Reference Set Research Articles

A two-stage cooperative scatter search algorithm with multi-population hierarchical learning mechanism

Scatter search (SS) is a population-based metaheuristic algorithm, which has been proved high efficiency and effective optimizer for complex continuous real value problems. A two-stage cooperative SS guided with the multi-population hierarchical learning mechanism (TCSSMH) to overcome the slow convergence speed of the original SS is proposed. Three strategies are applied to the original SS. Firstly, TCSSMH adopts an adaptive two-way selection search strategy based on the elite reference set (RefSet), which is elite-oriented and ensures the quality of the population. Secondly, the multi-group hierarchical learning mechanism is embedded in the updating process of the RefSet, and the population of the candidates is divided into three levels including excellent candidates, medium candidates, and inferior candidates according to the fitness value of the function. These three subpopulations cooperate to balance the exploration and exploitation ability of the algorithm in the process of evolution. Finally, each subpopulation adopts an interactive learning strategy to increase the diversity of the population and avoid premature convergence of solutions. The optimum of each subpopulation with high accuracy is obtained by the pattern search (PS) optimization. The stronger search ability and higher search efficiency of these additional proposed strategies are verified by extensive experiments. The TCSSMH algorithm is tested on the CEC2017 benchmark test suite and practical engineering problems. The experimental results show that the TCSSMH algorithm is superior to other state-of-the-art algorithms in global search ability and convergence on the benchmark problems.

Design of a Multi-Epitope Vaccine against Tropheryma whipplei Using Immunoinformatics and Molecular Dynamics Simulation Techniques.

Whipple’s disease is caused by T. whipplei, a Gram-positive pathogenic bacterium. It is considered a persistent infection affecting various organs, more likely to infect males. There is currently no licensed vaccination available for Whipple’s disease; thus, the development of a chimeric peptide-based vaccine against T. whipplei has the potential to be tremendously beneficial in preventing Whipple’s disease in the future. The present study aimed to apply modern computational approaches to generate a multi-epitope-based vaccine that expresses antigenic determinants prioritized from the core proteome of two T. whipplei whole proteomes. Using an integrated computational approach, four immunodominant epitopes were found from two extracellular proteins. Combined, these epitopes covered 89.03% of the global population. The shortlisted epitopes exhibited a strong binding affinity for the B- and T-cell reference set of alleles, high antigenicity score, nonallergenic nature, high solubility, nontoxicity, and excellent binders of DRB1*0101. Through the use of appropriate linkers and adjuvation with a suitable adjuvant molecule, the epitopes were designed into a chimeric vaccine. An adjuvant was linked to the connected epitopes to boost immunogenicity and efficiently engage both innate and adaptive immunity. The physiochemical properties of the vaccine were observed favorable, leading toward the 3D modeling of the construct. Furthermore, the vaccine’s binding confirmation to the TLR-4 critical innate immune receptor was also determined using molecular docking and molecular dynamics (MD) simulations, which shows that the vaccine has a strong binding affinity for TLR4 (−29.4452 kcal/mol in MM-GBSA and −42.3229 kcal/mol in MM-PBSA). Overall, the vaccine described here has a promising potential for eliciting protective and targeted immunogenicity, subject to further experimental testing.

Learning to classify relations between entities from noisy data - A meta instance reweighting approach

Relation classification between entities is a fundamental problem in knowledge extraction. It aims at determining if a semantic relation holds between a pair of entities based on textual descriptions. In general, the training data for each relation is limited. Distant supervision has thus been widely used to generate abundant weakly labeled data for the task. These data are noisy, containing many errors. A crucial problem is to select reliable instances for training or weigh them adequately. However, most existing approaches rely solely on statistics among the noisy data to generate classification features. They could be easily trapped by frequent wrong instances. To deal with this problem, we use a small set of manually annotated samples as reference data to guide the selection/weighting process. In this paper, we propose a new meta instance reweighting framework, which automatically adjusts the instance weights under the guidance of the reference data. The principle is that the classifier trained with the weighted data should perform well on the reference data. To further improve the approach, we propose to augment the limited reference data with a set of highly reliable instances — elite instances, selected from noisy data. The selection of elite instances is based on both their labeling quality and diversity so as to maximize the coverage of diverse expressions by the reference set. Extensive experiments on two public datasets by applying our method to two base neural models demonstrate the effectiveness of our method: It outperforms previous state-of-the-art approaches. We also show that the use of both reference data and elite instances is beneficial. The proposed approach is model-agnostic: it can be used on other classification models. It can also be applied to other classification tasks under distant supervision.

A COMMUNITY-WIDE DESCRIPTION AND KEY OF POLLEN FROM CO-FLOWERING PLANTS OF THE SERPENTINE SEEPS OF THE MCLAUGHLIN NATURAL RESERVE, LAKE COUNTY, CA

Pollen morphology is species-specific and its identification can inform many different research objectives, including those focused on understanding pollinator niche breadth, pollen transfer dynamics, and factors driving plant community composition. Community-wide characterization of pollen can thus accelerate research on plant-pollinator interactions on both contemporary and historical time frames. Here we present descriptions, a dichotomous key, and reference set of light microscopy images of pollen from 70 species in 28 families of co-flowering plants growing in and around serpentine seeps at the McLaughlin Natural Reserve in Lake County, CA. This represents a reference and a tool for the study of species interactions in this unique biodiversity hotspot.

Development and evaluation of a search filter to identify prognostic factor studies in Ovid MEDLINE

BackgroundSystematic reviews (SRs) are valuable resources as they address specific clinical questions by summarizing all existing relevant studies. However, finding all information to include in systematic reviews can be challenging. Methodological search filters have been developed to find articles related to specific clinical questions. To our knowledge, no filter exists for finding studies on the role of prognostic factor (PF). We aimed to develop and evaluate a search filter to identify PF studies in Ovid MEDLINE that has maximum sensitivity.MethodsWe followed current recommendations for the development of a search filter by first identifying a reference set of PF studies included in relevant systematic reviews on the topic, and by selecting search terms using a word frequency analysis complemented with an expert panel discussion. We evaluated filter performance using the relative recall methodology.ResultsWe constructed a reference set of 73 studies included in six systematic reviews from a larger sample. After completing a word frequency analysis using the reference set studies, we compiled a list of 80 of the frequent methodological terms. This list of terms was evaluated by the Delphi panel for inclusion in the filter, resulting in a final set of 8 appropriate terms. The consecutive connection of these terms with the Boolean operator OR produced the filter. We then evaluated the filter using the relative recall method against the reference set, comparing the references included in the SRs with our new search using the filter. The overall sensitivity of the filter was calculated to be 95%, while the overall specificity was 41%. The precision of the filter varied considerably, ranging from 0.36 to 17%. The NNR (number needed to read) value varied largely from 6 to 278. The time saved by using the filter ranged from 13–70%.ConclusionsWe developed a search filter for OVID-Medline with acceptable performance that could be used in systematic reviews of PF studies. Using this filter could save as much as 40% of the title and abstract screening task. The specificity of the filter could be improved by defining additional terms to be included, although it is important to evaluate any modification to guarantee the filter is still highly sensitive.

Low-temperature curing of adhesives – Large-scale experiments

ABSTRACT In contrast to mechanical joining techniques, which can be manufactured under any ambient conditions, adhesively bonded joints always require some minimum temperatures – typically above 10 °C – in order to cure safely. Due to this limitation, either many technical applications exclude adhesives right from the start or practitioners at high costs are forced to adapt their production processes to the specific environmental requirements of the polymers to be cured. As a response to these manufacturing challenges, a new process for accelerated curing – induction heating with Curie particles (CP) – was studied to cure three different structural adhesives (two epoxy resins and one polyurethane) at low temperatures. The CP are added to the adhesives as a typical filler material and can only be heated by an externally applied electromagnetic field up to their material-specific Curie temperature T c. Thus, a self-regulating process is created, which effectively prevents the adhesives from overheating while simultaneously eliminating the need to control curing temperatures by external measures like IR cameras. Recently, the authors published a series of papers in which CP were used to cure at ambient temperatures (+23 °C); the results of which are now to be extended towards low-temperature curing. For that, an experimental campaign was carried out using the exemplary connection type of Glued-in Rods (GiR), which are frequently used in various timber engineering applications. In detail, the heating behaviour of inductively cured GiR conditioned at –10 °C and –30 °C was compared to GiR inductively cured starting from +23 °C. In addition, reference sets with and without added CP were produced, whereby all GiR were subjected to tensile tests in the following. Afterwards, the series were compared on the level of load–displacement curves, failure loads, as well as observed fracture patterns. The results proved that CP-curing is capable of curing adhesives in temperature ranges currently not intended for structural adhesives by technical regulations. When cured under low-temperature conditions, the GiR involving epoxy adhesives showed a fracture behaviour that proved to be equal to that of the references. The failure loads of the 2 K-PUR dropped to 50–75% of the reference values, which, however, is strongly assumed to be related to moisture on the cold adherends and not to a general unsuitability of polyurethane adhesives for the process – an assumption that will be validated by low-temperature experiments using single lap shear joints presented in a subsequent study.

Reexamining Confederate symbols displayed on flags and monuments in public spaces: Two fallacies in the heritage versus hate debate

Objective This article examines ongoing arguments over the meanings of Confederate symbols—especially symbols displayed on flags and monuments—to assess two fallacies that frequently influence the debate. Method The article explores the historical record concerning public displays of Confederate symbols. Results The traditional debate is based on two fallacies. First, it presupposes that the meaning of a symbol can be limited to a single referent or set of referents and fixed in time. Second, it assumes that the meaning of Confederate symbols can be divorced from hateful messages of white supremacy and bigotry. Conclusion A symbol cannot be limited to its original meaning because the context is constantly evolving. Even if it could be limited, the original meaning of Confederate symbols was always hateful. The debate sometimes has been cast as “heritage versus hate.” Because displays of Confederate symbols in public spaces have always been in a context of “hate”—to defend a slaveholding republic, to promote white supremacy, to defy court-ordered integration of public schools, or to promote the agenda of racist advocacy groups—the meaning of Confederate flags and monuments was never about heritage alone. Hate was always part of the message.

A more global urban studies, besides empirical variation

An expanded set of sites, a more differentiated set of references and linguistic diversification have been discussed as needed changes in urban studies. The critiques of the limitations of urban studies, in terms of both the scholarship and the scholars, offer important and concrete responses to expanding the scope of the field. Yet this tremendous special issue on ‘Comparative Methods for Global Urban Studies’ with 10 papers cutting across a range of sites and topics is decidedly not only about empirical variation; this is an important distinction worth drawing more attention to. The creativity expressed in these papers comes at an auspicious time in urban studies where new routes for doing urban theory are needed to move past debates about singular versus plural epistemologies of the urban. As a kind of research that demands more translation, exchange and collaboration, perhaps comparative urban research as a mode of theory-building can help to humble the chest-pounding, posturing, privilege of thinking and speaking the language of theory. The theoretical ambitions of these very different papers show how urban theory need not only be about better understanding urbanisation within the epistemological confines of late capitalism. Rather than reifying a shared grammar of urbanisation as a necessity to understand each other, they may entice scholars everywhere to develop a broader vocabulary and perhaps even learn another language.

‘Something that’s very American’: The interactional role of Light-Head Relative Clauses

In this paper, we examine a specific type of Relative Clause (RC). We look at the construction consisting of a ‘light noun’, that is, a noun with highly non-specific lexical content which does not do referential work, plus a relative clause. It has generally been assumed that the functional contribution of RCs is to narrow the set of referents of the head noun to only those for which the predicate of the RC holds true. However, the ‘Light Head RC construction’ (LHRC) has various interactional affordances that mostly revolve around characterizing referents with discourse-relevant properties rather than establishing reference. We argue that these various functions of LHRCs revolve around participants’ orientations to categoryhood. Data are in English and Dutch.

Revisiting IoT definitions: A framework towards comprehensive use

New technologies constantly change the paradigm of how businesses will be run in the future. The Internet of Things (IoT) enables new business opportunities for data-driven transformation in organisations. The emergence of the IoT concept has resulted in numerous definitions, with earlier references primarily focussed on the technological aspects. This has hindered the broader diffusion of the term IoT as, arguably, the definitions do not integrate other non-technological elements of IoT and focus more on business and service provisions. To resolve this, our research identifies the most significant building blocks required in designing an IoT system. This is accomplished through a methodological review of 122 definitions and their consolidation into a novel definitional framework. The definitional framework unifies the traditional technology focus of the earlier definitions and integrates additional elements that are likely to increase the adoption of a comprehensive definition to support the development of future business applications. Furthermore, the framework serves as a reference set for scholarly societies and standards organisations who, in the future, can be tasked with formulating a definition of IoT, as has been the case with the NIST definition of Cloud Computing, which was preceded by several academic studies on defining the term.

Can Hyperspectral Imaging and Neural Network Classification Be Used for Ore Grade Discrimination at the Point of Excavation?

This work determines whether hyperspectral imaging is suitable for discriminating ore from waste at the point of excavation. A prototype scanning system was developed for this study. This system combined hyperspectral cameras and a three-dimensional LiDAR, mounted on a pan-tilt head, and a positioning system which determined the spatial location of the resultant hyperspectral data cube. This system was used to obtain scans both in the laboratory and at a gold mine in Western Australia. Samples from this mine site were assayed to determine their gold concentration and were scanned using the hyperspectral apparatus in the laboratory to create a library of labelled reference spectra. This library was used as (i) the reference set for spectral angle mapper classification and (ii) a training set for a convolutional neural network classifier. Both classification approaches were found to classify ore and waste on the scanned face with good accuracy when compared to the mine geological model. Greater resolution on the classification of ore grade quality was compromised by the quality and quantity of training data. The work provides evidence that an excavator-mounted hyperspectral system could be used to guide a human or autonomous excavator operator to selectively dig ore and minimise dilution.

Development of Electromyography Signal Contraction Detection Using the Double Threshold Method for Controlling Wheelchair

The consequence of spinal cord injury (SCI) is the malfunction of some parts of the body. This observation especially applies to trauma at the cervical level, because both patients' hands and legs are no longer functioning. The purpose of this study was to determine the mechanism of the comparator treshold system in electric wheelchairs for patients with paralysis. The research contribution is to get a treshold mechanism that can make a wheelchair run smoothly without being affected by the strength of the user's unstable muscle contractions. To set the muscle reference value, connect the transmitter to Android using USB Cable. On Android there are 2 settings modes, namely manual and auto settings. The method used in auto mode is the transmitter will take 100 contraction and 100 relaxation data and multiplied by certain constants to get the upper and lower threshold values. In setting the threshold manually, the value is set based on the strength of the patient's contractions that are displayed on Android manually. Where in the threshold setting manually obtained response 93.3% of forward movement, 100% of turn left movement, 100% of turn right movement and 93.3% of backward movement. Meanwhile, the threshold setting automatically gets 80% response for forward movements, 93.3% for turn left movements, 86.6% for turn right movements and 93.3% for backward movements. The results of the tapping into 4 parts of the muscle can be implemented to detect muscle contractions so that the wheelchair can move according to orders. The problem obtained in this study is that there are many tapping points on the face so that it interferes with the comfort and time of installing electrodes on the face, also the patient's muscle reference setting system on Android still needs the help of others. In the future, it is expected to reduce tapping points and improve the efficiency of muscle threshold settings.

Development and validation of search filters to identify articles on deprescribing in Medline and Embase

BackgroundDeprescribing literature has been increasing rapidly. Our aim was to develop and validate search filters to identify articles on deprescribing in Medline via PubMed and in Embase via Embase.com.MethodsArticles published from 2011 to 2020 in a core set of eight journals (covering fields of interest for deprescribing, such as geriatrics, pharmacology and primary care) formed a reference set. Each article was screened independently in duplicate and classified as relevant or non-relevant to deprescribing. Relevant terms were identified by term frequency analysis in a 70% subset of the reference set. Selected title and abstract terms, MeSH terms and Emtree terms were combined to develop two highly sensitive filters for Medline via Pubmed and Embase via Embase.com. The filters were validated against the remaining 30% of the reference set. Sensitivity, specificity and precision were calculated with their 95% confidence intervals (95% CI).ResultsA total of 23,741 articles were aggregated in the reference set, and 224 were classified as relevant to deprescribing. A total of 34 terms and 4 MeSH terms were identified to develop the Medline search filter. A total of 27 terms and 6 Emtree terms were identified to develop the Embase search filter. The sensitivity was 92% (95% CI: 83–97%) in Medline via Pubmed and 91% (95% CI: 82–96%) in Embase via Embase.com.ConclusionsThese are the first deprescribing search filters that have been developed objectively and validated. These filters can be used in search strategies for future deprescribing reviews. Further prospective studies are needed to assess their effectiveness and efficiency when used in systematic reviews.

EPR Spectroscopy of Cu(II) Complexes: Prediction of g-Tensors Using Double-Hybrid Density Functional Theory

Computational electron paramagnetic resonance (EPR) spectroscopy is an important field of applied quantum chemistry that contributes greatly to connecting spectroscopic observations with the fundamental description of electronic structure for open-shell molecules. However, not all EPR parameters can be predicted accurately and reliably for all chemical systems. Among transition metal ions, Cu(II) centers in inorganic chemistry and biology, and their associated EPR properties such as hyperfine coupling and g-tensors, pose exceptional difficulties for all levels of quantum chemistry. In the present work, we approach the problem of Cu(II) g-tensor calculations using double-hybrid density functional theory (DHDFT). Using a reference set of 18 structurally and spectroscopically characterized Cu(II) complexes, we evaluate a wide range of modern double-hybrid density functionals (DHDFs) that have not been applied previously to this problem. Our results suggest that the current generation of DHDFs consistently and systematically outperform other computational approaches. The B2GP-PLYP and PBE0-DH functionals are singled out as the best DHDFs on average for the prediction of Cu(II) g-tensors. The performance of the different functionals is discussed and suggestions are made for practical applications and future methodological developments.

Implementation and continued validation of the malaria Plasmodium falciparum lactate dehydrogenase-based colorimetric assay for use in antiplasmodial drug screening

Antimalarial drug discovery has been facilitated by the development of various in vitro drug susceptibility testing methods suitable for medium-throughput or high-throughput campaigns. Among many, the Plasmodium falciparum lactate dehydrogenase (PfLDH) assay has acceptable demand on equipment, labour, technical skills and affordability and offers a good opportunity for scientists in low- and middle-income countries to participate in the global effort of discovering future antimalarial drugs. Hence, to enable our search for novel antimalarial drugs, we implemented and examined assay conditions and validated the PfLDH-based method in our laboratory using a reference set of standard antimalarial drugs with known activity against Plasmodium falciparum strains. The PfLDH assay revealed acceptable linearity profiles of R2 = 0.97 and 0.92 for Pf3D7 and PfDd2, respectively, achieved at 2% parasitaemia and 1% haematocrit. The detection and quantitation limits (DL and QL) of the PfLDH-based assay were 0.09% and 0.4% parasitemia, respectively. The assay showed an acceptable average Z-factor between 0.76 and 0.79 and was considerably robust. The average interassay reproducibility via percent coefficient of variation (%CV) was 5.47 between independent experiments. Overall, the PfLDH-based method produced a reliable and reproducible drug screening profile for in vitro assays in our setting. There were no significant interassay variability or hazards of other screening assays.

Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis.

For decades, the prediction of bank failure has been a popular topic in credit risk and banking studies. Statistical and machine learning methods have been working well in predicting the probability of bankruptcy for different time horizons prior to the failure. In recent years, bank efficiency has attracted much interest from academic circles, where low productivity or efficiency in banks has been regarded as a potential reason for failure. It is generally believed that low efficiency implies low-quality management of the organisation, which may lead to bad performance in the competitive financial markets. Previous papers linking efficiency measures calculated by Data Envelopment Analysis (DEA) to bank failure prediction have been limited to cross sectional analyses. A dynamic analysis with the updated samples is therefore recommended for bankruptcy prediction. This paper proposes a nonparametric method, Malmquist DEA with Worst Practice Frontier, to dynamically assess the bankruptcy risk of banks over multiple periods. A total sample of 4426 US banks over a period of 15 years (2002–2016), covering the subprime financial crisis, is used to empirically test the model. A static model is used as the benchmark, and we introduce more extensions for comparisons of predictive performance. Results of the comparisons and robustness tests show that Malmquist DEA is a useful tool not only for estimating productivity growth but also to give early warnings of the potential collapse of banks. The extended DEA models with various reference sets and orientations also show strong predictive power.

Genome-wide association mapping identifies an SNF4 ortholog that impacts biomass and sugar yield in sorghum and sugarcane.

Sorghum is a feed/industrial crop in developed countries and a staple food elsewhere in the world. This study evaluated the sorghum mini core collection for days to 50% flowering (DF), biomass, plant height (PH), soluble solid content (SSC), and juice weight (JW), and the sorghum reference set for DF and PH, in 7-12 testing environments. We also performed genome-wide association mapping with 6 094 317 and 265 500 single nucleotide polymorphism markers in the mini core collection and the reference set, respectively. In the mini core panel we identified three quantitative trait loci for DF, two for JW, one for PH, and one for biomass. In the reference set panel we identified another quantitative trait locus for PH on chromosome 6 that was also associated with biomass, DF, JW, and SSC in the mini core panel. Transgenic studies of three genes selected from the locus revealed that Sobic.006G061100 (SbSNF4-2) increased biomass, SSC, JW, and PH when overexpressed in both sorghum and sugarcane, and delayed flowering in transgenic sorghum. SbSNF4-2 encodes a γ subunit of the evolutionarily conserved AMPK/SNF1/SnRK1 heterotrimeric complexes. SbSNF4-2 and its orthologs will be valuable in genetic enhancement of biomass and sugar yield in plants.

Sensorless angle and stiffness control of antagonistic PAM actuator using reference set

This paper proposes a simultaneous control method for the angle and stiffness of the joint in an antagonistic pneumatic artificial muscle (PAM) actuator system using only pressure measurements in the absence of an external force, and clarifies the allowable references for the PAM actuator system. To achieve a sensorless control, the proposed method estimates the joint angle and contraction forces using an unscented Kalman filter that employs a detailed model of the actuator system. Unlike the previous control methods, the proposed method does not require any encoder and force sensor to achieve angle and stiffness control of the PAM actuator system. Experimental validations using three control scenarios confirm that the proposed method can control the joint angle and stiffness simultaneously and independently. Moreover, it is shown that a reference admissible set can be used as an indicator to establish the reference values by demonstrating that the reference set covers the experimentally obtained trajectories of the angle and stiffness.

Computation and visualization of cell\u2013cell signaling topologies in single-cell systems data using Connectome

Single-cell RNA-sequencing data has revolutionized our ability to understand of the patterns of cell–cell and ligand–receptor connectivity that influence the function of tissues and organs. However, the quantification and visualization of these patterns in a way that informs tissue biology are major computational and epistemological challenges. Here, we present Connectome, a software package for R which facilitates rapid calculation and interactive exploration of cell–cell signaling network topologies contained in single-cell RNA-sequencing data. Connectome can be used with any reference set of known ligand–receptor mechanisms. It has built-in functionality to facilitate differential and comparative connectomics, in which signaling networks are compared between tissue systems. Connectome focuses on computational and graphical tools designed to analyze and explore cell–cell connectivity patterns across disparate single-cell datasets and reveal biologic insight. We present approaches to quantify focused network topologies and discuss some of the biologic theory leading to their design.

A reference set of clinically relevant adverse drug-drug interactions

The accurate and timely detection of adverse drug-drug interactions (DDIs) during the postmarketing phase is an important yet complex task with potentially major clinical implications. The development of data mining methodologies that scan healthcare databases for drug safety signals requires appropriate reference sets for performance evaluation. Methodologies for establishing DDI reference sets are limited in the literature, while there is no publicly available resource simultaneously focusing on clinical relevance of DDIs and individual behaviour of interacting drugs. By automatically extracting and aggregating information from multiple clinical resources, we provide a scalable approach for generating a reference set for DDIs that could support research in postmarketing safety surveillance. CRESCENDDI contains 10,286 positive and 4,544 negative controls, covering 454 drugs and 179 adverse events mapped to RxNorm and MedDRA concepts, respectively. It also includes single drug information for the included drugs (i.e., adverse drug reactions, indications, and negative drug-event associations). We demonstrate usability of the resource by scanning a spontaneous reporting system database for signals of DDIs using traditional signal detection algorithms.