IMESc – an interactive machine learning app for environmental sciences

The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics (NAMD) in large realistic systems has received high research interest in recent years. However, such NAMD simulations normally generate an enormous amount of time-dependent high-dimensional data, leading to a significant challenge in result analyses. Based on unsupervised machine learning (ML) methods, considerable efforts were devoted to developing novel and easy-to-use analysis tools for the identification of photoinduced reaction channels and the comprehensive understanding of complicated molecular motions in NAMD simulations. Here, we tried to survey recent advances in this field, particularly to focus on how to use unsupervised ML methods to analyze the trajectory-based NAMD simulation results. Our purpose is to offer a comprehensive discussion on several essential components of this analysis protocol, including the selection of ML methods, the construction of molecular descriptors, the establishment of analytical frameworks, their advantages and limitations, and persistent challenges.

Background Recently, the need for artificial intelligence (AI) and machine learning (ML) methods in drug development and research is gaining high concern and more grounds. Moreover, providing pharmaceutical and related schools with non-commercial, free-to-use programming languages, software and tools is becoming an unavoidable need. The R programming language can be easily used, through the correct and simplified codes and packages, in conducting unsupervised ML methods, such as principal component analysis (PCA) and hierarchical clustering analysis (HCA), after calculating relevant descriptors of drugs and molecules. Objective The objective of this study was to assess the enhancement of non-computer sciences-based students’ perception of the use of machine learning methods such as PCA and HCA using R-programming in drug formulation. Results Undergraduate students were taught to use R program to derive PCA distinguishable plots such as score, loading and scree, in addition to HCA dendrograms, in the context of developing new pharmaceutical formulations. Surveys conducted pre- and post-teaching the course proved that implementation of such ML methods can help in better understanding and exploring the data, in order to derive meaningful conclusions, and make informed decisions that help develop pharmaceutical formulations of premium quality, with minimal resources consumption. Conclusion We hereby report the easy use of R-programming in applications and activities that introduce undergraduate Pharmaceutical Engineering and Biotechnology students to ML methods. Student surveys showed better student satisfaction and understanding of AI applications in solving pharmaceutical problems. We claim that these students and early_career researchers, who are non-specialists in computer science, can utilize R-programming to perform important pharmaceutical applications through the step-by-step guide and codes provided in this article. Graphical Abstract

Domestic violence (DV) is a public health crisis that threatens both the mental and physical health of people. With the unprecedented surge in data available on the internet and electronic health record systems, leveraging machine learning (ML) to detect obscure changes and predict the likelihood of DV from digital text data is a promising area health science research. However, there is a paucity of research discussing and reviewing ML applications in DV research. Methods: We extracted 3588 articles from four databases. Twenty-two articles met the inclusion criteria. Results: Twelve articles used the supervised ML method, seven articles used the unsupervised ML method, and three articles applied both. Most studies were published in Australia (n = 6) and the United States (n = 4). Data sources included social media, professional notes, national databases, surveys, and newspapers. Random forest (n = 9), support vector machine (n = 8), and naïve Bayes (n = 7) were the top three algorithms, while the most used automatic algorithm for unsupervised ML in DV research was latent Dirichlet allocation (LDA) for topic modeling (n = 2). Eight types of outcomes were identified, while three purposes of ML and challenges were delineated and are discussed. Conclusions: Leveraging the ML method to tackle DV holds unprecedented potential, especially in classification, prediction, and exploration tasks, and particularly when using social media data. However, adoption challenges, data source issues, and lengthy data preparation times are the main bottlenecks in this context. To overcome those challenges, early ML algorithms have been developed and evaluated on DV clinical data.

BackgroundBreakdown of self-tolerance is an important common mechanism in autoimmunity. Despite considerable clinical heterogeneity, many autoimmune diseases exhibit common immunological mechanisms leading to a breakdown of self-tolerance. Identification of unique...

Oklahoma drew a significant amount of interest in the past years due to an increase in induced seismicity, which has been associated with the injection of wastewater into the sedimentary strata overlying the basement. Most of these seismogenic faults were unmapped and absent from the Oklahoma Geological Survey fault database until their reactivation revealed their existence. More interestingly, the seismicity reported was at basement depth, indicating that the faults reactivated were basement faults. Evidently, correctly knowing fault presence, orientation, and geometry, as well as the local stress field, is crucial for evaluating the reactivation potential of a fault and, therefore, the seismicity hazard associated. In this study, different geometric seismic attributes were analyzed and integrated using unsupervised machine-learning (ML) methods to identify potential basement-rooted faults and strike-slip-related structures in a seismic data set in northcentral Oklahoma. The ML results not only confirmed the existence of northeast–southwest faults that extended from the basement upward into the sedimentary section and that correlated with earthquake data but also the potential existence of other structurally controlled northeast–southwest features of antiform shape. Through a fault slip potential analysis, we also found that some of the basement-rooted faults interpreted have strikes that are favorable for reactivation if the conditions of pore pressure change were met.

In this paper, we provide an overview of state-of-the-art techniques that are being developed for efficient calculation of second and higher nuclear derivatives of quantum mechanical (QM) energy. Calculations of nuclear Hessians and anharmonic terms incur high costs and memory and scale poorly with system size. Three emerging classes of methods─machine learning (ML), automatic differentiation (AD), and matrix completion (MC)─have demonstrated promise in overcoming these challenges. We illustrate studies that employ unsupervised ML methods to reduce the need for multiple Hessian calculations in dynamics simulations and those that utilize supervised ML to construct approximate potential energy surfaces and estimate Hessians and anharmonic terms at reduced cost. By extension, if electronic structure operations could be written in a manner similar to functions underlying ML methods, rapid differentiation or AD routines can be employed to inexpensively calculate higher arbitrary-order derivatives. While ML approaches are typically black-box, we describe methods such as compressed sensing (CS) and MC, which explicitly leverage problem-specific mathematical properties of higher derivatives such as sparsity and low-rank, to complete higher derivative information using only a small, incomplete sample. The three classes of methods facilitate reliable predictions of observables ranging from infrared spectra to thermal conductivity and constitute a promising way forward in accurately capturing otherwise intractable higher-order responses of QM energy to nuclear perturbations.

Assessing students’ participation in science practices presents several challenges, especially when aiming to differentiate meaningful (vs. rote) forms of participation. In this study, we sought to use machine learning (ML) for a novel purpose in science assessment: developing a construct map for students’ consideration of generality, a key epistemic understanding that undergirds meaningful participation in knowledge-building practices. We report on our efforts to assess the nature of 845 students’ ideas about the generality of their model-based explanations through the combination of an embedded written assessment and a novel data analytic approach that combines unsupervised and supervised machine learning methods and human-driven, interpretive coding. We demonstrate how unsupervised machine learning methods, when coupled with qualitative, interpretive coding, were used to revise our construct map for generality in a way that allowed for a more nuanced evaluation that was closely tied to empirical patterns in the data. We also explored the application of the construct map as a framework for coding used as a part of supervised machine learning methods, finding that it demonstrates some viability for use in future analyses. We discuss implications for the assessment of students’ meaningful participation in science practices in terms of their considerations of generality, the role of unsupervised methods in science assessment, and combining machine learning and human-driven approach for understanding students’ complex involvement in science practices.

A systems-oriented review of machine learning (ML) over the entire flood management spectrum, encompassing fluvial flood control, pluvial flood management, and resiliency-risk characterization was undertaken. Deep learners like long short-term memory (LSTM) networks perform well in predicting reservoir inflows and outflows. Convolution neural networks (CNNs) and other object identification algorithms are being explored in assessing levee and flood wall failures. The use of ML methods in pump station operations is limited due to lack of public-domain datasets. Reinforcement learning (RL) has shown promise in controlling low-impact development (LID) systems for pluvial flood management. Resiliency is defined in terms of the vulnerability of a community to floods. Multi-criteria decision making (MCDM) and unsupervised ML methods are used to capture vulnerability. Supervised learning is used to model flooding hazards. Conventional approaches perform better than deep learners and ensemble methods for modeling flood hazards due to paucity of data and large inter-model predictive variability. Advances in satellite-based, drone-facilitated data collection and Internet of Things (IoT)-based low-cost sensors offer new research avenues to explore. Transfer learning at ungauged basins holds promise but is largely unexplored. Explainable artificial intelligence (XAI) is seeing increased use and helps the transition of ML models from black-box forecasters to knowledge-enhancing predictors.

To the Editor: We read with great interest the article by Hadanny et al,1 published in Neurosurgery. In this study, the authors used an approach which augmented unsupervised and supervised machine learning methods to predict response to spinal cord stimulators. This paper is an excellent example of how both approaches can be used together to prognosticate on patient outcomes, and the methodological implications span beyond the impact on patients with spinal cord stimulators. Unsupervised machine learning (UML) operates on the premise that the ground truth is not known—for this reason, there are no training data involved in the model. The outcomes of UML algorithms are usually unique clusters identified in the data set. This varies from supervised machine learning (SML) algorithms, which construct a model based on known input and output data. In the literature, UML methods are commonly used to identify novel and emergent phenotypes, whereas SML has commonly been used to perform outcome prediction.2,3 When using UML methods, there are several important parameters to consider which may influence the outcomes seen. First parameter is the cluster number identification. In this paper, the authors used the K-nearest neighbor algorithm (KNN), preceded by an elbow-based cluster optimization step. The KNN algorithm itself is not a completely unsupervised algorithm and requires the inclusion of the number of preset clusters. It requires a cluster optimization step to identify this parameter. In their supplementary methods, they argue that, using the elbow method, a pseudoelbow was created at K = 3. As a result, the decision is made that two clusters exist in the data. We encourage the authors to consider the use of other approaches to validate this finding, including the silhouette method, which has been shown to be more robust and quantifiable in some cases and may be less prone to subjective examination of the analyst.4 The number of clusters is also not readily apparent in the reprojections to the principal component analysis domain space. We suggest the use of other dimensionality reduction approaches to visualize the clusters, namely t-distributed stochastic network embedding or uniform manifold approximation and projection. While the clustering should remain in the multidimensional space, dimensionality reduction approaches have been useful in visualizing multidimensional data otherwise difficult to appreciate. Second parameter is the algorithm itself. KNN is a very well-documented partitioning algorithm. However, it has several limitations. First, the algorithm requires the use of numerical data, which may distort the categorical data, such as patient biological sex, diagnosis, smoking status, and present inherent to the data set. In addition, the sample size required for use of the KNN method is also relatively large, with some studies suggesting the need for a sample size of 2^n, where n is the number of features (24 features in this study).5,6 Other clustering algorithms, such as hierarchical clustering, have been shown to be far more effective for smaller data sets, and we encourage the authors to explore the use of more robust algorithms that would better fit the constraints of the data. As more neurosurgical studies use machine learning, specifically UML, approaches for the analysis of data, it becomes very important for researchers to consider the aforementioned parameters to strengthen their observations and findings. We applaud Hadanny et al1 for an excellent presentation of how both UML and SML approaches can be augmented, and we look forward to additional novel contributions using both approaches in the future to better help understand complex disease processes and ultimately help patients.

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then, the field has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildfire science and management. Our overall objective is to improve awareness of ML methods among wildfire researchers and managers, as well as illustrate the diverse and challenging range of problems in wildfire science available to ML data scientists. To that end, we first present an overview of popular ML approaches used in wildfire science to date and then review the use of ML in wildfire science as broadly categorized into six problem domains, including (i) fuels characterization, fire detection, and mapping; (ii) fire weather and climate change; (iii) fire occurrence, susceptibility, and risk; (iv) fire behavior prediction; (v) fire effects; and (vi) fire management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildfires within a data science context. In total, to the end of 2019, we identified 300 relevant publications in which the most frequently used ML methods across problem domains included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods — including deep learning and agent-based learning — in the wildfire sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods such as deep learning requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildfire research and management communities play an active role in providing relevant, high-quality, and freely available wildfire data for use by practitioners of ML methods.

Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

The test study area is the Miocene reservoir of Nam Con Son Basin, offshore Vietnam. In the study we used unsupervised learning to automatically cluster hydraulic flow units (HU) based on flow zone indicators (FZI) in a core plug dataset. Then we applied supervised learning to predict HU by combining core and well log data. We tested several machine learning algorithms. In the first phase, we derived hydraulic flow unit clustering of porosity and permeability of core data using unsupervised machine learning methods such as Ward’s, K mean, Self-Organize Map (SOM) and Fuzzy C mean (FCM). Then we applied supervised machine learning methods including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Boosted Tree (BT) and Random Forest (RF). We combined both core and log data to predict HU logs for the full well section of the wells without core data. We used four wells with six logs (GR, DT, NPHI, LLD, LSS and RHOB) and 578 cores from the Miocene reservoir to train, validate and test the data. Our goal was to show that the correct combination of cores and well logs data would provide reservoir engineers with a tool for HU classification and estimation of permeability in a continuous geological profile. Our research showed that machine learning effectively boosts the prediction of permeability, reduces uncertainty in reservoir modeling, and improves project economics.

Applications of machine learning in metal-organic frameworks

A machine learning tool to analyze spectroscopic changes in high-dimensional data.

Natural hazards, such as flooding, have been negatively impacting developed and emerging economies alike. The effects of floods are more prominent in countries of the Global South, where large parts of the population and infrastructure are insufficiently protected from natural hazards. From this scope, a lot of effort is required to mitigate these impacts by continuously providing new and more reliable tools to aid in mitigation and preparedness, during or after a flood event. Flood mapping followed by damage assessment plays an important role in all these stages. In this work we investigate a new dataset provided by DrivenData Labs based on Sentinel-1 (S1) imagery (VH, VV imagery and labels) to help map floods in the city of Beira in Mozambique. Exploiting Google Earth Engine (GEE), we deployed supervised and unsupervised machine learning (ML) methods on a dataset comprising imagery from 13 countries worldwide. We first mapped the floods country-by-country including Mozambique. This first part was helpful to understand the sensitivity of each method when applied to data from different regions and with different polarizations. We then trained the supervised model globally (in all 13 countries) and used it to predict floods in Beira. To assess the accuracy of the experiments we used the intersection over the union (IoU) metric, results of which we compared with the benchmark IoU achieved by the winner in the DrivenData competition for flood mapping in 2021. The implementation of unsupervised and supervised ML using VH and VV+VH produced satisfactory results, and showed to be better than using VV imagery; in Cambodia and Bolivia with VH polarization yielded IoUs values ranging from 0.819 to 0.856 which is above the benchmark (0.8094). The predictions in Beira using VH imagery yielded IoU of 0.568, which is a reasonable outcome. The proposed approach is a reliable alternative for flood mapping, especially in Mozambique due to its low cost and time effectiveness as even with unsupervised approaches, relatively high-quality results are yielded in near real-time. Finally, we used Sentinel-2 (S2) imagery for a land cover classification to perform damage assessment in Beira and integrated population data from Beira to enhance the quality the results. The results show that 20% of agricultural area and about 10% of built up area were flooded. Flooded built up area includes highly populated neighborhoods such as Chaimite and Ponta Gea that are located in the center of the city.