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Accurate prediction of protein-ligand binding affinity is important in that it can lower the overall cost of drug discovery in structure-based drug design. For more accurate prediction, many classical scoring functions and machine learning-based methods have been developed. However, these techniques tend to have limitations, mainly resulting from a lack of sufficient interactions energy terms to describe complex interactions between proteins and ligands. Recent deep-learning techniques show strong potential to solve this problem, but the search for more efficient and appropriate deep-learning architectures and methods to represent protein-ligand complexes continues. In this study, we proposed a deep-neural network for more accurate prediction of protein-ligand complex binding affinity. The proposed model has two important features, descriptor embeddings that contains embedded information about the local structures of a protein-ligand complex and an attention mechanism for highlighting important descriptors to binding affinity prediction. The proposed model showed better performance on most benchmark datasets than existing binding affinity prediction models. Moreover, we confirmed that an attention mechanism was able to capture binding sites in a protein-ligand complex and that it contributed to improvement in predictive performance. Our code is available at https://github.com/Blue1993/BAPA.Author summaryThe initial step in drug discovery is to identify drug candidates for a target protein using a scoring function. Existing scoring functions, however, lack the ability to accurately predict the binding affinity of protein-ligand complexes. In this study, we proposed a deep learning-based approach to extract patterns from the local structures of protein-ligand complexes and to highlight the important local structures via an attention mechanism. The proposed model showed good performance for various benchmark datasets compared to existing models.

Migraine is a type of primary headache with recurrent attacks, negatively affects the daily living activities of sufferers because of its severity, and causes a heavy economic burden. The economic, social, and physical burdens of migraine grow with the increasing frequency of headache attacks. Mechanism-based treatments are increasingly needed, especially for those with chronic migraine (CM) or episodic migraine (EM) with frequent attacks. Conventional migraine-preventive medications have been essentially developed for some other diseases and shown to be also effective against migraine headaches. Efficacy, safety, and tolerability issues limit their use for an adequate duration, and most patients are under the risk of medication-overuse headache because of the ineffectiveness of attack treatments. In recent decades, a better understanding of migraine pathophysiology has given a new direction to migraine drug research to fulfill the unmet need for the development of migraine-specific medications. Calcitonin gene-related peptide (CGRP) has attracted attention with its potential role in migraine pathogenesis and has become the focus of drug research in this area as of the 1990s. The first monoclonal antibody developed and approved for the treatment of migraine is erenumab. Being the only therapeutic antibody against the CGRP receptor, erenumab differs from the other monoclonal antibodies for migraine prevention that target the CGRP ligand. Erenumab is a fully human, immunoglobulin G2 class monoclonal antibody with no immunomodulatory effect. It blocks the CGRP receptor with high affinity and selectivity and prevents binding of the CGRP ligand to this receptor. It does not have a significant effect on other receptors in the calcitonin receptor family. Erenumab has been shown to diminish the number of migraine days and the need for attack treatment and to improve patient-reported outcomes in patients with EM and CM.

Background Rodents are thought to be produced their human-audible calls (AUDs, below 20 kHz) with phonation mechanism based on vibration of the vocal folds, whereas their ultrasonic vocalizations (USVs, over 20 kHz) are produced with aerodynamic whistle mechanism. Despite of different production mechanisms, the acoustic parameters (duration and fundamental frequency) of AUDs and USVs change in the same direction along ontogeny in collared lemming Dicrostonyx groenlandicus and fat-tailed gerbil Pachyuromys duprasi. We hypothesize that this unidirectional trend of AUDs and USVs is a common rule in rodents and test whether the AUDs of yellow steppe lemmings Eolagurus luteus would display the same ontogenetic trajectory (towards shorter and low-frequency calls) as their USVs, studied previously in the same laboratory colony. Results We examined for acoustic variables 1200 audible squeaks emitted during 480-s isolation-and-handling procedure by 120 individual yellow steppe lemmings (at 12 age classes from neonates to breeding adults, 10 individuals per age class, up to 10 calls per individual, each individual tested once). We found that the ontogenetic pathway of the audible squeaks, towards shorter and lower frequency calls, was the same as the pathway of USVs revealed during 120-s isolation procedure in a previous study in the same laboratory population. Developmental milestone for the appearance of mature patterns of the squeaks (coinciding with eyes opening at 9–12 days of age), was the same as previously documented for USVs. Similar with ontogeny of USVs, the chevron-like squeaks were prevalent in neonates whereas the squeaks with upward contour were prevalent after the eyes opening. Conclusion This study confirms a hypothesis of common ontogenetic trajectory of call duration and fundamental frequency for AUDs and USVs within species in rodents. This ontogenetic trajectory is not uniform across species.

A model having two degrees of freedom and incorporating a specific type of nonlinearity is proposed for the analysis of dynamics of a pipe robot. Specific type of nonlinearity has different values of viscous friction depending on the sign of velocity of the system. Numerical investigations for various parameters of the system are presented. Optimal frequency of excitation of a pipe robot is determined. The obtained results are used in the process of design of pipe robots: it is especially important to choose the frequency of excitation in order to ensure effective operation of a pipe robot.

Abstract Piperine is the main active component of Piper longum L., which is also the main component of anti-sciatica Mongolian medicine Naru Sanwei pill. It has many pharmacological activities such as anti-inflammatory and immune regulation. This paper aims to preliminarily explore the potential mechanism of piperine in the treatment of sciatica through network pharmacology and molecular docking. TCMSP, ETCM database and literature mining were used to collect the active compounds of Piper longum L. Swiss TargetPrediction and SuperPred server were used to find the targets of compounds. At the same time, CTD database was used to collect the targets of sciatica. Then the above targets were compared and analyzed to select the targets of anti-sciatica in Piper longum L. The Go (gene ontology) annotation and KEGG pathway of the targets were enriched and analyzed by Metascape database platform. The molecular docking between the effective components and the targets was verified by Autodock. After that, the sciatica model of rats was established and treated with piperine. The expression level of inflammatory factors and proteins in the serum and tissues of rat sciatic nerve were detected by ELISA and Western blot. HE staining and immunohistochemistry were carried out on the sciatica tissues of rats. The results showed that Piper longum L. can regulate the development of sciatica and affect the expressions of PPARG and NF-kB1 through its active ingredient piperine, and there is endogenous interaction between PPARG and NF-kB1. Graphic abstract

Introduction Benign paroxysmal positional vertigo (BPPV) of the horizontal semicircular canal (hSCC) can present with otoconia blocking its lumen (canalith jam), with signs and symptoms that make it difficult to distinguish from central nervous system pathology. Objective Here we report two cases of canalith jam affecting the hSCC and offer a theoretical mechanism based on known vestibular neurophysiology. Methods We use video‐oculography to document the canalith jam and show the moment the otoconia loosen. Results Canalith jam is a rare form of BPPV remedied with repositioning maneuvers. Conclusion Clinicians should consider canalith jam as a mechanism for BPPV when the nystagmus is (a) Direction fixed with fixation removed and during positional testing; (b) Velocity dependent on supine head position; (c) Converts to geotropic directional changing nystagmus.

In the traditional double auction market, the number of achievable tradable pairs are usually limited by the market size. To solve this problem, we propose new double auction mechanisms based on social networks to realize market expansion. In this paper, we examine the double auction market, which consists of a group of sellers and a group of buyers, where the sellers provide the same type of items, and the buyers respectively purchase one from them. In addition, every buyer can invite other potential buyers to enter the market through social networks. The goal of this paper is to propose mechanisms such that they can encourage buyers already in the market to invite other potential buyers to join the auction through social networks, and achieve an effective allocation of merchandises and increase profits for sellers, which cannot be achieved under the existing double auction mechanism. We found that the extended McAfee auction mechanism cannot motivate existing buyers to invite other potential ones, while the traditional Vickrey-Clark-Groves (VCG) mechanism cannot guarantee to break even. To solve these problems, we propose two mechanisms, which are called information network auction (INA) mechanism and double network auction (DNA) mechanism. Both of these mechanisms can encourage old buyers in the market to invite new potential customers to participate. Moreover, INA focuses on achieving more effective allocation and DNA focuses on ensuring a balance between income and expenditure.

A new optical fiber twist sensor with temperature compensation mechanism is designed and experimentally demonstrated. The main structure of this twist sensor is a torsion perturbation point cascaded with single mode-multimode-single mode (SMS) fiber structure. The experimental results show that the twist sensor has ability to distinguish the twist direction with sensitivities of 9.5 pm/° and 34.7 pm/° at the chosen dips in the range from -100° (CCW) to 140° (CW). Furthermore, the temperature response characteristic of the sensor is also researched through experiment. Temperature sensitivities of 71.8 pm/°C and 65.2 pm/°C can be obtained at the same dips in the range from 20 °C to 75 °C. In addition, the temperature's influence can be compensated with post-measurement calculation through the cross-matrix. The theoretical resolutions of twist and temperature simultaneous measurement are ±0.084° and ±0.32 °C, respectively.

Neural computations are currently investigated using two separate approaches: sorting neurons into functional populations, or examining the low-dimensional dynamics of collective activity. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from networks trained on neuroscience tasks, here we show that the dimensionality of the dynamics and cell-class structure play fundamentally complementary roles. While various tasks can be implemented by increasing the dimensionality in networks with fully random population structure, flexible input-output mappings instead required a non-random population structure that can be described in terms of multiple sub-populations. Our analyses revealed that such a population structure enabled flexible computations through a mechanism based on gain-controlled modulations that flexibly shape the dynamical landscape of collective dynamics. Our results lead to task-specific predictions for the structure of neural selectivity, inactivation experiments, and for the implication of different neurons in multi-tasking.

Most activity-based molecular probes are designed to target enzymes that catalyze the breaking of chemical bonds and the conversion of a unimolecular substrate into bimolecular products. However, DNA topoisomerases are a class of enzymes that alter DNA topology without producing any molecular segments during catalysis, which hinders the development of practical methods for diagnosing these key biomarkers in living cells. Here, we established a new strategy for the effective sensing of the expression levels and catalytic activities of topoisomerases in cell-free systems and human cells. Using our newly designed biosensors, we tricked DNA topoisomerases within their catalytic cycles to switch on fluorescence and resume new rounds of catalysis. Considering that human topoisomerases have been widely recognized as biomarkers for multiple cancers and identified as promising targets for several anticancer drugs, we believe that these DNA-based biosensors and our design strategy would greatly benefit the future development of clinical tools for cancer diagnosis and treatment. Published version This work was supported fnancially by the Northwestern Polytechnical University in China through research grant D5000211020.

In the field of image processing, tangling noise and artefacts elimination of objects are two essential tasks. Tangling noise and lack of intensity in certain applications also occur at the same time. In this paper, a new variational model is proposed based on total variation and l0 the norm for simultaneously removing the tangling noise, estimating the location of missing pixels, and filling in them. To be specific, the total variation is used to regularize the estimated image and use the l0 norm to make the missing pixel to be sparse. Moreover, the data fidelity term is given by a new forward description about the degraded process and the gamma noise assumption. Finally, an algorithm based on the alternating direction multiplier method is exploited to solve the model. By conducting simulated and real experiments, the damaged images can be effectively restored by the proposed method. In qualitative and quantitative terms, this approach works better.

The Industrial Internet of Things (IIoT) has led to the growth and expansion of various new opportunities in the new Industrial Transformation. There have been notable challenges regarding the security of data and challenges related to privacy when collecting real-time and automatic data while observing applications in the industry. This paper proposes an Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT. In FT-Block (Federated transfer learning blockchain), several blockchains are applied to preserve privacy and security for all types of industrial applications. Additionally, by introducing the authentication mechanism based on transfer learning, blockchains can enhance the preservation and security standards for industrial applications. Specifically, Novel Supportive Twin Delayed DDPG trains the user model to authenticate specific regions. As it is considered one of the most open and scalable interacting platforms of information, it successfully helps in the positive transfer of different kinds of data between devices in more significant and local operations of the industry. It is mainly due to a single authentication factor, and the poor adaptation to regular increases in the number of users and different requirements that make the current authentication mechanism suffer a lot in IIoT. As a result, it has been very clearly observed that the given solutions are very useful.

Case-based reasoning, where cases are described in terms of problem-solution pairs \(\mathtt{case}^{}=(\mathtt{x}, \mathtt{y})\), amounts to propose a solution to a new problem on the basis of past experience made of stored cases. On the one hand, the building of the solution to a new problem may be viewed as a form of belief revision of the solution of a retrieved case (whose problem part is similar to the new problem) constrained by domain knowledge. On the other hand, an extrapolation mechanism based on analogical proportions has been proposed. It exploits triplets of cases \((\mathtt{case}^{a}, \mathtt{case}^{b}, \mathtt{case}^{c})\) whose descriptions of problem parts \(\mathtt{x}^{a}\), \(\mathtt{x}^{b}\), \(\mathtt{x}^{c}\) form an analogical proportion with the new problem \(\mathtt{x}^{\mathtt{tgt}}\), in such a way that “\(\mathtt{x}^{a}\) is to \(\mathtt{x}^{b}\) as \(\mathtt{x}^{c}\) is to \(\mathtt{x}^{\mathtt{tgt}}\)”. Then, the analogical inference amounts to compute a solution \(\mathtt{y}^{\mathtt{tgt}}\) of \(\mathtt{x}^{\mathtt{tgt}}\) by solving (when possible) an equation expressing that “\(\mathtt{y}^{a}\) is to \(\mathtt{y}^{b}\) as \(\mathtt{y}^{c}\) is to \(\mathtt{y}^{\mathtt{tgt}}\)” (where \(\mathtt{y}^{a}\), \(\mathtt{y}^{b}\) and \(\mathtt{y}^{c}\) are respectively the solution parts of \(\mathtt{case}^{a}\), \(\mathtt{case}^{b}\) and \(\mathtt{case}^{c}\)). The paper investigates how the belief revision view and analogical extrapolation relate. Besides that it constitutes an unexpected bridge between areas which ignore each other, it casts some light on the adaptation mechanism in case-based reasoning. The paper is illustrated by a running example.

In meat processing, changes in the myofibrillar protein (MP) structure can affect the quality of meat products. High hydrostatic pressure (HHP) has been widely utilized to change the conformational structure (secondary, tertiary and quaternary structure) of MP so as to improve the quality of meat products. However, a systematic summary of the relationship between the conformational structure (secondary and tertiary structure) changes in MP, gel properties and product quality under HHP is lacking. Hence, this review provides a comprehensive summary of the changes in the conformational structure and gel properties of MP under HHP and discusses the mechanism based on previous studies and recent progress. The relationship between the spatial structure of MP and meat texture under HHP is also explored. Finally, we discuss considerations regarding ways to make HHP an effective strategy in future meat manufacturing.

With the popularity of social media, there has been an increasing interest in user profiling and its applications nowadays. This paper presents our system named UIR-SIST for User Profiling Technology Evaluation Campaign in SMP CUP 2017. UIR-SIST aims to complete three tasks, including keywords extraction from blogs, user interests labeling and user growth value prediction. To this end, we first extract keywords from a user's blog, including the blog itself, blogs on the same topic and other blogs published by the same user. Then a unified neural network model is constructed based on a convolutional neural network (CNN) for user interests tagging. Finally, we adopt a stacking model for predicting user growth value. We eventually receive the sixth place with evaluation scores of 0.563, 0.378 and 0.751 on the three tasks, respectively.

Introduction: In order to achieve the highest level of effective interaction between the insurance organizations and the health system, efforts should be made to identify existing communication challenges for policymakers and decision makers. The present study was conducted to identify interactive behaviors aimed at designing an insurance mechanism based on game theory to cover the existing gaps. Methods: This study consisted of two phases of qualitative and quantitative. In the qualitative phase, data were collected and classified by a semi-structured interview with a number of insurance professionals in insurance organizations and the health system. In the quantitative phase of the study, interactive behaviors between the two organizations were studied. Finally, with the help of Gambit software, the insurance mechanism was designed. Results: In the qualitative phase, the data were collected in three main categories: behavioral and communicative, structural and financial and deductibles and 11 sub-categories. In the quantitative phase, 35 optimal interactive behavioral strategies were delineated. Conclusion: The best behavioral strategy for interaction between insurance organizations and health system, to fill gaps in insurance system and to improve the interaction between the two organizations, was introduced in the form of 35 strategies to provide an insurance mechanism.

Older patients with hematologic malignancies are increasingly considered for allogeneic hematopoietic cell transplantation with encouraging outcomes. While aging-related thymic dysfunction remains a major obstacle to optimal and timely immune reconstitution post- transplantation, recent accumulating evidence has suggested that various aging hallmarks such as cellular senescence, inflamm-aging, and hematopoietic stem cell exhaustion, could also impact immune reconstitution post-transplantation in both thymic-dependent and independent manner. Here we review molecular and cellular aspects of immune senescence and immune rejuvenation related to allogeneic hematopoietic cell transplantation among older patients and discuss potential strategies for mechanism-based therapeutic intervention.

Proton pump inhibitors (PPIs) are the mainstay for treatment of acid-related diseases. This study developed a mechanism-based pharmacokinetic (PK) and pharmacodynamics (PD) model with ilaprazole as case drug, so as to support and accelerate the development of novel PPIs. The model was established and verified using the PK and PD data from 26 subjects receiving 5 to 30 mg of ilaprazole and 22 subjects receiving the loading dose of ilaprazole 20 mg followed by 10 mg once daily for 2 days. The nonlinear mixed-effects modeling approach was performed for the PK/PD model. A two-compartment model with linear elimination and covariates (body weight and gender) described the observed data well. The relationship between plasma concentrations of ilaprazole and gastric acid pH was well quantified with individual variability, in which the synthesis and degradation of H+/K+-ATPase, the food effect, the circular rhythms of gastric acid secretion, and the irreversible inhibition of H+/K+-ATPase by ilaprazole were integrated. This PK/PD model well predicted the PK and PD profile of ilaprazole in healthy subjects and patients with duodenal ulcers receiving wide range dose regimens. The mechanism-based PK/PD model provided a potential strategy to accelerate the development of novel PPIs by waiving the unnecessary clinical trials.

We demonstrate phase-matched second-harmonic generation (SHG) from three-dimensional metamaterials consisting of stacked metasurfaces. To achieve phase matching, we utilize a novel mechanism based on phase engineering of the metasurfaces at the interacting wavelengths, facilitating phase-matched SHG in the unconventional backward direction. By stacking up to five metasurfaces, we obtain the expected factor of 25 enhancement in SHG efficiency. Our results motivate further investigations to achieve higher conversion efficiencies also with more complex wavefronts., Comment: 5 pages, 3 figures, supplementary material

In recent years Local Energy Markets (LEM) have emerged as an innovative and versatile energy trade solution. They bring benefits when renewable energy sources are used and are more flexible for consumers. There are, however, security concerns that put the feasibility of the local energy market at risk. One of these security challenges is the integrity of data in the smart-grid that supports the local market. In this article the LEM and the types of attacks that can have a negative impact on it are presented, and a security mechanism based on a trust model is proposed. A case study is elaborated using a multi-agent system called Local Energy Market Multi-Agent System (LEMMAS), capable of simulating the LEM and testing the proposed security mechanism.

In this article, we discuss the issue of context-dependence of mechanism-based explanation in the social sciences. The different ways in which the context-dependence and context-independence of mechanism-based explanation have been understood in the social sciences are often motivated by different and apparently incompatible understandings of what explanatory mechanisms are. Instead, we suggest that the different varieties of context-dependence are best seen as corresponding to different research goals. Rather than conflicting with one another, these goals are complementary to each other and therefore pave the way to a methodologically more cooperative approach to mechanism-based explanation in the social sciences.

This paper introduces an information screening mechanism based on the general SEIR model of public opinion dissemination in complex networks. Specifically, networks users screen the information disseminated in complex networks based on their knowledge reserve, life experience, and personal preferences. This screening mechanism filters the public opinion disseminated. Based on the quantitative analysis of this mechanism, we establish a new SEIR model of public opinion dissemination in complex networks. The simulation results on the Facebook network data set provide theoretical guidance for the formulation of effective public opinion suppression strategies.

This paper considers a closed-loop supply chain (CLSC) consisting of a manufacturer and a retailer based on different corporate social responsibility (CSR) behaviors. In addition, the advertising investment and government’s reward-penalty mechanism (RPM) are also taken in account. To explore the impact of CSR behaviors on supply chain, we developed three models: (1) Only the manufacturer undertakes CSR (model M); (2) only the retailer undertakes CSR (model R); (3) the manufacturer and the retailer share CSR (model MR). By analyzing the results, we found that: (1) The advertising investment level and waste products return rate both increase with the increase of the extent to members undertake CSR when there is only retailer undertakes CSR and when the manufacturer and retailer share CSR, and remain unchanged when there is only the manufacturer undertakes CSR; (2) the overall social welfare increases with the increase of the extent to members undertake CSR no matter which member undertakes CSR, and reaches the highest when there is only the retailer undertakes CSR; (3) it is the most conductive for the performance of the closedloop supply chain when there is only the retailer undertakes CSR.

Neural machine translation has been widely concerned in recent years. The traditional sequential neural network framework of English translation has obvious disadvantages because of its poor ability to capture long-distance information, and the current improved framework, such as the recurrent neural network, still cannot solve this problem very well. In this paper, we propose a hybrid neural network that combines the convolutional neural network (CNN) and long short-term memory (LSTM) and introduce the attention mechanism based on the encoder-decoder structure to improve the translation accuracy, especially for long sentences. In the experiment, this model is implemented based on TensorFlow, and the results show that the BLEU value of the proposed method is obviously improved compared with the traditional machine learning model, which proves the effectiveness of our method in English-Chinese translation.

Lipid oxidation is a major concern in the food industry as it is one of the main contributors to the limited shelf life of lipid-based food products. This is a particular concern for food emulsions with high amounts of unsaturated fats, such as mayonnaise. In the food industry, accelerated shelf-life tests (ASLTs) are often applied to assess the oxidative stability of different formulations. These ASLTs are not used without drawbacks. First, it is well recognised that shelf-life testing under accelerating conditions may induce different mechanisms and therefore, different stability rankings than when performed under consumer-relevant conditions. Secondly, even under accelerated conditions, it can still take weeks of shelf-life before (late) off-flavour compounds can be detected. Thirdly, it is difficult to infer the involved mechanism from these late lipid oxidation products. To optimise formulation and processing of oxidation-stable food emulsions in a more rapid and rational manner there is a clear need for shelf-life tests that provide early (hours-days) markers that quantitatively predict generation of off-flavour and give an indication of the involved oxidative mechanism(s). The main goal of the thesis is to discover these mechanism-based early markers for lipid oxidation in food emulsions, possibly under consumer relevant conditions.An Nuclear Magnetic Resonance (NMR) method for the quantitative assessment of both hydroperoxides and aldehydes, respectively the primary and secondary oxidation products, was developed using band-selective selective 1H NMR pulse excitation. 1H NMR signals of hydroperoxides were assigned in a fatty acid and isomer specific way. The method allowed quantification of hydroperoxides and aldehydes with high throughput, dynamic range and precision. Explorative multivariate data modelling of the quantitative 1H NMR profiles revealed that shelf-life temperature had a significant impact on lipid oxidation mechanisms. The method was expanded to include the epoxides, using 2D 1H-13C HSQC. This addition allowed for the inclusion of also the major epoxide contribution in the assessment of environmental impact on lipid oxidation pathways. Different classes of epoxides were distinguished, based on stereospecificity and additional oxidation moieties. Limited potential for epoxides as a sole early marker was found, however. Combined assessment of hydroperoxides and epoxides will allow for capturing of main radical mechanisms.Radicals were monitored in early lipid oxidation using Electron Spin Resonance (ESR), combined with spin-trapping. By following spin adducts over time, oxidative stability of emulsion could be derived. Importantly, already at mild (=

Diffusion-reaction models derived within the frame of continuum thermodynamics of irreversible processes for reacting mixtures enjoy a number of advantages over purely phenomenological models. This paper explores the potential and challenges of this approach by means of a particular model for spurt oxidation in bitumen. Film ageing experiments are simulated considering different bitumen compositions in terms of SARA fractions. The predicted spurt completion times are contrasted with parameters characterizing the initial composition. The results demonstrate the consistency of the model. Furthermore, the trend regarding spurt completion time predicted by the model is in line with experimental results from the Strategic Highway Research Program (SHRP). In addition, the change in diffusivity due to oxidation is discussed for different compositions. The model consistently predicts a significant decrease in diffusivity due to oxidation in the absence of out-diffusion of reaction products.

We report on the design of a novel nanoresonator operating at visible wavelengths, in which light confinement is achieved by a hybrid mechanism based on total internal reflection and photonic band gap. We show that this structure can support resonant nanophotonic modes with mode volumes on the order of one cubic wavelength, and Q factors exceeding several tens of thousands. Its properties make it ideal for controlling and enhancing the light-matter interaction at sub-wavelength scales.

In social network group decision-making, adjusting the opinions of decision makers is often used to promote consensus. But decision makers are not always willing to accept the feedback mechanism, and decision-makers' trust relationships are not constant during the consensus process. This paper constructs a novel consensus model with double feedback mechanism based on dynamic relationship to solve social network group decision-making problems. It overcomes the deficiency of single feedback mechanism and improves consensus degrees. This double feedback mechanism comprises a weight feedback mechanism and an alternative-contents feedback mechanism. In each round of this double feedback mechanism, the trust relationships between decision makers are dynamic, the trust scores are adjusted to update the weights of decision makers on the basis of the weight feedback mechanism. Synchronously, adjust the contents of alternatives with low dominance degree according to the alternative-contents feedback mechanism, which can change the evaluation opinions of the decision makers. Moreover, an example of a decision-making problem about a Public-Private-Partnership construction project illustrates the feasibility and validity of the proposed method. Finally, the comparisons between this paper and existing studies help point out the advantages of the proposed methods which can improve decision makers' consensus degrees and satisfaction with alternatives.

Chemical substances are subjected to assessment of genotoxic and carcinogenic effects before being marketed to protect man and the environment from health risks. For agrochemicals, the long-term rodent carcinogenicity study is currently required from a regulatory perspective. Although it is the current mainstay for the detection of nongenotoxic carcinogens, carcinogenicity studies are shown to have prominent weaknesses and are subject to ethical and scientific debate. A transition toward a mechanism-based weight-of-evidence approach is considered a requirement to enhance the prediction of carcinogenic potential for environmental (agro)chemicals. The resulting approach should make optimal use of innovative (computational) tools and be less animal demanding. To identify the various mode of actions (MOAs) underlying the nongenotoxic carcinogenic potential of agrochemicals, we conducted an extensive analysis of 411 unique agrochemicals that have been evaluated for carcinogenicity by the United States Environmental Protection Agency (US EPA) and the European Chemicals Agency (ECHA). About one-third of these substances could be categorized as nongenotoxic carcinogens with an average of approximately two tumor types per substance, observed in a variety of organs. For two-third of the tumor cases, an underlying MOA (network) could be identified. This analysis demonstrates that a limited set of MOA (networks) is underlying nongenotoxic carcinogenicity of agrochemicals, illustrating that the transition toward a MOA-driven approach appears manageable. Ultimately the approach should cover relevant MOAs and its associated key events; this will also facilitate the evaluation of the human relevance. This manuscript describes the results of the analysis while identifying knowledge gaps and necessities to achieve a mechanism-based weight-of-evidence approach.

A damage mechanism based approach to the structural health monitoring of wind turbine blades is formulated. Typical physical mechanisms of wind turbine blade degradation, including surface erosion, adhesive fatigue, laminate cracking and in some cases compressive kinking and failure are reviewed. Examples of a local, damage mechanism based approach to the structural health monitoring of wind turbine blades are demonstrated, including the monitoring of leading edge erosion of wind turbine blades, adhesive bond failure, plydrop delamination, static and dynamic plydrop tests, and bolt and laminate fatigue. The examples demonstrate the possibilities of monitoring specific damage mechanisms, and specific localizations of wind turbine blades.

Immune checkpoint inhibitors (ICI) targeting CTLA4 or PD-1/PD-L1 have transformed cancer therapy but are associated with immune-related adverse events, including myocarditis. Here, we report a robust preclinical mouse model of ICI-associated myocarditis in which monoallelic loss of Ctla4 in the context of complete genetic absence of Pdcd1 leads to premature death in approximately half of mice. Premature death results from myocardial infiltration by T cells and macrophages and severe ECG abnormalities, closely recapitulating the clinical and pathologic hallmarks of ICI-associated myocarditis observed in patients. Using this model, we show that Ctla4 and Pdcd1 functionally interact in a gene dosage–dependent manner, providing a mechanism by which myocarditis arises with increased frequency in the setting of combination ICI therapy. We demonstrate that intervention with CTLA4–Ig (abatacept) is sufficient to ameliorate disease progression and additionally provide a case series of patients in which abatacept mitigates the fulminant course of ICI myocarditis. Significance: We provide a preclinical model of ICI-associated myocarditis which recapitulates this clinical syndrome. Using this model, we demonstrate that CTLA4 and PD-1 (ICI targets) functionally interact for myocarditis development and that intervention with CTLA4–Ig (abatacept) attenuates myocarditis, providing mechanistic rationale and preclinical support for therapeutic clinical studies. See related commentary by Young and Bluestone, p. 537. This article is highlighted in the In This Issue feature, p. 521

Similar to the use of electrocardiogram (ECG) for monitoring heartbeat, this article proposes an equipment electrocardiogram (EECG) mechanism based on fine-grained collection of data during the entire operating duration of the manufacturing equipment, with the purpose of the EECG to reveal the equipment performance degradation in smart manufacturing. First, the system architecture of EECG in smart manufacturing is constructed, and the EECG mechanism is explored, including the granular division of the duration of the production process, the matching strategy for process sequences, and several important working characteristics (e.g., baseline, tolerance, and hotspot). Next, the automatic production line EECG (APL-EECG) is deployed, to optimize the cycle time of the production process and to monitor the performance decay of the equipment online. Finally, the performance of the APL-EECG was validated using a laboratory production line. The experimental results have shown that the APL-EECG can monitor the performance degradation of the equipment in real-time and can improve the production efficiency of the production line. Compared with a previous factory information system, the APL-EECG has shown more accurate and more comprehensive understanding in terms of data for the production process. The EECG mechanism contributes to both equipment fault tracking and optimization of production process. In the long run, APL-EECG can identify potential failures and provide assistance in for preventive maintenance of the equipment.

International audience; In this paper, we investigate the problem of selective routing attack in wireless sensor networks by considering a novel threat, named the upstream-node effect, which limits the accuracy of the monitoring functions in deciding whether a monitored node is legitimate or malicious. To address this limitation, we propose a one-dimensional one-class classifier, named relaxed flow conservation constraint, as an intrusion detection scheme to counter the upstream node attack. Each node uses four types of relaxed flow conservation constraints to monitor all of its neighbors. Three constraints are applied by using one-hop knowledge, and the fourth one is calculated by monitoring two-hop information. The latter is obtained by proposing two-hop energy-efficient and secure reporting scheme. We theoretically analyze the security and performance of the proposed intrusion detection method. We also show the superiority of relaxed flow conservation constraint in defending against upstream node attack compared to other schemes. The simulation results show that the proposed intrusion detection system achieves good results in terms of detection effectiveness.

Mycobacterial infections continue to greatly affect global health and result in challenging histopathological examinations using digital whole-slide images (WSIs), histopathological methods could be made more convenient. However, screening for stained bacilli is a highly laborious task for pathologists due to the microscopic and inconsistent appearance of bacilli. This study proposed a computer-aided detection (CAD) system based on deep learning to automatically detect acid-fast stained mycobacteria. A total of 613 bacillus-positive image blocks and 1202 negative image blocks were cropped from WSIs (at approximately 20 &times, 20 pixels) and divided into training and testing samples of bacillus images. After randomly selecting 80% of the samples as the training set and the remaining 20% of samples as the testing set, a transfer learning mechanism based on a deep convolutional neural network (DCNN) was applied with a pretrained AlexNet to the target bacillus image blocks. The transferred DCNN model generated the probability that each image block contained a bacillus. A probability higher than 0.5 was regarded as positive for a bacillus. Consequently, the DCNN model achieved an accuracy of 95.3%, a sensitivity of 93.5%, and a specificity of 96.3%. For samples without color information, the performances were an accuracy of 73.8%, a sensitivity of 70.7%, and a specificity of 75.4%. The proposed DCNN model successfully distinguished bacilli from other tissues with promising accuracy. Meanwhile, the contribution of color information was revealed. This information will be helpful for pathologists to establish a more efficient diagnostic procedure.

Kinematic dimensional synthesis is one of the essential steps during the process of design. It is classified into three tasks: function generation, path generation, and motion generation. In this paper, a design method is presented to solve the synthesis of the slider-crank mechanism for function generation based on a closed-form solution with five design parameters. The success criterion of a method used for function generation depends on the structural error function within an operational domain of the slider-crank mechanism. The structural error reduction is related to the number of design parameters utilized in mechanism synthesis, and the effective maximum number of design parameters for the slider-crank mechanism is five. In this study, the closed-form synthesis is found using three methods: precision point method, sub-domain method, and galerkin method. The system of equations is reduced to a twelfth-degree univariate polynomial equation, and thus all the available solutions are obtained. The effectiveness of this design method is tested with some examples of commonly used test functions, namely ex, sin(x), tan(x) and ln(x), using a developed computer program. This design method gives lower structural error than traditional methods in kinematics literature.

Vital biological processes such as genome repair require fast and efficient binding of selected proteins to specific target sites on DNA. Here we propose an active target search mechanism based on "chromophoresis", the dynamics of DNA-binding proteins up or down gradients in the density of epigenetic marks, or colours (biochemical tags on the genome). We focus on a set of proteins that deposit marks from which they are repelled---a case which is only encountered away from thermodynamic equilibrium. For suitable ranges of kinetic parameter values, chromophoretic proteins can perform unidirectional motion and are optimally redistributed along the genome. Importantly, they can also locally unravel a region of the genome which is collapsed due to self-interactions and "dive" deep into its core, for a striking enhancement of the efficiency of target search on such an inaccessible substrate. We discuss the potential relevance of chromophoresis for the location of DNA lesions., Comment: 5 pages, 5 figures

Although many methods have been designed for ranking universities, there is no suitable system that focuses on the ranking of countries based on the performance of their universities. The overall ranking of the universities in a region can indicate the growth of interests in science among the people of that land. This paper introduces a novel ranking mechanism based on the rankings of universities. Firstly, we introduce and discuss two new rankings of countries, based on the rank of their universities. Secondly, we create rankings of countries according to the selected method, based on the top 12000 universities in webometrics.info (January 2012) and compare rankings of countries in 4 editions (January 2012 to July 2013). Firstly, we introduce two new methods of ranking countries based on their university rankings, Weighted Ranking (WR) and Average Ranking (AR). Secondly, we discuss how the introduced ranking systems, perform in ranking countries based on the two years of data. Thirdly, we choose QS (http://www.topuniversities.com) and webometrics.info as two different classification systems for comparing rankings of countries, based on the top 500 universities in these rankings. Results indicate that the methodology can be used to show the quality of the whole universities of each country used to compare rankings of countries in practice compare to other countries in the world., 13 pagges

Urgency of the research. Inspection tasks are frequently and very important from the view of safety. From this reason the topic is very actual.Target setting. The aim of the study is to investigate new kind of pipe mechanism based on differential frictions of bristles.Actual scientific researches and issues analysis. There are many mechanisms for inspection of narrow or hard-to-reach areas. Many of them are based on wheels or tank belt. This research investigates bristle-based pipe mechanism.Uninvestigated parts of general matters defining. Nowadays, the SMA materials or different kind of memory materials are always in the focus of researchers.The research objective is to develop and experimental test the new kind of mechanism based on SMA as well as steel spring mechanism.The statement of basic materials. The locomotion divided into two phases is introduced. Based on this locomotion the mathematical model was derived. Assuming mathematical model was developed control system for experimental pipe mechanism.Conclusions. The results of the experiments shows problems with cooling phase due to its long time consumption. For cooling was used external device. The cooling phase significantly decrease average velocity of pipe mechanism. The advantage of this kind of mechanism is simple control and utilization for the pipe with small diameters., Актуальність теми дослідження. Інспекційні завдання часті і дуже важливі з точки зору безпеки. З цієї причини тема дуже актуальна.Постановка проблеми. Метою роботи є дослідження нового типу трубного механізму на основі диференціального тертя щетинок.Аналіз останніх досліджень і публікацій. Існує багато механізмів огляду вузьких або важкодоступних місць. Більшість з них мають колісну або гусеничну базу. В цій роботі досліджується трубопровідний механізм з поверхнею у вигляді щетини.Виділення недосліджених частин загальної проблеми. У наш час SMA матерыали або різного роду запам’ятовуючі матеріали завжди знаходяться у центрі уваги дослідниківПостановка завдання полягає в розробці та експериментальному випробуванні нового типу механізму на основі SMA, а також сталевого пружинного механізму.Виклад основного матеріалу. Переміщення механізму розділене на дві фази. На основі цього переміщення було виведено математичну модель. Використовуючи математичну модель, була розроблена система управління експериментальним трубним механізмом.Висновки відповідно до статті. Результати експериментів показують проблеми з фазою охолодження через тривалу експлуатацію. Для охолодження використовувався зовнішній пристрій. Фаза охолодження значно знижує середню швидкість трубного механізму. Перевага цього механізму полягає в простому керуванні та використанні для труби невеликих діаметрів.

Unrestricted tumor growth requires a permanent supply of glucose that can be obtained from cancer-stimulated hepatic glucose production and/or glucose redirecting from host insulin resistant tissues to cancer cells. This study proposes a mechanism based on metabolic and hormonal changes that may provoke glucose delivery to cancer cells through two interconnected "vicious cycles" whose continuous activity drives cancer progression. As follows from the proposed here feedback model, these "vicious cycles" result from cancer-mediated manipulation of host glucose sensors. The derived conclusions contribute to a better understanding of cancer pathogenesis and identifying potential therapeutic targets.

Aiming at the problem that the banana de-handing device has poor radial deployment to the banana bunch stalk during the mechanized banana de-handing procedure, this paper presented a ring deployable mechanism based on a set of planar seven-bar linkages. It consists of multiple basic deployable units, which has great folding/deploying performance and is suitable for manufacturing a banana de-handing device, the diameter of which can be variably larger. The kinematics analysis of the mechanism was done, and the trajectory in space was obtained. When the mechanism is fully deployed, the diameter is 164 mm. The ratio of the folded height to the deployed diameter is 0.732, the ratio of the folded diameter to the deployed diameter is 0.262, and the ratio of the folded volume to the maximum of the deployed volume is 0.069. An experimental prototype of 500, 500, and 768 mm in length, width, and height was manufactured, and the deploying performance was analyzed to show the feasibility. Finally, experimental results show that 71.43% of the banana hands are de-handed successfully. The mechanism has a great deploying effect on banana bunch stalk, and the quality of the banana crown incision is good.

The current climate changes have increased the prevalence and intensity of heat stress (HS) conditions. One of the initial consequences of HS is the impairment of the intestinal epithelial barrier integrity due to hyperthermia and hypoxia following blood repartition, which often results in a leaky gut followed by penetration and transfer of luminal antigens, endotoxins, and pathogenic bacteria. Under extreme conditions, HS may culminate in the onset of “heat stroke”, a potential lethal condition if remaining untreated. HS-induced alterations of the gastrointestinal epithelium, which is associated with a leaky gut, are due to cellular oxidative stress, disruption of intestinal integrity, and increased production of pro-inflammatory cytokines. This review summarizes the possible resilience mechanisms based on in vitro and in vivo data and the potential interventions with a group of nutritional supplements, which may increase the resilience to HS-induced intestinal integrity disruption and maintain intestinal homeostasis.

This paper investigates the impact of government subsidies on the firm-level markups and its mechanism of action by measuring the firm-level markups based on a micro dataset of Chinese manufacturing firms from 2000 to 2007. The findings suggest that government subsidies significantly reduce the markups of Chinese firms. Moreover, there is significant heterogeneity in the negative impact of government subsidies among different types of firms. Furthermore, analysis of the impact mechanism based on the mediation effect model shows that the increase in rent-seeking cost is an important way for government subsidies to reduce the firm-level markups. Finally, based on the dynamic decomposition of the aggregate markup of the Chinese manufacturing industry, it is found that government subsidies significantly reduce the industry-level aggregate markup through within-firm and cross-firm effects. This study enriches the literature on government subsidies and markups, and provides a new perspective for understanding the micro-performance of government subsidies.

The tolerance mechanism based on dark channel prior (DCP) of a single image dehazing algorithm is less effective when there are large areas of the bright region in the hazy image because it cannot obtain the tolerance adaptively according to the characteristics of the image. It will lead to insufficient improvement of the transmission of image, so it is difficult to eliminate the color distortion and block effects in the restored image completely. Moreover, when a dense haze area or a third-party direct light source (sunlight, headlights and reflected glare) is misjudged as sky area, the use of tolerance will cause an inferior dehazing effect such as details lost. Regarding the issue above, this paper proposes an adaptive tolerance estimation algorithm. The tolerance is obtained according to the statistic characteristics of each image to make the estimation of transmission more accurately. The experimental results show that the proposed algorithm not only maintains high operational efficiency but also effectively compensates for the defects of the dark channel prior to some scenes. The proposed algorithm can effectively solve the problem of color distortion recovered by the DCP method in the bright regions of the image.

The synthesis of a series of vinylated analogues of sphingosine-1-phosphate together with their unambiguous configurational assignment by VCD methods is reported. Among them, compound RBM10-8 can irreversibly inhibit human sphingosine-1-phosphate lyase (hS1PL) while behaving also as an enzyme substrate. These findings, together with the postulated mechanism for S1PL activity, reinforce the role of RBM10-8 as a new mechanism-based hS1PL inhibitor. Copyright © 2019 American Chemical Society.

The extent of senescence due to damage accumulation (or aging) is evidently evolvable as it varies hugely between species and is not universal, suggesting that its fitness advantages depend on life history and environment. In contrast, repair of damage is present in all organisms studied. Repair and segregation of damage have not always been considered as alternatives, despite the fundamental trade-off between investing resources into repair or growth. For unicellular organisms, unrepaired damage could be divided asymmetrically between daughter cells, leading to aging of one and rejuvenation of the other. Repair of unicells has been shown to be advantageous in well-mixed environments such as chemostats. However, most microorganisms live in spatially structured systems such as biofilms with gradients of environmental conditions and cellular physiology as well as clonal population structure. We asked whether this clonal structure might favor aging by damage segregation as this can be seen as a division of labor strategy, akin to the germline soma division in multicellular organisms. We used an individual-based model with a newly developed adaptive repair strategy where cells respond to their current intracellular damage levels by investing into repair machinery accordingly. We found that the new adaptive repair strategy was advantageous whenever efficient and optimal, both in biofilms and chemostats. Thus, biofilms do not favor a germline soma-like division of labor between daughter cells in terms of damage segregation. We suggest that damage segregation is only beneficial when active and effective, extrinsic mortality is high and a degree of multicellularity is present.IMPORTANCEDamage is an inevitable consequence of life, leading to a trade-off between allocating resources into damage repair or into growth whilst allowing aging, i.e., segregation of damage upon cell division. Few studies considered repair as an alternative to aging. Moreover, all previous studies merely considered well-mixed environments, although the vast majority of unicellular organisms live in spatially structured environments, exemplified by biofilms, and fitness advantages in well-mixed systems often turn into disadvantages in spatially structured systems. We compared the fitness consequences of aging versus damage repair in biofilms with an individual-based model implementing an adaptive repair mechanism based on sensing damage. We found that aging is not beneficial. Instead, it is useful as a stress response to deal with damage that failed to be repaired when (i) clearly asymmetric cell division is feasible; (ii) extrinsic mortality is high; and (iii) a degree of multicellularity is present.

International audience; This article proposes a framework of personalized feedback mechanism to help multiple inconsistent experts to reach consensus in group decision making by allowing to select different feedback parameters according to individual consensus degree. The general harmony degree (GHD) is defined to determine the before/after feedback difference between the original and revised opinions. It is proved that the GHD index is monotonically decreasing with respect to the feedback parameter, which means that higher parameter values will result in higher changes of opinions. An optimization model is built with the GHD as the objective function and the consensus thresholds as constraints, with the solution being personalized feedback advices to the inconsistent experts that keep a balance between consensus (group aim) and independence (individual aim). This approach is, therefore, more reasonable than the unpersonalized feedback mechanisms in which the inconsistent experts are forced to adopt feedback generated with only consensus target without considering the extent of the changes acceptable by individual experts. Furthermore, the following interesting theoretical results are also proved: 1) the personalized feedback mechanism guarantees that the increase of consensus level after feedback advices are implemented; 2) the GHD by the personalized feedback mechanism is higher than that of the unpersonalized one; and 3) the personalized feedback mechanism generalizes the unpersonalized one as it is proved the latter is a particular type of the former. Finally, a numerical example is provided to model the feedback process and to corroborates these results when comparing both feedback mechanism approaches.

This study aims at the great limitations caused by the non-ROI (region of interest) information interference in traditional scene classification algorithms, including the changes of multiscale or various visual angles and the high similarity between classes and other factors. An effective indoor scene classification mechanism based on multiple descriptors fusion is proposed, which introduces the depth images to improve descriptor efficiency. The greedy descriptor filter algorithm (GDFA) is proposed to obtain valuable descriptors, and the multiple descriptor combination method is also given to further improve descriptor performance. Performance analysis and simulation results show that multiple descriptors fusion not only can achieve higher classification accuracy than principal components analysis (PCA) in the condition with medium and large size of descriptors but also can improve the classification accuracy than the other existing algorithms effectively.

The ongoing global pandemic of COVID-19 has brought life to almost a standstill with the implementation of lockdowns and social distancing as some of the preventive measures in the absence of any approved specific therapeutic interventions. To combat this crisis, research communities worldwide are falling back on the existing repertoire of approved/investigational drugs to probe into their anti-coronavirus properties. In this report, we describe our unique efforts in identifying potential drugs that could be repurposed against the main protease of SARS-CoV-2 (SARS-CoV-2 Mpro). To achieve this goal, we have primarily exploited the principles of 'neighbourhood behaviour' in the protein 3D (workflow-I) and chemical 2D structural space (workflow-II) coupled with docking simulations and insights into the possible modes of action of the selected candidates from the available literature. This integrative approach culminated in prioritizing 29 potential repurpose-able agents (20 approved drugs and 9 investigational molecules) against SARS-CoV-2 Mpro. Apart from the approved/investigational anti-viral drugs, other notable hits include anti-bacterial, anti-inflammatory, anti-cancer and anti-coagulant drugs. Our analysis suggests that some of these drugs have the potential to simultaneously modulate the functions of viral proteins and the host response system. Interestingly, many of these identified candidates (12 molecules from workflow-I and several molecules, belonging to the chemical classes of alkaloids, tetracyclines, peptidomimetics, from workflow-II) are suggested to possess anti-viral properties, which is supported by laboratory and clinical data. Furthermore, this work opens a new avenue of research to probe into the molecular mechanism of action of many drugs, which are known to demonstrate anti-viral activity but are so far not known to target viral proteases.

This paper investigates the hybrid-driven mechanism problem for Markov jump system, where both channel quantization (BCQ) and network-induced delay based on uncertain network are considered. Firstly, comparing with the traditional event-triggered scheme, a hybrid-driven mechanism is employed in networked control systems (NCSs) for the finite capacity of communication bandwidth resources and system performance in equilibrium. Then, the quantization technology is applied in the communication channel from sensor-to-controller and controller-to-actuator. The application of BCQ is for further investigation that mitigate data packet transmission rate. Thirdly, Markov jump system is modeled for the hybrid-driven mechanism and network-induced delay. By constructing the Lyapunov–Krasovskii function, a sufficient condition is derived as the stability criterion, and the controller is designed in which the nonlinear term is rewritten for simplifying the calculation. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed approach.

Querying average distances is useful for real-world applications such as business decision and medical diagnosis, as it can help a decision maker to better understand the users’ data in a database. However, privacy has been an increasing concern. People are now suffering serious privacy leakage from various kinds of sources, especially service providers who provide insufficient protection on user’s private data. In this paper, we discover a new type of attack in an average-distance query (AVGD query) with noisy results. The attack is general that it can be used to reveal private data of different dimensions. We theoretically analyze how different factors affect the accuracy of the attack and propose the privacy-preserving mechanism based on the analysis. We experiment on two real-life datasets to show the feasibility and severity of the attack. The results show that the severity of the attack is mainly influenced by the factors including the noise magnitude, the number of queries, and the number of users in each query. Also, we validate the correctness of our theoretical analysis by comparing with the experimental results and confirm the effectiveness of the privacy-preserving mechanism.