Set Of Pairs Research Articles

Chroma-dependence of CIEDE2000 acceptability thresholds for dentistry.

Determine visual 50:50% color difference acceptability thresholds (AT) for regions of the dental color space with varying chromaticity. A 40-observer panel belonging to two different groups (dentists and laypersons) evaluated 144 dental resin composites pairs (divided in three different sets of 48 pairs according to chroma value: Low Chroma (LC), Medium Chroma (MC) and High Chroma (HC) placed 40 cm away and inside of a viewing cabinet (D65 Standard light source; diffuse/0° geometry). A Takagi-Sugeno-Kang (TSK) fuzzy approximation was used for fitting the data points and calculate the 50:50% acceptability thresholds in CIEDE2000. A paired t-test was used to evaluate the statistical significance between thresholds differences and Bonferroni correction was applied. The CIEDE2000 50:50% AT were ∆E00 = 2.84, ∆E00 = 2.31 and ∆E00 = 1.80 for LC, MC and HC sets of sample pairs, respectively. The 50:50% AT values were statistically significant between the different sets of sample pairs, as well as the 50:50% AT values obtained for different observer groups. 50:50% CIEDE2000 acceptability thresholds for dentistry are significantly different depending on the chromaticity of the samples. Observers show higher acceptability for more achromatic samples (low chroma value) than for more chromatic samples. The difference in the AT for distinct regions of the dental color space can assist professionals as a quality control tool to assess clinical performance and interpret visual and instrumental findings in clinical dentistry, dental research, and subsequent standardization processes.

Theoretical and experimental characterization of non-Markovian anti-parity-time systems

Open systems with anti-parity-time ({{{{{{{mathcal{APT}}}}}}}}) or {{{{{{{mathcal{PT}}}}}}}} symmetry exhibit a rich phenomenology absent in their Hermitian counterparts. To date all model systems and their diverse realizations across classical and quantum platforms have been local in time, i.e., Markovian. Here we propose a non-Markovian system with anti-{{{{{{{mathcal{PT}}}}}}}}-symmetry where a single time-delay encodes the retention of memory, and experimentally demonstrate its consequences with two time-delay coupled semiconductor lasers. A transcendental characteristic equation with infinitely many eigenvalue pairs sets our model apart. We show that a sequence of amplifying-to-decaying dominant mode transitions is induced by the time delay in our minimal model. The signatures of these transitions quantitatively match results obtained from four, coupled, nonlinear rate equations for laser dynamics, and are experimentally observed as constant-width sideband oscillations in the laser intensity profiles. Our work introduces a paradigmatic non-Hermitian system with memory, paves the way for its realization in classical systems, and may apply to time-delayed feedback-control for quantum systems.

Adaptive control of a linear first-order discrete-time system providing linear-like behaviour and asymptotic tracking

Adaptive control is an approach used to deal with systems having uncertain and/or time-varying parameters. In this paper, we consider the problem of designing an adaptive controller for a discrete-time first-order plant. Recently, Shahab et.al. considered this problem and proposed an approach which provides linear-like behaviour: exponential stability and a convolution bound on the input–output behaviour, together with robustness to slow time-variations and unmodelled dynamics. However, asymptotic tracking of a general reference signal was not provided. Here, we extend the aforementioned work with the aim to achieve asymptotic tracking while retaining linear-like closed-loop behaviour. We replace this uncertainty set with a pair of convex sets, one for each sign of the input gain, which enables us to use two parameter estimators — one for each convex set. We design these estimators using the modified version of the original projection algorithm. For each estimator, there is the corresponding one-step-ahead control law. A dynamic performance signal based switching rule is then adopted that decides which controller should be used at each time step. It is shown that the proposed approach preserves linear-like behaviour. In addition to that, we also have shown asymptotic trajectory tracking for two different circumstances: when the reference signal is asymptotically strongly persistently exciting of order two, and for a fairly general reference signal but the plant is unstable. Numerical simulations are presented to demonstrate the efficacy of the proposed approach.

Concentration and condition of American lobster postlarvae in small‐scale convergences

AbstractInvertebrate larvae are often abundant in the surface ocean, which plays a key role in their dispersal and connectivity. Pelagic microhabitats characterized by small‐scale hydrographic variability are complex and ubiquitous in the coastal ocean, but their study is challenging, and they have been largely neglected in meroplankton ecology. Surface convergences, i.e., surface microhabitats featuring convergent horizontal currents, may aggregate the last larval stage of the American lobster and could provide shelter and food for Stage IV postlarvae and thus enhance their condition. We tested these hypotheses by conducting a series of cruises in the southwestern Gulf of Maine in summer 2021, sampling 15 paired sets of potential convergences and off‐convergence unstructured habitat. We measured postlarval abundance, surface hydrography, acoustic backscatter, and circulation. Experiments and image analysis compared condition, color, and morphology of postlarvae sampled inside and outside potential convergences. Potential convergences varied in near‐surface hydrographic patterns, with most displaying consistency among two transects and diverse patterns in salinity and temperature (e.g., across‐convergence gradients with equal or different signs). While the highest postlarval abundances were found in convergences, abundance patterns on and off convergences were not consistent, and another analysis indicated higher abundance in convergences than in a 7‐year untargeted surface ocean data set. Experiments indicated no survivorship differences among convergence and non‐convergence individuals at two temperatures, while image analyses revealed differences in color and size. Physical measurements and qualitative neuston community analyses indicated substantial heterogeneity among potential convergences. Our results reinforce that small‐scale heterogeneities are highly variable but important to the ecology of meroplankton, including the pelagic and neustonic habitats where lobster postlarvae are abundant.

Computational Inference of Gene Regulatory Network Using Genome-wide ChIP-X Data.

Gene regulatory network is the architecture of transcription factors (TFs) and their gene targets, which help in controlling their expression as required by a phenotype during various environmental perturbations. Inferring the regulatory network from the high-throughput data needs an algorithmic approach involving statistical analysis. There are several interaction databases such as JASPAR and SwissRegulon that provide information for TFs-targets pair interaction, which are estimated based on experimental and prediction procedures. These repositories are majorly used for predicting the complex structure of GRNs either with or without gene expression data. Here we described and discussed the step-wise procedures to extract the interaction data for a desired set of target-TFs from the JASPAR database, and used that information to infer the network by using the igraph library. Further, we also mentioned the important parameters for analyzing the different properties of the network. The described procedure will be helpful in discerning the GRN based on the set of TF-gene pairs.

Computation of the Hausdorff Distance between Two Compact Convex Sets.

The Hausdorff distance between two closed sets has important theoretical and practical applications. Yet apart from finite point clouds, there appear to be no generic algorithms for computing this quantity. Because many infinite sets are defined by algebraic equalities and inequalities, this a huge gap. The current paper constructs Frank-Wolfe and projected gradient ascent algorithms for computing the Hausdorff distance between two compact convex sets. Although these algorithms are guaranteed to go uphill, they can become trapped by local maxima. To avoid this defect, we investigate a homotopy method that gradually deforms two balls into the two target sets. The Frank-Wolfe and projected gradient algorithms are tested on two pairs of compact convex sets, where: (1) is the box translated by and is the intersection of the unit ball and the non-negative orthant; and (2) is the probability simplex and is the unit ball translated by . For problem (2), we find the Hausdorff distance analytically. Projected gradient ascent is more reliable than the Frank-Wolfe algorithm and finds the exact solution of problem (2). Homotopy improves the performance of both algorithms when the exact solution is unknown or unattained.

Improving toxicity prediction of metal-contaminated sediments by incorporating sediment properties

For the purpose of sediment quality assessment, the prediction of toxicity risk-levels for aquatic organisms based on simple environmental measurements is desirable. One commonly used approach is the comparison of total contaminant concentrations with corresponding water and sediment quality guideline values, serving as a Line of Evidence (LoE) based on chemistry-toxicity effects relationships. However, the accuracy of toxicity predictions can be improved by considering the factors that modify contaminant bioavailability. In this study we used paired chemistry-ecotoxicity data sets for sediments to evaluate the improvement in toxicity risk predictions using bioavailability-modified guidelines. The sediments were predominantly contaminated with metals, and measurements of sediment particle size, total organic carbon (TOC) and acid volatile sulfide (AVS) were used to modify hazard quotients (HQ). To further assess the predictive efficacy of the bioavailability-modified guideline models, sediments with differing contamination levels were tested for toxicity to a benthic amphipod's reproduction. To account for differences between laboratory exposure and field exposure scenarios, where the latter creates greater dilution, both static-renewal and flow-through test procedures were employed, and flow-through resulted in lower dissolved metal concentrations in the overlying waters. We also investigated how lower AVS concentration by oxidation modified the toxicity. This study reaffirmed that consideration of factors that influence contaminant bioavailability improves toxicity risk predictions, however the improvements may be modest. The sediment particle size data had the greatest influence on the modified HQ, indicating that higher percentage of fine particle size (<63 μm) contributed most to a lower predicted toxicity. The comparison of the static-renewal and flow-through test results continue to raise important questions about the relevance of static or static-renewal toxicity test results for risk assessment decisions, as both these test designs may cause unrealistically high contributions of dissolved metals in overlying waters to toxicity. Overall, this study underscores the value of incorporating outcomes from simple and routine sediment analysis (e.g., particle size, TOC, and consideration of AVS) to enhance the predictive efficacy of toxicity risk assessments in the context of sediment quality risk assessment.

Synthesis, solvent role (water and DMSO), antimicrobial activity, reactivity analysis, inter and intramolecular charge transfer, topology, and molecular docking studies on adenine derivative

In the current study, a molecule (E)-3-(((9H-purin-6-yl)imino)methyl)phenol (A3H) was made and its structure was characterized through the application of spectroscopic techniques. The optimization of the structure of the title chemical was performed using the B3LYP/cc-pVDZ basis set, employing the most stable conformational structure according to the DFT approach. The geometrical characteristics, vibrational wavenumbers, and electrical properties have also been investigated. The electronic characteristics of HOMO-LUMO, UV–Vis, and MEP maps were analysed, including different solvation effects. When compared to the other solvents the water has the lowest energy gap value, due to solvent interaction. The NLO activity has been investigated using the DFT/B3LYP approach, employing the cc-pVDZ basis set. In the NLO study water has the highest dipole moment, polarizability, and hyper-polarizability values. In this study, we have successfully obtained a set of lone pairs of donor–acceptor contacts and a hydrogen bond donor/acceptor surface. These findings provide a basis for explaining a charge transfer process. The utilization of molecular docking techniques has been employed to get insight into the binding interaction between the ligand and targeted proteins.

Isomorphism classes of cut loci for a cube

We prove that a face of a cube can be optimally partitioned into 193 connected sets on which the cut locus, or ridge tree, is constant up to isomorphism as a labeled graph. These are 60 connected open sets, curves bounding them, and intersection points of curves. Polynomial equations for the curves are given. Sixteen pairs of sets support the same cut locus class. We present the 177 distinct cut locus classes.

Convolutional Neural Network‐Based Adaptive Localization for an Ensemble Kalman Filter

AbstractFlow‐dependent background error covariances estimated from short‐term ensemble forecasts suffer from sampling errors due to limited ensemble sizes. Covariance localization is often used to mitigate the sampling errors, especially for high dimensional geophysical applications. Most applied localization methods, empirical or adaptive ones, multiply the Kalman gain or background error covariances by a distance‐dependent parameter, which is a simple linear filtering model. Here two localization methods based on convolutional neural networks (CNNs) learning from paired data sets are proposed. The CNN‐based localization function (CLF) aims to minimize the sampling error of the estimated Kalman gain, and the CNN‐based empirical localization function (CELF) aims to minimize the posterior error of state variables. These two CNN‐based localization methods can provide localization functions that are nonlinear, spatially and temporally adaptive, and non‐symmetric with respect to displacement, without requiring any prior assumptions for the localization functions. Results using the Lorenz05 model show that CLF and CELF can better capture the structures of the Kalman gain than the best Gaspari and Cohn (GC) localization function and the adaptive reference localization method. For both perfect‐ and imperfect‐model experiments, CLF produces smaller errors of the Kalman gain, prior and posterior than the best GC and reference localization, especially for spatially averaged observations. Without model error, CELF has smaller prior and posterior errors than the best GC and reference localization for spatially averaged observations, while with model error, CELF has smaller prior and posterior errors than the best GC and reference localization for single‐point observations.

Optimizing the Pairs of Radiologists That Double Read Screening Mammograms.

Background Despite variation in performance characteristics among radiologists, the pairing of radiologists for the double reading of screening mammograms is performed randomly. It is unknown how to optimize pairing to improve screening performance. Purpose To investigate whether radiologist performance characteristics can be used to determine the optimal set of pairs of radiologists to double read screening mammograms for improved accuracy. Materials and Methods This retrospective study was performed with reading outcomes from breast cancer screening programs in Sweden (2008-2015), England (2012-2014), and Norway (2004-2018). Cancer detection rates (CDRs) and abnormal interpretation rates (AIRs) were calculated, with AIR defined as either reader flagging an examination as abnormal. Individual readers were divided into performance categories based on their high and low CDR and AIR. The performance of individuals determined the classification of pairs. Random pair performance, for which any type of pair was equally represented, was compared with the performance of specific pairing strategies, which consisted of pairs of readers who were either opposite or similar in AIR and/or CDR. Results Based on a minimum number of examinations per reader and per pair, the final study sample consisted of 3 592 414 examinations (Sweden, n = 965 263; England, n = 837 048; Norway, n = 1 790 103). The overall AIRs and CDRs for all specific pairing strategies (Sweden AIR range, 45.5-56.9 per 1000 examinations and CDR range, 3.1-3.6 per 1000; England AIR range, 68.2-70.5 per 1000 and CDR range, 8.9-9.4 per 1000; Norway AIR range, 81.6-88.1 per 1000 and CDR range, 6.1-6.8 per 1000) were not significantly different from the random pairing strategy (Sweden AIR, 54.1 per 1000 examinations and CDR, 3.3 per 1000; England AIR, 69.3 per 1000 and CDR, 9.1 per 1000; Norway AIR, 84.1 per 1000 and CDR, 6.3 per 1000). Conclusion Pairing a set of readers based on different pairing strategies did not show a significant difference in screening performance when compared with random pairing. © RSNA, 2023.

Risk analysis of cruise ship supply chain based on the set pair analysis-Markov chain model

The cruise industry, which serves as a prominent exemplar of globalized business, is characterized by unique features that render its supply chain operations highly susceptible to various risk factors, thereby giving rise to significant consequences. Evaluating the risks associated with major disruptive events and enhancing the robustness of the cruise supply chain have become crucial issues of concern for various stakeholders. This study first develops an indicator evaluation system for assessing the risks of China's cruise supply chain, drawing upon relevant literature and expert interview opinions. Secondly, set pair analysis is utilized to identify the scores and trends of risk indicators in China's cruise supply chain. Thirdly, the Set pair analysis-Markov chain model is employed to forecast the future risk status of China's cruise supply chain system, and the influence of different weights on the prediction results is discussed. Finally, the risk evolution network of China's cruise supply chain is constructed to identify common risk consequences and visualize the evolution path of risk events, with the aim of providing reference for the formulation of prevention and control measures. The findings of this article have important consequences for improving risk mitigation strategies and enhancing the resilience of the cruise supply chain.

An adaptive greedy heuristic for large scale airline crew pairing problems

A crew pairing represents a sequence of flight legs that constitute a crew work allocation, starting and ending at the same crew base. A complete set of crew pairings covers all flight legs in the timetable of an airline for a given planning horizon. That determines the rosters for the crew and their quality, since those pairings would potentially include layovers, deadheads and connection times which are the key factors which directly contribute to the operational crew costs. Considering that crew costs form the second largest bit in the overall operational cost, generating optimized crew pairings is a vital process for the airline companies. In this study, a score-based adaptive greedy heuristic and a genetic algorithm are presented for solving large scale instances of airline crew pairing problems. Both solution methods are applied to a set of real-world problem instances from Turkish Airlines which is one of the largest carriers in the world. The empirical results show that the proposed approaches are indeed capable of generating high quality solutions for crew pairing, even for the large scale problem instances.

Application of a Novel Methodology for Efficient Optimization of Hippocampal Avoidance Whole-Brain Radiotherapy

A growing awareness of the long-term neurocognitive toxicity associated with whole-brain radiotherapy (WBRT) has led to the development of newer delivery methods to achieve relative hippocampal sparing. In order to accomplish the required target coverage and dose constraints, however, hippocampal-avoidance WBRT (HA-WBRT) can prove labor intensive and time-consuming. We therefore developed a simplified and efficient planning model for generating optimized HA-WBRT treatment. Target volumes and organs at risk (OARs) were contoured per the NRG-CC001 protocol, including the whole-brain parenchyma, bilateral hippocampi, and optic apparatus. Treatment was planned using Eclipse 16.1 for a TrueBeam STx 2.7 LINAC, applying customizable optimization and clinical goals templates to rapidly generate uniform plans. An isocenter was placed at the point corresponding to the center of mass between the bilateral contoured 3-dimensional hippocampi. Volumetric modulated arc therapy (VMAT) using 6MV photons was designed using two sets of bi-directional full arcs, with collimator rotations of 85° and 95°. The two paired sets independently covered the superior and inferior portions of the treatment volume, respectively. Arcs shared a total 2cm overlap in the superior-inferior (X Jaws) direction. A total of 10 consecutive cases of HA-WBRT were retrospectively re-optimized using the described planning model. All plans were normalized to cover 95% of the treatment volume with prescription dose (V30Gy = 95%). The model achieved favorable dosimetry relative to NRG-CC001 per protocol constraints. Average maximal dose to the hottest 2% and minimal dose to 98% of the volume were 32.22 ± 0.26 Gy and 29.82 ± 1.46 Gy, well within protocol limits of 37.5 Gy and 25 Gy, respectively. The average maximal dose to 100% of the hippocampi (D100%) and to its hottest 0.03cc (Dmax) were 8.50 ± 0.34 Gy and 12.85 ± 0.70 Gy, compared to protocol limits of 9Gy and 16Gy, respectively. Mean Dmax to the total optic apparatus was 29.67 ± 0.52 Gy, with a maximum value within the cohort of 30 Gy, matching protocol constraints. The above presented methodology for optimizing HA-WBRT can consistently and efficiently generate plans with excellent target coverage and strict hippocampal avoidance per current collaborative group constraints. This method can be broadly applied to decrease the burden and heterogeneity across HA-WBRT planning.

Dynamic learnable degradation for blind super-resolution

Blind super-resolution endeavors to restore clear and high-resolution images from obscure degraded images. To comprehensively encompass the diverse range of real image degradations, prevailing research methods either explicitly or implicitly simulate image degradation. Nevertheless, a majority of prior approaches employ conventional interpolation algorithms for resize operations, thereby impeding the breadth of image degradation diversity. In the case of implicit models, we propose a dynamic learnable degradation (DLD) model, achieved through self-supervised training. This model leverages a dynamic downsampling module to generate a multitude of low-resolution images. By introducing DLD as a novel downsampling method, we devise an original explicit degradation model, which generates additional sets of training image pairs to enhance the practical restoration outcomes of ESRGAN for natural images, called Dynamic-ESRGAN. We also account for the overall degradation and put forth a hybrid-order degradation model to encompass the manifold degradations prevalent in natural images. Exhaustive experimental comparisons conducted on both synthetic and real datasets have unequivocally demonstrated the significant advantages of our DLD and Dynamic-ESRGAN models, both in terms of objective indicators and subjective perception.

Decompositions of optimal averaged Gauss quadrature rules

Optimal averaged Gauss quadrature rules provide estimates for the quadrature error in Gauss rules, as well as estimates for the error incurred when approximating matrix functionals of the form uTf(A)v with a large matrix A∈RN×N by low-rank approximations that are obtained by applying a few steps of the symmetric or nonsymmetric Lanczos processes to A; here u,v∈RN are vectors. The latter process is used when the measure associated with the Gauss quadrature rule has support in the complex plane. The symmetric Lanczos process yields a real tridiagonal matrix, whose entries determine the recursion coefficients of the monic orthogonal polynomials associated with the measure, while the nonsymmetric Lanczos process determines a nonsymmetric tridiagonal matrix, whose entries are recursion coefficients for a pair of sets of bi-orthogonal polynomials. Recently, it has been shown, by applying the results of Peherstorfer, that optimal averaged Gauss quadrature rules, which are associated with a nonnegative measure with support on the real axis, can be expressed as a weighted sum of two quadrature rules. This decomposition allows faster evaluation of optimal averaged Gauss quadrature rules than the previously available representation. The present paper provides a new self-contained proof of this decomposition that is based on linear algebra techniques. Moreover, these techniques are generalized to determine a decomposition of the optimal averaged quadrature rules that are associated with the tridiagonal matrices determined by the nonsymmetric Lanczos process. Also, the splitting of complex symmetric tridiagonal matrices is discussed. The new splittings allow faster evaluation of optimal averaged Gauss quadrature rules than the previously available representations. Computational aspects are discussed.

On the distance-edge-monitoring numbers of graphs

Foucaud et al. (2022) recently introduced and initiated the study of a new graph-theoretic concept in the area of network monitoring. For a set M of vertices and an edge e of a graph G, let P(M,e) be the set of pairs (x,y) with a vertex x of M and a vertex y of V(G) such that dG(x,y)≠dG−e(x,y). For a vertex x, let EM(x) be the set of edges e such that there exists a vertex v in G with (x,v)∈P({x},e). A set M of vertices of a graph G is distance-edge-monitoring set if every edge e of G is monitored by some vertex of M, that is, the set P(M,e) is nonempty. The distance-edge-monitoring number of a graph G, denoted by dem(G), is defined as the smallest size of distance-edge-monitoring sets of G. In this paper, we continue the study of distance-edge-monitoring sets. In particular, we give upper and lower bounds of P(M,e), EM(x), dem(G), respectively, and extremal graphs attaining the bounds are characterized. We also characterize the graphs with dem(G)=3. In addition, we give some properties of the graph G with dem(G)=n−2.

Evaluation Model and Application of the Implementation Effectiveness of the River Chief System (RCS)—Taking Henan Province as an Example

To scientifically evaluate the implementation of the River Chief System (RCS), accelerate the overall improvement of the water ecological environment, and promote the sustainable development of river and lake functions, this study selects 26 evaluation indicators from six aspects, including the effectiveness of organization and management, the effectiveness of water resources protection, the effectiveness of water environment management, the effectiveness of water pollution prevention and control, the effectiveness of water ecological restoration, and the effectiveness of the management of the waterside shoreline, and establishes an evaluation system for the effectiveness of the implementation of the RCS. Among the 26 indicators, data for the qualitative indicators mainly come from a series of statistical yearbooks and RCS reports, while data for the quantitative indicators are obtained through scoring by more than 20 experts and calculating the average. The CRITIC objective weighting method is improved from three aspects of comparison intensity, correlation coefficient, and degree of variation, and the subjective weighting of indicators is carried out using the AHP 1–5 scale method. The optimal linear combination of subjective and objective weighting results is obtained using the combination weighting method with game theory, which is auxiliary to the set pair analysis. Considering the “certainty” and “uncertainty” in the evaluation process, the four-element connection number model of set pair analysis is established to evaluate the implementation effect of the RCS in Henan Province from 2018 to 2021. The results show that the implementation effect of the RCS in Henan Province improves year by year and reaches excellent in 2019. The results of this study can be used as a reference for evaluating the work of the RCS in other regions and can also provide a reference for the study of evaluation problems in other fields.

중학교 수학 학습을 위한 질의응답 챗봇의 설계와 활용

Objectives The purpose of this study is to determine the most effective approach for constructing training data comprising questions and answers for the development of a mathematical question-answering chatbot. This involves a theoretical analysis of mathematical knowledge and an empirical analysis of questions posed by students using the chatbot.&#x0D; Methods The study targeted second-year middle school students and created a total of 1,936 pairs of math-related question-answer sets virtually. The chatbot was trained using the Doc2Vec technique, which is based on similarity. During the data construction process, various considerations related to mathematical knowledge and math learning were taken into account to design different types of questions. The data was then used for training the chatbot. Additionally, students utilized the trained chatbot during their classes for seven months, and their input questions were collected and analyzed.&#x0D; Results A comparison between the collected dataset of 1,636 questions from 92 middle school students and the types of question-answer pairs used for training the chatbot revealed that conceptual knowledge questions accounted for over 70% of the student-generated data. Students also posed questions to test the chatbot's capabilities and inquire about attitudes and emotional aspects related to mathematics. Within the category of conceptual knowledge questions, deeper inquiries involving comparisons, extensions, and connections of concepts were observed.&#x0D; Conclusions The study revealed that when students used the question-answering chatbot, inquiries related to c conceptual understanding of mathematics when students interact with the question-answering chatbot highlights the importance of focusing on conceptual questions. Furthermore, the activity of questioning concepts holds the potential to evolve into more advanced inquiries, fostering connections and extensions of mathematical concepts. Further research is needed to explore more suitable and effective ways of responding to students through the chatbot and utilizing it in educational contexts.

Killing of a Multispecies Biofilm Using Gram-Negative and Gram-Positive Targeted Antibiotic Released from High Purity Calcium Sulfate Beads.

Multispecies biofilm orthopedic infections are more challenging to treat than mono-species infections. In this in-vitro study, we aimed to determine if a multispecies biofilm, consisting of Gram positive and negative species with different antibiotic susceptibilities could be treated more effectively using high purity antibiotic-loaded calcium sulfate beads (HP-ALCSB) containing vancomycin (VAN) and tobramycin (TOB) in combination than alone. Three sets of species pairs from bioluminescent strains of Pseudomonas aeruginosa (PA) and Staphylococcus aureus (SA) and clinical isolates, Enterococcus faecalis (EF) and Enterobacter cloacae were screened for compatibility. PA + EF developed intermixed biofilms with similar cell concentrations and so were grown on 316L stainless steel coupons for 72 h or as 24 h agar lawn biofilms and then treated with HP-ALCSBs with single or combination antibiotics and assessed by viable count or bioluminescence and light imaging to distinguish each species. Replica plating was used to assess viability. The VAN + TOB bead significantly reduced the PA + EF biofilm CFU and reduced the concentration of surviving antibiotic tolerant variants by 50% compared to single antibiotics. The combination of Gram-negative and positive targeted antibiotics released from HP-ALCSBs may be more effective in treating multispecies biofilms than monotherapy alone.