Internal Metrics Research Articles

Corporate Strategies for Managing Regulatory Changes in Resource-Intensive Industries

Resource-intensive industries face continuous challenges due to evolving regulatory environments, which demand strategic responses to ensure compliance and sustain competitive advantage. The objective of this study is to explore how corporations in these industries adapt their strategies to manage regulatory changes, with a focus on identifying the key themes and strategies employed by corporate leaders to navigate this complex landscape. This qualitative study is based on semi-structured interviews with 31 participants holding key roles in strategic management, compliance, and operations across various resource-intensive sectors. The methodology aimed at achieving theoretical saturation involves in-depth interviews designed to explore corporate strategies, challenges, and adaptive practices in response to regulatory changes. Thematic analysis was employed to identify and categorize the main themes from the data. Three main themes were identified: Strategic Adaptation, Organizational Impact, and Stakeholder Engagement. Strategic Adaptation includes risk assessment, regulatory compliance strategies, and innovation. Organizational Impact covers internal communication, change management, resource allocation, and performance metrics. Stakeholder Engagement focuses on government relations, public relations, and partner collaboration. Each theme is detailed with specific strategies and concepts that corporations use to adapt to regulatory pressures. The study concludes that effective management of regulatory changes in resource-intensive industries requires a multifaceted strategic approach that includes robust risk management, proactive organizational changes, and dynamic stakeholder engagement. These strategies are not only vital for compliance but also for fostering innovation and sustainability within corporations.

A mixed-methods analysis to understand the implementation of a multistakeholder research consortium: Environmental influences on child health outcomes (ECHO).

Large, transdisciplinary research consortia have increasingly been called upon to address complex and challenging health problems. The National Institutes of Health's (NIH) Environmental influences on Child Health Outcomes (ECHO) Program developed multisite collaboration strategies to promote impactful collaborative observational research on child health. Team science and implementation science offer theoretical and methodological structure to answer questions about the strategies that facilitate successful consortia. We sought to characterize the elements and conditions that influence the implementation of a complex, interdisciplinary longitudinal research program, ECHO. Informed by the Practical, Robust, Implementation and Sustainability Model, our ethnographic research included semi-structured interviews with internal stakeholders and program evaluation metrics. We conducted template and matrix analysis and triangulated the qualitative and quantitative data to understand the implementation of ECHO. Between February and May 2022, we conducted 24 virtual interviews with representatives from ECHO components. The main cross-cutting topics that emerged from thematic analysis were collaboration and team science; communication and decision-making; data processes and harmonization; and diversity, equity, and inclusion. Both the qualitative and secondary quantitative evaluation data provided insights into the reach, adoption, implementation, and effectiveness of the program. A large, multidisciplinary research consortium such as ECHO has produced conceptual, instrumental, capacity building, and connectivity impact for internal and external stakeholders. Facilitators included infrastructure that supported collaboration and learning, alignment of data processes, and harmonization. Opportunities for enhanced impact include multidisciplinary, multimethod communication strategies, and alignment of research priorities.

Comparison of internal and external load metrics between won and lost game segments in elite beach handball

Purpose.The extraordinary evolution of beach handball requires the relationship between the specific internal and external load in this sport to be determined in order to continue its development. For this reason, the objective of this study was to compare won and lost game segments in beach handball matches to determine if there is a relationship between winning and losing a set and the external and internal loads experienced by an elite beach handball player.Methods.The data of 57 elite beach handball players were collected over ten matches, which were divided into time segments of 2 minutes each, making a total of 100 game segments. Data were collected using GPS and GNSS technology.Results.The results showed significant differences in the variables distance per minute (<i>d</i> = large in male and medium in female), velocity band 2 distance (<i>d</i> = very large in male and large in female) and band 3 distance (<i>d</i> = very large in male and medium in female), accelerations (number) (<i>d</i> = large in male and medium in female), player load per minute (<i>d</i> = medium in male and in female), velocity work/rest ratio (<i>d</i> = very large in male and in female) and total jumps (number) (<i>d</i> = medium in male and large in female).Conclusions.We conclude that the external and internal load variables that determine winning or losing a game segment in elite beach handball are distance per minute, velocity bands 2 (6–8.9 km/h) and 3 (9–11.9 km/h) distance, accelerations (number), player load per minute, velocity work/rest ratio and total jumps (number), both in the male and female categories. The highest values of these variables are observed in the won segments, except for the number of jumps in women.

A Novel Neighborhood Granular Meanshift Clustering Algorithm

The most popular algorithms used in unsupervised learning are clustering algorithms. Clustering algorithms are used to group samples into a number of classes or clusters based on the distances of the given sample features. Therefore, how to define the distance between samples is important for the clustering algorithm. Traditional clustering algorithms are generally based on the Mahalanobis distance and Minkowski distance, which have difficulty dealing with set-based data and uncertain nonlinear data. To solve this problem, we propose the granular vectors relative distance and granular vectors absolute distance based on the neighborhood granule operation. Further, the neighborhood granular meanshift clustering algorithm is also proposed. Finally, the effectiveness of neighborhood granular meanshift clustering is proved from two aspects of internal metrics (Accuracy and Fowlkes–Mallows Index) and external metric (Silhouette Coeffificient) on multiple datasets from UC Irvine Machine Learning Repository (UCI). We find that the granular meanshift clustering algorithm has a better clustering effect than the traditional clustering algorithms, such as Kmeans, Gaussian Mixture and so on.

Risk assessment of aromatic organic chemicals to T. pyriformis in environmental protection using regression-based QSTR and Read-Across algorithm

Hazardous aromatic compounds have a high probability of entering the surrounding environment, posing a threat to humans and other habitats. It is, thus, necessary to estimate the toxicity of these chemicals as a preventive measure before being marketed. The present study involves the development of in silico-based predictive 2D-QSTR models using a PLS regression approach for the exploration of the structural features responsible for the toxicity of T. pyriformis using simple and easily interpretable 2D descriptors employing a dataset containing 892 chemicals. The developed model was extensively validated by evaluating the model's reliability and predictability using internationally accepted external and internal validation metrics. We have also used the “Intelligent Consensus Predictor” tool and Read-Across software, which shows better results for test set compounds as compared with individual PLS models. We have also validated the models using a set of 383 external set compounds which are not used for the development of models. The results suggested that molecules with aliphatic aldehyde groups are much more toxic to the protozoan whereas chemicals containing C–N fragments at the topological distance 3 & 4, polar and alcoholic groups are less toxic to the protozoan. In conclusion, the developed QSTR and Read-Across models might be extremely beneficial as guides for researchers to estimate the toxicity profile of novel compounds against T. pyriformis.

How Did They Get There? What Perspectives From the Top Tell Us About Developing Women Leaders in Academic Anesthesiology.

How Did They Get There? What Perspectives From the Top Tell Us About Developing Women Leaders in Academic Anesthesiology.

Data clustering using leaders and followers optimization and differential evolution

Data clustering is an important research topic in data mining. Although cluster analysis based on optimization algorithms has attracted great attention, optimization-based techniques face difficulties due to the non-linear objective function and complicated search space. Leaders and followers optimization (LaF) introduced in the 2015 IEEE Congress on Evolutionary Computation, and differential evolution algorithm (DE) are two efficient evolutionary computation methods, and they own some special advantages. The key power of LaF is the exploration in multi-modal search spaces, but it has a poor performance in the exploitation. On the other hand, DE based on the DE/best/1 mutation operator significantly promotes the exploitation process. In this study, the strong properties of LaF and DE are combined to balance exploration and exploitation in the search space to discover the cluster centroids. Besides, the proposed clustering method, i.e., LaF-DE, does not need parameter settings, unlike the existing optimization-based partitional clustering methods. Hence, this study proposes a straightforward, parameter-free, and efficient novel hybrid algorithm for the optimization-based partitional data clustering problem. Many experiments on the functions from CEC2017 test suite show that LaF-DE has better optimization performance and higher stability than the state-of-the-art metaheuristic algorithms. LaF-DE has been compared with well-known clustering techniques on the UCI and Shape datasets. The experimental results and statistical tests indicate that LaF-DE outperforms the well-known partitional clustering methods on 8 of 12 datasets in terms of internal performance metrics. Besides, LaF-DE performs better than density peaks clustering on 9 of 12 datasets in terms of external performance metrics.

Match Loads May Predict Neuromuscular Fatigue and Intermittent-Running Endurance Capacity Decrement after a Soccer Match.

How the match-derived load metrics relate to post-match fatigue in soccer is scarcely researched. Thus, the aim of this study was to determine the associations between soccer match-related internal and external loads, neuromuscular performance decrease and intermittent-running endurance capacity decrement immediately post-match. Vertical jump (countermovement jump), straight-line sprinting (10- and 20-m sprint), change of direction ability (T-test) and intermittent-running endurance capacity (YO-YO intermittent recovery level 2) were measured one day before and immediately after a friendly match in male soccer players. During the match, players' internal and external loads were also monitored, including heart rate-derived indices, total distance at various speed thresholds, average running velocity, maximal running velocity, number of sprints and number of accelerations and decelerations at various intensity thresholds. The results show that match-induced fatigue was reflected on neuromuscular performance and intermittent-running endurance capacity immediately post-match (p < 0.05). The quantification of percentage change of match external-load metrics, particularly accelerations and decelerations, provides a useful non-invasive predictor of subsequent neuromuscular fatigue status in soccer players immediately post-match (p < 0.05). However, only internal load metrics present a practical application for predicting intermittent-running endurance capacity impairment (p < 0.05). In summary, internal and external load metrics may allow for predicting the extent of acute fatigue, and variability between halves may represent a valuable alternative to facilitate the analysis of match-related fatigue both for research and applied purposes.

Intelligent consensus predictions of bioconcentration factor of pharmaceuticals using 2D and fragment-based descriptors

Bioconcentration factors (BCFs) are markers of chemical substance accumulation in organisms, and they play a significant role in determining the environmental risk of various chemicals. Experiments to obtain BCFs are expensive and time-consuming; therefore, it is better to estimate BCF early in the chemical development process. The current research aims to evaluate the ecotoxicity potential of 122 pharmaceuticals and identify possible important structural attributes using BCF as the determining feature against a group of fish species. We have calculated the theoretical 2D descriptors from the OCHEM platform and SiRMS descriptor calculating software. The regression-based quantitative structure–property relationship (QSPR) modeling was used to identify the chemical features responsible for acute fish bioconcentration. Multiple models with the “intelligent consensus” algorithm were employed for the regression-based approach improving the predictive ability of the models. To ensure the robustness and interpretability of the developed models, rigorous validation was performed employing various statistical internal and external validation metrics. From the developed models, it can be specified that the presence of large lipophilic and electronegative moieties greatly enhances the bioaccumulative potential of pharmaceuticals, whereas the hydrophilic characteristics have shown a negative impact on BCF. Furthermore, the developed models were employed to screen the DrugBank database (https://go.drugbank.com/) for assessing the BCF properties of the entire database. The evidence acquired from the modeled descriptors might be used for aquatic risk assessment in the future, with the added benefit of providing an early caution of their probable negative impact on aquatic ecosystems for regulatory purposes.

Exploring quantitative structure-property relationship models for environmental fate assessment of petroleum hydrocarbons.

The rate and extent of biodegradation of petroleum hydrocarbons in the different aquatic environments is an important element to address. The major avenue for removing petroleum hydrocarbons from the environment is thought to be biodegradation. The present study involves the development of predictive quantitative structure-property relationship (QSPR) models for the primary biodegradation half-life of petroleum hydrocarbons that may be used to forecast the biodegradation half-life of untested petroleum hydrocarbons within the established models' applicability domain. These models use easily computable two-dimensional (2D) descriptors to investigate important structural characteristics needed for the biodegradation of petroleum hydrocarbons in freshwater (dataset 1), temperate seawater (dataset 2), and arctic seawater (dataset 3). All the developed models follow OECD guidelines. We have used double cross-validation, best subset selection, and partial least squares tools for model development. In addition, the small dataset modeler tool has been successfully used for the dataset with very few compounds (dataset 3 with 17 compounds), where dataset division was not possible. The resultant models are robust, predictive, and mechanisticallyinterpretable based on both internal and external validation metrics (R2 range of 0.605-0.959. Q2(Loo) range of 0.509-0.904, and Q2F1 range of 0.526-0.959). The intelligent consensus predictor tool has been used for theimprovement of the prediction quality for test set compounds which provided superior outcomes to those from individual partial least squares models based on several metrics (Q2F1 = 0.808 and Q2F2 = 0.805 for dataset 1 in freshwater). Molecular size and hydrophilic factor for freshwater, frequency of two carbon atoms at topological distance 4 for temperate seawater, and electronegative atom count relative to size for arctic seawater were found to be the most significant descriptors responsible for the regulation of biodegradation half-life of petroleum hydrocarbons.

МЕТОДЫ И АЛГОРИТМЫ КЛАСТЕРИЗАЦИИ ТЕКСТОВЫХ ДАННЫХ (ОБЗОР)

The article deals with one of the important tasks of artificial intelligence – machine processingof natural language. The solution of this problem based on cluster analysis makes it possibleto identify, formalize and integrate large amounts of linguistic expert information under conditionsof information uncertainty and weak structure of the original text resources obtained fromvarious subject areas. Cluster analysis is a powerful tool for exploratory analysis of text data,which allows for an objective classification of any objects that are characterized by a number offeatures and have hidden patterns. A review and analysis of modern modified algorithms for agglomerativeclustering CURE, ROCK, CHAMELEON, non-hierarchical clustering PAM, CLARAand the affine transformation algorithm used at various stages of text data clustering, the effectivenessof which is verified by experimental studies, is carried out. The paper substantiates therequirements for choosing the most efficient clustering method for solving the problem of increasing the efficiency of intellectual processing of linguistic expert information. Also, the paper considersmethods for visualizing clustering results for interpreting the cluster structure and dependencieson a set of text data elements and graphical means of their presentation in the form ofdendograms, scatterplots, VOS similarity diagrams, and intensity maps. To compare the quality ofthe algorithms, internal and external performance metrics were used: "V-measure", "AdjustedRand index", "Silhouette". Based on the experiments, it was found that it is necessary to use ahybrid approach, in which, for the initial selection of the number of clusters and the distribution oftheir centers, use a hierarchical approach based on sequential combining and averaging the characteristicsof the closest data of a limited sample, when it is not possible to put forward a hypothesisabout the initial number of clusters. Next, connect iterative clustering algorithms that providehigh stability with respect to noise features and the presence of outliers. Hybridization increasesthe efficiency of clustering algorithms. The research results showed that in order to increase thecomputational efficiency and overcome the sensitivity when initializing the parameters of clusteringalgorithms, it is necessary to use metaheuristic approaches to optimize the parameters of thelearning model and search for a global optimal solution.

Submarine Landslide Susceptibility and Spatial Distribution Using Different Unsupervised Machine Learning Models

A submarine landslide is a well-known geohazard that can cause significant damage to offshore engineering facilities. Most standard predicting and mapping methods require expert knowledge, supervision, and fieldwork. In this research, the main objective was to analyze the potential of unsupervised machine learning methods and compare the performance of three different unsupervised machine learning models (k-means, spectral clustering, and hierarchical clustering) in modeling the susceptibility of the submarine landslide. Nine groups of geological factors were selected as the input parameters, which were obtained through field surveys. To estimate submarine landslide susceptibility, all input factors were separated into three or four groups based on data features and environmental variables. Finally, the goodness-of-fit and accuracy of models were validated with both internal metrics (Calinski–Harabasz index, silhouette index, and Davies–Bouldin index) and external metrics (existing landslide distribution, hydrodynamic distribution, and liquefication distribution). The findings of k-means, spectral clustering, and hierarchical clustering performed commendably and accurately in forecasting the submarine landslide susceptibility. Spectral clustering has the greatest congruence with environmental geology parameters. Therefore, the unsupervised machine learning model can be used in submarine-landslide-predicting studies, and the spectral clustering method performed best. Furthermore, machine learning can improve submarine landslide mapping in the future with the development of models and the extension of geological data related to submarine landslides.

Metric Based Dynamic Control Charts for Edge Anomaly Detection in Factory Logistics

The optimization of transport logistics in production environments is a holistic task for the factory of the future. Autonomous guided vehicles that perform transport jobs in factories are facing this challenge and have to detect, react and prepare to unforeseen changes and anomalies in the production system. Due to data protection concerns, details like production plans are often not available for an external transportation system. Hence the anomaly detection has to be based on self-collected and observed data of the transport system like occurred transport needs or the evolution of internal metrics. In this paper we infused a production system with manufacturing process anomalies and demonstrate a detection based on the observation of transport needs to overcome the gab caused by restricted information. For that detection we extended classic control charts to work with expected values based on learned dynamic production characteristics. The system sets a tolerance field as narrow as possible around dynamically determined values, resulting in an average precision of 95% for detection unusual number of transport jobs.

A Veterans Affairs' perspective in response to Best Practice Model Subcommittee attribute statements for outpatient psychiatric pharmacists.

We applaud the work published by Lee et al1 and the best practice model subcommittee in their attribute statements for defining the optimal recommendations for outpatient psychiatric pharmacy practice. This serves as an excellent foundation in standardizing the expectations for clinical pharmacist practitioners (CPP) within outpatient mental health (MH) settings and our group believes these defined statements are being applied at the Department of Veterans Affairs (VA).2 Measurement-based care, suicide assessment, and comprehensive medication management (CMM) are all standards of care for VA outpatient MH CPP.3 The goal of our letter is to highlight the VA MH CPP model and the advancements in access, quality impact, and standardization.Integrating the MH CPP into the team-based care model significantly improves access to care, primarily when the CPP is utilized as a primary MH provider. Many strong CPP practices within the VA have been identified, including CPP integration into primary care MH services, outpatient MH clinics, specialty MH, and residential rehabilitation programs. Other strategies utilized by VA MH CPP to optimize access include utilizing analytics to target specific clinical interventions (population management), posthospital discharge appointments, and enhanced patient care in suicide risk stratification.In October 2017, the VA Pharmacy Benefits Management (PBM) partnered with the Office of Rural Health to increase access to care for veterans by utilizing MH CPPs as MH prescribers. Within 2½ years from its inception, 40 MH CPPs were hired, which increased direct patient care access and quality by providing CMM for 51 210 rural-residing patients.4 Due to the initiative's positive impact on patient care, an additional 46 MH CPP were hired in FY20/21, which demonstrates the importance of optimizing the MH CPP role in the team-based care model.MH CPP within the VA have established guidance for how to create a positive impact in the VA's internal performance metrics, including population management, continuity of care, experience of care, behavioral health screenings, and suicide risk management. They are also instrumental in serving as facility champions for psychotropic stewardship and safety. One of the attribute statements proposed by Lee et al1 recommends prescriptive authority and/or a collaborative practice agreement. In collaboration with VA PBM, a care coordination agreement (CCA) was created for MH CPP and establishes a partnership between MH and pharmacy services. This CCA covers many of the attribute statements, including attribute statements 14-24 and 27. The CCA is approved by service leadership and the chief of staff to ensure these attribute statements are being upheld. The PBM has also developed a business rules document to guide standardization of MH CPP practice within the VA. This includes in-depth descriptions defining clinical pharmacy practice, patient care delivery, optimizing access and integration, and documentation of workload.Overall, the VA has taken great strides to advance the practice of MH CPPs. Current practice by VA MH CPPs incorporates many of the attribute statements. We are hopeful that these well-developed attribute statements will help foster all psychiatric pharmacists to practice at the highest level of care, achieve reimbursement for clinical services, and bolster patient and provider satisfaction.

Ensemble modeling approach to predict the past and future climate suitability for two mangrove species along the coastal wetlands of peninsular India

Mangroves support numerous ecosystem services and help in reducing coastal ecological risks, yet they are declining rapidly due to climate change, sea level fluctuations and human activities. It is important to understand their responses to climate and sea level changes and identify conservation target areas at spatio-temporal scales, specifically in regions of rich mangrove biodiversity. In this study, we predicted the potential impact of past (Middle Holocene, ∼6000 years), current and future (2050s, 2070s; RCP 2.6 and RCP 8.5) climate change scenarios on the two dominant species in the coastal mangrove forest wetlands of India, i.e., Rhizophora mucronata and Avicennia officinalis through an ensemble species distribution modeling approach. The ensemble modeling has been carried out by integrating eight single algorithm methods. Based on the receiver operating characteristics of area under the curve (AUC) and true skill statistics (TSS) values the ensemble modeling has yielded the highest predictive performance for SVM for both the species and lowest by CART for R. mucronata and BIOCLIM for A. officinalis. The internal evaluation metrics of the resulting Species distribution models (SDMs) tested its robustness with AUC-0.97 and TSS-0.89 for A. officinalis and AUC-0.99 and TSS-0.90 for R. mucronata. Precipitation of Wettest Month (Bio 13) and Mean Temperature of Warmest Quarter (Bio 10) was the most important variable (54–67%) for the distribution of A. officinalis and Precipitation Seasonality (Bio 15) and Precipitation of Warmest Quarter (Bio 18) for R. mucronata. High precipitation and sea-level highstand during middle Holocene led to the maximum range expansion of suitable habitat for the mangrove species which is also validated in the present study by the fossil pollen datasets. Total mangrove habitat in current and future climatic scenarios decreased in 2.6 and 8.5 Representative Concentration Pathways (RCPs) for 2050 and 2070 which indicates the vulnerability of the species to climate change impacts. Mangrove species are projected to shift their ranges more towards land in future experiencing a decrease in the amount of suitable coastal area available to them throughout the Indian coastline. The plausible cause for this range shift may be due to higher precipitation that is usually associated with longer period of soil inundation and because of the rise in sea level. Our findings will assist in formulating species-specific restoration plans for these keystone species in context of climate change in the Indian Subcontinent.

Quasiconformality to quasisymmetry via weak (L,M)-quasisymmetry

This paper is devoted to the study of a fundamental problem in the theory of quasiconformal analysis: under what conditions local quasiconformality of a homeomorphism implies its global quasisymmetry. In particular, we introduce the concept of weak \((L,M)\)-quasisymmetry, serving as a bridge between local quasiconformality and global quasisymmetry. We show that in general metric spaces local regularity and some connectivity together with the Loewner condition are sufficient for a quasiconformal map to be weakly \((L,M)\)-quasisymmetric, and subsequently, quasisymmetric with respect to the internal metrics.

TSFNFR: Two-stage fuzzy neighborhood-based feature reduction with binary whale optimization algorithm for imbalanced data classification

Recently developed imbalanced data classification models are mainly focused on the majority class samples. In addition, several whale optimization-based feature reduction models are inefficient for high-dimensional data, readily fall into the local optimum, and are subject to difficulties associated with achieving a global optimal feature subset, due to high costs. To overcome the drawbacks, in this study, a two-stage feature reduction model using fuzzy neighborhood rough sets (FNRS) and the binary whale optimization algorithm (BWOA) was developed for imbalanced data classification. First, to indicate the sample fuzziness of mixed data, a similarity measure between samples based on fuzzy neighborhood was defined to investigate the similarity matrix and fuzzy neighborhood granule, and a new FNRS model was presented by constructing lower and upper approximations. By considering the uneven distribution of classes, the boundary-based feature significance measure was developed to minimize the influence of the uncertainty in boundary region for imbalanced data. Second, fuzzy neighborhood roughness and decision entropy were investigated based on FNRS, and by integrating these above measures, fuzzy neighborhood decision entropy was proposed to evaluate the fuzziness and roughness of the fuzzy neighborhood for imbalanced data. The external and internal significance metrics were proposed to achieve the preselected feature subset in the first stage. Third, in this second stage, a new control factor was defined to control the position of whales, and a novel fitness function was developed to evaluate the selected feature subset from imbalanced datasets. Thereafter, the immune regulation strategy of artificial immune was introduced into the BWOA to design the mixed selection probability, to divide the whale population. Two local interference strategies were applied to adjust the whale position and prevent BWOA trapped in the local optimum. Thus, an optimal feature subset was achieved by constantly iterating the BWOA. Finally, a two-stage feature reduction algorithm was designed to handle imbalanced and high-dimensional data, where the particle swarm optimization (PSO) algorithm was employed to determine the different optimized parameters for this two-stage algorithm. Experiments conducted on 22 datasets revealed that the proposed algorithm is efficient for two-class and multiclass datasets.

QSPR models to predict quantum chemical properties of imidazole derivatives using genetic algorithm–multiple linear regression and back‐propagation–artificial neural network

AbstractImidazole derivatives are the foundation of different types of drugs with a wide range of biological activities. In this study, the genetic algorithm–multiple linear regression (GA–MLR), and backpropagation–artificial neural network (BP–ANN) were applied to design QSPR models to predict the quantum chemical properties like the entropy (S) and enthalpy of formation (∆Hf) of imidazole derivatives. In order to draw molecular structure of 84 derivative compounds Gauss View 05 program was used. These structures were optimized at DFT‐B3LYP/6‐311G* level with Gaussian09W. The Dragon software was used to calculate a set of different molecular descriptors, and the genetic algorithm procedure and backward stepwise regression were applied for the selection of descriptors. The resulting quantitative GA–MLR model of ∆Hf, showed that there is good linear correlation between the selected descriptors and ∆Hf of compounds. Also the results show that the BP–ANN model appeared to be superior to GA–MLR model for prediction of entropy. Different internal and external validation metrics were adopted to verify the predictive performance of QSPR models. The predictive powers of the models were found to be acceptable. Thus, these QSPR models may be useful for designing new series of imidazole derivatives and prediction of their properties.

A scalable and unbiased discordance metric with H.

A standard unsupervised analysis is to cluster observations into discrete groups using a dissimilarity measure, such as Euclidean distance. If there does not exist a ground-truth label for each observation necessary for external validity metrics, then internal validity metrics, such as the tightness or separation of the clusters, are often used. However, the interpretation of these internal metrics can be problematic when using different dissimilarity measures as they have different magnitudes and ranges of values that they span. To address this problem, previous work introduced the "scale-agnostic" $G_{+}$ discordance metric; however, this internal metric is slow to calculate for large data. Furthermore, in the setting of unsupervised clustering with $k$ groups, we show that $G_{+}$ varies as a function of the proportion of observations assigned to each of the groups (or clusters), referred to as the group balance, which is an undesirable property. To address this problem, we propose a modification of $G_{+}$, referred to as $H_{+}$, and demonstrate that $H_{+}$ does not vary as a function of group balance using a simulation study and with public single-cell RNA-sequencing data. Finally, we provide scalable approaches to estimate $H_{+}$, which are available in the $\mathtt{fasthplus}$ R package.

A Generic Pharmacokinetic Model for Quantifying Mother-to-Offspring Transfer of Lipophilic Persistent Environmental Chemicals.

Lipophilic persistent environmental chemicals (LPECs) can accumulate in a woman's body and transfer to her developing child across the placenta and via breast milk. To assess health risks associated with developmental exposures to LPECs, we developed a pharmacokinetic (PK) model that quantifies mother-to-offspring transfer of LPECs during pregnancy and lactation and facilitates internal dosimetry calculations for offspring. We parameterized the model for mice, rats, and humans using time-varying functions for body mass and milk consumption rates. The only required substance-specific parameter is the elimination half-life of the LPEC in the animal species of interest. We used the model to estimate whole-body concentrations in mothers and offspring following maternal exposures to hexachlorobenzene (HCB) and 2,2',4,4',5,5'-hexachlorobiphenyl (PCB 153) and compared these with measured concentrations from animal studies. We also compared estimated concentrations for humans to those generated using a previously published human LPEC PK model. Finally, we compared human equivalent doses (HEDs) calculated using our model and an allometric scaling method. Estimated and observed whole-body concentrations of HCB and PCB 153 in offspring followed similar trends and differed by less than 60%. Simulations of human exposure yielded concentration estimates comparable to those generated using the previously published model, with concentrations in offspring differing by less than 12%. HEDs calculated using our PK model were about 2 orders of magnitude lower than those generated using allometric scaling. Our PK model can be used to calculate internal dose metrics for offspring and corresponding HEDs and thus informs assessment of developmental toxicity risks associated with LPECs.