Showing papers by "North Carolina State University published in 2018"

Universal Language Model Fine-tuning for Text Classification

Abstract: Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 18-24% on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100 times more data. We open-source our pretrained models and code.

Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data.

Abstract: The accuracy of microbial community surveys based on marker-gene and metagenomic sequencing (MGS) suffers from the presence of contaminants—DNA sequences not truly present in the sample. Contaminants come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate it. Here we introduce decontam ( https://github.com/benjjneb/decontam ), an open-source R package that implements a statistical classification procedure that identifies contaminants in MGS data based on two widely reproduced patterns: contaminants appear at higher frequencies in low-concentration samples and are often found in negative controls. Decontam classified amplicon sequence variants (ASVs) in a human oral dataset consistently with prior microscopic observations of the microbial taxa inhabiting that environment and previous reports of contaminant taxa. In metagenomics and marker-gene measurements of a dilution series, decontam substantially reduced technical variation arising from different sequencing protocols. The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome and that some low-frequency taxa seemingly associated with preterm birth were contaminants. Decontam improves the quality of metagenomic and marker-gene sequencing by identifying and removing contaminant DNA sequences. Decontam integrates easily with existing MGS workflows and allows researchers to generate more accurate profiles of microbial communities at little to no additional cost.

QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science

Abstract: We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.

Ultrawide-Bandgap Semiconductors: Research Opportunities and Challenges

Abstract: J. Y. Tsao,* S. Chowdhury, M. A. Hollis,* D. Jena, N. M. Johnson, K. A. Jones, R. J. Kaplar,* S. Rajan, C. G. Van de Walle, E. Bellotti, C. L. Chua, R. Collazo, M. E. Coltrin, J. A. Cooper, K. R. Evans, S. Graham, T. A. Grotjohn, E. R. Heller, M. Higashiwaki, M. S. Islam, P. W. Juodawlkis, M. A. Khan, A. D. Koehler, J. H. Leach, U. K. Mishra, R. J. Nemanich, R. C. N. Pilawa-Podgurski, J. B. Shealy, Z. Sitar, M. J. Tadjer, A. F. Witulski, M. Wraback, and J. A. Simmons

Gut microbiome–mediated bile acid metabolism regulates liver cancer via NKT cells

Abstract: INTRODUCTION Primary liver tumors and liver metastasis currently represent the leading cause of cancer-related deaths. The liver intimately cross-talks with the gut and performs many essential functions related to digestion, metabolism of nutrients, and clearance of bacterial metabolites. Diseased livers are often associated with altered gut bacterial composition, or dysbiosis, and it has been suggested that gut bacterial products contribute to malignant transformation of hepatocytes. The liver is exposed to the gut microbiome through the portal vein and is an immunological organ that is heavily populated by immune cells. Emerging studies have shown that gut commensal bacteria are important regulators of antitumor immunity. Although it has been established that the gut microbiome influences the efficacy of cancer immunotherapy, the role of gut bacteria in antitumor surveillance in the liver is poorly understood. RATIONALE The liver is exposed to gut bacterial metabolites and products by way of blood from the intestine, which comprises 70% of the whole liver blood supply. Changes in the gut microbiome may affect immune cell function in the liver, and commensal bacteria can mediate the metabolism of primary into secondary bile acids, which recirculate back into the liver through the enterohepatic circulation. Given that bile acids are known to be involved in liver cancer development, we focused on the role of bile acids in immunosurveillance of tumors growing in the liver. We altered gut bacteria and examined changes of hepatic immune cells and antitumor immunity directed against liver tumors. Uncovering how the gut microbiome uses bile acids to shape immunity to liver cancer may have future therapeutic applications. RESULTS Using one primary liver model and three liver metastasis models, we found that altering commensal gut bacteria induced a liver-selective antitumor effect. A selective increase of hepatic CXCR6 + natural killer T (NKT) cells was observed, independent of mouse strain, gender, or presence of liver tumors. The accumulated hepatic NKT cells showed an activated phenotype and produced more interferon-γ upon antigen stimulation. In vivo studies using both antibody-mediated cell depletion and NKT-deficient mice confirmed that NKT cells mediated the inhibition of tumor growth in the liver. Further investigation showed that NKT cell accumulation was regulated by the expression of CXCL16, the solo ligand for CXCR6, on liver sinusoidal endothelial cells, which form the lining of liver capillaries and the first barrier for the blood coming from the gut entering the liver. Primary bile acids increased CXCL16 expression, whereas secondary bile acids showed the opposite effect. Removing gram-positive bacteria by antibiotic treatment with vancomycin, which contains the bacteria mediating primary-to-secondary bile acid conversion, was sufficient to induce hepatic NKT cell accumulation and decrease liver tumor growth. Feeding secondary bile acids or colonization of bile acid–metabolizing bacteria, reversed both NKT cell accumulation and inhibition of liver tumor growth in mice with altered gut commensal bacteria. In nontumor liver tissue from human patients with primary liver cancer, primary bile acid chenodeoxycholic acid (CDCA) levels correlated with CXCL16 expression, whereas an inverse correlation was observed with secondary bile acid glycolithocholate (GLCA), suggesting that the finding may apply to humans. CONCLUSION We describe a mechanism by which the gut microbiome uses bile acids as messengers to control a chemokine-dependent accumulation of hepatic NKT cells and antitumor immunity in the liver, against both primary and metastatic liver tumors. These findings not only have possible implications for future cancer therapeutic studies but also provide a link between the gut microbiome, its metabolites, and immune responses in the liver.

Moving in the Anthropocene : global reductions in terrestrial mammalian movements

Abstract: Animal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission.

Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics.

Abstract: Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes.

A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions

Abstract: The fifth generation (5G) wireless network technology is to be standardized by 2020, where main goals are to improve capacity, reliability, and energy efficiency, while reducing latency and massively increasing connection density. An integral part of 5G is the capability to transmit touch perception type real-time communication empowered by applicable robotics and haptics equipment at the network edge. In this regard, we need drastic changes in network architecture including core and radio access network (RAN) for achieving end-to-end latency on the order of 1 ms. In this paper, we present a detailed survey on the emerging technologies to achieve low latency communications considering three different solution domains: 1) RAN; 2) core network; and 3) caching. We also present a general overview of major 5G cellular network elements such as software defined network, network function virtualization, caching, and mobile edge computing capable of meeting latency and other 5G requirements.

A Wide Band Gap Polymer with a Deep Highest Occupied Molecular Orbital Level Enables 14.2% Efficiency in Polymer Solar Cells.

Abstract: To simultaneously achieve low photon energy loss (Eloss) and broad spectral response, the molecular design of the wide band gap (WBG) donor polymer with a deep HOMO level is of critical importance in fullerene-free polymer solar cells (PSCs). Herein, we developed a new benzodithiophene unit, i.e., DTBDT-EF, and conducted systematic investigations on a WBG DTBDT-EF-based donor polymer, namely, PDTB-EF-T. Due to the synergistic electron-withdrawing effect of the fluorine atom and ester group, PDTB-EF-T exhibits a higher oxidation potential, i.e., a deeper HOMO level (ca. −5.5 eV) than most well-known donor polymers. Hence, a high open-circuit voltage of 0.90 V was obtained when paired with a fluorinated small molecule acceptor (IT-4F), corresponding to a low Eloss of 0.62 eV. Furthermore, side-chain engineering demonstrated that subtle side-chain modulation of the ester greatly influences the aggregation effects and molecular packing of polymer PDTB-EF-T. With the benefits of the stronger interchain π–π int...

Liquid metals: fundamentals and applications in chemistry

Abstract: Post-transition elements, together with zinc-group metals and their alloys belong to an emerging class of materials with fascinating characteristics originating from their simultaneous metallic and liquid natures. These metals and alloys are characterised by having low melting points (i.e. between room temperature and 300 °C), making their liquid state accessible to practical applications in various fields of physical chemistry and synthesis. These materials can offer extraordinary capabilities in the synthesis of new materials, catalysis and can also enable novel applications including microfluidics, flexible electronics and drug delivery. However, surprisingly liquid metals have been somewhat neglected by the wider research community. In this review, we provide a comprehensive overview of the fundamentals underlying liquid metal research, including liquid metal synthesis, surface functionalisation and liquid metal enabled chemistry. Furthermore, we discuss phenomena that warrant further investigations in relevant fields and outline how liquid metals can contribute to exciting future applications.

American Gut: an Open Platform for Citizen Science Microbiome Research.

Abstract: Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the United States, United Kingdom, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes compared to environmental samples; demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations; uncover new molecules and kinds of molecular communities in the human stool metabolome; and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education. IMPORTANCE We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples.

Datamonkey 2.0: A Modern Web Application for Characterizing Selective and Other Evolutionary Processes.

Abstract: Inference of how evolutionary forces have shaped extant genetic diversity is a cornerstone of modern comparative sequence analysis. Advances in sequence generation and increased statistical sophistication of relevant methods now allow researchers to extract ever more evolutionary signal from the data, albeit at an increased computational cost. Here, we announce the release of Datamonkey 2.0, a completely re-engineered version of the Datamonkey web-server for analyzing evolutionary signatures in sequence data. For this endeavor, we leveraged recent developments in open-source libraries that facilitate interactive, robust, and scalable web application development. Datamonkey 2.0 provides a carefully curated collection of methods for interrogating coding-sequence alignments for imprints of natural selection, packaged as a responsive (i.e. can be viewed on tablet and mobile devices), fully interactive, and API-enabled web application. To complement Datamonkey 2.0, we additionally release HyPhy Vision, an accompanying JavaScript application for visualizing analysis results. HyPhy Vision can also be used separately from Datamonkey 2.0 to visualize locally executed HyPhy analyses. Together, Datamonkey 2.0 and HyPhy Vision showcase how scientific software development can benefit from general-purpose open-source frameworks. Datamonkey 2.0 is freely and publicly available at http://www.datamonkey.org, and the underlying codebase is available from https://github.com/veg/datamonkey-js.

High-entropy high-hardness metal carbides discovered by entropy descriptors

Abstract: High-entropy materials have attracted considerable interest due to the combination of useful properties and promising applications. Predicting their formation remains the major hindrance to the discovery of new systems. Here we propose a descriptor-entropy forming ability-for addressing synthesizability from first principles. The formalism, based on the energy distribution spectrum of randomized calculations, captures the accessibility of equally-sampled states near the ground state and quantifies configurational disorder capable of stabilizing high-entropy homogeneous phases. The methodology is applied to disordered refractory 5-metal carbides-promising candidates for high-hardness applications. The descriptor correctly predicts the ease with which compositions can be experimentally synthesized as rock-salt high-entropy homogeneous phases, validating the ansatz, and in some cases, going beyond intuition. Several of these materials exhibit hardness up to 50% higher than rule of mixtures estimations. The entropy descriptor method has the potential to accelerate the search for high-entropy systems by rationally combining first principles with experimental synthesis and characterization.

Quantitative relations between interaction parameter, miscibility and function in organic solar cells

Abstract: Although it is known that molecular interactions govern morphology formation and purity of mixed domains of conjugated polymer donors and small-molecule acceptors, and thus largely control the achievable performance of organic solar cells, quantifying interaction–function relations has remained elusive. Here, we first determine the temperature-dependent effective amorphous–amorphous interaction parameter, χaa(T), by mapping out the phase diagram of a model amorphous polymer:fullerene material system. We then establish a quantitative ‘constant-kink-saturation’ relation between χaa and the fill factor in organic solar cells that is verified in detail in a model system and delineated across numerous high- and low-performing materials systems, including fullerene and non-fullerene acceptors. Our experimental and computational data reveal that a high fill factor is obtained only when χaa is large enough to lead to strong phase separation. Our work outlines a basis for using various miscibility tests and future simulation methods that will significantly reduce or eliminate trial-and-error approaches to material synthesis and device fabrication of functional semiconducting blends and organic blends in general. This work reports a quantitative investigation of the interaction parameter and miscibility of donor and acceptor organic molecules and their relationship with the fill factor and photovoltaic performance of bulk-heterojunction organic solar cells.

Blockchains for Business Process Management - Challenges and Opportunities

Abstract: Blockchain technology offers a sizable promise to rethink the way interorganizational business processes are managed because of its potential to realize execution without a central party serving as a single point of trust (and failure). To stimulate research on this promise and the limits thereof, in this article, we outline the challenges and opportunities of blockchain for business process management (BPM). We first reflect how blockchains could be used in the context of the established BPM lifecycle and second how they might become relevant beyond. We conclude our discourse with a summary of seven research directions for investigating the application of blockchain technology in the context of BPM.

Crop pests and predators exhibit inconsistent responses to surrounding landscape composition

Abstract: The idea that noncrop habitat enhances pest control and represents a win-win opportunity to conserve biodiversity and bolster yields has emerged as an agroecological paradigm. However, while noncrop habitat in landscapes surrounding farms sometimes benefits pest predators, natural enemy responses remain heterogeneous across studies and effects on pests are inconclusive. The observed heterogeneity in species responses to noncrop habitat may be biological in origin or could result from variation in how habitat and biocontrol are measured. Here, we use a pest-control database encompassing 132 studies and 6,759 sites worldwide to model natural enemy and pest abundances, predation rates, and crop damage as a function of landscape composition. Our results showed that although landscape composition explained significant variation within studies, pest and enemy abundances, predation rates, crop damage, and yields each exhibited different responses across studies, sometimes increasing and sometimes decreasing in landscapes with more noncrop habitat but overall showing no consistent trend. Thus, models that used landscape-composition variables to predict pest-control dynamics demonstrated little potential to explain variation across studies, though prediction did improve when comparing studies with similar crop and landscape features. Overall, our work shows that surrounding noncrop habitat does not consistently improve pest management, meaning habitat conservation may bolster production in some systems and depress yields in others. Future efforts to develop tools that inform farmers when habitat conservation truly represents a win-win would benefit from increased understanding of how landscape effects are modulated by local farm management and the biology of pests and their enemies.

Nanomaterial-Enabled Wearable Sensors for Healthcare.

Abstract: Highly sensitive wearable sensors that can be conformably attached to human skin or integrated with textiles to monitor the physiological parameters of human body or the surrounding environment have garnered tremendous interest. Owing to the large surface area and outstanding material properties, nanomaterials are promising building blocks for wearable sensors. Recent advances in the nanomaterial-enabled wearable sensors including temperature, electrophysiological, strain, tactile, electrochemical, and environmental sensors are presented in this review. Integration of multiple sensors for multimodal sensing and integration with other components into wearable systems are summarized. Representative applications of nanomaterial-enabled wearable sensors for healthcare, including continuous health monitoring, daily and sports activity tracking, and multifunctional electronic skin are highlighted. Finally, challenges, opportunities, and future perspectives in the field of nanomaterial-enabled wearable sensors are discussed.

Climate change, human impacts, and carbon sequestration in China.

Abstract: The scale of economic growth in China during the past three decades is unprecedented in modern human history. China is now the world’s second largest economic entity, next to the United States. However, this fast economic growth puts China’s environment under increasing stresses. China can be viewed as a massive “laboratory” with complex interactions between socioeconomic and natural systems, providing an excellent opportunity to examine how environmental changes and intensive human economic activities influence natural systems. This special feature explores the impacts of climate change and human activities on the structure and functioning of ecosystems, with emphasis on quantifying the magnitude and distribution of carbon (C) pools and C sequestration in China’s terrestrial ecosystems. We also document how species diversity, species traits, and nitrogen (N) and phosphorus (P) stoichiometry mediate ecosystem C pool and vegetation production. This overview paper introduces the background and scientific significance of the research project, presents the underlying conceptual framework, and summarizes the major findings of each paper. Reducing CO2 emissions to mitigate regional and global climate change is one of the most challenging issues facing humanity (1). At present, China has the largest annual CO2 emissions in the world ( Upper graph in Fig. 1), placing it in the spotlight of efforts to manage global C emissions and design climate-change policy. It is therefore critical to improve our understanding of the C budget and its dynamics in China to mitigate climate change at both regional and global scales. Fig. 1. Evolution in total national GDP, population, and fossil fuel CO2 emissions, together with trajectory of the national policies in China between 1945 and 2015. ( Upper ) GDP, population, and CO2 emissions. The CO2 emissions data were from Oak Ridge National Laboratory (cdiac.ess-dive.lbl.gov/), and population and GDP data from the World Bank (https://data.worldbank.org/country/). ( Lower ) National … [↵][1]1To whom correspondence may be addressed. Email: jyfang{at}urban.pku.edu.cn, fangjingyun{at}ibcas.ac.cn, or terry.chapin{at}alaska.edu. [1]: #xref-corresp-1-1

A High-Efficiency Organic Solar Cell Enabled by the Strong Intramolecular Electron Push-Pull Effect of the Nonfullerene Acceptor.

Abstract: Besides broadening of the absorption spectrum, modulating molecular energy levels, and other well-studied properties, a stronger intramolecular electron push-pull effect also affords other advantages in nonfullerene acceptors. A strong push-pull effect improves the dipole moment of the wings in IT-4F over IT-M and results in a lower miscibility than IT-M when blended with PBDB-TF. This feature leads to higher domain purity in the PBDB-TF:IT-4F blend and makes a contribution to the better photovoltaic performance. Moreover, the strong push-pull effect also decreases the vibrational relaxation, which makes IT-4F more promising than IT-M in reducing the energetic loss of organic solar cells. Above all, a power conversion efficiency of 13.7% is recorded in PBDB-TF:IT-4F-based devices.

Structural evolutions of metallic materials processed by severe plastic deformation

Abstract: Bulk nanostructured (ns)/ultrafine-grained (UFG) metallic materials possess very high strength, making them attractive for high strength, lightweight and energy efficient applications. The most effective approach to produce bulk ns/UFG metallic materials is severe plastic deformation (SPD). In the last 30 years, significant research efforts have been made to explore SPD processing of materials, SPD-induced microstructural evolutions, and the resulting mechanical properties. There have been a few comprehensive reviews focusing mainly on SPD processing and the mechanical properties of the resulting materials. Yet no such a review on SPD-induced microstructural evolutions is available. This paper aims to provide a comprehensive review on important microstructural evolutions and major microstructural features induced by SPD processing in single-phase metallic materials with face-centered cubic structures, body-centered cubic structures, and hexagonal close-packed structures, as well as in multi-phase alloys. The corresponding deformation mechanisms and structural evolutions during SPD processing are discussed, including dislocation slip, deformation twinning, phase transformation, grain refinement, grain growth, and the evolution of dislocation density. A brief review on the mechanical properties of SPD-processed materials is also provided to correlate the structure with mechanical properties of SPD-processed materials, which is important for guiding structural design for optimum mechanical properties of materials.

The Bioavailability, Transport, and Bioactivity of Dietary Flavonoids: A Review from a Historical Perspective.

Abstract: Flavonoids are plant‐derived dietary components with a substantial impact on human health. Research has expanded massively since it began in the 1930s, and the complex pathways involved in bioavailability of flavonoids in the human body are now well understood. In recent years, it has been appreciated that the gut microbiome plays a major role in flavonoid action, but much progress still needs to be made in this area. Since the first publications on the health effects of flavonoids, their action is understood to protect against various stresses, but the mechanism of action has evolved from the now debunked simple direct antioxidant hypothesis into an understanding of the complex effects on molecular targets and enzymes in specific cell types. This review traces the development of the field over the past 8 decades, and indicates the current state of the art, and how it was reached. Future recommendations based on this historical analysis are (a) to focus on key areas of flavonoid action, (b) to perform human intervention studies focusing on bioavailability and protective effects, and (c) to carry out cellular in vitro experiments using appropriate cells together with the chemical form of the flavonoid found at the site of action; this could be the native form of compounds found in the food for studies on digestion and the intestine, the conjugated metabolites found in the blood after absorption in the small intestine for studies on cells, or the chemical forms found in the blood and tissues after catabolism by the gut microbiota.

Precision and accuracy of single-molecule FRET measurements—a multi-laboratory benchmark study

Abstract: Single-molecule Forster resonance energy transfer (smFRET) is increasingly being used to determine distances, structures, and dynamics of biomolecules in vitro and in vivo. However, generalized protocols and FRET standards to ensure the reproducibility and accuracy of measurements of FRET efficiencies are currently lacking. Here we report the results of a comparative blind study in which 20 labs determined the FRET efficiencies (E) of several dye-labeled DNA duplexes. Using a unified, straightforward method, we obtained FRET efficiencies with s.d. between ±0.02 and ±0.05. We suggest experimental and computational procedures for converting FRET efficiencies into accurate distances, and discuss potential uncertainties in the experiment and the modeling. Our quantitative assessment of the reproducibility of intensity-based smFRET measurements and a unified correction procedure represents an important step toward the validation of distance networks, with the ultimate aim of achieving reliable structural models of biomolecular systems by smFRET-based hybrid methods.

Database of Absorption and Fluorescence Spectra of >300 Common Compounds for use in PhotochemCAD.

Abstract: The design of new molecules for photochemical studies typically requires knowledge of spectral features of pertinent chromophores beginning with the absorption spectrum (λabs ) and accompanying molar absorption coefficient (e, m-1 cm-1 ) and often extending to the fluorescence spectrum (λem ) and fluorescence quantum yield (Φf ), where the fluorescence properties may be of direct relevance or useful as proxies to gain insight into the nature of the first excited singlet state. PhotochemCAD databases, developed over a period of 30 years, are described here. The previous databases for 150 compounds have been expanded to encompass 339 compounds for which absorption spectra (including e values), fluorescence spectra (including Φf values) and references to the primary literature have been included where available (552 spectra altogether). The compounds exhibit spectra in the ultraviolet, visible and/or near-infrared spectral regions. The compound classes and number of members include acridines (21), aromatic hydrocarbons (41), arylmethane dyes (11), azo dyes (18), biomolecules (18), chlorins/bacteriochlorins (16), coumarins (14), cyanine dyes (19), dipyrrins (7), heterocycles (26), miscellaneous dyes (13), oligophenylenes (13), oligopyrroles (6), perylenes (5), phthalocyanines (11), polycyclic aromatic hydrocarbons (16), polyenes/polyynes (10), porphyrins (34), quinones (24) and xanthenes (15). A database of 31 solar spectra also is included.

Composite of CH3NH3PbI3 with Reduced Graphene Oxide as a Highly Efficient and Stable Visible-Light Photocatalyst for Hydrogen Evolution in Aqueous HI Solution

Abstract: A facile and efficient photoreduction method is employed to synthesize the composite of methylammonium lead iodide perovskite (MAPbI3 ) with reduced graphene oxide (rGO). This MAPbI3 /rGO composite is shown to be an outstanding visible-light photocatalyst for H2 evolution in aqueous HI solution saturated with MAPbI3 . Powder samples of MAPbI3 /rGO (100 mg) show a H2 evolution rate of 93.9 µmol h-1 , which is 67 times faster than that of pristine MAPbI3 , under 120 mW cm-2 visible-light (λ ≥ 420 nm) illumination, and the composite is highly stable showing no significant decrease in the catalytic activity after 200 h (i.e., 20 cycles) of repeated H2 evolution experiments. The electrochemiluminescence performance of MAPbI3 is investigated to explore the charge transfer process, to find that the photogenerated electrons in MAPbI3 are transferred to the rGO sites, where protons are reduced to H2 .

Heterogeneous Defect Prediction

Abstract: Many recent studies have documented the success of cross-project defect prediction (CPDP) to predict defects for new projects lacking in defect data by using prediction models built by other projects. However, most studies share the same limitations: it requires homogeneous data; i.e., different projects must describe themselves using the same metrics. This paper presents methods for heterogeneous defect prediction (HDP) that matches up different metrics in different projects. Metric matching for HDP requires a “large enough” sample of distributions in the source and target projects—which raises the question on how large is “large enough” for effective heterogeneous defect prediction. This paper shows that empirically and theoretically, “large enough” may be very small indeed. For example, using a mathematical model of defect prediction, we identify categories of data sets were as few as 50 instances are enough to build a defect prediction model. Our conclusion for this work is that, even when projects use different metric sets, it is possible to quickly transfer lessons learned about defect prediction.