Search

Accumulation of very long chain fatty acids (VLCFAs) due to defects in ATP binding cassette protein D1 (ABCD1) is thought to underlie the pathologies observed in adrenoleukodystrophy (ALD). Pursuing a substrate reduction approach based on the inhibition of elongation of very long chain fatty acid 1 enzyme (ELOVL1), we explored a series of thiazole amides that evolved into compound 27─a highly potent, central nervous system (CNS)-penetrant compound with favorable in vivo pharmacokinetics. Compound 27 selectively inhibits ELOVL1, reducing C26:0 VLCFA synthesis in ALD patient fibroblasts, lymphocytes, and microglia. In mouse models of ALD, compound 27 treatment reduced C26:0 VLCFA concentrations to near-wild-type levels in blood and up to 65% in the brain, a disease-relevant tissue. Preclinical safety findings in the skin, eye, and CNS precluded progression; the origin and relevance of these findings require further study. ELOVL1 inhibition is an effective approach for normalizing VLCFAs in models of ALD.

Dredged materials from navigation channel maintenance represent a potentially valuable resource for wetland creation and restoration. In the northern Chesapeake Bay, fine-grained sediments from Baltimore Harbor approach channels are transported by barge southward for creation of wetlands on the site of an eroded island. High concentrations of ammonium, soluble reactive phosphorus, dissolved iron, and iron sulfide minerals in channel deposits are altered by the transport and drying of these materials prior to wetland development. The oxidation of iron sulfide minerals results in low pH, with the initiation of tidal inundation removing sulfuric acid from near-surface soil horizons and moderating the soil pH. Despite the loss of ammonium during dewatering and soil processing, the resultant soils retained high concentrations of dissolved and adsorbed ammonium. Iron-associated inorganic phosphorus represented a large pool of potentially labile phosphorus and along with the high ammonium, resulted in high nutrient concentrations for plant growth. Combined with results on plant growth presented elsewhere, these data suggest that fine-grained dredged materials from non-contaminated environments are well suited for the creation of tidal wetlands after placement and seasoning 1–2 years.

Predicting the properties of materials prior to their synthesis is of great importance in materials science. Magnetic and superconducting materials exhibit a number of unique properties that make them useful in a wide variety of applications, including solid oxide fuel cells, solid-state refrigerants, photon detectors and metrology devices. In all these applications, phase transitions play an important role in determining the feasibility of the materials in question. Here, we present a pipeline for fully integrating data extracted from the scientific literature into machine-learning tools for property prediction and materials discovery. Using advanced natural language processing (NLP) and machine-learning techniques, we successfully reconstruct the phase diagrams of well-known magnetic and superconducting compounds, and demonstrate that it is possible to predict the phase-transition temperatures of compounds not present in the database. We provide the tool as an online open-source platform, forming the basis for further research into magnetic and superconducting materials discovery for potential device applications.

Breast tumour kinase (Brk/PTK6) is overexpressed in up to 86% of breast cancers and is associated with poorer patient outcomes. It is considered a potential therapeutic target in breast cancer, even though the full spectrum of its kinase activity is not known. This study investigated the role of the kinase domain in promoting tumour growth and its potential in sensitising triple negative breast cancer cells to standard of care chemotherapy. Triple negative human xenograft models revealed that both kinase-inactive and wild-type Brk promoted xenograft growth. Suppression of Brk activity in cells subsequently co-treated with the chemotherapy agents doxorubicin or paclitaxel resulted in an increased cell sensitivity to these agents. In triple negative breast cancer cell lines, the inhibition of Brk kinase activity augmented the effects of doxorubicin or paclitaxel. High expression of the alternatively spliced isoform, ALT-PTK6, resulted in improved patient outcomes. Our study is the first to show a role for kinase-inactive Brk in human breast tumour xenograft growth; therefore, it is unlikely that kinase inhibition of Brk, in isolation, would halt tumour growth in vivo. Breast cancer cell responses to chemotherapy in vitro were kinase-dependent, indicating that treatment with kinase inhibitors could be a fruitful avenue for combinatorial treatment. Of particular prognostic value is the ratio of ALT-PTK6:Brk expression in predicating patient outcomes. Germany. Taxol resistant T47D were a gift from Helen Coley, University of Surrey, UK.

The ever-growing abundance of data found in heterogeneous sources, such as scientific publications, has forced the development of automated techniques for data extraction. While in the past, in the physical sciences domain, the focus has been on the precise extraction of individual properties, attention has recently been devoted to the extraction of higher-level relationships. Here, we present a framework for an automated population of ontologies. That is, the direct extraction of a larger group of properties linked by a semantic network. We exploit data-rich sources, such as tables within documents, and present a new model concept that enables data extraction for chemical and physical properties with the ability to organize hierarchical data as nested information. Combining these capabilities with automatically generated parsers for data extraction and forward-looking interdependency resolution, we illustrate the power of our approach via the automatic extraction of a crystallographic hierarchy of information. This includes 18 interrelated submodels of nested data, extracted from an evaluation set of scientific articles, yielding an overall precision of 92.2%, across 26 different journals. Our method and associated toolkit, ChemDataExtractor 2.0, offers a key step toward the seamless integration of primary literature sources into a data-driven scientific framework.

Tidal marsh restoration using dredged material is being undertaken in many coastal areas to replace lost habitat and ecosystem services due to tidal marsh loss. The fate of high levels of nitrogen (N) in fine-grained dredged material used as a substrate for marsh restoration is uncertain, but if exported tidally may cause subtidal habitat degradation. In this study, a mass balance was developed to characterize N fluxes in a two-year-old restored tidal marsh constructed with fine-grained dredged material at Poplar Island, MD, in Chesapeake Bay, and to evaluate the potential impact on the adjacent submersed aquatic vegetation (SAV) habitat. Denitrification and N accumulation in Spartina organic matter were identified as the major sinks (21.31 and 28.5 mg N m−2 d−1, respectively), while tidal export of TN was more modest (9.4 mg N m−2 d−1) and inorganic N export was low (1.59 mg N m−2 d−1). Internal cycling helped retain N within the marsh. Mineralization of N associated with labile organic matter in the dredged material was likely a large, but unquantified, source of N supporting robust plant growth and N exports. Exceedances of SAV water quality habitat requirements in the subtidal region adjacent to the marsh were driven by elevated Chesapeake Bay concentrations rather than enrichment by the marsh.

Generative models have been successfully used to synthesize completely novel images, text, music, and speech. As such, they present an exciting opportunity for the design of new materials for functional applications. So far, generative deep-learning methods applied to molecular and drug discovery have yet to produce stable and novel 3-D crystal structures across multiple material classes. To that end, we, herein, present an autoencoder-based generative deep-representation learning pipeline for geometrically optimized 3-D crystal structures that simultaneously predicts the values of eight target properties. The system is highly general, as demonstrated through creation of novel materials from three separate material classes: binary alloys, ternary perovskites, and Heusler compounds. Comparison of these generated structures to those optimized via electronic-structure calculations shows that our generated materials are valid and geometrically optimized.

Rho kinase (ROCK) inhibitors are potential therapeutic agents for the treatment of a variety of disorders including hypertension, glaucoma and erectile dysfunction. Here we disclose a series of potent and selective ROCK inhibitors based on a substituted 7-azaindole scaffold. Substitution of the 3-position of 7-azaindole led to compounds such as 37, which possess excellent ROCK inhibitory potency and high selectivity against the closely related kinase PKA.