Long-Range Proton Conduction Across Free-Standing Serum Albumin

Free‐standing serum‐albumin mats can transport protons over millimetre length‐scales. The results of photoinduced proton transfer and voltage‐driven proton‐conductivity measurements, together with temperature‐dependent and isotope‐effect studies, suggest that oxo‐amino‐acids of the protein serum albumin play a major role in the translocation of protons via an “over‐the‐barrier” hopping mechanism. The use of proton‐conducting protein mats opens new possibilities for bioelectronic interfaces

Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground

We provide a comprehensive evaluation of salient object detection (SOD) models. Our analysis identifies a serious design bias of existing SOD datasets which assumes that each image contains at least one clearly outstanding salient object in low clutter. The design bias has led to a saturated high performance for state-of-the-art SOD models when evaluated on existing datasets. The models, however, still perform far from being satisfactory when applied to real-world daily scenes. Based on our analyses, we first identify 7 crucial aspects that a comprehensive and balanced dataset should fulfill. Then, we propose a new high quality dataset and update the previous saliency benchmark. Specifically, our SOC (Salient Objects in Clutter) dataset, includes images with salient and non-salient objects from daily object categories. Beyond object category annotations, each salient image is accompanied by attributes that reflect common challenges in real-world scenes. Finally, we report attribute-based performance assessment on our dataset.Comment: ECCV 201

Polymeric templating synthesis of anatase TiO₂ nanoparticles from low-cost inorganic titanium sources

A novel facile and cost-effective synthesis method for anatase TiO₂ nanoparticles has been developed by using poly-acrylic acid hydrogel as template at room temperature. The newly developed synthesis method avoids the use of hazardous reagents and/or hydrothermal steps, and enables production of highly active TiO₂ nanoparticles from low cost inorganic titanium sources. The synthesized TiO₂ nanoparticles have been studied in several applications including dye-sensitized solar cells as a photoanode as well as in organics degradation of methyl orange in aqueous media. Good photocatalytic performances were obtained in both applications

Systematic alteration of ATAC-seq for profiling open chromatin in cryopreserved nuclei preparations from livestock tissues.

The use of Assay for Transposase-Accessible Chromatin (ATAC-seq) to profile chromatin accessibility has surged over the past years, but its applicability to tissues has been very limited. With the intent of preserving nuclear architecture during long-term storage, cryopreserved nuclei preparations from chicken lung were used to optimize ATAC-seq. Sequencing data were compared with existing DNase-seq, ChIP-seq, and RNA-seq data to evaluate library quality, ultimately resulting in a modified ATAC-seq method capable of generating high quality chromatin accessibility data from cryopreserved nuclei preparations. Using this method, nucleosome-free regions (NFR) identified in chicken lung overlapped half of DNase-I hypersensitive sites, coincided with active histone modifications, and specifically marked actively expressed genes. Notably, sequencing only the subnucleosomal fraction dramatically improved signal, while separation of subnucleosomal reads post-sequencing did not improve signal or peak calling. The broader applicability of this modified ATAC-seq technique was tested using cryopreserved nuclei preparations from pig tissues, resulting in NFR that were highly consistent among biological replicates. Furthermore, tissue-specific NFR were enriched for binding motifs of transcription factors related to tissue-specific functions, and marked genes functionally enriched for tissue-specific processes. Overall, these results provide insights into the optimization of ATAC-seq and a platform for profiling open chromatin in animal tissues

Underwriting and investment risks in the property-liability insurance industry: Evidence prior to the 9-11 event

Underwriting and investment are two important and related business activities of insurance companies. However, studies on the interrelation between underwriting and investment risks of Property-Liability (P-L) insurance companies are sparse in the literature. Using a sample of US P-L insurers, this article conducts an empirical investigation of how these two risks are associated with each other in the 1994-2000 period (before the September 11th terrorist attack in 2001). Our results, robust to various estimations, suggest that there is no significant relationship between the underwriting and investment risks among our sample firms. Such results based on pre 9-11 event period provide some support for the conjecture of Achleitner et al. (Geneva Pap Risk Insur Issues Pract 27:275-282, 2002) that many insurance companies may have failed to take an integrated approach to risk management. This resulted in a heavy loss due to dual exposures in both underwriting and investment in the 9-11 event. In the aftermath of the recent global financial crisis, risk taking and risk management of financial institutions have received more attention and increasing scrutiny. We believe the current paper provides some useful insights in this vein. © 2010 Springer Science+Business Media, LLC.postprin

Evaluation of mTOR-regulated mRNA translation.

mTOR, the mammalian target of rapamycin, regulates protein synthesis (mRNA translation) by affecting the phosphorylation or activity of several translation factors. Here, we describe methods for studying the impact of mTOR signalling on protein synthesis, using inhibitors of mTOR such as rapamycin (which impairs some of its functions) or mTOR kinase inhibitors (which probably block all functions).To assess effects of mTOR inhibition on general protein synthesis in cells, the incorporation of radiolabelled amino acids into protein is measured. This does not yield information on the effects of mTOR on the synthesis of specific proteins. To do this, two methods are described. In one, stable-isotope labelled amino acids are used, and their incorporation into new proteins is determined using mass spectrometric methods. The proportions of labelled vs. unlabeled versions of each peptide from a given protein provide quantitative information about the rate of that protein's synthesis under different conditions. Actively translated mRNAs are associated with ribosomes in polyribosomes (polysomes); thus, examining which mRNAs are found in polysomes under different conditions provides information on the translation of specific mRNAs under different conditions. A method for the separation of polysomes from non-polysomal mRNAs is describe

Magnetic plasmonic particles for SERS-based bacteria sensing: A review

This review describes recent advances in the use of magnetic-plasmonic particles (MPPs) for bacteria detection by Surface-Enhanced Raman Scattering (SERS). Pathogenic bacteria pollution has always been a major threat to human health and safety. SERS spectroscopy has emerged as a powerful and promising technique for sensitive and selective detection of pathogen bacte-ia. MPPs are considered as a versatile SERS platform for their excellent plasmonic properties and good magnetic responsiveness. Improved preparation method and typical characterization technique of MPPs are introduced, focusing on the thin and continuous metallic shell covering process. Consequently, the SERS-based sensing methods for bacteria identification were discussed, including the label-free and label-based methods. Finally, an overview of the current state of the field and our perspective on future development directions are given

Statistical analysis driven optimized deep learning system for intrusion detection

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018

Isolation and characterization of antimicrobial, anti-inflammatory and chemopreventive flavones from premna odorata blanco

Premna odorata Blanco (Verbenaceae) is a native tree of the Philippines where its leaves are used traditionally for vaginal irrigation and tuberculosis. It is one of the seven components of a commercialized Philippine herbal preparation called "Pito-Pito". Its medicinal uses, however, have not been scientifically validated. This tree is not commonly cultivated and thrive in the less accessible limestone forests of the Philippines. Solvent partitioning and fractionation of the ethanolic crude extract of the leaves isolated two yellow amorphous powders. The identities of these compounds were determined by LC/MS/MS and NMR spectroscopic analyses, and their spectra were compared with literature data. The isolates were flavone aglycones which were the widespread acacetin and the nonwidespread diosmetin. These flavones were isolated from the P. odorata for the first time ever. They had been reported by earlier studies to exhibit medicinal properties as antimicrobial, anti-inflammatory and chemopreventive. Thus, the current study has provided a scientific evidence of the medicinal properties of the leaves of P. odorata that could become the popular basis for the plant's sustainable use, conservation and cultivation. © 2011 Academic Journals.published_or_final_versio

Dynamic E-Supply Chain Integration – A Knowledge-Based Decision and Coordination Framework

Assistant Professor, Division of Information & Technology Studies, The University of Hong Kong - Dr. Maggie Minhong Wang is a newly appointed Assistant Professor in the Division of Information & Technology Studies, the University of Hong Kong. She received her PhD in information systems from City University of Hong Kong in 2005. Her research interests bridge the business and academic communities in information technologies and their applications in information and knowledge management, business intelligence systems and technology-mediated learning. Her research is a synergy of multi-disciplinary background, teaching and industrial working experience in information management and computer engineering. She has published papers in Information & Management, Knowledge-based Systems, Expert Systems with Applications, International Journal of Technology and Human Interaction, and in international conferences, including BPM, CAiSE, HICSS, AMCIS, PRICAI, CEC/EEE among others.With e-business emerging as a key enabler to drive supply chains, the focus of supply chain management has been shifted from production efficiency to customer-driven and partnership synchronization approaches. Such approaches depend on the match between the requirements and offerings that deliver the services. It can be ensured by separating requirements from the means of realization as well as dynamically assigning available resources to requests. To achieve this, we need coordinate the flow of information among the services and link their business processes under various constraints. The problem is complicated as a result of undetermined requirements of individual services and unpredictable solutions contributed by individual service providers. This paper examines an agent-mediated and knowledge-based decision and coordination approach to dynamic supply chain integration in a web-based environment. Each agent works as a broker for each service, exploring individual service decisions as well as interacting with each other by knowledge creating and sharing for achieving compatibility and coherence among the decisions of all services. Based on the approach, a prototype is implemented with simulated experiments highlighting the effectiveness of the approach.published_or_final_versionCentre for Information Technology in Education, University of Hong Kon