A universal equivariant graph neural network for the elasticity tensors of any crystal system

The elasticity tensor that describes the elastic response of a material to external forces is among the most fundamental properties of materials. The availability of full elasticity tensors for inorganic crystalline compounds, however, is limited due to experimental and computational challenges. Here, we report the materials tensor (MatTen) model for rapid and accurate estimation of the full fourth-rank elasticity tensors of crystals. Based on equivariant graph neural networks, MatTen satisfies the two essential requirements for elasticity tensors: independence of the frame of reference and preservation of material symmetry. Consequently, it provides a universal treatment of elasticity tensors for all crystal systems across diverse chemical spaces. MatTen was trained on a dataset of first-principles elasticity tensors garnered by the Materials Project over the past several years (we are releasing the data herein) and has broad applications in predicting the isotropic elastic properties of polycrystalline materials, examining the anisotropic behavior of single crystals, and discovering new materials with exceptional mechanical properties. Using MatTen, we have discovered a hundred new crystals with extremely large maximum directional Young's modulus and eleven polymorphs of elemental cubic metals with unconventional spatial orientation of Young's modulus

A representation-independent electronic charge density database for crystalline materials

In addition to being the core quantity in density functional theory, the charge density can be used in many tertiary analyses in materials sciences from bonding to assigning charge to specific atoms. The charge density is data-rich since it contains information about all the electrons in the system. With increasing utilization of machine-learning tools in materials sciences, a data-rich object like the charge density can be utilized in a wide range of applications. The database presented here provides a modern and user-friendly interface for a large and continuously updated collection of charge densities as part of the Materials Project. In addition to the charge density data, we provide the theory and code for changing the representation of the charge density which should enable more advanced machine-learning studies for the broader community

Proteomic profiling of urinary proteins in renal cancer by surface enhanced laser desorption ionisation (SELDI) and neural-network analysis: Identification of key issues affecting potential clinical utility.

Recent advances in proteomic profiling technologies, such as surface enhanced laser desorption ionization mass spectrometry, have allowed preliminary profiling and identification of tumor markers in biological fluids in several cancer types and establishment of clinically useful diagnostic computational models. There are currently no routinely used circulating tumor markers for renal cancer, which is often detected incidentally and is frequently advanced at the time of presentation with over half of patients having local or distant tumor spread. We have investigated the clinical utility of surface enhanced laser desorption ionization profiling of urine samples in conjunction with neural-network analysis to either detect renal cancer or to identify proteins of potential use as markers, using samples from a total of 218 individuals, and examined critical technical factors affecting the potential utility of this approach. Samples from patients before undergoing nephrectomy for clear cell renal cell carcinoma (RCC; n 48), normal volunteers (n 38), and outpatients attending with benign diseases of the urogenital tract (n 20) were used to successfully train neural-network models based on either presence/absence of peaks or peak intensity values, resulting in sensitivity and specificity values of 98.3–100%. Using an initial “blind” group of samples from 12 patients with RCC, 11 healthy controls, and 9 patients with benign diseases to test the models, sensitivities and specificities of 81.8–83.3% were achieved. The robustness of the approach was subsequently evaluated with a group of 80 samples analyzed “blind” 10 months later, (36 patients with RCC, 31 healthy volunteers, and 13 patients with benign urological conditions). However, sensitivities and specificities declined markedly, ranging from 41.0% to 76.6%. Possible contributing factors including sample stability, changing laser performance, and chip variability were examined, which may be important for the long-term robustness of such approaches, and this study highlights the need for rigorous evaluation of such factors in future studies

Understanding the impacts of novel coronavirus outbreaks on people who use drugs: A systematic review to inform practice and drug policy responses to COVID-19

People who use drugs (PWUD) experience many social and health harms and are considered at greater risk of acquiring COVID-19. Little research has examined the impact of coronaviruses either on PWUD, or on services targeted to PWUD. We report the findings of a systematic review of empirical evidence from studies which have examined the impact of coronaviruses (Severe Acute Respiratory Syndrome (SARS-CoV-1) and Middle Eastern Respiratory Syndrome (MERS-CoV) and COVID-19) on PWUD or on service responses to them. Five databases were searched (MEDLINE, PsycINFO, CINAHL, ASSIA and EMBASE) as well as COVID-19 specific databases. Inclusion criteria were studies reporting any impact of SARS, MERS or COVID-19 or any service responses to those, published between January 2000 and October 2020. Weight of Evidence judgements and quality assessment were undertaken. In total, 27 primary studies were included and grouped by seven main themes: treatment/recovery services; emergency medical settings; low-threshold services; prison setting, PWUD/substance use disorder (SUD) diagnosis; people with SUD and HIV; ‘Sexual minority’ men. Overall, research in the area was scant, and of average/poor quality. More robust research is required to inform on-going and future responses to coronavirus epidemics for PWUD.This review was funded by Scottish Government under the auspices of the Drugs Death Taskforce, Grant No DDTFRF0

Simvastatin inhibits TLR8 signaling in primary human monocytes and spontaneous TNF production from rheumatoid synovial membrane cultures

Simvastatin has been shown to have anti-inflammatory effects that are independent of its serum cholesterol lowering action, but the mechanisms by which these anti-inflammatory effects are mediated have not been elucidated. To explore the mechanism involved, the effect of simvastatin on Toll-like receptor (TLR) signalling in primary human monocytes was investigated. A short pre-treatment with simvastatin dose-dependently inhibited the production of tumor necrosis factor-α (TNF) in response to TLR8 (but not TLRs 2, 4, or 5) activation. Statins are known inhibitors of the cholesterol biosynthetic pathway, but intriguingly TLR8 inhibition could not be reversed by addition of mevalonate or geranylgeranyl pyrophosphate; downstream products of cholesterol biosynthesis. TLR8 signalling was examined in HEK 293 cells stably expressing TLR8, where simvastatin inhibited IKKα/β phosphorylation and subsequent NF-κB activation without affecting the pathway to AP-1. Since simvastatin has been reported to have anti-inflammatory effects in RA patients and TLR8 signalling contributes to TNF production in human RA synovial tissue in culture, simvastatin was tested in these cultures. Simvastatin significantly inhibited the spontaneous release of TNF in this model which was not reversed by mevalonate. Together, these results demonstrate a hitherto unrecognized mechanism of simvastatin inhibition of TLR8 signalling that may in part explain its beneficial anti-inflammatory effects

Opposing transcriptional programs of KLF5 and AR emerge during therapy for advanced prostate cancer.

Endocrine therapies for prostate cancer inhibit the androgen receptor (AR) transcription factor. In most cases, AR activity resumes during therapy and drives progression to castration-resistant prostate cancer (CRPC). However, therapy can also promote lineage plasticity and select for AR-independent phenotypes that are uniformly lethal. Here, we demonstrate the stem cell transcription factor Krüppel-like factor 5 (KLF5) is low or absent in prostate cancers prior to endocrine therapy, but induced in a subset of CRPC, including CRPC displaying lineage plasticity. KLF5 and AR physically interact on chromatin and drive opposing transcriptional programs, with KLF5 promoting cellular migration, anchorage-independent growth, and basal epithelial cell phenotypes. We identify ERBB2 as a point of transcriptional convergence displaying activation by KLF5 and repression by AR. ERBB2 inhibitors preferentially block KLF5-driven oncogenic phenotypes. These findings implicate KLF5 as an oncogene that can be upregulated in CRPC to oppose AR activities and promote lineage plasticity

Provenancing Archaeological Wool Textiles from Medieval Northern Europe by Light Stable Isotope Analysis (δ13C, δ15N, δ2H)

We investigate the origin of archaeological wool textiles preserved by anoxic waterlogging from seven medieval archaeological deposits in north-western Europe (c. 700-1600 AD), using geospatial patterning in carbon (δ13C), nitrogen (δ15N) and non-exchangeable hydrogen (δ2H) composition of modern and ancient sheep proteins. δ13C, δ15N and δ2H values from archaeological wool keratin (n = 83) and bone collagen (n = 59) from four sites were interpreted with reference to the composition of modern sheep wool from the same regions. The isotopic composition of wool and bone collagen samples clustered strongly by settlement; inter-regional relationships were largely parallel in modern and ancient samples, though landscape change was also significant. Degradation in archaeological wool samples, examined by elemental and amino acid composition, was greater in samples from Iceland (Reykholt) than in samples from north-east England (York, Newcastle) or northern Germany (Hessens). A nominal assignment approach was used to classify textiles into local/non-local at each site, based on maximal estimates of isotopic variability in modern sheep wool. Light element stable isotope analysis provided new insights into the origins of wool textiles, and demonstrates that isotopic provenancing of keratin preserved in anoxic waterlogged contexts is feasible. We also demonstrate the utility of δ2H analysis to understand the location of origin of archaeological protein samples