The advancement of embedded sensor systems allowed the monitoring of complex processes based on connected devices. As more and more data are produced by these sensor systems, and as the data are used in increasingly vital areas of applications, it is of growing importance to also track the data quality of these systems. We propose a framework to fuse sensor data streams and associated data quality attributes into a single meaningful and interpretable value that represents the current underlying data quality. Based on the definition of data quality attributes and metrics to determine real-valued figures representing the quality of the attributes, the fusion algorithms are engineered. Methods based on maximum likelihood estimation (MLE) and fuzzy logic are used to perform data quality fusion by utilizing domain knowledge and sensor measurements. Two data sets are used to verify the proposed fusion framework. First, the methods are applied to a proprietary data set targeting sample rate inaccuracies of a micro-electro-mechanical system (MEMS) accelerometer and second, to the publicly available Intel Lab Data set. The algorithms are verified against their expected behavior based on data exploration and correlation analysis. We prove that both fusion approaches are capable of detecting data quality issues and providing an interpretable data quality indicator. Embedded sensor systems generate massive amounts of data for many different use cases. Applications include predictive maintenance, robotics, and health care. Many of these use cases have significant business impacts but are also often related to the safety or security of the monitored facility. It is crucial for these sensor systems to monitor their data quality level, ensuring the reliability of the observed process and the actual application. Embedded sensor systems are resource constrained, making it necessary to define and aggregate data quality-related attributes into a single meaningful indicator. This step is often necessary because of a limited communication bandwidth or power consumption budget. These constraints limit the ability of embedded sensor systems to transmit raw data frequently, making it necessary to transfer already preprocessed data.Most sensor systems consist of a microcontroller, and one or several sensors embedded in one device . These sThe workflow consists of five steps. The goal is to transform real valued sensor measurements into data quality attributes and fuse them into a single meaningful and interpretable data quality indicator. The first and second steps utilize domain knowledge to derive data quality attributes based on the available sensor data. Then the fusion algorithms are defined based on statistical properties and expert knowledge. Finally, the process results in a data quality fusion algorithm, which outputs the calculated data quality indicator. The output of the fusion algorithm is a single meaningful number that represents the data quality of the observed sensor system. The proposed method is applied to two data sets. The first data set covers the temperature dependence of the sampling rate from an industry-grade MEMS accelerometer. The precision of the sampling rate of these systems influences the overall data quality, as algorithms such as frequency domain-based approaches require accurate sampling rates. The second data set is publicly available. It consists of distributed sensor systems that monitor environmental parameters such as temperature, light, and device-related metadata.We bridge existing gaps by allowing the direct use of the data quality attributes, their metrics, and the defined bounds for data fusion, using maximum likelihood estimation and fuzzy logic. Both algorithms can fuse multiple inputs into one single meaningful indicator. The algorithms do not require scaling or normalization. All sensor readings can be input directly into the algorithms, addressing limitations of previous works in the field. In addition, we contribute a semi-automated approach to defining fuzzy logic membership and likelihood functions, making it possible to create quality fusion algorithms with minimal effort. Existing work requires manual specification of the properties of the fusion algorithms. Our semi-automatic approach limits the required amount of manual work to specify the properties of the algorithms. The predefined set of supported rules and distributions limits the initial degrees of freedom, supporting the quick setup of a data quality fusion algorithm and its test.Various approaches have been proposed to track the data quality of sensors. In , a capacDifferent authors have previously defined a variety of data quality attributes. These attributes reflect important aspects of sensor signals, influencing the signal quality of the complete system. A summary of commonly used attributes is given in Frequency domain-based methods have been used to monitor machine conditions . A proceThis section introduces the methods and data used in this work. First, we introduce the overall architecture of the framework. Second, the used data quality attributes are defined, and the metrics used to calculate the indicators are established. Then, the fusion and aggregation algorithms are introduced. Finally, the data sets used for this work are described.In order to aggregate multiple attributes derived from the metrics into a single data quality indicator, a data fusion algorithm is added to the architecture. The task of the fusion algorithm is to calculate a meaningful and interpretable value, representing the overall quality of the data in the complete system. Depending on the application scenario, a layered fusion approach is applied, resulting in a fused data quality indicator This section introduces the attributes used for this work and defines the metrics for their calculation.AccuracyThis attribute is related to the error of a sensor signal with respect to a reference value or overall precision . UsuallyAnother approach called dynamic uncertainty, proposed by Gatian et al.  relates Consistencyt. Both values represent measurements of the same physical quantity.Inconsistent measurements deviate significantly from similar measurements, closely related in time and space. This attribute is related to the coherence between sensor signals and is calculated based on sensors that measure the same physical quantity. The consistency metric is defined by calculating the absolute error over two values as follows:CompletenessX represents the number of measured samples, giving a measure of completeness.This quality parameter relates to missing values  in a serTimelinessThe timeliness attribute is addressed in multiple use cases and is defined slightly differently depending on the usage. It is often related to its currency or up-to-dateness, an additional aspect in embedded sensor systems. Gutierrez et al.  define tBased on empirical data, statistical properties of the data quality attributes can be derived. Statistical properties are used to derive probability distributions L is the product of all individual probability functions f [f is constant. If this is not the case, it is necessary to derive individual distributions for every value L with respect to The combined likelihood function ctions f . For ind follows :(5)θ^ML= follows :(6)fNormThe variance The value ameter μ :(8)fExp demonstrates the case of low data quality. The attributes are or-ed, meaning that any low data quality sample results in low data qualityDefuzzification is the final step of the process. The results of the inference step are transformed back into a crisp output value, representing the current data quality To reduce manual labor during the initial setup of a data quality fusion algorithm, we propose a semi-automatic framework for the definition of likelihood functions or fuzzy logic membership functions. The data-driven approach uses available measurements or domain knowledge, such as data sheets, to create a working fusion algorithm with minimal effort. To enable the semi-automatic rule definition, a set of supported distributions is defined, such as normal or exponential. The measured observations or domain knowledge is fed into an array such that an observation resolves to an assigned data quality. For purely data-driven parameters, the histogram can be used as a basis, considering more frequent observations to have higher data quality compared to observations occurring less often. If domain knowledge is used to perform data quality indication, a table must be provided that maps observations to data quality levels.The procedure for the semi-automatic definition of fusion algorithms is shown in After finding the most suitable statistical model for the quality attributes, this information is further used to define quality fusion algorithms. The statistical model found in the previous step can be used directly for data fusion using maximum likelihood. For fuzzy logic-based fusion, bounds for high and low data quality need to be defined, which are derived from the statistical properties. This is achieved by defining multiples of the allowed variance of the fitted distributions centered around the found mean, which still represents a high data quality. A sufficient granularity of fuzzy representations is achieved by defining three membership functions, low, medium, and high. Low and high memberships represent poor data quality, as they are too far off compared to the expected mean and variance. The membership medium represents high data quality, as the newly captured observation is well within the expected range.The semi-automatic definition of fuzzy logic membership functions also relies on properties derived from data and observations. The support points of the membership functions are derived from the mean and standard deviation. The proposed framework supports Gaussian membership functions for average, Z-shaped membership functions for low, and S-shaped membership functions for high. An example of defined membership functions is given in The S- and Z-shaped membership functions are defined so that the points a, b, c, and d according to Equations (11) and (12) cross the maximum and minimum of each side of the Gaussian membership function, respectively.This data set was acquired based on a custom-designed PCB, equipped with an STMicroelectronics STM32L4A6 microcontroller. The sensor system carries an STMicroelectronics LSM6DSL inertial measurement unit, a Texas Instruments TMP461 temperature sensor, and a BOSCH BMP280 pressure and temperature sensor. The purpose of this data set is to measure the influence of temperature changes on the sample rate of LSM6DSL. This temperature dependence is used to derive data quality attributes such as accuracy, timeliness, completeness, and consistency based on the measured data and the available data sheet information. The sensors are configured as follows: LSM6DSL is configured to measure acceleration only, in FIFO mode , at a frequency of 6664 Hz and a sensitivity of 2 mg . A new sThe fuzzy logic rule set used for this data set is defined as follows:Equation (16) defines the set of rules for low data quality, given observations with low membership. Rule 2 defines poor data quality readings for sensor values that are too high. The third rule defines the average quality of the data if the completeness is high, which means that more samples were acquired than needed. The last rule specifies the conditions for high data quality if all observations are in the medium range.This data set was collected by Madden et al.  in 2004.An exploratory data analysis showed that some motes did not record data over the entire time period, while others showed significant gaps. Motes 46 to 48 were found to have complete recordings and also show the effect of voltage drop in the battery. All three sensors are located close to each other, in the left part of the laboratory, according to The fuzzy logic rules, derived from the Intel Lab Data, are given in Equations (20)–(22): Three rules were derived, representing the possible data quality levels poor, average, and high. The quality attribute of the voltage data was calculated based on the inverse of the actual voltage. This was done by changing the significance of the values, making large values small and small values large. High data quality is assumed when the inverse of the voltage is low or medium. Inverting is helpful because large voltages represent a higher data quality. This way, the accuracy attribute calculated through voltage can be better mapped in the memberships of low, average, and poor. This procedure was chosen to map the characteristics of the data set to the proposed semi-automatic rule definition approach.The maximum likelihood and fuzzy logic-based data fusion methods introduced before were applied to the Accelerometer Data Set and the Intel Lab Data. The semi-automatic rule definition was applied to create the fusion algorithms for the used data sets. The behavior of the fusion algorithms is verified by calculating correlation coefficients and evaluating the similarity of the signals.Based on the conducted measurement and the acquired data, a total of eight data quality attributes were derived. The semi-automatic definition method was applied to all attributes except LSM6DSL sample rate accuracy, using a knowledge-based approach for this attribute. Based on the proposed method, all automatically defined rules consist of normally distributed values. The timeliness is the average time difference between subsequent samples in seconds. A medium timeliness for LSM6DSL of 0.052 s, 0.263 s for TMP461, and 1.052 s for BMP280 is found. Similarly, completeness attributes were calculated for a time slot of one second; thus, the resulting values have a unit of samples per second. The semi-automatic rule definition yields an average completeness of 19.85 S/s for LSM6DSL, 3.98 S/s for TMP461, and 0.99 S/s for BMP280. The completeness of BMP280 was calculated over a time window of 10 s and then normalized to 1 s. For the other values, an interval of 1 s was chosen. The average consistency between the temperature measurements was 0.15 °C. The mean squared error values, used to evaluate the goodness of fit range between 0.1 and 0.05.The LSM6DSL sample rate and temperature are plotted in The results in this section were acquired using data from sensor Mote IDs 46, 47, and 48. The timeliness and completeness attributes were calculated based on the timestamps provided by the data set, using a time window of 10 min for completeness. One instance of measured values is bound to one timestamp, so specific attributes for a particular measurement cannot be calculated. Furthermore, the voltage level is the quality attribute related to accuracy, as it significantly affects the validity of temperature measurements . Based oIn This work proposes a framework to aggregate data quality attributes into a single meaningful data quality indicator. The results are discussed in the following section. The Accelerometer Data Set reflects our initial assumption that the temperature of the sensor system influences the overall quality of the data. Based on the proposed knowledge-driven and data-driven approach, a set of rules has been established to aggregate data quality attributes into a single meaningful indicator. Interpretability is gained through the outputs of the indicators themselves. Both indicators, obtained by fuzzy logic and the maximum likelihood method, are in the range between 0 and 1, where higher values indicate higher data quality. The general information content of the fused outputs regarding their inputs was analyzed with a correlation analysis.Despite the different characteristics of the two used data sets, our approach provided consistent results. In both data sets, the proposed timeliness attributes do not contribute significantly to the overall data quality indicator, leaving room for further improvement. A possible reason for this could be the used metric. The interpretation of the results of the fuzzy logic and maximum likelihood-based approaches shows the same trend of reporting worse data quality for similar inputs. However, fuzzy logic is more responsive to issues related to data quality. This is because the fuzzy rule set can be finely tuned to the desired data quality level. In contrast, the MLE-based approach is completely determined by the distribution used. Tuning this method is only possible by modifying these distributions, making it less flexible but also less effort to construct compared to the fuzzy logic-based approach. This comes at the cost of a less sensitive data quality output. The nature of MLE is to compute the likelihood given a set of observations; if, for example, only one input value is of low quality, statistically, the overall data quality could still be high. This work covers only a limited number of membership functions and distributions. The supported algorithmic properties could be extended further to give developers more degrees of freedom when designing a quality fusion system. In addition, the algorithms have not been implemented directly in the sensor systems. Furthermore, the noise level of the fused data quality indicators requires further analysis. A possible solution could be to apply a moving average filter, limiting the dynamics of the data quality indicator. Additionally, to address the real time requirements as well, a more sophisticated approach can be considered, such as variable mode decomposition . MoreoveCompared to previous work in the field of data quality indication in sensor systems, our framework provides an end-to-end approach to building a quality fusion algorithm. Starting with data and domain knowledge, statistical properties, and fuzzy rule sets are extracted. Based on these, the actual data-fusion algorithms are parameterized. Wu et al.  proposedIn the next step, we aim to port the proposed fusion methods to sensor systems to show their feasibility in live scenarios. While porting, the latency of the fusion algorithm, running in a resource-constrained system, has to be addressed. Furthermore, a suitable description of the data quality indicators and the necessary metadata are required.


This paper aims to explore the potential offered by emotion recognition systems to provide a feasible response to the growing need for audience understanding and development in the field of arts organizations. Through an empirical study, it was investigated whether the emotional valence measured on the audience through an emotion recognition system based on facial expression analysis can be used with an experience audit to: (1) support the understanding of the emotional responses of customers toward any clue that characterizes a staged performance; and (2) systematically investigate the customer’s overall experience in terms of their overall satisfaction. The study was carried out in the context of opera live shows in the open-air neoclassical theater Arena Sferisterio in Macerata, during 11 opera performances. A total of 132 spectators were involved. Both the emotional valence provided by the considered emotion recognition system and the quantitative data related to customers’ satisfaction, collected through a survey, were considered. Results suggest how collected data can be useful for the artistic director to estimate the audience’s overall level of satisfaction and make choices about the specific characteristics of the performance, and that emotional valence measured on the audience during the show can be useful to predict overall customer satisfaction, as measured using traditional self-report methods. For decades, service research has measured customers’ emotions by using self-report methods. This approach has been strongly supported by the assumption that customers’ emotions are strong mediators of overall customer satisfaction ,19,20 anx or y). The concrete applications of this technology are countless, both in the physical and digital worlds. Compared with the other tools and methods for emotional recognition and analysis, this system presents many advantages, such as:Non-invasive solutions that integrate several technologies;A modular architecture of technologies that let each module work as a stand-alone tool so that the functionality of the system is not compromised when modules are missing;Web-based user interface, easily accessible remotely yet protected by security protocols. Thus, the user can access data in a cloud-based environment;Emotion recognition technology inserted in a customer experience context.The emotion recognition system adopted in this study represents a state-of-the-art system for emotion recognition based on the analysis of facial expressions. Such software implements a combination of facial recognition and gaze-tracking technologies based on artificial intelligence algorithms, as described in detail in  and 49]49]. It eSeveral studies have been carried out to determine the impact of such a tool on customer service management in many fields, ranging from automotive  to retaiThis study aims to evaluate the usefulness of the proposed system to analyze, at a qualitative level, the experience behaviors expressed by the audience during the opera staging. To this end, starting with the assumption that “customer satisfaction can be considered essentially the culmination of a series of customer experiences or (…) the net result of the good ones minus the bad ones” , this stThe proposed emotion recognition system was used to monitor the audience during 11 opera performances. This monitoring was specifically conducted during 4 performances of Carmen, 3 performances of Macbeth, and 4 performances of Rigoletto. During each performance, 12 spectators were selected for the study whose profiles fit with the target audience. They were asked to sit in the central sector , where aA Foscam R2 1080p infrared IP Camera;An Asus VivoMini UN62 with Intel Core i5-4210U processor, 4 GB RAM.Using the infrared mode, frame acquisition was conducted in grayscale; this did not cause any particular problems for the aforementioned CNN , trainedThe qualitative analysis conducted with the emotion recognition system to detect the emotions of the audience was flanked by a quantitative survey aimed at investigating the level of customer satisfaction reported by spectators.Based on the model of customer satisfaction in opera proposed in , we consThe audience sample had been selected to correspond to the five Sferisterio buyer personas , elaboraThe actual experiment consisted of monitoring participants during an opera performance through an emotion recognition system and asking them to answer a survey at the end of the performance. A total of 132 people were involved. Participation was encouraged by the offer of free tickets. Participants were informed about the objectives of the study, the types of data, and how they would be collected and processed, and they signed an informed consent form.V_s experienced during the performance was computed according to the following equation:ith frame and n is the total number of video frames analyzed for the considered spectator during the spectacle. This equation represents the average emotion value throughout the recording, distributed over all frames belonging to the recording for each subject. This calculation was required by the behavior held by the emotion analysis software, which, during the analysis of the recordings, processed the frames frame-by-frame and returned the emotional values for each of these frames. As can be observed, spectators who attended the performances of Carmen generally showed a higher level of negative emotions compared with those who attended Rigoletto or Macbeth. The performances of Macbeth were the ones that elicited the most positive emotions. For each spectator, the mean emotional valence In the case of a complex performance such as a lyric opera performed in an open space at night, many factors can contribute to arousing emotions in the audience, such as the plot, the acting and direction, the scenography elements, the performances of the orchestra, the chorus, and the principal singers, the popularity of the main arias, the musical characteristics , and so on. By comparing the results of the analysis on the audience of the different representations, it is possible not only to indicate which opera the audience mostly appreciated but also to understand the consequences on the lived experience due to the differences among the representations of the same opera.Act I, scene I: Witches chorus Che faceste? dite su!;Act I, scene V: the cavatina of Lady Macbeth Vieni! t’affretta!/Come! Hurry!;Act II, scene V: brindisi Si colmi il calice/Fill up the cup;Act III, scene V: duet Vi trovo alfin!/I’ve found you at last;Act IV, scene I: the Scottish refugees chorus Patria oppressa/Downtrodden country;Act IV, scene I: aria of Macduff Ah, la paterna mano/Ah, the paternal hand;Act IV, scene IV: aria of Lady Macbeth Una macchia è qui tuttora/Yet here’s a spot.For example, by comparing the trend of the emotional valence registered during the three representations of Macbeth , it can The data collected also evidenced a significant increase in positive emotional reactions during the moments of applause; these highlight how the act of showing appreciation to the performers evokes powerful emotions in the spectators themselves. The link between applause and strong pleasant emotions should not be surprising; the results of several studies suggest that there is a link between clapping and happiness ,60,61. Ip < 0.01), valence and AQ , and valence and SQD . While there was no significant correlation between valence and SQBA, positive correlations were observed between CS and AQ  and between CS and SQD . There were no statistically significant correlations between the considered variables and SQBA.Pearson product-moment correlation coefficients were computed to assess the relationship between the emotional valence and the levels of CS, AQ, SQD, and SQBA reported by the audiences. Results  evidenceAnalyzing these results, it can be observed how the emotional valence measured during the performance can be useful in determining the overall level of satisfaction experienced by the spectators. Measured emotions seem to be directly elicited in the visitors by the stimuli they perceived during the performance . In contrast, results suggest that the quality of services offered before and during the performance  did not affect the emotions detected. These results suggest that the proposed emotion recognition system can serve as an experience audit tool to collect information regarding the level of satisfaction manifested by the audience during the performance. Monitoring the emotional valence would seem to be a possible alternative to collecting the subjective opinions of viewers through surveys.This paper investigates whether the analysis of the dynamic nature of emotions manifested by the audience of performing arts  captured through a non-invasive emotion recognition system can support the understanding of the emotional satisfaction of customers toward any clue that characterizes the staged performance. Moreover, it suggests how the analysis of emotional valence manifested by the audience during the show can be useful to predict overall customer satisfaction, as measured using traditional self-report methods. The discussed results suggest that collected data related to audiences can be useful for the artistic director to estimate the audience’s overall level of satisfaction and better guide choices regarding the specific characteristics of the performance and casting, in order to improve the artistic offering. Furthermore, they seem to be useful in supporting the management of arts organizations in defining medium- and long-term strategies as well as marketing, both at a strategic and operational level. These results highlight the potential of a facial expression recognition system as an experience audit tool in the context of opera performances. However, future studies should be carried out to better understand its effectiveness even in the absence of integration with traditional research tools. Moreover, future studies should aim at investigating its effectiveness as well as its pros and cons in other performance art contexts. Once integrated into the marketing database activities, such information may constitute an important ingredient for the development of the CRM (Customer Relationship Management) system. In this sense, it will be possible to create offers that meet the qualitative and emotional expectations of customers, which are essential for building and nurturing a customer satisfaction path . This ca


Accurate identification of Drug-Target Interactions (DTIs) plays a crucial role in many stages of drug development and drug repurposing. (i) Traditional methods do not consider the use of multi-source data and do not consider the complex relationship between data sources. (ii) How to better mine the hidden features of drug and target space from high-dimensional data, and better solve the accuracy and robustness of the model.To solve the above problems, a novel prediction model named VGAEDTI is proposed in this paper. We constructed a heterogeneous network with multiple sources of information using multiple types of drug and target dataIn order to obtain deeper features of drugs and targets, we use two different autoencoders. One is variational graph autoencoder (VGAE) which is used to infer feature representations from drug and target spaces. The second is graph autoencoder (GAE) propagating labels between known DTIs. Experimental results on two public datasets show that the prediction accuracy of VGAEDTI is better than that of six DTIs prediction methods. These results indicate that model can predict new DTIs and provide an effective tool for accelerating drug development and repurposing. The therapeutic effect of a drug on a disease from its action on a target protein and its effect on its expression . TherefoTraditional calculation methods  mainly iIn recent years, heterogeneous networks of some deep learning algorithms have integrated information related to multiple drugs, diseases and targets for DTIs prediction. Compared with homogeneous networks, heterogeneous networks cover multiple entities and complex interaction relationships between different types of entities . For exaIn this paper, we propose a new prediction model named VGAEDTI in Fig. The VGAEDTI model uses multi-source drug information and target similarity to build a heterogeneous network, learning their embeddings through known association relationships and unknown associations.The in-depth features of drugs and targets are learned through collaborative training with VGAE and GAE in VGAEDTI model.The major contributions of this research are as follows:The two datasets we use were downloaded from several public databases, DrugBank, UniProt and MalaCard. DrugBank contains information on the molecular structure of drugs, target proteins, etc. UniProt is a protein-related database with a large amount of protein information. MalaCard is a human disease database that collects information on symptoms and related drug data. We download the chemical structure information of drugs and the targets information of all chemical drugs from DrugBank. Protein sequence information was obtained from UniProt, and drug indications were obtained from the MalaCard database. These two datasets use involves 3508 targets, 2015 drugs, 9702 diseases, and contains 207,540 known drug-disease association information and and 8947 known DTIs and some other types of data, these two data sets were summarized into Table The n drugs in the dataset are denoted by entclass1pt{minimaThe lgorithm , and theentclass1pt{minimaentclass1pt{minimaIn order to better extract the internal connections between drug and target nodes, and perform deep learning on the common topological information representation of drug and target nodes, a heterogeneous network The adjacency matrix of a heterogeneous network of drugs, targets, and diseases is represented as follows,H p r as a drug and the characteristics of the target node matrix VGAE and GAE serve as feature extractors for drug space and targets space. These two autoencoders extract the potential feature information from the two Spaces through feature inference and label propagation, respectively. For a drug or target node, the association and similarity with it can be regarded as the feature attribute of the node, So take For example, for the targets space, The decoding process of VAE is as follows,We use VGAE to extract the spatial information of the input target feature matrix For the targets space, the loss function of VGAE is the sum of reconstruction error If the feature follows Gaussian distribution, the reconstruction error is the mean square error, when the feature follows Bernoulli distribution, the reconstruction error is cross-entropy loss as follows,For the target space, the following equation is the reconstruction error We propose the VGAEDTI model, design a representation learning framework that integrates the feature inference network and labels propagation network and use the integrated variational inference to train the variational EM algorithm. VGAEDTI alternates the following two steps until convergence occurs.E-step (Feature inference): The VGAE is used for feature inference.M-step (Label propagation): The GAE is used for label propagation.Taking training spatial target information as an example, the variational EM algorithm is implemented by alternately minimizing the loss of the VGAE and GAE, after the variational EM algorithm alternately trains the two autoencoders until convergence as follows,In this paper, the VGAE and GAE are co-trained, and the co-training loss is represented by learning from drug and target space respectively as follows,The total optimized loss The total optimized loss The first step is to sample the data. The samples in the training set are sampled in the form of put back, and the data set is sampled for Then, the Gini coefficient is used to calculate the optimal segmentation variable, and the decision tree is constructed by node attribute splitting.Obtain N decision trees by repeating the previous steps In this paper, in order to get better score prediction and avoid the negative impact of feature dimension and the importance of feature information on the prediction of drugs and targets, a Random Forest classifier  is used.The Gini coefficient is as follows,In order to evaluate the performance of our proposed VGAEDTI model for predicting DTIs. We use fivefold cross-validation. The dataset we use contained 1307 drugs, and the dataset was randomly divided into five groups of the same size, one of which was the test set in turn, and the remaining four groups were the training set. All the known drug target information were positive samples, and the remaining unknown drug target associations were negative samples, and the negative data contained all unknown or nonexistent DTI, it can be seen from Table GRMF: DTIs prediction using graph regularized matrix factorization .DTINet: A network integration method for predicting DTIs and computing drug repurposing from heterogeneous information.MolTrans: Transformer of molecular interactions for DTIs prediction .NGDTP: Graph convolution autoencoder and Generative adversarial network approach for predicting DTIs .DeepDTNet: Identify targets between known drugs by deep learning from heterogeneous networks .AEFS: An autoencoder-based approach to predict DTIs by maintaining consistency in pharmacochemical properties and functions .HNM: Drug repositioning by integrating target information through a heterogeneous network model The epochs of our VGAEDTI model are 500, the learning rate is 0.1, the weight decay rate is We adopted a fivefold cross-validation method for training, and the following are some evaluation indicators:In the above formula, TN is the true negative; FN is a false negative; FP is a false negative, TP is truly positive, FPR is the false positive rate, and TPR is the true positive rate. TPR and FPR can draw receiver operating characteristic (ROC) curves, and the area under the ROC curve (AUROC) and the area under the accuracy-recall curve (AUPR) are important indicators to measure the performance and stability of binary classification models.In order to better demonstrate that our method can extract deep drug-target information from high-dimensional feature information, In order to maintain the fairness of the experiment, we used the same data processing methods, and the input data were the same. The scores of the other models were derived from AEFS , we compTable In order to better evaluate the performance of the model, we decided to use the recall rate of the top k DTIs candidates . The recall rate can reflect whether the model can reasonably predict the performance of DTIs. We still selected the average recall rate of these methods to compare the performance of these methods with our method, as shown in Fig. In the first data set, the average value of recall of our model before  is better than that of the six methods, and in the first 30%, our method is slightly lower than AEFS. In the second dataset, our model outperformed all DTIs methods in the top , reflecting our model's strong performance in identifying drug-target associations.Evaluating the performance of a model is mainly based on accuracy and practicality. We trained the VGAEDTI model using known DTIs datasets to predict the natural association of drug-targets. We will predict the interaction between drug-target scores in the top 15 for recording. In order to verify the accuracy of the prediction score, we verified its authenticity by querying the source data set of Uniprot and DrugBank databases; the database contains a large number of drugs and targets of the associated information, so that supported by data authenticity.In the Table To further validate this novel interaction, we performed computational docking and utilized the docking program AutoDock to infer the possible binding modes of the new predicted DTI. Docking results showed that Gentamicin can dock the structure of 2M0P. More specifically, Ibrutinib binds to 2MOP by forming hydrogen bonds with residues LEU-23, PBU-22, and ASN-305.We use pymol for molecular docking and hydrogen bond coloring, as shown in Fig. VGAEDTI model combines drugs space and target space information, so two spatial information are integrated to co-training improve the ability of its important feature information extraction. The pattern of co-training on performance evaluation of the VGAEDTI model has an important influence. Therefore, this paper set up a set of ablation experiments on its effectiveness.The AUROC and AUPR of the VGAEDTI model with and without co-training under two different datasets are shown in Fig. In VGAEDTI, based on the embedding features of drug and target, we use random forest to calculate drug-target association scores. In order to confirm that random forest can obtain better score prediction, we performed the following ablation experiments as Fig. By integrating the feature information of drug space and target space trained alternately by two autoencoders using a variational EM algorithm, the VGAEDTI model can get more accurate feature information so as to better predict its association. To select the suitable weight parameters of the two spaces to maintain the balance between them and ensure the contribution of different spatial feature information outputs to the prediction performance of the model, we use different datasets for testing.It can be seen from the above Fig. How to accurately identify DTIs is one of the most important steps in drug repurposing and new drug development. In this study, we propose a novel model VGAEDTI to predict DTIs. Firstly, the VGAEDTI model calculates the similarity of multi-source drug information, target information and disease information, and then constructs a heterogeneous network through the known association information and feature information among them, so as to better extract more potentially complex relationships among drugs, targets and diseases. Then it is input to the VGAE and GAE for feature information extraction. The VGAE deduces the feature representation from the drug and target space respectively, while the GAE propagates the label between the known drug and target associations, and uses the variational EM algorithm for alternating training until convergence. Also, the co-training starategy is used to capture the feature information of drug space and target space, which enhances the ability of VGAEDTI to capture efficient low-dimensional representations from high-dimensional features, thereby improving the robustness and accuracy of predicting the unknown DTIs. In this way, the obtained drug and target feature information is more accurate and comprehensive. In order to obtain better score prediction and avoid the negative effects of feature dimension and importance of feature information on predicting drugs and targets, we use random forest classifier, which can judge the importance of features and the interaction between different features. For imbalanced data sets, it can balance the error. If a large part of the features are lost, the accuracy can still be maintained, and the model has strong robustness and generalization ability. In order to evaluate the performance of the proposed VGAEDTI model for predicting DTIs. We use fivefold cross validation to compare the performance of six methods on two different datasets, all of which achieved better results in some aspects, and also proved that our model has strong generalization ability. In general, our model VGAEDTI can be used as an effective and accurate tool for predicting DTIs.Although VGAEDTI model has good performance at present, there are also some potential drawbacks in extracting information from heterogeneous networks, recently inspired by Zhao et al. , existin


Background: The reconstruction of midface skin defects represents a challenge for the head and neck surgeon due to the midface’s significant role in defining important facial traits. Due to the high complexity of the midface region, there is no possibility to use one definitive flap for all purposes. For moderate defects, the most common reconstructive techniques are represented by regional flaps. These flaps can be defined as donor tissue with a pedunculated axial blood supply not necessarily adjacent to the defect. The aim of this study is to highlight the more common surgical techniques adopted for midface reconstruction, providing a focus on each technique with its description and indications. Methods: A literature review was conducted using PubMed, an international database. The target of the research was to collect at least 10 different surgical techniques. Results: Twelve different techniques were selected and cataloged. The flaps included were the bilobed flap, rhomboid flap, facial-artery-based flaps , cervicofacial flap, paramedian forehead flap, frontal hairline island flap, keystone flap, Karapandzic flap, Abbè flap, and Mustardè flap. Conclusions: The study of the facial subunits, the location and size of the defect, the choice of the appropriate flap, and respect for the vascular pedicles are the key elements for optimal outcomes. The reconstruction of midface skin defects represents a challenge for the head and neck surgeon due to its renowned role in defining important facial traits . MidfaciInitial background research was conducted to outline the conceptual foundations of the study: the reconstruction of midface soft tissue. A literature review was conducted using PubMed, an international database. The keywords used were: “head and neck surgery”, “head and neck reconstruction”, “head and neck skin defects”, and “midface skin defects”. The database used was screened using the keywords in combination with “surgical flaps”, and “locoregional flaps” included in the research string.The review of the literature was conducted in November 2022. The researchers decided to not include a chronological limit to the research. The target of the research was to collect at least 10 different surgical techniques with diverse complexity indexes. Adopted inclusion criteria were (a) articles written in English; (b) technique studies, retrospective studies, and prospective studies; (c) studies describing the surgical reconstruction of a midface skin defect; and (d) the adoption of pedunculated flaps harvested with only soft tissue. Exclusion criteria were (a) articles not describing the surgical technique adopted; (b) articles involving more complex or multilayered defects; and (c) the adoption of free flaps. The studies matching the inclusion criteria were recognized and were read in full to perform the data extraction.After individuating the different techniques, data were extracted accounting for complete technique explanations, descriptions of application areas, and descriptions of the defect’s diameter. When the same technique was reported in more than one study, the most precise was preferred and selected. All data were stored in a digital database including the technique name, the type of the adopted flap, the main midface region treated, suggested secondary regions, and skin defect size . The classification in sizes followed a centimetric division found in the literature: 1.5–2 cm for “small”, 2–3 cm for “medium”, and >3 cm for “large”. Additional or missing information was compared among similar studies and collected in the digital database.Among the studies obtained, 12 different techniques were selected and cataloged. The main characteristics of each technique are reported in The type of flap was divided into transpositional , rotational , and advancement flaps (2/12 16%). Just one flap was considered both rotational and advancement. The cheek and nose areas were the most described regions , followed by the upper lip area . Defect size indications were mostly “medium” .Bilobed flap: the bilobed flap (BF) is a single pedicle random pattern cutaneous transposition flap composed of two lobes. Its applications are various, including the hand, scalp, foot, and eyelids, but its main adoption is in nasal reconstruction for its excellent color and texture match with adjacent skin. The BF is used for small nasal defects (1.5 cm). Nasal ala defects are approached with a medially based flap, while tip defects are approached with a laterally based one. The surgical technique requires precise measurements: the first measure is the diameter of the defect (D), then the pivot point is placed at least 0.5 D from the defect; from the pivot point, the outer flap edge is obtained by rotating a suture around the pivot point; an inner arch is marked 0.5 D inside the outer arch; and the first lobe is demarcated with a diameter equal to D, while the second lobe is marked with a width 0.5 higher than D (r than D . The disThe highlights of the techniques included are described in the following list:2.Rhomboid flap: the rhomboid flap (RF), also known as the Limberg flap, is a transposing rhomboid-shaped flap, used typically for medium-sized defects of the cheek (2–2.5 cm). It can be designed in every direction. The main advantage is that, being adjacent to the defect, it matches the skin color and texture. Its plan is geometrical: the defect is surrounded by a rhomboid with 60° and 120° angles; then, the flap is designed on the short axis (120°), as shown in 3.nasolabial flap (NLF) is the most adopted advancement flap in the reconstruction of the nasal side wall and ala defects and also cheek and upper lip regions. The NLF is used to cover medium defects close to the flap. It is based on the branches of the facial artery, most commonly on the angular artery. It is triangular-shaped, with the base of the triangle direct to the defect, while the apex is directed toward the pedicle. Once the flap is mobilized, the surgical wound is closed in a V-Y fashion [Facial-artery-based flaps: The  fashion ,15  is a transpositional flap used in the reconstruction of the lower half of the nose, the lower eyelid, and the medial cheek. It is based on the angular artery, detected preoperatively by a Doppler. The flap is harvested from the nasolabial fold, with the first incision made in the distal part, to identify the angular pedicle. After isolating the vascular pedicle, the flap is islanded distal to medial with a subfascial dissection. After subcutaneous tunneling, the flap is inserted into the gap [The  the gap   is adopted to increase the mobility of the distance covered by the classic nasolabial flap and is harvested using the procerus muscle near the defect as a pedicle. It is commonly used in the upper nasal region, the lower eyelid, and the medial canthal area. The flap is elevated under the muscle on the non-pedicle side and then advanced to the defect area. The donor site is closed with a V-Y technique. It can also be tunneled to reach more distant regions [The  regions ,16  defects of the cheek and is extendible to the periorbital and neck regions. It represents a combination of an advancement and rotational flap that can be moved anteriorly or posteriorly and anteriorly. The incision is a variant of the rhytidectomy approach but continues through the zygomatic arch, the lateral cantus, and the lower eyelid line until the nasolabial groove is reached in its most inferior part. The incision can be prolonged posteriorly, as in a parotidectomy with a horizontal limb. The flap is elevated superficially to the superficial musculoaponeurotic system (SMAS) and the masseteric fascia to avoid facial nerve branch injuries. After rotation and advancement, the donor site is closed by direct suture. Due to the great dimension of the flap, anchoring the flap to the orbital–zygomatic periosteum is necessary [ecessary ,17  is an interpolated rotational flap harvested from the forehead and used for reconstructing large and complex defects of the nose. It is harvested based on the blood supply of the supratrochlear artery. The PMFF requires an accurate template of the defect to fill due to the complex three-dimensionality of the nasal region  is a transpositional tunneled island flap harvested from the forehead and nourished by the supraorbital and supratrochlear arteries. It is used for medial canthal, upper nasal dorsum, and nasolabial small–medium defects. The FHiH is harvested in an elliptical shape at the level of the frontal hairline. Once the upper incision is made, the pedicle is dissected subperiosteally toward the orbital rim. The flap is transposed subcutaneously in a tunneled passage to the gap and sutured to the adjacent skin. The donor site is closed by direct suture. This flap represents a tunneled version of the paramedian flap but with indications for smaller skin defects [ defects   is a random-pattern multi-perforator advancement flap applicable to all defect dimensions, as it can be performed in all facial subunits. After tumor excision, the flap is islanded to the subcutaneous tissue with the shape of a Roman arch or vault keystone. The ratio between the width of the defect and the flap should be 1:1. The flap should not be undermined to not injure the multiple perforator vessels. It is important to preserve the superficial venous system and to keep intact the deep fascia. It is divided into four categories: type I is the classic keystone flap, type II includes skin grafting, type III is a double classic keystone, and type IV is based on undermining the skin around the keystone flap. The flap is then inserted into the gap and the closure starts with the edge closer to the defect. The longitudinal angles are closed in a V–Y fashion [ fashion ,20  is a rotation and advancement flap used in the upper lip and commissure reconstructions of full-thickness defects. It can fill medium and large defects. It is based on the facial artery branches. The KF is often harvested bilaterally with two “L-shaped” opposing flaps, eventually of different lengths. Once the upper lip skin defect has been created, a bull-horn skin excision is made in both alar sulci to increase the advancement of the flaps. The flap dissection is performed gently through the skin and subcutaneous tissue to preserve the vascular pedicle and the neural bundle. The two advancement flaps are moved until they meet centrally. Sutures are made by direct closure [ closure ,22  is a rotational flap usually harvested for the reconstruction of the upper lip. Its indications are defects involving 50% or more of the philtrum and defects of the medial element of the lateral subunit greater than 1.5 cm. The AF is considered a cross-lip switch flap, where the donor site is represented by a quadrangular full-thickness portion of the lower lip, frequently in its center–lateral part. The flap is based on the labial artery, the pedicle of the region, which can be medial or lateral in the flap harvested. The AF amount is calculated based on the defect to fill and then transposed with markers on the lower lip. Once the defect in the upper lip is created, the flap is rotated to fill the gap, while the lower lip is closed primarily. This technique temporarily reduces lip movement and mouth-opening due to the closure. The flap requires a second stage of division where the pedicle is cut and the flap is inserted. The flap can also be harvested for defects in the lower lip [ower lip ,24 (Figu10.Mustardè flap: the Mustardè flap is a rotational flap adopted for the reconstruction of large cheek defects, the lateral nose wall, and the lower eyelid region. Its harvesting begins from the edge of the excision defect and extends through the lower eyelid to the lateral canthus and ends with an incision superior and lateral to the temple region. The dissection is performed subcutaneously to not injure any facial nerve branches. As the MF is a flap of great dimensions, anchoring it to the zygomatic arch helps to avoid excessive tension on the flap’s insertion. The donor site is closed by direct suture, with the help of Burow’s triangles [riangles ,26 (FiguThe reconstruction of facial defects after skin cancer excision can be challenging, especially in the midface, a region that requires both aesthetic and functional outcomes . The divThe utility of these subunits is double: on one hand, they delimitate areas with circumscribed histology, thickness, and texture; on the other hand, their separation lines represent marks for surgical incisions to keep areas with different characteristics separated . When deThe gradual but significant evolution of reconstructive techniques has led to several solutions in the hand of a maxillofacial surgeon’s repertoire . The mosAs shown in Locoregional flaps, while safe and reliable, are not without complications. The rate of complications is directly related to flap complexity and the dimension of the defect. Smaller and easier locoregional flaps are the most successful ones. The most common complications are the general ones such as edema, perioperative hemorrhage, hematomas, and infections. Due to the nature of pedunculated flaps, total flap loss is very rare. Partial flap necrosis can occur more often in rotational flaps for pedicle suffering: debridement resolves the issue and allows second-intention healing . TechniqThe authors described the most established and standardized techniques for the reconstruction of a complex area. Taking into account the potentially infinite imagination in the design of locoregional flaps, including all of them was conceptually unachievable. For these reasons, the technique selection considered mainstream flaps, not contemplating all possible technical variants and eventually derived techniques. Another limitation of the study concerns the adoption of only one international database as a technique source.The midface is one of the most challenging regions to reconstruct for maxillofacial surgeons because of its dynamic functional and aesthetic components. The midfacial district has a complex structure that requires a detailed understanding of the facial subunits, the location and size of the defect, and the choice of the appropriate flap to achieve optimal outcomes. The reconstruction of the midfacial district with locoregional flaps involves a variety of surgical techniques. The use of these techniques requires a great emphasis on pre-surgical planning. In the conducted study, it was found that locoregional pedunculated flaps, due to their great variety, often represent the best solution for midfacial skin defects.The success of the reconstruction of the midface using locoregional flaps depends on several key elements. One of the most critical elements is pre-surgical planning, which includes a detailed study of the facial subunits to identify the location, size, and extent of the defect. The surgeon must also consider the choice of the appropriate flap, which is crucial in achieving optimal outcomes. This should be carried out while paying attention to the vascular pedicles, which can make a significant difference in the success of the procedure.In summary, the reconstruction of the midface using locoregional flaps is a complex procedure that requires a great emphasis on pre-surgical planning. Choosing the right flap, respecting the vascular pedicles, and understanding the facial subunits are key elements that determine the success of the procedure. Locoregional pedunculated flaps offer a great variety of options, making them often the best solution for midfacial skin defects.


The basic reproduction number,  The values of such parameters can be obtained from individual-level observations  for SEI4R) returns a 95% CI of . As noted earlier, the estimates obtained from this default heuristic or traditional approach are sensitive to the uncertainty in the infectious period distribution. On the contrary, when we fix the mean generation time in the SEIjR  to a value (2.85 days) obtained from the literature [SEI4R, bolstering the fact that the actual infectious period is far from being exponentially distributed.Leveraging the knowledge gained from the synthetic data, the last step in this work consists of exploiting the relationship between the basic reproduction number and the mean generation time to update the  surveys  to deter surveys ,22 emploterature , we deriribution . Notice . 5SEIR framework can negatively impact the estimation of I0 and ρ) redress the misspecification. We leveraged this knowledge to formulate an alternative parametrization that is more robust to the uncertainty of the epidemiological delays. However, there is a caveat with this alternative formulation. Although it exploits a local symmetry (incidence dynamics) of the SEIR framework, such symmetry does not extend to the other states of the system. Therefore, the usefulness of the alternative parametrization is confined to the estimation of I0, we would obtain unreliable estimates. Furthermore, the alternative SEIR with exponentially distributed delays will be as overoptimistic as its traditional counterpart in predicting the critical vaccination proportion or the effectiveness of an imperfect HIV treatment in the context of within-host dynamics [The misspecification of various assumptions within the dynamics . TherefoDespite the significant computational effort of simulation analyses, a single study cannot offer overarching statements. Further work is required to test the validity of these insights in stricter or more elaborated contexts. For instance, we assumed the complete availability of the incidence time-series throughout this study. This assumption restricts the validity of the approach to retrospective analyses. However, other situations exist where only fewer incidence measurements are available to the modellers, such as the early phase of a pandemic response. Hence, it remains to be seen the effect of various levels of data availability on the performance of the suggested approach. Furthermore, for simplicity, we ignored age-related effects in the dynamics of the infectious disease as well as process stochasticity  and time-varying contact rates. We expect that future research builds on the findings provided by this study and addresses the aforementioned challenges to construct ever more reliable inference approaches.


The SABA Reliance Questionnaire (SRQ) is a validated tool for evaluating patients beliefs about SABAs that may lead to overreliance and overuse.Patient beliefs about their asthma and its treatment may contribute to overreliance on short-acting βOur aim was to evaluate the psychometric properties of the Spanish version of the SRQ.This was an observational, cross-sectional, single-country questionnaire validation study in adults with asthma. Reliability  and validity (convergent and discriminant) of SRQ were evaluated. Concurrent validity was assessed with the Beliefs about Medication Questionnaire, the Treatment Satisfaction Questionnaire for Medication, and a visual analog scale item to assess patients’ perceptions of the importance of their reliever inhaler. Discriminant validity was assessed through differences in mean SRQ sum score between patients with high adherence to inhaled corticosteroids and those with low adherence, as measured by the Medication Adherence Report Scale-9 and the Test of Adherence to Inhalers.The Spanish-SRQ exhibited good psychometric properties among 131 patients with asthma. Internal consistency was confirmed with an ordinal α of 0.85. All 5 items were useful for measuring patients’ beliefs about SABAs that may lead them to be overreliant on SABAs. Concurrent validity with the Beliefs about Medication Questionnaire, Treatment Satisfaction Questionnaire for Medication, and a visual analog scale item assessing patients’ perceptions of the importance of their reliever inhaler was demonstrated.The Spanish version of the SRQ is a valid tool for evaluating potential overreliance on SABAs in Spanish-speaking patients to enable early intervention and support. With the 2021 Global Initiative for Asthma, however, there has now been a dramatic shift.Asthma is a chronic inflammatory disease of the airways resulting in bronchial hyperresponsiveness and variable air flow obstruction.High levels of SABA use  is a marker of poor asthma control and an indicator of an increased risk of potentially life-threatening asthma exacerbation.r = 0.216; P < .0001) and negative correlation with the Medication Adherence Report Scale (MARS)-ICS (adherence) scores . These observations indicate that patients with stronger beliefs in the personal necessity of a SABA (those with high SRQ scores) are significantly more likely to self-report low adherence to ICS therapy.SABA overreliance is likely related to patients' beliefs about their asthma and its treatment. Patients’ beliefs have been shown to influence treatment engagement and adherence.Here we report the results of a study undertaken to evaluate the psychometric properties, reliability, validity, and internal consistency of a Spanish version of the SRQ.www.jaci-global.org). Before the psychometric validation study, a linguistic and cultural adaptation of the original English version of Reliever Reliance Test (RRT) questionnaire (which includes the SRQ) was performed following the methodology recommended by the International Society for Pharmacoeconomics and Outcomes Research .This was an observational, cross-sectional, multicenter, single-country study undertaken to determine the psychometric properties and validity of the Spanish version of the SRQ, which was developed to enable evaluation of overreliance on SABA among Spanish-speaking patients with asthma  with a confirmed diagnosis of asthma (regardless of severity stage) who were currently receiving SABA as part of their asthma management and attending a routine asthma-related health care consultation with their primary care physician at one of the participating centers (on-site or remotely). In addition, patients were required to have been prescribed a SABA at least 12 months before inclusion, with at least 12 months' availability of electronic medical records, to be receiving an ICS for maintenance treatment, and to be able to complete the SRQ questionnaire and other patient-reported outcome (PRO) questionnaires using an online survey platform. Patients currently enrolled in a clinical trial, those receiving institutional care, and those with mental illnesses that would prevent them from correctly completing the questionnaires were excluded from participation. All eligible patients provided informed consent before participation.Data were collected at 9 primary care centres in Spain. At the study visit, patients were asked to complete a set of questionnaires, including the SRQ, the Beliefs about Medication Questionnaire (BMQ), the MARS-9, the Test of Adherence to Inhalers (TAI), the Treatment Satisfaction Questionnaire for Medication (TSQM) and the VAS item “How important is your reliever (SABA) medication?” Because of the COVID pandemic, the study visit was conducted either by telephone or videoconference. All PRO questionnaires were completed online by the patient via a secure link sent to each patient and associated with the patient identifier in order to merge the PRO information with an electronic case report form.The acceptability (feasibility) of the SRQ was evaluated by estimating the percentage of complete responses and the ceiling and floor effects. The reliability of the SRQ was evaluated by estimating the internal consistency. Concurrent validity was assessed with the BMQ-Specific subscale (referring to SABAs), the TSQM, and a VAS item to assess patients’ perceptions of the importance of their reliever inhaler. Discriminant validity was assessed by comparing SRQ scores depending on patient’s adherence to ICS, as measured by MARS-9 questionnaire and TAI (12 items). The conceptual hypotheses informing these assessments is that patients overrelying on SABA (those with high SRQ scores) will also have high scores on the BMQ-Necessity subscale, the TSQM, and the VAS item, because they have strong beliefs regarding the necessity of SABA medication  and are satisfied with it (as measured by the TSQM). Also, it was hypothesized that patients overrelying on their SABA would be more likely to have poor adherence to maintenance ICS therapy, resulting in a negative association between patients scoring high on the SRQ and adherence to ICS therapy, as measured by MARS-9 and TAI. All analyses were based on the full analysis set .All variables were analyzed descriptively with appropriate statistical methods: categoric variables were analyzed by frequency tables (absolute and relative frequencies) and continuous variables were analyzed by sample statistics .a priori to evaluate the convergent and discriminant validity of the Spanish version of SRQ. These included testing of the null hypothesis for concurrent validity and the null hypothesis for discriminant validity. The null hypothesis for concurrent validity was defined as a Pearson correlation between the SRQ total score and the BMQ-Specific VAS item to assess patients’ perceptions of the importance of their reliever inhaler and a TSQM score of 0 . The null hypothesis for discriminant validity was defined as the difference between means  for the SRQ total score in patients with good adherence to ICSs (higher scores on the MARS-9 and/or TAI) versus patients with poor adherence (low scores on the MARS-9 and/or TAI) of 0 . The cutoff scores for low and high adherence were determined by the sample responses to the MARS-9 by calculating the maximum potential score on the MARS-9,Several hypothesis tests were set t test to compare the overall mean score of SRQ in patients with good adherence to ICS therapy (as measured by the score in the MARS-9 adherence questionnaire in percentile 66 [the highest third]) versus in patients showing no good adherence (percentile 33 of the score in MARS-9 [the lowest third]). The Student t test was also used to compare the overall mean score of SRQ in patients with good adherence to ICS (as measured by a score of 50 on the TAI) versus in patients showing no good adherence (a score of ≤45 on the TAI). To further understand the relationship between SRQ scores and adherence, an exploratory ANOVA or Kruskal-Wallis test  was conducted. In this way, the mean SRQ scores among the different types of patient nonadherence , as defined by the TAI, were compared. To check whether using different definitions of high and low adherence would have an impact on the analysis, a sensitivity analysis was conducted by using different MARS-9-ICS cutoff points; high and low adherence groups were defined as those scoring in the top and bottom 30% of the sample (as opposed to in the top and bottom thirds).The feasibility of the questionnaire was assessed by calculating the percentage of patients not answering all items of the SRQ. Floor and ceiling effects were assessed by calculating the percentage of patients answering the lowest-option response in all the items of the SRQ and the highest-option response, respectively and their 95% CI. Internal consistency was assessed using ordinal α for the whole SRQ questionnaire. Also, ordinal α excluding every item was calculated to evaluate the consistency of the item with the scale. Concurrent validity was evaluated by calculating the Pearson or Spearman correlation (depending on the variable distribution) of the overall score of SRQ with the validated Spanish versions of the BMQ-Specific, the VAS item to assess patients’ perceptions of the importance of their reliever inhaler, and the TSQM. Discriminant validity was evaluated by using the Student No definite guidelines exist regarding required sample size for psychometric validation. However, the sample size for the psychometric validation of a questionnaire should be large enough to meet a desired level of measurement precision or SE. Sample sizes of around 200 participants for psychometric analyses have been proposed, although the sample size requirements for reliability and validity assessment depend on the specific circumstances and analytical tools. For the current study, because the SRQ scale consists of only 5 items, a factor analysis was not considered necessary and a smaller sample size would be acceptable. Most of the psychometric indicators for reliability and validity in our study were based on correlation coefficients, and a sample size of 124 patients was anticipated to provide 80% power  to detect a correlation between 2 variables of 0.25 as being statistically significant. A sample size of 124 patients was also anticipated to be sufficient for the exploratory objective to assess the mean difference between the SRQ total score of patients with good adherence to ICS and nonadherent patients, as this sample would be sufficient to detect a difference of 0.50 SD (effect size) between 2 independent groups of subjects of equal size with an α risk of 0.05 and a statistical power of 0.80.A total of 131 subjects with a confirmed diagnosis of asthma (regardless of the severity stage), with SABA prescriptions, and seeking asthma care at any of the participant sites were recruited in the study and included in the analyses . The amoThe characteristics of the participating patients are described in In general, patients did not present with asthma symptoms within the week before the study visit. Among those experiencing asthma symptoms, the intensity was generally low or moderate . SABA uswww.jaci-global.org). Floor and ceiling effects are shown in All patients answered all questions on the SRQ, indicating a good level of understanding and acceptable feasibility. The distribution of SRQ scores is shown in Analysis of the internal reliability of the SRQ is shown in , the VAS item , and the TSQM  and global satisfaction  dimensions  versus among patients showing no good adherence (percentile 33 of the score in MARS-9 [the lowest third] or a score of 45 or lower on the TAI ). All ofThe psychometric evaluation reported here showed that the Spanish version of the SRQ exhibits good psychometric properties in terms of acceptability, reliability, and concurrent validity with the BMQ-Specific subscale (referring to SABAs), the TSQM effectiveness and global satisfaction dimensions, and a VAS item assessing patients’ perceptions of the importance of their reliever inhaler. These results support the use of the Spanish version of the SRQ for evaluation of Spanish-speaking patients for their potential overreliance on SABA as part of their asthma self-management. Identification of patients at risk for SABA overreliance can enable early interventions, including education and support to encourage patients to adhere to preventer medication regimens for symptom control consistent with current treatment guidelines.The results of the psychometric validation of the SRQ presented in this study indicate that Spanish-speaking patients can now also benefit from the RRT and access appropriate support and interventions to reduce SABA overreliance and improve asthma control through adherence to preventer medications.Discriminant validity was not demonstrated in the current study with regard to patient’s adherence to ICS therapy as measured by the MARS-9 questionnaire and the TAI. However, this concept in particular depends not only on the quality and discriminatory properties of the SRQ itself but also on the psychometric attributes of the questionnaires/measures with which it is compared. As the study cohort consisted of a sample of patients attending for routine consultations, it is possible that the relative stability of this group from the standpoint to their asthma symptoms limited the variability in relation to the variables of interest for the determination of discriminant validity. Additionally, a recent study found that 51.16% of patients with good adherence according to the TAI did not collect at least 80% of their prescribed medication, indicating that the TAI may be overestimating adherence to asthma medication.This study has limitations. The findings are most relevant to Spanish-speaking adults with asthma and cannot be generalized to those speaking other languages. Also, the study population included mainly patients with mild and moderate asthma, with 52.7% of participants not having used their SABA in the past week.In conclusion, our analyses support the acceptability, reliability, and concurrent validity of the Spanish version of the SRQ with the other validated measures assessing patient perceptions and beliefs about their medication. These results support the use of the Spanish version of the SRQ for the evaluation of Spanish-speaking patients from the standpoint of their potential overreliance on SABA as part of their asthma self-management, enabling early intervention and support.


Deep-learning-assisted medical diagnosis has brought revolutionary innovations to medicine. Breast cancer is a great threat to women’s health, and deep-learning-assisted diagnosis of breast cancer pathology images can save manpower and improve diagnostic accuracy. However, researchers have found that deep learning systems based on natural images are vulnerable to attacks that can lead to errors in recognition and classification, raising security concerns about deep systems based on medical images. We used the adversarial attack algorithm FGSM to reveal that breast cancer deep learning systems are vulnerable to attacks and thus misclassify breast cancer pathology images. To address this problem, we built a deep learning system for breast cancer pathology image recognition with better defense performance. Accurate diagnosis of medical images is related to the health status of patients. Therefore, it is very important and meaningful to improve the security and reliability of medical deep learning systems before they are actually deployed. With the rapid development of information technology, artificial intelligence technology has achieved great success ,2,3. DeeBreast cancer is one of the most common malignant tumors in women, its incidence is the highest among female malignant tumors, and it is developing earlier and becoming more prevalent ,14,15. CThe rapid development of deep learning has revolutionized many fields, among which its use to assist in medical diagnosis has been groundbreaking ,23,24. DWe used transfer learning to build a deep learning system that can accurately identify benign and malignant breast tumor pathology images, and the model achieved an average recognition accuracy of 98.72%.We used an adversarial attack algorithm to attack the trained model so that the deep learning system misclassified the breast cancer images, which reduced the model’s recognition accuracy for breast cancer images from 98.90% to 10.99%. It was demonstrated that the above breast cancer deep learning system has security vulnerabilities and can be affected by adversarial attack.To address the security vulnerabilities in the deep learning system for breast cancer pathology images, we built a defense deep learning system for breast cancer pathology images with better defense performance. The defense model could defend against the adversarial attack algorithm, and the recognition accuracy for breast cancer images decreases from 96.70% to 27.47% in the face of the same adversarial attack algorithm.Deep learning has greatly improved the accuracy of breast cancer pathology image recognition and classification, and it has also provided effective diagnostic aids in the early detection and graded treatment of breast cancer . HoweverIn this section, we introduce adversarial attacks and defenses against adversarial attacks in deep learning systems. Deep neural networks have shown powerful capabilities for image recognition and classification ,41,42. Hx is the input data, f is the deep learning model, and the classification result of the model is f(x). If there is a perturbation ϵ satisfying the following equation, we can refer to x as the adversarial sample of the model f.Suppose Since Szegedy discovered this phenomenon, it has attracted many researchers to study adversarial attacks and to pay more attention to the security and reliability of deep learning systems. Goodfellow et al. proposed the fast gradient sign method (FGSM) to generate adversarial samples by performing only one gradient step, and this method reduces the computational cost of generating adversarial samples . MomentuAccording to different attack algorithms, adversarial attacks can be gradient-based, optimization-based, or adversarial-network-based . A gradiAccording to different attack environments, adversarial attacks can be white-box attacks, black-box attacks, or gray-box attacks . White-bAccording to the purpose of the adversarial attack, it can be divided into targeted and untargeted attacks . The purFaced with the threat of adversarial samples, researchers have proposed some methods of adversarial sample defense to protect deep learning models. Most white-box attacks obtain an adversarial sample by computing the gradient of the model, so if the gradient of the model cannot be computed, the attack will be ineffective. Gradient masking changes the model to some extent, thus making the gradient useless and resisting the adversarial sample well. Florian Tramèr et al. proposed powerful single-step attack strategies and integrated adversarial training by migrating perturbation inputs from other pre-trained models, thus decoupling the two processes of adversarial sample generation and model parameter training and increasing the diversity of perturbations in the training process . Dongyu The method proposed in this paper consists of three parts. Firstly, benign pathological images of breast cancer and malignant pathological images are used as input images, and the two types of images are trained based on the transfer learning method with Desnet-121 as the skeleton to obtain a deep learning model that can accurately identify the two types of images. The trained model is attacked with adversarial attacks to generate adversarial images, which makes the model misclassify the adversarial images, thus also leading to a decrease in the accuracy of the model for image recognition. To overcome this problem, we propose a defensive approach against adversarial attacks, building up a more secure, reliable, and robust defense deep learning system. The methods are shown in We constructed a deep learning system for breast cancer using the transfer learning approach. The model construction consists of two parts: model training and performance testing . In thisThe breast cancer pathology image data in this paper were obtained from the breast cancer pathological database (BreakHis) . This daTransfer learning is a common approach in deep learning, whereby trained models are used to accomplish new tasks by exploiting the similarity between models and targets. By using transfer learning, we can take an existing trained model, migrate it to our task, and then fine-tune the model for our task-specific requirements to save training costs and time and quickly achieve the task requirements. Due to the small amount of data from the breast cancer pathology images, we adopted a transfer learning approach using the DenseNet121 model pre-trained on chest X-rays to achieve better results . The Denx is the input sample, y is the label corresponding to sample x, advx is the adversarial sample, θ is the weight parameter of the model, the manually set perturbation parameter of the model is ϵ, and the loss function of the model is J.The fast gradient sign method (FGSM) is a gradient-based method for generating adversarial samples that maximize the loss function in the opposite direction of the decreasing gradient during the data propagation and updating of a neural network. The expression of FGSM is shown below, where One of the security risks of medical image deep learning systems is that the original breast cancer images are modified into benign tumor images by maliciously tampering with medical images, thus making the models misclassify them. This leads to the misdiagnosis of the patient’s condition, thus making the patient miss the best time for treatment. In order to test the security of the breast cancer deep learning system, we conducted an adversarial attack on the trained model using breast cancer images as the research object and added subtle interference to the test set, which caused the deep learning model to misclassify the images. We use the FGSM algorithm to attack the trained model and generate adversarial samples that are difficult to distinguish with the eye.We used an adversarial attack to attack the deep learning model by slightly altering the original image’s pixel to generate an adversarial image, thus fooling the model and making it misclassify the image. If we added noise to the original image before training the model, the new image was trained so that the model could obtain more feature information from the noisy image .We used the noisy images as input data to train and build a defense deep learning system with the same model and parameters as the original model and test the performance of the defense model with the original test set without added noise. Gaussian noise is noise whose probability density function obeys a Gaussian distribution. In the construction of the adversarial defense model, we chose to add Gaussian noise to the original image, and the comparison between the Gaussian noise image and the original image was as follows . We normIn this study, the performance of the breast cancer deep learning system was evaluated using the metric of accuracy. The accuracy metric was used to measure the overall correctness of the model’s classifications. True positives (TPs) represented the number of breast cancer images that were correctly identified as breast cancer images. False positives (FPs) indicated the number of benign tumor images that were incorrectly classified as breast cancer images. True negatives (TNs) represented the number of benign tumor images that were accurately identified as benign tumor images. Finally, false negatives (FNs) indicated the number of breast cancer images that were mistakenly classified as benign tumor images.The experiments were coded in Python 3.8 with Pytorch on a personal computer with an NVIDIA GeForce 3070 graphics processing unit (GPU) with 8 Gb of random access memory (RAM) .This section shows the experimental results.The performance test results of the deep learning model are shown in To reveal the threat of adversarial attacks on breast cancer deep learning systems and better simulate the security risk of real-world deep systems based on breast cancer images, we used breast cancer images as the research object and attacked the pre-trained model with the FGSM adversarial attack algorithm and then tested the defense capabilities of the original model and the defense model against the adversarial attacks, and the results are shown in To better illustrate the adversarial attack, we compared the original image with the adversarial image, as shown in We tested and studied a deep learning system for pathological images of breast cancer. In contrast with previous studies on breast cancer deep learning, our research focused on the security and reliability of deep learning systems based on breast cancer pathology images, and we demonstrated through adversarial attacks that deep learning systems based on breast cancer pathology images have security vulnerabilities and can be easily attacked.We trained a deep learning model that can accurately identify benign and malignant breast tumors using transfer learning with an average accuracy of 98.72%, but the model is susceptible to attacks by adversarial attack algorithms. To better reveal the security risks that exist in real-world breast cancer deep learning systems, we demonstrated that the model’s recognition accuracy of images dropped from 98.90% to 10.99% after being attacked using breast tumor images as the study object.To better defend against this adversarial attack, we superimposed Gaussian noise onto the input images at the beginning of the training phase of the model and then retrained the model and tested the performance of the defense model with the same test set as the original model, and the model achieved a recognition accuracy of 98.08% for the original test set, which indicates that superimposing noise on the input data does not affect the recognition accuracy of the whole model. However, when we attacked the defense model with the same adversarial attack algorithm, we found that the accuracy of the model only decreased from 96.70% to 27.47%, which is a 16.48% improvement compared with the original model, indicating that the model has a defensive performance against the adversarial attack algorithm and can effectively defend against an adversarial attack. While this may not appear as a substantial advancement, it indisputably demonstrates the defensive model’s superior security and robustness in the face of adversarial attack challenges.In this work, we studied adversarial attack and defense in breast cancer deep learning systems. In contrast with previous studies, we demonstrated the excellence of using deep learning in medical-image-assisted diagnosis while revealing its limitations. More importantly, breast cancer deep learning systems are vulnerable to adversarial attacks that misclassify medical images. To address the threat of adversarial attack on the security of breast cancer deep learning systems, we propose a method that can defend against adversarial attack, thus effectively reducing the success rate of adversarial attack and improving the security and reliability of the deep learning system.Undoubtedly, there remains a need for further research and development to effectively apply the breast cancer deep learning model in real world scenarios. Adapting the model to real world environments will require addressing various challenges and potential threats. Continual refinement and exploration of novel defensive mechanisms will be necessary to fortify the deep learning model’s defenses against increasingly sophisticated adversarial attacks. In future work, we will continue to study the security and reliability of the medical deep learning system. We also hope that more researchers will pay attention to the security of medical deep learning systems.