Miracle-dz/UniFAR-FTUs huggingface dataset:
A High-Resolutio Rapid and Conti Translocation ac

Protein translocation is a fundamental process in biology. Major gaps in our understanding of this process arise due the poor sensitivity, low time resolution and irreproducibility of translocation assays. To address this, we applied NanoLuc split-luciferase to produce a new strategy for measuring protein transport. The system reduces the timescale of data collection from days to minutes and allows for continuous acquisition with a time resolution in the order of seconds, yielding kinetics parameters suitable formechanistic elucidation andmathematical fitting. To demonstrate its versatility, we implemented and validated the assay in vitro and in vivo for the bacterial Sec systemand themitochondrial protein import apparatus. Overall, this technology represents amajor step forward, providing a powerful new tool for fundamental mechanistic enquiry of protein translocation and for inhibitor (drug) screening, with an intensity and rigor unattainable through classical methods. © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Two‐dimensional MXenes for lithium‐sulfur batteries

Rechargeable lithium-sulfur (Li-S) batteries have attracted significant research attention due to their high capacity and energy density. However, their com-mercial applications are still hindered by challenges such as the shuttle effect of soluble lithium sulfide species, the insulating nature of sulfur, and the fast capacity decay of the electrodes. Various efforts are devoted to address these problems through questing more conductive hosts with abundant polysulfide chemisorption sites, as well as modifying the separators to physically/chemi-cally retard the polysulfides migration. Two dimensional transition metal carbides, carbonitrides and nitrides, so-called MXenes, are ideal for confining the polysulfides shuttling effects due to their high conductivity, layered structure as well as rich surface terminations. As such, MXenes have thus been widely studied in Li-S batteries, focusing on the conductive sulfur hosts, polysulfides interfaces, and separators. Therefore, in this review, we summarize the significant progresses regarding the design of multifunctional MXene-based Li-S batteries and discuss the solutions for improving electrochemical performances in detail. In addition, challenges and perspectives of MXenes for Li-S batteries are also outlined.
Tailless Control of a Double Clap-and-Fling Flapping Wing MAV

This paper presents the study on a tailless control mechanism designed to tilt the wing roots of a double clap-and-fling flapping wing micro-airvehicle (MAV). The MAV has made stable hovering flight with tail and stabilizers, the work presented in this paper paves the way for the implementation of tailless flight control on the MAV. A wing tilting mechanism driven by three linear servos was designed and fabricated. Study shows that tilting the wing roots creates a linear trend of pitching and rolling moments if the centre of gravity (CG) falls on the correct location. Wing kinematics was captured and it was found that the higher drag due to higher angle of attack could be a contributing factor to the pitching and rolling moments. Another contributing factor could be the lift dissymmetry of the opposite pairs of wings. The effects of pitch-roll coupling are also presented.
A Review of Contemporary Techniques for Measuring Ergonomic Wear Comfort of Protective and Sport Clothing

Abstract Protective and sport clothing is governed by protection requirements, performance, and comfort of the user. The comfort and impact performance of protective and sport clothing are typically subjectively measured, and this is a multifactorial and dynamic process. The aim of this review paper is to review the contemporary methodologies and approaches for measuring ergonomic wear comfort, including objective and subjective techniques. Special emphasis is given to the discussion of different methods, such as objective techniques, subjective techniques, and a combination of techniques, as well as a new biomechanical approach called modeling of skin. Literature indicates that there are four main techniques to measure wear comfort: subjective evaluation, objective measurements, a combination of subjective and objective techniques, and computer modeling of human–textile interaction. In objective measurement methods, the repeatability of results is excellent, and quantified results are obtained, but in some cases, such quantified results are quite different from the real perception of human comfort. Studies indicate that subjective analysis of comfort is less reliable than objective analysis because human subjects vary among themselves. Therefore, it can be concluded that a combination of objective and subjective measuring techniques could be the valid approach to model the comfort of textile materials.
The Impact of Human Mobility on Edge Data Center Deployment in Urban Environments

Multi-access Edge Computing (MEC) brings storage and computational capabilities at the edge of the network into so-called Edge Data Centers (EDCs) to better low-latency applications. To this end, effective placement of EDCs in urban environments is key for proper load balance and to minimize outages. In this paper, we specifically tackle this problem. To fully understand how the computational demand of EDCs varies, it is fundamental to analyze the complex dynamics of cities. Our work takes into account the mobility of citizens and their spatial patterns to estimate the optimal placement of MEC EDCs in urban environments in order to minimize outages. To this end, we propose and compare two heuristics. In particular, we present the mobility-aware deployment algorithm (MDA) that outperforms approaches that do not consider citizens mobility. Simulations are conducted in Luxembourg City by extending the CrowdSenSim simulator and show that efficient EDCs placement significantly reduces outages.
ATTRIBUTE GAUSSIAN MIXTURE MODELLING

Unsupervised image-to-image translation aims to learn a mapping between several visual domains by using unpaired training pairs. Recent studies have shown remarkable success in image-to-image translation for multiple domains but they suffer from two main limitations: they are either built from several two-domain mappings that are required to be learned independently and/or they generate lowdiversity results, a phenomenon known as model collapse. To overcome these limitations, we propose a method named GMM-UNIT based on a content-attribute disentangled representation, where the attribute space is fitted with a GMM. Each GMM component represents a domain, and this simple assumption has two prominent advantages. First, the dimension of the attribute space does not grow linearly with the number of domains, as it is the case in the literature. Second, the continuous domain encoding allows for interpolation between domains and for extrapolation to unseen domains. Additionally, we show how GMM-UNIT can be constrained down to different methods in the literature, meaning that GMM-UNIT is a unifying framework for unsupervised image-to-image translation.
Economining consumer opinions in product reviews

Growth of the commercial Web has dramatically changed the way that consumers express and share their opinions about products. Popularized by Amazon.com and other electronic markets, consumer product reviews have become useful source of valuable information both for businesses and individual customers. The problem of automated review sentiment analysis has been studied extensively in the last few years in the data mining and natural language processing communities. In this paper we consider economic aspects of the problem and investigate whether text information from product reviews can be used for estimation of the product demand. In order to tackle the issue, we develop and implement a hybrid text mining and econometric technique that models consumer reviews as elements in a tensor product of feature space and evaluation space and the quantitative impact of consumer reviews on the product demand as a linear functional from this space. We show how to use low rank approximation of this functional to significantly reduce the number of model parameters while providing good experimental results. The technique is validated on a panel of more than 1300 products and related consumer reviews extracted from Amazon.com during a one year period.
Towards Automatic, Scalable Quality Assurance in Open Education

With the emergence of Open Education Resources (OERs), educational content creation has boomed to a whole new scale. For AI-driven OER platforms such as X5GON, scalable quality assurance is highly impactful. As the quality of OERs could vary significantly, the quality assurance process plays a key role in maintaining a high-quality learner experience when using OERs. Managing this problem at large scale demands automating the quality assurance process as a whole or in parts. Prior research on automating quality assurance in the context of education is surprisingly scarce. We present our ongoing work of building Quality Assurance Models, a novel approach to using crossmodal features from OERs to predict quality using machine learning. While developing quality models, we extended our search beyond the education domain to identify features that indicate content quality that can be categorised into five main quality verticals. In the future, these features will enable us to leverage scalable quality assurance on OERs of different modalities. Furthermore, quality features will also become useful in learning quality preferences of learners when recommending content. Altogether, the expected outcomes of this research will mark a significant step towards Automatic, Scalable Quality Assurance in Open Education.
TION METHOD FOR USE IN NATURAL DIALOGUE

The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research. Current architectures only take care of semantic and contextual information for a given query and fail to completely account for syntactic and external knowledge which are crucial for generating responses in a chit-chat system. To overcome this problem, we propose an end to end multi-stream deep learning architecture which learns unified embeddings for query-response pairs by leveraging contextual information from memory networks and syntactic information by incorporating Graph Convolution Networks (GCN) over their dependency parse. A stream of this network also utilizes transfer learning by pre-training a bidirectional transformer to extract semantic representation for each input sentence and incorporates external knowledge through the neighborhood of the entities from a Knowledge Base (KB). We benchmark these embeddings on next sentence prediction task and significantly improve upon the existing techniques. Furthermore, we use AMUSED to represent query and responses along with its context to develop a retrieval based conversational agent which has been validated by expert linguists to have comprehensive engagement with humans.
BLOCKCHAIN FOR USE IN COLLABORATIVE INTRUSION DETECTION SYSTEMS

As the threat of cyber attack grows ever larger, new approaches to security are required. While there are several different types of intrusion detection systems (IDS), collaborative IDS (CIDS) offers particular promise in identifying distributed, coordinated attacks that might otherwise elude detection. Even for this type of IDS, there are unresolved issues associated with trusting participants and aggregating data. Blockchain technology appears capable of addressing those issues. This thesis is focused on presenting a proof-of-concept experiment leveraging an Ethereum-based private blockchain for a CIDS that uses pluggable authentication modules (PAM) to track login activity toward detection of doorknob rattling attacks.
Intrinsic Motivation for Encouraging Synergistic Behavior

We study the role of intrinsic motivation as an exploration bias for reinforcement learning in sparse-reward synergistic tasks, which are tasks where multiple agents must work together to achieve a goal they could not individually. Our key idea is that a good guiding principle for intrinsic motivation in synergistic tasks is to take actions which affect the world in ways that would not be achieved if the agents were acting on their own. Thus, we propose to incentivize agents to take (joint) actions whose effects cannot be predicted via a composition of the predicted effect for each individual agent. We study two instantiations of this idea, one based on the true states encountered, and another based on a dynamics model trained concurrently with the policy. While the former is simpler, the latter has the benefit of being analytically differentiable with respect to the action taken. We validate our approach in robotic bimanual manipulation tasks with sparse rewards; we find that our approach yields more efficient learning than both 1) training with only the sparse reward and 2) using the typical surprise-based formulation of intrinsic motivation, which does not bias toward synergistic behavior. Videos are available on the project webpage: https://sites.google. com/view/iclr2020-synergistic.
Optimization of Ultrasound-Assisted Phenolic Extraction from Red Pepper Seed by Response Surface Methodology

In this study, phenolic compounds of Kahramanmaras red pepper (Capsicum annuum L.) seeds which are waste products from red pepper processing line were extracted by ultrasound-assisted (UAE) and conventional solvent extraction (SE) methods. Two different extraction methods were evaluated in terms of total phenolic contents (TPC) and antioxidant capacity. Response surface methodology (RSM) was used to optimize UAE conditions of phenolic extracts including extraction temperature (40, 50 and 60 °C), extraction time (20, 40 and 60 min) and solvent volume (50, 75 and 100 mL) for obtaining maximum total phenolics with higher antioxidant capacity. Effects of temperature, time and volume of solvent on extraction of phenolics were evaluated. As a result of RSM analysis, the optimum conditions determined as an extraction temperature of 51 °C, an extraction time of 60 min, and solvent volume of 50 mL. The results showed that ultrasonic treatment was more effective than conventional extraction method on phenolic extraction yield.
MACRO ACTION ENSEMBLE SEARCHING METHODOL-

In this paper, we propose to improve the performance of deep reinforcement learning (DRL) methods by searching for a feasible macro action ensemble to augment the action space of an agent. A macro action ensemble is composed of multiple macro actions, which are typically defined as sequences of primitive actions. A well-defined macro action ensemble enables a DRL agent to achieve higher performance than conventional DRL methods on a variety of tasks. However, macro actions generated by previous approaches are either not necessarily promising, or limited to specific forms. As a result, in this study, we investigate a searching method to learn the macro action ensemble from the environment of interest. The proposed method is inspired by the concepts of neural architecture search techniques, which are capable of developing network architectures for different tasks. These search techniques, such as NASNet or MetaQNN, have been proven to generate high-performance neural network architectures in large search spaces. In order to search in large macro action ensemble spaces, we propose to embrace Deep Q-Learning to search the macro action ensemble space for a good ensemble. Our approach iteratively discovers new ensembles of macro actions with better performance on the learning task. The proposed method is able to search finite macro action ensemble spaces directly, that the other contemporary methods have yet to achieve. Our experimental results show that the scores attained by the policy trained with the discovered macro action ensemble outperforms those without it. Moreover, the policies using our macro action ensemble are more efficient in exploration and able to converge faster. We further perform a comprehensive set of ablative analyses to validate the proposed methodology.
Curcumin exerts protective effects against hypoxia-reoxygenation injury via the enhancement of apurinic/apyrimidinic endonuclease 1 in SH-SY5Y cells: Involvement of the PI3K/AKT pathway

Curcumin, a polyphenolic compound extracted from the plant Curcuma longa, has been reported to exert neuroprotective effects against cerebral ischemia reperfusion (I/R) injury. However, the mechanisms underlying these effects remain to be fully elucidated. Emerging evidence indicated that apurinic/apyrimidinic endonuclease 1 (APE1), a multifunctional enzyme, participates in neuronal survival against I/R injury. Therefore, the aim of the present study was to investigate whether curcumin alleviates oxygen-glucose deprivation/reper-fusion (OGD/R)-induced SH-SY5Y cell injury, which serves as an in vitro model of cerebral I/R injury, by regulating APE1. The results revealed that curcumin increased cell viability, decreased LDH activity, reduced apoptosis and caspase-3 activity, downregulated the pro-apoptotic protein Bax expression and upregulated the anti-apoptotic protein Bcl-2 expression in SH-SY5Y cells subjected to OGD/R. Simultaneously, curcumin eliminated the OGD/R-induced decreases in APE1 protein and mRNA expression, as well as 8-hydroxy-2′-deoxyguanosine (8-OHdG) level and AP sites in SH-SY5Y cells. However, APE1 knockdown by siRNA transfection markedly abrogated the protective effects of curcumin against OGD/R-induced cytotoxicity, apoptosis and oxidative stress, as illustrated by the decreases in reactive oxygen species production and NADPH oxidase 2 expression, and the increase in superoxide dismutase activity and glutathione levels in SH-SY5Y cells. Furthermore, curcumin mitigated the OGD/R-induced activation of phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) signaling pathway. Treatment with LY294002, an inhibitor of PI3K/AKT pathway activity, attenuated the protective effects of curcumin on cytotoxicity and apoptosis, and reversed the curcumin-induced upregulation of APE1 protein expression in SH-SY5Y cells subjected to OGD/R. Taken together, these results demonstrated that curcumin protects SH-SY5Y cells against OGD/R injury by inhibiting apoptosis and oxidative stress, and via enhancing the APE1 level and activity, promoting PI3K/AKT pathway activation.
An Area-Context-Based Credibility Detection for Big Data in IoT

In order to improve the credibility of big data analysis platform’s results in IoT, it is necessary to improve the quality of IoT data. Many detection methods have been proposed to filter out incredible data, but there are certain deficiencies that performance is not high, detection is not comprehensive, and process is not credible. So this paper proposes an area-context-based credibility detection method for IoT data, which can effectively detect point anomalies, behavioral anomalies, and contextual anomalies. The performance of the context determination and the data credibility detection of the device satisfying the area characteristics is superior to the similar algorithms. As the experiments show, the proposed method can reach a high level of performance with more than 97% in metrics, which can effectively improve the quality of IoT data.
A Multi-Attribute Decision Making Process with Immediate Probabilistic Interactive Averaging Aggregation Operators of T-Spherical Fuzzy Sets and Its Application in the Selection of Solar Cells

The objective of this paper is to present new interactive averaging aggregation operators by assigning associate probabilities for T-spherical fuzzy sets (T-SFSs). T-SFS is a generalization of several existing theories such as intuitionistic fuzzy sets and picture fuzzy sets to handle imprecise information. Under such an environment, we developed a series of averaging interactive aggregation operators under the features that each element is represented with T-spherical fuzzy numbers. Various properties of the proposed operators are also investigated. Further, to rank the different T-SFSs, we exhibit the new score functions and state their some properties. To demonstrate the presented algorithm, a decision-making process algorithm is presented with T-SFS features. To save non-renewable resources and to the protect environment, the use of renewable resources is important. Solar energy is one of the best renewable energy resources and is also environment-friendly and thus the selection of solar cells is typically a multi-attribute decision-making problem. Therefore, the applicability of the developed algorithm is demonstrated with a numerical example in the selection of the solar cells and comparison of their performance with the several existing approaches.
Randomized Controlled Trials of Tianma Gouteng Decoction Combined with Nifedipine in the Treatment of Primary Hypertension: A Systematic Review and Meta-Analysis

Background Hypertension is a primary risk factor for cardiovascular disease (CVD). Tianma Gouteng decoction (TGD), originating from Zabingzhengzhixinyi, has been used for thousands of years in China to treat hypertension, giddiness, and migraine. This updated meta-analysis aimed at assessing the efficacy and safety of TGD combined with nifedipine in the treatment of primary hypertension. Methods Related research published prior to September 1, 2019, was found in electronic databases without language limitations. Fourteen studies were selected and analyzed for specified criteria, including the quality of the studies. All outcomes were recorded exhaustive. Data management and analysis were performed using RevMan 5.3 software. Results A total of 1,537 (769 cases in the experimental group and 768 cases in the control group) patients were enrolled. The total efficacy rate was improved significantly for the combination of nifedipine with TGD compared to nifedipine treatment alone (I2 = 22%, RR = 1.17, and 95% CI: 1.12 to 1.22). Traditional Chinese medicine (TCM) symptoms of patients were obviously improved in the experimental group than in the control group (I2 = 22%, RR = 1.17, and 95% CI: 1.12 to 1.22). Traditional Chinese medicine (TCM) symptoms of patients were obviously improved in the experimental group than in the control group (I2 = 22%, RR = 1.17, and 95% CI: 1.12 to 1.22). Traditional Chinese medicine (TCM) symptoms of patients were obviously improved in the experimental group than in the control group (I2 = 22%, RR = 1.17, and 95% CI: 1.12 to 1.22). Traditional Chinese medicine (TCM) symptoms of patients were obviously improved in the experimental group than in the control group (I2 = 22%, RR = 1.17, and 95% CI: 1.12 to 1.22). Traditional Chinese medicine (TCM) symptoms of patients were obviously improved in the experimental group than in the control group (P < 0.00001) when two studies (shicaihong 2017 and xiaoyugao 2017) were removed. And the results of DBP showed no heterogeneity (I2 = 22%, RR = 1.17, and 95% CI: 1.12 to 1.22). Traditional Chinese medicine (TCM) symptoms of patients were obviously improved in the experimental group than in the control group (P < 0.00001) when two studies (shicaihong 2017 and xiaoyugao 2017) were removed. And the results of DBP showed no heterogeneity ( Conclusion The combination of TGD and nifedipine has a better effect in the treatment of hypertension, including blood pressure lowering and patients' TCMs improving. However, our findings must be handled with care because of the small sample size and low quality of clinic trials cited. Other rigorous and large-scale RCTs are in need to confirm these results.