Electroplated Electrodes for Continuous and Mass-Efficient Electrochemical Hydrogenation.

Electrocatalytic hydrogenations (ECH) enable the reduction of organic substrates upon usage of electric current and present a sustainable alternative to conventional processes if green electricity is used. Opposed to most current protocols for electrode preparation, this work presents a one-step binder- and additive-free production of silver- and copper-electroplated electrodes. Controlled adjustment of the preparation parameters allows for the tuning of catalyst morphology and its electrochemical properties. Upon optimization of the deposition protocol and carbon support, high faradaic efficiencies of 93 % for the ECH of the Vitamin A- and E-synthon 2-methyl-3-butyn-2-ol (MBY) are achieved that can be maintained at current densities of 240 mA cm-2 and minimal catalyst loadings of 0.2 mg cm-2, corresponding to an unmatched production rate of 1.47 kgMBE gcat -1 h-1. For a continuous hydrogenation process, the protocol can be directly transferred into a single-pass operation mode giving a production rate of 1.38 kgMBE gcat -1 h-1. Subsequently, the substrate spectrum was extended to a total of 17 different C-C-, C-O- and N-O-unsaturated compounds revealing the general applicability of the reported process. Our results lay an important groundwork for the development of electrochemical reactors and electrodes able to directly compete with the palladium-based thermocatalytic state of the art.

A review on endophytic fungi: a potent reservoir of bioactive metabolites with special emphasis on blight disease management.

Phytopathogenic microorganisms have caused blight diseases that present significant challenges to global agriculture. These diseases result in substantial crop losses and have a significant economic impact. Due to the limitations of conventional chemical treatments in effectively and sustainably managing these diseases, there is an increasing interest in exploring alternative and environmentally friendly approaches for disease control. Using endophytic fungi as biocontrol agents has become a promising strategy in recent years. Endophytic fungi live inside plant tissues, forming mutually beneficial relationships, and have been discovered to produce a wide range of bioactive metabolites. These metabolites demonstrate significant potential for fighting blight diseases and provide a plentiful source of new biopesticides. In this review, we delve into the potential of endophytic fungi as a means of biocontrol against blight diseases. We specifically highlight their significance as a source of biologically active compounds. The review explores different mechanisms used by endophytic fungi to suppress phytopathogens. These mechanisms include competing for nutrients, producing antifungal compounds, and triggering plant defense responses. Furthermore, this review discusses the challenges of using endophytic fungi as biocontrol agents in commercial applications. It emphasizes the importance of conducting thorough research to enhance their effectiveness and stability in real-world environments. Therefore, bioactive metabolites from endophytic fungi have considerable potential for sustainable and eco-friendly blight disease control. Additional research on endophytes and their metabolites will promote biotechnology solutions.

Rethinking the applicability domain analysis in QSAR models.

Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates.

Multimodal Model to Predict Tissue-to-Blood Partition Coefficients of Chemicals in Mammals and Fish.

Tissue-to-blood partition coefficients (Ptb) are key parameters for assessing toxicokinetics of xenobiotics in organisms, yet their experimental data were lacking. Experimental methods for measuring Ptb values are inefficient, underscoring the urgent need for prediction models. However, most existing models failed to fully exploit Ptb data from diverse sources, and their applicability domain (AD) was limited. The current study developed a multimodal model capable of processing and integrating textual (categorical features) and numerical information (molecular descriptors/fingerprints) to simultaneously predict Ptb values across various species, tissues, blood matrices, and measurement methods. Artificial neural network algorithms with embedding layers were used for the multimodal modeling. The corresponding unimodal models were developed for comparison. Results showed that the multimodal model outperformed unimodal models. To enhance the reliability of the model, a method considering categorical features, weighted molecular similarity density, and weighted inconsistency in molecular activities of structure-activity landscapes was used to characterize the AD. The model constrained by the AD exhibited better prediction accuracy for the validation set, with the determination coefficient, root mean-square error, and mean absolute error being 0.843, 0.276, and 0.213 log units, respectively. The multimodal model coupled with the AD characterization can serve as an efficient tool for internal exposure assessment of chemicals.

Chemical Space Covered by Applicability Domains of Quantitative Structure-Property Relationships and Semiempirical Relationships in Chemical Assessments.

A significant number of chemicals registered in national and regional chemical inventories require assessments of their potential "hazard" concerns posed to humans and ecological receptors. This warrants knowledge of their partitioning and reactivity properties, which are often predicted by quantitative structure-property relationships (QSPRs) and other semiempirical relationships. It is imperative to evaluate the applicability domain (AD) of these tools to ensure their suitability for assessment purpose. Here, we investigate the extent to which the ADs of commonly used QSPRs and semiempirical relationships cover seven partitioning and reactivity properties of a chemical "space" comprising 81,000+ organic chemicals registered in regulatory and academic chemical inventories. Our findings show that around or more than half of the chemicals studied are covered by at least one of the commonly used QSPRs. The investigated QSPRs demonstrate adequate AD coverage for organochlorides and organobromines but limited AD coverage for chemicals containing fluorine and phosphorus. These QSPRs exhibit limited AD coverage for atmospheric reactivity, biodegradation, and octanol-air partitioning, particularly for ionizable organic chemicals compared to nonionizable ones, challenging assessments of environmental persistence, bioaccumulation capability, and long-range transport potential. We also find that a predictive tool's AD coverage of chemicals depends on how the AD is defined, for example, by the distance of a predicted chemical from the centroid of the training chemicals or by the presence or absence of structural features.

A Review on Small Organic Colorimetric and Fluorescent Hosts for the Detection of Cobalt and Nickel Ion.

In recent years, there has been a notable increase in efforts to advance efficient hosts for detecting cobalt and nickel ions, driven by their extensive industrial applications and environmental significance. This review meticulously examines the progress made in small organic colorimetric and fluorescent hosts tailored specifically for the sensitive and selective detection of cobalt and nickel ions. It delves into a diverse range of molecular architectures, including organic ligands, elucidating their unique attributes such as sensitivity, selectivity, and response time. Moreover, the review precisely explores the underlying principles governing the colorimetric and fluorescent mechanisms employed by these hosts, shedding light on the intricate interactions between the sensing moieties and the target metal ions. Furthermore, it critically evaluates the practical applicability of these hosts, considering crucial factors such as detection limits, recyclability, and compatibility with complex sample matrices. Additionally, exploration extends to potential challenges and prospects in the field, emphasizing the imperative for ongoing innovation to address emerging environmental and analytical demands. Eventually, through this comprehensive examination, the review seeks to contribute to the ongoing endeavor to develop robust and efficient tools for monitoring and detecting cobalt and nickel metal ions in diverse analytical scenarios.

Multi-task machine learning models for simultaneous prediction of tissue-to-blood partition coefficients of chemicals in mammals.

Tissue-to-blood partition coefficients (Ptb) are crucial for assessing the distribution of chemicals in organisms. Given the lack of experimental data and laborious nature of experimental methods, there is an urgent need to develop efficient predictive models. With the help of machine learning algorithms, i,e., random forest (RF), and artificial neural network (ANN), this study developed multi-task (MT) models that can simultaneously predict Ptb values for various mammalian tissues, including liver, muscle, brain, lung, and adipose. Single-task (ST) models using partial least squares regression, RF, and ANN algorithms for each endpoint were established for comparison. Overall, the performances of MT models were superior to those of ST models. The MT model using ANN algorithms showed the highest prediction accuracy with determination coefficients ranging from 0.704 to 0.886, root mean square errors between 0.223 and 0.410, and mean absolute errors ranging from 0.178 to 0.285 log units. Results showed that lipophilicity and polarizability of molecules significantly influence their partition behavior in organisms. Applicability domains (ADs) of the models were characterized by weighted molecular similarity density, and weighted inconsistency in molecular activities of structure-activity landscapes. When constrained by ADs, the models displayed enhanced predictive accuracy, making them valuable tools for the risk assessment and management of chemicals.

New horizons in clinical practice guidelines for use with older people.

Globally, more people are living into advanced old age, with age-associated frailty, disability and multimorbidity. Achieving equity for all ages necessitates adapting healthcare systems. Clinical practice guidelines (CPGs) have an important place in adapting evidence-based medicine and clinical care to reflect these changing needs. CPGs can facilitate better and more systematic care for older people. But they can also present a challenge to patient-centred care and shared decision-making when clinical and/or socioeconomic heterogeneity or personal priorities are not reflected in recommendations or in their application. Indeed, evidence is often lacking to enable this variability to be reflected in guidance. Evidence is more likely to be lacking about some sections of the population. Many older adults are at the intersection of many factors associated with exclusion from traditional clinical evidence sources with higher incidence of multimorbidity and disability compounded by poorer healthcare access and ultimately worse outcomes. We describe these challenges and illustrate how they can adversely affect CPG scope, the evidence available and its summation, the content of CPG recommendations and their patient-centred implementation. In all of this, we take older adults as our focus, but much of what we say will be applicable to other marginalised groups. Then, using the established process of formulating a CPG as a framework, we consider how these challenges can be mitigated, with particular attention to applicability and implementation. We consider why CPG recommendations on the same clinical areas may be inconsistent and describe approaches to ensuring that CPGs remain up to date.

Creating more comparable cohorts in observational palliative care studies: A proposed framework to improve applicability and replicability of research.

BACKGROUND: Palliative care is characterised by heterogeneous patient and caregiver populations who are provided care in different health systems and a research base including a large proportion of observational, mostly retrospective studies. The inherent diversity of palliative care populations and the often inadequate study descriptions challenge the application of new knowledge into practice and reproducibility for confirmatory studies. Being able to define systematically study populations would significantly increase their generalisability and effective translation into practice. PROPOSAL: Based on an informal consensus process by active palliative care researchers challenged by this problem and a review of the current evidence, we propose an approach to creating more comparable cohorts in observational (non-randomised) palliative care studies that relies on defining the study population in relation to a fixed, well-defined event from which analyses are built ('anchoring'). In addition to providing a detailed and complete description of the study population, anchoring is the critical step in creating more comparable cohorts in observational palliative care studies. Anchoring can be done with respect to a single or multiple data points, and can support both prospective and retrospective data collection and analysis. DISCUSSION: Anchoring the cohort to reproducible data points will help create more comparable cohorts in palliative care whilst mitigating its inherent heterogeneity. This, in turn, will help optimise the generalisability, applicability and reproducibility of observational palliative care studies to strengthen the evidence base and improve practice.

How the Xenopus eleutheroembryonic thyroid assay compares to the amphibian metamorphosis assay for detecting thyroid active chemicals.

The Xenopus Eleutheroembryonic Thyroid Assay (XETA) was recently published as an OECD Test Guideline for detecting chemicals acting on the thyroid axis. However, the OECD validation did not cover all mechanisms that can potentially be detected by the XETA. This study was therefore initiated to investigate and consolidate the applicability domain of the XETA regarding the following mechanisms: thyroid hormone receptor (THR) agonism, sodium-iodide symporter (NIS) inhibition, thyroperoxidase (TPO) inhibition, deiodinase (DIO) inhibition, glucocorticoid receptor (GR) agonism, and uridine 5'-diphospho-glucuronosyltransferase (UDPGT) induction. In total, 22 chemicals identified as thyroid-active or -inactive in Amphibian Metamorphosis Assays (AMAs) were tested using the XETA OECD Test Guideline. The comparison showed that both assays are highly concordant in identifying chemicals with mechanisms of action related to THR agonism, DIO inhibition, and GR agonism. They also consistently identified the UDPGT inducers as thyroid inactive. NIS inhibition, investigated using sodium perchlorate, was not detected in the XETA. TPO inhibition requires further mechanistic investigations as the reference chemicals tested resulted in opposing response directions in the XETA and AMA. This study contributes refining the applicability domain of the XETA, thereby helping to clarify the conditions where it can be used as an ethical alternative to the AMA.

A systematic review of instrumented assessments for upper limb function in cerebral palsy: current limitations and future directions.

INTRODUCTION: Recently, interest in quantifying upper limb function in cerebral palsy has grown. However, the lack of reference tasks and protocols, have hindered the development of quantified movement analysis in clinical practice. This study aimed to evaluate existing instrumented assessments of upper limb function in cerebral palsy, with a focus on their clinical applicability, to identify reasons for the lack of adoption and provide recommendations for improving clinical relevance and utility. METHODS: A systematic review was conducted by a multidisciplinary team of researchers and clinicians (Prospero CRD42023402382). PubMed and Web of Science databases were searched using relevant keywords and inclusion/exclusion criteria. RESULTS: A total of 657 articles were initially identified, and after the selection process, 76 records were included for analysis comprising a total of 1293 patients with cerebral palsy. The quality assessment of the reviewed studies revealed a moderate overall quality, with deficiencies in sample size justification and participant information. Optoelectronic motion capture systems were predominantly used in the studies (N = 57/76). The population mainly consisted of individuals with spastic cerebral palsy (834/1293) with unilateral impairment (N = 1092/1293). Patients with severe functional impairment (MACS IV and V) were underrepresented with 3.4% of the 754 patients for whom the information was provided. Thirty-nine tasks were used across the articles. Most articles focused on unimanual activities (N = 66/76) and reach or reach and grasp (N = 51/76). Bimanual cooperative tasks only represented 3 tasks present in 4 articles. A total of 140 different parameters were identified across articles. Task duration was the most frequently used parameter and 23% of the parameters were used in only one article. CONCLUSION: Further research is necessary before incorporating quantified motion analysis into clinical practice. Existing protocols focus on extensively studied populations and rely on costly equipment, limiting their practicality. Standardized unimanual tasks provide limited insights into everyday arm use. Balancing methodological requirements and performance evaluation flexibility is a challenge. Exploring the correlation between outcome parameters and therapeutic guidance could facilitate the integration of quantified movement assessment into treatment pathways.

Evaluating Factors Associated With Patient-Perceived Applicability of the QuickDASH Survey in Reflecting Their Goals of Care Prior to Hand Surgery.

PURPOSE: Patient-reported outcome measures intend to capture patients' perspectives on their health status. However, the patient-perceived applicability of many of these patient-reported outcome measures is unknown. We hypothesized that patients experiencing greater upper extremity disability and greater pain interference would be more likely to report that the Quick Disabilities of the Arm, Shoulder, and Hand questionnaire (QuickDASH) survey content is responsive to their daily lives and goals in seeking surgical care. METHODS: Adult preoperative hand surgery patients at a single tertiary academic center were recruited prospectively. QuickDASH, Patient-Reported Outcomes Measurement Information System Pain Interference computerized-adaptive-testing, and the Godin Leisure-Time Exercise Questionnaire (GLTEQ)-a validated adult physical activity level metric-data were collected. The following two Likert response questions were also asked: question (1) "How applicable is the above questionnaire to your treatment goals for your upper extremity condition?" and question (2) "How applicable is the overall questionnaire to your daily life?" Multivariable binary logistic regression was performed to define the factors associated with patients reporting that the survey was "very applicable." RESULTS: Of the 133 included patients, the mean age was 49 ± 18 years, 40% were women, and the mean GLTEQ score was 54.1 ± 5.5 (consistent with a high level of activity). For questions 1 and 2, 32% and 29% of the patients reported that QuickDASH was "very applicable," respectively. The multivariable model demonstrated that for every 10-point increase in QuickDASH, there was a 45% to 49% greater odds of respondents reporting that the survey was "very applicable," and for every 5-point increase in pain interference computerized-adaptive-testing, the odds increased by 55% to 70%. No association with GLTEQ was observed. CONCLUSIONS: Patients with greater upper extremity disability and pain interference were more likely to find the QuickDASH content to be applicable to their daily lives and goals in seeking surgical care. CLINICAL RELEVANCE: These findings suggest that QuickDASH may not be an optimal instrument when evaluating upper extremity function in cohorts with mild disability and low pain interference.

Progress in Predicting Ames Test Outcomes from Chemical Structures: An In-Depth Re-Evaluation of Models from the 1st and 2nd Ames/QSAR International Challenge Projects.

The Ames/quantitative structure-activity relationship (QSAR) International Challenge Projects, held during 2014-2017 and 2020-2022, evaluated the performance of various predictive models. Despite the significant insights gained, the rules allowing participants to select prediction targets introduced ambiguity in model performance evaluation. This reanalysis identified the highest-performing prediction model, assuming a 100% coverage rate (COV) for all prediction target compounds and an estimated performance variation due to changes in COV. All models from both projects were evaluated using balance accuracy (BA), the Matthews correlation coefficient (MCC), the F1 score (F1), and the first principal component (PC1). After normalizing the COV, a correlation analysis with these indicators was conducted, and the evaluation index for all prediction models in terms of the COV was estimated. In total, using 109 models, the model with the highest estimated BA (76.9) at 100% COV was MMI-VOTE1, as reported by Meiji Pharmaceutical University (MPU). The best models for MCC, F1, and PC1 were all MMI-STK1, also reported by MPU. All the models reported by MPU ranked in the top four. MMI-STK1 was estimated to have F1 scores of 59.2, 61.5, and 63.1 at COV levels of 90%, 60%, and 30%, respectively. These findings highlight the current state and potential of the Ames prediction technology.

Supporting the applicability and transferability of the "Sortir Ensemble & Se Respecter" program in France / Accompagner l'applicabilité et la transférabilité du programme « Sortir Ensemble & Se Respecter ¼ en Frances

INTRODUCTION: The prevention of intimate partner violence (IPV) among young people is a major challenge for public policies. Nevertheless, there are a few prevention programs that have proven effective in France. "Sortir Ensemble & Se Respecter" (SE&SR) is a Swiss adaptation of "Safe Dates," an American intervention program that has reduced violent behavior by young perpetrators and victims of IPV alike. The aim of this article is to analyze the applicability and "potential transferability" of SE&SR in France. METHODS: We described the SE&SR intervention by explaining the intervention theory, the key functions (i.e., the "ingredients" allowing the SE&SR program to work), and we commented on its applicability from a perspective of adapting and transferring it to the French context. We used the ASTAIRE tool and the FIC (key functions, implementation, context) approach. RESULTS: The intervention theory highlighted various factors, acting at the individual level (i.e., beliefs/representations, knowledge, life skills) and at the level of the living environment (i.e., facilities welcoming young people; families; public policies; networks of actors), that can prevent IPV among young people. Ten key functions have been identified, revealing the "skeleton" of the Swiss intervention. We drew on these results to comment on the intervention's applicability, with a view to transferability, specifying the contextual elements to consider before implementing SE&SR in France. CONCLUSION: This study aims to make the process of evaluating applicability, with a view to transferring an evidence-based program to the French context, more accessible.

Introduction: La prévention des violences dans les relations amoureuses (VRA) chez les jeunes est un enjeu fort des politiques publiques. Néanmoins, il existe peu de programmes de prévention ayant fait la preuve de leur efficacité en France. « Sortir Ensemble & Se Respecter ¼ (SE&SR) est une adaptation suisse de « Safe Dates ¼, un programme d'intervention américain qui a montré des résultats en matière de réduction des comportements violents tant du côté des jeunes victimes que des auteurs. L'objectif de cet article est d'analyser l'applicabilité et la « potentielle transférabilité ¼ de SE&SR en France. Méthodes: L'approche adoptée consistait à décrire l'intervention SE&SR en explicitant la théorie d'intervention, les fonctions clés (soit les « ingrédients ¼ permettant que le programme SE&SR fonctionne) et en proposant des commentaires d'applicabilité en vue d'une transférabilité adaptée au contexte français. L'outil ASTAIRE et la démarche FIC ont été utilisés. Résultats: La théorie d'intervention a mis en évidence différents facteurs, agissant au niveau individuel (i.e. connaissances, croyances/représentations, compétences psychosociales) et au niveau des milieux de vie (i.e. structures accueillant les jeunes, familles, politiques publiques/réseaux d'acteurs), qui peuvent prévenir les VRA chez les jeunes. Dix fonctions clés ont été identifiées, dégageant le « squelette ¼ de l'intervention suisse. À la suite de ces résultats, des commentaires d'applicabilité en vue d'une transférabilité ont permis de préciser les éléments de contexte à prendre en compte avant la mise en œuvre de SE&SR en France. Conclusion: Cette étude souhaite rendre accessibles les process d'applicabilité en vue d'une transférabilité d'un programme probant en contexte français.

Simulation Tests of Methods in Evolution, Ecology, and Systematics: Pitfalls, Progress, and Principles.

Complex statistical methods are continuously developed across the fields of ecology, evolution, and systematics (EES). These fields, however, lack standardized principles for evaluating methods, which has led to high variability in the rigor with which methods are tested, a lack of clarity regarding their limitations, and the potential for misapplication. In this review, we illustrate the common pitfalls of method evaluations in EES, the advantages of testing methods with simulated data, and best practices for method evaluations. We highlight the difference between method evaluation and validation and review how simulations, when appropriately designed, can refine the domain in which a method can be reliably applied. We also discuss the strengths and limitations of different evaluation metrics. The potential for misapplication of methods would be greatly reduced if funding agencies, reviewers, and journals required principled method evaluation.

Novel approach for verification of a human PBPK modeling strategy using chimeric mice in the health risk assessment of epyrifenacil.

In the human risk assessment by physiologically based pharmacokinetic modeling (PBPK), verification of the modeling strategy and confirmation of the reliability of the output data are important when the clinical data are not available. A new herbicide, epyrifenacil, is metabolized to S-3100-CA in mammals and causes hepatotoxicity in mice. S-3100-CA is transferred to the liver by transporters and eliminated by biliary excretion and metabolism. In the previous human PBPK research, we succeeded in predicting S-3100-CA pharmacokinetics by obtaining human hepatic parameters from chimeric mice with humanized liver after we checked the model's quantitative performance using mouse experimental data. To further enhance the reliability of human PBPK data, verification of the following two points was considered effective: 1) verification of model applicability to pharmacokinetics prediction in multiple animal species, and 2) verification of the parameter acquisition methods. In this study, we applied the same modeling strategy to rats, i.e., we obtained rat hepatic parameters for PBPK from chimeric mice with rat hepatocytes, not from rats. As the simulation results, rat internal dosimetry was precisely reproduced, although it tended to be slightly overestimated by approximately two times. From the results of the sensitivity analysis, this overestimation was mainly due to hepatic parameters from chimeric mice. Therefore, it is suggested that a similar slight prediction error may occur also in human PBPK using chimeric mice, but considering the degree of error, it can be said that our modeling strategy is robust and the predicted human internal dosimetry in the previous research is reliable.

Improvement of multi-task learning by data enrichment: application for drug discovery.

Multi-task learning in deep neural networks has become a topic of growing importance in many research fields, including drug discovery. However, applying multi-task learning poses new challenges in improving prediction performance. This study investigated the potential of training data enrichment to enhance multi-task model prediction quality in drug discovery. The study evaluated four scenarios with varying degrees of information capacity of the training data and applied two types of test data to evaluate prediction performance. We used three datasets: ViralChEMBL, which consisted of binary activities of compounds against viral species, was applied for the classification task; pQSAR(159) and pQSAR(4267), which consisted of bio-activities of compounds and assays from the research of the profile-QSAR method, were applied for regression tasks. We built multi-task models based on the feed-forward DNNs using the PyTorch framework. Our findings showed that training data enrichment could be an effective means of enhancing prediction performance in multi-task learning, but the degree of improvement depends on the quality of the training data. The more unique compounds and targets the training data included, the more new compound-target interactions are required for prediction improvement. Also, we found out that even using multi-task learning, one could not predict the interactions of compounds that are highly dissimilar from those used for model training. The study provides some recommendations for effectively employing multi-task learning in drug discovery to improve prediction accuracy and facilitate the discovery of novel drug candidates.

A Graph Neural Network Model with a Transparent Decision-Making Process Defines the Applicability Domain for Environmental Estrogen Screening.

The application of deep learning (DL) models for screening environmental estrogens (EEs) for the sound management of chemicals has garnered significant attention. However, the currently available DL model for screening EEs lacks both a transparent decision-making process and effective applicability domain (AD) characterization, making the reliability of its prediction results uncertain and limiting its practical applications. To address this issue, a graph neural network (GNN) model was developed to screen EEs, achieving accuracy rates of 88.9% and 92.5% on the internal and external test sets, respectively. The decision-making process of the GNN model was explored through the network-like similarity graphs (NSGs) based on the model features (FT). We discovered that the accuracy of the predictions is dependent on the feature distribution of compounds in NSGs. An AD characterization method called ADFT was proposed, which excludes predictions falling outside of the model's prediction range, leading to a 15% improvement in the F1 score of the GNN model. The GNN model with the AD method may serve as an efficient tool for screening EEs, identifying 800 potential EEs in the Inventory of Existing Chemical Substances of China. Additionally, this study offers new insights into comprehending the decision-making process of DL models.

Determination of Microplastics' Vertical Concentration Transport (Rouse) Profiles in Flumes.

The transport behavior of microplastics (MPs) in the fluvial environment is scarcely researched. Besides settling velocities and critical shear stress for erosion, only a few investigations aim at MPs' vertical concentration profile and the underlying theory required. Therefore, this paper's experiments investigate vertical concentration profiles of approximately spherical MP particles (d = 1-3 mm) with densities close to water (0.91-1.13 g/cm3) in flow channels, coupling them with fundamental theory for the first time. The experiments were conducted in a tiling flume (slope of 0-2.4%) at 67 and 80 mm water depth, with a turbulent flow, velocities ranging from 0.4 to 1.8 m/s, and turbulence kinetic energy from 0.002 to 0.08 m2/s2. The measured profiles confirm the assumption that the concentration profile shapes of settling plastics are similar to those of sediments and running reversed for buoyant plastics. Furthermore, the hypothesis of the Rouse formula's applicability for floating and sinking plastics could be confirmed for approximately uniform flows. Future studies tying in with this research should increase particle properties and hydraulic parameter variation.

Applicability Domains Based on Molecular Graph Contrastive Learning Enable Graph Attention Network Models to Accurately Predict 15 Environmental End Points.

In silico models for predicting physicochemical properties and environmental fate parameters are necessary for the sound management of chemicals. This study employed graph attention network (GAT) algorithms to construct such models on 15 end points. The results showed that the GAT models outperformed the previous state-of-the-art models, and their performance was not influenced by the presence or absence of compounds with certain structures. Molecular similarity density (ρs) was found to be a key metrics characterizing data set modelability, in addition to the proportion of compounds at activity cliffs. By introducing molecular graph (MG) contrastive learning, MG-based ρs and molecular inconsistency in activities (IA) were calculated and employed for characterizing the structure-activity landscape (SAL)-based applicability domain ADSAL{ρs, IA}. The GAT models coupled with ADSAL{ρs, IA} significantly improved the prediction coefficient of determination (R2) on all the end points by an average of 14.4% and enabled all the end points to have R2 > 0.9, which could hardly be achieved previously. The models were employed to screen persistent, mobile, and/or bioaccumulative chemicals from inventories consisting of about 106 chemicals. Given the current state-of-the-art model performance and coverage of the various environmental end points, the constructed models with ADSAL{ρs, IA} may serve as benchmarks for future efforts to improve modeling efficacy.