Does lindane (gamma-hexachlorocyclohexane) increase the rapid delayed rectifier outward K(+) current (I(Kr)) in frog atrial myocytes?

BACKGROUND: The effects of lindane, a gamma-isomer of hexachlorocyclohexane, were studied on transmembrane potentials and currents of frog atrial heart muscle using intracellular microelectrodes and the whole cell voltage-clamp technique. RESULTS: Lindane (0.34 microM to 6.8 microM) dose-dependently shortened the action potential duration (APD). Under voltage-clamp conditions, lindane (1.7 microM) increased the amplitude of the outward current (I(out)) which developed in Ringer solution containing TTX (0.6 microM), Cd(2+) (1 mM) and TEA (10 mM). The lindane-increased I(out) was not sensitive to Sr(2+) (5 mM). It was blocked by subsequent addition of quinidine (0.5 mM) or E-4031 (1 microM). E-4031 lengthened the APD; it prevented or blocked the lindane-induced APD shortening. CONCLUSIONS: In conclusion, our data revealed that lindane increased the quinidine and E-4031-sensitive rapid delayed outward K(+) current which contributed to the AP repolarization in frog atrial muscle

MythQA: Query-Based Large-Scale Check-Worthy Claim Detection through Multi-Answer Open-Domain Question Answering

Check-worthy claim detection aims at providing plausible misinformation to downstream fact-checking systems or human experts to check. This is a crucial step toward accelerating the fact-checking process. Many efforts have been put into how to identify check-worthy claims from a small scale of pre-collected claims, but how to efficiently detect check-worthy claims directly from a large-scale information source, such as Twitter, remains underexplored. To fill this gap, we introduce MythQA, a new multi-answer open-domain question answering(QA) task that involves contradictory stance mining for query-based large-scale check-worthy claim detection. The idea behind this is that contradictory claims are a strong indicator of misinformation that merits scrutiny by the appropriate authorities. To study this task, we construct TweetMythQA, an evaluation dataset containing 522 factoid multi-answer questions based on controversial topics. Each question is annotated with multiple answers. Moreover, we collect relevant tweets for each distinct answer, then classify them into three categories: "Supporting", "Refuting", and "Neutral". In total, we annotated 5.3K tweets. Contradictory evidence is collected for all answers in the dataset. Finally, we present a baseline system for MythQA and evaluate existing NLP models for each system component using the TweetMythQA dataset. We provide initial benchmarks and identify key challenges for future models to improve upon. Code and data are available at: https://github.com/TonyBY/Myth-QAComment: Accepted by SIGIR 202

Sensitivity analysis of rockfall trajectory simulations to material properties

International audienceMany tools have been developed to manage rockfall risk. In particular, many softwares are designed to simulate rockfall trajectories. These softwares require the definition of many parameters, especially those describing the mechanical properties of soils (rigidity, roughness, etc.). Choosing appropriate values for these parameters remains a difficult task and will depend on the expert know-how. Here, we propose a simple method that can be used routinely to evaluate the relative influence of these parameters (about 50 parameters for the examples below) on the simulation results. The objective is 1) to identify the parameters that are playing a key or predominant role in the simulations and that require additional characterization efforts, 2) to estimate the uncertainty that exists on the simulation results. The application cases for this sensitivity analysis are two busy roads on Reunion island (France) when considering the residual rockfall risk after a major rockfall event

ChronoR: Rotation Based Temporal Knowledge Graph Embedding

Despite the importance and abundance of temporal knowledge graphs, most of the current research has been focused on reasoning on static graphs. In this paper, we study the challenging problem of inference over temporal knowledge graphs. In particular, the task of temporal link prediction. In general, this is a difficult task due to data non-stationarity, data heterogeneity, and its complex temporal dependencies. We propose Chronological Rotation embedding (ChronoR), a novel model for learning representations for entities, relations, and time. Learning dense representations is frequently used as an efficient and versatile method to perform reasoning on knowledge graphs. The proposed model learns a k-dimensional rotation transformation parametrized by relation and time, such that after each fact's head entity is transformed using the rotation, it falls near its corresponding tail entity. By using high dimensional rotation as its transformation operator, ChronoR captures rich interaction between the temporal and multi-relational characteristics of a Temporal Knowledge Graph. Experimentally, we show that ChronoR is able to outperform many of the state-of-the-art methods on the benchmark datasets for temporal knowledge graph link prediction

Can Knowledge Graphs Simplify Text?

Knowledge Graph (KG)-to-Text Generation has seen recent improvements in generating fluent and informative sentences which describe a given KG. As KGs are widespread across multiple domains and contain important entity-relation information, and as text simplification aims to reduce the complexity of a text while preserving the meaning of the original text, we propose KGSimple, a novel approach to unsupervised text simplification which infuses KG-established techniques in order to construct a simplified KG path and generate a concise text which preserves the original input's meaning. Through an iterative and sampling KG-first approach, our model is capable of simplifying text when starting from a KG by learning to keep important information while harnessing KG-to-text generation to output fluent and descriptive sentences. We evaluate various settings of the KGSimple model on currently-available KG-to-text datasets, demonstrating its effectiveness compared to unsupervised text simplification models which start with a given complex text. Our code is available on GitHub.Comment: Accepted as a Main Conference Long Paper at CIKM 202

Structure-function studies of an engineered scaffold protein derived from stefin A. I: Development of the SQM variant

Non-antibody scaffold proteins are used for a range of applications, especially the assessment of protein–protein interactions within human cells. The search for a versatile, robust and biologically neutral scaffold previously led us to design STM (stefin A triple mutant), a scaffold derived from the intracellular protease inhibitor stefin A. Here, we describe five new STM-based scaffold proteins that contain modifications designed to further improve the versatility of our scaffold. In a step-by-step approach, we introduced restriction sites in the STM open reading frame that generated new peptide insertion sites in loop 1, loop 2 and the N-terminus of the scaffold protein. A second restriction site in ‘loop 2’ allows substitution of the native loop 2 sequence with alternative oligopeptides. None of the amino acid changes interfered significantly with the folding of the STM variants as assessed by circular dichroism spectroscopy. Of the five scaffold variants tested, one (stefin A quadruple mutant, SQM) was chosen as a versatile, stable scaffold. The insertion of epitope tags at varying positions showed that inserts into loop 1, attempted here for the first time, were generally well tolerated. However, N-terminal insertions of epitope tags in SQM had a detrimental effect on protein expression