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The phase behavior of polymers in solution is crucial to many applications in polymer processing, synthesis, self-assembly, and purification. Quantitative prediction of polymer solubility space for an arbitrary polymer–solvent pair and across a large composition range is challenging. Qualitative agreement is provided by many current theoretical models, but only a portion of the phase space is quantitatively predicted. Here, we utilize a curated database for binary polymer solutions comprised of 21 linear polymers, 61 solvents, and 97 unique polymer–solvent combinations (6524 cloud point temperatures) to construct phase diagrams from machine learning predictions. A generalizable feature vector is developed that includes component descriptors concatenated with state variables and an experimental data descriptor (phase direction). The impact of several types of descriptors (Morgan fingerprints, molecular descriptors, and Hansen solubility parameters) to encode polymer–solvent interactions is assessed. Hansen solubility parameters are also introduced as a means to understand the general breadth of the linear polymer–solvent space as well as the density and distribution of curated data. Two common regression algorithms (XGBoost and neural networks) establish the generality of the descriptors; provide a root mean squared error (RMSE) within 3 °C for predicted cloud points in the test set; and offer excellent agreement with upper and lower critical solubility curves, isopleths, and closed-loop phase behavior by a single model. The ability to extrapolate to polymers that are very dissimilar from the curated data is poor, but with as little as 20 cloud points or a single phase boundary, RMSE error of predictions are within 5 °C. This implies that the current model captures aspects of the underlying physics and can readily exploit correlations to reduce required data for additional polymer–solvent pairs. Finally, the model and data are accessible via the Polymer Property Predictor and Database (3PDb).

Silk is a promising material for biomedical applications, and much research is focused on how application-specific, mechanical properties of silk can be designed synthetically through proper amino acid sequences and processing parameters. This protocol describes an iterative process between research disciplines that combines simulation, genetic synthesis, and fiber analysis to better design silk fibers with specific mechanical properties. Computational methods are used to assess the protein polymer structure as it forms an interconnected fiber network through shearing and how this process affects fiber mechanical properties. Model outcomes are validated experimentally with the genetic design of protein polymers that match the simulation structures, fiber fabrication from these polymers, and mechanical testing of these fibers. Through iterative feedback between computation, genetic synthesis, and fiber mechanical testing, this protocol will enable a priori prediction capability of recombinant material mechanical properties via insights from the resulting molecular architecture of the fiber network based entirely on the initial protein monomer composition. This style of protocol may be applied to other fields where a research team seeks to design a biomaterial with biomedical application-specific properties. This protocol highlights when and how the three research groups (simulation, synthesis, and engineering) should be interacting to arrive at the most effective method for predictive design of their material.

This paper describes the creation of a cyberinfrastructure to facilitate collaborative materials research in a laboratory environment that supports the discovery, development, and sustainment of materials and processing solutions. The infrastructure provides a web-based interface supporting group and project spaces within which researchers can easily organize, share, and collaborate on the results of their experimental and computational efforts. It seamlessly connects researchers with experimental and computational resources for easy generation, collection, and storage of digital data to provide instant access to results with no intermediate transfers. Persistent identifiers and metadata tagging are used to ensure historical research data are discoverable, interpretable, and reusable. The architecture is designed to be modular and agile and is based on federation of both applications and data through a central service bus that brokers all transactions. It is comprised of a number of open-source, commercial, and non-commercial software packages that provide the specific functionality needed to meet the large number of system requirements. This collaborative environment is essential to enabling a large research organization to conduct a research program consistent with the discipline of Integrated Computational Materials Engineering by allowing the seamless connection of experiment to model through pedigreed digital data with complete provenance.

De novo assembly is a widely used methodology in bioinformatics. However, the conventional short-read-based de novo assembly is incapable of reliably reconstructing the large-scale structures of human genomes. Recently, a novel optical label-based technology has enabled reliable large-scale de novo assembly. Despite its advantage in large-scale genome analysis, this new technology requires a more computationally intensive alignment algorithm than its conventional counterpart. For example, the runtime of reconstructing a human genome is on the order of 10,000 hours on a sequential CPU. Therefore, in order to practically apply this new technology in genome research, accelerated approaches are desirable. In this article, we present three different accelerated approaches, multicore CPU, GPU, and FPGA. Against the sequential software baseline, our multicore CPU design achieved an 8.4× speedup, while the GPU and FPGA designs achieved 13.6× and 115× speedups, respectively. We also discuss the details of the design space exploration of this new assembly algorithm on these three different devices. Finally, we compare these devices in performance, optimization techniques, prices, and design efforts.

We present RIFFA 2.1, a reusable integration framework for Field-Programmable Gate Array (FPGA) accelerators. RIFFA provides communication and synchronization for FPGA accelerated applications using simple interfaces for hardware and software. Our goal is to expand the use of FPGAs as an acceleration platform by releasing, as open source, a framework that easily integrates software running on commodity CPUs with FPGA cores. RIFFA uses PCI Express (PCIe) links to connect FPGAs to a CPU’s system bus. RIFFA 2.1 supports FPGAs from Xilinx and Altera, Linux and Windows operating systems, and allows multiple FPGAs to connect to a single host PC system. It has software bindings for C/C++, Java, Python, and Matlab. Tests show that data transfers between hardware and software can reach 97% of the achievable PCIe link bandwidth.

N-Phenylanthranilic acid (NPAA) causes renal papillary necrosis (RPN) in the rat following repeated oral dosing. Non-invasive early detection of RPN is difficult, but a number of potential biomarkers have been investigated, including phospholipid and uronic acid excretion. This study used 1H-nuclear magnetic resonance (NMR) spectroscopic analysis of urine to investigate urinary metabolic perturbations occurring in the rat following exposure to NPAA. Male Alderley Park rats received NPAA (300, 500 or 700 mg kg--1 day--1 orally) for 7 days, and urine was collected on days 7-8, 14-15, 21-22 and 28-29. In a separate study, urine was collected on days 1-2, 3-4, 5-6 and 7-8 from rats receiving 500 mg kg--1 day--1. Samples were analysed by copy combined with multivariate data analysis and clinical chemistry. Histopathology and clinical chemistry analysis of terminal blood samples was carried out following termination on days 4, 6, 8 and 29 (4 week time course) and days 2, 4, 6 and 8 (8 day study). Urine analysis revealed a marked, though variable, excretion of β-hydroxybutyrate, acetoacetate and acetone (ketone bodies) seen on days 3-4, 5-6 and 7-8 of the study. It is postulated that the ketonuria might be secondary to an alteration in fatty acid metabolism due to inhibition of prostaglandin synthesis. In addition, an elevation in urinary ascorbate was observed during the first 8 days of the study. Ascorbate is considered to be a biomarker of hepatic response, probably reflecting an increased hepatic activity due to glucuronidation of NPAA.

The article discusses the role of armed forces in making wrist watch one of the fashion accessories. According to a 1918 article, the wrist watch was a flop in the men's fashion market when it first appeared and sported only by a highly suspect caste of men. It was the role that it played during World War I that helped make it a standard fashion accessory among males. Before the war, the wrist watch was only used as a piece of jewel by women.