Rugulopteryx okamurae of North Pacific origin. According to the scarce literature, the algae first settled in the south shore, probably following commercial exchanges with French ports where it was accidentally introduced together with Japanese oysters imported for mariculture. There is no certainty, however, that the algae first colonized the south shore of the Strait and, from there, spread to the north. It could well have been the opposite. Whatever the case, it spread all over the Strait and surrounding areas with amazing rapidity. Human-mediated vectors  can be behind the spread from the shore initially settled to the algae-free shore on the opposite side. But it could also have happened by means of hydrodynamic processes without direct human intervention. This possibility is assessed in this paper by revisiting historical current meter profiles collected in the Strait of Gibraltar searching for secondary cross-strait flows. All the stations present an intermediate layer of northward cross-strait velocity near the interface of the mean baroclinic exchange along with a surface layer above of southward velocity, whose lower part also overlaps the interface zone. The first one would back the south-to-north transport of algal fragments, the second one, the north-to south. In both cases, algae must reach the depth of the interface. The vertical velocity field in the area, which far exceeds the small sedimentation velocity of the algae, allows their vertical displacements throughout the water column. Its endurance to survive under the weak or no light conditions that will prevail during the cross-strait transport and its capability of reactivating the metabolism after this unfavorable period, offers chances for colonizing the opposite shore. Therefore, the propagation of the algae by hydrodynamic processes, without human intervention, cannot be ruled out.Presently, the Strait of Gibraltar is undergoing an unprecedented invasion of the alien alga  Rugulopteryx okamurae [Since the mid-2010s, the coasts around the Strait of Gibraltar  and neighboring basins  are suffokamurae  where, aokamurae –7 where okamurae . On the okamurae . Currentokamurae . More wookamurae  that it The explosive spreading would be linked to favorable environmental conditions, since the photic zone in the area gathers suitable year-round temperature for growth and reproduction of this subtropical species , 13, butThe plausible hypothesis that algae first settled in a shore of the SoG, likely the south one according to the scarce available literature, poses the question of how did it spread to the other shore. Different mechanisms could be behind the spreading. The first and more plausible one is linked to the heavy ship traffic in the area, which includes continuous north-to-south ferry and other commercial ships crossing the Strait. The alga shows huge capability to colonize any type of hard substrates, not only rocky seafloor but glass, ceramic, iron, tires, etc. , so the The SoG holds a well-known two-way (baroclinic) exchange necessary to compensate for the freshwater and buoyancy losses in the Mediterranean basin. It results in intense, zonal-oriented currents: an eastward Atlantic inflow into the Mediterranean Sea at the surface, and a subsurface Mediterranean outflow towards the Atlantic Ocean beneath  point-wise current meter data collected at points N, C, and S in the eastern SoG see  in the shttps://portus.puertos.es) for the period August 2020 –July 2021 at a 1-minute sampling rate in the buoy of Palma de Mallorca (see https://climate.copernicus.eu) with 1-hour temporal and ∼25 km spatial resolutions.Atmospheric pressure is provided by Puertos del Estado, Spain, . The The surface of instantaneous null velocity, which is the intuitive interface separating inflow and outflow, exhibits large tidally-driven oscillations. Even more, it often disappears because the entire water column moves in one direction during parts of the tidal cycle , 20. OnlA mean local interface has been defined as the time-averaged depth of the surface of null velocity obtained from the vertical profiles of velocity at each station. The horizontal black lines in Figs The successful algae spreading between both shores depends on the cross-strait current, as the floating spores or seaweed fragments eventually torn from the substrate behave as passive nearly neutrally-buoyant particles (see next Section). However, the success is mediated by the along-strait current, which can displace those particles beyond the open boundaries of the SoG before they are transported from one shore to the other. A favorable coupling between both velocity components is thus necessary.-1 (4 km·day-1) or less in the intermediate and deep layers, thus requiring about 4 days at least to cross the SoG at its narrowest section (∼14 km) and longer through other sections. Along-strait mean velocity in the Mediterranean layer changes with depth and geographical location. In its core, the velocity increases from few tens of cm·s-1 in the eastern part to nearly 1 m·s-1 in the west  is considered. The intermediate layer of positive cross-strait velocity displayed in Velocity fluctuations in The along-strait velocity is negatively correlated with the atmospheric pressure over the western Mediterranean above the interface and positively below it . High prFocusing on the upper and intermediate layers that extend over the depth range occupied by the interface Figs  and 5, FOne of the clearer features in The joint consideration of the outcome of the correlation analysis allows to stress favorable opportunity windows and unfavorable scenarios for cross-strait connections. Regarding meteorological forcing, northward flow within the intermediate layer would increase under high pressure over the western Mediterranean basin and local winds from the southwest. If, moreover, these particular conditions are met during neap tides, the resulting scenario would gather the best conditions for south-to-north connectivity. The opposite would happen under low pressure and local northeasterly in spring tides, which would diminish the chances. As for the upper layer, the southwards flow that favors north-to south connection would increase under low atmospheric pressure and local northwesterly along with neap tides. Chances would decrease with low pressure and southeasterly in spring tides.Let us consider one of the shores of the SoG with algae settlements while the other is still free of them. What conditions must be met to achieve a successful colonization of the algae-free shore without human intervention? First of all is the existence of the cross-strait system of flows described in the previous sections. The current to take advantage of depends on which shore holds the initial settlement: should it be the north one, the right current is that at the surface layer, but if the initially colonized is the south shore, the proper current lays in the intermediate layer. In addition to this secondary circulation, the successful colonization requires other set of circumstances that include physiological traits and endurance of algae.The weak cross-strait flow is able to transport products from a shore to the other only if they are available. For south-to-north transport, for instance, algal fragments must reach the interface, situated 100–150 m below the sea surface in the south, be carried to the north at these depths and, once there, be moved upwards into the illuminated layer to settle and thrive. The journey will take days to weeks and be done in dark conditions. The colonization success will thus depend on whether or not the algae arrive in good conditions for reproduction after that period of weak light or no-light conditions.Rugulopteryx okamurae is very slightly denser than seawater. Simple experiments performed in calm sea conditions in which thalli were dropped near the surface and allowed to fall freely, yielded sedimentation velocities in the range of 1 to 4 cm·s-1, depending on the size and shape of the thalli and on whether they form larger aggregates. Similar experiments carried out under more controlled conditions in the laboratory gave a little broader sedimentation velocity range (from 1 to 6.5 cm·s-1) and confirmed its dependence on the shape and size of the specimens. Variations of seawater density within realistic intervals will hardly change that velocity. The small density difference makes the algae be easily re-suspended by wind-induced or current-induced turbulence, carried to the surface and eventually beached on the shores, often in astonishing amounts [Macroalgal strains are attached to rocky illuminated bottom and other type of substrates in the subtidal nearshore zone of the SoG to a maximum observed depth of 40 m . There a amounts , 40 see -1 [Flow-topography interaction in the SoG forms internal hydraulic jumps with high levels of turbulence nearby Camarinal sill . They de-1 , greaterThe combination of the very small negative buoyancy of algal fragments and the structure and strength of vertical velocities in the SoG makes it feasible for the algae to reach the suitable depth to be advected across the SoG. They would do it in one direction or the other, depending on the depth range in which they stay cf., . Once onRugulopteryx okamurae specimens collected in Tarifa , a heterokont alga responsible for the harmful brown tides, was able to survive for at least 30 days in the dark according to [Rugulopteryx okamurae. Further studies are required on this. However, the point of interest here is that there are real possibilities of survival during the time interval needed for transportation from one shore to the other.This outcome is not new in the algae realm. Microscopic stages of macroalgae can withstand long periods of darkness and subsequently develop when conditions improve –47. Aurerding to . Otherwirding to –51. The Rugulopteryx okamurae in both shores of the SoG few years ago seems highly improbable. A much more likely situation is that the algae settled in one shore, from which it invaded the opposite one. Favorable environmental conditions propitiated its subsequent explosive spreading. Algae capability to colonize any type of hard substrates [Simultaneous appearance of bstrates  opens thThe possibility has been assessed in this study using current meter observations collected in the area, which show layers of no null velocity in the cross-strait direction. Chances for cross-strait transport reduce to the depth range around the interface of null along-strait velocity, located at around 100–150 m depth. Within this range, layers of weak cross-strait currents heading south and north have been identified above and below the interface, respectively. They could be used by nearly-neutral buoyant seaweed fragments for being transported from one shore to the other. Outside of this depth range, possibilities drop null, as particles will be advected beyond the lateral open boundaries of the SoG by the strong inflow and outflow.The successful connection, however, relies on a chain of circumstantial links. Algae should be ripped out from the seafloor by natural (wind-induced or current-induced turbulence) or human (bottom-trawling nets) agents in the surface illuminated layer at the colonized shore. Their propagules or spores should be displaced offshore in the horizontal and to the depth range of the interface in the vertical. Then they should be transported across the deepest part of the SoG until reaching the opposite continental slope, be raised to the surface and displaced to the shore and, finally, settle and thrive in the new environment. The process will take weeks, much of the time the algae being in extreme low or even no-light conditions. This study argues that each and every of these contingencies, considered one by one, are surmountable. For a single experiment, however, the concatenation of the full sequence of events leading to a successful connection is extremely improbable. Obviously, it is only the massive amount of available specimens for transport that can provide a number of successful connections. Even if this number of connections is reduced, it would be enough to colonize the new ecosystem taking into account the huge adaptability of the algae. The feasibility of this possibility could be tested with the help of very high resolution fully 3D numerical models coupled to high resolution advection schemes. The results of these numerical experiments would either support the possibility of a successful hydrodynamic connection between both shores or, on the contrary, they would provide additional proofs to confirm its high improbability and, therefore, reject it. Such a study is currently in progress.S1 File(PDF)Click here for additional data file.


The healthy function of the vestibular system (VS) is of vital importance for individuals to carry out their daily activities independently and safely. This study carries out Tsallis entropy (TE)-based analysis on insole force sensor data in order to extract features to differentiate between healthy and VS-diseased individuals. Using a specifically developed algorithm, we detrend the acquired data to examine the fluctuation around the trend curve in order to consider the individual’s walking habit and thus increase the accuracy in diagnosis. It is observed that the TE value increases for diseased people as an indicator of the problem of maintaining balance. As one of the main contributions of this study, in contrast to studies in the literature that focus on gait dynamics requiring extensive walking time, we directly process the instantaneous pressure values, enabling a significant reduction in the data acquisition period. The extracted feature set is then inputted into fundamental classification algorithms, with support vector machine (SVM) demonstrating the highest performance, achieving an average accuracy of 95%. This study constitutes a significant step in a larger project aiming to identify the specific VS disease together with its stage. The performance achieved in this study provides a strong motivation to further explore this topic. The vestibular system (VS) is a perceptual system responsible for providing the brain with information regarding spatial orientation, head position, and motion. Additionally, it plays a crucial role in maintaining balance and stability . DespiteVarious methods are employed in the literature to identify the specific VS problem but the most popular clinical method is still computerized dynamic posturography (CDP) . The staThe gait data are especially used to give information about balance disorders related to different diseases. Within this context, gait analysis has emerged as a valuable tool in the diagnosis and monitoring of neurodegenerative diseases, providing objective measures to assess motor impairments associated with these conditions. It has been extensively utilized in the evaluation of diseases such as Parkinson’s disease (PD), Huntington’s disease (HD), amyotrophic lateral sclerosis (ALS), and other related disorders. Numerous studies have demonstrated the effectiveness of gait analysis in identifying disease-specific gait abnormalities and distinguishing between different neurodegenerative conditions. As an example, Nir Giladi et al. proposed a new clinical classification scheme for gait and posture and discussed the use of gait analysis in identifying disease-specific gait abnormalities . BovonsuAs an example of the use of gait data to evaluate balance disorders associated with dysfunction in the VS, A. R. Wagner et al. discussed how gait analysis can be used to assess vestibular-related impairments in older adults, and how these impairments can impact balance control . In 7],,7], IkizIn this study, our aim was to utilize contemporary classification methods to extract pertinent characteristics from gait data for the purpose of diagnosing VS-dysfunction-based balance disorders. To accomplish this objective, we employed an innovative approach that involved TE values as the feature. TE offers a framework for characterizing the statistical properties of complex systems and thus it is capable of defining non-extensive systems. TE has proven to be effective in diverse domains such as physics, information theory, and economics, enabling a more comprehensive analysis and understanding of systems with long-range correlations and heavy-tailed distributions . As an eThis study is an important step within a larger project which we are conducting together with the audiologists at The Medical School Cerrahpaşa-Istanbul. We aim to develop a diagnosing system to identify the specific disease that is the source of the VS dysfunction causing imbalance. We also aim to determine the stage of the problem. The first step in this process is the classification of the individual as healthy or suffering. For this classification, we are searching for primary discriminative features. We collect various features which will then enter a feature reduction/selection process. According to the experience of the audiologists, these primary features are expected to be obtained from relatively short data acquisition periods, in order to not put the patient in stress, and thus increase the accuracy of the whole system. In , we discWe can briefly summarize the contributions we have brought with this study as follows: Most studies have focused on features related to gait analysis, such as stride time, stance time, etc., which require a relatively long walking time. This study aims to shorten the data acquisition period by capturing features from short walks. Pressure data collected from wearable insole sensors are used for feature extraction. This approach allows data to be obtained in daily life, helping the patient avoid the stress of the clinical environment and potentially improving the accuracy of the diagnosis ,19. We dAfter feature extraction, the extracted features were used to train models using classification methods. The main classification categories included decision trees (DT), discriminant analysis, logistic regression, naïve Bayes, support vector machine (SVM), k-nearest neighbors (KNN), kernel approximation, ensemble, and neural networks. Considering the flow of the study, the rest of this article is structured as follows: The Materials and Methods section provides comprehensive details on TE. Subsequently, in the Data Acquisition Process section, a thorough explanation is given regarding the data collection process. In the Data Processing section, the step-by-step procedures for transforming the raw data into distinct features are elaborated upon. The outcomes of the subsequent experiments are presented comparatively within the Results section. Lastly, in the Discussion section, the results are analyzed, inferences are drawn, and future prospects regarding the utilization of the outcomes within the broader project are mentioned.N is the number of microstates, and Entropy is a property that is mostly used as a measure to describe the chaotic level of a dynamic system. The well-known Shannon entropy (SE) based on Boltzmann–Gibbs statistical mechanics and formulated aselations ,21. In  were manufactured. Prior to the commencement of the experiment, the correctly sized insoles were inserted into the subjects’ shoes. For the production of the insoles, a durable and soft plastic material commonly employed in the manufacturing of orthopedic products was utilized.Force-sensitive resistors (FSR) were chosen as pressure sensors, as they are widely used in gait analysis applications and offer several advantages . ConsideSome explanatory information about the characteristics in Data collection was carried out in the clinical setting of the Audiology Department at Cerrahpaşa Medical School, Istanbul University—Istanbul, Türkiye. The process was conducted in compliance with the principles outlined in the Helsinki Declaration. Before starting the process, approval was obtained from the Istanbul University Ethics Committee . In addition, informed consent was obtained from all subjects before participation in the study. For individuals with VS problems, their conditions had already been diagnosed by the audiologists using conventional systems (computerized dynamic posturography-CDP).Data were collected on weekends to minimize the subjects’ stress and avoid interference from other nearby devices. The subjects were asked to walk the 12 m long path twice. The first walk aimed to help them become familiar with the environment and reduce any possible stress, while the data from the second walk were used for analysis in general. In some cases, subjects walked a third time when needed as a result of the audiologists’ observations. The pressure sensor data collected with the Arduino Mega device placed on the subjects were transferred to the laptop wirelessly via an HC-06 Bluetooth unit. Sampling was performed from all sensors simultaneously at a rate of 20 samples per second. In order to convert the force to voltage, a 1 kΩ resistor in series with the FSR served as a voltage divider. As the next step, we calibrated this structure in the lab since the FSR has a highly non-linear characteristic curve. Supplying the structure with 5 V DC voltage presented an average function asInformative data about the participants are listed in The distribution of the subjects whose specific disease was detected by CDP by audiologists is given in To ensure the confidentiality and privacy of all participants, their identities have been anonymized for publication of this article.In order to interpret the results more accurately on the basis of the subject, the obtained data were preprocessed before feature extraction. Thus, the feature extraction process was carried out in six stages.Stage 1—Framing useful dataWe framed the useful part of the whole walk, and data corresponding to the first and last steps were extracted from the overall data. Thus, data on steps with missing dynamic behavior were excluded from the evaluation.Stage 2—Determining the intervals when the foot is actively touching the groundAll the sensor data were normalized to the range 0–1 asOf all the gait data, only those corresponding to the time intervals during which the foot is actively touching the ground provide useful information. These intervals were determined for each foot as follows: The maximum of all sensor data , We had four sensors under each foot, so, eight sensors in total. Using both the TE value of the entire gait cycle for each sensor as well as the stepwise variation in the TEs, we had a total of 16 features that served for machine learning. For the classification process, we used the Matlab R2021b Classification Learner Tool (on MSI GE75 Raider 10875H). A 10-fold cross-validation technique was applied, where approximately 25% of the total data (from 15 subjects) was used for testing and the remainder (from 45 subjects) for training.The process of classification training involved utilizing nine different model categories: decision trees (DT), discriminant analysis, logistic regression, naïve Bayes, support vector machine (SVM), k-nearest neighbors (KNN), kernel approximation, ensemble, and neural networks. Considering the sub-models of these categories that were used, such as ‘Course: 4, Medium: 20, Fine: 100’ for the maximum number of splits in the decision tree category, a total of thirty-two models were involved in the process. Among all the classifiers examined, SVM (Gaussian), KNN , and logistic regression showed the three best performances. Regarding these classifiers, the KNN algorithm determines the class membership of an object/vector by examining its k nearest neighbors . In thisIn this section, a comparative analysis is made based on data collected from both healthy and VS-diseased individuals. The comparison commences from the detrending stage of processing the sensor data, as described in the Data Processing section.To see the effect of the proposed trending algorithm, trend curves were created using 2nd-, 3rd-, and 4th-degree curve-fitting polynomials and the results were compared. The classification accuracies obtained with the different trending methods are listed in As described in Data Processing section, thirty-two classifiers provided by the Classification Learner Tool in Matlab were trained using sixteen features with ten-fold cross-validation. The average accuracies of the major classification algorithms are listed in This study was carried out in conjunction with a project where our ultimate goal is to identify the specific diseases of individuals suffering from VS dysfunction, along with the level of the problem. In the full version of the project, a machine learning process will be conducted using distinctive features as input. For this purpose, features that will be effective in defining the problem are being sought and all of them will be placed in the candidate features basket, that is, they will be selected to take part in the feature reduction stage. According to the experience of the audiologists with whom we conducted the experiments, some important points need to be considered when collecting data from patients in order to achieve a high level of accuracy in diagnosis. These are particularly obtaining the data in a short time and collecting it under stress-free conditions. Having taken these guidelines into account, and thus aiming to capture the features from a short walk, we performed multifractal detrended fluctuation analysis (MFDFA) in our previous study . Our curIn this study, we utilized TE-based methods for feature extraction from gait data collected from insole pressure/force sensors. The reason for considering the TE was its ability to capture the level of the fluctuations in the detrended data, providing insight into the complexity and irregularity of the gait pattern. Unlike other entropies, TE enables a parameterized analysis, offering flexibility in quantifying uncertainty and capturing certain characteristics of the data distribution.Data from eight insole sensors, four under each foot, were first normalized and then detrended to provide information about fluctuation around the trend curve of the individual. With this process, we aimed to consider the gait habit of the person in order not to misinterpret an unusual gait habit as identifying a balance disorder. As one of the effective innovations brought by this study, we developed an algorithm that determines the trend curve at each step. The efficiency of this algorithm can be seen when the results are compared with other curve fitting methods. Using our algorithm, we achieved an average accuracy of 95% in distinguishing VS patients from healthy subjects, while the best rate was 86.7% even with a fourth-order curve-fitting polynomial. A total of sixteen features were involved in the classification process, eight of which were derived from the TEs of the entire gait cycle and the other eight from the step-by-step TE change for each sensor. The TE value for the entire gait cycle and the step-by-step variation in the TE value were observed to be greater in VS patients than in healthy individuals, which we explained by the high data deviation around the trend curve for these individuals. The TE parameter Regarding the data collection time, the subjects had to walk for around 10–15 s. As we described in detail in , this tiThe SVM with Gaussian kernel and logistic regression performed best in the classification process with 95%, followed by KNN (cosine) and neural network (wide) with 93.3%. At this point, we would like to emphasize that we had defined our criterion for categorizing any feature as distinctive and labeling it as a candidate for feature reduction as an individual accuracy level threshold of 90% ; thus, tIn addition to the numerical values presented in the Results section, we provide further statistical data in Currently, we are conducting experiments for the binary classification phase of the larger project so that an individual can be described as ‘suffering’ or ‘healthy’. As we stated in , feature


The mobile medical platform effectively complements offline medical services as it can provide patients with broader and more convenient medical services, effectively solving the shortage of medical resources in the public health system. Although the public interest in healthcare service platforms continues to rise, the market data shows that the adoption and acceptance have not reached a high level. How to increase the utilization rate of the mobile medical platform to relieve medical pressure has become an urgent issue to be discussed. Based on the framework of “trust-intention” this research introduces innovation acceptance and technical risk concerns as the two moderating variables to attempt to build a research model of users' intention to use the mobile medical platform. The analysis illustrated that users' trust in the mobile medical platform would positively affect their use intention. The researchers further explored the moderating role of innovation acceptance and technical risk concerns.Use questionnaire to collect data in China, then use the OLS least square method for the regression test.The results showed that users with high personal innovation acceptance would positively promote the relationship between trust and use intention. In contrast, users who are more concerned about the risks of innovative technologies will weaken the relationship between trust and use intention.The findings theoretically extend the academic research of use intention to the specific context of the mobile medical platform and enrich the research framework of “trust-intention”. The global spread of COVID-19 threatens human health and challenges the current level of medical services. The sheer number of people infected during the same period led to hospitals being the most severely infected areas. Thus, many countries and regions have introduced home isolation policies to alleviate the tremendous pressure of the shortage of medical resources, which has caused difficulties for people to access medical care. The mobile medical platform has received unprecedented attention and frequency of use from the community at this particular time due to its unique advantage of spanning distances. Patients can connect with doctors in different regions through the mobile medical platform and are no longer limited by geography. In addition, online access to medical care can effectively avoid contact infections caused by a large number of patients gathering in hospitals –4. ThereExciting research on users' use intention is mainly based on the framework of “trust-intention”. RegardiScholars in the consumer field have extensively studied the use of intention and expanded the definition of intention . Dodds aThe intermediate mechanisms of the “trust-intention” framework have been studied in two ways, namely, perceived value and perceived risk. The studies of perceived value focus more on the balance between “profit” and “loss” . The perBased on the existing research, this study introduces two contextual variables, namely, personal innovation and technical risk concern to explore the contextual factors of the “trust-intention” framework. Personal innovation refers to an underlying personal characteristic of an individual's intention to accept new changes and ideas . PersonaBased on the “trust-intention” framework, this study aims to elucidate the explanatory mechanism of “trust-intention” from the perspective of perceived value and perceived risk and introduces personal innovation and technical risk concerns as two moderating variables to further explore the framework in specific contexts. Therefore, this study establishes a theoretical framework, proposes corresponding research hypotheses, and empirically tests the hypotheses using a questionnaire survey. An overview of the conceptual framework of the study is shown in Trust is critical in maintaining dependent exchange relationships and significantly impacts establishing and sustaining profitable long-term relationships . Users' First, amplify the perceived value. Perceived value is the users' overall evaluation of the effect of a product or service after weighing its perceived benefits against the costs paid . It combSecond, weaken the perceived risk. Perceived risk is that users will consider purchasing a product as rational economic agents and effectively avoid the risk during the purchase process . Prior rThe two mechanisms amplify the perceived value and weaken the perceived risk act on the relationship between trust and use intention. They have different temporalities, in which the amplifying perceived value tends to be more sustainable. In summary, this research proposes research Hypothesis H1:Hypothesis 1 (H1): Users' trust positively affects their use intention of the mobile medical platform.Personal innovation is an analysis of the individual characteristics of consumers, and it has been suggested that users who are early adopters of innovative technologies can be classified as innovators . InnovatFirst, personal innovation positively moderates the relationship between trust and use intention as groups with higher levels of personal innovation are more likely to search for positive news about new technologies and amplify their perceived value . InnovatSecond, personal innovation positively moderates the relationship between trust and use intention because groups with higher levels of individual innovation are optimistic about technological risks, thereby weakening perceived risks. Innovations are by nature uncertain, imprecise, and risky; the risks are potential and require users to perceive them. In innovation diffusion, groups with higher levels of individual innovation tend to be more tolerant of the risks of new technologies, thus, they are more willing to try them and are well prepared for the risks they may entail . Highly Groups with higher levels of personal innovation tend to have positive attitudes toward technology and are more willing to accept the creation of a mobile medical platform. Thus, personal innovation tends to amplify the perceived value and weaken perceived risk, strengthening the relationship between trust in the mobile medical platform and use intention. In summary, this study proposes research Hypothesis H2.Hypothesis 2 (H2): Personal innovation positively regulates the relationship between users' trust in the mobile medical platform and their use intention.Technology risk concerns refer to users' concerns about the potential risks associated with new technologies . It has First, groups with high levels of technical risk concerns tend to be pessimistic and reluctant to amplify perceived value, which will negatively moderate the relationship between trust and use intention. Groups with high levels of technical risk concern tend to be pessimistic about adopting new technologies and believe that their use may pose technical risks . The serSecond, groups with higher technical risk concerns have fearful attitudes toward technology, enhancing perceived risk and negatively moderating the relationship between trust and use intention. Groups with high levels of technical risk concerns tend to fear new technologies, seeing them as “poison” that disrupts their lives and equating technology with risk . Groups Groups with higher levels of technical risk concerns are pessimistic or even fearful of technology and are reluctant to accept the creation of the mobile medical platform. In this process, they tend to amplify the perceived value but strengthen the perceived risk, weakening the relationship between users' trust in the mobile medical platform and their use intention. In summary, this study proposes the research Hypothesis H3.Hypothesis 3 (H3): Technical risk concerns negatively affect the relationship between users' trust in the mobile medical platform and their use intention.via email from 1 February to 1 May 2020. The questionnaire was filled out anonymously to ensure the authenticity of the answers, and the researchers returned a total of 678 questionnaires. Based on this, the study eliminated the sample according to the following principles: (1) the presence of missing answers and (2) the apparent regularity of the answers. Finally, the study obtained a total of 625 valid questionnaires.The sample for this study was the mobile medical platform users. The survey period was from February to May 2020 during COVID-19, in which the online diagnosis for individuals could be more convenient due to the need for urban spatial isolation. What is more, our research subjects are users of mobile medical platforms, who are more familiar with online research methods. We can obtain valid data by this way. This is because treatment at offline hospitals increases the risk of infection, and hospitals are primarily responding to the epidemic. In addition, many cities and regions have adopted quarantine measures such as suspending public transportation, making offline access to medical care more difficult. In this context, exploring the intention of using the mobile medical platform makes more sense. This study commissioned Wenjuanxing Inc. (WJX), a professional market research firm, to conduct this survey. The researchers randomly sampled from the WJX sample database and sent the questionnaire According to the research variables, three instruments were used to collect the required data. Through extensive literature reading and research, the researcher integrated and adapted these three research instruments to fit the situation and the main research objectives of this study. The questionnaire was divided into four sections, including control variables, independent variables, dependent variables, and moderating variables. The independent, dependent, and moderating variables are measured using a 7-point Likert scale, where 1 indicates strong disagreement and 7 indicates strong agreement. The control variables include age, gender, educational background, and epidemic regions (the Chinese government made judgments based on the number of cases in the COVID-19 epidemic regions and divided the region into high-risk regions and low-risk regions). Since this study was conducted in the specific context of COVID-19, the degree of variation in epidemic regions affects users' trust in healthcare, thus, the epidemic regions are included as control variables in this study.In the reliability test, Cronbach'α coefficient of trust is 0.92 (>0.7), indicating that the internal structure of the trust scale has a good fit. The trust scale in this study is adapted using mature scales in appropriate contexts and has good content validity. The result of the factor analysis is shown in Existing studies have used correlation coefficients between variables and variance inflation factors (VIFs) to test whether multiple linear regression models have multicollinearity problems. According to the results of the VIF test, when the VIF is >10, there is a multicollinearity problem among variables. The test results of this study showed that the VIF of all variables did not exceed 10, and the corresponding tolerances were all >0.1, indicating no multicollinearity problem.According to the conclusions of previous studies , the resThe empirical tests of this article are in the following order: first, descriptive statistics are done according to the sample situation. Second, the Pearson correlation test is done to test the correlation between the variables according to the regression analysis method. Finally, the OLS least square method is used for the regression test. All of the aforementioned processes are performed using STATA statistical software, and the specific results are shown in the result section.The samples' demographic information is shown in The results of the Pearson correlation coefficients between the variables are listed in The regression test results are shown in This study empirically tested the relationship between users' trust in the mobile medical platform and their use intention. The dependent variable of Model 1 is use intention, and this model contains control variables and independent variables (trust). From Model 1, the data showed that trust positively affects the use intention. The coefficient of the path was 0.8777, and a P-value of < 0.01, which means that trust improves users' use intention. Thus, H1 is supported.Based on the H1, this study introduces two moderating variables: personal innovation and technical risk concerns. The empirical results are shown in Models 2 and 3 in This article focuses on developing a mobile medical platform in the field of public health during COVID-19. Through the survey results of 625 individuals, it is found that there is a positive correlation between trust and the use of the mobile medical platform. The relationship between trust and use intention is explained through two interpretation mechanisms, namely, perceived value and perceived risk. On this basis, two moderating variables of personal innovation and technical risk concerns are introduced. The empirical results show that personal innovation positively moderates the relationship between trust and use intention. At the same time, technical risk concerns have a negative impact on the relationship between trust and use intention. This provides new inspiration for the development of the mobile medical platform.The research conclusions further promote the development of a mobile medical platform for public health, especially during COVID-19 . This reThe theoretical innovation of this research is mainly reflected in two aspects. One is to extend the academic research of use intention to the specific context of the mobile medical platform. Traditional research on use intention is mainly based on the consumer domain, such as e-commerce platforms , 15, 23.The positive relationship between trust and use intention in the mobile medical platform is consistent with the findings of previous research on “trust-intention” that trust promotes use intention , 8. ThisH2 illustrates the positive moderating impact of personal innovation on the relationship between trust and use intention. Similar to the theory of new product adoption, this research believed that personal innovation is essential in expressing their use intention. When individuals are more receptive to innovative affairs, they are more willing to try emerging things such as mobile medicine , 26, 50.The result from the testing of H3 predicts that technical risk concerns moderate the relationship between trust and use intention. Internet technology has two sides. On the one hand, it improves users' convenience, such as mobile medical and e-commerce. On the other hand, it also brings risk challenges to daily life, such as data leakage. For this reason, technical risk concerns must be considered in the research of the mobile medical platform. This study takes technological risk concerns as a moderating variable and empirically tests that technological risk concerns negatively moderate the relationship between trust and use intention.The current study provides several managerial implications for the practice of public administration in the context of public health.First, promoting the mobile medical platform to address public health and medical issues requires building users' trust. The development of the mobile medical platform is an effective complement to the existing healthcare system as the development and improvement of the mobile medical platform can help expand the scope of healthcare services and improve efficiency. However, the development of the mobile medical platform is not satisfactory at present because the trust of users has not been established. Therefore, the findings of this study provide a practical reference for building users' trust in practice. The study concluded that platforms need to enhance users' perceived value and reduce their perceived risk to improve their trust in the platform and, thus, their willingness to use it. COVID-19 was a critical period for the development of the mobile medical platform , 50. ForSecond, it is vital for individuals to actively cultivate the spirit of innovation and learn to accept new things. Personal innovation positively impacts the “trust-intention” framework, and the more innovative a group is, the more willing it is to use the mobile medical platform. This means that different groups of individual innovators have other intentions to use the mobile medical platform . TherefoThird, the mobile medical platform should avoid technical risks and reduce the perceived risks to users. For the mobile medical platform, it is critical to reduce the perceived risk to users, especially the privacy issues that come with technology. Therefore, platforms need to pay great attention to the adverse effects of technology and reduce users' perceived risk through various methods. First, it can be updated through technology to solve the problem of privacy leakage brought about by insufficient technology. Second, it can introduce internal rules of the platform to avoid privacy leakage through standard norms and conventions. Third, it can increase publicity. Information asymmetry is the main reason for users' distrust of the platform. Promotion can reduce information asymmetry and make more users participate in using the mobile medical platform.Fourth, the government should recognize the importance of the mobile medical platform to public health development and guide users to adopt the mobile medical platform through policies. As a policymaker, the government should recognize that the mobile medical platform can effectively alleviate the problems of difficult access and complicated processes and help more people break through the limitations of distance. For this reason, the development of the mobile medical platform has a significant role in improving public health services. Especially in the COVID-19 period, when medical resources are scarce, the mobile medical platform can realize the efficient allocation of medical help. For the government to effectively guide more users to use the mobile medical platform through policies to reduce the pressure of offline medical care, it is necessary to start from the policy aspect to guide the benign development of users, the mobile medical platform, and medical service providers.Despite the contributions of this study, there are limitations, some of which can be the basis for future research. First, this research used the survey method to verify the research hypotheses. Using experimental methods can improve the external validity of conclusions, especially in the context of COVID-19. The moderating effect of personal innovation and technical risk concerns can be further verified through experiments. Second, the data sources of this study are mainly from China. The perception of internet healthcare in China is different from that of other regions, thus, in other countries or regions, the external validity of the conclusions will all be reduced. Future research can test the findings of this article in different countries.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.Ethical review and approval was not required for the study of human participants in accordance with the local legislation and institutional requirements. Written informed consent from the patients/participants was not required to participate in this study in accordance with the national legislation and the institutional requirements.The author confirms being the sole contributor of this work and has approved it for publication.


The human microbiome plays a critical role in maintaining human health. Due to the recent advances in high-throughput sequencing technologies, the microbiome profiles present in the human body have become publicly available. Hence, many works have been done to analyze human microbiome profiles. These works have identified that different microbiome profiles are present in healthy and sick individuals for different diseases. Recently, several computational methods have utilized the microbiome profiles to automatically diagnose and classify the host phenotype.In this work, a novel deep learning framework based on boosting GraphSAGE is proposed for automatic prediction of diseases from metagenomic data. The proposed framework has two main components, (a). Metagenomic Disease graph (MD-graph) construction module, (b). Disease prediction Network (DP-Net) module. The graph construction module constructs a graph by considering each metagenomic sample as a node in the graph. The graph captures the relationship between the samples using a proximity measure. The DP-Net consists of a boosting GraphSAGE model which predicts the status of a sample as sick or healthy. The effectiveness of the proposed method is verified using real and synthetic datasets corresponding to diseases like inflammatory bowel disease and colorectal cancer. The proposed model achieved a highest AUC of 93%, Accuracy of 95%, F1-score of 95%, AUPRC of 95% for the real inflammatory bowel disease dataset and a best AUC of 90%, Accuracy of 91%, F1-score of 87% and AUPRC of 93% for the real colorectal cancer dataset.The proposed framework outperforms other machine learning and deep learning models in terms of classification accuracy, AUC, F1-score and AUPRC for both synthetic and real metagenomic data. The human body is the habitat for trillions of diverse and complex microbes (microbiota or microbiome). These microbes reside in various body sites  and play a vital role in (a) shaping and controlling human health, (b) developing the human immune system and (c) affecting human metabolism . The mosThe microbiome sequence data is used to characterize the microbiome profiles for a deeper analysis. Many tools such as QIIME , MetaPhlDue to the availability of vast amounts of human metagenomic data, machine learning (ML) and deep learning (DL) techniques are being used to analyze and identify the relationships between different microbes and the relationship between microbes and host diseases. Recently, many works have been published in automatic disease prediction using human gut microbiome metagenomic data with ML and DL techniques –10. ThesResearchers have always tried to improve and optimize classification models to achieve better accuracy. Ensemble learning is a widely used technique to improve the classification accuracy . It coulThough several ML and DL models are used for metagenomic disease prediction, it could be noted that the graph neural network (GNN) models such as Graph SAmple and aggreGatE (GraphSAGE)  and grapInspired by ensemble learning and GCNs, this work proposes a novel framework for automatic metagenomic disease prediction. The proposed framework involves three steps. Firstly, the distance matrix is calculated from the OTU table. Secondly, a metagenomic disease graph (MD-graph) is constructed using the distance matrix. The MD-graph models the samples as the nodes. The edges between the graph nodes are constructed based on the assumption that similar samples exhibit similar features. Finally, the Disease prediction Network (DP-Net) module is constructed with ensemble of GraphSAGE models. The DP-Net is used to predict the sample phenotype.An ensemble model based on GraphSAGE for disease prediction using human gut microbiome metagenomic data.A novel graph construction method to construct the MD-graph from the gut microbiome metagenomic data.The efficiency of the proposed framework is studied by applying it to two different real disease datasets like IBD, and CRC. Also the model’s performance is assessed using synthetic datasets.All things considered, the proposed framework put forward the following contributions. This section aims to review the works done in disease prediction with human gut metagenomic data using ML and DL methods. The study done in  performek-mer abundances and OTU abundances.Subsequent to this, the work conducted in  analyzedAn automated software called MetaDP was developed by the study of  to perfoIn , a DL moIn  a new mek nearest neighbors of the OTUs in the dataset. The proposed CNN reported promising results when compared with state-of-the-art ML algorithms. Another recent study by [A CNN based model proposed in  implemenstudy by  proposedTable In this work, the metagenomic data is converted to a graph called MD-graph using a novel graph construction method and a boosting GraphSAGE is used to predict the class label of samples in the dataset.Disease prediction from metagenomic samples is the task of predicting if a given sample is healthy or sick based on the microbiome profile. The architecture of the proposed disease prediction framework is illustrated in Fig. N be the total number of samples in a dataset. Then, the set of samples in a dataset is denoted as f (i.e.) each sample contains a f-dimensional feature vector. Let the feature vector The dataset in the form of an OTU table, along with the class labels, is given as input to the framework. For each sample in the dataset, the OTU table captures the species level relative abundance profiles (OTUs). The OTUs are considered as the features of a sample. Let Human gut metagenomic datasets of two different diseases, such as IBD  and CRC This work uses the technique implemented by  for geneD is a N (i.e.) The next step in the proposed framework is the construction of the distance (dissimilarity) matrix. The distance matrix t is used in this work to determine if two graph nodes are neighbors. The distance threshold defines the maximum value for the distance between two graph nodes to become neighbors (i.e.) if the distance between two MD-graph nodes is less than or equal to the threshold t then, they are neighbors otherwise they are not neighbors. In this work, to begin with, we use the median of the values (m) of the distance matrix as the threshold value t. The reason behind selecting the median value as the threshold is that the median of a set of values mark the middle value. Moreover, choosing the median value resulted in inclusion of reasonable amount of neighbors (that is less than the maximum number of neighbors for a graph node) for all the graph nodes for all the datasets. If we do not use a distance threshold, then every graph node is considered as the neighbor of every other graph node. In that case, the resulting MD-graph would be too dense. The graph may also capture unnecessary relationships between nodes.A distance threshold The neighbor threshold mentclass2pt{minimG. The distance matrix D is used to construct the edges between the MD-graph nodes. The assumption is that similar graph nodes exhibit similar features and hence will belong to the same class . Hence, in the MD-graph G an edge is present between similar graph nodes A is obtained from it. In this work, the MD-graph is an unweighted, undirected graph with N nodes. The steps in the MD-Graph construction are detailed in the following subsections.After obtaining the distance matrix, the next step is to construct the MD-graph for a dataset. This is a challenging task because unlike the previous work where the GNNs have been applied on datasets with inherent graph structure, this work aims to apply GraphSAGE to metagenomic disease datasets that lack graph structure. The graph structure is imposed to the raw OTU table as follows: The samples in a dataset D, the neighbor threshold t and an empty adjacency matrix A of size The distance matrix t is set to t. An edge is drawn between graph nodes t and the current maximum number of neighbors of the node is less than or equal to Let The above procedure can be applied to graph nodes of imbalanced dataset. However the condition to insert an edge is given by Eqs. We observe that the above edge construction procedure at times result in a MD-graph where few graph nodes have less than the specified number of neighbors (sparse nodes). In some cases we also observed few standalone nodes in the MD-graph. The edge refinement step is used to handle standalone and sparse graph nodes.S denote the set of all standalone and sparse graph nodes obtained after the edge construction process. In order to ensure that every node t in steps and invoke the edge construction process on the graph node v until it acquires v is a sick graph node (or)v is a healthy graph nodeAt every step, the t value is incremented by m is the median of the distance matrix values.Let After the edge refinement step we obtain the MD-graph. For the purpose of GraphSAGE, the graph nodes are assumed to contain features. In this work, the OTU feature vector The algorithm for the edge refinement and adding the feature vectors to MD-graph nodes is illustrated in Algorithm 2.The GraphSAGE model utilizes the inherent structure of the graph structured data to generate feature representations of graph nodes. The node feature representations are helpful for prediction and graph analysis tasks. GraphSAGE uses an inductive approach, where the model discovers rules from the train samples, which are then applied to the test samples. Also, GraphSAGE has two improvements to the original GCN. Firstly, unlike the full graph training used in GCN, GraphSAGE uses a small batch training method by sampling the neighbors of a graph node. Secondly, the algorithm implements different aggregator functions for aggregating the features of the neighbors. Thus, GraphSAGE generates feature representation for each node by sampling and aggregating the features of nodes in its neighborhood. This is based on the idea that nodes in the same neighborhood should have similar features.K is the total number of graph convolution layers in the GraphSAGE model. Each graph convolution layer has a set of parameters like a sample number (k), a weight matrix . Then, aggregate the features of all the nodes in the sampled neighborhood using a differentiable aggregator function v. This is denoted by Eq. u is one of the nodes from the sampled neighborhood of node v and u. In this work, the Mean aggregator function is used to aggregate information from neighborhood nodes. The mean aggregator takes the elementwise mean of the vectors in a nodes sampled neighborhood. Figure Choose a target node, entclass1pt{minimaConcatenate the previous layer representation of the target node Normalize the node representation to prevent gradient explosion. This is given by Eq. The forward propagation iterates over the search depth At the end of the forward propagation, the feature representations of the nodes are passed through a softmax layer which predicts the class labels for the nodes. The loss function computes the error using the predicted labels and actual labels. During the backward propagation, the gradient descent algorithm is used to optimize the weight matrix and the parameters in the aggregation function.The DP-Net comprises of a boosting GraphSAGE model. The boosting GraphSAGE model uses multiple GraphSAGE models as the base estimators and the AdaBoost  algorithX is split into a train set N is the total number of samples in the train set. The iterative phase comprises of M boosting rounds. At each boosting round, bootstrap samples of size N are drawn from the train set. These samples are used to train the base GraphSAGE at that round. Once the base GraphSAGE at a boosting round is learned, the train set is supplied as input to the model, which predicts the class labels of all the samples in the train set. The predictions are used to calculate the error rate and the importance of the base GraphSAGE. It should be noted that if the error rate is high then, the importance of the base GraphSAGE is low and vice versa. The importance of the base GraphSAGE is then used to update the weights of the samples in the train set for the next boosting round. Thus, the boosting GraphSAGE model works by assigning adaptive weights to each sample in the train set (i.e) the weights of the samples are changed at each boosting round. The sample weights also play an essential role in drawing the bootstrap samples (i.e.) the higher the weight of a sample, the higher the probability of the sample being included in the bootstrap. Formally, the steps in boosting GraphSAGE algorithm can be explained as followsIt comprises of two phases: Initialization phase and Iterative phase. During the initialization phase the original dataset The entire dataset is split into train and test set, where the train set is used to train the M base GraphSAGEs in the boosting GraphSAGE model sequentially.The weight vector N from Draw bootstrap sample Train the base GraphSAGE using Predict the class labels of all the samples in m, Calculate the error If Calculate the weight mentclasspt{minimaUpdate the weights of the samples in Normalize the weights of all the samples.The trained boosting GraphSAGE can be used to predict the category of a test sample  is selected based on the F1-score on the two real datasets. Figure The best dissimilarity measure for constructing the edges between the nodes in the MD-graph is selected by conducting experiments using one base GraphSAGE model. The optimum number of neighbors for a node for a dataset is based on some percentage of the total number of samples in the dataset. In order to find the optimum number of neighbors for each dataset, experiments are conducted by varying the percentage value  be the number of sick samples that are correctly predicted as samples with disease by a classifier model, False Positive (FP) be the healthy samples that are incorrectly predicted as samples with disease, True Negative (TN) be the healthy samples that are correctly predicted as healthy samples and False Negative (FN) be the sick samples that are incorrectly predicted as healthy samples.Then, Accuracy (ACC) is the ratio of total number of predictions that are correct to the total number of predictions made and is given by the Eq. In order to evaluate the performance of the model when working with imbalanced datasets, a useful metric called area under the precision-recall curve (AUPRC) is also used. AUPRC is the area under the precision-recall curve, drawn with recall on the x-axis and precision on the y-axis. If the model is able to identify all of the positive examples (sick samples) without accidentally marking any healthy samples as sick, then the model achieves a perfect AUPRC. AUPRC value of 0 indicates a poor model and 1 indicates a perfect model.In this section, the capability of the boosting GraphSAGE method in classifying the metagenomic samples into sick and healthy class is compared with other ML, DL and ensemble models. The comparison is made according to the results achieved by each model in terms of the AUC, ACC, F1-score and AUPRC. Table For the IBD dataset, the results show that ML models like SVM, RF, DF, XGB and MLP exhibit low F1-score. Among the ML models the SVM classifier achieves the best F1-score. One possible reason for the low performance of these classifiers can be the data imbalance. By analyzing the results of the single GNN models, the single GraphSAGE model outperforms the single GCN. In addition, the single GNNs outperform all the individual ML and ensemble ML models. Even though all the ensemble GNN models significantly improve the performance, the boosting GraphSAGE outperforms all the other models for all considered evaluation metrics. The boosting GraphSAGE achieves the best performance with an AUC of 93%, an ACC of 95%, an F1-score of 95% and an AUPRC of 95%.For the CRC dataset, it is evident from the results that all the individual ML and ensemble ML models face difficulty achieving good results even though the dataset is balanced. The poor results may be due to the small sample size and high dimensionality of the dataset. From the results, it can be observed that SVM is the worst-performing classifier. The XGB performs better when compared to individual ML and ensemble classifiers. When analyzing the GNN models, it is observed that all the GNN models (i.e.) the single and ensemble models outperform the individual ML and ensemble models. Out of the GNN models, the single GCN performs poorly. The boosting and bagging GCN perform better than the single GCN but their performance is less than that of a single GraphSAGE model. However, the boosting GraphSAGE model achieves the best results, of 90% AUC, 91% ACC, 87% F1-score and 93% AUPRC.Table The results of CRC_synthetic dataset show that the SVM, MLP and all the variations of the GCN model perform poorly when compared to the other models with respect to ACC, AUC, F1-score and AUPRC. The best AUC of 99% is achieved by boosting GraphSAGE, XGB, and RF classifiers. The boosting GraphSAGE achieves an ACC of 98%, which is slightly lower than the ACC achieved by GraphSAGE and the DF classifier. Also, the best AUPRC of 99% is achieved by RF classifier. However, the best F1-score is achieved by RF, DF, and all variations of GraphSAGE classifiers including the boosting GraphSAGE.To sum up, the single GraphSAGE model is able to accurately discriminate the sick and healthy metagenomic samples in real and synthetic disease datasets. The sampling of neighbors and the aggregation of features from the sampled neighbors positively help the GraphSAGE model to get a better representation of the nodes and, thereby, effectively classify the nodes. Since, single GraphSAGE performs well, the ensemble GraphSAGE model improves the classification performance. Out of the two ensembles, bagging GraphSAGE and boosting GraphSAGE, the boosting GraphSAGE outperforms because the boosting GraphSAGE is able to tackle the misclassifications at each base classifier. However, it should be noted that for synthetic data, most of the ensemble ML models and the proposed model perform similarly. Comparing the results of ensemble ML models on real and synthetic datasets, the models achieve an increased performance in synthetic dataset because of the increased number of samples. But, still the proposed model is able to handle the data imbalance in both the real and the synthetic datasets. It could be observed from the results that the proposed model is not able to achieve the best ACC on both the synthetic datasets. However, it is evident that the performance of the proposed model exceeds all the other models for the real datasets.To show the effectiveness of the proposed framework, a comparative analysis is conducted with the previous works , 27, 36 Figure In summary, the improved results of the proposed framework prove that the boosting GraphSAGE model can effectively generate a powerful representation for each node by gaining relevant information from its neighbors. The ensembling technique has helped to improve the classification performance by overcoming the challenge of high dimensionality coupled with low sample size associated with metagenomic datasets. Additionally, the results also favor the modeling of the metagenomic datasets in the form of a graph for the application of graph based models to yield better performance. Furthermore, it should be noted that the proposed framework achieves considerable results for highly imbalanced datasets such as IBD, where all the other models failed to achieve good results. One reason for the improved result is the MD-graph construction procedure for the imbalanced dataset which has effectively addressed the imbalance problem by setting a different neighbor threshold for nodes from different classes. The different neighbor threshold helps the MD-graph to avoid unnecessary connections and to keep only the relevant relationship between the nodes.The case study aims to analyze the effectiveness of the MD-graph construction module of the proposed framework in capturing the neighbors of a graph node for the two real datasets.Two sick samples S2, S10 and two healthy samples S7, S16 are considered from the real IBD dataset and the abundance of the microbial biomarkers in these samples is analyzed. The microbial biomarkers for the IBD disease are identified from few biological studies as Clostridiales, Enterobacteriaceae, Eubacterium, and Ruminococcus , 38. WhiPeptostreptococcus, Streptococcus, Prevotella and Ruminococcus [Similarly, two sick samples S7, S10 and two healthy samples S8, S11 are considered from the real CRC dataset and the abundance of the microbial biomarkers in these samples is analyzed. The microbial biomarkers for the CRC disease are identified from a few biological studies as nococcus . While eIn this paper, we have developed a new framework consisting of boosting GraphSAGE model to analyze human gut microbiome metagenomic data to classify the disease status as sick or healthy. In particular, the ability of GraphSAGE to work effectively on graph-based data is used for the binary classification task. We also proposed a novel graph construction approach using a dissimilarity measure for computing the dissimilarity between the samples. The graph was constructed by considering the data imbalance present in the dataset, which helped the proposed model to achieve the best results on the highly imbalanced dataset. The experimental results using real metagenomic disease datasets and synthetic datasets show that the proposed framework outperformed most competing methods in classification performance. Also, from the results, we confirm that the proposed framework can be effectively used for disease prediction and personalized medicine for microbiome-related diseases. In future, the proposed framework can be applied to other metagenomic and medical datasets for disease prediction.


Significant advances in the technological development of mass spectrometry in the field of proteomics and the generation of extremely large amounts of data require a very critical approach to assure the validity of results. Commonly used procedures involved liquid chromatography followed by high-resolution mass spectrometry measurements. Proteomics analysis is used in many fields including the investigation of the metabolism of biologically active substances in organisms. Thus, there is a need to care about the validity of the obtained results. In this work, we proposed a standardized protocol for proteomic analysis using liquid chromatography–high-resolution mass spectrometry, which covers all of these analytical steps to ensure the validity of the results. For this purpose, we explored the requirements of the ISO/IEC 17025:2017 standard as a reference document for quality control in biochemistry research-based mass spectrometry. The reliability and validity of analytical results are the most important and fundamental features in the evaluation of any measurement system , particuTetrahymena thermophila to elucidate the involvement of many key proteins in the basis of biological processes 5; m/z = 371.1018), one of the common background contamination ions in MS derived from the glass emitter in the ion source.Mass accuracy is an essential value for the significant interpretation of the mass spectra of compounds of interest in a wide range of biochemical research. To obtain accurate mass (m/z) measurements, mass spectrometers require calibration using ions of known m/z. The appropriate selection of the calibration mixture of interest should be adjusted to the type of mass spectrometer and the purpose of the analysis. The preparation of the calibration mixture, the ionization efficiency of the reagent, and memory effects should also be taken into consideration . The rigibration . To veri1)Sample a statistically significant number of mass spectra to allow valid statistical interpretation of the results.2)Cover a wide mass range, or at least the mass range of interest.3)Sample ions of different relative abundances to determine the effect of the number of ions on mass measurements.4)Statistically evaluate the mass measurement accuracy and precision for individual masses along with the overall mass measurement accuracy and precision of the entire data set.5)Statistically analyze the error distributions to aid in the search for systematic errors.In 1984, Sack et al.  proposedIf the stability and proper functioning of the MS system are confirmed, we can proceed to the next step of ensuring the validity of the results.Escherichia coli proteins mixed in defined proportions [Monitoring the stability of chromatographic parameters in proteomic measurements is crucial in properly identifying proteins of interest and changes in their expression whenever a comparison is required between study groups. The reference standard ISO/IEC 17025:2017 (Section 6.4 “Equipment”)  requiresportions . The firThe sequence coverage parameter is the number of amino acids in a specific protein sequence that was found in the peptides sequenced in the MS/MS study. Analysis of the fragmentation spectra obtained during measurements allows us to determine the amino acid sequence of the analyzed protein mixture. Using dedicated software containing information about protein sequences and comparing the experimentally obtained fragmentation spectra with theoretical spectra, we can determine the sequence coverage.Retention time is the time elapsed between sample introduction (beginning of the chromatogram) and the maximum signal of the given compound at the detector, which means the amount of time that the given peptide is retained on the column after it has been injected. As an example, for the bovine serum albumin sample containing several different peptides  as a control sample in many complex analyses might not be sufficient. This is a major problem in quantitative proteomic studies without the use of isotopic labeling ,6, whereAn efficient sample separation method has to be coupled with the best peptide separation system to characterize as many unique peptides and identify as many proteins in a given lysate as possible. To achieve maximum efficiency in identifying and quantifying proteins, the separation efficiency of the chromatography system and the detection speed of the mass spectrometer must match in resolution and speed, respectively. Efficient chromatography allows not only for better separation of the analytes but also improves ionization. To achieve accurate quantitation, the concentration of each protein has to be measured both accurately and reproducibly, which requires a well-established and highly validated MS method.To develop a mass spectrometry method before the analysis of high-complexity protein samples, proteomics laboratories most often use the HeLa Protein Digest Standard ,39. The The approach proposed in our latest papers ,6 is alsIn this way, the prepared sample represented an excellent degree of the qualitative and quantitative composition of the test group. The sample was measured before and after the measurement series. In this approach, we also measured blank samples to control the carry-over effect  to identBesides the parameters described above that are relevant to the operation of the measurement system in proteomics research, other additional parameters are worth analyzing as well. Significant advances in instrument software also allow us to monitor those parameters that can affect the number of identified proteins and further biological interpretation of the results. In the event of instability in the operation of the LC–MS system, information on these parameters can prove to be very useful and will enable the early reaction to return the system to the correct state.This parameter relates to the stability of ionization in time. It is the singular most important parameter because it ensures the proper creation of ions and in that sense determines the performance of the mass spectrometer and the outcome of the calibration. Spray stability can be affected by the flow speed of the calibrant solution, which is being applied using a syringe pump. Additional parameters influencing spray stability are auxiliary and sheath gas flows, which need to be optimized to ensure the best possible spray stability. The stability of the spray is measured as a relative standard deviation  from a moving average of total ion chromatogram intensity. Ideal spray stability would be an RSD of around 2%. In most cases, this is not achievable; nonetheless, RSD should not exceed 5%. The software-provided threshold value, above which spray is considered to be unstable, is 15% RSD.This parameter relates to the ratio of ions, which reach the detector, to the number of ions generated in the ion source. The large surface area detector (LSAD) is located in the ion trap. The ideal value for this parameter would be 100%, which means that all of the generated ions reach the detector, which is unfeasible in a real-life situation. Ion loss is an inevitable fact of mass spectrometric measurements, especially in the case of a mass spectrometer with nonlinear ion optics. In our experience, the transmission of 60–70% is satisfactory. One thing worth mentioning is that ion transmission decreases in time as the number of measured samples increases. It is worth evaluating this parameter and recording its values. When transmission drops below 50%, cleaning of the ion optics, including the S-lens, quadrupole, and ion-routing multipole, is advised. As one can imagine, the lower the transmission through the quadrupole, the lower the number of ions measured in the Orbitrap, which can lead to unsatisfactory and lower-than-expected numbers of peptide spectral matches, peptide IDs, and, in turn, protein IDs.This parameter relates to the ability of a mass analyzer to separate ions with relatively close m/z ratios. Resolution in mass spectrometry is calculated as the ratio of the m/z value of a particular peak and its width at 50% of the peak height (FWHM). Quadrupole resolution is important, since during the measurement, it functions as an ion filter, so the proper separation of ions based on their m/z ratio is crucial. In the case of the Orbitrap mass analyzer, where the distinction between ions with m/z ratios identical up to the third decimal place is necessary, proper calibration of their resolution is needed. During proteomic measurements, an Orbitrap resolution depends on the instrument model, however, it is important to consider MS2 spectra acquisition with a lower resolution than MS1.Fourier transform is a mathematical transform that decomposes functions depending on space or time into functions depending on spatial or temporal frequency. In the case of Orbitrap, the Fourier transform is used for decomposing frequency measured by the Orbitrap for each ion into the m/z value of a particular ion, which allows for the acquisition of mass spectra. Its proper calibration is important because, without it, the acquisition of mass spectra is not feasible, even if the mass analyzer is functioning properly as such, i.e., it is acquiring proper frequency values for each ion.The active gain control (AGC) target refers to the cumulative number of ions accumulated in the C-trap, which are to be injected into the Orbitrap mass analyzer. When using Orbitrap, each measurement requires the accumulation of a certain number of ions in the C-trap. This amount is specified within the measurement method. The calibration of the AGC target is important, as it is a pivotal parameter that allows for proper proteomic analysis. This parameter can have an enormous impact on the final results, as it controls the actual amounts of ions that are collected in the C-trap and that in turn can be analyzed in the Orbitrap. If the AGC target prediction has not been checked and is not properly tuned, this might result in unsatisfactory numbers of peptide spectral matches and peptide and protein identifications. In advanced spectrometers, parameters such as ‘Fourier transform’ and ‘AGC target prediction’ are calibrated automatically according to the relevant frequency, but their significance must be considered during measurements.The statistical quality control technique is used to determine the control limits and for exploring the changes that require improving the process. In our laboratory, we most frequently use the Shewhart control chart for quality control of the results. The Shewhart control chart has a baseline and upper and lower limits, shown as dashed lines, that are symmetrical about the baseline. Measurements are plotted on the chart versus a timeline. The measurements that are outside of the limits are considered to be out of control. In proteomics studies, the Shewhart control charts are successfully applied to check the monitoring of valid proteomics results. An example of Shewhart charts for peak width at the baseline for the selected peptide LVNELTEFAK m/z = 582.3193 from the BSA control sample is shown in In the case of proteomic analysis, the obtained results are used in many areas, including in the evaluation of protein expression under drug administration, the identification of novel biomarkers in neurodegenerative disorders, the metabolism of psychotropic substances, or the investigation of plant physiology and metabolism. Thus, there is a big expectation to obtain valid results, as they could be used to conclude the status of organisms.The requirements of ISO/IEC 17025 and ISO 15189 are widely used in accredited laboratories. Unfortunately, quality control issues are often omitted in scientific papers, making it impossible to compare published results and thus draw general conclusions. For the use of advanced mass spectrometry techniques in routine applications such as medical diagnostics, as well as in many regulated areas, ensuring the validity of the reported results and consistency of analytical performance is crucial.The use of complex protein mixtures during the calibration process would adversely affect the quality and resolution of individual signals. Simple calibration mixtures with a low number of peptides provide very high-resolution peaks from the individual peptides, allowing more detailed analysis. Moreover, the use of complex control solutions increases the probability of peptide co-elution, which impedes the process of data analysis. It is worth mentioning that calibration with a simple peptide mixture greatly facilitates perception and adjustment of errors that occurred during the measurement, e.g., it is exceptionally challenging to notice a significant loss in the number of identified proteins/peptides using calibration solutions rich in proteins/peptides, which may attest to the leakiness of the nano-liquid chromatography system. Due to the numerous advantages of simple calibration mixtures, proteomic laboratories frequently use peptide mixes obtained after the enzymatic hydrolysis of bovine serum albumin (BSA) as a control sample to monitor chromatographic parameters. Another interesting approach is to prepare a control sample that represents the quantitative and qualitative composition of all samples from the measurement series. The analysis of such a control sample allows the simple evaluation of chromatographic parameters, i.e., retention time, peak intensity, peak width, carry-over effect, and their comparison data obtained during previous calibrations and data suggested by the manufacturer. Additionally, the proper calibration of all of the necessary parameters of the high-resolution mass spectrometer is of utmost importance. This paper, as an example, uses the Orbitrap mass analyzer-based mass spectrometer, which is often used in the field of proteomics. Aside from the most well-known parameters such as spray stability and mass analyzer resolution, it is worth mentioning lesser-known parameters that also need to be carefully monitored, since they have an impact on the results of the analysis, i.e., quadrupole transmission and automated gain control target adjustment and monitoring.Analytical grade chemicals and analytical standards were obtained from Merck , Promega , Thermo Scientific , and EMD Millipore . Deionized water obtained from the Milli-Q system  was used for samples and standard dilution.The instrumentation used for sample preparation was as follows: mechanical homogenizer Ultra-Turrax , laboratory incubator CLN 240 , vacuum centrifuge 5804/5804 R , vortex shaker , thermomixer Eppendorf Comfort , and vacuum concentrator SpeedVac Concentrator Plus . Reversed-phase capillary nano-UHPLC separations were performed using an UltiMate 3000 nanosystem  coupled online with a high-resolution tandem mass spectrometer .TM TribridTM mass spectrometer  coupled online to a nanoflow UHPLC instrument . One microgram of enzymatically-cleaved peptides was separated over a 52 min gradient on a reverse-phase 50 cm long C18 in-house packed column ). Solvent A consisted of 0.1% formic acid in water; solvent B consisted of 0.1% formic acid in 80% acetonitrile. The eluted peptides were ionized in the positive ion mode in the nano-ESI source with a capillary voltage of 1.9 kV. Mass spectrometric analysis was performed in data-dependent acquisition mode, with dynamic exclusion set to 60 s. Survey scans  were acquired from the Orbitrap mass analyzer at a resolving power of 60,000. MS/MS spectra were acquired from the Orbitrap mass analyzer in the top-speed mode for the most abundant, multiply-charged ions. Precursor ions with an intensity above 1 × 106 were chosen for higher-energy collisional dissociation (HCD)-based fragmentation . The MS/MS scans were acquired at a resolving power of 15,000 .Liquid chromatography–tandem mass spectrometry analysis was performed using an Orbitrap FusionRaw MS data were converted to mgf files using MSConvert (v. 3.0.18294-6c5e69c23) software. All MS/MS data were searched using Mascot . The following parameters were set: trypsin was set as a digestion enzyme; precursor and fragment ion mass tolerances were 5 ppm and 10 ppm, respectively; carbamidomethylation on cysteine was a fixed modification; carbamylation on lysine and N-terminus were variable modifications; up to two missed cleavage sites were allowed. Proteins and peptides (minimum six amino acids) were identified using the target-decoy approach with the reversed database. The results were processed with a false discovery rate set to 0.01 at the level of both peptides and proteins.In this work, we drew attention to the need to develop a unified protocol for assuring the quality of proteomic analysis results. The purpose of the presented protocol was the need to ensure the validity of the results in studies that are often related to medical research. The developed detailed standardized guideline includes all essential items enabling ensure the validity of the results in proteomic studies based on international standards such as ISO/IEC 17025 and ISO 15189. The presented scenario demonstrates the calibration of the high-resolution mass spectrometer, monitoring of the stability of chromatographic conditions, and suitable tools for analyzing trends in changes during the monitoring of parameters. Undoubtedly, standardized procedures impress obligations on laboratories working in the regime of quality systems, in accordance with the relevant standards. On the other hand, academic laboratories are not subject to such strictly formalized procedures; researchers can choose the most optimal measurement conditions for their aims. Nevertheless, the purpose of this paper was to draw attention to the fact that in scientific research, the quality of results is equally as important. Comparing results, and thus verifying hypotheses requires at least a good and detailed description of the experimental conditions and proper supervision of the stability of the measurement system. In our view, the proposed scenario fulfills both the requirements of routine testing as well as research laboratories, assuring confidence in the validity of the results. This guideline is intended to be used by quality managers and analytical staff in both industry and academia who are involved in the planning, performance, and management of nonroutine measurements in analytical science and associated research and development.


Postmortem magnetic resonance imaging (MRI) can provide a bridge between histological observations and the in vivo anatomy of the human brain. Approaches aimed at the co-registration of data derived from the two techniques are gaining interest. Optimal integration of the two research fields requires detailed knowledge of the tissue property requirements for individual research techniques, as well as a detailed understanding of the consequences of tissue fixation steps on the imaging quality outcomes for both MRI and histology. Here, we provide an overview of existing studies that bridge between state-of-the-art imaging modalities, and discuss the background knowledge incorporated into the design, execution and interpretation of postmortem studies. A subset of the discussed challenges transfer to animal studies as well. This insight can contribute to furthering our understanding of the normal and diseased human brain, and to facilitate discussions between researchers from the individual disciplines. Grasping the full complexity of the structure and function of the human brain cannot be achieved using a single research modality. Today’s knowledge results from incremental data obtained through a broad spectrum of research approaches, ranging from the extrapolation of findings from a variety of non-human species, to data obtained through a plethora of brain imaging techniques applied in humans. The most commonly used approach to image the whole human brain in vivo with the purpose of studying neuroanatomy and function is magnetic resonance imaging (MRI). MRI techniques are continuously undergoing technological developments aimed to further improve hardware, MR sequences as well as algorithms used for image reconstructions. An approach that is gaining popularity within the field of structural MRI is the investigation of human postmortem specimens. In an increasing number of studies, detailed MRI is followed by histological processing of the tissue. The integration of the methodologies when combining MRI and histology requires the balancing of the opportunities and challenges associated with the individual techniques, optimization choices may differ from choices made if only histological or MRI techniques are uses. This overview provides insight in the choices that are made. In spite of the compromises, the combined approach is stirring excitement in the field, since the spatial alignment of MRI and histological reconstructions bring forward new opportunities that allow fine-grained anatomical validation of MRI results, and more detailed studies on the 3D structure of the brain Fig. . The acqThe scientific value of pushing the signal-to-noise ratio and spatial resolution of (quantitative) MRI to further improve anatomical contrast is recognized by the research community, and the same holds for efforts moving histology from a 2D to a 3D field. Building a bridge between modalities gives rise to synergistic research that allows the creation of unified maps of the human brain registered to a common template space . An open discussion between researchers, sharing the background knowledge, understanding the limitations and workload involved, will help to bridge modalities. When developing an integrated research protocol, the leading question should be the research question at hand and both disciplines should be open to adapting their operationalized protocols, and adjusting their standards in service of the bridging to the other field. In practice, this means that researchers from the field of microscopy may not be able to study the anatomy at the level of detail to which they are accustomed to, since only very few groups in the world have the resources to achieve these levels of detail, and subsequently creating 3D reconstructions of the whole brain, or tissue blocks. At the same time, researchers from the field of MRI will need to accommodate changes in MRI characteristics resulting from steps required to prevent tissue decay that would interfere with histological processing. In addition, the time required to process and analyze large human brain specimens hampers the rapid publication of studies applying state-of-the-art MRI techniques.Postmortem studies come with inherent unknowns on the translatability of the observations to the in vivo situation. Important questions that immediately rise are: is the postmortem brain still  representative of the living brain? What does postmortem MRI tell us about the in vivo situation? What non-MRI related factors differ between in vivo and postmortem scans? What factors can and cannot be controlled for in postmortem studies?A straightforward approach to determine to what extent the human postmortem brain is representative of the living brain is to perform a within subject study providing a direct comparison between the in vivo and postmortem state. Although this approach has the potential to answer a crucial question, setting up such a study is ethically delicate, and this may explain the largely absent data in scientific literature. We are aware of only a single unique case report that provides a direct, within subject comparison of ante- and postmortem MRI before tissue fixation. A 37-year-old male patient with familial early-onset Alzheimer’s disease was scanned 4 days ante- and 9 h postmortem using the same imaging setup and protocol allowing direct comparisons  characteristics differ between age groups  lesions). Such visual comparisons only require approximate alignment with the brain, and can be performed without detailed co-registrations, which makes this approach also feasible for studies using smaller tissue blocks with a limited number of available anatomical landmarks for registration purposes. Quantitative approaches can include the correlating of MRI measurements of cortical thickness to postmortem assessments of the same parameters  shape alterations. Histological sections are subsequently cut usually at a < 10 µm thickness, at which thickness they are prone to tearing as a result of burrs on the cutting knife, or the subsequent manipulation of the sections. More severe damage of sections requires labor-intensive manual repair of tissue artifacts.An additional challenge is posed by the evaluation of the accuracy of the transformations. The accuracy of transformations can for instance be evaluated through the relative overlap of region labels available in both the tissue block and target template, and the alignment of anatomical fiducials , which have underdone updating and continuous improvements over the years . MNI templates play a prominent in the field of MRI, allowing co-registration of many types of acquired data  ensure the availability of mutual information between imaging modalities for registration purposes. A straightforward approach is to perform histological staining that allows visualization of fiber bundles. This can be achieved using classical Luxol or Bielschowsky stainings. The derived contrast can be used for alignment with T1-derived MR contrasts. (2) Perform blockface imaging during the cutting process. The blockface reconstruction provides an important shape prior which is invaluable for preventing Z-shift effects and for reducing the image reconstruction from a 2D–3D to a 2D–2D problem. (3) Adapt tissue fixation to accommodate both MRI and histology. Formalin cross-linking prevents further tissue degradation at the expense of the MRI contrast. Consider performing (part of) the MRI imaging prior to fixation if possible, alternatively consider limiting formalin fixation and rinsing tissues extensively in buffered saline to partially recover shortened MRI decay times. (4) Consider compromising on the level of histological detail, to allow the publication together with state-of-the-art MRI data. Postmortem histological studies are very labor intensive and time consuming and represent a scientific tour de force. To decrease chances of MRI data no longer being state-of-the-art when histological processing is completed, compromises in acquiring histological detail can be considered. We would like to stress, however, that datasets with older MRI acquisitions should not be considered outdated, and still represent high scientific value. 5) Consider the prerequisites for optimizing the reusability of the data, particularly the registration to MRI templates. The applicability and reusability of the data is increased if the brain can be compared directly to in vivo brains, and more even if the data can be used to co-register annotations that cannot be made on in vivo MRI scans with lower anatomical detail and image contrast. To conclude, it is also important to keep in mind that template space is also subject to updating and continuous improvement. It is, therefore, important to not only share any created dataset in standard reference space, but also in individual template space, to allow also researchers developing new registration tools to make optimal use of the created datasets moving the field further forward.