Graph neural networks-enhanced relation prediction for ecotoxicology (GRAPE).

Exposure to toxic chemicals threatens species and ecosystems. This study introduces a novel approach using Graph Neural Networks (GNNs) to integrate aquatic toxicity data, providing an alternative to complement traditional in vivo ecotoxicity testing. This study pioneers the application of GNN in ecotoxicology by formulating the problem as a relation prediction task. GRAPE's key innovation lies in simultaneously modelling 444 aquatic species and 2826 chemicals within a graph, leveraging relations from existing datasets where informative species and chemical features are augmented to make informed predictions. Extensive evaluations demonstrate the superiority of GRAPE over Logistic Regression (LR) and Multi-Layer Perceptron (MLP) models, achieving remarkable improvements of up to a 30% increase in recall values. GRAPE consistently outperforms LR and MLP in predicting novel chemicals and new species. In particular, GRAPE showcases substantial enhancements in recall values, with improvements of ≥ 100% for novel chemicals and up to 13% for new species. Specifically, GRAPE correctly predicts the effects of novel chemicals (104 out of 126) and effects on new species (7 out of 8). Moreover, the study highlights the effectiveness of the proposed chemical features and induced network topology through GNN for accurately predicting metallic (74 out of 86) and organic (612 out of 674) chemicals, showcasing the broad applicability and robustness of the GRAPE model in ecotoxicological investigations. The code/data are provided at https://github.com/csiro-robotics/GRAPE.

Gas adsorption and framework flexibility of CALF-20 explored via experiments and simulations.

In 2021, Svante, in collaboration with BASF, reported successful scale up of CALF-20 production, a stable MOF with high capacity for post-combustion CO2 capture which exhibits remarkable stability towards water. CALF-20's success story in the MOF commercialisation space provides new thinking about appropriate structural and adsorptive metrics important for CO2 capture. Here, we combine atomistic-level simulations with experiments to study adsorptive properties of CALF-20 and shed light on its flexible crystal structure. We compare measured and predicted CO2 and water adsorption isotherms and explain the role of water-framework interactions and hydrogen bonding networks in CALF-20's hydrophobic behaviour. Furthermore, regular and enhanced sampling molecular dynamics simulations are performed with both density-functional theory (DFT) and machine learning potentials (MLPs) trained to DFT energies and forces. From these simulations, the effects of adsorption-induced flexibility in CALF-20 are uncovered. We envisage this work would encourage development of other MOF materials useful for CO2 capture applications in humid conditions.

Preparation, characterization, and photocatalytic performance of a PVDF/cellulose membrane modified with nano Fe3O4 for removal of methylene blue using RSM under visible light.

In this work, a polymeric membrane-based polyvinylidene fluoride coated with cellulose and loaded with iron oxide nanoparticles (PVDF/cellulose/Fe3O4) was synthesized and was characterized using FESEM, XRD, AFM, and contact angle measurements. The activity and modification of the PVDF/cellulose/Fe3O4 membrane under visible light for the removal of methylene blue were studied using the central composite design. The effect of influential variables such as pH, methylene blue concentration, amount of Fe3O4 in the membrane, and irradiation time on MB removal was investigated. Analysis of variance was used to determine the significance of experimental factors and their interactions. About 72.5% methylene blue removal using the PVDF/cellulose/Fe3O4 membrane under visible light was achieved at optimum conditions of a pH of 9, methylene blue concentration of 600 mg L-1, Fe3O4 amount of 0.03 g, and irradiation time of 117 min. Finally, results confirmed that the proposed membrane has good performance for methylene blue removal under visible light.

CL3: Generalization of Contrastive Loss for Lifelong Learning.

Lifelong learning portrays learning gradually in nonstationary environments and emulates the process of human learning, which is efficient, robust, and able to learn new concepts incrementally from sequential experience. To equip neural networks with such a capability, one needs to overcome the problem of catastrophic forgetting, the phenomenon of forgetting past knowledge while learning new concepts. In this work, we propose a novel knowledge distillation algorithm that makes use of contrastive learning to help a neural network to preserve its past knowledge while learning from a series of tasks. Our proposed generalized form of contrastive distillation strategy tackles catastrophic forgetting of old knowledge, and minimizes semantic drift by maintaining a similar embedding space, as well as ensures compactness in feature distribution to accommodate novel tasks in a current model. Our comprehensive study shows that our method achieves improved performances in the challenging class-incremental, task-incremental, and domain-incremental learning for supervised scenarios.

The Prevalence of Hypertension and Obesity in Iranian Professional Drivers.

Background: Professional driving is associated with overworking, lack of physical activity, and high stress, which are susceptible to cardiovascular diseases (CVDs). We aimed to determine the prevalence of hypertension and obesity in Iranian professional drivers. Methods: Overall, 132,452 drivers were included by census sampling methods and those who did not pass periodic examinations were excluded. Demographics and anthropometric data, including height and weight and the driver's blood pressure, were recorded. The criteria for hypertension assumed as the systolic blood pressure ≥ 130 mm and/or diastolic blood pressure ≥ 80 mm, and the criteria for prehypertension assumed as 120-129 systolic and < 80 mm Hg. In addition, body mass index (BMI) ≥ 25 is assumed as overweight, and BMI ≥ 30 is assumed as obesity. Results: Overall, 113,856 male drivers were included in the final analysis. The prevalence of HTN, pre-HTN, and abnormal blood pressure (HTN + pre-HTN) was calculated to be 14.2%, 57.4%, and 71.6%, respectively. Khuzestan, West Azerbaijan, and Yazd had the most prevalence of abnormal blood pressure. The prevalence of overweight, obesity, and abnormal weight (overweight + obesity) was calculated to be 50.9%, 22.6%, and 73.5%, respectively, and the northwest provinces had the highest prevalence of abnormal weight. Conclusion: Professional Iranian drivers have a high prevalence of abnormal blood pressure and weight associated with job-related risk factors. Preventive measures should be taken to confront a possible outbreak of CVDs in this population.

Augmented Reality for Enhanced Visualization of MOF Adsorbents.

Augmented reality (AR) is an emerging technique used to improve visualization and comprehension of complex 3D materials. This approach has been applied not only in the field of chemistry but also in real estate, physics, mechanical engineering, and many other areas. Here, we demonstrate the workflow for an app-free AR technique for visualization of metal-organic frameworks (MOFs) and other porous materials to investigate their crystal structures, topology, and gas adsorption sites. We think this workflow will serve as an additional tool for computational and experimental scientists working in the field for both research and educational purposes.

Subspace distillation for continual learning.

An ultimate objective in continual learning is to preserve knowledge learned in preceding tasks while learning new tasks. To mitigate forgetting prior knowledge, we propose a novel knowledge distillation technique that takes into the account the manifold structure of the latent/output space of a neural network in learning novel tasks. To achieve this, we propose to approximate the data manifold up-to its first order, hence benefiting from linear subspaces to model the structure and maintain the knowledge of a neural network while learning novel concepts. We demonstrate that the modeling with subspaces provides several intriguing properties, including robustness to noise and therefore effective for mitigating Catastrophic Forgetting in continual learning. We also discuss and show how our proposed method can be adopted to address both classification and segmentation problems. Empirically, we observe that our proposed method outperforms various continual learning methods on several challenging datasets including Pascal VOC, and Tiny-Imagenet. Furthermore, we show how the proposed method can be seamlessly combined with existing learning approaches to improve their performances. The codes of this article will be available at https://github.com/csiro-robotics/SDCL.

The study of the removal of penconazole fungicide from surface water using carboxymethyl tragacanth-based hydrogel grafted with poly (acrylic acid-co-acrylamide).

In this study, a polymeric adsorbent based on carboxymethyl tragacanth (CMT) grafted by poly acrylic acid-co-acrylamide (AAc-co-AAm) synthesized by radical polymerization for the first time was used to remove the fungicide penconazole (PEN) or Topas 20% from surface water. The parameters of solution pH, adsorption isotherm, and adsorption kinetics of PEN were studied by the synthetic adsorbent. The surface morphology and functional groups of CMT-g-poly (AAc-co-AAm) were confirmed by XRD, SEM and FT-IR techniques. Adsorption of PEN on CMT-g-poly (AAc-co-AAm) follows the Freundlich and pseudo-second-order models. The significant maximum adsorption capacity of the synthesized polymer was found to be 196.08 mg/g. The synthetic adsorbent had good reproducibility in PEN removal for up to 5 cycles. CMT-g-poly (AAc-co-AAm) is a cost-effective and non-toxic adsorbent for the decontamination of surface water from pesticides.

Exploiting Field Dependencies for Learning on Categorical Data.

Traditional approaches for learning on categorical data underexploit the dependencies between columns (a.k.a. fields) in a dataset because they rely on the embedding of data points driven alone by the classification/regression loss. In contrast, we propose a novel method for learning on categorical data with the goal of exploiting dependencies between fields. Instead of modelling statistics of features globally (i.e., by the covariance matrix of features), we learn a global field dependency matrix that captures dependencies between fields and then we refine the global field dependency matrix at the instance-wise level with different weights (so-called local dependency modelling) w.r.t. each field to improve the modelling of the field dependencies. Our algorithm exploits the meta-learning paradigm, i.e., the dependency matrices are refined in the inner loop of the meta-learning algorithm without the use of labels, whereas the outer loop intertwines the updates of the embedding matrix (the matrix performing projection) and global dependency matrix in a supervised fashion (with the use of labels). Our method is simple yet it outperforms several state-of-the-art methods on six popular dataset benchmarks. Detailed ablation studies provide additional insights into our method.

DigiMOF: A Database of Metal-Organic Framework Synthesis Information Generated via Text Mining.

The vastness of materials space, particularly that which is concerned with metal-organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties.

Computational Characterization of Zr-Oxide MOFs for Adsorption Applications.

Zr-oxide secondary building units construct metal-organic framework (MOF) materials with excellent gas adsorption properties and high mechanical, thermal, and chemical stability. These attributes have led Zr-oxide MOFs to be well-recognized for a wide range of applications, including gas storage and separation, catalysis, as well as healthcare domain. Here, we report structure search methods within the Cambridge Structural Database (CSD) to create a curated subset of 102 Zr-oxide MOFs synthesized to date, bringing a unique record for all researchers working in this area. For the identified structures, we manually corrected the proton topology of hydroxyl and water molecules on the Zr-oxide nodes and characterized their textural properties, Brunauer-Emmett-Teller (BET) area, and topology. Importantly, we performed systematic periodic density functional theory (DFT) calculations comparing 25 different combinations of basis sets and functionals to calculate framework partial atomic charges for use in gas adsorption simulations. Through experimental verification of CO2 adsorption in selected Zr-oxide MOFs, we demonstrate the sensitivity of CO2 adsorption predictions at the Henry's regime to the choice of the DFT method for partial charge calculations. We characterized Zr-MOFs for their CO2 adsorption performance via high-throughput grand canonical Monte Carlo (GCMC) simulations and revealed how the chemistry of the Zr-oxide node could have a significant impact on CO2 uptake predictions. We found that the maximum CO2 uptake is obtained for structures with the heat of adsorption values >25 kJ/mol and the largest cavity diameters of ca. 6-7 Å. Finally, we introduced augmented reality (AR) visualizations as a means to bring adsorption phenomena alive in porous adsorbents and to dynamically explore gas adsorption sites in MOFs.

The development of a comprehensive toolbox based on multi-level, high-throughput screening of MOFs for CO/N2 separations.

The separation of CO/N2 mixtures is a challenging problem in the petrochemical sector due to the very similar physical properties of these two molecules, such as size, molecular weight and boiling point. To solve this and other challenging gas separations, one requires a holistic approach. The complexity of a screening exercise for adsorption-based separations arises from the multitude of existing porous materials, including metal-organic frameworks. Besides, the multivariate nature of the performance criteria that needs to be considered when designing an optimal adsorbent and a separation process - i.e. an optimal material requires fulfillment of several criteria simultaneously - makes the screening challenging. To address this, we have developed a multi-scale approach combining high-throughput molecular simulation screening, data mining and advanced visualization, as well as process system modelling, backed up by experimental validation. We have applied our recent advances in the engineering of porous materials' morphology to develop advanced monolithic structures. These conformed, shaped monoliths can be used readily in industrial applications, bringing a valuable strategy for the development of advanced materials. This toolbox is flexible enough to be applied to multiple adsorption-based gas separation applications.

Hydrogen storage in MOFs: Machine learning for finding a needle in a haystack.

In recent years, machine learning (ML) has grown exponentially within the field of structure property predictions in materials science. In this issue of Patterns, Ahmed and Siegel scrutinize several redeveloped ML techniques for systematic investigations of over 900,000 metal-organic framework (MOF) structures, taken from 19 databases, to discover new, potentially record-breaking, hydrogen-storage materials.

Data visualization for Industry 4.0: A stepping-stone toward a digital future, bridging the gap between academia and industry.

Here, we analyze the potential of advanced data-visualization dashboards as an enabling technology for Industry 4.0. High-dimensional, real-time visualization allows the graphical expression of complex process variables at a fraction of the cost of full-scale digitalization. It is therefore a more achievable goal for smaller firms looking to transition to digital manufacturing and poses a realistic stepping-stone approach for Industry 4.0.

Catalyst-Enabled In Situ Linkage Reduction in Imine Covalent Organic Frameworks.

New linkages for covalent organic frameworks (COFs) have been continuously pursued by chemists as they serve as the structure and property foundation for the materials. Developing new reaction types or modifying known linkages have been the only two methods to create new COF linkages. Herein, we report a novel strategy that uses H3PO3 as a bifunctional catalyst to achieve amine-linked COFs from readily available amine and aldehyde linkers. The acidic proton of H3PO3 catalyzes the imine framework formation, which is then in situ reduced to the amine COF by the reductive P-H moiety. The amine-linked COF outperforms its imine analogue in promoting Knoevenagel condensation because of the more basic sites and higher stability.

Pt(II)-Decorated Covalent Organic Framework for Photocatalytic Difluoroalkylation and Oxidative Cyclization Reactions.

A new covalent organic framework (COF) based on imine bonds was assembled from 2-(4-formylphenyl)-5-formylpyridine and 1,3,6,8-tetrakis(4-aminophenyl)pyrene, which showed an interesting dual-pore structure with high crystallinity. Postmetallation of the COF with Pt occurred selectively at the N donor (imine and pyridyl) in the larger pores. The metallated COF served as an excellent recyclable heterogeneous photocatalyst for decarboxylative difluoroalkylation and oxidative cyclization reactions.

Wiz: A Web-Based Tool for Interactive Visualization of Big Data.

In an age of information, visualizing and discerning meaning from data is as important as its collection. Interactive data visualization addresses both fronts by allowing researchers to explore data beyond what static images can offer. Here, we present Wiz, a web-based application for handling and visualizing large amounts of data. Wiz does not require programming or downloadable software for its use and allows scientists and non-scientists to unravel the complexity of data by splitting their relationships through 5D visual analytics, performing multivariate data analysis, such as principal component and linear discriminant analyses, all in vivid, publication-ready figures. With the explosion of high-throughput practices for materials discovery, information streaming capabilities, and the emphasis on industrial digitalization and artificial intelligence, we expect Wiz to serve as an invaluable tool to have a broad impact in our world of big data.

Thermal Infrared Imaging Can Differentiate Skin Temperature Changes Associated With Intense Single Leg Exercise, But Not With Delayed Onset of Muscle Soreness.

Muscle damage and soreness associated with increased exercise training loads or unaccustomed activity can be debilitating and impact the quality of subsequent activity/performance. Current techniques to assess muscle soreness are either time consuming, invasive or subjective. Infrared thermography has been identified as a quick, non-invasive, portable and athlete friendly method of assessing skin temperature. This study assessed the capability of thermal infrared imaging to detect skin temperature changes that may accompany the inflammatory response associated with delayed onset muscular soreness (DOMS). Eight recreationally trained participants (age 25 ± 3 years, mass 74.9 ± 13.6 kg, training minutes 296 ± 175 min·wk-1) completed 6 sets of 25 maximal concentric/eccentric contractions of the right knee flexors/extensors on a dynamometer to induce muscle damage and DOMS. The left knee extensors acted as a non-exercise control. Neuromuscular performance, subjective pain assessment and infrared thermography were undertaken at baseline, 24 and 48 hr post the DOMS-inducing exercise protocol. Data were analysed using Bayesian hierarchical regression and Cohen's d was also calculated. Maximal voluntary contraction torque was statistically lower at 24 hr (d = -0.70) and 48 hr (d = -0.52) compared to baseline, after the DOMS-inducing exercise protocol. These neuromuscular impairments coincided with statistically higher ratings of muscle soreness at 24 hr (d = 0.96) and 48 hr (d = 0.48). After adjusting for ambient temperature, anterior thigh skin temperature was statistically elevated at 24 hr, but not 48 hr, compared with baseline, in both the exercised and non-exercised leg. Thigh temperature was not different statistically between legs at these time points. Infrared imaging was able to detect elevations in skin temperature, at 24 hrs after the DOMS inducing exercise protocol, in both the exercised and non-exercised thigh. Elevations in the skin temperature of both thighs, potentially identifies a systemic inflammatory response occurring at 24 hr after the DOMS-inducing exercise protocol.

(Thio)urea-Based Covalent Organic Framework as a Hydrogen-Bond-Donating Catalyst.

Two-dimensional urea- and thiourea-containing covalent organic frameworks (COFs) were synthesized at ambient conditions at large scale within 1 h in the absence of an acid catalyst. The site-isolated urea and thiourea in the COF showed enhanced catalytic efficiency as a hydrogen-bond-donating organocatalyst compared to the molecular counterparts in epoxide ring-opening reaction, aldehyde acetalization, and Friedel-Crafts reaction. The COF catalysts also had excellent recyclability.

Targeted classification of metal-organic frameworks in the Cambridge structural database (CSD).

Large-scale targeted exploration of metal-organic frameworks (MOFs) with characteristics such as specific surface chemistry or metal-cluster family has not been investigated so far. These definitions are particularly important because they can define the way MOFs interact with specific molecules (e.g. their hydrophilic/phobic character) or their physicochemical stability. We report here the development of algorithms to break down the overarching family of MOFs into a number of subgroups according to some of their key chemical and physical features. Available within the Cambridge Crystallographic Data Centre's (CCDC) software, we introduce new approaches to allow researchers to browse and efficiently look for targeted MOF families based on some of the most well-known secondary building units. We then classify them in terms of their crystalline properties: metal-cluster, network and pore dimensionality, surface chemistry (i.e. functional groups) and chirality. This dynamic database and family of algorithms allow experimentalists and computational users to benefit from the developed criteria to look for specific classes of MOFs but also enable users - and encourage them - to develop additional MOF queries based on desired chemistries. These tools are backed-up by an interactive web-based data explorer containing all the data obtained. We also demonstrate the usefulness of these tools with a high-throughput screening for hydrogen storage at room temperature. This toolbox, integrated in the CCDC software, will guide future exploration of MOFs and similar materials, as well as their design and development for an ever-increasing range of potential applications.