PEGA: Personality-Guided Preference Aggregator for Ephemeral Group Recommendation

Recently, making recommendations for ephemeral groups which contain dynamic users and few historic interactions have received an increasing number of attention. The main challenge of ephemeral group recommender is how to aggregate individual preferences to represent the group's overall preference. Score aggregation and preference aggregation are two commonly-used methods that adopt hand-craft predefined strategies and data-driven strategies, respectively. However, they neglect to take into account the importance of the individual inherent factors such as personality in the group. In addition, they fail to work well due to a small number of interactive records. To address these issues, we propose a Personality-Guided Preference Aggregator (PEGA) for ephemeral group recommendation. Concretely, we first adopt hyper-rectangle to define the concept of Group Personality. We then use the personality attention mechanism to aggregate group preferences. The role of personality in our approach is twofold: (1) To estimate individual users' importance in a group and provide explainability; (2) to alleviate the data sparsity issue that occurred in ephemeral groups. The experimental results demonstrate that our model significantly outperforms the state-of-the-art methods w.r.t. the score of both Recall and NDCG on Amazon and Yelp datasets

Comparative venom gland transcriptome analysis of the scorpion Lychas mucronatus reveals intraspecific toxic gene diversity and new venomous components

<p>Abstract</p> <p>Background</p> <p><it>Lychas mucronatus </it>is one scorpion species widely distributed in Southeast Asia and southern China. Anything is hardly known about its venom components, despite the fact that it can often cause human accidents. In this work, we performed a venomous gland transcriptome analysis by constructing and screening the venom gland cDNA library of the scorpion <it>Lychas mucronatus </it>from Yunnan province and compared it with the previous results of Hainan-sourced <it>Lychas mucronatus</it>.</p> <p>Results</p> <p>A total of sixteen known types of venom peptides and proteins are obtained from the venom gland cDNA library of Yunnan-sourced <it>Lychas mucronatus</it>, which greatly increase the number of currently reported scorpion venom peptides. Interestingly, we also identified nineteen atypical types of venom molecules seldom reported in scorpion species. Surprisingly, the comparative transcriptome analysis of Yunnan-sourced <it>Lychas mucronatus </it>and Hainan-sourced <it>Lychas mucronatus </it>indicated that enormous diversity and vastly abundant difference could be found in venom peptides and proteins between populations of the scorpion <it>Lychas mucronatus </it>from different geographical regions.</p> <p>Conclusions</p> <p>This work characterizes a large number of venom molecules never identified in scorpion species. This result provides a comparative analysis of venom transcriptomes of the scorpion <it>Lychas mucronatus </it>from different geographical regions, which thoroughly reveals the fact that the venom peptides and proteins of the same scorpion species from different geographical regions are highly diversified and scorpion evolves to adapt a new environment by altering the primary structure and abundance of venom peptides and proteins.</p

X-ray performance of a customized large-format scientifc CMOS detector

In recent years, the performance of Scientifc Complementary Metal Oxide Semiconductor (sCMOS) sensors has been improved signifcantly. Compared with CCD sensors, sCMOS sensors have various advantages, making them potentially better devices for optical and X-ray detection, especially in time-domain astronomy. After a series of tests of sCMOS sensors, we proposed a new dedicated high-speed, large-format X-ray detector in 2016 cooperating with Gpixel Inc. This new sCMOS sensor has a physical size of 6 cm by 6 cm, with an array of 4096 by 4096 pixels and a pixel size of 15 um. The frame rate is 20.1 fps under current condition and can be boosted to a maximum value around 100 fps. The epitaxial thickness is increased to 10 um compared to the previous sCMOS product. We show the results of its frst taped-out product in this work. The dark current of this sCMOS is lower than 10 e/pixel/s at 20C, and lower than 0.02 e/pixel/s at -30C. The Fixed Pattern Noise (FPN) and the readout noise are lower than 5 e in high-gain situation and show a small increase at low temperature. The energy resolution reaches 180.1 eV (3.1%) at 5.90 keV for single-pixel events and 212.3 eV (3.6%) for all split events. The continuous X-ray spectrum measurement shows that this sensor is able to response to X-ray photons from 500 eV to 37 keV. The excellent performance, as demonstrated from these test results, makes sCMOS sensor an ideal detector for X-ray imaging and spectroscopic application.Comment: 20 pages. published in PAS

Magnetically assisted DNA assays: high selectivity using conjugated polymers for amplified fluorescent transduction

We report a strategy for conjugated polymer (CP)-based optical DNA detection with improved selectivity. The high sensitivity of CP-based biosensors arises from light harvesting by the CP and the related amplified fluorescent signal transduction. We demonstrate that the use of magnetic microparticles significantly improves the selectivity of this class of DNA sensors. Compared with previously reported DNA sensors with CP amplification, this novel sensing strategy displays excellent discrimination against non-cognate DNA in the presence of a protein mixture or even human serum. We also demonstrate that the magnetically assisted DNA sensor can conveniently identify even a single-nucleotide mismatch in the target sequence

A Scorpion Defensin BmKDfsin4 Inhibits Hepatitis B Virus Replication in Vitro

Hepatitis B virus (HBV) infection is a major worldwide health problem which can cause acute and chronic hepatitis and can significantly increase the risk of liver cirrhosis and primary hepatocellular carcinoma (HCC). Nowadays, clinical therapies of HBV infection still mainly rely on nucleotide analogs and interferons, the usage of which is limited by drug-resistant mutation or side effects. Defensins had been reported to effectively inhibit the proliferation of bacteria, fungi, parasites and viruses. Here, we screened the anti-HBV activity of 25 scorpion-derived peptides most recently characterized by our group. Through evaluating anti-HBV activity and cytotoxicity, we found that BmKDfsin4, a scorpion defensin with antibacterial and Kv1.3-blocking activities, has a comparable high inhibitory rate of both HBeAg and HBsAg in HepG2.2.15 culture medium and low cytotoxicity to HepG2.2.15. Then, our experimental results further showed that BmKDfsin4 can dose-dependently decrease the production of HBV DNA and HBV viral proteins in both culture medium and cell lysate. Interestingly, BmKDfsin4 exerted high serum stability. Together, this study indicates that the scorpion defensin BmKDfsin4 also has inhibitory activity against HBV replication along with its antibacterial and potassium ion channel Kv1.3-blocking activities, which shows that BmKDfsin4 is a uniquely multifunctional defensin molecule. Our work also provides a good molecule material which will be used to investigate the link or relationship of its antiviral, antibacterial and ion channel–modulating activities in the future

DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation

Sequential recommendations have made great strides in accurately predicting the future behavior of users. However, seeking accuracy alone may bring side effects such as unfair and overspecialized recommendation results. In this work, we focus on the calibrated recommendations for sequential recommendation, which is connected to both fairness and diversity. On the one hand, it aims to provide fairer recommendations whose preference distributions are consistent with users' historical behaviors. On the other hand, it can improve the diversity of recommendations to a certain degree. But existing methods for calibration have mainly relied on the post-processing on the candidate lists, which require more computation time in generating recommendations. In addition, they fail to establish the relationship between accuracy and calibration, leading to the limitation of accuracy. To handle these problems, we propose an end-to-end framework to provide both accurate and calibrated recommendations for sequential recommendation. We design an objective function to calibrate the interests between recommendation lists and historical behaviors. We also provide distribution modification approaches to improve the diversity and mitigate the effect of imbalanced interests. In addition, we design a decoupled-aggregated model to improve the recommendation. The framework assigns two objectives to two individual sequence encoders, and aggregates the outputs by extracting useful information. Experiments on benchmark datasets validate the effectiveness of our proposed model