Effective conductivity of composites of graded spherical particles

We have employed the first-principles approach to compute the effective response of composites of graded spherical particles of arbitrary conductivity profiles. We solve the boundary-value problem for the polarizability of the graded particles and obtain the dipole moment as well as the multipole moments. We provide a rigorous proof of an {\em ad hoc} approximate method based on the differential effective multipole moment approximation (DEMMA) in which the differential effective dipole approximation (DEDA) is a special case. The method will be applied to an exactly solvable graded profile. We show that DEDA and DEMMA are indeed exact for graded spherical particles.Comment: submitted for publication

Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing

Motivation: Transcriptome-based computational drug repurposing has attracted considerable interest by bringing about faster and more cost-effective drug discovery. Nevertheless, key limitations of the current drug connectivity-mapping paradigm have been long overlooked, including the lack of effective means to determine optimal query gene signatures. Results: The novel approach Dr Insight implements a frame-breaking statistical model for the ‘hand-shake’ between disease and drug data. The genome-wide screening of concordantly expressed genes (CEGs) eliminates the need for subjective selection of query signatures, added to eliciting better proxy for potential disease-specific drug targets. Extensive comparisons on simulated and real cancer datasets have validated the superior performance of Dr Insight over several popular drug-repurposing methods to detect known cancer drugs and drug–target interactions. A proof-of-concept trial using the TCGA breast cancer dataset demonstrates the application of Dr Insight for a comprehensive analysis, from redirection of drug therapies, to a systematic construction of disease-specific drug-target networks

Every Step Counts: Adapting Qualtrics to Encourage Student Engagement in Library Orientations

Many libraries have adopted gamification strategies to enhance their orientation programmes, in the hope of encouraging better student engagement via goal-based design. A literature review reveals a lack of in-depth, granular data on participant behavior in these gamified activities, with evidence generally limited to post-event feedback and comments. Such data could potentially provide insights to help assess the extent to which orientation programme outcomes have been achieved. An example of an orientation activity with in-depth usage data is provided by Hong Kong Baptist University Library’s paperless, mobile-assisted Library Mystery Challenge (the Challenge). Designed using the popular data collection and analysis platform Qualtrics, the Challenge is presented to students as a scenario where they are tasked with helping the Library to find a missing student. Participants are given a series of clues that lead them to various locations, and at each stop they are provided with information on the nearby facilities and services. The Challenge has been run three times since Fall 2016, with close to 100% positive feedback from participants. Game design using Qualtrics is cost-effective, customizable and scalable, and has required minimal staffing resources. The authors will present the design principles of the Challenge, with a particular emphasis on how librarians analyzed student data recorded at each step of completion in the Challenge, for example, participation and retention, average time to completion, etc., and how these insights into student behavior were used to refine the user experience in subsequent iterations of the Challenge. Practical suggestions and advice for making informed decisions through the use of data analytics tools will also be shared

The shrimp hyperglycemic hormone-like neuropeptide is encoded by multiple copies of genes arranged in a cluster1The DNA sequences of the MeCHH-like cDNA and four (43-1, 43-2, 43-3 and 43-4) out of six CHH-like genes have been submitted to the GenBank sequence database with the accession numbers AF109775, AF109776, AF109777, AF109778 and AF109779 respectively.1

AbstractThe crustacean hyperglycemic hormone (CHH) plays an important role in the regulation of glucose metabolism. We have cloned and sequenced several cDNAs encoding the preproCHH-like of the shrimp, Metapenaeus ensis. The preproCHH-like peptide of the shrimp consists of a signal peptide, a CHH precursor-like peptide (CPRP) and the CHH-like peptide. Comparative analysis revealed that the signal peptide and the CPRP of the shrimp peptide are the shortest among all the CHHs reported. MeCHH-like is expressed in the eyestalk, but it is not expressed in the heart, hepatopancreas, muscle, nerve cord and pre-hatch embryo. To study the structural organization of the shrimp CHH-like gene, we have screened the genomic DNA library constructed from one shrimp. Three groups of overlapping genomic clones have been isolated. The results from both genomic Southern blot analysis and library screening indicate that the shrimp genome contains at least six copies of the CHH-like genes arranged in a cluster on the chromosome. The size of the CHH-like genes is 1.5–2.1 kb. DNA sequence determinations indicate that the CHH-like genes share 98–100% amino acid sequence identity. There are three exons and two introns in each CHH-like gene. The first intron separates the signal peptide and the second intron separates the mature peptide in the coding region. The 150–200 bp of the upstream 5′ flanking region of the CHH-like genes contains promoters with characteristics similar to most eukaryotic genes. Several putative cis-acting elements are also identified in the first 400 bp 5′ end upstream region. The organization of the shrimp CHH-like genes is similar to that of the molt inhibiting hormone gene of the same shrimp and the crab, Charybdis feriatus

Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network

The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and many different methods have been published. The rapid development of machine learning computational algorithms, coupled with the large volume of publicly available protein–ligand 3D structures, makes it possible to apply deep learning techniques in binding site comparison. Our method uses a cutting-edge spherical convolutional neural network based on the DeepSphere architecture to learn global representations of protein-binding sites. The model was trained on TOUGH-C1 and TOUGH-M1 data and validated with the ProSPECCTs datasets. Our results show that our model can (1) perform well in protein-binding site similarity and classification tasks and (2) learn and separate the physicochemical properties of binding sites. Lastly, we tested the model on a set of kinases, where the results show that it is able to cluster the different kinase subfamilies effectively. This example demonstrates the method’s promise for lead hopping within or outside a protein target, directly based on binding site information

The role of cool versus warm colors in B2B versus B2C firm-generated content for boosting positive eWOM

While the importance of electronic Word-of-Mouth (eWOM) for Business-to-Business (B2B) firms is increasing, the use of B2B firm-generated content for driving positive eWOM is less understood. Given the emergence of image-oriented social media platforms, this study investigates how color features increase positive eWOM in the B2B versus B2C context by analyzing 13,356 images on Instagram. The results reveal key differences in color features in the contexts of B2B and B2C. Specifically, cool colors are more appealing in B2B content, while warm colors work better in B2C content. Further, darkness, saturation, and colorfulness moderate the cool effect in B2B content, such that darker, less saturated, and more varied colors increase the effect of cool color. In the B2C context, only colorfulness increases the effect of warm color. The findings of this research contribute to the literature examining the different drivers of eWOM between B2B and B2C social media and offer managerial implications for B2B and B2C firms on ways to encourage positive eWOM

Empirical regularities of opening call auction in Chinese stock market

We study the statistical regularities of opening call auction using the ultra-high-frequency data of 22 liquid stocks traded on the Shenzhen Stock Exchange in 2003. The distribution of the relative price, defined as the relative difference between the order price in opening call auction and the closing price of last trading day, is asymmetric and that the distribution displays a sharp peak at zero relative price and a relatively wide peak at negative relative price. The detrended fluctuation analysis (DFA) method is adopted to investigate the long-term memory of relative order prices. We further study the statistical regularities of order sizes in opening call auction, and observe a phenomenon of number preference, known as order size clustering. The probability density function (PDF) of order sizes could be well fitted by a $q$-Gamma function, and the long-term memory also exists in order sizes. In addition, both the average volume and the average number of orders decrease exponentially with the price level away from the best bid or ask price level in the limit-order book (LOB) established immediately after the opening call auction, and a price clustering phenomenon is observed.Comment: 11 pages, 6 figures, 3 table