Search

The ability to predict cell-permeable candidate molecules has great potential to assist drug discovery projects. Large molecules that lie beyond the Rule of Five (bRo5) are increasingly important as drug candidates and tool molecules for chemical biology. However, such large molecules usually do not cross cell membranes and cannot access intracellular targets or be developed as orally bioavailable drugs. Here, we describe a random forest (RF) machine learning model for the prediction of passive membrane permeation rates developed using a set of over 1000 bRo5 macrocyclic compounds. The model is based on easily calculated chemical features/descriptors as independent variables. Our random forest (RF) model substantially outperforms a multiple linear regression model based on the same features and achieves better performance metrics than previously reported models using the same underlying data. These features include: (1) polar surface area in water, (2) the octanol-water partitioning coefficient, (3) the number of hydrogen-bond donors, (4) the sum of the topological distances between nitrogen atoms, (5) the sum of the topological distances between nitrogen and oxygen atoms, and (6) the multiple molecular path count of order 2. The last three features represent molecular flexibility, the ability of the molecule to adopt different conformations in the aqueous and membrane interior phases, and the molecular "chameleonicity." Guided by the model, we propose design guidelines for membrane-permeating macrocycles. It is anticipated that this model will be useful in guiding the design of large, bioactive molecules for medicinal chemistry and chemical biology applications.

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.

Funder: Finnish Foundation for Cardiovascular Research, Funder: Finnish Diabetes Research Foundation, Funder: University of Helsinki, Funder: Helsinki University Hospital Government Research Funds, Funder: University of Helsinki and Helsinki University Hospital Government Research, Funder: The Finnish Medical Foundation, Weight loss and increased physical activity may promote beneficial modulation of the metabolome, but limited evidence exists about how very low-level weight loss affects the metabolome in previously non-obese active individuals. Following a weight loss period (21.1 ± 3.1 weeks) leading to substantial fat mass loss of 52% (−7.9 ± 1.5 kg) and low body fat (12.7 ± 4.1%), the liquid chromatography-mass spectrometry-based metabolic signature of 24 previously young, healthy, and normal weight female physique athletes was investigated. We observed uniform increases (FDR < 0.05) in bile acids, very-long-chain free fatty acids (FFA), and oxylipins, together with reductions in unsaturated FFAs after weight loss. These widespread changes, especially in the bile acid profile, were most strongly explained (FDR < 0.05) by changes in android (visceral) fat mass. The reported changes did not persist, as all of them were reversed after the subsequent voluntary weight regain period (18.4 ± 2.9 weeks) and were unchanged in non-dieting controls (n = 16). Overall, we suggest that the reported changes in FFA, bile acid, and oxylipin profiles reflect metabolic adaptation to very low levels of fat mass after prolonged periods of intense exercise and low-energy availability. However, the effects of the aforementioned metabolome subclass alteration on metabolic homeostasis remain controversial, and more studies are warranted to unravel the complex physiology and potentially associated health implications. In the end, our study reinforced the view that transient weight loss seems to have little to no long-lasting molecular and physiological effects.

Exciton relaxation dynamics in multichromophore systems are often modeled using Redfield theory, where bath fluctuations mediate the relaxation among the exciton eigenstates. Identifying the vibrational or phonon modes that are implicated in exciton relaxation allows more detailed understanding of exciton dynamics. Here we focus on a well-studied light-harvesting II complex (LH2) isolated from the photosynthetic purple bacterium

Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods—data collection, featurization, model generation, and model evaluation—and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature–property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.

Oscillatory features observed in two-dimensional electronic spectroscopy (2DES) manifest coherent vibrational and electronic dynamics and even the interplay of them. Recently, we developed a 2DES technique utilizing a pair of synchronized repetition-frequency-stabilized lasers, which enables the wide dynamic range measurements of 2DES signals rapidly. Here, we apply this dual-laser 2DES technique to investigate the electronic energy transfer (EET) process in bacterial light-harvesting complex II consisting of B800 and B850 circular aggregates at ambient temperature, and the coherent vibrational wavepakcet associated with the EET between the two aggregates are measured. Examining the principal component analysis of the time-resolved 2DES signal, we found that the EET from B800 to low-lying B850 states is modulated by a low-frequency (156 cm-1) vibrational mode of the exciton donor (B800). This observation suggests that the donor transition density is modulated by this vibration, which, in turn, modulates the energy transfer dynamics.

This study presents the diversity of mosquitoes collected from communes, endemic with malaria and dengue, located in Khanh Hoa and Binh Phuoc Provinces, Vietnam. A total of 10,288 mosquitoes were collected in the village and forested sites using standard larval dippers, cow-baited traps, ultra-violet light traps, and mechanical aspirators. Mosquito taxa were identified morphologically and species complexes/groups were further characterized molecularly. Five genera of mosquitoes were morphologically identified: Anopheles Meigen (21 species), Aedes Meigen (2 species), Culex Linnaeus (5 species), Mansonia Blanchard sp., and Armigeres Theobald sp. The PCR-based identification methods allowed the distinction of members of Maculatus Group, Funestus Group, and Dirus Complex; and DNA barcodes enabled the further identification of the Barbirostris Complex. Data reported here include the first report of An. saeungae Taai & Harbach and An. wejchoochotei Taai & Harbach from Vietnam, and re-emphasizes the significance of using molecular data in an integrated systematic approach to identify cryptic species and better understand their role in disease transmission.

Ionic liquids (ILs) are well classified as designer solvents based on the ease of tailoring their properties through modifying the chemical structure of the cation and anion. However, while many structure–property relationships have been developed, these generally only identify the most dominant trends. Here, we have used machine learning on existing experimental data to construct robust models to produce meaningful predictions across a broad range of cation and anion chemical structures. Specifically, we used previously collated experimental data for the viscosity and conductivity of protic ILs [T. L. Greaves and C. J. Drummond, Chem. Rev. 115, 11379–11448 (2015)] as the inputs for multiple linear regression and neural network models. These were then used to predict the properties of all 1827 possible cation–anion combinations (excluding the input combinations). These models included the effect of water content of up to 5 wt. %. A selection of ten new protic ILs was then prepared, which validated the usefulness of the models. Overall, this work shows that relatively sparse data can be used productively to predict physicochemical properties of vast arrays of ILs.

The light-harvesting complex 2 (LH2) of purple bacteria is one of the most studied photosynthetic antenna complexes. Its symmetric structure and ring-like bacteriochlorophyll arrangement make it an ideal system for theoreticians and spectroscopists. LH2 complexes from most bacterial species are thought to have eightfold or ninefold symmetry, but recently a sevenfold symmetric LH2 structure from the bacterium Mch. purpuratum was solved by Cryo-Electron microscopy. This LH2 also possesses unique near-infrared absorption and circular dichroism (CD) spectral properties. Here we use an atomistic strategy to elucidate the spectral properties of Mch. purpuratum LH2 and understand the differences with the most commonly studied LH2 from Rbl. acidophilus. Our strategy exploits a combination of molecular dynamics simulations, multiscale polarizable quantum mechanics/molecular mechanics calculations, and lineshape simulations. Our calculations reveal that the spectral properties of LH2 complexes are tuned by site energies and exciton couplings, which in turn depend on the structural fluctuations of the bacteriochlorophylls. Our strategy proves effective in reproducing the absorption and CD spectra of the two LH2 complexes, and in uncovering the origin of their differences. This work proves that it is possible to obtain insight into the spectral tuning strategies of purple bacteria by quantitatively simulating the spectral properties of their antenna complexes.

Strong light-matter interaction leads to the formation of hybrid polariton states and alters the photophysical dynamics of organic materials and biological systems without modifying their chemical structure. Here, we experimentally investigated a well-known photosynthetic protein, light harvesting 2 complexes (LH2) from purple bacteria under strong coupling with the light mode of a Fabry-Perot optical microcavity. Using femtosecond pump probe spectroscopy, we analyzed the polariton dynamics of the strongly coupled system and observed a significant prolongation of the excited state lifetime compared with the bare exciton, which can be explained in terms of the exciton reservoir model. Our findings indicate the potential of tuning the dynamic of the whole photosynthetic unit, which contains several light harvesting complexes and reaction centers, with the help of strong exciton-photon coupling, and opening the discussion about possible design strategies of artificial photosynthetic devices.

All purple photosynthetic bacteria contain RC–LH1 ‘Core’ complexes. The structure of this complex from Rhodobacter sphaeroides, Rhodopseudomonas palustris and Thermochromatium tepidum has been solved using X-ray crystallography. Recently, the application of single particle cryo-EM has revolutionised structural biology and the structure of the RC–LH1 ‘Core’ complex from Blastochloris viridis has been solved using this technique, as well as the complex from the non-purple Chloroflexi species, Roseiflexus castenholzii. It is apparent that these structures are variations on a theme, although with a greater degree of structural diversity within them than previously thought. Furthermore, it has recently been discovered that the only phototrophic representative from the phylum Gemmatimonadetes, Gemmatimonas phototrophica, also contains a RC–LH1 ‘Core’ complex. At present only a low-resolution EM-projection map exists but this shows that the Gemmatimonas phototrophica complex contains a double LH1 ring. This short review compares these different structures and looks at the functional significance of these variations from two main standpoints: energy transfer and quinone exchange.

Water is a unique solvent that is ubiquitous in biology and present in a variety of solutions, mixtures, and materials settings. It therefore forms the basis for all molecular dynamics simulations of biological phenomena, as well as for many chemical, industrial, and materials investigations. Over the years, many water models have been developed, and it remains a challenge to find a single water model that accurately reproduces all experimental properties of water simultaneously. Here, we report a comprehensive comparison of structural and dynamic properties of 30 commonly used 3-point, 4-point, 5-point, and polarizable water models simulated using consistent settings and analysis methods. For the properties of density, coordination number, surface tension, dielectric constant, self-diffusion coefficient, and solvation free energy of methane, models published within the past two decades consistently show better agreement with experimental values compared to models published earlier, albeit with some notable exceptions. However, no single model reproduced all experimental values exactly, highlighting the need to carefully choose a water model for a particular study, depending on the phenomena of interest. Finally, machine learning algorithms quantified the relationship between the water model force field parameters and the resulting bulk properties, providing insight into the parameter-property relationship and illustrating the challenges of developing a water model that can accurately reproduce all properties of water simultaneously.

Water is a unique solvent that is ubiquitous in biology and present in a variety of solutions, mixtures, and materials settings. It therefore forms the basis for all molecular dynamics simulations of biological phenomena, as well as for many chemical, industrial, and materials investigations. Over the years, many water models have been developed, and it remains a challenge to find a single water model that accurately reproduces all experimental properties of water simultaneously. Here, we report a comprehensive comparison of structural and dynamic properties of 30 commonly used 3-point, 4-point, 5-point, and polarizable water models simulated using consistent settings and analysis methods. For the properties of density, coordination number, surface tension, dielectric constant, self-diffusion coefficient, and solvation free energy of methane, models published within the past two decades consistently show better agreement with experimental values compared to models published earlier, albeit with some notable exceptions. However, no single model reproduced all experimental values exactly, highlighting the need to carefully choose a water model for a particular study, depending on the phenomena of interest. Finally, machine learning algorithms quantified the relationship between the water model force field parameters and the resulting bulk properties, providing insight into the parameter-property relationship and illustrating the challenges of developing a water model that can accurately reproduce all properties of water simultaneously.

C-radical borylation is an significant approach for the construction of carbon−boron bond. Photochemical borylation of aryl halides successfully applied this strategy. However, precise mechanisms, such as the generation of aryl radicals and the role of base additive(TMDAM) and water, remain controversy in these reactions. In this study, photochemical borylation of aryl halides has been researched by density functional theory (DFT) calculations. Indeed, the homolytic cleavage of the C−X bond under irradiation with UV-light is a key step for generation of aryl radicals. Nevertheless, the generation of aryl radicals may also undergo the process of single electron transfer and the heterolytic carbon-halogen bond cleavage sequence, and the latter is favorable during the reaction.

Janus particles, which are named after the two-faced Roman god Janus, have two distinct sides with different surface features, structures, and compositions. This asymmetric structure enables the combination of different or even incompatible physical, chemical, and mechanical properties within a single particle. Much effort has been focused on the preparation of Janus particles with high homogeneity, tunable size and shape, combined functionalities, and scalability. With their unique features, Janus particles have attracted attention in a wide range of applications such as in optics, catalysis, and biomedicine. As a biomedical device, Janus particles offer opportunities to incorporate therapeutics, imaging, or sensing modalities in independent compartments of a single particle in a spatially controlled manner. This may result in synergistic actions of combined therapies and multi-level targeting not possible in isotropic systems. In this review, we summarize the latest advances in employing Janus particles as therapeutic delivery carriers, in vivo imaging probes, and biosensors. Challenges and future opportunities for these particles will also be discussed.

Lyotropic liquid crystalline lipid nanomaterials have shown promise as delivery vehicles for small therapeutic drugs, protein, peptides, and in vivo imaging contrast agents. To design effective lipid-based delivery systems, it is important to understand and be able to predict their self-assembly processes. In this study, we utilized a machine learning approach to study the phase behavior of a nanoparticulate system consisting of a base lipid, monoolein, or phytantriol and varied the concentration of saturated and unsaturated fatty acids. The experimental data sets acquired by high throughput characterization techniques were used to train the “machine” using two separate models, i.e., multiple linear regression (MLR) and Bayesian regularized artificial neural networks (ANNs). The models were accurate (>70%) in predicting the phase behavior for data used to train the neural networks. The ANN model appeared to be more accurate than the MLR model in predicting mesophases. We then used the obtained ANN models ...

The bicontinuous lipidic cubic phase (LCP), which is based on the fundamental structure of the lipid bilayer, is increasingly used in a range of applications including drug delivery, in meso crystallization of membrane proteins, biosensors, and biofuel cells. The majority of LCPs investigated to date have been formulated from a single lipid or a combination of two lipids in water. Such systems lack tunability, with only a narrow range of lattice parameters adopted. In addition, the lipid bilayer of these materials lacks the complexity of natural cell membranes, which are composed of hundreds of different lipids and which may be essential to retaining the functionality of proteins embedded within them. In this work, we investigate the phase behavior of quaternary lipid-water systems consisting of three different lipids (monoolein-cholesterol-phospholipid) and water using a combination of experimental and simulation techniques. This study provides a large library of lipidic materials with bilayer compositions, which more effectively mimic the native cell membrane and significantly increased tunability based on nanostructural parameters such as lattice parameter, aqueous channel size, and bilayer thickness. Importantly, the library contained several extremely swollen cubic phases with a maximum lattice parameter of up to 342.5 Å. Many of these cubic phases were successfully dispersed into highly swollen cubosomes. The swollen cubic phases described in this article contain only uncharged lipids and are therefore particularly useful for applications with a high salt concentration, including encapsulation of larger therapeutic proteins and peptides for in vivo delivery, or for the crystallization of large membrane proteins such as GPCRs.(1).

The ability to tailor the constituent ions in ionic liquids (ILs) is highly advantageous as it provides access to solvents with a range of physicochemical properties. However, this benefit also leads to large compositional spaces that need to be explored to optimise systems, often involving time consuming experimental work. The use of machine learning methods is an effective way to gain insight based on existing data, to develop structure–property relationships and to allow the prediction of ionic liquid properties. Here we have applied machine learning models to experimentally determined rate constants of a representative organic process (the reaction of pyridine with benzyl bromide) in IL–acetonitrile mixtures. Multiple linear regression (MLREM) and artificial neural networks (BRANNLP) were both able to model the data well. The MLREM model was able to identify the structural features on the cations and anions that had the greatest effect on the rate constant. Secondly, predictive MLREM and BRANNLP models were developed from the full initial set of rate constant data. From these models, a large number of predictions (>9000) of rate constant were made for mixtures of different ionic liquids, at different proportions of ionic liquid and molecular solvent, at different temperatures. A selection of these predictions were tested experimentally, including through the preparation of novel ionic liquids, with overall good agreement between the predicted and experimental data. This study highlights the benefits of using machine learning methods on kinetic data in ionic liquid mixtures to enable the development of rigorous structure–property relationships across multiple variables simultaneously, and to predict properties of new ILs and experimental conditions.

The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.

Supplemental material, sj-docx-1-jdr-10.1177_00220345211024634 for Degradable RGD-Functionalized 3D-Printed Scaffold Promotes Osteogenesis by P.-C. Chang, Z.-J. Lin, H.-T. Luo, C.-C. Tu, W.-C. Tai, C.-H. Chang and Y.-C. Chang in Journal of Dental Research

Recently superconductivity and topological charge-density wave (CDW) were discovered in the Kagome metals $A$V$_3$Sb$_5$ ($A$ = Cs, Rb, and K), which have an ideal Kagome lattice of vanadium. Here we report resistance measurements on thin flakes of CsV$_3$Sb$_5$ to investigate the evolution of superconductivity and CDW with sample thickness. The CDW transition temperature ${\it T}_{\rm CDW}$ decreases from 94 K in bulk to a minimum of 82 K at thickness of 60 nm, then increases to 120 K as the thickness is reduced further to 4.8 nm (about five monolayers). Since the CDW order in CsV$_3$Sb$_5$ is quite three-dimensional (3D) in the bulk sample, the non-monotonic evolution of ${\it T}_{\rm CDW}$ with reducing sample thickness can be explained by a 3D to 2D crossover around 60 nm. Strikingly, the superconducting transition temperature ${\it T}_{\rm c}$ shows an exactly opposite evolution, increasing from 3.64 K in the bulk to a maximum of 4.28 K at thickness of 60 nm, then decreasing to 0.76 K at 4.8 nm. Such exactly opposite evolutions provide strong evidence for competing superconductivity and CDW, which helps us to understand these exotic phases in $A$V$_3$Sb$_5$ Kagome metals., Comment: 4 pages, 4 figures

Preventing biological contamination (biofouling) is key to successful development of novel surface and nanoparticle-based technologies in the manufacturing industry and biomedicine. Protein adsorption is a crucial mediator of the interactions at the bio – nano -materials interface but is not well understood. Although general, empirical rules have been developed to guide the design of protein-resistant surface coatings, they are still largely qualitative. Herein we demonstrate that this knowledge gap can be addressed by using machine learning approaches to extract quantitative relationships between the material surface chemistry and the protein adsorption characteristics. We illustrate how robust linear and non-linear models can be constructed to accurately predict the percentage of protein adsorbed onto these surfaces using lysozyme or fibrinogen as prototype common contaminants. Our computational models could recapitulate the adsorption of proteins on functionalised surfaces in a test set with an r2 of 0.82 and standard error of prediction of 13%. Using the same data set that enabled the development of the Whitesides rules, we discovered an extension to the original rules. We describe a workflow that can be applied to large, consistently obtained data sets covering a broad range of surface functional groups and protein types.

We report the 2.4 Å resolution structure of the light harvesting 2 complex (LH2) from Marichromatium (Mch.) purpuratum determined by electron cryo-microscopy. The structure contains a heptameric ring that is unique among all known LH2 structures, explaining the unusual spectroscopic properties of this bacterial antenna complex. Two sets of distinct carotenoids are identified in the structure, and a network of energy transfer pathways from the carotenoids to bacteriochlorophyll a molecules is shown. The geometry imposed by the heptameric ring controls the resonant coupling of the long wavelength energy absorption band. Together, these details reveal key aspects of the assembly and oligomeric form of purple bacterial LH2 complexes that were previously inaccessible by any technique.One Sentence SummaryThe structure of a heptameric LH2 antenna complex reveals new energy transfer pathways and the basis for assembling LH rings.

The advances of carbon usage for Carbon Fibre Reinforce Polymer (CFRP) structures led to multiple applications in a large number of industries. This chapter presents methods for CFRP material characterization and usage for aeronautic, automotive and satellite applications. The major CFRP components used for antennas and microwave applications within these industries are presented. The accelerated adoption of carbon-based composites, current challenges and future directions are also reported.

Mesophase structures of self-assembled lyotropic liquid crystalline nanoparticles are important factors that directly influence their ability to encapsulate and release drugs and their biological activities. However, it is difficult to predict and precisely control the mesophase behavior of these materials, especially in complex systems with several components. In this study, we report the controlled manipulation of mesophase structures of monoolein (MO) and phytantriol (PHYT) nanoparticles by adding unsaturated fatty acids (FAs). By using high throughput formulation and small-angle X-ray scattering characterization methods, the effects of FAs chain length, cis-trans isomerism, double bond location, and level of chain unsaturation on self-assembled systems are determined. Additionally, the influence of temperature on the phase behavior of these nanoparticles is analyzed. We found that in general, the addition of unsaturated FAs to MO and PHYT induces the formation of mesophases with higher Gaussian surface curvatures. As a result, a rich variety of lipid polymorphs are found to correspond with the increasing amounts of FAs. These phases include inverse bicontinuous cubic, inverse hexagonal, and discrete micellar cubic phases and microemulsion. However, there are substantial differences between the phase behavior of nanoparticles with trans FA, cis FAs with one double bond, and cis FAs with multiple double bonds. Therefore, the material library produced in this study will assist the selection and development of nanoparticle-based drug delivery systems with desired mesophase.

The development of low cost, portable diagnostic tools for in-field detection of viruses and other pathogenic microorganisms is in great demand but remains challenging. In this study, a novel approach based on reduced graphene oxide-polyaniline (rGO-PANi) film for the in situ detection of loop-mediated-isothermal-amplification (LAMP) products by means of open circuit potential measurement is proposed. The pH-sensitive conducting polymer PANi was electro-deposited onto rGO coated screen printed electrodes and tuned to be at the emeraldine state at which the pH sensitivity was maximized. By combining PANi and rGO, the pH sensitivity of the system was modulated up to about −64 mV per pH unit. This enabled the number of amplified amplicons resulting from the isothermal amplification process to be monitored. The sensor was then examined for monitoring LAMP reactions using Hepatitis B virus (HBV) as a model. This simple, low-cost, reproducible and sensitive interfacing layer is expected to provide a new possibility for designing point-of-care sensors under limited-resource conditions.

Heat release rate is an important fire reaction property used to quantify the flammability of composite materials in fire. In this study, an artificial neural network (ANN) model was developed to predict the heat release properties of composites. The ANN model was trained using 10,419 data points for heat release rate extracted from the results of cone calorimetry tests performed on 14 sets of composite laminates. Two machine learning algorithms of Multiple Linear Regression (MLR) and Bayesian regularized artificial neural network with Gaussian prior (BRANNGP) are compared. The composites used to demonstrate the predictive accuracy of the ANN model were phenolic-based laminates containing different types and amounts of flame retardant additives. The BRANNGP model is capable of predicting the heat release rate-time curve, peak heat release value and total heat release of the composites. In addition, the BRANNGP model with outlier-elimination strategy can estimate with good accuracy the complex non-linear relationship between heat release rate and heat flux exposure time without considering the mechanistic interactions between the input and output parameters.

Summary New photocatalysts are traditionally identified through trial-and-error methods. Machine learning has shown considerable promise for improving the efficiency of photocatalyst discovery from a large potential pool. Here, we describe a multi-step, target-driven consensus method using a stacking meta-learning algorithm that robustly predicts bandgaps and H2 evolution activities of photocatalysts. Trained on small datasets, these models can rapidly screen a large space (>10 million materials) to identify promising, non-toxic compounds as candidate water splitting photocatalysts. Two effective compounds and two controls possessing optimal bandgap values (∼2 eV) but not photoactivity as predicted by the models were synthesized. Their experimentally measured bandgaps and H2 evolution activities were consistent with the predictions. Conspicuously, the two compounds with strong photoactivities under UV and visible light are promising visible-light-driven water splitting photocatalysts. This study demonstrates the power of machine learning and the potential of big data to accelerate discovery of next-generation photocatalysts., Graphical abstract, Highlights • Stacking models predict bandgap and H2 evolution activity of oxide photocatalysts • Models predict robustly across a wide range of material structures • Models rapidly identify promising photocatalysts from 10 million materials • Four compounds are synthesized and confirm predicted results, Chemistry; Catalysis; Computational chemistry.

The development of green spaces in urban areas is rapidly on the rise as more people are keen to maintain a clean and green atmosphere around where they live and work. Also, the link between the physical world and the internet has been a driving force in enhancing people's quality of life which has resulted in the most recent and rising technologies, collectively referred to as the Internet of Things (IoT). The adoption of vertical gardens (VG) and/or vertical farms (VF) can be beneficial for maintaining a sustainable environment, as well as for expanding food security in an urban context around the world with limited land space. IoT technologies have the potential to be key enablers in the accelerated adoption of VG. In this study, we investigate the critical parameters for automating sustainable vertical gardening systems by using the IoT concept in smart cities towards smart living. This involves collection and review of data from 30 peer-reviewed publications published between 2004 and 2018, including real-world VG implementations. The key criteria considered include: (i) crop/plant type, (ii) VG topology (size), (iii) sensing data, (iv) used hardware (sensors, actuators, etc.), (v) power supplies, (vi) velocity or frequency of data collection, (vii) data storage method, (viii) communication technologies, (ix) data analysis methods/algorithm, (x) other used strategies, and (xi) countries that implemented VGs. The data were subsequently analyzed to obtain a detailed understanding of using IoT in VGs. The results of the analysis revealed that most of the studies used 6-20 tiers (40%) when implementing VGs, and the most popular crop was lettuce (28.6%). The sensors used were commonly connected to AC power and battery (each 44.4%), while only a small proportion of VGs used solar power (11.1%). The majority of IoT sensors used were to measure room temperature (22.5%), light intensity (21.1%), humidity level (14%) and soil nutrition (7%). The frequency of data collection by these sensors was between 1 and 3 minutes (42.8%). The frequently used data transmission technology was Zigbee and Wi-Fi (42.8%) for collecting sensor data from VGs. We also found that, using the server database, remote data management platform and cloud were the most popular data storage methods (each 25%). After data collection, many studies used threshold-based algorithms (50%) for the decision making, and the soil-based (42%) and hydroponic (38%) were the most popular plant cultivation technologies. The use of recycled and reused water (30%), solar power (20%) and controlled indoor environment, without sun or soil (20%) are some of the other essential considerations in VGs. Furthermore, it was found that the most significant focus on automation of VGs incorporating IoT were in USA (41.2%) and China (23.5%). The impact of vertical cultivation walls on human well-being was discussed. In addition to this, eight international patents on VGs have been analyzed to acquire an implementation understanding of autonomous control or using IoT in vertical gardens.

Ionic liquid containing solvent systems are candidates for very large compositional space exploration due to the immensity of the possible combination of ions and molecular species. The prediction of key properties of such multicomponent solvent systems plays a vital role in the design and optimization of their structures for specific applications. In this study, we have explored two machine learning algorithms for predicting the surface tension and liquid nanostructure of solvents containing a protic ionic liquid (PIL) with water and excess acid or base present. Machine learning algorithms of multiple linear regression (MLR) and Bayesian regularized artificial neural networks (ANNs) were used to develop semiempirical structure-property models for the data set, which was comprised of 207 surface tension and 80 liquid nanostructure data elements which we previously reported ( Phys. Chem. Chem. Phys. 2019, 21, 6810-6827). On the basis of the models, the significance levels for the impact of the alkyl chain length and the presence of hydroxyl groups on cation, type of anion, nonstoichiometry, and presence of water were elucidated. Both models are statistically applicable for designing new PIL containing solvent systems. Furthermore, the generated models were used to create response-surface plots, for both surface tension and liquid nanostructure, interpolated across the compositional space. An additional surface tension data set with 18 new data points within the same compositional space was used to test the prediction ability of models, and the results showed all of the models were successful for prediction. These machine learning approaches are highly suited to the development of structure-property relationships for ionic liquids and particularly for the increasing use of ionic liquid-molecular solvent mixtures.

Zinc oxide nanoparticles have found wide application due to their unique optoelectronic and photocatalytic characteristics. However, their safety aspects remain of critical concern, prompting the use of physicochemical modifications of pristine ZnO to reduce any potential toxicity. However, the relationships between these modifications and their effects on biology are complex and still relatively unexplored. To address this knowledge gap, a library of 45 types of ZnO nanoparticles with varying particle size, aspect ratio, doping type, doping concentration, and surface coating is synthesized, and their biological effects measured. Three biological assays measuring cell damage or stress are used to study the responses of human umbilical vein endothelial cells (HUVECs) or human hepatocellular liver carcinoma cells (HepG2) to the nanoparticles. These experimental data are used to develop quantitative and predictive computational models linking nanoparticle properties to cell viability, membrane integrity, and oxidative stress. It is found that the concentration of nanoparticles the cells are exposed to, the type of surface coating, the nature and extent of doping, and the aspect ratio of the particles make significant contributions to the cell toxicity of the nanoparticles tested. Our study shows that it is feasible to generate models that could be used to design or optimize nanoparticles with commercially useful properties that are also safe to humans and the environment.

Dispersed amphiphile-fatty acid systems are of great interest in drug delivery and gene therapies because of their potential for triggered release of their payload. The mesophase behavior of these systems is extremely complex and is affected by environmental factors such as drug loading, percentage and nature of incorporated fatty acids, temperature, pH, and so forth. It is important to study phase behavior of amphiphilic materials as the mesophases directly influence the release rate of the incorporated drugs. We describe a robust machine learning method for predicting the phase behavior of these systems. We have developed models for each mesophase that simultaneous and reliably model the effects of amphiphile and fatty acid structure, concentration, and temperature and that make accurate predictions of these mesophases for conditions not used to train the models.

Rodents are important reservoirs of many human pathogens transmitted via arthropod vectors. Arthropod-borne bacteria belonging to the family Rickettsiaceae cause acute febrile diseases in humans worldwide, but the real burdens of rickettsial diseases appear to be underestimated in Hanoi, Vietnam, because differential diagnosis on the basis of clinical signs and symptoms is confounded by the presence of other tropical infectious diseases with similar signs and symptoms. To know the prevalence of bacteria of the family Rickettsiaceae among small mammals in Hanoi, 519 animals thriving in the public places were captured and examined for the presence of bacterial sequences using duplex PCR. Nucleotide sequences specific for Orientia tsutsugamushi were detected in seven samples (1.3%). Out of seven animals, two were captured in a market, whereas five were in hospitals. None of the captured small mammals tested positive for the genus Rickettsia. The nucleotide sequence analysis of the genes encoding the 47-kDa high-temperature requirement A (47-kDa HtrA) and 56-kDa type-specific antigen (TSA) showed that these seven isolates were indistinguishable from each other. O. tsutsugamushi isolated in this study was closely related phylogenetically to the Gilliam strain, which was originally isolated at the border of Assam and Burma, rather than to those isolated in the central to southern part of Vietnam. It should be emphasized that Vietnamese hospitals were heavily infested by small rodents and some of them harbored O. tsutsugamushi. Strict hygienic control should be implemented to mitigate the potential risk posed by O. tsutsugamushi in hospital settings.

Nanomaterials are used increasingly in diagnostics and therapeutics, particularly for malignancies. Efficient targeting of nanoparticles to specific cells is an important requirement for the development of successful nanoparticle-based theranostics and personalized medicines. Gold nanoparticles are surface modified using a library of small organic molecules, and optionally folate, to investigate their ability to target four cell lines from common cancers, three having high levels of folate receptors expression. Uptake of these nanoparticles varies widely with surface chemistriy and cell lines. Sparse machine learning methods are used to computationally model surface chemistry-uptake relationships, to make quantitative predictions of uptake for new nanoparticle surface chemistries, and to elucidate molecular aspects of the interactions. The combination of combinatorial surface chemistry modification and machine learning models will facilitate the rapid development of targeted theranostics. Efficient targeting of nanoparticles to specific cells is an important requirement for the development of successful nanoparticle-based cancer theranostics and personalized medicines. The cancer cell targeting ability of gold nanoparticles coated with a library of small organic molecules plus folate is modeled. Computational models can predict the degree of uptake of the nanoparticles as a function of surface chemistry.

Most medicinal chemists understand that chemical space is extremely large, essentially infinite. Although high-throughput experimental methods allow exploration of drug-like space more rapidly, they are still insufficient to fully exploit the opportunities that such large chemical space offers. Evolutionary methods can synergistically blend automated synthesis and characterization methods with computational design to identify promising regions of chemical space more efficiently. We describe how evolutionary methods are implemented, and provide examples of published drug development research in which these methods have generated molecules with increased efficacy. We anticipate that evolutionary methods will play an important role in future drug discovery.

Online banking is a substantial service in the enlargement strategy of the modern bank sector. Nevertheless, this service has not been widely used, because of users are still scared of the risks of online transactions. Thus, the in-depth and distinct works of risks in risk awareness are an essential and meaningful assignment for the banking sector. This study investigates the role of perceived risk on intention to use online banking. The results of SEM (structural equation modelling) evidence risk factors (privacy risk, security risk, social risk, time risk, and financial-performance risk) in perceived risk, which has a negative effect on intention to use online banking in Vietnam. Research results can help to propose solutions for enhancing the safety and mitigating the risks in online banking.

While experimental assessment of the biological effects of nanomaterials is essential to properly assign risk to these materials, computational methods provide considerable promise in supplementing experimental approaches. Indeed, although the biological effects of nanomaterials will be more difficult to model than those of small molecules, drugs and chemicals, recent reports have shown that quantitative structure–activity relationships and quantum chemical methods can provide very useful mechanistic and predictive information for nanomaterials. In the present chapter, we explain why in silico computational methods are an essential addition to the nanomaterial research toolkit and why nanomaterials may be more difficult to model than single molecules, and provide examples of recent successful models of the biological effects of nanomaterials.

In meso crystallization uses bicontinuous cubic lipidic mesophases as matrices for the crystallization of membrane proteins. In this work, we look at the impact of a screen specifically marketed as compatible with the cubic mesophase, the Cubic crystallization screen (Emerald BioSystems), on the cubic mesophases formed by three different lipids: monoolein, monopalmitolein, and phytantriol. The Cubic screen was found to be compatible with cubic mesophase retention under most crystallization conditions for all three lipids studied. The effect of the individual components comprising the multicomponent screen was deconvoluted in two ways. Initially, the effect of specific poly(ethylene glycol) (PEG) and salt components on the cubic mesophase was determined using small-angle X-ray scattering (SAXS). The effect of high-molecular-weight (Mw) PEG was shown to dominate the phase behavior within the screen. The effect of additional salts present within the screen becomes important for low Mw PEG molecules. Finally,...

Ahead of Print article withdrawn by publisher

The increase in recent power failures, with negative impacts on humans and the economy, has been largely attributed to environmental effects and the aging of the power network. These have been accelerated in the last years due to two main factors: an increased load on the power network and material degradation owing to the presence of environmental pollutants. These factors together with specific weather conditions create the incipient conditions for power network degradation. In this paper, a review of the influence of environmental factors on high-voltage (HV) materials and components is provided. Sensing and artificial intelligence (AI) technologies developed to prevent the failure of the material structure and HV components are also reported.