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Although thermally activated delayed fluorescence (TADF) based organic light‐emitting diodes (OLEDs) continue to achieve high efficiencies, their device stability still does not necessarily meet industrial standards. To improve device stability, careful design of a stable host material is crucial. Herein, with reference to a common p‐type host material, 3,3′‐di(9H‐carbazol‐9‐yl)‐1,1′‐biphenyl (mCBP), three novel host materials based on a pyridine unit, mCBP‐1N, CzPyPhCz, and CzPyBF, are systematically designed to improve the stabilities efficiently. The green and blue TADF OLEDs are fabricated with these three new hosts. The maximum external quantum efficiencies (EQEmaxs) of green OLEDs are 20.3 % and 14.0% for CzPyBF and mCBP hosts, respectively. Further, high EQEs of 19.2% and 12.9% are maintained even at 1000 cd m−2. Most importantly, the device lifetime of the CzPyBF host can achieve LT95 of 62.7 h, which is 2.5 times longer than that of mCBP. Also, lower turn‐on voltages are achieved by using CzPyPhCz in blue and green devices. With host engineering, the TADF OLEDs are simultaneously improved with lower turn‐on voltages, higher EQEs, and longer device lifetimes. [ABSTRACT FROM AUTHOR]

In this paper, we study the following chemotaxis system with a signal-dependent motility and logistic source: { u t = Δ (γ (v) u) + μ u (1 − u α) , x ∈ Ω , t > 0 , 0 = Δ v − v + u r , x ∈ Ω , t > 0 , u (x , 0) = u 0 (x) , x ∈ Ω under homogeneous Neumann boundary conditions in a smooth bounded domain Ω ⊂ R 2 , where the motility function γ (v) satisfies γ (v) ∈ C 3 ([ 0 , ∞)) with γ (v) > 0 , and | γ ′ (v) | 2 γ (v) is bounded for all v > 0 . The purpose of this paper is to prove that the model possesses globally bounded solutions. In addition, we show that all solutions (u , v) of the model will exponentially converge to the unique constant steady state (1 , 1) as t → + ∞ when μ ≥ K 4 1 + r with K = max 0 < v ≤ ∞ | γ ′ (v) | 2 γ (v) . [ABSTRACT FROM AUTHOR]

An increasingly popular method for investigating visuospatial working memory assumes stored features of objects such as color and orientation vary along continua subject to internal noise. It adapts the stimulus adjustment procedure from perceptual psychophysics to assess the precision with which stored features are represented in memory. This contrasts with methods using discrete, categorical measures of feature retention. The current study examined the replicability of some phenomena documented using conventional methodology when assessed using a continuous measure of feature recall. These concern memory for a short series of objects and include effects of recency, prioritizing an individual object, and presenting an irrelevant additional object after the last item (a poststimulus 'suffix'). In two experiments we find broadly similar results using a continuous measure of color-orientation binding to those obtained previously using categorical measures, with small differences we regard as minor. We interpret the convergence between methods in terms of a simple analogy between categorical memory and categorical perception whereby categorical retrieval involves the application of a discrete criterion to an underlying continuum of stored feature information. We conclude by discussing some of the advantages and limitations of continuous and categorical measures of retention. [ABSTRACT FROM AUTHOR]

The identification of influential nodes in complex networks is an interesting problem. Traditional approaches, such as metric- and machine learning-based approaches, often fail to adequately consider the network structure or node features, limiting their applications in certain scenarios. Deep learning-based methods, particularly those using graph neural networks (GNNs), have yielded remarkable results in this field. However, there is a lack of utilization of the regular equivalence-based similarity between nodes to feature construction, which is critical to deep learning-based methods. Nevertheless, regular equivalence-based similarity is extremely suitable to the quantification of the similarity between influential nodes, as these nodes usually have similar structural properties but do not require common neighbors. Building upon graph convolutional network (GCN, a specific type of GNN) and the regular equivalence-based similarity, this study proposes a novel method for identifying influential nodes in complex networks. Particularly, the regular equivalence-based similarities between nodes are considered to construct node features; a GCN is enhanced to improve the identification of influential nodes. Experiments on several datasets indicate that the proposed method is superior to state-of-the-art methods, demonstrating the advantages of the proposed feature construction based on regular equivalence-based similarity over the identification of influential nodes. [ABSTRACT FROM AUTHOR]

The persistence of the global COVID-19 pandemic caused by the SARS-CoV-2 virus has continued to emphasize the need for point-of-care (POC) diagnostic tests for viral diagnosis. The most widely used tests, lateral flow assays used in rapid antigen tests, and reverse-transcriptase real-time polymerase chain reaction (RT-PCR), have been instrumental in mitigating the impact of new waves of the pandemic, but fail to provide both sensitive and rapid readout to patients. Here, we present a portable lens-free imaging system coupled with a particle agglutination assay as a novel biosensor for SARS-CoV-2. This sensor images and quantifies individual microbeads undergoing agglutination through a combination of computational imaging and deep learning as a way to detect levels of SARS-CoV-2 in a complex sample. SARS-CoV-2 pseudovirus in solution is incubated with acetyl cholinesterase 2 (ACE2)-functionalized microbeads then loaded into an inexpensive imaging chip. The sample is imaged in a portable in-line lens-free holographic microscope and an image is reconstructed from a pixel superresolved hologram. Images are analyzed by a deep-learning algorithm that distinguishes microbead agglutination from cell debris and viral particle aggregates, and agglutination is quantified based on the network output. We propose an assay procedure using two images which results in the accurate determination of viral concentrations greater than the limit of detection (LOD) of 1.27 × 103 copies per mL, with a tested dynamic range of 3 orders of magnitude, without yet reaching the upper limit. This biosensor can be used for fast SARS-CoV-2 diagnosis in low-resource POC settings and has the potential to mitigate the spread of future waves of the pandemic. [ABSTRACT FROM AUTHOR]

Community detection is an essential task in network analysis and is challenging due to the rapid growth of network scales. Recently, discovering communities from the local perspective of some specified nodes called seeds, rather than requiring the global information of the entire network, has become an alternative approach to addressing this challenge. Some seeding algorithms have been proposed in the literature for finding seeds, but many of them require an excessive amount of effort because of the global information or intensive computation involved. In our study, we formally summarize a unified framework for local seeding by considering only the local information of each node. In particular, both popular local seeding algorithms and new ones are instantiated from this unified framework by adopting different centrality metrics. We categorize these local seeding algorithms into three classes and compare them experimentally on a number of networks. The experiments demonstrate that the degree-based algorithms usually select the fewest seeds, while the denseness-based algorithms, except the one with node mass as the centrality metric, select the most seeds; using the conductance of the egonet as the centrality metric performs best in discovering communities with good quality; the core-based algorithms perform best overall considering all the evaluation metrics; and among the core-based algorithms, the one with the Jaccard index works best. The experimental results also reveal that all the seeding algorithms perform poorly in large networks, which indicates that discovering communities in large networks is still an open problem that urgently needs to be addressed. [ABSTRACT FROM AUTHOR]

Exoglucanase (CBH) is the rate limiting enzyme in the process of cellulose degradation. The carbohydrate binding module (CBM) can improve the accessibility of cellulase to substrate, thereby promoting the enzymatic hydrolysis of cellulase. In this study, the influence of CBM on the properties of GH6 exoglucanase from Chaetomium thermophilum (CtCBH) is systematically explored from three perspectives: the fusion of exogenous CBM, the exogenous CBM replacement of its own CBM, and the deletion of its own CBM. The parental and reconstructed CtCBH presented the same optimum pH (6.0) and temperature (60 °C) for maximum activity. Fusion of exogenous CBM increased the binding capacity of CtCBH to Avicel by 8% and 9%, respectively, but it had no significant effect on its catalytic activity. The exogenous CBM replacement of its own CBM resulted in a 12% reduction in the binding ability of CtCBH to Avicel, and a 26% reduction in the catalytic activity of Avicel. The deletion of its own CBM significantly reduced the binding ability of CtCBH to Avicel by approximately 53%, but its catalytic activity was not obviously reduced. These observations suggest that binding ability of CBM is not necessary for the catalysis of CtCBH. • CBM is not necessary for the catalysis of CtCBH. • The deletion of CBM significantly reduced the binding ability of CtCBH. • The deletion of CBM did not significantly reduce its catalytic activity. • The fusion of exogenous CBM can improve the binding ability of CtCBH. • The replacement of CBM affected the catalytic activity and binding ability of CtCBH. [ABSTRACT FROM AUTHOR]

Abstract Influenza virus infection causes substantial morbidity and mortality worldwide. The limited efficacy of oseltamivir in delayed treatment, coupled with the increasing incidences of oseltamivir-resistant strains, calls for next-generation of antiviral drugs. In this study, we discovered NMS-873, an allosteric and specific p97 inhibitor, as a broad-spectrum influenza antiviral through forward chemical genomics screening. NMS-873 shows potent antiviral activity with low-nanomolar EC 50 s against multiple human influenza A and B viruses, including adamantine-, oseltamivir-, or double resistant strains. Our data further showed that silencing of p97 via siRNA or inhibiting p97 by NMS-873 both inhibited virus replication and retained viral ribonucleoproteins (vRNPs) in the nucleus, confirming p97 is the drug target. Mechanistic studies have shown that the nuclear retention of vRNP with NMS-873 treatment is a combined result of two effects: the reduced viral M1 protein level (indirect effect), and the disruption of p97-NP interactions (direct effect). Taken together, our results suggest that p97 could be a novel antiviral target and its inhibitor, NMS-873, is a promising antiviral drug candidate. Graphical abstract Unlabelled Image [ABSTRACT FROM AUTHOR]

A lattice model is proposed for three-lane traffic flow. The extended lane-changing rates are presented to mimic the lane-changing behavior by considering the congestion degree at the present site and the acceptance degree at the targeted site. The stability condition of the model is derived by applying the linear stability method. The spatiotemporal evolution of traffic flow transfer rates is investigated. The results demonstrate that the new model can reproduce the characteristics of lane changing. [ABSTRACT FROM AUTHOR]

This paper deals with the stabilization of state-constrained linear parameter-varying systems subject to parameter uncertainties and input saturation. Based on a class of parameter-dependent Lyapunov functions, and the set invariance, sufficient conditions for the stabilization problem of the linear parameter-varying systems are established in terms of parameterized linear matrix inequalities. Further, these conditions are converted into linear matrix inequalities by using a parameter relaxation technique. Finally, detailed simulation results are presented to illustrate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Abstract: Previous research on memory for a short sequence of visual stimuli indicates that access to the focus of attention (FoA) can be achieved in either of two ways. The first is automatic and is indexed by the recency effect, the enhanced retention of the final item. The second is strategic and based on instructions to prioritize items differentially, a process that draws on executive capacity and boosts retention of information deemed important. In both cases, the increased level of retention can be selectively reduced by presenting a poststimulus distractor (or suffix). We manipulated these variables across three experiments. Experiment 1 generalized previous evidence that prioritizing a single item enhances its retention and increases its vulnerability to interference from a poststimulus suffix. A second experiment showed that the enhancement from prioritizing one or two items comes at a cost to the recency effect. A third experiment showed that prioritizing two items renders memory for both vulnerable to interference from an irrelevant suffix. The results suggest that some but not all items in working memory compete to occupy a narrow FoA and that this competition is determined by a combination of perceptually driven recency and internal executive control. [ABSTRACT FROM AUTHOR]

In this paper, we consider the stability analysis and control synthesis of finite-time boundedness problems for linear parameter-varying (LPV) systems subject to parameter-varying time delays and external disturbances. First, the concepts of uniform finite-time stability and uniform finite-time boundedness are introduced to LPV systems. Then, sufficient conditions, which guarantee LPV systems with parameter-varying time delays finite-time bounded, are presented by using parameter-dependent Lyapunov-Krasovskii functionals and free-weight matrix technologies. Moreover, on the basis of the results on the uniform finite-time boundedness, the parameter-dependent state feedback controllers are designed to finite-time stabilize LPV systems. Both analysis and synthesis conditions are delay-dependent, and they are formulated in terms of linear matrix inequalities by using efficient interior-point algorithms. Finally, results obtained in simulation demonstrate the effectiveness of the proposed approach. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The emergence of multidrug-resistant influenza viruses poses a persistent threat to public health. The current prophylaxis and therapeutic interventions for influenza virus infection have limited efficacy due to the continuous antigenic drift and antigenic shift of influenza viruses. As part of our ongoing effort to develop the next generation of influenza antivirals with broad-spectrum antiviral activity and a high genetic barrier to drug resistance, in this study we report the discovery of dapivirine, an FDA-approved HIV nonnucleoside reverse transcriptase inhibitor, as a broad-spectrum antiviral against multiple strains of influenza A and B viruses with low micromolar efficacy. Mechanistic studies revealed that dapivirine inhibits the nuclear entry of viral ribonucleoproteins at the early stage of viral replication. As a result, viral RNA and protein synthesis were inhibited. Furthermore, dapivirine has a high in vitro genetic barrier to drug resistance, and its antiviral activity is synergistic with oseltamivir carboxylate. In summary, the in vitro antiviral results of dapivirine suggest it is a promising candidate for the development of the next generation of dual influenza and HIV antivirals. [ABSTRACT FROM AUTHOR]

Community structure is a fundamental feature for many networks. The problem of discovering communities in those networks thus has been attracting a lot of research. However, due to the rapid increase of networks' scale and the availability of real communities in many networks, the task of detecting communities in large real networks remains a challenging problem. In this paper, we study the structure of various large real networks and their ground-truth community structures and observe an interesting phenomenon: the difference of degrees (abbreviated as dod) of connected nodes follows a heavy-tailed distribution with an approximate power-law tail for large dod, in both original network and community structure but to different extents. With the aim to explore the effect of this observation on identifying communities in real large networks, we propose a weighting strategy and further embed it into two prominent community detection algorithms. Comparisons against the state of the arts demonstrate a very promising performance of the proposed weighting strategy. [ABSTRACT FROM AUTHOR]

The efficacy of current influenza vaccines and small molecule antiviral drugs is curtailed by the emerging of multidrug-resistant influenza viruses. As resistance to the only FDA-approved oral influenza antiviral, oseltamivir (Tamiflu), continues to rise, there is a clear need to develop the next-generation of antiviral drugs. Since more than 95% of current circulating influenza A viruses carry the S31N mutation in their M2 genes, the AM2-S31N mutant proton channel represents an attractive target for the development of broad-spectrum antivirals. In this study we report the design and synthesis of the first class of organosilanes that have potent antiviral activity against a panel of human clinical isolates of influenza A viruses, including viruses that are resistant to amantadine, oseltamivir, or both. [ABSTRACT FROM AUTHOR]

Adamantanes such as amantadine ( 1 ) and rimantadine ( 2 ) are FDA-approved anti-influenza drugs that act by inhibiting the wild-type M2 proton channel from influenza A viruses, thereby inhibiting the uncoating of the virus. Although adamantanes have been successfully used for more than four decades, their efficacy was curtailed by emerging drug resistance. Among the limited number of M2 mutants that confer amantadine resistance, the M2-V27A mutant was found to be the predominant mutant under drug selection pressure, thereby representing a high profile antiviral drug target. Guided by molecular dynamics simulations, we previously designed first-in-class M2-V27A inhibitors. One of the potent lead compounds, spiroadamantane amine ( 3 ), inhibits both the M2-WT and M2-V27A mutant with IC 50 values of 18.7 and 0.3 μM, respectively, in in vitro electrophysiological assays. Encouraged by these findings, in this study we further examine the in vitro and in vivo antiviral activity of compound 3 in inhibiting both amantadine-sensitive and -resistant influenza A viruses. Compound 3 not only had single to sub-micromolar EC 50 values against M2-WT- and M2-V27A-containing influenza A viruses in antiviral assays, but also rescued mice from lethal viral infection by either M2-WT- or M2-V27A-containing influenza A viruses. In addition, we report the design of two analogs of compound 3, and one was found to have improved in vitro antiviral activity over compound 3 . Collectively, this study represents the first report demonstrating the in vivo antiviral efficacy of inhibitors targeting M2 mutants. The results suggest that inhibitors targeting drug-resistant M2 mutants are promising antiviral drug candidates worthy of further development. [ABSTRACT FROM AUTHOR]

The analysis of communities and their evolutionary behaviors in dynamic networks is a challenging topic. Although a growing body of work on this topic is emerging, there are few methods which can reveal and track meaningful communities over time and can also deal with large networks efficiently. In this paper, we propose a method to track dynamic communities and their evolutionary behaviors. The main idea behind our method is to discover dynamic communities by exploring the local views of nodes that change. Moreover, based on the discovered dynamic communities, the global community structure can be derived by updating the historical community structure and the evolutionary behaviors of communities can also be tracked. To discover the dynamic communities, we apply the technique of approximate personalized PageRank vector; to track the evolutionary behaviors of the communities, we introduce a partial evolutionary graph. We compare the proposed method with several existing methods by performing experiments on nine synthetic networks and one real network. The experimental results show that the proposed method performs well on discovering communities as well as tracking their evolution in dynamic networks, and spends much less running time than the existing methods. [ABSTRACT FROM AUTHOR]

We examined the role of executive control in stimulus-driven and goal-directed attention in visual working memory using probed recall of a series of objects, a task that allows study of the dynamics of storage through analysis of serial position data. Experiment 1 examined whether executive control underlies goal-directed prioritization of certain items within the sequence. Instructing participants to prioritize either the first or final item resulted in improved recall for these items, and an increase in concurrent task difficulty reduced or abolished these gains, consistent with their dependence on executive control. Experiment 2 examined whether executive control is also involved in the disruption caused by a post-series visual distractor (suffix). A demanding concurrent task disrupted memory for all items except the most recent, whereas a suffix disrupted only the most recent items. There was no interaction when concurrent load and suffix were combined, suggesting that deploying selective attention to ignore the distractor did not draw upon executive resources. A final experiment replicated the independent interfering effects of suffix and concurrent load while ruling out possible artifacts. We discuss the results in terms of a domain-general episodic buffer in which information is retained in a transient, limited capacity privileged state, influenced by both stimulus-driven and goal-directed processes. The privileged state contains the most recent environmental input together with goal-relevant representations being actively maintained using executive resources. [ABSTRACT FROM AUTHOR]

As a further step towards the modernization of acupuncture, the objective of this review was to figure out the frequency and severity of adverse complications and events in acupuncture treatment reported from 1980 to 2013 in China. All first-hand case reports of acupuncture-related complications and adverse events that could be identified in the scientific literature were reviewed and classified according to the type of complication and adverse event, circumstance of the event, and long-term patient outcome. The selected case reports were published between 1980 and 2013 in 3 databases. Relevant papers were collected and analyzed by 2 reviewers. Over the 33 years, 182 incidents were identified in 133 relevant papers. Internal organ, tissue, or nerve injury is the main complications of acupuncture especially for pneumothorax and central nervous system injury. Adverse effects also included syncope, infections, hemorrhage, allergy, burn, aphonia, hysteria, cough, thirst, fever, somnolence, and broken needles. Qualifying training of acupuncturists should be systemized and the clinical acupuncture operations should be standardized in order to effectively prevent the occurrence of acupuncture accidents, enhance the influence of acupuncture, and further popularize acupuncture to the rest of the world. [ABSTRACT FROM AUTHOR]

Software systems are widely employed in society. With a limited amount of testing resource available, testing resource allocation among components of a software system becomes an important issue. Most existing research on the testing resource allocation problem takes a single-objective optimization approach, which may not adequately address all the concerns in the decision-making process. In this paper, an architecture-based multi-objective optimization approach to testing resource allocation is proposed. An architecture-based model is used for system reliability assessment, which has the advantage of explicitly considering system architecture over the reliability block diagram (RBD)-based models, and has good flexibility to different architectural alternatives and component changes. A system cost modeling approach which is based on well-developed software cost models is proposed, which would be a more flexible, suitable approach to the cost modeling of software than the approach adopted by others which is based on an empirical cost model. A multi-objective optimization model is developed for the testing resource allocation problem, in which the three major concerns in the testing resource allocation problem, i.e., system reliability, system cost, and the total amount of testing resource consumed, are taken into consideration. A multi-objective evolutionary algorithm (MOEA), called multi-objective differential evolution based on weighted normalized sum (WNS-MODE), is developed. Experimental studies are presented, and the experiments show several results. 1) The proposed architecture-based multi-objective optimization approach can identify the testing resource allocation strategy which has a good trade-off among optimization objectives. 2) The developed WNS-MODE is better than the MOEA developed in recent research, called HaD-MOEA, in terms of both solution quality and computational efficiency. 3) The WNS-MODE seems quite robust from the sensitivity analysis results. [ABSTRACT FROM PUBLISHER]

Along with the development of internet, our personal online social networks become bigger and more jumbled than before, and it is necessary to provide a good way to organize them. Social networking sites allow users to manually categorize their friends into social circles (e.g., ‘list’ on Facebook and Twitter), but it is laborious. The problem of social circles identifying is thus posed on a user’s ego network, while there are currently few efficient as well as effective methods to identify user’s social circles. In this paper, we propose a new method, named enhanced link clustering, for social circles identifying on ego networks. The proposed method integrates node profile and network structure by constructing an edge profile for each edge. Utilization of both node profile and network structure information makes the proposed method more effective. Taking edge similarity instead of node similarity to discriminate nodes into different circles allows us to detect overlapping circles. Moreover, we observe that nodes in one circle appear transitive similarity and some nodes are only densely connected, or share common properties. These observations make the process of edge clustering efficient. Experiments on several real datasets demonstrate that our method is much faster, and also more effective compared with maximization likelihood-like method, which has been proved to dominate most methods. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 main protease (Mpro) is a major therapeutic target. The Mpro inhibitor, nirmatrelvir, is the antiviral component of Paxlovid, an orally available treatment for COVID-19. As Mpro inhibitor use increases, drug resistant mutations will likely emerge. We have established a non-pathogenic system, in which yeast growth serves as an approximation for Mpro activity, enabling rapid identification of mutants with altered enzymatic activity and drug sensitivity. The E166 residue is known to be a potential hot spot for drug resistance and yeast assays identified substitutions which conferred strong nirmatrelvir resistance and others that compromised activity. On the other hand, N142A and the P132H mutation, carried by the Omicron variant, caused little to no change in drug response and activity. Standard enzymatic assays confirmed the yeast results. In turn, we solved the structures of Mpro E166R, and Mpro E166N, providing insights into how arginine may drive drug resistance while asparagine leads to reduced activity. The work presented here will help characterize novel resistant variants of Mpro that may arise as Mpro antivirals become more widely used. Author summary: The SARS-CoV-2 virus has proven to be highly adept at evading the newly developed vaccines. Antiviral drugs provide an important alternative that can reduce disease severity. Nirmatrelvir, an orally available drug approved by the FDA to treat COVID-19, inhibits the SARS-CoV-2 main or 3C-like protease (Mpro or 3CLpro) and reduces the severity of COVID-19 infections. However, continued use of nirmatrelvir will likely drive the emergence of drug resistant Mpro mutations. Our study reports a rapid, inexpensive, and non-pathogenic system using baker's yeast that can determine if potential Mpro mutations may confer drug resistance. Furthermore, we solve the structures of two Mpro mutants first characterized in yeast that provide insights into how E166 can change to drive resistance versus leading to inactivity. Our results can aid in advancing our understanding of resistance mechanisms that will be important as nirmatrelvir and other protease inhibitors become more widely used. [ABSTRACT FROM AUTHOR]

The job-shop scheduling problems with fuzzy processing time and fuzzy due date are investigated in this paper. The ranking concept among fuzzy numbers based on possibility and necessity measures which are developed in fuzzy sets theory is introduced. And on the basis of two consistent measures in this concept, several novel objective functions are proposed. The purpose of our research is to obtain the optimal schedules based on these objective functions. A modified DE algorithm will be designed to solve these objective functions. Several jop-shop scheduling problems with fuzzy processing time and fuzzy due date are experimented to show the efficiency and comparability of our approach. Through the experimental results, the potential application of the possibility and necessity theory in the real world is shown. [ABSTRACT FROM AUTHOR]

Abstract: Clustering is used to group data objects into sets of disjoint classes called clusters so that objects within the same class are highly similar to each other and dissimilar from the objects in other classes. K-harmonic means (KHM) is one of the most popular clustering techniques, and has been applied widely and works well in many fields. But this method usually runs into local optima easily. A hybrid data clustering algorithm based on an improved version of Gravitational Search Algorithm and KHM, called IGSAKHM, is proposed in this research. With merits of both algorithms, IGSAKHM not only helps the KHM clustering to escape from local optima but also overcomes the slow convergence speed of the IGSA. The proposed method is compared with some existing algorithms on seven data sets, and the obtained results indicate that IGSAKHM is superior to KHM and PSOKHM in most cases. [Copyright &y& Elsevier]

Previous research indicates that visual attention can be automatically captured by sensory inputs that match the contents of visual working memory. However, Woodman and Luck (2007) showed that information in working memory can be used flexibly as a template for either selection or rejection according to task demands. We report two experiments that extend their work. Participants performed a visual search task while maintaining items in visual working memory. Memory items were presented for either a short or long exposure duration immediately prior to the search task. Memory was tested by a change-detection task immediately afterwards. On a random half of trials items in memory matched either one distractor in the search task (Experiment 1) or three (Experiment 2). The main result was that matching distractors speeded or slowed target detection depending on whether memory items were presented for a long or short duration. These effects were more in evidence with three matching distractors than one. We conclude that the influence of visual working memory on visual search is indeed flexible but is not solely a function of task demands. Our results suggest that attentional capture by perceptual inputs matching information in visual working memory involves a fast automatic process that can be overridden by a slower top-down process of attentional avoidance. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new type acceptor with fused pyrazine and maleimide is developed. • A "locked" biphenyl bridge is designed to reduce ΔE ST and boost Φ PL. • An EQE max of 26.0% with a low efficiency roll off is realized for orange – red OLEDs. The strategy to develop orange – red thermally activated delayed fluorescent (TADF) materials for high-performance organic light-emitting diodes (OLEDs) with both high maximum external quantum efficiencies (EQEs max) and low efficiency roll-off, is ongoing pursued yet challenging. Herein, two efficient orange – red TADF emitters, T -DMAC-PPyM and P -DMAC-BPyM, with a new acceptor fused by pyrazine and maleimide, were designed and synthesized. Notably, the highly twisted conformation of P -DMAC-BPyM can inhibit the close π-π stacking, so as to reduce exciton quenching, while the "locked" biphenyl bridge can enhance the molecular rigidity, thus reducing the non-radiative decay associated with intramolecular rotation. Consequently, P -DMAC-BPyM exhibits outstanding electroluminescent performance with a remarkably high EQE max of 26.0% and an impressively low efficiency roll-off (EQE 100 is 21.7%; EQE 1000 is 18.3%), which are among the best results for orange – red TADF OLEDs. [ABSTRACT FROM AUTHOR]

In the connected vehicle environment, a vehicle can obtain the information of multiple front and rear vehicles. This driving strategy of bi-directional visual field and multiple anticipation can be implemented to traffic flow modeling. In this paper, an extended car-following model with consideration of bi-directional visual field and multiple anticipation is proposed. A novel macroscopic traffic flow model is developed based on the relationship between the microscopic and macroscopic variables, and the validity of the model is confirmed through the theoretical and numerical studies. The stability analysis shows that the multiple anticipation can improve the stability of traffic flow, and the results of numerical simulation illustrates that the new model can reproduce the local clustering phenomenon of traffic flow. • An extended car following model in the connected vehicle environment is proposed. • A multi-anticipative bi-directional visual field macroscopic model is developed. • The stability of the model is analyzed. [ABSTRACT FROM AUTHOR]

Traffic flow models are important tools for traffic management applications such as traffic incident detection and traffic control. In this paper, we propose a novel numerical approximation method for second-order macroscopic traffic flow models. The method is based on the semi-discrete central-upwind numerical flux and high-order reconstructions for spatial discretizations. We then apply the designed high-resolution schemes to three representative types of second-order traffic flow models and perform a variety of numerical experiments to validate the proposed methods. The simulation results illustrate the effectiveness, simplicity and universality of the central-upwind scheme as numerical approximation method for macroscopic traffic flow models. [ABSTRACT FROM AUTHOR]

Highlights • UV-C illumination at dose of 4.24 KJ m-2 maintained whiteness and texture of WBS. • The UV-C illumination inhibited lignification and chlorophylls biosynthesis. • The UV-C treatment prevented the loss of antioxidant capacity. • Bioactive compounds and antioxidant enzymes were induced by UV-C treatment. Abstract Ultraviolet-C (UV-C) treatment has been used as a method to maintain postharvest quality for many kinds of fruits and vegetables. In this study, we investigated the effects of UV-C treatment on physicochemical changes in water bamboo shoots (WBS) during storage. WBS were treated with UV-C at the doses of 0 (control), 1.06, 2.12, 3.18, 4.24, 5.30, and 6.36 kJ m−2 at 20 °C ± 2 °C for 2 d. The increase in greenness of WBS was inhibited by all UV-C treatments, but the 4.24 kJ m−2 UV-C treatment maintained lightness and whiteness index values of WBS better than other treatments. Thus, the 4.24 kJ m−2 UV-C treatment was selected for use to determine the physicochemical quality of WBS during refrigeration at 10 °C. The UV-C treatment retarded the increases in greenness, chlorophyll a , chlorophyll b, and total chlorophyll content, resulting in maintenance of the whiteness index and colour attributes. It also prevented toughness by retarding lignin increase during storage. Moreover, it induced antioxidant activities and bioactive compounds, especially total phenol, and maintained the activities of antioxidant enzymes such as phenylalanine ammonia lyase and catalase during storage. However, the UV-C treatment did not affect ascorbic acid content and the activities of ascorbic acid peroxidase and guaiacol-peroxidase. These results indicated that UV-C treatment at the dose of 4.24 kJ m-2 is a potential alternative for maintaining physicochemical quality and improving the nutritional quality of WBS during storage. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a dynamic surface control (DSC)–based robust adaptive control scheme for a class of semi‐strict feedback systems with full‐state and input constraints. In the control scheme, a constraint transformation method is employed to prevent the transgression of the full‐state constraints. Specifically, the state constraints are firstly represented as the surface error constraints, then, an error transformation is introduced to convert the constrained surface errors into new equivalent variables without constraints. By ensuring the boundedness of the transformed variables, the violation of the state constraints can be prevented. Moreover, in order to obtain magnitude limited virtual control signal for the recursive design, the saturations are incorporated into the control law. The auxiliary design systems are constructed to analyze the effects of the introduced saturations and the input constraints. Rigorous theoretical analysis demonstrates that the proposed control law can guarantee all the closed‐loop signals are uniformly ultimately bounded, the tracking error converges to a small neighborhood of origin, and the full‐state constraints are not violated. Compared with the existing results, the key advantages of the proposed control scheme include: (i) the utilization of the constraint transformation can handle both time‐varying symmetric and asymmetric state constraints and static ones in a unified framework; (ii) the incorporation of the saturations permits the removal of a feasibility analysis step and avoids solving the constrained optimization problem; and (iii) the “explosion of complexity” in traditional backstepping design is avoided by using the DSC technique. Simulations are finally given to confirm the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, the problem of robust H control for a class of uncertain neutral stochastic systems with mixed delays is investigated. The parameter uncertainties are assumed to be norm-bounded. A delay-dependent sufficient condition is derived in terms of the nonlinear matrix inequality by constructing proper Lyapunov-Krasovskii functional, using matrix inequality techniques and introducing free weighting matrices. The new result obtained in this paper can be tested numerically by using the so-called cone complementarity linearization (CCL) algorithm. Two examples provided in the literature show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

A multiple look-ahead model is extended to take into account the reaction-time delay of drivers. The stability condition of this model is obtained by using the linear stability theory. Through nonlinear analysis, the Korteweg-de Vries (KdV) equation near the neutral stability line and the modified KdV (mKdV) equation near the critical point are derived. Both the analytical and simulation results demonstrate that the stabilization of traffic flow is weakened with increasing the reaction-time delay of drivers, and multiple look-ahead consideration could partially remedy this unfavorable effect. [ABSTRACT FROM AUTHOR]

In this paper, an improved multianticipative cooperative adaptive cruise control (CACC) model is proposed based on fully utilizing multivehicle information obtained by vehicle-to-vehicle communication. More flexible, effective and practical spacing strategy is embedded into the model. We design a new lane-changing rule for CACC vehicles on the freeway. The rule considers that CACC vehicles are more inclined to form a platoon for coordinated control. Furthermore, we investigate the effect of CACC vehicles on two-lane traffic flow. The results demonstrate that introducing CACC vehicles into mixed traffic and forming CACC platoon to cooperative control can improve traffic efficiency and enhance road capacity to a certain extent. [ABSTRACT FROM AUTHOR]

Pretreatment can improve the hydrolysis efficiency of cellulose, in which biological pretreatment plays an important role. In the present study, we uncovered that Rhodococcus has the ability of lignin degradation, which can decompose lignin and serve as a carbon source to meet the needs of its own growth. We used Rhodococcus to pretreat the corn stalks and evaluate the effect on cellulose hydrolysis. The concentration of reducing sugar produced by the hydrolysis of corn stalk after pretreatment of Rhodococcus is 2.95 g/L. SEM imaging showed that Rhodococcus pretreatment resulted the surface of corn stalk to be no longer complete, some lamellar structures fall off, and leave obvious traces, and obvious delamination was found at the edge of the fault. AFM imaging showed that the pretreatment changed the lignin structure of the corn stalk material surface, resulting in a higher surface roughness of 9.37. These results indicated that Rhodococcus pretreatment can improve the saccharification efficiency of cellulose by removing lignin and increasing the surface roughness of the material. [ABSTRACT FROM AUTHOR]

• Compound 6aw inhibits multiple strains of EV-D68, EV-A71 and CVB3. • Compound 6aw has a high selectivity index. • Compound 6aw is stable in mouse liver microsomes. • Compound 6aw targets viral 2C protein as confirmed by thermal shift binding assay. Non-polio enteroviruses such as enterovirus A71 (EV-A71), EV-D68, and coxsackievirus B3 (CVB3) are significant human pathogens with disease manifestations ranging from mild flu-like symptoms to more severe encephalitis, myocarditis, acute flaccid paralysis/myelitis, and even death. There is currently no effective antivirals to prevent or treat non-polio enterovirus infection. In this study, we report our progress in developing potent and broad-spectrum antivirals against these non-polio enteroviruses. Starting from our previously developed lead compounds that had potent antiviral activity against EV-D68, we synthesized 43 analogs and profiled their broad-spectrum antiviral activity against additional EV-D68, EV-A71, and CVB3 viruses. Promising candidates were also selected for mouse microsomal stability test to prioritize lead compounds for future in vivo mouse model studies. Collectively, this multi-parameter optimization process revealed a promising lead compound 6aw that showed single-digit to submicromolar EC 50 values against two EV-D68 strains (US/KY and US/MO), two EV-A71 strains (Tainan and US/AK), and one CVB3 strain, with a high selectivity index. Encouragingly, 6aw was stable in mouse microsomes with a half-life of 114.7 min. Overall, 6aw represents one of the most potent broad-spectrum antiviral against non-polio enteroviruses, rendering it a promising lead candidate for non-polio enteroviruses with translational potential. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the effect of preconception examinations programs on the prevention of birth defects in Dongguan City during 2013-2017.Methods: The data were from preconception examinations system and the birth defects surveillance system during 2013-2017. The study population included 63,175 infants born to mothers accepted preconception examinations during pregnancy (the screening group) and 649,862 infants whose mother did not check (the control group). The infants included for stillbirth, dead fetus, live birth between 28 weeks of gestation and 7 days after birth and legal pregnancy termination. The risk ratios (RRs) and 95% confidence intervals (CIs) were calculated to examine for the association between birth defects and preconception examinations. We also conducted a stratified analysis based on infant gender and disease classification, and maternal age and region.Results: The incidence of birth defects in the screening group was 134.55/10,000, while that in the control group was 241.53/10,000. Preconception examinations can effectively reduce the incidence of birth defects (BDs) (RR: 0.557, 95%CI: 0.520-0.597). Bifid spine (RR: 0.076, 95%CI: 0.011-0.545), anencephalia (RR: 0.134, 95%CI: 0.033-0.543) and anorectal atresia or stenosis (RR: 0.151, 95%CI: 0.048-0.471) were controlled best, and Down syndrome (RR: 0.684, 95%CI: 0.435-1.075) was no effect. Young maternal age (14-19 years) and old maternal age will increase the risk of birth defects. Preconception examinations had the best effect for pregnant women under 25 years of age (RR: 0.465, 95%CI: 0.387-0.559), and were relatively poor for women aged 30-34 years (RR: 0.678, 95%CI: 0.593-0.776). The incidence of the urban was significantly higher than that of the rural. The effect of preconception examinations to prevent birth defects was more effective in urban areas (RR: 0.453, 95%CI: 0.391-0.525) than in rural areas (RR: 0.577, 95%CI: 0.533-0.625). The incidence of BDs in males was higher than that in females.Conclusions: By implementing preconception examinations project, birth defects in Dongguan have been well controlled. This can provide reference for other developing countries to prevent birth defects. [ABSTRACT FROM AUTHOR]

Community detection is a crucial research orientation in complex network owing to its practical applications. Recently, the convolutional neural network (CNN) based edge classification community detection approach achieved impressive performance as a representative graph embedding method. However, existing methods rely heavily on the manually defined relationship between nodes and local feature representations of the edges, leading to the potential limitation of feature representation in complex real-world networks. To tackle this issue, in this paper, we propose a novel m ulti- f eature f usion net work for community detection, namely MFF-Net, which can embed edges of the network into comprehensive feature representations according to the intrinsic property of the network. Specifically, instead of utilizing local features of the edges only, we first propose to simultaneously consider local and non-local relationships of the edges through the local neighbors of the nodes and random node sequences sampled from a customized random walk. Then, we propose to convert local and non-local feature representations of the edges into grayscale images for edge classification via introducing a quantitative relationship between nodes. More importantly, those obtained feature representations are fused with an implicit manner in a latent space, which can leverage more comprehensive relationships for complex real-world networks. Extensive experiments on the computer-generated networks, small-scale and large-scale real-world networks, show that the proposed MFF-Net can achieve better performance compared with existing methods, in terms of quantitative and visualized results. [ABSTRACT FROM AUTHOR]

Genes encoding two proteins corresponding to elongation factor G (EF-G) were cloned from Pseudomonas aeruginosa. The proteins encoded by these genes are both members of the EFG I subfamily. The gene encoding one of the forms of EF-G is located in the str operon and the resulting protein is referred to as EF-G1A while the gene encoding the other form of EF-G is located in another part of the genome and the resulting protein is referred to as EF-G1B. These proteins were expressed and purified to 98% homogeneity. Sequence analysis indicated the two proteins are 90/84% similar/identical. In other organisms containing multiple forms of EF-G a lower degree of similarity is seen. When assayed in a poly(U)-directed poly-phenylalanine translation system, EF-G1B was 75-fold more active than EF-G1A. EF-G1A pre-incubate with ribosomes in the presence of the ribosome recycling factor (RRF) decreased polymerization of poly-phenylalanine upon addition of EF-G1B in poly(U)-directed translation suggesting a role for EF-G1A in uncoupling of the ribosome into its constituent subunits. Both forms of P. aeruginosa EF-G were active in ribosome dependent GTPase activity. The kinetic parameters (KM) for the interaction of EF-G1A and EF-G1B with GTP were 85 and 70 μM, respectively. However, EF-G1B exhibited a 5-fold greater turnover number (observed kcat) for the hydrolysis of GTP than EF-G1A; 0.2 s-1 vs. 0.04 s-1. These values resulted in specificity constants (kcatobs/KM) for EF-G1A and EF-G1B of 0.5 x 103 s-1 M-1 and 3.0 x 103 s-1 M-1, respectively. The antibiotic fusidic acid (FA) completely inhibited poly(U)-dependent protein synthesis containing P. aeruginosa EF-G1B, but the same protein synthesis system containing EF-G1A was not affected. Likewise, the activity of EF-G1B in ribosome dependent GTPase assays was completely inhibited by FA, while the activity of EF-G1A was not affected. [ABSTRACT FROM AUTHOR]

Neuraminidase (NA) inhibitors play a prime role in treating influenza. However, a variety of viruses containing mutant NAs have developed severe drug resistance towards NA inhibitors, so it is of crucial significance to solve this problem. Encouraged by urea-containing compound 12 disclosed by our lab, we designed a series of oseltamivir derivatives bearing hydrazide fragment for targeting the 150 cavity. Among the synthesized compounds, compound 17a showed 8.77-fold, 4.12-fold, 203-fold and 6.23-fold more potent activity than oseltamivir carboxylate against NAs from H5N1, H1N1, H5N1–H274Y, H1N1–H274Y, respectively. Meanwhile, the best compound 17a exhibited satisfactory metabolic stability in vitro. This study offers an important reference for the structural optimization of oseltamivir aiming at potent inhibition against H274Y mutant of NAs. • Hydrazide-containing oseltamivir analogues were designed and synthesized. • Compound 17a showed potent inhibitions on wild-type and H274Y-mutant neuraminidases. • Compound 17a exhibited satisfactory metabolic stability in vitro. [ABSTRACT FROM AUTHOR]

Community detection is a fundamental problem for many networks, and many methods have been proposed to resolve it. However, due to rapid increases in the scale and diversity of networks, the modular organization at the global level in many large networks is often extremely difficult to recognize. In this paper, we propose a new method based on deep learning on ground-truth communities, with the aim of revealing community structure in large real-world networks. The contributions of this paper are 1) proposing an edge-to-image (E2I) model that can transfer the edge structure to an image structure; 2) construction of a community network (ComNet) to classify the two types of edges, which are those in the same community and others between different communities; 3) making it easier to obtain local views of network communities by breadth-first search based on edge classification; and 4) merging preliminary communities with local modularity R, making it easy to optimize the community structure and obtain the final community structure of given networks. The experimental results show that the proposed edge classification method based on deep convolution neural networks can increase the accuracy of community structure evaluation compared with the existing methods in computer-generated networks and large-scale real-world networks. • Proposing an edge-to-image (E2I) model that can transfer the edge structure to an image structure. • Construction of CNN(ComNet) to classify the two types of edges which are those in the same community and others between different communities. • The community structure of a network can be divided by removing the edges between different communities. • Use the local modularity R to merge communities to optimize the community structure. • The experimental results have better performance and accuracy in large scale network. [ABSTRACT FROM AUTHOR]

Community detection in networks plays a key role in understanding their structures, and the application of clustering algorithms in community detection tasks in complex networks has attracted intensive attention in recent years. In this paper, based on the definition of uncertainty of node community belongings, the node density is proposed first. After that, the DD (the combination of node density and node degree centrality) is proposed for initial node selection in community detection. Finally, based on the DD and k-means clustering algorithm, we proposed a community detection approach, the density-degree centrality-jaccard-k-means method (DDJKM). The DDJKM algorithm can avoid the problem of random selection of initial cluster centers in conventional k-means clustering algorithms, so that isolated nodes will not be selected as initial cluster centers. Additionally, DDJKM can reduce the iteration times in the clustering process and the over-short distances between the initial cluster centers can be avoided by calculating the node similarity. The proposed method is compared with state-of-the-art algorithms on synthetic networks and real-world networks. The experimental results show the effectiveness of the proposed method in accurately describing the community. The results also show that the DDJKM is practical a approach for the detection of communities with large network datasets. [ABSTRACT FROM AUTHOR]