In Vitro Functional Analysis Of Novel Single Nucleotide Polymorphisms In OATP1B1 And Potential Clinical Relevance

Statin-induced myopathy is a common adverse reaction of statin therapy. Patients with elevated plasma concentration of statins are thought to be at greater risk for myopathy. Statins are transported from the blood to hepatocytes via organic anion transporting polypeptide 1B1 (OATP1B1). Although single nucleotide polymorphisms (SNPs) in OATP1B1 have been associated with increased statin concentrations, we hypothesize that there may be other SNPs in OATP1B1 that can also contribute to reduced transport activity and increased plasma statin concentrations. OATP1B1 cDNA packaged in pEF6/V5-His TOPO was used as template, and 6 SNPs — c.298G\u3eA, c.419C\u3eT, c.463C\u3eA (*4), c.1007C\u3eG, c.1463G\u3eC (*9), and c.1738C\u3eT — were introduced separately and expressed in adenovirus. 3 SNPs abolished transport activity, 1 SNP decreased transport, 1 increased transport, and 1 did not affect transport activity. Our data support the hypothesis that there are additional loss of function SNPs in OATP1B1

Trade-gender alignment of international trade agreements: insufficiencies and improvements

There is a growing call for gender equality in international trade. Enhancing trade-gender alignment of international trade agreements (ITAs) is a major way to make ITAs gender-responsive. In recent years, an increasing number of gender provisions have been incorporated in ITAs, which can be roughly categorized in four major types from normative perspective: declaratory, aspirational, obligatory, and exceptive. While these provisions help draw attention to gender issues associated with international trade and investment, their normativity, enforceability and effectiveness remain at an insufficient level in general. Given the sensitivity and complexity of gender issues in many states, it seems unrealistic and undesirable to transform ITAs into a major discourse for states to address gender concerns, but it still makes sense to enhance trade-gender alignment of ITAs. This impliedly calls for incorporating larger number and more types of gender provisions in ITAs, enhancing their normativity and enforceability through suitable means, and harmonizing national gender laws and ITA gender provisions to create synergies for pursuing gender equality

GelFlow: Self-supervised Learning of Optical Flow for Vision-Based Tactile Sensor Displacement Measurement

High-resolution multi-modality information acquired by vision-based tactile sensors can support more dexterous manipulations for robot fingers. Optical flow is low-level information directly obtained by vision-based tactile sensors, which can be transformed into other modalities like force, geometry and depth. Current vision-tactile sensors employ optical flow methods from OpenCV to estimate the deformation of markers in gels. However, these methods need to be more precise for accurately measuring the displacement of markers during large elastic deformation of the gel, as this can significantly impact the accuracy of downstream tasks. This study proposes a self-supervised optical flow method based on deep learning to achieve high accuracy in displacement measurement for vision-based tactile sensors. The proposed method employs a coarse-to-fine strategy to handle large deformations by constructing a multi-scale feature pyramid from the input image. To better deal with the elastic deformation caused by the gel, the Helmholtz velocity decomposition constraint combined with the elastic deformation constraint are adopted to address the distortion rate and area change rate, respectively. A local flow fusion module is designed to smooth the optical flow, taking into account the prior knowledge of the blurred effect of gel deformation. We trained the proposed self-supervised network using an open-source dataset and compared it with traditional and deep learning-based optical flow methods. The results show that the proposed method achieved the highest displacement measurement accuracy, thereby demonstrating its potential for enabling more precise measurement of downstream tasks using vision-based tactile sensors

Anisotropically Shaped Magnetic/Plasmonic Nanocomposites for Information Encryption and Magnetic-Field-Direction Sensing.

Instantaneous control over the orientation of anisotropically shaped plasmonic nanostructures allows for selective excitation of plasmon modes and enables dynamic tuning of the plasmonic properties. Herein we report the synthesis of rod-shaped magnetic/plasmonic core-shell nanocomposite particles and demonstrate the active tuning of their optical property by manipulating their orientation using an external magnetic field. We further design and construct an IR-photoelectric coupling system, which generates an output voltage depending on the extinction property of the measured nanocomposite sample. We employ the device to demonstrate that the nanocomposite particles can serve as units for information encryption when immobilized in a polymer film and additionally when dispersed in solution can be employed as a new type of magnetic-field-direction sensor

Performance of Early Retransmission Scheme and Delay Based Protocol in Video Streaming

In this paper, we propose an early retransmission scheme to improve TCP's performance in delivering time-sensitive media. Our extensive ns2 simulations show significant improvement. When integrated into a traditional TCP variant, namely TCP-SACK, the early retransmission scheme can substantially reduce the latency caused by retransmission timeout. As a result, it can help TCP-SACK achieve a considerably higher success rate in delivering real time media. Early Retransmission also enhances the performance of a delay-based TCP variant, namely PERT. Furthermore, we also explore the improvement brought by employing a fine-grained retransmission timer, and compare it with ER. We find out that ER outperforms the fine grained timer in a variety of conditions and the combination of the two can further improve performance

Neural Document Expansion with User Feedback

This paper presents a neural document expansion approach (NeuDEF) that enriches document representations for neural ranking models. NeuDEF harvests expansion terms from queries which lead to clicks on the document and weights these expansion terms with learned attention. It is plugged into a standard neural ranker and learned end-to-end. Experiments on a commercial search log demonstrate that NeuDEF significantly improves the accuracy of state-of-the-art neural rankers and expansion methods on queries with different frequencies. Further studies show the contribution of click queries and learned expansion weights, as well as the influence of document popularity of NeuDEF's effectiveness.Comment: The 2019 ACM SIGIR International Conference on the Theory of Information Retrieva

Multi-Clue Reasoning with Memory Augmentation for Knowledge-based Visual Question Answering

Visual Question Answering (VQA) has emerged as one of the most challenging tasks in artificial intelligence due to its multi-modal nature. However, most existing VQA methods are incapable of handling Knowledge-based Visual Question Answering (KB-VQA), which requires external knowledge beyond visible contents to answer questions about a given image. To address this issue, we propose a novel framework that endows the model with capabilities of answering more general questions, and achieves a better exploitation of external knowledge through generating Multiple Clues for Reasoning with Memory Neural Networks (MCR-MemNN). Specifically, a well-defined detector is adopted to predict image-question related relation phrases, each of which delivers two complementary clues to retrieve the supporting facts from external knowledge base (KB), which are further encoded into a continuous embedding space using a content-addressable memory. Afterwards, mutual interactions between visual-semantic representation and the supporting facts stored in memory are captured to distill the most relevant information in three modalities (i.e., image, question, and KB). Finally, the optimal answer is predicted by choosing the supporting fact with the highest score. We conduct extensive experiments on two widely-used benchmarks. The experimental results well justify the effectiveness of MCR-MemNN, as well as its superiority over other KB-VQA methods