Combinatorial screening yields discovery of 29 metal oxide photoanodes for solar fuel generation

Combinatorial synthesis combined with high throughput electrochemistry enabled discovery of 29 ternary oxide photoanodes, 15 with visible light response for oxygen evolution. Y₃Fe₅O₁₂ and trigonal V₂CoO₆ emerge as particularly promising candidates due to their photorepsonse at sub-2.4 eV illumination

Analyzing machine learning models to accelerate generation of fundamental materials insights

Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool. Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces, such as that mapped by a combinatorial materials science experiment. Measuring a performance metric in a given materials space provides direct information about (locally) optimal materials but not the underlying materials science that gives rise to the variation in performance. By building a model that predicts performance (in this case photoelectrochemical power generation of a solar fuels photoanode) from materials parameters (in this case composition and Raman signal), subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses. Human interpretation of these key relationships produces the desired fundamental understanding, demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist. We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space, such as the addition of specific alloying elements, that may increase performance by moving beyond the confines of existing data

Combinatorial screening yields discovery of 29 metal oxide photoanodes for solar fuel generation

Combinatorial synthesis combined with high throughput electrochemistry enabled discovery of 29 ternary oxide photoanodes, 15 with visible light response for oxygen evolution. Y₃Fe₅O₁₂ and trigonal V₂CoO₆ emerge as particularly promising candidates due to their photorepsonse at sub-2.4 eV illumination

Machine learning of optical properties of materials - predicting spectra from images and images from spectra

As the materials science community seeks to capitalize on recent advancements in computer science, the sparsity of well-labelled experimental data and limited throughput by which it can be generated have inhibited deployment of machine learning algorithms to date. Several successful examples in computational chemistry have inspired further adoption of machine learning algorithms, and in the present work we present autoencoding algorithms for measured optical properties of metal oxides, which can serve as an exemplar for the breadth and depth of data required for modern algorithms to learn the underlying structure of experimental materials science data. Our set of 178 994 distinct materials samples spans 78 distinct composition spaces, includes 45 elements, and contains more than 80 000 unique quinary oxide and 67 000 unique quaternary oxide compositions, making it the largest and most diverse experimental materials set utilized in machine learning studies. The extensive dataset enabled training and validation of 3 distinct models for mapping between sample images and absorption spectra, including a conditional variational autoencoder that generates images of hypothetical materials with tailored absorption properties. The absorption patterns auto-generated from sample images capture the salient features of ground truth spectra, and band gap energies extracted from these auto-generated patterns are quite accurate with a mean absolute error of 180 meV, which is the approximate uncertainty from traditional extraction of the band gap energy from measurements of the full transmission and reflection spectra. Optical properties of materials are not only ubiquitous in materials applications but also emblematic of the confluence of underlying physical phenomena yielding the type of complex data relationships that merit and benefit from neural network-type modelling

Early intervention for obsessive compulsive disorder : An expert consensus statement

© 2019 Elsevier B.V.and ECNP. All rights reserved.Obsessive-compulsive disorder (OCD) is common, emerges early in life and tends to run a chronic, impairing course. Despite the availability of effective treatments, the duration of untreated illness (DUI) is high (up to around 10 years in adults) and is associated with considerable suffering for the individual and their families. This consensus statement represents the views of an international group of expert clinicians, including child and adult psychiatrists, psychologists and neuroscientists, working both in high and low and middle income countries, as well as those with the experience of living with OCD. The statement draws together evidence from epidemiological, clinical, health economic and brain imaging studies documenting the negative impact associated with treatment delay on clinical outcomes, and supporting the importance of early clinical intervention. It draws parallels between OCD and other disorders for which early intervention is recognized as beneficial, such as psychotic disorders and impulsive-compulsive disorders associated with problematic usage of the Internet, for which early intervention may prevent the development of later addictive disorders. It also generates new heuristics for exploring the brain-based mechanisms moderating the ‘toxic’ effect of an extended DUI in OCD. The statement concludes that there is a global unmet need for early intervention services for OC related disorders to reduce the unnecessary suffering and costly disability associated with under-treatment. New clinical staging models for OCD that may be used to facilitate primary, secondary and tertiary prevention within this context are proposed.Peer reviewe

Treatments used for obsessive-compulsive disorder-An international perspective

© 2019 John Wiley & Sons, Ltd.OBJECTIVE: The objective of this study was to characterise international trends in the use of psychotropic medication, psychological therapies, and novel therapies used to treat obsessive-compulsive disorder (OCD). METHODS: Researchers in the field of OCD were invited to contribute summary statistics on the characteristics of their samples. Consistency of summary statistics across countries was evaluated. RESULTS: The study surveyed 19 expert centres from 15 countries (Argentina, Australia, Brazil, China, Germany, Greece, India, Italy, Japan, Mexico, Portugal, South Africa, Spain, the United Kingdom, and the United States) providing a total sample of 7,340 participants. Fluoxetine (n = 972; 13.2%) and fluvoxamine (n = 913; 12.4%) were the most commonly used selective serotonin reuptake inhibitor medications. Risperidone (n = 428; 7.3%) and aripiprazole (n = 415; 7.1%) were the most commonly used antipsychotic agents. Neurostimulation techniques such as transcranial magnetic stimulation, deep brain stimulation, gamma knife surgery, and psychosurgery were used in less than 1% of the sample. There was significant variation in the use and accessibility of exposure and response prevention for OCD. CONCLUSIONS: The variation between countries in treatments used for OCD needs further evaluation. Exposure and response prevention is not used as frequently as guidelines suggest and appears difficult to access in most countries. Updated treatment guidelines are recommended.Peer reviewe

Ion Transport across Biological Membranes by Carborane-Capped Gold Nanoparticles

Carborane-capped gold nanoparticles (Au/carborane NPs, 2-3 nm) can act as artificial ion transporters across biological membranes. The particles themselves are large hydrophobic anions that have the ability to disperse in aqueous media and to partition over both sides of a phospholipid bilayer membrane. Their presence therefore causes a membrane potential that is determined by the relative concentrations of particles on each side of the membrane according to the Nernst equation. The particles tend to adsorb to both sides of the membrane and can flip across if changes in membrane potential require their repartitioning. Such changes can be made either with a potentiostat in an electrochemical cell or by competition with another partitioning ion, for example, potassium in the presence of its specific transporter valinomycin. Carborane-capped gold nanoparticles have a ligand shell full of voids, which stem from the packing of near spherical ligands on a near spherical metal core. These voids are normally filled with sodium or potassium ions, and the charge is overcompensated by excess electrons in the metal core. The anionic particles are therefore able to take up and release a certain payload of cations and to adjust their net charge accordingly. It is demonstrated by potential-dependent fluorescence spectroscopy that polarized phospholipid membranes of vesicles can be depolarized by ion transport mediated by the particles. It is also shown that the particles act as alkali-ion-specific transporters across free-standing membranes under potentiostatic control. Magnesium ions are not transported

A pilot study of a nurse-delivered cognitive behavioral therapy intervention (Ziphamandla) for adherence and depression in HIV in South Africa

Depression is prevalent among people living with HIV in South Africa and interferes with adherence to antiretroviral therapy. This study evaluated a nurse-delivered, cognitive behavioral therapy intervention for adherence and depression among antiretroviral therapy users with depression in South Africa (n = 14). Primary outcomes were depression, antiretroviral therapy adherence, feasibility, and acceptability. Findings support robust improvements in mood through a 3-month follow up. Antiretroviral therapy adherence was maintained during the intervention period. Participant retention supports acceptability; however, modest provider fidelity despite intensive supervision warrants additional attention to feasibility. Future effectiveness research is needed to evaluate this nurse-delivered cognitive behavioral therapy intervention for adherence and depression in this context