A TOPIC SENSITIVE SIMRANK (TSSR) MODEL FOR EXPERTS FINDING ON ONLINE RESEARCH SOCIAL PLATFORMS

As an efficient online academic information repository and information channel with crowds’ contribution, online research social platforms have become an efficient tool for various kinds of research & management applications. Social network platforms have also become a major source to seek for field experts. They have advantages of crowd contributions, easy to access without geographic restrictions and avoiding conflict of interests over traditional database and search engine based approaches. However, current research attempts to find experts based on features such as published research work, social relationships, and online behaviours (e.g. reads and downloads of publications) on social platforms, they ignore to verify the reliability of identified experts. To bridge this gap, this research proposes an innovative Topic Sensitive SimRank (TSSR) model to identify “real” experts on social network platforms. TSSR model includes three components: LDA for Expertise Extension, Topic Sensitive Network for Reputation Measurement, and Topic Sensitive SimRank for unsuitable experts detection. We also design a parallel computing strategy to improve the efficiency of the proposed methods. Last, to verify the effectiveness of the proposed model, we design an experiment on one of the research social platforms-ScholarMate to seek for experts for companies that need academic-industry collaboration

Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior Enhancement

The reconstruction of indoor scenes from multi-view RGB images is challenging due to the coexistence of flat and texture-less regions alongside delicate and fine-grained regions. Recent methods leverage neural radiance fields aided by predicted surface normal priors to recover the scene geometry. These methods excel in producing complete and smooth results for floor and wall areas. However, they struggle to capture complex surfaces with high-frequency structures due to the inadequate neural representation and the inaccurately predicted normal priors. To improve the capacity of the implicit representation, we propose a hybrid architecture to represent low-frequency and high-frequency regions separately. To enhance the normal priors, we introduce a simple yet effective image sharpening and denoising technique, coupled with a network that estimates the pixel-wise uncertainty of the predicted surface normal vectors. Identifying such uncertainty can prevent our model from being misled by unreliable surface normal supervisions that hinder the accurate reconstruction of intricate geometries. Experiments on the benchmark datasets show that our method significantly outperforms existing methods in terms of reconstruction quality

Expression, Purification and Bioactivities Analysis of Recombinant Active Peptide from Shark Liver

The Active Peptide from Shark Liver (APSL) was expressed in E. coli BL21 cells. The cDNA encoding APSL protein was obtained from shark regenerated hepatic tissue by RT-PCR, then it was cloned in the pET-28a expression vector. The expressed fusion protein was purified by Ni-IDA affinity chromatography. SDS-PAGE and HPLC analysis showed the purity of the purified fusion protein was more than 98%. The recombinant APSL (rAPSL) was tested for its biological activity both in vitro, by its ability to improve the proliferation of SMMC7721 cells, and in vivo, by its significant protective effects against acute hepatic injury induced by CCl4 and AAP (acetaminophen) in mice. In addition, the rAPSL could decrease the blood glucose concentration of mice with diabetes mellitus induced by alloxan. Paraffin sections of mouse pancreas tissues showed that rAPSL (3 mg/kg) could effectively protect mouse islets from lesions induced by alloxan, which indicated its potential application in theoretical research and industry

Construction and Characterization of a Chimeric Virus (BIV/HIV-1) Carrying the Bovine Immunodeficiency Virus \u3ci\u3egag\u3c/i\u3e-\u3ci\u3epol\u3c/i\u3e Gene: Research Letters

HIV-1HXB2 5′LTR region, most of BIVR29 gag-pol segment and HIV-1HXB2 pol IN-3′LTR region were respectively amplified. A chimeric clone, designated as pHBIV3753, was constructed by cloning three fragments sequentially into pUC18. MT4 cells were transfected with pHBIV3753. The replication and expressions of the chimeric virus (HBIV3753) were monitored by RT activity and IFA. The results firstly demonstrated that it is possible to generate a new type of the BIV/HIV-1 chimeric virus containing BIV gag-pol gene

Role of redox centre in charge transport investigated by novel self-assembled conjugated polymer molecular junctions

Molecular electronics describes a field that seeks to implement electronic components made of molecular building blocks. To date, few studies have used conjugated polymers in molecular junctions despite the fact that they potentially transport charge more efficiently than the extensively investigated small-molecular systems. Here we report a novel type of molecular tunnelling junction exploring the use of conjugated polymers, which are self-assembled into ultrathin films in a distinguishable ‘planar' manner from the traditional vertically oriented small-molecule monolayers. Electrical measurements on the junctions reveal molecular-specific characteristics of the polymeric molecules in comparison with less conjugated small molecules. More significantly, we decorate redox-active functionality into polymeric backbones, demonstrating a key role of redox centre in the modulation of charge transport behaviour via energy level engineering and external stimuli, and implying the potential of employing tailor-made polymeric components as alternatives to small molecules for future molecular-scale electronics

Robust Fine Registration of Multisensor Remote Sensing Images Based on Enhanced Subpixel Phase Correlation

Automatic fine registration of multisensor images plays an essential role in many remote sensing applications. However, it is always a challenging task due to significant radiometric and textural differences. In this paper, an enhanced subpixel phase correlation method is proposed, which embeds phase congruency-based structural representation, L1-norm-based rank-one matrix approximation with adaptive masking, and stable robust model fitting into the conventional calculation framework in the frequency domain. The aim is to improve the accuracy and robustness of subpixel translation estimation in practical cases. In addition, template matching using the enhanced subpixel phase correlation is integrated to realize reliable fine registration, which is able to extract a sufficient number of well-distributed and high-accuracy tie points and reduce the local misalignment for coarsely coregistered multisensor remote sensing images. Experiments undertaken with images from different satellites and sensors were carried out in two parts: tie point matching and fine registration. The results of qualitative analysis and quantitative comparison with the state-of-the-art area-based and feature-based matching methods demonstrate the effectiveness and reliability of the proposed method for multisensor matching and registration.TU Berlin, Open-Access-Mittel – 202

Mental Disorder Symptoms during the COVID-19 Pandemic in Latin America – A Systematic Review and Meta-analysis

Aims There is a lack of evidence related to the prevalence of mental health symptoms as well as their heterogeneities during the coronavirus disease 2019 (COVID-19) pandemic in Latin America, a large area spanning the equator. The current study aims to provide meta-analytical evidence on mental health symptoms during COVID-19 among frontline healthcare workers, general healthcare workers, the general population and university students in Latin America. Methods Bibliographical databases, such as PubMed, Embase, Web of Science, PsycINFO and medRxiv, were systematically searched to identify pertinent studies up to August 13, 2021. Two coders performed the screening using predefined eligibility criteria. Studies were assigned quality scores using the Mixed Methods Appraisal Tool. The double data extraction method was used to minimise data entry errors. Results A total of 62 studies with 196 950 participants in Latin America were identified. The pooled prevalence of anxiety, depression, distress and insomnia was 35%, 35%, 32% and 35%, respectively. There was a higher prevalence of mental health symptoms in South America compared to Central America (36% v. 28%, p \u3c 0.001), in countries speaking Portuguese (40%) v. Spanish (30%). The pooled prevalence of mental health symptoms in the general population, general healthcare workers, frontline healthcare workers and students in Latin America was 37%, 34%, 33% and 45%, respectively. Conclusions The high yet heterogenous level of prevalence of mental health symptoms emphasises the need for appropriate identification of psychological interventions in Latin America

De Novo Transcriptome of Safflower and the Identification of Putative Genes for Oleosin and the Biosynthesis of Flavonoids

Safflower (Carthamus tinctorius L.) is one of the most extensively used oil crops in the world. However, little is known about how its compounds are synthesized at the genetic level. In this study, Solexa-based deep sequencing on seed, leaf and petal of safflower produced a de novo transcriptome consisting of 153,769 unigenes. We annotated 82,916 of the unigenes with gene annotation and assigned functional terms and specific pathways to a subset of them. Metabolic pathway analysis revealed that 23 unigenes were predicted to be responsible for the biosynthesis of flavonoids and 8 were characterized as seed-specific oleosins. In addition, a large number of differentially expressed unigenes, for example, those annotated as participating in anthocyanin and chalcone synthesis, were predicted to be involved in flavonoid biosynthesis pathways. In conclusion, the de novo transcriptome investigation of the unique transcripts provided candidate gene resources for studying oleosin-coding genes and for investigating genes related to flavonoid biosynthesis and metabolism in safflower

A Distributed Agents QoS Routing Algorithm to Transmit Electrical Power Measuring Information in Last Mile Access Wireless Sensor Networks

Internet of Things or wireless sensor networks (WSNs) can be utilized in monitoring electrical power consumption. For electrical power application, the main issue is how to effectively apply self-organized WSNs technology to handle the last mile communication and supply the reliable, real-time transmission. For example, great number of renewable generators' instantaneous voltage and power parameters should be reported in real time to dispatching center, which is the primary guarantee to keep the power system's stability. In this paper, integrating traffic engineering and distributed agent technologies, a novel distributed agents QoS routing algorithm is proposed to transmit electrical information flows with multi-QoS constraints. The algorithm can explore fast forward path with multiagents and guarantee transmitting quality with smooth allocating different traffic. We also present the mathematical analysis to prove the algorithm's validity. Finally, in the computer simulation, the average end-to-end delay, routing overhead, and links' bandwidth occupation ratio are computed to evaluate the algorithm performance. Coincident results show that the new algorithm can provide short end-to-end transmission with optimal utilized communication resource. A health infrastructure with load balance can effectively avoid the potential congestion and has robust capability to bear abrupt strong traffic flows