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Single-molecule topological insulators are promising candidates as conducting wires over nanometre length scales. A key advantage is their ability to exhibit quasi-metallic transport, in contrast to conjugated molecular wires which typically exhibit a low conductance that decays as the wire length increases. Here, we study a family of oligophenylene-bridged bis(triarylamines) with tunable and stable mono- or di-radicaloid character. These wires can undergo one- and two-electron chemical oxidations to the corresponding mono-cation and di-cation, respectively. We show that the oxidized wires exhibit reversed conductance decay with increasing length, consistent with the expectation for Su–Schrieffer–Heeger-type one-dimensional topological insulators. The 2.6-nm-long di-cation reported here displays a conductance greater than 0.1 G 0 , where G 0 is the conductance quantum, a factor of 5,400 greater than the neutral form. The observed conductance–length relationship is similar between the mono-cation and di-cation series. Density functional theory calculations elucidate how the frontier orbitals and delocalization of radicals facilitate the observed non-classical quasi-metallic behaviour. Designing long and highly conducting molecular wires has been a great challenge for decades. It has now been shown that a singly oxidized 2.6-nm-long oligophenylene-bridged bis(triarylamine) can show a single-molecule junction conductance over 0.1 G 0 .

The ability to advance our understanding of multiple exciton generation (MEG) in organic materials has been restricted by the limited number of materials capable of singlet fission. A particular challenge is the development of materials that undergo efficient intramolecular fission, such that local order and strong nearest-neighbour coupling is no longer a design constraint. Here we address these challenges by demonstrating that strong intrachain donor–acceptor interactions are a key design feature for organic materials capable of intramolecular singlet fission. By conjugating strong-acceptor and strong-donor building blocks, small molecules and polymers with charge-transfer states that mediate population transfer between singlet excitons and triplet excitons are synthesized. Using transient optical techniques, we show that triplet populations can be generated with yields up to 170%. These guidelines are widely applicable to similar families of polymers and small molecules, and can lead to the development of new fission-capable materials with tunable electronic structure, as well as a deeper fundamental understanding of MEG. Design rules for the synthesis of donor–acceptor systems with efficient intramolecular singlet fission are now proposed. These guidelines have been applied to both small molecules and polymeric chains.

Consumer-wearable activity trackers are small electronic devices that record fitness and health-related measures. The purpose of this systematic review was to examine the validity and reliability of commercial wearables in measuring step count, heart rate, and energy expenditure. We identified devices to be included in the review. Database searches were conducted in PubMed, Embase, and SPORTDiscus, and only articles published in the English language up to May 2019 were considered. Studies were excluded if they did not identify the device used and if they did not examine the validity or reliability of the device. Studies involving the general population and all special populations were included. We operationalized validity as criterion validity (as compared with other measures) and construct validity (degree to which the device is measuring what it claims). Reliability measures focused on intradevice and interdevice reliability. We included 158 publications examining nine different commercial wearable device brands. Fitbit was by far the most studied brand. In laboratory-based settings, Fitbit, Apple Watch, and Samsung appeared to measure steps accurately. Heart rate measurement was more variable, with Apple Watch and Garmin being the most accurate and Fitbit tending toward underestimation. For energy expenditure, no brand was accurate. We also examined validity between devices within a specific brand. Commercial wearable devices are accurate for measuring steps and heart rate in laboratory-based settings, but this varies by the manufacturer and device type. Devices are constantly being upgraded and redesigned to new models, suggesting the need for more current reviews and research.

Pregnane X receptor (PXR) is activated by chemicals to transcriptionally regulate drug disposition and possibly decrease drug efficacy and increase resistance, suggesting therapeutic value for PXR antagonists. We previously reported the antagonist SPA70 and its analog SJB7, which unexpectedly is an agonist. Here, we describe another unexpected observation: mutating a single residue (W299A) within the PXR ligand-binding domain converts SPA70 to an agonist. After characterizing wild-type and W299A PXR activity profiles, we used molecular dynamics simulations to reveal that in wild-type PXR, agonists stabilize the activation function 2 (AF-2) helix in an “inward” position, but SPA70 displaces the AF-2. In W299A, however, SPA70 stabilizes the AF-2 “inward”, like agonists. We validated our model by predicting the antagonist SJC2 to be a W299A agonist, which was confirmed experimentally. Our work correlates previously unobserved ligand-induced conformational changes to PXR cellular activity and, for the first time, reveals how PXR antagonists work.

PurposeIndustrial symbiosis network (ISN) facilitation tools seek to holistically evaluate the environmental and economic performance of ISNs through life cycle assessment (LCA) and life cycle costing (LCC). ISNs have many stakeholders with diverse interests in the LCA and LCC results thus requiring multi-level analysis. The objective of this review was to examine the state-of-the-art methodologies used in LCAs and LCCs of ISNs and understand how multi-level analysis can be conducted.MethodsThe systematic literature review methodology was applied to develop a corpus of peer-reviewed LCA and LCC studies of ISNs published between 2010 and 2019 without any geographic boundary. Abstracts were reviewed to shortlist studies that conducted an LCA or LCC of an ISN with numerical results. LCA and LCC methodologies used in the shortlisted studies were collected and categorized. Each methodology was examined to understand how the foreground and background systems are represented, how waste-to-resource exchanges are analyzed, and how the results can be computed at the network, entity, and flow levels.Results and discussionThe review yielded 42 LCA studies and 11 LCC studies of ISNs that used eight different methodologies. Process-based LCA was used in 71% of the LCA studies, whereas tiered hybrid LCA was used in 14% of the studies. Waste-to-resource exchanges in ISN scenarios were represented either through process analysis or as a black box. Fewer LCC studies that evaluate the economic performance of ISNs exist compared with LCA studies. Economic studies often evaluated financial feasibility, net present value, profitability, or payback period of specific waste-to-resource exchanges or the network overall.ConclusionsThe insights derived from this review chart future areas of research in multi-level modeling and analysis of the life cycle environmental and economic performance of ISNs. To improve the model construction and analysis process, research should be explored in developing a methodology for constructing a single model that represents multiple entities linked together by waste-to-resource exchanges and can provide LCA and LCC results for different stakeholder perspectives. The lack of LCC studies of ISNs merits the need for more research in this area at both the network and entity levels to quantify potential economic trade-offs between stakeholders. Developing a methodology for unified LCA and LCC modeling and analysis of ISNs can help ISN facilitation tool developers conduct simultaneous life cycle environmental and economic analysis of the potential symbiosis connections identified and how they contribute to the overall network.

Materials informatics employs techniques, tools, and theories drawn from the emerging fields of data science, internet, computer science and engineering, and digital technologies to the materials science and engineering to accelerate materials, products and manufacturing innovations. Manufacturing is transforming into shorter design cycles, mass customization, on-demand production, and sustainable products. Additive manufacturing or 3D printing is a popular example of such a trend. However, the success of this manufacturing transformation is critically dependent on the availability of suitable materials and of data on invertible processing–structure–property–performance life cycle linkages of materials. Experience suggests that the material development cycle, i.e. the time to develop and deploy new material, generally exceeds the product design and development cycle. Hence, there is a need to accelerate materials innovation in order to keep up with product and manufacturing innovations. This is a major challenge considering the hundreds of thousands of materials and processes, and the huge amount of data on microstructure, composition, properties, and functional, environmental, and economic performance of materials. Moreover, the data sharing culture among the materials community is sparse. Materials informatics is key to the necessary transformation in product design and manufacturing. Through the association of material and information sciences, the emerging field of materials informatics proposes to computationally mine and analyze large ensembles of experimental and modeling datasets efficiently and cost effectively and to deliver core materials knowledge in user-friendly ways to the designers of materials and products, and to the manufacturers. This paper reviews the various developments in materials informatics and how it facilitates materials innovation by way of specific examples.

Life cycle assessment (LCA) is a well-established methodology to quantify the environmental impacts of products, processes, and services. An advanced branch of this methodology, dynamic LCA, is increasingly used to reflect the variation in such potential impacts over time. The most common form of dynamic LCA focuses on the dynamism of the life cycle inventory (LCI) phase, which can be enabled by digital models or sensors for a continuous data collection. We adopt a systematic literature review with the aim to support practitioners looking to apply dynamic LCI, particularly in Industry 4.0 applications. We select 67 publications related to dynamic LCI studies to analyze their goal and scope phase and how the dynamic element is integrated in the studies. We describe and discuss methods and applications for dynamic LCI, particularly those involving continuous data collection. Electricity consumption and/or electricity technology mixes are the most used dynamic components in the LCI, with 39 publications in total. This interest can be explained by variability over time and the relevance of electricity consumption as a driver of environmental impacts. Finally, we highlight eight research gaps that, when successfully addressed, could benefit the diffusion and development of sound dynamic LCI studies.

Phenotypic screening through high-content automated microscopy is a powerful tool for evaluating the mechanism of action of candidate therapeutics. Despite more than a decade of development, however, high content assays have yielded mixed results, identifying robust phenotypes in only a small subset of compound classes. This has led to a combinatorial explosion of assay techniques, analyzing cellular phenotypes across dozens of assays with hundreds of measurements. Here, using a minimalist three-stain assay and only 23 basic cellular measurements, we developed an analytical approach that leverages informative dimensions extracted by linear discriminant analysis to evaluate similarity between the phenotypic trajectories of different compounds in response to a range of doses. This method enabled us to visualize biologically-interpretable phenotypic tracks populated by compounds of similar mechanism of action, cluster compounds according to phenotypic similarity, and classify novel compounds by comparing them to phenotypically active exemplars. Hierarchical clustering applied to 154 compounds from over a dozen different mechanistic classes demonstrated tight agreement with published compound mechanism classification. Using 11 phenotypically active mechanism classes, classification was performed on all 154 compounds: 78% were correctly identified as belonging to one of the 11 exemplar classes or to a different unspecified class, with accuracy increasing to 89% when less phenotypically active compounds were excluded. Importantly, several apparent clustering and classification failures, including rigosertib and 5-fluoro-2'-deoxycytidine, instead revealed more complex mechanisms or off-target effects verified by more recent publications. These results show that a simple, easily replicated, minimalist high-content assay can reveal subtle variations in the cellular phenotype induced by compounds and can correctly predict mechanism of action, as long as the appropriate analytical tools are used.

Survival in high-risk pediatric neuroblastoma has remained around 50% for the last 20 years, with immunotherapies and targeted therapies having had minimal impact. Here, we identify the small molecule CX-5461 as selectively cytotoxic to high-risk neuroblastoma and synergistic with low picomolar concentrations of topoisomerase I inhibitors in improving survival in vivo in orthotopic patient-derived xenograft neuroblastoma mouse models. CX-5461 recently progressed through phase I clinical trial as a first-in-human inhibitor of RNA-POL I. However, we also use a comprehensive panel of in vitro and in vivo assays to demonstrate that CX-5461 has been mischaracterized and that its primary target at pharmacologically relevant concentrations, is in fact topoisomerase II beta ( TOP2B ), not RNA-POL I. This is important because existing clinically approved chemotherapeutics have well-documented off-target interactions with TOP2B, which have previously been shown to cause both therapy-induced leukemia and cardiotoxicity—often-fatal adverse events, which can emerge several years after treatment. Thus, while we show that combination therapies involving CX-5461 have promising anti-tumor activity in vivo in neuroblastoma, our identification of TOP2B as the primary target of CX-5461 indicates unexpected safety concerns that should be examined in ongoing phase II clinical trials in adult patients before pursuing clinical studies in children. CX-5461 recently progressed through phase I clinical trial as a first-inhuman inhibitor of RNA-POL I. Here, the authors demonstrate that CX-5461 synergizes with topoisomerase I inhibitors to inhibit neuroblastoma cells and that its primary target in this disease is topoisomerase II beta and not RNA-POL I.

Drug combinations are essential to modern medicine, but their discovery remains slow and inefficient as experimental complexity expands rapidly with each additional drug tested. Although modern liquid handling systems enable complex and highly customizable experimental designs, a lack of strategies integrating these technologies with combination-specific analytical methods has limited throughput. Here we introduce Combocat, an open-source and streamlined framework that combines acoustic liquid handling protocols with machine learning-based inference to achieve ultrahigh-throughput drug combination screening. Using Combocat, we generate a reference dataset of over 800 unique combinations in a dense 10 × 10 matrix format across multiple cell types, and use this to train a predictive model that accurately infers drug combination effects from sparse data, drastically reducing the number of experimental measurements required. As proof of concept, we screened 9,045 combinations in a neuroblastoma cell line—the largest number of combinations tested in a single cell line to date—achieved using minimal resources. By integrating advanced drug dispensing technologies with predictive computational modeling, Combocat provides a scalable solution to accelerate the discovery of novel drug combinations. Drug combination discovery remains slow and challenging. Here, the authors introduce Combocat, an open-source framework that combines acoustic liquid handling protocols with machine learning to achieve ultrahigh-throughput drug combination screening; as proof of concept, they use Combocat to screen 9,045 drug combinations in a neuroblastoma cell line.