Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding repolarization in the intracardiac unipolar electrogram: A long-lasting controversy revisited

Background: The optimal way to determine repolarization time (RT) from the intracardiac unipolar electrogram (UEG) has been a topic of debate for decades. RT is typically determined by either the Wyatt method or the “alternative method,” which both consider UEG T-wave slope, but differently. Objective: To determine the optimal method to measure RT on the UEG. Methods: Seven pig hearts surrounded by an epicardial sock with 100 electrodes were Langendorff-perfused with selective cannulation of the left anterior descending (LAD) coronary artery and submersed in a torso-shaped tank containing 256 electrodes on the torso surface. Repolarization was prolonged in the non-LAD-regions by infusing dofetilide and shortened in the LAD-region using pinacidil. RT was determined by the Wyatt (tWyatt) and alternative (tAlt) methods, in both invasive (recorded with epicardial electrodes) and in non-invasive UEGs (reconstructed with electrocardiographic imaging). tWyatt and tAlt were compared to local effective refractory period (ERP). Results: With contact mapping, mean absolute error (MAE) of tWyatt and tAlt vs. ERP were 21 ms and 71 ms, respectively. Positive T-waves typically had an earlier ERP than negative T-waves, in line with theory. tWyatt -but not tAlt-shortened by local infusion of pinacidil. Similar results were found for the non-invasive UEGs (MAE of tWyatt and tAlt vs. ERP were 30 ms and 92 ms, respectively). Conclusion: The Wyatt method is the most accurate to determine RT from (non) invasive UEGs, based on novel and historical analyses. Using it to determine RT could unify and facilitate repolarization assessment and amplify its role in cardiac electrophysiology.
MAPKAP Kinase-2 phosphorylation of PABPC1 controls its interaction with 14-3-3 proteins after DNA damage: A combined kinase and protein array approach

14-3-3 proteins play critical roles in controlling multiple aspects of the cellular response to stress and DNA damage including regulation of metabolism, cell cycle progression, cell migration, and apoptotic cell death by binding to protein substrates of basophilic protein kinases following their phosphorylation on specific serine/threonine residues. Although over 200 mammalian proteins that bind to 14-3-3 have been identified, largely through proteomic studies, in many cases the relevant protein kinase responsible for conferring 14-3-3-binding to these proteins is not known. To facilitate the identification of kinase-specific 14-3-3 clients, we developed a biochemical approach using high-density protein filter arrays and identified the translational regulatory molecule PABPC1 as a substrate for Chk1 and MAPKAP Kinase-2 (MK2) in vitro, and for MK2 in vivo, whose phosphorylation results in 14-3-3-binding. We identify Ser-470 on PABPC1 within the linker region connecting the RRM domains to the PABC domain as the critical 14-3-3-binding site, and demonstrate that loss of PABPC1 binding to 14-3-3 results in increased cell proliferation and decreased cell death in response to UV-induced DNA damage.
Editorial: ADAM, ADAMTS and astacin proteases: Challenges and breakthroughs in the -Omics era-Volume II

Cancer Centre, Faculty of Health Sciences, MOE Frontiers Science Center for Precision Oncology, University of Macau, Avenida de Universidade, Taipa, China, Institute of Life Course andMedical Sciences, University of Liverpool, Liverpool, United Kingdom, Institute of Cardiovascular Science, University College London, London, United Kingdom, Proteomics Group of Fondazione Ri.MED, Department of Research IRCCS ISMETT, Palermo, Italy, Department of Biochemical Sciences, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
Global research trends of endoscope in early gastric cancer: A bibliometric and visualized analysis study over past 20 years

Objectives Early gastric cancer (EGC) is defined as aggressive gastric cancer involving the gastric mucosa and submucosa. Early detection and treatment of gastric cancer are beneficial to patients. In recent years, many studies have focused on endoscopic diagnosis and therapy for EGC. Exploring new methods to analyze data to enhance knowledge is a worthwhile endeavor, especially when numerous studies exist. This study aims to investigate research trends in endoscopy for EGC over the past 20 years using bibliometric analysis. Methods Original articles and reviews examining the use of endoscopy for EGC published from 2000 to 2022 were retrieved from the Web of Science Core Collection, and bibliometric data were extracted. Microsoft Office Excel 2016 was used to show the annual number of published papers for the top 10 countries and specific topics. VOSviewer software was used to generate network maps of the cooccurrences of keywords, authors, and topics to perform visualization network analysis. Results In total, 1,009 published papers met the inclusion criteria. Japan was the most productive country and had the highest number of publications (452, 44.8%), followed by South Korea (183, 18.1%), and China (150, 14.9%). The National Cancer Center of Japan was the institution with the highest number of publications (48, 4.8%). Ono was the most active author and had the highest number of cited publications. Through the network maps, exploring endoscopic diagnosis and therapy were major topics. Artificial intelligence (AI), convolutional neural networks (CNNs), and deep learning are hotspots in endoscopic diagnosis. Helicobacter pylori eradication, second-look endoscopy, and follow-up management were examined. Conclusions This bibliometric analysis investigated research trends regarding the use of endoscopy for treating EGC over the past 20 years. AI and deep learning, second-look endoscopy, and management are hotspots in endoscopic diagnosis and endoscopic therapy in the future.
Modelling and control of manufacturing systems subject to context recognition and switching

ABSTRACT Finite-State Automata (FSA) are foundations for modelling, synthesis, verification, and implementation of controllers for manufacturing systems. However, FSA are limited to represent emerging features in manufacturing, such as the ability to recognise and switch contexts. One option is to enrich FSA with parameters that carry details about the manufacturing, which may favour design and control. A parameter can be embedded either on transitions or states of an FSA, and each approach defines its own modelling framework, so that their comparison and integration are not straightforward, and they may lead to different control solutions, modelled, processed and implemented distinctly. In this paper, we show how to combine advantages from parameters in manufacturing the modelling and control. We initially present a background that allows to understand each parameterisation strategy. Then, we introduce a conversion method that translates a design-friendly model into a synthesis-efficient structure. Finally, we use the converted models is synthesis, highlighting their advantages. Examples are used throughout the paper to illustrate and compare our results and tooling support is also provided.
Improving Oracle-Guided Inductive Synthesis by Efficient Question Selection

Oracle-guided inductive synthesis (OGIS) is a widely-used framework to apply program synthesis techniques in practice. The question selection problem aims at reducing the number of iterations in OGIS by selecting a proper input for each OGIS iteration. Theoretically, a question selector can generally improve the performance of OGIS solvers on both interactive and non-interactive tasks if it is not only effective for reducing iterations but also efficient. However, all existing effective question selectors fail in satisfying the requirement of efficiency. To ensure effectiveness, they convert the question selection problem into an optimization one, which is difficult to solve within a short time. In this paper, we propose a novel question selector, named LearnSy. LearnSy is both efficient and effective and thus achieves general improvement for OGIS solvers for the first time. Since we notice that the optimization tasks in previous studies are difficult because of the complex behavior of operators, we estimate these behaviors in LearnSy as simple random events. Subsequently, we provide theoretical results for the precision of this estimation and design an efficient algorithm for its calculation. According to our evaluation, when dealing with interactive tasks, LearnSy can offer competitive performance compared to existing selectors while being more efficient and more general. Moreover, when working on non-interactive tasks, LearnSy can generally reduce the time cost of existing CEGIS solvers by up to 43.0%.
Optimization by decomposition and coordination: A unified approach

In the general framework of inifinite-dimensional convex programming, two fundamental principles are demonstrated and used to derive several basic algorithms to solve a so-called "master" (constrained optimization) problem. These algorithms consist in solving an infinite sequence of "auxiliary" problems whose solutions converge to the master's optimal one. By making particular choices for the auxiliary problems, one can recover either classical algorithms (gradient, Newton-Raphson, Uzawa) or decomposition-coordination (two-level) algorithms. The advantages of the theory are that it clearly sets the connection between classical and two-level algorithms, It provides a framework for classifying the two-level algorithms, and it gives a systematic way of deriving new algorithms.
Privacy-preserving Charging Station Recommendation System for Electric Vehicles over Blockchain

Electric vehicle (EV) charging station recommendation system is used to recommend charging stations to EVs in the same area for specific purposes, such as reducing traffic congestion, balancing grid load, or fully utilizing charging station resources. Most of the current recommended systems for electric vehicle charging stations mainly adopt centralized architecture. However, a centralized architecture can still generate service trust issues, and the collection of location privacy information by untrusted systems can reveal sensitive personal information of car owners such as home location, work address, etc. To solve the above problems, we propose a blockchain-based privacy-preserving electric vehicle charging station recommendation system in this paper. Specifically, the system follows the decentralized structure of blockchain and uses a K-anonymity based charging station recommendation algorithm to protect location privacy. We also introduced an incentive mechanism to encourage vehicles to participate in the recommendation and to guarantee the operation of the system. In this paper we give the detailed design and implementation of the system. Simulation results show that the system is able to achieve good performance in the EV charging station recommendation process in a service trusted as well as privacy preserving manner.
Randomized Testing of Byzantine Fault Tolerant Algorithms

Byzantine fault-tolerant algorithms promise agreement on a correct value, even if a subset of processes can deviate from the algorithm arbitrarily. While these algorithms provide strong guarantees in theory, in practice, protocol bugs and implementation mistakes may still cause them to go wrong. This paper introduces ByzzFuzz, a simple yet effective method for automatically finding errors in implementations of Byzantine fault-tolerant algorithms through randomized testing. ByzzFuzz detects fault-tolerance bugs by injecting randomly generated network and process faults into their executions. To navigate the space of possible process faults, ByzzFuzz introduces small-scope message mutations which mutate the contents of the protocol messages by applying small changes to the original message either in value (e.g., by incrementing the round number) or in time (e.g., by repeating a proposal value from a previous message). We find that small-scope mutations, combined with insights from the testing and fuzzing literature, are effective at uncovering protocol logic and implementation bugs in real-world fault-tolerant systems. We implemented ByzzFuzz and applied it to test the production implementations of two popular blockchain systems, Tendermint and Ripple, and an implementation of the seminal PBFT protocol. ByzzFuzz detected several bugs in the implementation of PBFT, a potential liveness violation in Tendermint, and materialized two theoretically described vulnerabilities in Ripple’s XRP Ledger Consensus Algorithm. Moreover, we discovered a previously unknown fault-tolerance bug in the production implementation of Ripple, which is confirmed by the developers and fixed.
Exploration in Open-World Videogames: Environment, Items, Locations, Quests, and Combat in The Witcher 3

The purpose of this research is to examine how players engage in exploration in videogames. Open-world games are becoming increasingly popular, however, despite this growing popularity, empirical research into how players engage in these spaces is still emerging. By understanding how players will explore, designers can continue to create engaging open-world experiences. A diary study was conducted to examine participants’ gameplay experiences while playing an open-world game. Twelve participants took part in the study by playing The Witcher 3 for two weeks. Through a thematic analysis of participants’ diary entries, five activity themes were found – including exploring quests, items, combat, locations, and interactive environments. The results presented in this paper expands on the emerging research, and provides a deeper understanding of how players engage in exploration in videogames.
Exploring the K+ binding site and its coupling to transport in the neurotransmitter:sodium symporter LeuT

The neurotransmitter:sodium symporters (NSSs) are secondary active transporters that couple the reuptake of substrate to the symport of one or two sodium ions. One bound Na+ (Na1) contributes to the substrate binding, while the other Na+ (Na2) is thought to be involved in the conformational transition of the NSS. Two NSS members, the serotonin transporter (SERT) and the Drosophila dopamine transporter (dDAT), also couple substrate uptake to the antiport of K+ by a largely undefined mechanism. We have previously shown that the bacterial NSS homologue, LeuT, also binds K+, and could therefore serve as a model protein for the exploration of K+ binding in NSS proteins. Here, we characterize the impact of K+ on substrate affinity and transport as well as on LeuT conformational equilibrium states. Both radioligand binding assays and transition metal ion FRET (tmFRET) yielded similar K+ affinities for LeuT. K+ binding was specific and saturable. LeuT reconstituted into proteoliposomes showed that intra-vesicular K+ dose-dependently increased the transport velocity of [3H]alanine, whereas extra-vesicular K+ had no apparent effect. K+-binding induced a LeuT conformation distinct from the Na+- and substrate-bound conformation. Conservative mutations of the Na1 site residues affected the binding of Na+ and K+ to different degrees. The Na1 site mutation N27Q caused a >10-fold decrease in K+ affinity but at the same time a ∼3-fold increase in Na+ affinity. Together, the results suggest that K+-binding to LeuT modulates substrate transport and that the K+ affinity and selectivity for LeuT is sensitive to mutations in the Na1 site, pointing toward the Na1 site as a candidate site for facilitating the interaction with K+ in some NNSs.
Tristetraprolin promotes survival of mammary progenitor cells by restraining TNFα levels

Tristetraprolin (TTP) is a RNA binding protein that destabilizes mRNA of factors that up-regulate proliferation, invasiveness and inflammation. Here we show that TTP expression is higher in mammary progenitor cells than in other cell populations, and that reducing its levels impairs mammary gland morphogenesis in vivo and mammosphere formation in culture. Knocking down TTP in stem-like HC11 mouse mammary cell line increased inflammatory cytokine mRNAs and signaling cascades involving NFκB, STAT3 and MAPK p38 activation, which led to apoptosis. Importantly, TNFα overexpression and the consequent p38 phosphorylation would be the leading cause of progenitor cell death upon TTP expression restriction. Taken together, our results reveal the relevance of negative posttranscriptional regulation on TNFα, exerted by TTP, for the maintenance of the progenitor cell compartment in the mammary gland.
A framework for linking hemispheric, full annual cycle prioritizations to local conservation actions for migratory birds

The conservation of migratory birds poses a fundamental challenge: their conservation requires coordinated action across the hemisphere, but those actions must be designed and implemented locally. To address this challenge, we describe a multi-level framework for linking broad-scale, full annual cycle prioritizations to local conservation actions for migratory birds. We developed hemisphere-scale spatial prioritizations for the full annual cycle of migratory birds that breed in six different ecosystems in North America. The full annual cycle prioritizations provide a hemispheric context within which regional priorities can be identifieed. Finer resolution, regional prioritizations can then inform local conservation actions more effectively. We describe the importance of local conservation practitioner contributions at each level of the process and provide two examples of regional spatial prioritizations that were developed to guide local action. The fierst example focused on coastal North and South Carolina, USA, and used information on marsh birds, shorebirds, ecological integrity, and co-benefiets for people to identify Cape Romain, South Carolina as a high-priority site for conservation action. The second example in Colombia used information on migrant and resident birds to identify the Cauca Valley as a high priority site. The multi-level conceptual framework we describe is one pathway for identifying sites for implementation of local conservation actions that are guided by conservation priorities for migratory birds across their full annual cycle.
Modification of amyloplast size in wheat endosperm through mutation of PARC6 affects starch granule morphology

Starch granule morphology is a major factor determining the functional and nutritional properties of starch. Here, we reveal amyloplast structure plays an important role in starch granule morphogenesis in wheat endosperm. Wheat amyloplasts contain large discoid A-type granules and small spherical B-type granules. We isolated a mutant in durum wheat defective in the plastid division protein PARC6, which had increased plastid size in both leaves and endosperm. Endosperm amyloplasts of the mutant contained more A- and B-type granules than those of the wild type. In mature grains, the mutant had larger A- and B-type granules than the wild type, and its A-type granules had a highly aberrant, lobed surface. This defect in granule morphology was already evident at early stages of grain development, when granule size was identical between the mutant and the wild type, and occurred without obvious alterations in starch polymer structure and composition. Plant growth and photosynthetic efficiency, as well as the size, number and starch content of grains, were not affected in the Ttparc6 mutants despite the large changes in plastid size. Interestingly, mutation of the PARC6 paralog, ARC6, in durum wheat did not increase plastid or starch granule size. We suggest this is because TtPARC6 can complement disrupted TtARC6 function by interacting with PDV2, the outer plastid envelope protein that typically interacts with ARC6 to promote plastid division. We propose that amyloplast compartment size and available stromal volume play important roles in determining starch granule size, shape and number per amyloplast.
Mechanism of ADP-Inhibited ATP Hydrolysis in Single Proton-Pumping FoF1-ATP Synthase Trapped in Solution

FoF1-ATP synthases in mitochondria, in chloroplasts and in most bacteria are the proton-driven membrane enzymes supplying the cells with ATP made from ADP and phosphate. To monitor and prevent the reverse chemical reaction of fast wasteful ATP hydrolysis by the enzymes, different control mechanisms exist including mechanical or redox-based blockade of catalysis and ADP inhibition. In general product inhibition is expected to slow down the mean catalytic turnover. However, biochemical assays are ensemble measurements and cannot discriminate between a mechanism affecting all enzymes equally or individually. For example, all enzymes could work slower at a decreasing substrate/product ratio, or more and more individual enzymes are blocked completely. Here, we examined how increasing amounts of ADP affected ATP hydrolysis of single Escherichia coli FoF1-ATP synthases in liposomes. We observed individual catalytic turnover of the enzymes one after another by monitoring the internal subunit rotation using single-molecule Förster resonance energy transfer (smFRET). Observation times of single FRET-labeled FoF1-ATP synthase in solution were increased up to seconds using a confocal Anti-Brownian electrokinetic trap (ABEL trap). By counting active versus inhibited enzymes we revealed that ADP inhibition did not decrease the catalytic turnover of all FoF1-ATP synthases equally. Instead, increasing ADP in the ADP/ATP mixture reduced the number of the remaining active enzymes which were operating at similar catalytic rates for varying substrate/product ratios.
GILEA: GAN Inversion-enabled latent eigenvalue analysis for phenome profiling and editing

Modeling heterogeneous disease states by data-driven methods has great potential to advance biomedical research. However, a comprehensive analysis of phenotypic heterogeneity is often challenged by the complex nature of biomedical datasets and emerging imaging methodologies. Here, we propose a novel GAN Inversion-enabled Latent Eigenvalue Analysis (GILEA) framework and apply it to phenome profiling and editing. As key use cases for fluorescence and natural imaging, we demonstrate the power of GILEA using publicly available SARS-CoV-2 datasets stained with the multiplexed fluorescence cell-painting protocol as well as real-world medical images of common skin lesions captured by dermoscopy. The quantitative results of GILEA can be biologically supported by editing latent representations and simulating dynamic phenotype transitions between physiological and pathological states. In conclusion, GILEA represents a new and broadly applicable approach to the quantitative and interpretable analysis of biomedical image data. The GILEA code and video demos are publicly available at https://github.com/CTPLab/GILEA.