang et al., 2020). [ABSTRACT FROM AUTHOR]

Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have often been used within the geosciences to most accurately identify a desired output given a set of inputs, with the interpretation of what the network learns used as a secondary metric to ensure the network is making the right decision for the right reason. Neural network interpretation techniques have become more advanced in recent years, however, and we therefore propose that the ultimate objective of using a neural network can also be the interpretation of what the network has learned rather than the output itself. We show that the interpretation of neural networks can enable the discovery of scientifically meaningful connections within geoscientific data. In particular, we use two methods for neural network interpretation called backward optimization and layerwise relevance propagation, both of which project the decision pathways of a network back onto the original input dimensions. To the best of our knowledge, LRP has not yet been applied to geoscientific research, and we believe it has great potential in this area. We show how these interpretation techniques can be used to reliably infer scientifically meaningful information from neural networks by applying them to common climate patterns. These results suggest that combining interpretable neural networks with novel scientific hypotheses will open the door to many new avenues in neural network‐related geoscience research. Plain Language Summary: Neural networks, a form of machine learning, have become popular in geoscience over the recent past. A common limitation of neural networks in geoscience has been the belief that they are "black boxes," and their decision‐making process is uninterpretable. This has sometimes made geoscientists hesitant to use neural networks, since an understanding of how and why our models make decisions is important to our science. Methods for interpreting neural networks have become more advanced, however, and so we highlight two such methods that we think have particular promise in geoscientific applications. The methods are called backward optimization and layerwise relevance propagation, both of which help identify which inputs into the neural network were most helpful in the neural network's decision‐making process. Layerwise relevance propagation has not yet been introduced to the geoscientific community, and we think it offers particularly useful interpretation traits, so we introduce it here. We apply the methods to two commonly studied climate patterns, the El Niño Southern Oscillation, and its impacts on seasonal climate patterns over North America, to showcase their utility. Our results suggest that these two interpretation methods open many new avenues for the usage of neural networks within geoscience. Key Points: Interpretable neural networks can identify the coherent spatial patterns of known modes of Earth system variabilityThe layerwise relevance propagation and backward optimization methods enable new ways to use neural networks for geoscientific researchWe propose that the interpretation of what a neural network has learned can be used as the ultimate scientific outcome of a trained network [ABSTRACT FROM AUTHOR]

Lung cancer is the most frequently diagnosed cancer overall and the deadliest cancer in North America. Early diagnosis through current bronchoscopy techniques is limited by poor diagnostic yield and low specificity, especially for lesions located in peripheral pulmonary locations. Even with the emergence of robotic-assisted platforms, bronchoscopy diagnostic yields remain below 80%. The aim of this study was to determine whether in situ single-point fingerprint (800 to 1700 cm − 1) Raman spectroscopy coupled with machine learning could detect lung cancer within an otherwise heterogenous background composed of normal tissue and tissue associated with benign conditions, including emphysema and bronchiolitis. A Raman spectroscopy probe was used to measure the spectral fingerprint of normal, benign, and cancer lung tissue in 10 patients. Each interrogated specimen was characterized by histology to determine cancer type, i.e., small cell carcinoma or non-small cell carcinoma (adenocarcinoma and squamous cell carcinoma). Biomolecular information was extracted from the fingerprint spectra to identify biomolecular features that can be used for cancer detection. Supervised machine learning models were trained using leave-one-patient-out cross-validation, showing lung cancer could be detected with a sensitivity of 94% and a specificity of 80%. This proof of concept demonstrates fingerprint Raman spectroscopy is a promising tool for the detection of lung cancer during diagnostic procedures and can capture biomolecular changes associated with the presence of cancer among a complex heterogeneous background within less than 1 s. [ABSTRACT FROM AUTHOR]

Current generation general circulation models (GCMs) simulate synoptic-scale climate state variables such as geopotential heights, specific humidity, and integrated vapor transport (IVT) more reliably than mesoscale precipitation. Statistical downscaling methods that condition precipitation on GCM-based, synoptic-scale climate features have shown promise in the reproduction of local precipitation. However, current approaches to climate-state-informed downscaling impose some limitations on the skill of precipitation reproduction, including hard clustering of climate modes into a discrete set of states, utilization of numerical clustering methodologies poorly suited to nonnormal data, and a tendency to focus on relationships to a limited set of large-scale climate modes. This study presents a methodology based on emerging machine learning techniques to develop global approximators of regional precipitation and discharge extremes given a suite of synoptic-scale climate state variables. Archetypal analysis is first used to define regional modes of winter and summer extreme precipitation and discharge across the eastern contiguous United States. A 2D convolution neural network (NN) is then used to predict the co-occurrence of the archetypes using 300- and 700-hPa geopotential heights, 300- and 700-hPa specific humidity, and IVT. Results suggest that 300-hPa geopotential height, 700-hPa specific humidity, and IVT yield the most reliable predictions, although with some important differences by season and region. Finally, we demonstrate that the trained activations of NN convolutional layers can be used to infer the causal pathways between synoptic-scale climate features and regional extremes. [ABSTRACT FROM AUTHOR]

The Laurentian Great Lakes comprise the largest assemblage of inland waterbodies in North America, with vast geographic, environmentally complex nearshore benthic substrate and associated habitat. The Great Lakes Water Quality Agreement, originally signed in 1972, aims to help restore and protect the basin, and ecosystem monitoring is a primary objective to support adaptive management, environmental policy, and decision making. Yet, monitoring ecosystem trends remains challenging, potentially hindering progress in lake management and restoration. Consistent, high-resolution maps of nearshore substrate and associated habitat are fundamental to support management needs, and the nexus of high-quality remotely sensed data with improvements to analytical methods are increasing opportunities for large-scale nearshore benthic mapping at project-relevant spatial resolutions. This study attempts to advance the integration of high-fidelity data (airborne imagery and lidar, satellite imagery, in situ observations, etc.) and machine learning to identify and classify nearshore benthic substrate and associated habitat using a case study in southwest Lake Michigan along Illinois Beach State Park, Illinois, USA. Data inputs and analytical methods were evaluated to better understand their implications with respect to the Coastal and Marine Ecological Classification Standard (CMECS) classification hierarchy, resulting in an approach that could be easily applied to other shallow coastal environments. Classification of substrate and biotic components were iteratively classified in two Tiers in which classes with increasing specificity were identified using different combinations of airborne and satellite data inputs. Classification accuracy assessments revealed that for the Tier 1 substrate component (3 classes), average overall accuracy was 90.10 ± 0.60% for 24 airborne data combinations and 89.77 ± 1.02% for 12 satellite data combinations, whereas the Tier 1 biotic component (2 classes) average overall accuracy was 93.58 ± 0.91% for 24 airborne data combinations and 92.67 ± 0.71% for 11 satellite data combinations. The Tier 2 result for the substrate component (2 classes) was 93.28% for 2 airborne data combinations and 95.25% for the biotic component (2 classes). The study builds on foundational efforts to move towards a more integrated data approach, whereby data strengths and limitations for mapping nearshore benthic substrate and associated habitat, expressed through classification accuracy, were evaluated within the context of the CMECS classification hierarchy, and has direct applicability to critical monitoring needs in the Great Lakes. [ABSTRACT FROM AUTHOR]

Approximately 50 million Americans have allergic diseases. Airborne plant pollen is a significant trigger for several of these allergic diseases. Ambrosia (ragweed) is known for its abundant production of pollen and its potent allergic effect in North America. Hence, estimating and predicting the daily atmospheric concentration of pollen (ragweed pollen in particular) is useful for both people with allergies and for the health professionals who care for them. In this study, we show that a suite of variables including meteorological and land surface parameters, as well as next-generation radar (NEXRAD) measurements together with machine learning can be used to estimate successfully the daily pollen concentration. The supervised machine learning approaches we used included random forests, neural networks, and support vector machines. The performance of the training is independently validated using 10% of the data partitioned using the holdout cross-validation method from the original dataset. The random forests (R= 0.61, R2= 0.37), support vector machines (R= 0.51, R2= 0.26), and neural networks (R= 0.46, R2= 0.21) effectively predicted the daily Ambrosia pollen, where the correlation coefficient (R) and R-squared (R2) values are given in brackets. Three independent approaches—the random forests, correlation coefficients, and interaction information—were employed to rank the relative importance of the available predictors. [ABSTRACT FROM AUTHOR]