Food frequency questionnaires (FFQs) are the most commonly selected tools in nutrition monitoring, as they are inexpensive, easily implemented and provide useful information regarding dietary intake. They are usually carefully drafted by experts from nutritional and/or medical fields and can be validated by using other dietary monitoring techniques. FFQs can get very extensive, which could indicate that some of the questions are less significant than others and could be omitted without losing too much information. In this paper, machine learning is used to explore how reducing the number of questions affects the predicted nutrient values and diet quality score. The paper addresses the problem of removing redundant questions and finding the best subset of questions in the Extended Short Form Food Frequency Questionnaire (ESFFFQ), developed as part of the H2020 project WellCo. Eight common machine-learning algorithms were compared on different subsets of questions by using the PROMETHEE method, which compares methods and subsets via multiple performance measures. According to the results, for some of the targets, specifically sugar intake, fiber intake and protein intake, a smaller subset of questions are sufficient to predict diet quality scores. Additionally, for smaller subsets of questions, machine-learning algorithms generally perform better than statistical methods for predicting intake and diet quality scores. The proposed method could therefore be useful for finding the most informative subsets of questions in other FFQs as well. This could help experts develop FFQs that provide the necessary information and are not overbearing for those answering. As proper dietary habits are very important for a healthy lifestyle, the application includes a module that monitors and encourages dietary habits. The essential part of the nutrition module is a food frequency questionnaire named the Extended Short Form Food Frequency Questionnaire (ESFFFQ), through which the virtual coach acquires information about the users’ dietary habits and returns recommendations based on the answers and quality scores calculated from them. The ESFFFQ [Adopting and maintaining healthy habits is extremely important and can at the same time be quite challenging. WellCo  are time-consuming, and the selection of food items relies greatly on personal expertise; hence, an automated approach could be of great help. Gerdessen et al.  showed tThe users included in the WellCo project are elderly and it was of great importance to find ways for them to answer as few questions as possible while still providing the system with sufficient information about their nutritional habits. Since they are supposed to answer questionnaires regarding other aspects of a healthy lifestyle as well, the number of questions could quickly become overwhelming. This problem could be overcome by finding a method to detect which questions in the ESFFFQ are really informative and which are redundant. Statistical methods, such as multiple imputation and zero imputation could, of course, be used to impute the answers to the questions the users did not answer, but it is reasonable to expect that at least zero imputation will underestimate the true intake and therefore return wrong diet quality scores. Besides, these methods, contrary to machine learning, are not designed to deal with intentionally missing answers.Therefore, the paper proposes an approach that finds the questions that provide the most information, and estimates nutrient intake and diet quality scores from a subset of answers using machine learning. The hypothesis of the paper is that by using the proposed method, machine learning could better predict the nutrient intake and diet quality scores when using just the selected questions in comparison to statistical methods.In the WellCo project, the aim of nutrition monitoring was to find a way to ask elderly users as few questions about their dietary habits as possible and still get sufficient information to evaluate their nutrition.The problem was explored as a set of single-target problems, which means that it was explored for each of the targets, diet quality scores, separately. The problem was explored for five of the targets—fat intake, sugar intake, fiber intake, protein intake and salt intake. The remaining three targets  are only dependent on one or two questions, and therefore the problem of reducing and ranking the features is trivial.The answers to ESFFFQ were collected from 92 adults from Slovenia, Italy, Spain and Denmark as a part of the WellCo project and were then joined with answers from 197 adults included in SIMenu, the Slovenian EU Menu research project  aimed atAdults answering either of the questionnaires were between 18 and 80 years old. A more detailed description of the population in the SIMenu is available in the paper by Zupanič et al. .Input data consisted of answers to the questionnaire. The questions were of the multi-choice type, providing discrete answers. As not all of the questions had the same number of possible answers—and besides, some of the answers never contributed data—the answers were standardized to avoid one variable dominating others by subtracting the mean, and we scaled them to unit variance. Once normalized, the answers to questions were used as input features to machine-learning algorithms.Regression problem: In regression problems we try to predict continuous values. In our case, we try to predict the values of the intake of nutrient selected per food group.Classification problem: In classification problems the predicted values are discrete. In our case, we try to predict the diet quality scores for chosen nutrients and food groups.In this study, the performance of machine learning on different subsets of questions was explored for the following two types of machine-learning problems:For simplicity, the shorter expressions “amounts” and “scores” will be used when speaking of nutrient values’ amounts and diet quality scores respectively.For comparison, besides machine-learning algorithms, the scores were also computed by using statistical methods. Statistical methods are commonly used as the state-of-the-art when dealing with missing questions, and could therefore be used for dealing with subsets of questions. First, the scores were calculated by using just the answers to the questions from a subset and setting the remaining questions to the frequency “rarely or never”. This is actually the so-called zero imputation method . An alteTo calculate the scores, the amounts of nutrients were calculated based on users’ answers, and the amounts were further transformed to diet quality scores. For easier understanding, the example for target “vegetable intake” is presented. The amounts were calculated from sums of average amounts in In the field of machine learning, the expression features is used for the data that we feed to machine-learning methods. In our case, the features are the answers to the questions, and subsets of features represent answers to the questions included in the subset of questions.i and remove one of the two questions that have a correlation higher than this threshold. Thresholds were chosen experimentally. Pearson correlation coefficients were calculated and the thresholds were chosen in such way that number of elements in subsets determined by the thresholds fell approximately linearly. The set F0 represents the complete set of features. To produce the first subset of features F1, one of the two features whose correlation coefficient exceeded the chosen threshold was removed, and the same was done for every such pair of features. The procedure was repeated for the next thresholds. For thresholds TH1, TH2, …, THn, we get subsets of features (questions) F1, F2, …, Fn, which is shown in The paper explores how reducing the number of questions in FFQs affects the diet quality scores, which could further be used to optimize the selection of the FFQ questions and shorten very extensive FFQs. First, Pearson correlation coefficients between the features were calculated. This approach was used as a Pearson correlation coefficient measures the statistical relationship between two variables. Features with a high correlation are more linearly dependent and therefore have a similar effect on the dependent variable. If two features are highly correlated, one of the two could be dropped. To get a subset of questions, we set a threshold THEight classification and eight regression models were built—some of them very simple, such as linear/logistic regression, decision tree and k-nearest neighbors; and the others more complex, including support vector machine, random forest, gradient boosting classifier/regressor, and finally, the voting classifier/regressor that combines all the previous models . To builWhen dealing with a comparison of different models on different subsets of features, many evaluation measures can be used, and drawing clear summaries and conclusions from them may become a very challenging task. Besides, incorporating and comparing more different metrics makes the evaluation more robust and fair. We decided to combine different metrics by which models and subsets of features are compared. Specifically, the approach proposed by Eftimov and Kocev , which f0, F1, …, F5  is presented in k models are built with methods M1, M2, …, Mk on each of n feature subsets F1, F2, …, Fn with previously described dimensionality reduction. The models are built on all subsets for all m targets  T1, T2, …, Tm. For each target and each feature subset the decision matrix D is built. In D, j for method i. In this paper, performance measure First, M1 and M2 the j-th preference measure would be defined with the equation:The next step is to make pairwise comparisons between all methods for each performance measure. The preference function Q is the indifference threshold, the greatest amount of difference that is insignificant.There are six types of preference function . We chosAfter calculating preference measures nd Kocev .The final step is to compute the positive, the negative and the net preference flows. The positive preference flow for The negative preference flow quantifies how method Finally, the positive and negative flow are aggregated into net flow by subtracting negative flow from positive flow:The higher the net flow for method D was formatted a bit differently. For example, for method D represent the performance measures on feature sets—the first row presents the performance on the feature set F0, in the second row the performance measures on the feature set F1, etc. The above-listed equations were adjusted. This returned the most optimal feature set for method M1, M2, …, kM. The pipeline for this example, on how to get the most optimal feature set FiTw for target i and the best feature set Fw for all targets is shown in To get the most optimal subset for each method, the matrix Rank methods—for each of the subsets of questions F0, F1, F2, F3, F4 and F5 we rank the methods by their performances on that subset;Ranked subsets of questions—for each of the models  we rank the subsets of questions by the performance of that model on them.The PROMETHEE was used to:To run the experiments, the dataset was split into five splits and 5-fold cross-validation was used to test the performances of the models. We trained on four splits and tested on the fifth one. We repeated the same for all combinations of splits. The same splits were used for all models and all subsets for a fair comparison.1, F2, F3, F4 and F5 respectively. As the ESFFFQ was already designed to have a minimal number of questions included, the low correlations between the questions were expected. Therefore, the thresholds were quite low. The subsets of features (number of questions and list of questions) are listed in 0, contains 27 questions. The full questionnaire is listed in The Pearson correlation coefficients were calculated for all features. The more correlated the two features are, the higher the coefficient. Based on the calculated correlation coefficients, we chose thresholds 0.40, 0.30, 0.25, 0.20 and 0.10, and from those we got subsets of features FThe results are presented in the following subsections. For each of the targets  a table representing ranked methods and a table representing ranked subsets are included. In each table, the best performing method or subset is marked in bold. In tables ranking methods the results are compared column-wise and the subsets are compared row-wise.For each subset of features the performances of the classification models were measured with precision, recall and F1-score and the regression models with mean average error (MAE), mean squared error (MSE), root mean squared error (RMSE) and coefficient of determination (R2-score). Precision, recall, F-score and R2-score are maximizing metrics (higher is better), while MAE, MSE and RMSE are minimizing metrics (lower is better). The results, ranked methods across all subsets of questions and subsets across all methods, are presented in the tables. To compare the performances of methods, elements are compared column-wise and the subsets row-wise. For better representation the best rank is marked in bold.0 of questions were the statistical methods, as in this case we were not working with any missing data and the calculations were actually the ground truth. This was same for all other targets as well .In 0 and F1 were the only subsets where statistical methods performed better than machine learning; and for the regression problem, machine learning performed better on all subsets. The overall best methods on all subsets were SVM for the classification problem and voting classifier for the regression problem.For the classification problem, F0. This again stands for all the targets and will not be repeated in the following subsections. For target fat intake, machine learning also worked the best on the full set of questions. For the regression problem, the same is true, except that the SVM worked the best on the subset F1. Additionally, it is possible to see that the performances of the kNN on F0 and F1 are very similar. However, the best subset for the regression and classification problems is the full subset of questions. Moreover, for most methods the loss of information between subsets F0 and F1 is lower than the loss of information between F1 and F2.When ranking the subsets see , it is a0. However, as the subsets got equal to or smaller than subset F3, statistical methods started to perform far worse and machine-learning algorithms worked better. The overall best algorithm was the gradient boosting classifier for both the classification and the regression problem.As seen in 2, which included 16 questions , as this is actually the ground truth. Predictive models based on ML do not work so well because of overfitting, which happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. However, the aim of this paper is to show that when only answers are available, the precision of the scores could be improved by using machine learning. When dealing with full questionnaires, it is obvious that the conventional calculation of amounts and scores is a more reasonable choice than the use of machine learning.It is expected that the statistical methods will work better than machine learning when dealing with the full questionnaire  , in comb


The results showed that the incorporated GnPs did not markedly influence the melting temperature of PA6 but affected the crystallization temperature, fiber bulk structure, crystallinity, and mechanical properties. Furthermore, GnPs increased the PA6 complex viscosity, which resulted in the need to adjust the parameters of melt spinning to enable continuous filament production. Although the incorporation of GnPs did not provide a reinforcing effect of PA6 fibers and reduced fiber tensile properties, the thermal stability of the PA6 fiber increased. The increased melt viscosity and graphene anti-dripping properties postponed melt dripping in the vertical flame spread test, which consequently prolonged burning within the samples.Studies of the production of fiber-forming polyamide 6 (PA6)/graphene composite material and melt-spun textile fibers are scarce, but research to date reveals that achieving the high dispersion state of graphene is the main challenge to nanocomposite production. Considering the significant progress made in the industrial mass production of graphene nanoplatelets (GnPs), this study explored the feasibility of production of PA6/GnPs composite fibers using the commercially available few-layer GnPs. To this aim, the GnPs were pre-dispersed in molten  In order to expand the use of PA6 plastic and textile materials in areas with more demanding performance requirements, such as the aerospace and automobile industries, the application of graphene has dramatically increased in the development of different high-performance electrically and thermally conductive composite and nanocomposite materials [Polyamide 6 (PA6) is an important fiber-forming polymer with excellent melt processing, crystallinity, and mechanical, abrasion, and wear properties. Furthermore, the chemical recyclability of PA6 to monomer aterials ,2. The aaterials ,4,5,6 anaterials ,8 has alaterials . Additioaterials .2-hybridized carbon atoms arranged in a hexagonal lattice. Although the development of a procedure for the large-scale production of single-layer graphene remains a challenge, commercially available graphene nanopowder products include the granular form of aggregated few-layer graphene nanoplatelets (GnPs). The strong π–π interactions between graphene layers and the resulting agglomerates restrict the achievement of a conductive network in the polymer matrix at concentrations that provide the benefit of the polymer’s mechanical properties [2 carbon atoms into sp3-hybridized carbon atoms, which consequently reduces conductivity [2-hybridized system, which provides a conductivity advantage [Graphene as a single graphite layer represents an atomic layer of spoperties . A seconoperties ,12. To ioperties ,12,13. Tuctivity ; by contdvantage ,15,16. Tdvantage ,18,19 andvantage ,21,22,23dvantage .ε-caprolactam monomer, which has a lower viscosity, provides a better medium for dispersing GnPs and enables more efficient retention of the inherent properties of graphene [2– or –COOH-functionalized graphene surface increased compatibility with the PA6 matrix in composites, which significantly enhanced the reinforcing effect in melt-spun PA6 fibers. To achieve the fiber reinforcing effect, the optimal concentration of GO incorporated into the PA6 matrix by in situ hydrolytic polymerization is reported to be approximately 0.05 wt % [ε-caprolactam at concentrations of 1 and 2 wt.%. The dispersion state of the incorporated GnPs and their influence on the melt rheology, melt spinning process, dynamic mechanical properties, and thermal and flammability properties of melt-spun PA6 multifilament yarns were analyzed in detail.Although significant scientific research focused on the production of high-performance PA6/GnP nanocomposite bulk plastic materials has been undertaken, studies of the production of fiber-forming PA6 composite material and melt-spun textile fibers are scarce. Existing research has identified a demanding challenge regarding the achievement of uniformly nanodispersed graphene to increase fiber conductivity while retaining fiber tensile properties ,26,27,28graphene ,31,32. Hgraphene ,5,6. Thegraphene ,33. Faci.05 wt % ; by in s.05 wt % . Further.05 wt % ,36. Consε-Caprolactam was kindly supplied by Brüggemann . The exfoliated graphene nanopowder 0541DX (GN) was purchased from SkySpring Nanomaterials, Inc. , with a platelet morphology, average particle diameter of 15 microns, and thickness of 6–8 nm.ε-caprolactam monomer in the presence of graphene nanoplatelets. First, vacuum-dried ε-caprolactam monomer was melted in a Teflon chamber at 180 °C on a temperature-controlled hot plate provided with a magnetic stirrer and placed inside a fume hood under an inert argon atmosphere. The graphene nanopowder (GN) was pre-dispersed in the molten ε-caprolactam at concentrations of 1 and 2 wt % using the IKA ULTRA-TURRAX disperser (T 25)  at a speed of 5500 rpm for a period of 30 min. Subsequently, the temperature was gradually reduced to 80 °C, and then 1 wt % of water was added dropwise. Then, the Teflon chamber was placed in a hydrothermal autoclave reactor and the mixture was polymerized at 250 °C for 10 h under autogenous pressure. For comparison, the PA6 sample in the absence of graphene was synthesized using the same method. The resulting composite materials containing GN at concentrations of 1 and 2 wt % were coded as PA6/1GN and PA6/2GN, respectively.Polyamide 6/graphene composites were prepared by the in situ water-catalyzed ring-opening polymerization of the PA6, PA6/1GN, and PA6/2GN composite filament yarns were produced using the laboratory-scale melt spinning and drawing device . The temperatures of the extruder (three zones), metering pump, and spin-pack (two zones) were set to 210 °C for the melt spinning of PA6. In the case of the melt spinning of PA6/1GN and PA6/2GN, the temperature of the extruder was set to 210 °C, and the temperatures of the metering pump and spin-pack were set to 220 °C. The spinneret for multifilament spinning had 10 holes with a diameter of 0.35 mm. The extruded multifilament yarns were quenched in air at room temperature and were wound on a godet operating at a speed of 80 m/min. Photographs of the wound PA6/2GN composite filament yarns produced by the melt spinning process and the corresponding fiber strand sample are shown in −1 with a resolution of 4 cm−1 using a laser with a 532 nm wavelength. An approximate laser power of 10 mW was used for all recorded spectra. The single spectra were taken on at least 8 positions for each of the samples. Peak positions were determined based on the actual measurements and not on the fitted data.The Raman spectra were acquired using a WiTec Alpha 300 confocal microscope . Spectra were recorded in the spectral range from 70 to 3600 cmRheological measurements of the melts were conducted on an Anton Paar Physica MCR 301 rotational rheometer , using a parallel-plate geometry with a diameter of 25 mm and a gap of 0.5 mm. The measurement temperature was 225 °C, with a fixed shear strain of 5% and over a frequency range of 0.01 to 100 rad/s.Scanning electron microscopy (SEM) was performed on a Zeiss SUPRA 35VP SEM microscope . The samples were coated with Cr.Tm) and crystallization (Tc) temperatures using a Mettler Toledo DSC1 instrument  at temperatures ranging from 25 to 280 °C (or just above Tm of each sample) with heating and cooling rates of 10 °C/min in a nitrogen atmosphere at a flow rate of 30 mL/min using aluminum standard 40 µL crucibles with a pierced lid. The sample masses were approximately equal to 3 mg. The heating runs were analyzed to determine the melting temperatures of the first and second heating runs  of each sample. Equation (1) was used to determine the degree of crystallization, Xc:Hm1 is the melting enthalpy from the first heating run, x corresponds to the weight percentage of PA6 polymer in the sample.Differential scanning calorimetric (DSC) analysis was used to measure the melting  was performed using a TA instrument  with a controlled gas cooling accessory . The dimensions of the samples were 12.5 cm × 0.5 cm. The samples were heated from 0 to 160 °C at a constant rate of 3 °C/min. During heating, the test samples were deformed in the tension mode at a constant amplitude (strain) of 10 μm and a frequency of 10 Hz.The tensile properties were analyzed with an Instron 5567 dynamometer  in accordance with ISO 13934–1:2013. The method was adjusted so that the gauge length was 100 mm and the deformation rate was 350 mm/min. At least six different yarns from the same sample (10 filaments per yarn) were tested, the results averaged, and standard error calculated for Young’s modulus, tenacity, and elongation-at-break values.ε-caprolactam conversion, CLconv, in accordance with the method proposed by Zhang et al. [Thermogravimetric (TG) analyses of approximately 10 mg samples in an air and a nitrogen atmosphere at a gas flow rate of 50 mL/min were performed on a Mettler Toledo simultaneous TGA/DSC1 Thermogravimetric Analyzer  from 25 to 800 °C with a heating rate of 10 °C/min in open 150 µL platinum pans. Blank curves were subtracted for all measurements. Thermogravimetric analyses were also used for the determination of g et al. .The standard vertical flame spread test (ASTM D6413)  was performed on the fiber strand samples. The fiber strand samples  were prepared by twisting several filaments together. Additionally, the cotton indicators positioned below the fiber strand and knitted fabric samples in accordance with the standard UL 94 were used in order to evaluate the flammability of the melt drips.Scanning electron microscopy analysis of graphene nanoplatelets prior to their dispersion revealed that the diameter of the aggregated graphene species was higher than 30 µm . As the −1 can be related to the structural defects (the co-existing sp3 carbons in the sp2-hybridized carbon sheets) and to the relative motion of sp2 carbons, respectively [2 destruction and to the interactions of the PA6 matrix with the sp3 graphene domains. In the case of the PA6/1GN and PA6/2GN samples, the overlapping of the D and G graphene bands occurred with the most intense PA6 band at 1445 cm−1 (CH2 bending) [3 and sp2 C atoms, there was a shift toward higher D/G ratios for the spectra taken on PA6/1GN and PA6/2GN in comparison to that of the GN. This indicates that the used dispersal process produced local defects in the sp2-hybridized carbon sheets due to the disaggregation process.Raman spectroscopy was performed on the as-received graphene nanoplatelets (GN) and PA6, PA6/1GN, and PA6/2GN fiber samples. Single spectra were taken on various positions for each of the samples. The representative results are shown in ectively . These tbending) . Due to μ*), storage modulus (G’), loss modulus (G’’), and loss factor (tanδ) are presented in ω), signifying the more pronounced shear thinning behavior in the case of composites (G’), and viscous (G’’), portions to the applied angular frequencies show no intersection of the moduli, with G’’ values higher than G’, which corresponds to liquid-like behavior, with G’’ >> G’ , indicating the formation of an interconnected structure. This implies that interfacial interactions between graphene and the PA6 matrix succeeded in holding the microstructure under angular frequencies of up to 0.1 rad/s, at which the applied force collapsed the microstructure and the materials started to flow, showing the liquid-like behavior with G’’ >> G’ above 0.1 rad/s. In the case of the PA6/2GN melt, the responses of the solid- and liquid-like behaviors were equal during the whole frequency region between 0.01 and 0.1 rad/s. The values of the loss factor (tanδ) , where the percolated network of graphene restricts polymer chain mobility [G’ values compared to G’’ at angular frequencies lower than 0.1 rad/s indicate that this composite behaved like an elastic solid.To evaluate the impact of the in situ-incorporated graphene on the melt flow properties of the PA6 matrix, melt rheology measurements were conducted. The results for complex viscosity  c decreasulus G’’ d shows tmobility ,42. In tThe values of the complex viscosities and viscous moduli of the PA6 and PA6/1GN melts were approximately equal in the high-frequency region above 100 rad/s, where short-range dynamics of polymer chains dominate, and the rheological behavior of the melted system is dominated by the matrix properties . In the To overcome the higher melt viscosities of the PA6/1GN and PA6/2GN composite samples and achieve a continuous melt spinning process for the PA6/1GN and PA6/2GN samples without filament breakage, the spinning pump and spin pack temperatures were increased, and the spinning pump speed was lowered, compared to those used for the PA6. However, clogging of the spin filter and an increase in the pressure in the spinning pack during the melt spinning process occurred in the case of both PA6/1GN and PA6/2GN samples, indicating the presence of the micro-species. To obtain more insights into the microstructure of the PA6/1GN and PA6/2GN composite textile fibers and the dispersion and distribution of graphene nanoplatelets, scanning electron microscopy (SEM) was used. Representative SEM images of the fibers’ longitudinal and cross-sectional views are presented in Tm) and crystallization (Tc) temperatures, and for the degree of crystallinity (Xc). The endothermic melting peaks of the neat PA6 from the first and second heating runs, Tm1 and Tm2, appeared at 218 and 217 °C, respectively. The shoulder at 211 °C appearing on the melting peak from the second heating run  only in the case of the PA6/1GN sample  as a function of the temperature (Tg) because the molecular motion of the polymer main chain segments is maximized at this temperature [Tg [The dynamic-mechanical properties of the PA6, PA6/1GN, and PA6/2GN composite filament yarns were analyzed by measuring the stress response during the exposure of the samples to a sinusoidal strain over a temperature range of 0–160 °C. The results are presented in terms of the storage modulus (E’), loss modulus (E’’), and loss tangent  of PA6 fibers by about 30–40 °C, increasing the thermal stability of the PA6 composite fiber to approximately 380 °C and the temperature of the maximum in the weight loss rate (Tmax) by about 20–30 °C. A shift in the initial decomposition temperature and the temperature of the maximum in the weight loss rate toward higher temperatures was also reported for the PA6 fibers with non- or functionalized graphene sheets, which were incorporated into the PA6 matrix either by the melt compounding [Tmax for PA6/1GN and PA6/2GN compared to that of PA6 suggest that structural characteristics of the formed char were not efficient in protecting the underlying polymer at higher temperatures. The weight percentages of the residues above 500 °C correspond to the amount of graphene in composites [To investigate how the incorporated graphene influenced the thermal stability of PA6 fibers, thermal decomposition of the as-received graphene nanoplatelets, neat PA6, and PA6/1GN and PA6/2GN fiber samples was conducted by simultaneous TG and DSC analyses in nitrogen and air environments. TG, DTG, and DSC graphs are shown in pounding  or the imposites . FurtherTmax1 and Tmax2 of 444.3 and 727.3 °C, respectively. The first and second decomposition step can be assigned to the combustion of amorphous carbon with a weight loss of 18.5 wt % and to the graphene nanoplatelets with a weight loss of 81.5 wt %, respectively. Furthermore, as observed from the DTG curve  for the PA6/1GN and PA6/2GN samples compared to that of neat PA6 (a–c), which consequently could not increase the weight percentage of the residue at 600 °C.When thermal decomposition occurred in an air environment, the heat-induced decomposition was supported by oxygen, which induced the reduction in initial decomposition temperatures for GN, PA6, PA6/1GN, and PA6/2GN samples  in compaTG curve b, the seta-f), weight loss (Δm), and number of drips (Nd), are presented in The vertical burning behavior of the PA6, PA6/1GN, and PA6/2GN fiber strand samples was investigated according to the standard vertical flame spread test (ASTM D6413). The results for the fiber strand samples, including the after-flame time (ε-caprolactam need to be optimized, which will be the subject of our future work. Furthermore, the increased thermal stability due to the incorporated graphene and graphene’s anti-dripping properties indicates that GnPs could provide effective support for gas-phase active flame retardants to obtain anti-dripping self-extinguishing properties for PA6 textile materials.In this research, we investigated the feasibility of using commercially available few-layer GnPs to produce PA6/graphene textile fibers by applying GnPs pre-dispersed in molten ε-caprolactam at concentrations equal to 1 and 2 wt %, followed by the in situ water-catalyzed ring-opening polymerization of ε-caprolactam and the pilot-scale melt spinning process. Thus, the graphene incorporated in the composite PA6 textile fibers did not significantly change the melting temperature of PA6, but slightly increased its crystallization temperature and crystallinity, which is advantageous for future applications in the synthesis of a linear PA6 polymer suitable for the production of textile fibers. The results indicate a low degree of the achieved GnP disaggregation and deagglomeration, which finally led to the incorporation of microsized graphene particles in the PA6 textile fibers, caused clogging of the filter in the spinning pack, and, consequently, reduced fiber tensile properties. This suggests that the applied parameters of the graphene dispersion in


Hand, foot and mouth disease (HFMD) is a growing threat to children's health, causing a serious public health burden in China. The relationships between associated meteorological factors and HFMD have been widely studied. However, the HFMD burden due to relative humidity from the perspective of attributable risk has been neglected. This study investigated the humidity-HFMD relationship in three comprehensive perspectives, humidity-HFMD relationship curves, effect modification and attributable risks in the Sichuan Basin between 2011 and 2017. We used multistage analyses composed of distributed lag nonlinear models (DLNMs), a multivariate meta-regression model and the calculations of attributable risk to quantify the humidity-HFMD association. We observed a J-shaped pattern for the pooled cumulative humidity-HFMD relationship, which presented significant heterogeneity relating to the geographical region and number of primary school students. Overall, 27.77% (95% CI 25.24–30.02%) of HFMD infections were attributed to humidity. High relative humidity resulted in the greatest burden of HFMD infections. The proportion of high humidity-related HFMD in the southern basin was higher than that in the northern basin. The findings provide evidence from multiple perspectives for public health policy formulation and health resource allocation to develop priorities and targeted policies to ease the HFMD burden associated with humidity. It mostly occurs in children under 5 years old. The disease is mainly transmitted via the fecal–oral route, followed by the respiratory route2. The clinical symptoms in most patients are mild, self-limited and generally tend to disappear or decrease in 7–10 days. Some patients, however, may rapidly develop severe complications, such as central nervous system damage, cardiopulmonary failure, and even death3. In the past few decades, several HFMD epidemics and outbreaks have been reported in the Asian-Pacific region6. In China, HFMD, a primary infectious disease, has the highest annual incidence, ranging from 37.01/100,000 to 205.06/100,0007, compared with all the other notifiable infectious diseases . Moreover, since 2008, the number of reported HFMD cases has been increasing dramatically, and the cumulative number has reached over 22 million, surpassing the numbers of cases in most Asian-Pacific countries8. Based on the severity of the HFMD situation and its growing threat to children, HFMD has recently become an important public health issue that has attracted much attention.Hand, foot, and mouth disease (HFMD) is a common acute infection caused by a series of enteroviruses and coxsackie viruses15. The majority of studies focused on exploring the impacts of temperature and relative humidity on HFMD infections. Numerous studies analyzed and found a consistent relationship between temperature and HFMD. However, for the effects of relative humidity, the existing literature has suggested inconsistent results. For example, early studies generally showed a simple linear relationship. A16 study in Beijing showed that relative humidity was negatively associated with HFMD infections. In contrast, other studies from Thailand17, the Mekong Delta region in Vietnam18, Taiwan19, Shandong20 and Guangdong21 showed positive associations. Recently, epidemiologists have shown increasing interest in explicating potential complicated nonlinear impacts of relative humidity. A study in Hong Kong22 revealed a J-shaped relationship, while another study in Guangdong23 found an increasing risk of HFMD with relative humidity, with 86.9% relative humidity at the peak of the exposure–response curve. The heterogeneity of results might be associated with the differences in geographic locations, socioeconomic statuses and health services.Many epidemiological studies have revealed that meteorological factors tend to have complex nonlinear and delayed effects on HFMD24. Moreover, the impacts of relative humidity on HFMD were primarily quantified by measuring risk ratios  in published studies27, and the extra burden of HFMD attributed to exposure was not adequately assessed. Risk ratios and similar effect estimates are more difficult to understand from a public health perspective, while attributable numbers or fractions are easier to comprehend. Thus, calculating attributable risk indexes, such as the attributable number (AN) or attributable fraction (AF), is appropriate to reflect the overall disease burden associated with exposure factors and provide new evidence for effective interventions, health resource allocation, and predictions of the effects of climate change. Very few studies have explored the morbidity burden of HFMD infection attributable to relative humidity. To the best of our knowledge, no studies have focused on revealing the attributable risk in consideration of the possible complex nonlinear impacts and delayed impacts in the humidity-HFMD relationship. Therefore, the present study quantified the attributable risks of HFMD due to relative humidity based on the complex non-linear and lagged exposure–response relationship in the Sichuan Basin, an area with diverse climatic and socioeconomic characteristics among cities, which provides a more comprehensive and rational insight into the association.Most previous studies constructed single-city time series regressions with different modeling approaches and parameter determinations, which limit the comparability of city-specific findings. Diverse city-level factors may also cause inconsistent results across different cities, limiting generalizability. Therefore, a multicity distributed lag nonlinear modeling approach should be developed in which relationships in each city are assessed, and then the city-specific effects are pooled to explore the source of the heterogeneous resultsThe Sichuan Basin was chosen as our research area based on the following two reasons. First, the Sichuan region has been experiencing a severe HFMD epidemic since 2009, and the annual incidence continues to increase. In 2018, more than 13 thousand cases were reported, which was 6 times higher than that in 2009. Second, the Sichuan Basin has a complex terrain, and the numerous rivers lead to high and variable air humidity. Moreover, 17 cities in the Sichuan Basin have variable degrees of economic development, demographic characteristics, and health services. These factors may modify the effect of relative humidity on HFMD.We thus aimed to analyze the time series association of relative humidity with HFMD in 17 Sichuan Basin cities between 2011 and 2017, explore the modification effects of city-level variables, and quantify the infection burden of HFMD attributed to relative humidity to narrow the knowledge gap left by previous studies.A total of 427,788 HFMD cases were reported in the 17 cities in the Sichuan Basin between 2011 and 2017. On average, the daily number of reported HFMD cases was 10 (range 0–33). According to city-specific total numbers of reported HFMD cases, Chengdu, Meishan, Mianyang, and Deyang were the top 4 cities with the highest numbers of cases  to 52.5% and 51.1%, respectively. We found no significant modification effects for other variables . Figure Table The estimated city-specific ANs and AFs for total relative humidity and high relative humidity above the 75th percentile and low relative humidity below the 25th percentile are shown in Fig. The results quantified the influence of relative humidity on HFMD in children in the Sichuan Basin from multiple perspectives. This research has two main strengths. To our knowledge, this is the first study conducted in a basin region with diverse climatic and socioeconomic characteristics using a multicity statistical analysis strategy to explore the association between relative humidity and HFMD in children. The complex nonlinear and delayed impacts of relative humidity were firstly included to measure the risk of HFMD in children attributable to relative humidity.28 also suggested a monotonic increasing curve in southwestern China. Although research on the exact mechanisms is lacking, some evidence suggests that relative humidity may influence HFMD through several complex pathways. First, enteroviruses, the pathogens of HFMD, can survive in the environment for a period of time, increasing the chance to infect vulnerable individuals29. Relative humidity may impact the survival time and reproductive capacity of enteroviruses, thereby increasing or decreasing the chance of contact30. Second, on days with high relative humidity, infected people may excrete more enteroviruses into the environment31, which could easily attach to the small particles on the surface of toys or in the air32. This process could lead to the accumulation of the virus in the environment, thus accelerating the spread of HFMD. Third, humoral or cellular immunity could be affected by climatic factors33, especially in children with relatively immature immune systems and poor self-care abilities34. As relative humidity increases, body sweats and metabolism are restricted, making children more susceptible to the enteroviruses that cause HFMD. Therefore, the impact of relative humidity should be taken into account for future control and prevention policies for HFMD.This study evaluated the effects of relative humidity in 17 Sichuan Basin cities through a multicity two-stage time series modeling strategy. Consistent with previous studies in this region, this study found an approximate nonlinear J-shaped relationship, in which the risk of HFMD increased with increasing relative humidity. A study in mainland China2 statistics from the Cochran test show moderate heterogeneity among the variables, which is consistent with most previous studies23. Among all city-level variables, geographical region and the number of primary school students remained the two main potential effect modifiers leading to significant heterogeneity. Increased susceptibility to HFMD has been identified in the southern basin, which has high relative humidity. A previous study conducted in Sichuan Province suggested that the dominant HFMD serotypes also showed obvious regional differences, which varied from 2011 to 201735. Different strains of pathogens may have different behaviors due to relative humidity30. In the southern basin, under the condition of high relative humidity, pathogens may be able to thrive depending on humidity, resulting in longer survival times, and have stronger infectiousness. Further research is required to investigate regional differences in pathogen-specific performance depending on meteorological factors in the time scale. Moreover, previous virological studies reported that temperature plays an important role in the survival of enteroviruses and can affect the daily activities of the host and the excretion of enteroviruses36. The southern basin tends to experience relatively high temperatures, increasing the effect of high relative humidity on HFMD38, which may be another mechanism of the impact of relative humidity on HFMD. Similar to the findings of the present research, a study of 8 cities in Guangdong Province23 found that geographic location indicators, such as longitude and latitude, partially explained the heterogeneity of results among cities. Similarly, the effects of regional differences in humidity on HFMD were also observed in the second peak, especially in northwest and south regions, which show increased susceptibility to HFMD. More comprehensive stratified analyses are still needed to examine the season-stratified effects of meteorological factors such as relative humidity on childhood HFMD.In this study, a distributed lag nonlinear model (DLNM) combined with a multivariate meta-regression model was applied to conduct a unified analysis of multicity data to eliminate the possible impact of heterogeneity due to methodology. The I39. On the other hand, children in developed areas with better infrastructure and public transport may also tend to engage in more outdoor activities and have more social contact, thus causing higher HFMD burdens40.On the other hand, primary school students, the crucial HFMD-susceptible group, gather in fixed public places, such as schools, for a long time. More students results in closer the contact between susceptible students and infected students, consequently promoting the spread of HFMD. Notably, GDP growth was also fairly close to reaching significance. On one hand, people who are more economically well off may have greater health awareness and a limited need for extra day care, with higher socioeconomic status therefore corresponding to lower susceptibilityOur results revealed that humidity above the 75th percentile of relative humidity accounted for more than 50% of humidity-related HFMD cases, which is far higher than the proportion of 6% with humidity below the 25th percentile of relative humidity, indicating that the relative humidity-related attributable risk of HFMD primarily depends on relatively high humidity. Therefore, policymakers should increase parents’ awareness of the adverse health effects of wet environments and allocate additional health resources to implement effective public health interventions, especially for children in the northern basin. In addition, the AF attributable to relative humidity varied from 9.72 to 43.27% among cities. In general, the AF associated with high relative humidity in the northern basin was higher than that in the southern basin, while the AF of low relative humidity was higher in the southern basin. Compared with children in the southern basin, children in the northern basin are more susceptible to HFMD under high relative humidity conditions. One explanation is that people change their physical appearance and individual behaviors to adapt to local environmental conditions. The popularity of humidity control devices can help partly reduce the impact of high and low humidity on HFMD infections in children.41. Second, this study took city-level geographic, demographic, economic, health resource and traffic variables into account, but they accounted for only a small part of the heterogeneity. Some other important meta-predictors may also be related to HFMD, such as vegetation coverage and prevention and control measures for infectious diseases, which may partly explain the heterogeneity. However, due to limited access to data, these factors were not considered in this study. In addition, modeling the relationship between weather and infectious diseases while controlling residual autocorrelations by including an autoregressive term of past case counts into time-series regression models remains controversial. Some studies believe that lagged case counts are needed to control autocorrelations42, while other studies argue that including lagged case counts into the causal path of the overall weather-lag-disease relationship would result in underestimation of the true relationship44. In this study, we did not include the autoregressive term in the base model to avoid a possible downward bias.This study has several limitations. First, this was an ecological study, and the meteorological data were obtained from fixed monitoring stations rather than the real exposure of individuals. Therefore, measurement error is inevitable and may be randomly distributed, consequently underestimating the humidity-HFMD associationIn conclusion, this study reveals a J-shaped relationship between relative humidity and HFMD in the Sichuan Basin, though the relationship varied from city to city. Geographic region and number of primary school students were the two primary effect modifiers. High relative humidity contributed to most of the HFMD burden attributable to relative humidity. Our findings can help policy makers and parents improve the negative health effects of low and high relative humidity on child morbidity due to HFMD. These research findings may also provide an important basis from multiple perspectives for relevant public health decision-making and allow the allocation of additional health resources to mitigate the adverse health impacts of relative humidity, especially for people living in regions with relatively high humidity.2. Seventeen cities with a population of over 82 million are present in Sichuan Basin (according to 2014 demographic data). According to the national standard of meteorological and geographic division, the Sichuan Basin is divided into 4 regions .The Sichuan Basin, located in southeastern China, covers a total of 185,757 km46. City-specific clinical HFMD case information, including basic demographic information , disease information  and report information , was obtained from the China Information System for Disease Control and Prevention in the Sichuan Basin from 1 January 2011 to 31 December 2017. The China Center for Disease Control and Prevention assured and assessed the quality of the dataset. Among the dataset, children aged 0–15 years accounted for more than 99.7% of HFMD cases. Therefore, we selected reported HFMD cases in children under 15 years old throughout the Sichuan Basin for further analysis. Finally, we aggregated all cases under the age of 15 with symptom presentation into the daily HFMD counts at the prefecture level in 17 cities in the Sichuan Basin.HFMD was listed as a class “C” notifiable disease for reporting and management in China in 2004. Each HFMD case clinically diagnosed according to a standard guideline should be reported online within 24 h47. For cities with more than one meteorological monitoring station, we selected data from the station closest to the city geometric center14.Daily meteorological surveillance data, including the mean relative humidity (%), mean temperature (℃), minimum temperature (℃), maximum temperature (℃), mean atmosphere pressure (hPa), mean wind velocity (m/s) and sunshine duration (h), were collected from the China Meteorological Data Sharing Service System. For humidity, temperature, atmosphere pressure and wind velocity, the missing values were imputed using linear interpolation; for sunshine duration, the missing values were filled by zero depending on the nature of meteorological data. However, the missing data issue should be negligible given the very small proportion of missing values (approximately 0.1%) and the high quality of the monitoring data. For cities with no meteorological monitoring stations within the city administrative boundaries, data from the station in adjacent cities closest to the city center were usedWe obtained city-level geographic characteristics (region), demographic characteristics , economic characteristics (gross domestic product (GDP) per capita and GDP growth), health resource characteristics  and traffic characteristics (travel passengers) from the China City Statistical Yearbooks from 2011 to 2017. For each city, the arithmetic averages of all the city-level variables were calculated as a proxy for socioeconomic differences among cities in the Sichuan Basin. In the second-stage analysis, these average variables were added as modifiers to explore modification effects on the humidity-HFMD relationship.We used a two-stage multicity time series analysis strategy to analyze data from the Sichuan Basin from 2011 to 2017. First, a distributed lag nonlinear model (DLNM) was applied for each city’s data to obtain city-specific effect estimates. Second, we applied a multivariate meta-analysis to combine the effect estimates obtained from the first-stage analysis, and city-level variables were included to further investigate whether they could explain the heterogeneity between cities. Then, the risk of HFMD attributed to relative humidity was calculated by a backward perspective method.48. A cross-basis function was used to describe the two-dimensional exposure-lag-response association. We used natural cubic splines for the effects of relative humidity with 3 degrees of freedom (dfs) and natural cubic splines for lag effects with 4 dfs. Based on the parametrization in previous studies37 and the incubation period (approximately 3–5 days)46, we chose 3–17 days as the lag range to study the lag structure of the humidity-HFMD relationship. Mean temperature, atmospheric pressure, sunshine duration, and wind velocity were important confounding factors in the humidity-HFMD relationship. Therefore, we controlled for temperature by calculating simple moving weighted averages in the same lag range as relative humidity and a natural cubic spline with 3 dfs. The other three factors were controlled by calculating exponential moving weighted averages in the same lag range as relative humidity. A natural cubic spline with eight dfs per year was used to control for seasonality and long-term trends of HFMD. The day of week and holidays were set as two indicator variables in the model.For each city, we constructed a DLNM with a quasi-Poisson distribution to measure the nonlinear and lag relationship between relative humidity and HFMD50. Based on the intercept-only model, we individually included city-level variables in the intercept-only model to construct univariable meta-regression models to explore effect modifications. The likelihood ratio (LR) test was applied to test the significance of these variables. We identified and quantified residual heterogeneity through multivariate extension of the Cochran Q test and I2 statistic50. Stratified analyses were conducted for the first peak (April–July) and the second peak (September–December) to further examine whether regional differences in humidity effects on HFMD existed in two peaks periods.For the second-stage analysis, to obtain the overall effect estimates of relative humidity in the Sichuan Basin, we reduced the estimates from DLNMs and then fitted an intercept-only multivariate meta-regression model51, was considered the counterfactual exposure reference to calculate the infection burden of HFMD attributable to relative humidity. City-specific minimum-morbidity relative humidity was identified from the city-specific exposure–response relationship curve. We adopted a backward perspective considering that current risk was the accumulation of the exposure effect of a previous period52. We summed the contributions from all days in the series to compute the total number of HFMD cases attributed to relative humidity. The ratio of the number to the actual total cases in the corresponding city or region is the AF. We calculated the components attributable to high relative humidity above the 75th percentile and low relative humidity below the 25th percentile. Empirical confidence intervals (eCIs) were derived by Monte Carlo simulations52.The minimum-morbidity relative humidity, corresponding to the relative humidity associated with the minimum morbidity risk between the 5th and 95th percentiles12 and prior knowledge (see more details in Online Appendix Text Considering the systematic sensitivity analysis strategy proposed by XiaoAll HFMD surveillance data were collected from the China Information System for Disease Control and Prevention. This study was analyzed at the aggregate level and the research protocol was approved by the institutional review board of the School of Public Health, Sichuan University. All methods were carried out in accordance with relevant guidelines and regulations. All of the patients’ records were anonymized and deidentified, thus the need for informed consent was waived by the Ethics Review Committee of the Sichuan Provincial Center for Disease Control and Prevention.Supplementary Information.


Device performance was demonstrated by touch sensing and thermal distribution mapping-based shape detection. Sensor output voltage in the latter application was on the order of 300 μV with a signal-to-noise ratio (SNR) of 35, thus enabling accurate detection of objects of different shapes and sizes. The results imply that films based on multilayer graphene ink are highly suitable to thermoelectric sensing applications, while the ink phase enables facile integration into existing fabrication processes.Emergent applications in wearable electronics require inexpensive sensors suited to scalable manufacturing. This work demonstrates a large-area thermal sensor based on distributed thermocouple architecture and ink-based multilayer graphene film. The proposed device combines the exceptional mechanical properties of multilayer graphene nanocomposite with the reliability and passive sensing performance enabled by thermoelectrics. The Seebeck coefficient of the spray-deposited films revealed an inverse thickness dependence with the largest value of 44.7 μV K Temperature sensing is pivotal in a wide variety of applications, ranging from monitoring environmental conditions  to food −1 [2σ > 600 μW m−1 K−2 [Graphene, the two-dimensional allotrope of carbon, has emerged as an active material in a range of sensor architectures due to its remarkable mechanical durability ,10,11,12−1  and powe m−1 K−2 ,16, rendDespite the favorable characteristics, thermoelectric applications exploiting graphene remain scarcely reported. Previous reports considering the utilization of graphene as an active material for thermoelectric devices include the exploitation of both CVD-grown and solution-processed graphene ,18,19,20The possibility to produce graphene from bulk graphite via a range of liquid-phase methods, e.g., ultrasonic-assisted liquid phase exfoliation (UALPE) ,28, furtWhile different realizations of flexible temperature sensor arrays have been reported previously ,37,38,39The ink approach used to fabricate the graphene film provides a novel and accessible way of preparing flexible and mechanically durable active layers for large-area thermoelectric sensors. Compared to previous temperature-sensing approaches exploiting graphene, the proposed sensor is simple and inexpensive to fabricate, while still comprising multiple pixels, a feature that is lacking in many alternative designs utilizing the thermoelectric effect. The device is demonstrated both as a touch sensor and a real-time thermal distribution sensor, making it suitable for a wide range of applications, for example, in wearable and flexible electronics ,41.The graphene inks were prepared through a process similar to that reported previously . First, 2. The film was then thermally annealed at 375 °C for 30 min to remove the weakly conductive PVP stabilizer from the films, resulting in an order of magnitude increase in electrical conductivity. Following the annealing treatment, 250 nm Al2O3 dielectric and 30/250 nm Ti/Au readout leads were deposited by e-beam evaporation (Edwards E306A) through a shadow mask. External wiring was connected by the use of either silver epoxy  or conductive carbon paste . The produced thermoelectric signals were analyzed with National Instruments SignalExpress software via the NI cDAQ 9213 data acquisition module.The thermal distribution sensor was fabricated as follows: The prepared multilayer graphene ink (45 mL) was spray-deposited onto Kapton polyimide substrate over an area of 6 × 6 cmStructural analysis of the device was carried out using Zeiss Supra 40 scanning electron microscope (SEM), Bruker DekTak XT surface profilometer and WiTec Alpha 300 RA+ Raman spectroscopy equipment. Seebeck coefficients and resistivities of the samples were determined using a Linseis LSR-3 measurement system equipped with a thin film adapter. Five different temperature gradients were used for the measurement and carried out at an average temperature of 34 °C in an inert He atmosphere. The measured temperature difference and voltage values were analyzed using linear regression, and the Seebeck coefficient was obtained from the slope of the fit. Charge carrier mobilities and concentrations of the samples were obtained with Ecopia HMS-5300 Hall measurement system using van der Pauw contact geometry. An infrared camera (Testo 865) was used to acquire reference heat maps for the thermal distribution characterization. The electrical resistance of a sample film was measured under controlled deformation using a Fluke 289 multimeter, as well as under controlled humidity in a closed chamber using a Keithley 2602B source meter.The device architecture was reproduced in finite-element method (FEM) simulation software (COMSOL Multiphysics). The heat directed onto the device surface was modelled as a boundary heat source with a Gaussian spatial temperature distribution. The temperature rise produced by the boundary heat source was limited to 1.2 K, replicating the finite temperature rise resulting from the touch of a finger. Further details concerning simulation parameters and modelling results are included in Figure S5 and Table S1, Supplementary Materials.2O3 dielectric layer separates the remainders of the readout leads from the graphene film. Each readout lead forms an effective thermocouple with the graphene film acting as a common leg. When the contact between a readout lead and the graphene film is heated, a temperature gradient forms between the contact point and the reference ground, resulting in a thermoelectric voltage determined by the effective Seebeck coefficient of the graphene film and the readout lead. As a result, the spatially distributed effective thermocouples act as a sensor array accepting input signals of thermal origin. Since the voltage generation takes place within a single open circuit, the electrical readout wiring of the device is remarkably simplified compared to arrays of individual sensors, requiring only half of the electrical wiring.D/IG ratio of 0.26 before and after the treatment presented in 2D/IG ratio of 0.36 acquired from the post-annealing spectrum [spectrum .2 areas. After annealing, the samples were characterized as summarized in −1 with decreasing film thickness, in sound agreement with the peak voltages obtained in touch sensing experiments. The obtained hole concentration values agreed well with previously reported values for few-layer graphene flakes fabricated via a similar process [−1 K−2 depending on the sample. The results were not largely dependent on film thickness. However, thinner films appeared preferential for sensor application due to both a higher Seebeck coefficient and higher optical transparency, which promotes applications where transparency is important from either a practical or aesthetic standpoint, such as wearable or window applications. Additionally, to observe any possible changes in electrical properties due to deformation of the graphene film, the electrical resistance of a sample film was measured with the film bent to different bending radii, showing only minor changes at the smallest bending radii and thus highlighting the excellent mechanical durability of the films. The relative change in resistance is presented in To investigate the effect of film thickness on the sensor thermoelectric properties, 4, 8, 12 and 16 mL of ink were deposited on 2 × 3 cm process . The decThe transient performance of the device was characterized by carrying out a series of experiments, where the pixels were gently touched by a finger wearing a nitrile glove. Thermal distribution mapping was demonstrated in two separate experiments. First, the device was attached onto the outer surface of a beaker containing warm water at a temperature of 36 °C. The signal was recorded after 15 s, sufficient for the voltage reading to reach a steady state. −1, which corresponded to that of the film of equivalent thickness as presented in Magnitude of the thermoelectric signals in the presented shape detection experiments was typically on the order of 250–370 μV, while thermal noise present in the signals was approximately 8–10 μV. Therefore, the resulting signal-to-noise ratio (SNR) in temperature-based shape detection was roughly 35. SNR was noted to vary depending on the relative temperature between the reference ground and the ceramic piece.2σT κ−1, where κ denotes thermal conductivity, sensing architectures exploiting the thermoelectric effect are poorly compared via these indicators. On the other hand, the thermoelectric sensor performance is largely determined by the Seebeck coefficient, which directs the future search for materials possessing both a high Seebeck coefficient and compatibility with liquid-phase processing.Contrary to the thermoelectric energy harvesting devices conventionally evaluated via power factor or the thermoelectric figure of merit ZT = S−1 [−1 [To assess the performance of the sensor device in comparison with alternative carbon nanomaterials, large-area carbon nanomaterial films with thermoelectric parameters reported are compared to the multilayer graphene films studied in this work. Films made of semiconducting single-walled carbon nanotubes (SWCNTs) have been measured to possess S > 150 μV K−1 . The use−1 ,48. On t−1 [−1 , thus sh2 V−1 s−1 as a function of increasing film thickness [When compared to results from previous studies on p-type liquid-phase exfoliated graphene films, the electronic properties agree well with atmospherically doped films . Howeverhickness , agreeinIn applications of graphene requiring optical transparency, the desired performance is often achieved by reducing the film thickness, resulting in reduced electrical conductance . ConcurrInk formulation similar to the one utilized here has previously been optimized for inkjet printing , paving The demonstrated large-area thermoelectric performance of ink-based graphene films suggests that the concept could be scaled to suit fabrication methods such as gravure and flexographic printing, both of which have already been experimented with 2D material-based inks ,53. In aIn summary, a large-area thermal distribution sensor utilizing a thermoelectric film deposited from low-cost multilayer graphene ink was presented. The inexpensive and facile sensor configuration could detect thermal input from intermittent heat sources, such as human touch, as well as detect objects based on temperature distribution mapping. The finite-element model utilized in studying the device performance provides an accurate reproduction of the concept and allows for optimization of device parameters for the application at hand. The applied fabrication processes are readily scalable into high volumes, which, together with the simple device architecture, offers an accessible route to using multilayer graphene for thermoelectric sensing in application areas such as wearable and flexible electronics.


Findings from molecular studies suggesting that several infectious agents cause sarcoidosis are intriguing yet conflicting and likely biased due to their cross-sectional design. As done in other inflammatory diseases to overcome this issue, prospectively-collected register data could be used, but reverse causation is a threat when the onset of disease is difficult to establish. We investigated the association between infectious diseases and sarcoidosis to understand if they are etiologically related. We conducted a nested case–control study (2009–2013) using incident sarcoidosis cases from the Swedish National Patient Register (n = 4075) and matched general population controls . Infectious disease was defined using inpatient/outpatient visits and/or antimicrobial dispensations starting 3 years before diagnosis/matching. Adjusted odds ratios (aOR) of sarcoidosis were estimated using conditional logistic regression and tested for robustness assuming the presence of reverse causation bias. The aOR of sarcoidosis associated with history of infectious disease was 1.19 . Upper respiratory and ocular infections conferred the highest OR. Findings were similar when we altered the infection definition or varied the infection-sarcoidosis latency period (1–7 years). In bias analyses assuming one in 10 infections occurred because of preclinical sarcoidosis, the observed association was completely attenuated . Our findings, likely induced by reverse causation due to preclinical sarcoidosis, do not support the hypothesis that common symptomatic infectious diseases are etiologically linked to sarcoidosis. Caution for reverse causation bias is required when the real disease onset is unknown.The online version of this article (10.1007/s10654-020-00611-w) contains supplementary material, which is available to authorized users. The etiology of sarcoidosis remains to this day a mystery . The conIn fact, infectious agents gained the most research attention throughout the years largely due to the clinical and histological similarities between sarcoidosis and tuberculosis. Except mycobacteria , 5, the Findings from epidemiological assessments are equally conflicting. Exposure to moldy environments rich in aerosolized infectious agents , and theQuantifying the potential contribution of infectious disease to the etiology of sarcoidosis has profound implications for clinical practice and research, facilitating diagnosis and treatment and guiding future research efforts. To determine whether infectious disease could be etiologically linked to sarcoidosis development, we performed a nested case–control study using information derived from Swedish population-based registers. Our objective was to estimate relative risks of sarcoidosis associated with a history of infectious disease, overall, by sarcoidosis phenotype, type, and temporal proximity of infectious disease to sarcoidosis diagnosis and test the robustness of our findings in the presence of reverse causation bias.We conducted a case–control study nested in the Swedish population (2009–2013). We used the National Patient Register (NPR) to identify individuals with sarcoidosis. The NPR holds high quality data on hospitalizations (nationwide since 1987) and outpatient visits to specialists in public and private practices since 2001. Visits were coded using the International Classification of Diseases (ICD) system . IndividIndividuals with progressive sarcoidosis or debilitating symptoms receive pharmacologic treatment, commonly, systemic corticosteroids, methotrexate, or azathioprine in Sweden , 26. BecUp to 10 controls per case were sampled from the general population at index date from the Total Population Register and matched on birth year, sex, and residential location. Only controls who lived in Sweden at the date their matched case had their second visit for sarcoidosis and had no hematopoietic or lung malignancy within 6 months before or after the index date were included in the analyses.Ethical approval for this study was granted by the Regional Ethics Review Board in Stockholm (Decision No. 2014/230-31).History of infectious disease was defined using information on visits in the NPR listing an ICD code for infectious disease and/or antimicrobial dispensations in the Prescribed Drug Register. Thus, only symptomatic infections listed in Table S1 that led to an interaction with healthcare were captured by this exposure definition. The primary definition was at least one inpatient or outpatient visit listing an ICD code for infectious disease as primary or contributory discharge diagnosis. To approximate infectious disease severity and reduce misclassification, we restricted to hospitalizations, or to visits where infection was the primary discharge diagnosis, or required two or more visits for infection. To capture infections diagnosed and treated solely in primary care, we expanded the primary definition to include at least one dispensation of an antimicrobial medication.Sarcoidosis at a preclinical stage may last for more than 2 years before diagnosis in some cases , 29–31, We obtained demographic information from the Total Population Register including the birth date , sex, birth country , civil status , and county of residence at diagnosis/matching, which we classified into six healthcare regions . From the Longitudinal Integration Database for Health Insurance and Labor Market Studies, we retrieved data on education  and annual gross salary adjusted to 2014 inflation rate  of the yWe hypothesized that a higher genetic susceptibility to or the presence of a comorbid autoimmune disease in sarcoidosis might confound the association between history of infection and sarcoidosis development . To adjuUsing conditional logistic regression models, we estimated odds ratios (OR) for sarcoidosis associated with a history of infectious disease. We reported ORs adjusted for matching factors and ORs further adjusted for deciles of a high-dimensional propensity score estimated separately for each analysis. As detailed in the Supplement, we used a semi-automated algorithm  and dataWe performed several other analyses. First, we calculated the population attributable fraction (as outlined in the Supplement) to assess the contribution of infectious disease to sarcoidosis occurrence. Second, we examined the odds of sarcoidosis conferred by specific types of infection . In addition, we used acne as a proxy of infection by propionibacteria to corroborate reports on their role in sarcoidosis, and urinary tract infections as negative control. Third, we varied the latency period from 0 to 7 years to investigate whether there was effect measure modification by the time since infectious disease diagnosis. Last, to examine if ORs varied by sarcoidosis severity around diagnosis or phenotype, we estimated the association separately for treated and not treated sarcoidosis, and for Löfgren’s and non-Löfgren’s disease, the latter using data from our clinical cohort.Despite the fact that exposure and outcome were collected at least 3 years apart, a critical concern was differential exposure misclassification (reverse causation bias). That is, compared to the mostly healthy controls, individuals who were diagnosed with sarcoidosis were more likely to receive the diagnosis of infection due to undiagnosed preclinical sarcoidosis. Another concern was potential confounding of the association by smoking, data for which we did not have. To check the robustness of the conventional analyses against these two systematic errors, we performed a series of probabilistic bias analysis described in detail in the Supplement. In brief, we estimated bias-corrected ORs from three simulation analyses for differential exposure misclassification and from one simulation for unmeasured confounding. For reverse causation bias, we opted for three bias scenarios to account for the uncertainty around the bias parameters because data on the proportion of cases that had preclinical sarcoidosis were not available .Data were managed and analyzed using SAS software version 9.4  and R version 3.5.3 .We included 4075 newly diagnosed sarcoidosis cases and 40 688 matched general population controls. Cases and controls were on average 51 years old (SD 15.2), 45% were female, and comparable with respect to birth country, civil status, education, and salary  for developing sarcoidosis associated with a history of symptomatic infectious disease resulting in healthcare interaction. This weak association, which did not vary by time between infection and sarcoidosis diagnosis, attenuated completely when tested for reverse causation bias. Most of the observed risk was conferred by infections at sites related to sarcoidosis (e.g. respiratory), but associations with infections at unrelated sites (e.g. urinary tract infections) were also observed. We believe that these contradictory findings are a result of reverse causation bias induced by preclinical sarcoidosis, which may last for years in some cases before symptoms lead to sarcoidosis diagnosis.At first glance, several of our findings  sarcoidosis that spans years before diagnosis. In support of this notion, studies in sarcoidosis have shown that individuals appear to enter a diseased state characterized by sick leave absence, impaired productivity, visits to the doctor, and increased medication dispensing many years before diagnosis is established , 30, 37.In addition to findings from bias analyses, many of the main results support this alternative explanation when more carefully interpreted Table . First, Second, and contrary to previous studies on the role of propionibacteria –9 or mycThird, ORs of sarcoidosis did not vary with time since infection (except when no latency period was used) suggesting that there was no time window during which an infectious agent might have triggered sarcoid inflammation. This finding is in line with our hypothesis that immune disturbance is present for years before sarcoidosis diagnosis. However, we should emphasize that this observation does not preclude the possibility that a pathogen was either dormant or acted more than 7 years before sarcoidosis was diagnosed, or even that infection was simply asymptomatic.Last, assuming that individuals treated for sarcoidosis around diagnosis probably due to sarcoidosis severity are more likely to have a higher burden of immune disturbance compared to individuals with non-severe disease could explain the higher OR of treated sarcoidosis versus a very small increased OR of non-treated sarcoidosis. Based on the distinct genetics , seasonaOur findings should be interpreted in light of the limitations of this study. In contrast to molecular studies, we could not explicitly identify specific pathogens; hence, the effect of one or more infectious agents may have been masked. To mitigate this issue, we used several definitions of infectious disease including healthcare visits and antimicrobial medication dispensations. Moreover, some misclassification of sarcoidosis is expected as we relied on ICD codes and results of histological examinations, bronchoscopies, or imaging were not available. As previously shown , 27, eveDespite the challenges, using prospectively collected population-based data provided us with the necessary power for robust inference, and most importantly, allowed us to address (at least in part) the issue of reverse causation bias and use methods to effectively capture otherwise unmeasured confounding. Finally, we believe that our findings are transportable to populations that are similar in terms of environmental exposures, genetics, and standard of care to the Swedish context.In this large case–control study we observed a weak association between symptomatic infectious disease and the development of sarcoidosis, which is identifiable years before sarcoidosis diagnosis. This association attenuated in bias analyses for reverse causation bias. Overall, we could not find enough evidence implicating common symptomatic infectious diseases in the etiology of the sarcoidosis. On the contrary, we believe that a latent immune disturbance might be the main reason behind the slightly higher burden of infectious disease observed before sarcoidosis diagnosis is established. Despite the challenges, large prospective studies with regular longitudinal examinations are warranted to further investigate this. Additionally, understanding the underlying immunologic mechanisms of this preclinical period is of critical significance to prevent or shorten the time to diagnosis and improve the prognosis of sarcoidosis. Until then, caution for reverse causation bias is required when using register data to define the exposure when the real disease onset is unknown.Supplementary material 1 (DOCX 647 kb)Below is the link to the electronic supplementary material.


Graphene-based hyperbolic metamaterials provide a unique scaffold for designing nanophotonic devices with active functionalities. In this work, we have theoretically demonstrated that the characteristics of a polarization-dependent tunable hyperbolic microcavity in the mid-infrared frequencies could be realized by modulating the thickness of the dielectric layers, and thus breaking periodicity in a graphene-based hyperbolic metamaterial stack. Transmission of the tunable microcavity shows a Fabry–Perot resonant mode with a Q-factor > 20, and a sixfold local enhancement of electric field intensity. It was found that by varying the gating voltage of graphene from 2 to 8 V, the device could be self-regulated with respect to both the intensity (up to 30%) and spectrum (up to 2.1 µm). In addition, the switching of the device was considered over a wide range of incident angles for both the transverse electric and transverse magnetic modes. Finally, numerical analysis indicated that a topological transition between elliptic and type II hyperbolic dispersion could be actively switched. The proposed scheme represents a remarkably versatile platform for the mid-infrared wave manipulation and may find applications in many multi-functional architectures, including ultra-sensitive filters, low-threshold lasers, and photonic chips. Metamaterials can be realized via different architectures having been designed and manipulated to realize exotic effects arising from their structure sequence and configuration. Recently, the attention in metamaterial research has primarily been directed towards tunable and reconfigurable structures that can be used for an active wavefront shaping. This can serve as an effective tool for regulation and switching of electromagnetic waves spreading in multifunctional structured devices10. Active metamaterial functionalities can be obtained using liquid crystals14, superconductors16, nonlinear media18, and carrier injection schemes20. Nonetheless, despite the numerous available methods, studies on active graphene-based metamaterials are of a particular importance22. This is because their properties can be reversibly switched by changing the chemical potential of graphene sheets via electric biasing or optical pumping. Among the different classes of metamaterials, graphene-based hyperbolic metamaterials (GHMMs) have gained considerable attention because of their unique hyperbolic isofrequency, i.e., their permittivity and permeability tensor elements  are of opposite signs, resulting in a strong anisotropy, support for propagating high k-modes, and an enhanced photonic density of states32. Graphene, as a completely two-dimensional (2D) material that only conducts in the plane, shows an anisotropicity that is appropriate for hyperbolic metamaterials (HMMs). Considering an extraordinary wave (TM polarized) in a uniaxial medium, the isofrequency relation can be stated as ω is the wave frequency, and c is the speed of light31. Hence, such a medium has a dielectric response defined by a tensor εx = εy = ε∥ and the out-of-plane component is εz = ε⊥. The spherical isofrequency surface of the vacuum distorts to an ellipsoid for the anisotropic case. However, when extreme anisotropy is present, such that ε∥ ⋅ ε⊥ < 0, the isofrequency surface opens into an open hyperboloid2. Thereby, the same material may affect the incident radiation as a metal or as a dielectric depending on the orientation of the wavevector 33, intensified spontaneous emission34, negative refraction35, strengthened superconductivity36, and increased reflection23. Importantly, HMMs are most commonly created with two-structure classes such as metal-dielectric multilayers29 and metallic nanorod arrays37. In the case of GHMMs, graphene provides conducting electrons making the extreme anisotropicity achievable. As the thinnest material available, graphene also functions as a perfect building slab for multilayer systems, as it enables the minimum possible period and, consequently, the highest possible cutoff for the high k-modes; this has been limited in semiconductor and metal-based HMMs by the high thickness of those materials30. The unusual effects mentioned above have increased a significant amount of theoretical studies focused on the utilization of graphene as an active constituent of HMMs29. Nevertheless, the reported properties relate mainly to the potential of the active switching hyperbolic properties of graphene/dielectric planar heterostructures. Additionally, studies investigating the use of GHMMs as effective photonic devices remain insufficient; therefore, a further progress is necessary.The introduction of artificially engineered composite materials that function as electromagnetic metamaterials has allowed for a novel light-matter interaction at subwavelength scalesεx = εy = ε∥ and the out-of-plane component εz = ε⊥ allowed for determination of the topological transition between elliptic and type II hyperbolic dispersion.In this work, we report a polarization-dependent tunable microcavity based on a GHMM operating in the mid-infrared (mid-IR) frequencies. The operating principle of a tunable device was based on the thickness modulation of the dielectric layers in a GHMM stack. Thereby, breaking the periodicity in a GHMM is manifested in cavity resonant modes. We have modeled a stack consisting of 20-unit cells in which gaps between the graphene sheets are supposed to be filled with a dielectric of varying thickness described by a triangle wave function. It was shown that a subwavelength confinement attributed to volume plasmon polaritons can be optimized and efficiently controlled by acting both on the base dielectric thickness that we modulate and on the gate voltage of graphene. Our calculations proved that the obtained microcavity can be reversibly tuned with respect to both intensity (up to 30%) and spectrum (up to 2.1 µm) by varying the voltage-dependent sheet conductivity of graphene from 2 to 8 V; thus, adjusting the Fermi level via the chemical potential allowing for an ultrafast electrical modulation. Additionally, switching of the device was tested over a wide range of incident angles for both transverse electric and transverse magnetic (TE/TM) modes. Finally, determination of both the in-plane isotropic components ,23. Based on our recent studies, here, we set this value to a constant 23, and the thickness of the graphene monolayer was set to Full-wave numerical simulations of the tunable graphene-based microresonator were performed using Lumerical FDTD Solutions software which employs an algorithm based on the conformal finite-difference time-domain method. The modeled HMM geometry shown in Fig. 31:The effective medium permittivities for an anisotropic multilayer composite were computed using the effective medium theory (EMT). The method follows a generalized Maxwell–Garnett approach to obtain analytical expressions for the effective permittivity in parallel (||) and perpendicular (⊥) directions defined below for the multilayer metamaterial38:40. Without taking into account the external magnetic field, the isotropic surface conductivity intra and the inter-band term inter:5. The chemical potential (22:6 m/s), 16 m−2 V−1 is an empirical constant, and 21. In the sub-wavelength limit, the graphene-dielectric layered architecture can be examined as a homogeneous effective medium with the anisotropic permittivity tensor 32.If we assume that the electronic band structure of a graphene sheet cannot be affected by the neighboring graphene sheets, the effective permittivity of graphene mentclass2pt{minim2 averaged material data. To design the GHMM-based microcavity, the thickness of the dielectric layers in the HMM stack was modulated. The modulation was based on a half period triangle wave function, and the base dielectric thickness in the first unit cell was set as In the proposed model, the dielectric permittivity was set to a constant It should be noted that the dielectric thickness increased until reaching a maximum value in the middle of the stack and, then, it decreased to reach the initial value again in the last unit cell.−7. The described methodology allowed us to adapt the GHMM to design a tunable hyperbolic microcavity in the mid-IR frequencies.In our analysis we used periodic boundary conditions (PBC) for normal incidence, and broadband fixed angle source technique (BFAST) boundary conditions for obtaining the angular characteristics. In addition, to ensure both stability of the algorithm and high accuracy of calculations, we used uniform spatial grid with 100 nm step and additional mesh override regions around graphene layers with 1 nm step and 10 nm buffer; the auto shutoff threshold was set as 10z direction, as depicted by white arrows. In Fig. 23. In addition, the wavelength at which this transition occurs can be self-regulated via a voltage-controlled chemical potential. Then, using the procedure described above, we modulated the dielectric thickness in the HMM stack with a triangle wave function for In order to demonstrate that modulation of the dielectric thickness in the GHMM stack results in a microcavity, we designed two stacks, i.e., a stack with an unmodulated (constant) dielectric thickness and a modulated one. Then, the spatial distribution of their electric field intensities was studied, as shown in Fig. entclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minima41 and by Avrutsky et al.42. Graphene-based hyperbolic microcavity supports plasmonic modes which are commonly denominated gap plasmon polaritons (GPPs) and are strongly confined inside the stack43. Noteworthy is the fact that when the number of adjacent nanocavities (intended as the number of dielectric slabs loaded between two graphene sheets) increases, a collective repulsion of these modal indices appears. This interplay between these cavity plasmon modes stimulates their hybridization44, thus giving rise to extremely confined volume modes called volume plasmon polaritons (VPPs)45. Indeed, when the parameters of a subwavelength graphene-based multilayer composite are modulated (or superstructured) on a larger, wavelength scale, the propagation of VPPs in the resulting multiscale hyperbolic metamaterials is accompanied by photonic-band-gap phenomena. In our case, modulation of the dielectric thickness with a triangle wave function enabled us to achieve a great degree of control over such plasmons in the entire volume of the stack. It should be noted that when the stack geometry is periodic , stop bands due to Bragg reflection form within the volume a plasmonic band47. Contrarily, when cavity layers are sandwiched in an otherwise periodic stack, resonance peak of the Fabry–Perot (FP) nature arises within the stop band. Specifically, mid-IR waves reflecting from a partially reflecting graphene separated by a distance described by the triangle wave function can interfere constructively and form a resonating FP mode. At this point, it is worth noting that instead of mirrors (used in most classic resonance cavities) as a separate section, interface of dielectric/magnetic materials can act as a reflecting surface depending on the strength of dielectric permittivity (εr) and magnetic permeability (μr), and it can support FP modes in varieties of dielectric resonators48. A rectangular bar of silicon nanowire and dielectric nano ribbons are selected examples for supporting this statement50.An extensive explanation of the light propagation inside anisotropic multilayer composites has been given by Meierg = 2 V and Vg = 8 V, the transition from elliptic to type II hyperbolic dispersion can be tuned effectively up to ~ 1.9 μm for unmodulated and up to ~ 2.2 μm for modulated GHMM. Furthermore, by modifying the base dielectric thickness ε⊥) is constant while only the parallel permittivity component (ε∥) changes with wavelength. Interestingly, the change in the gate voltage of graphene does not affect the perpendicular permittivity of the HMM and, therefore, the dashed lines in Fig. In order to determine the maximum tunability of both unmodulated and modulated GHMM, based on the method described above, we determined real part of the permittivity tensor components as a function of a wavelength for two extreme voltages , as depicted in Fig. 52. Therefore, the presented concept based on GHMMs seems to be particularly promising. Additionally, unlike classic resonant microcavities, the Q-factor increases with increasing electrical stimuli, and thus the efficiency of the device also increases. It is crucial in state-of-the-art technologies, as it gives the possibility of increasing the Q-factor and tunability at the same time.The above-mentioned properties of the hyperbolic microcavity, i.e., enhancement of the electric field intensity, shift of the FP resonance spectral position, and microcavity Q-factor can be expressed as a function of both, the base dielectric thickness In the last step, we studied the angular reflection characteristics, as well as the spectral position of the dip (corresponding to the FP peak in transmission) for both the TE and TM modes for the hyperbolic microcavity, as shown in Fig. 52. The practical implementation of our scheme allowed a blue/red shift of the transition wavelength by adjusting the base dielectric thickness or controlling the Fermi level of graphene. The latter is especially effective if accomplished via electrical tuning54. Hence, shifting the transition wavelength further into the IR can be carried out by adopting the lower chemical potential of graphene or a thicker dielectric. Taking into account the angular reflection spectra, this class of microcavities was sensitive to the incident light angle/polarization, which is crucial in applications such as angular-selective and polarization-dependent plasmonic architectures. It is worth noting that in multi-layer graphene structures is interlayer charge screening effect55. However, it can be seen that the carrier density share of the 3rd layer is already quite low, and that of any layer above the 3rd layer is negligibly small55. We believe that the concept outlined here provides a novel and promising platform for a variety of applications that depend on a mid-IR energy enhancement and precise spectral switching of the electromagnetic resonances.In this work, a novel technique to design a tunable hyperbolic microcavity utilizing GHMMs was proposed and theoretically demonstrated. Further, we demonstrated a scheme for modulating the dielectric thickness in the HMM stack via the use of a triangle wave function. Therefore, the proposed plasmonic architecture allowed us to fully use the structural properties of graphene-based multilayered HMMs as a fundamental element of the active hyperbolic microcavities. The results clearly show that the resonance of the metadevice could be continuously and reversibly switched both with respect to its intensity (up to 30%) and wavelength (up to 2.1 µm) in a controllable manner. Furthermore, our calculations indicated that the graphene-dielectric multilayer structure displays an optical topological transition from an elliptical to a type II hyperbolic dispersion in the mid-IR range, confirming the theoretical predictions from previous works


Evidence is sparse and inconsistent on the role of a dual marker strategy (DMS) combining Copeptin with cardiac troponin T (cTnT) for instant rule-out of a non-ST-segment myocardial infarction (NSTEMI) when high sensitivity cardiac troponin T (hs-cTnT) is used.Data on 10,329 patients from 5 trials were pooled to evaluate initial Copeptin in combination with hs-cTnT against a single marker strategy (SMS) based on hs-cTnT < limit of detection. Endpoints were sensitivities and negative predictive values (NPV) for rule-out of NSTEMI, 30-day all-cause mortality and rates of applicability for DMS or SMS.p = 1.0], but applicability was 2.4-fold higher  with DMS than SMS. In a secondary analysis on DMS after inclusion of high risk patients, performance and applicability were similar.NPV for rule-out of NSTEMI was high, exceeding 99.0% for the lower limits of the 95% confidence intervals (99.0% vs 99.2%) for DMS and SMS, and NPV for all cause death at 30 days was similar with very low mortality after rule-out [0.07% (0.0–0.4%) vs 0.0% (0.0–1.2%), Findings corroborate the 2015 European Society of Cardiology recommendation to use dual marker strategy for instant rule-out of NSTEMI, extending evidence to hs-cTnT. Novel data demonstrate a comparably safe and effective instant rule-out with Copeptin in combination with hs-cTnT versus a single marker strategy based on very low hs-cTnT but a more than twofold higher applicability of the dual marker strategy without the need to exclude very early presenters or other important subgroups.Dual marker strategy using hs-cTnT at 99th percentile and Copeptin versus ESC 0-h immediate rule-out based on hs-cTnT < limit of detection The use of high-sensitivity cardiac troponin (hs-cTn) assays has enabled acceleration of the diagnostic process of acute myocardial infarction (MI) in patients presenting with suspected non-ST-segment elevation acute coronary syndrome (NSTE-ACS). The various options recommended by current ESC guidelines  include Both strategies have shortcomings that have to be balanced against their advantages. In particular, the single hs-cTn-based strategy (SMS) requires an appropriate interval of 2–3 h between onset of symptoms and blood sampling to avoid under-diagnosis of MI due to the “troponin blind interval”. In addition, the single low hs-cTn strategy can only be applied for a small proportion 10–25%) of eligible candidates for rule-out .The most striking difference was the 2.3-fold higher applicability of DMS versus the single hs-cTnT strategy  and sensitivities [96.2% (95%CI 93.8–97.7) vs 99.6% (95% CI 97.8–99.9)] to rule out an NSTEMI, and a consistently higher applicability [58.7% (95% CI 57.1–60.2) vs 27.9% (95% CI 26.2–29.7)] of DMS versus SMS.Likewise, negative predictive values of DMS vs SMS were similar for prediction of death at 30 days [99% (95% CI 99.6–99.9) vs 100% (95% CI 98.8–100)].Thus, the diagnostic and prognostic performance of DMS remained high despite inclusion of high-risk patients, while rates of suitable candidates for the algorithm (applicability) slightly declined as only a fraction of the patients at high risk are anticipated to present with normal values of hs-cTnT and Copeptin. As shown in Fig. These data extend available information on the usefulness of DMS combined with hs-cTn, particularly for the Roche 5th gen. hs-cTnT assay at the 99th percentile cutoff. In addition, accumulating evidence from the Biomarker-in-Cardiology 8 (BIC-8) trial, a large randomized intervention trial  backed bA potential disadvantage, that hitherto has limited the faster adoption of DMS, is the need to run a non-fully automated analyzer , 3. Neve