We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa . The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance. When electromagnetic waves are incident on a material’s surface, some part of it is absorbed and some of it is reflected back. The reflected electromagnetic waves from a surface depends on its material composition, physical and chemical properties, surface roughness and the geometry of incident and reflected waves. The behavior of reflected waves from a surface in a particular wavelength region is known as the spectral signature of that material. To obtain the spectral signature from a surface, devices like spectrophotometers and hyperspectral cameras are used. The advantage of hyperspectral devices is the acquisition of high resolution spectral and spatial information of a material or scene, whereas the spectrophotometer is able to acquire spectral information from one point of the selected surface.For the acquisition of hyperspectral images, push broom cameras have been extensively used . In the The analysis of hyperspectral images is performed for image data exploration, classification and quantification tasks. There are various multivariate analysis techniques that are applied to hyperspectral images for data decomposition, pre-processing and performing regression or classification analyses. Some of the methods for hyperspectral data analysis include the principal component analysis , linear Hyperspectral images contain more spectral information compared to color images. The high dimensionality of hyperspectral image data is an open challenge, and a trade-off between high information content and practical handling is often required. To evaluate this compromise and assess its performance, high quality data with high spatial and high spectral resolutions are required. Creating such a dataset takes time and effort, but it is necessary to the research community.Considering these needs, we present a hyperspectral image dataset of 112 textured objects falling into five different categories. Images are reflectance data that span the visible and near infrared (NIR) parts of the electromagnetic spectrum. The data is referred to as HyTexiLa . The idea behind creating this dataset is to provide a platform for the benchmark analysis of various applications and processing. The areas where this data could be used are in the fields of image processing, computational imaging and computer vision, such as surface identification, spatio-spectral analysis of textured surfaces, image sensor simulation, color reproduction, image relighting and so on. The availability of a high spectral and spatial resolution dataset will provide easy access for the evaluation of different techniques and will allow the results to be compared. In this paper, we present that dataset and analyze its spatial and spectral properties. We define the image acquisition protocol, the distortions that occur during the acquisition of the objects, the method for the correction of such distortions and the effect of such corrections. Our focus in this paper is to present a hyperspectral dataset to the community and to provide an analysis on our dataset as a benchmark for further research. We provide the methodology for pre-processing the raw hyperspectral data before using it for the intended applications. This paper is organized as follows: First we analyze the existing hyperspectral datasets and then, in Several hyperspectral image datasets are publicly available. In this section, we provide an overview of the existing hyperspectral image datasets that consist of close range images, including natural scenes and indoor images of objects. In this section, we provide a brief review of existing hyperspectral image datasets and compare them with the properties of our proposed dataset. We do not include the remote sensing datasets in this section, because remote sensing does not assume the same imaging model. We also exclude reflectance datasets obtained from pointwise spectrometers because they will not contain any spatial relationship between spectra as well as those multispectral image datasets which contain less than 10 channels.asets in ,43 have In this paper, we provide a new dataset of hyperspectral images that has been made publicly available. This dataset consists of samples from five different categories of materials. The spectral range of this dataset is from Our dataset was spectrally sampled at about In this section, we present and discuss the objects being imaged and the hyperspectral image acquisition setup. The issues faced during image acquisition and protocols to correct for those issues are also part of this section. We describe how we correct for distortions inherent to the system and how we compute reflectance images from the input radiance data.K channels of K spectral bands, centered at wavelengths k-th channel of the respective hyperspectral images. A pixel coordinate, p, of an image is denoted by a couple of bounded integer values: We first list the notations and conventions used in this article. Functions are usually denoted in roman font, with sets in calligraphic font and scalar variables in italic font. All indexes start from 0, and The different objects chosen for the dataset were divided into five classes: textile, wood, vegetation, food and stone. These samples are a set of textured object surfaces that exhibit different properties while remaining fairly flat. The imaged objects were reasonably flat so that the blurring effect due to distance variations from camera was minimal during the scanning process. After the completion of image acquisition, we analyzed each image thoroughly and discarded a few samples which were too blurry. We also excluded materials that exhibited high gloss in the considered viewing conditions to maintain a simple imaging model, as in Equation , which aDon De Soie located in Templeuve in France. In the wood category, there are 18 different samples of wood that can be found in the area around Mjøsa lake in Norway. There are 15 images for the vegetation category, which consist of leaves, flowers and one mushroom. The food category consists of 10 samples of tea, coffee and spices, and there are four samples in the stone category. Image labels include the material category, the acquisition sequence number and a specific name if the material has been precisely identified. For the textile, wood, and vegetation samples, the labels also include the color of the material (suffix _colorname if there are at least two samples of the same type but with different colors) and/or the acquisition side . We provide the sRGB rendering of each image along with the dataset for natural visualization of the imaged object. The sRGB values were computed from colorimetric computation on the spectral data , while the height, N, is the number of pixels in the scanning direction (y-axis) and is physically bounded by the size of the scene and the translation stage. During image acquisition, we observed spatial distortions in the output radiance images, which are addressed in the next section.The output of the HySpex VNIR-1800 camera consists of a radiance image, x-axis and a shear distortion on the y-axis of the output images. These distortions were observed during the acquisition of test targets . The correction of these distortions can be modeled by two functions, We observed a cross-track distortion on the The function urer see . The funtion see .The acquired images present a distortion across the scan direction called cross-track distortion. This distortion is due to the fact that each pixel is associated with a surface element whose size varies and is the same for each line of a channel and for each channel. x coordinate. Note that To correct this distortion on acquired images, we measure the cumulative ratio Finally the sensor correction The pixel coordinate is rounded to the nearest integer to avoid artifacts due to the interpolation of pixel values.y-axis due to the geometry of the set-up. Indeed, the direction to which the material moves and the line sensor are not exactly perpendicular. This implies that a rectangle in the scene becomes a parallelogram in the acquired image (shear distortion). This can be assumed to be an affine transform, leading to the correctiona and b. The pixel coordinate is again rounded here to the nearest integer to avoid interpolation artifacts. These parameters are experimentally estimated using the following method: we acquire 10 images of a grid with different rotations . We use the Otsu and Hough techniques here since they require few parameters and are adequate for the estimation of lines in these simple grid images which are just made of dark straight lines on a light background. The angular resolution for the Hough transform is set to a and b giveAfter the sensor correction, the image still exhibits a distortion on the rrection∀∈[Msholding  and Hougsholding  for all Equation . It can x-axis (Equation (y-axis (Equation (Equation ) or y-axx-axis. Then, we measure the pixel resolution in pixels·mm−1 thanks to the spaces between the markings of the single-channel image resulting from an average on 19 spectral channels without corrections—after sensor correction and after sensor and affine correction. To quantify the impact of the sensor and affine correction on the pixel resolution, we acquire an image of a ruler with equally spaced markings of 1 mm along the x-axis which means that the sensor correction correctly decreases the cross-track distortion. Moreover, we notice that the pixel resolution, on average, is close to the theoretical one given by −1 . After sensor correction or both corrections, the pixel resolution is constant w.r.t. the x coordinate (sides of squares remain constant) which means that the cross-track distortion has been reduced. Moreover, we observe on the image after both corrections that the shear distortion has been reduced (squares have right angles).To illustrate the impact of the corrections, images of the 49th channel ∀k∈[Reflectance images are cropped to As a reference, we also compute the reflectance image of a Macbeth ColorChecker. ments in , reflecthttp://color.univ-lille.fr/datasets and https://www.ntnu.edu/web/colourlab/software). Each one of the 112 texture images is provided as both (i) a hyperspectral reflectance image in ENVI format and (ii) a sRGB reconstructed image in PPM format. (i) The ENVI format consists of two files: a binary file (extension .raw) of single-precision floating-point variables (32-bit) that stores the raw hyperspectral reflectance image in the BSQ  data organization (channel by channel) and a text file (extension .hdr) which contains the metadata of the image. The .hdr text file holds:A description of camera parameters . These metadata were written by the HySpex acquisition software and we decided to keep them unchanged.Required information to open the associated binary raw file ,Default bands  whose band centers match with the primary Red, Green and Blue of sRGB standards. Generally these are used to generate false color RGB images using three channels.Wavelength array that contain values of Illumination array that contains the values of illumination Equation . The illEquation . The aveMoreover, we provide ImageJ plugins and Python code for opening and processing the data. Matlab users can call the following instruction to open one hyperspectral reflectance image:R = multibandread; These codes are available as The dataset is available on the websites of the team/laboratory of the authors (PPM format. (ii) The reflectance images are converted from the 186-channel domain to the sRGB color space. This transformation is performed using the CIE XYZ 2° standard observer functions. Then, the reference white D65 is used to convert values from XYZ to the sRGB color space. The resulting sRGB images are provided in In hyperspectral images, the adjacent spectral bands are highly correlated . Even thThere are various methods used for the computation of the effective dimension of spectral data, which can mainly be divided into supervised and unsupervised techniques. Supervised methods aim to preserve the a priori information, while unsupervised methods do not assume any prior information about the input data. Among the unsupervised methods, principal component analysis (PCA) ,61 and iDespite several previous studies being performed, there is still no agreement on the number of effective dimensions to represent the spectral data. There are different conclusions by various authors for the number of effective dimensions that can represent the spectral information. As an example, for the Munsell reflectance dataset, different authors have provided different dimensions that effectively represent it, ranging from 3 to 18 dimensional representations . The reqIn this section we investigate the effective spectral dimension of the acquired hyperspectral images. We do not assume any a priori knowledge about the data and provide a benchmark analysis in the spectral domain. Having these goals in mind, we used the principal component analysis (PCA) technique, which is mainly used to reduce the dimensionality of data consisting of a large number of inter-related variables, while retaining the variance in data . PCA hasFor the computation of effective spectral dimensions, each image is processed individually. First, a covariance matrix is computed, and then eigen decomposition is applied on this matrix to obtain the eigenvalues and eigenvectors. The eigenvectors are arranged relative to their eigenvalues in descending order. These eigenvectors consist of the characteristic reflectances of the input hyperspectral image. For each eigenvalue, corresponding eigenvectors exist, and a strong concentration of variance is present in the first few eigenvectors. If reflectance spectra are linearly independent, then the eigenvalues will all be significantly above zero. This shows that each wavelength contains independent information and is not redundant. However, in the case of hyperspectral imaging, typically, only a few eigenvalues are significantly higher, while the remaining values are close to zero, which indicates that their corresponding eigenvectors contain less variance and are mostly redundant. For an effective representation of the spectral data, we select only the first few eigenvalues, and the projection of spectra on the corresponding eigenvectors is able to provide adequate representation of the acquired data. This means that if we select the principal components containing 95% variance, the rest of principal components can be discarded with a loss of 5% of information. The decision to keep a certain percentage of the variance depends on the application for which a reduced set of representative data is required. For this analysis, we computed the effective dimension for each hyperspectral image which retained 99% and 95% of the total variance .textile_05 image) and found that 82.55% of the variance is retained by the first PC while 12.02% variance is retained by the second component. The rest of components add very little to the information, for example, the third PC contains 0.7% of the variance in this image. If the desired value of retained variance is reduced to 95%, then most of the images in our dataset can be defined with one or two principal components, while six images from the “textile” require a third component. From these figures, it can be observed that the number of effective dimensions is dependent on the nature of the materials being imaged and may be used as a feature to estimate the category of imaged object. The reason why some of the images require a higher dimension for the description of their spectral properties is due to the presence of anomalies, for example, a tiny part with specular reflection which causes the effective dimension to rise. The effective dimension analysis of our dataset on the basis of PCA, as described in linear mixing model which states that the spectral signature of each pixel is a linear combination of the spectral signature of end members at a particular spatial location.NMF is able to compute the spectral signature of end-members in a given hyperspectral image and the presence of each end-member in each pixel in the image in the form of abundance maps. The assumption behind this computation is the vegetation_leaf_03_green image from our dataset, and In NMF-based decomposition, the number of components to be retained is provided by the user, which is mostly based on the prior knowledge of the end-members. For our dataset, we used PCA to compute the required number of components. For the illustration of NMF based decomposition results, we provide two examples in textile_22_cyan image. The first and second components contain information about the reflectance from the textile surface which causes its particular color appearance. It is interesting to note that although the spectral signatures of first two components are almost the same in terms of shape in the visible region, they have different intensities in the near-infrared region. Upon closer inspection of the abundance maps in Although we have provided only two examples from the NMF-based decomposition of the spectral data, the results indicate that such an analysis can provide interesting information about the spectral composition of the materials present in an image. For example, the amount of chlorophyll in vegetation, information about pigments and dyes in a textile, and the quality of food materials on the basis of their spectral analysis. We used PCA to get information about the spectral dimension before decomposing it through NMF. From the comparison of spectral behavior and the effective dimension, it can be seen that effective spectral dimension of most of the objects in our dataset is within two to eight. However, this dimension should not be directly related to the required number of spectral channels needed for acquisition. Although there are some studies which have aimed to find the number of filters that can best be used to acquire the maximum spectral information , there iK-channel radiance image, A texture image is a characterization of the spatial and spectral properties of the physical structure of a material or an object. A texture analysis can be seen as a set of measures that quantify and provide information about the spatial arrangement of the spectral responses of an object. During texture classification, the goal is to assign an unknown sample texture image to a known texture class. For this purpose, discriminant texture features are extracted from test images and compared to those extracted from training images whose classes are known. To extract a feature from an image, a descriptor is needed. The Local Binary Pattern (LBP) method is one of the most robust feature descriptors, and characterizes the local level variation in a neighborhood of each pixel. Due to its discrimination power and computational efficiency, LBP has become a popular approach for various applications . LBP-basP pixels at a spatial distance, d, from each pixel, p, of a gray-level image. The outputs of the LBP operator over the multi-channel image are summarized as a concatenated histogram to form a texture feature. Here, we extend four color LBP operators to hyperspectral LBP operators so that the LBP operator is applied on a K-channel radiance image, The LBP operator, , charactk, compares with those in the neighborhood, p.Marginal LBP (LBP): The basic LBP operator is applied marginally to each channel p as [q, of p in s, is defined as Kxel p as (11)LBPkThe Opponent Band LBP (OBLBP): Mäenpää et al. [ä et al.  improvedThe final texture feature results from the concatenation of the Luminance-Local Color Contrast LBP (L-LCCLBP): This approach considers an image to have both spatial information of luminance and inter-channel information of different bands. The spatial information of the luminance results from the LBP operator being applied to the pseudo panchromatic image (PPI) ch pixel :(13)IPPp, and the average value, Regarding the inter-channel content, Cusano et al.  define tThe LCC operator is then given byThe final texture feature is the concatenation of the histogram of Luminance-Opponent Band Angles LBP (L-OBALBP): As for L-LCCLBP, this approach first applies the LBP operator to the PPI e et al.  considerWe now detail each LBP operator and how their radiance value, L channels whose indexes uniformly ranged in Since 186 channels is too many to perform the proposed LBP descriptors, we propose to spectrally downsample the 112 radiance images. We selected, for each radiance image, L, required to provide the best discrimination between channels, we studied the normalized covariance matrix between each couple of channels over the whole dataset computed in L, that ranged from 1 to 27 channels. In colorimetry and in the colour imaging communities, it has been accepted that sampling spectra at 10 nm provides enough discrimination [In order to determine the minimum number of channels, mination . It is itrain.txt and test.txt). In a learning phase, LBP histogram features were extracted from each training image. Then, to assign a test image to one of the classes, the same features were extracted from each test image and compared to those of each training image. This comparison was performed using a similarity/dissimilarity measure between test and train features. Finally, each test image was assigned to the class of the training image with the best match by using a decision algorithm. The performances of a classification algorithm are determined by the rate of well classified test images, and depend on three main parts of classification, namely, the choice of discriminating textural features, the feature similarity measure and the decision algorithm. In order to determine the most discriminant texture features with respect to the number of channels, we propose retaining the 1-Nearest Neighbor decision algorithm coupled with the similarity measure based on intersection between histograms [A texture feature assessment was performed on our dataset by considering the 112 texture images as 112 different classes. Each 8-bit hyperspectral radiance texture image, stograms , since tL  PCA channels that are considered for the LBP operator. By taking only the PCA channel with the highest variance (1st channel), classification performances are lower than by taking one real channel (93th centered at 699 nm). This can be explained by the specificity of our dataset. Since PCA is a projection of all channels into one channel, two similar textures with different spectral properties are barely discriminated using the PCA. For illustration purposes, textile_14_orange and textile_14_blue). As can be seen, images of the 93th channel of the two textures have different spatial properties and intensities, so they can be easily discriminated, whereas the 1st PCA channels of both textures have similar spatial properties and intensities, so they can be hardly discriminated. By increasing the number of PCA bands, useful information from all channels is used so that it becomes more discriminative than taking each channel separately. In In order to study the discrimination of PCA for hyperspectral texture classification, we propose the application of the marginal LBP operator over the projections of the 186-channel hyperspectral images on the principal axis of the covariance matrix (denoted as PCA channels in the following). Note that the OBLBP, L-LCCLBP and L-OBALBP operators are not relevant for this study since they use spectral correlation which, by definition, does not exist between PCA channels. PCA channels are computed for each image of the dataset by first projecting the 186-channel reflectance image onto each principal axis, such that the first PCA channel is associated with the eigenvector with the highest eigenvalue and so on. For a fair comparison with the previous operators, PCA channels are then quantized on 8-bits using the same quantization function for all PCA channels of all 112 images. The LBP-PCA accuracy is shown In this paper, we introduced and described a hyperspectral reflectance dataset of close range textured object surfaces. The spectral range of this dataset is from 405 to 995 nm, thus providing spectral information in both visible and NIR regions. There are five classes of objects, and a total of 112 images are provided. We discussed the hyperspectral image acquisition protocol and the corrections that were applied to the data.We performed a spectral analysis to quantify the spectral complexity of the samples and showed that a limited number of components permits a good description of their reflectance. Furthermore, we extended local binary pattern operators to a hyperspectral texture analysis. We observed that increasing the number of bands permits better texture classification. We also showed that the opponent band local binary pattern performed the best among the tested texture descriptors. Indeed, such descriptor uses an inter-channel correlation to discriminate textures using their spectral properties.This dataset is available for scientific use and simulations, and it provides a benchmark to test various computer vision algorithms that are related to object classification and material identification. This dataset will also help in designing optimal spectral sensors, computational imaging systems and spectral reconstruction algorithms.


Epitope prediction based on random peptide library screening has become a focus as a promising method in immunoinformatics research. Some novel software and web-based servers have been proposed in recent years and have succeeded in given test cases. However, since the number of available mimotopes with the relevant structure of template-target complex is limited, a systematic evaluation of these methods is still absent. In this study, a new benchmark dataset was defined. Using this benchmark dataset and a representative dataset, five examples of the most popular epitope prediction software products which are based on random peptide library screening have been evaluated. Using the benchmark dataset, in no method did performance exceed a 0.42 precision and 0.37 sensitivity, and the MCC scores suggest that the epitope prediction results of these software programs are greater than random prediction about 0.09–0.13; while using the representative dataset, most of the values of these performance measures are slightly improved, but the overall performance is still not satisfactory. Many test cases in the benchmark dataset cannot be applied to these pieces of software due to software limitations. Moreover chances are that these software products are overfitted to the small dataset and will fail in other cases. Therefore finding the correlation between mimotopes and genuine epitope residues is still far from resolved and much larger dataset for mimotope-based epitope prediction is desirable.  A B-cell epitope is defined as a part of protein antigen recognized by either a particular B cell receptor (BCR) or a particular antibody molecule of the immune system . It may The identification of B-cell epitopes is important to many immunodetection and immunotherapeutic applications because they elicit humoral immune responses ,5. The oetc. . If sets have all the same data, they were combined as one independent set, and the mimotope sequences of these sets were combined. The MS01105, MS01110 and MS01115 meet this condition, and we combined them as MS01105/10/15. If a template-target complex structure has several pairs of template and target chains with the same structure and binding sites, only one pair was selected and the template chain was extracted. The genuine epitopes of these mimotopes sets were determined by CED, IEDB-3D or CMA. In this way, 47 test sets with 28 corresponding complex structures formed the final benchmark dataset. These 47 test sets contain 18 sets with the antigen-antibody complex and 29 sets with the protein-protein interactions. Then for the same template-target complex, we selected only one test set according to the prediction results as the representative dataset . Finally, 30 test sets formed the representative dataset.We consulted and analyzed 11 recently-developed methods see , and fin–3, Pep-3D-Search will not give any prediction result; the online Pepsurf and Pepitope have the restriction that the length of mimotope sequence cannot be longer than 14; and EpiSearch has the restriction that the number of mimotope sequences cannot be longer than 30. Considering these restrictions, we defined a representative dataset for further evaluation of the prediction methods. Being aware of above problems and limitations, we also applied the following performance measures in this study to provide a complete and fair evaluation of the methods:There is no commonly acceptable standard for evaluating mimotope based B-cell epitope prediction methods. Some authors measure only the number of predicted epitope residues and the true epitope residues on a few test cases ,45. SomeIn our study, TP is the number of predicted epitope residues proven to be the true epitope residues. FP is the number of predicted epitope residues proven not to be the true epitope residues. TN is the predicted non-epitope residues proven not to be the true epitope residues. FN is the number of predicted non-epitope residues proven to be the true epitope residues. In these performance measures, MCC is in essence a correlation coefficient between the identified and the true epitope residues; it returns a value between –1 and +1. The value +1 represents a perfect prediction, the value 0 represents an average random prediction and –1 represents an inverse prediction. Because the five epitope prediction methods adopt different strategies to define the surface amino acids of the antigen, to be fair we took the amino acid number of the whole antigen as TP+FP+FN+TN instead of the surface amino acids number for calculating the above performance measures. According to analysis of the testing results with the true epitopes, we found that the first resulting cluster did not always performs best, and then we defined the best resulting cluster as the cluster whose TP is the highest through comparison with the true epitopes in this paper in order to achieve the best performance of the methods for evaluation.The performance measures were applied in evaluating the methods as follows: all methods were tested on the benchmark dataset and the best resulting cluster which correctly predicted the most epitope residues among the first three resulting clusters was retained. MCC, for all data have been calculated to evaluate the methods through the benchmark dataset; then scatter diagrams of sensitivity with respect to 1-specificity were figured to compare the methods with the random prediction. Finally, the average of all performance measures on the two datasets were calculated to give a direct view of the prediction performance of all methods on different datasets.We evaluated the methods through the benchmark dataset using the MCC, and the results are shown in We reexamined the data with the number of antigen amino acids ranging from 30 to 500, and the number of mimotope sequence is equal or larger than 3 and less than 30 in the benchmark dataset. Through evaluation, we found that the MCC of the following data are still lower: MS00465, MS00671, MS01061, MS01162 and MS001154. The MS00465 and MS00464 have the same target-template complex, and MS00464 has two mimotope sequences while MS00465 has three, but the overall performance of MS00464 is slightly better through the results of five methods. The similarities of mimotopes and epitopes may become the reason that causes this difference. MS00671 and MS01154 have 163 and 400 amino acids in antigen, 13 and eight mimotopes respectively, and the low MCC may also result by the similarities of mimotopes and epitopes in this situation. MS01061 and MS01062 share the same target-template complex [PDB id: 1MQ8], and the antigen of the two data is neither small nor large: 177 amino acids, and the numbers of mimotopes in these two data are 13 and eight, respectively. Moreover there are another two data have the same target-template complex as MS01061 and MS01062: MS01004 and MS01063, but these two data were removed at the beginning of this step for they have one mimotope. The MCCs to the four data are all low, nearly negative in all methods. The reasons for the low MCCs of these four data seems to be complicated: the small numbers of mimotope sequences, and the low affinities between mimotopes and antibodies, as well as the complicated structures of the antigens.Through MCC we evaluate the methods through the benchmark dataset, and try to find the reasons for different MCCs in all methods. From the analysis, we find that the small antigens are less significant, and the large antigens with small number of mimotope sequences work worse in all methods due to the fact that they contain relatively less epitope features. For the medium antigen with not a small number of mimotope sequences, the reason for low MCCs may be the similarities of mimotopes and epitopes and the complicated structures of the antigens. In addition, the mimotope sequences cannot reflect the whole features of genuine epitope, for example, a mimotope does not contain structure features of epitopes. Methods which considering other features of epitopes may work better. To sum up, finding the relationship between mimotope sequence and the genuine epitope will be an open problem for further research. As seen from Through the scatter diagrams of sensitivity with respect to 1-specificity, we evaluated the methods with the random prediction. From the analysis above we can see that for most test cases the five epitope prediction methods can all precisely localize epitope regions. The predicted results of all methods are totally better than random prediction. But due to the restrictions of each methods and the mimotope sequence itself, there is still distance from a satisfactory predicting precision. Moreover, to make a systematic evaluation of epitope prediction methods, the quantity and diversity of this benchmark dataset is still not enough, more reliable experiment data are expected.We calculated the average value of sensitivity, specificity, PPV and MCC using the benchmark dataset and the representative dataset. Meanwhile, we also gave the average values of these performance measures using antigen-antibody and protein-protein interactions for each method respectively. As one can see in Seen from this result, the overall performance was poor for all methods. When testing the methods using the representative dataset, the performances of all methods are just slightly improved. The poor performances results from two aspects. The one is the method itself. These five methods have different restrictions. The restrictions lead to the methods not working with some test cases. The other aspect for the poor performance is the dataset itself. Seen from the performance measures of every data in the benchmark dataset , there aThe datasets were derived from MimoDB and PDB:Benchmark dataset—test sets with corresponding 3D structure of template-target complex. This dataset is intended for evaluation the performance of existing epitope prediction methods to each other and development of new method. The benchmark dataset contains 47 test sets with 28 3D structures of template-target complex. Representative dataset—a subset of the benchmark dataset. It contains only one test set for each template-target complex in the benchmark dataset. This dataset is intended for further verifying the validity of the epitope prediction methods and accessing the performance of the methods. The representative dataset contains 30 test sets with 28 3D structures of template-target complex. The following steps 1-4 relate to the benchmark dataset; steps 1–5 relate to the representative dataset. Step 1: 62 test sets which are related to 53 solved structures of template-target complex were collected from MimoDB. The corresponding crystal structure of template-target complexes were downloaded from the PDB. If a test set has several complex structures we selected one according to the resolution and release date. Among these test sets, there are 18 sets with solved 3D structures of Ag-Ab complex, and 44 sets with solved 3D structures of protein-protein complex. The five methods evaluated in the paper all developed for epitope prediction, and the test sets with the protein-protein interactions could be taken as supplementary data to verify the efficiency of the methods. The number of peptide sequence in these test sets is range from 1 to 41. Step 2: all the test sets were checked and filtered carefully in this step. The test sets with either different library names or different experimental methods were considered as different sets. The test sets which were published in different papers by different research teams were considered as different, while the ones which published in different papers by the same research team were combined as one test set. For the test sets with different round of biopanning in experiment and the other conditions are the same, we choose the last round one. The MS01153 and MS01154 ones were in this situation. The MS01153 was obtained from two rounds of biopanning while the MS01154 was obtained after three rounds of biopanning, so the MS01153 was excluded. The test sets from MS01101 to MS01115 were slightly more complicated. The template of these sets was the same and the targets were obtained in three different ways, and these 15 data sets were obtained according five rounds of biopanning. Through background knowledge we choose the last round ones as results, therefore the MS01105, MS01110 and MS01115 were retained and the others were excluded. The target of these three sets were the same antibody from different organisms rather than different antibodies, thus we combined these three data sets as one test set-MS01105/10/15. Then we checked the sequence identity of the test sets whose number of peptide sequence is more than 20. There are six such test sets. Through pairwise alignment for each set and taking the identity of 70% as the threshold, we found that there are no more than three identity sequences. Then we excluded one sequence from each identity peptide sequence pairs according to the peptide binding affinity, and the mimotope sequences in following three sets changed: MS00054, MS00058 and MS01105/10/15. After this step, 47 test sets were retained.Step 3: for each 3D structure of template-target complex, the template chain was extracted and the target chain which interacts with the template was recorded to locate the epitopes. There are two cases: the first is the 3D structure has one template chain, and we extracted the single chain directly and recorded the target chain in this situation; the second situation is the 3D structure has several template-target pairs, since the template and target are identical and the binding sites are the same, so we choose one pair randomly and extracted the template chain and recorded the target chain.Step 4: for each test set, the mimotope sequences, the 3D structure of template-target complex and the structure of template are all ready. In this step the epitopes were determined. We confirmed the epitopes through three tools: CED . In addition, for finding the correlation between mimotopes and genuine epitopes based on machine learning methods, it is pre-requisite that larger dataset which is used for training are available. Therefore, more experiment data including available mimotopes and corrsponding structure of template-target complex are strongly needed.


The short-term forecast of earthquakes associated with fault rupture is a challenge in seismology and rock mechanics. The evolution of mechanical characteristics of a local fault segment may be encoded in the ambient noise, thus, converting the ambient noise to an efficient source of information about the fault stress-strain conditions. In laboratory experiments we investigate micro-vibrations of a block-fault system induced by weak external disturbances with the purpose of getting reliable evidence of how the system transits to the metastable state. We show that precursory changes of spectral characteristics of micro-vibrations are observed for the complete spectrum of failure modes. In the course of experiments we systematically change the properties of interface to perform the transition from stick-slip to steady sliding and observe the characteristics of micro-vibrations of the laboratory block-fault system. Detected were systematical alterations of the system natural frequency and those alterations were determined by the evolution of fault stiffness. The detected regularities suggest that the final stage of seismic event preparation can be revealed in analyzing the spectral characteristics of ambient noise. The detection of natural oscillations of a block-fault system can be a new useful tool to monitor active faults in real time. Taking laboratory stick-slip as a small-scale mechanical representation of the processes in the seismic source, various aspects of seismic source phenomena were studied in detail such as friction law, radiation efficiency, precursory effects, etc7. Such tests are by no means a sort of scale modeling since it is simply impossible to fulfill all the similarity criteria in this case8. They should be considered more as a study of fundamental properties of geomaterials and their structural properties which determine fault evolution.The concept that frictional instability is the most likely mechanism of shallow earthquakes is now dominating in seismology and rock mechanics9 and foreshock activity12, variations of geo-acoustic emission13 and water level in wells14, radon anomaly15, alterations of velocities of seismic wave propagation18, Q-factor of the medium19, physical and chemical properties of fluids20, and others were considered as earthquake precursors. Some analogues of the above phenomena are regularly observed in laboratory experiments24. The listed phenomena do not characterize the rupture zone itself, they rather deal with a larger area of elastic energy accumulation. For this reason, considerable efforts have not resulted so far in any reliable short-term earthquake precursor, although some positive progress has been made at least in post mortem precursory signal analysis25.One of the problems being solved in seismology is the search for macroscopic characteristics that could be used in monitoring seismically active areas in order to predict earthquakes. The phenomena of seismic quiescence27.As an alternative to observing characteristics of a huge area where earthquake precursors may be revealed, various methods of local monitoring seismically active faults can be used. Seismic methods seem to be most suitable for this purpose since they are supported by excellent instrument provision, as well as by well developed techniques of processing the data on kinematic and dynamic characteristics of vibrations in different frequency bandsBelow we present laboratory experiments on a spring-slider model set-up designed to study the possibility to recover some information about fault stress-strain conditions from the ambient seismic noise. To investigate the complete spectrum of frictional slip behaviors, we altered gouge content in the model fault. The ambient noise was imitated by slider micro-vibrations produced by the acoustic white noise of a loudspeaker. In a greater detail the description of experiments is presented in the section Methods. We investigate spectral characteristics of ambient noise in detail and detect the main resonant frequencies. We find some reliable evidences of a laboratory earthquake nucleation associated with the evolution of mechanical characteristics of the fault.30. The effect of non-linearity becomes particularly striking when the acting stresses are close to the fault ultimate strength. In particular, the non-linear deformational behavior can explain the effects we observe in experiments presented below.It is well known that damaged rocks  typically have a nonlinear behavior31:σ and τ are the normal and shear effective stresses acting in the vicinity of discontinuity, x and u are the relative normal and shear displacements of its sides. Differentiating the stress-displacement curve τ(u) allows us to measure the static value of fault shear stiffness. Fault slip modes are controlled by the ratio of two parameters – the maximal rate of frictional resistance weakening of the fault (the specific unloading stiffness of the fault) to the stiffness of enclosing rock massif6. The transition from stick-slip to aceismic creep has been studied in detail both analytically and in laboratory experiments6.We find it convenient to characterize the deformation properties of a fault by its normal and shear stiffnesses32. At the final stage of shear stress growth the stiffness can become very small, however, the dynamic rupture accompanied by elastic wave generation starts only when the modulus of the shear resistance decrease  becomes larger than the stiffness of the surrounding rock. Though the static stiffness of a fault cannot be measured in situ, laboratory experiments demonstrate correlation between the static and dynamic fault stiffnesses33. The normal and shear dynamic stiffnesses of a fault can be estimated through measured parameters of seismic waves transmitted through it. The theory of seismic waves interacting with a cut in an elastic medium is developed rather exhaustively35. We should take into account the strong non-linear dependence of the dynamic stiffness (estimated by seismic methods) on the intensity of disturbance28. Ultrasonic precursors are observed in laboratory experiments during stick-slip just before the failure of the frictional discontinuity occurs. Those precursors are attributable to a decrease of the shear stiffness of discontinuity37. Precursory changes of seismic velocities occur in laboratory faults for a full range of failure modes similar to those of tectonic faults7.As shear stresses approach the limit of frictional strength, and the source zone transits to the metastable state, the shear stiffness of the source zone should noticeably decrease42. However, it should be emphasized that many authors concern in pre-slip model. They observed irregular patterns observed at the final stage of an earthquake nucleation44.It is likely that a slow preliminary sliding and, consequently, a decrease of fault stiffness before the dynamic rupture does exist in nature. According to the pre-slip model, prior to a large earthquake, an area emerges around the hypocenter, where the pre-slip occurs. Some authors assume, that the size of this pre-slip area is proportional to the final earthquake magnitudein situ with the purpose to forecast the moment of earthquake is in fact impossible so far, because of insoluble difficulties associated with regular measurements of stresses and displacements at seismogenic depths. The seismic method of fault stiffness monitoring seems more suitable in situ. But in practice, it is difficult to perform active monitoring of seismogenic faults. To control the stiffness of a local fault section, first of all, it is necessary to choose the section itself, which is, naturally, very problematic. Besides, it is desirable to have records of both the incident and the transmitted waves, which is often impossible. In this regard, using the ambient noise to control the conditions of a seismogenic fault seems to be an exciting idea46.It is clear, that stress and deformation monitoring C is the velocity of propagation of longitudinal or shear wave and L is the characteristic block size, which is determined by the structure of a rock massif and the frequency of seismic waves used for investigations.The spectrum of ambient noise should inevitably contain components corresponding to natural frequencies of a blocky structure. One set of frequencies – the natural frequencies of elastic blocks – origins directly from the reflections of waves from interblock boundaries (just like standing waves in a resonator) and is defined by the characteristic block size:47. If the Q-factor of such a block-fault system is not extremely low, the natural frequency of these free harmonic block oscillations should be detectable in the spectra of recorded ambient seismic noise47.The other set of frequencies originates in a blocky medium, because some blocks, separated one from another by rather compliant faults, can execute free harmonic oscillations triggered by external disturbancesThe simplest 1D mechanical analogue of such an oscillating system is the harmonic “mass on spring” oscillator  stiffness So, for blocks tens of kilometers in length, the characteristic periods of oscillations can reach tens of seconds. If a fault approaches the metastable state, and its shear stiffness decreases, the specific natural frequency that could be detected in the spectrum of ambient seismic noise should decrease too.50.The reality of the above scenario was verified in laboratory tests at a slider set-up, where a block under normal and shear loads slides along a base, while weak stochastic vibrations are excited in the base by a special striker (see Methods). The fault model was filled with a layer of granular material composed of quartz sand and clay. First, we observe a spectrum of slip behaviors as a function of quartz sand/clay gouge composition Fig. . We syst51, which in the frequency range of  was calculated as follows:Weak acoustic pulses in the base produce micro-shear-deformation of the interface, which, in turn, excites oscillations of the upper block relatively the base Fig. . In a prHereinafter the first main maximum corresponds to the extended frequency band of 750–1250 Hz, the second maximum – to the frequency band of 400–650 Hz Fig. , inset.ko52. Duration of this stage essentially depends on the type of events being realized: the more intensive the event is, the longer the “interseismic” stage is. At the final preseismic stage the accumulating elastic energy and deformation results in initially slow, and just before the rupture – in a dramatic increase of slip velocity.The combination of microseismic and mechanical characteristics provides a comprehensive view of the fault state evolution during a seismic cycle Fig. . RepeatiΔfI  Hz dynamic slip episodes are accompanied by an abrupt decrease of the spectral centroid fcI. However, changes of the value of centroid at the “interseismic” stage do not correlate with alterations of stress-strain conditions. In the range of ΔfII  Hz alteration of the value of spectral centroid coheres with the stages of slip mode. Sections that correspond to the four stages of slip cycle can be clearly traced in the range of ΔfII. During the dynamic rupture, the value of fcII comes to minimum. After the rupture stops, fcII grows fast reaching its maximal value of ko at the loading stage. At the “interseismic” stage variations of the spectral centroid fcII are relatively small. During the process of elastic energy accumulation, long before the ultimate strength is reached, fcII starts to decrease, the rate of decrease gradually growing as the moment of dynamic rupture approaches. We can assume that it is the frequency range of ΔfII that corresponds to the main tone of natural harmonic oscillations of the laboratory block-fault system and carries the information about evolution of the block-fault system and its transition to the metastable state. The fact that the main natural frequency of the system lies in the frequency range of ΔfII is supported by independent special experiments at a vibration exciter (see Supplementary Section S2 for details). It is worth mentioning that using vibrations of higher amplitudes leads to a more pronounced effect of fcII alteration ..4).in situ for estimations by equations  are required. A possible trigger of such oscillations can be, for example, surface waves of distant powerful earthquakes.Analysis of the ambient seismic noise, containing traces of those oscillations, can provide an important information about stress-strain conditions of the fault and its transition to the metastable state. It should be emphasized that it is unclear so far, what characteristic size should be used quations  and 4)..in situ 54. The most vivid manifestation of this phenomenon happened on the eve of the M9.2 Sumatra earthquake, 26 December 2004, where the period of such oscillations (the oscillations appeared more than two days before the main shock) had been increasing rapidly as the moment of the main event approached . It is important to note that those oscillations were triggered by seismic waves from another distant earthquake near the island of Macquarie  that took place about 7000 km away. In other cases  triggering effects of external disturbances were also reported: very low frequency oscillations have been detected synchronously at several stations54. Low frequency oscillations with periods of up to 200 s were also observed some time before the main shock of the event that occurred in the North-West Pacific region55.It is worth mentioning that our experimental results are supported by a similar pattern of alteration of the characteristic frequency of oscillations, which was observed before several earthquakes57.The process of fault recovering after an earthquake was traced in a number of works using trapped waves37 have revealed the change in velocity speed during stick-slip instabilities. For such an effect to be detected in situ, one must have both the source of seismic waves and the measuring profile intersecting the fault zone. Our experiments have demonstrated that the shift of the characteristic frequency of natural oscillations toward low frequencies may serve as an indicator of a dramatic decrease of the fault shear stiffness and the transition of the block-fault system to a metastable state. It is evident that detecting natural frequencies of blocks in the ambient noise is undoubtedly a separate problem, requiring special methods of observations and data processing. Apparently, most advantageous for detecting the characteristic values for a certain region, can be the intervals of seismic records after the passage of surface waves from distant earthquakes. These vibrations with periods of several tens of seconds have noticeable amplitudes and rather long durations, which promotes excitation of resonant harmonic oscillations of block-fault systems. Determination of such frequencies, specific for each area, and tracing their variations can, in our opinion, make the base for a new approach to local monitoring of seismogenic faults and to solution of the problem of short-term earthquake forecast.Previous experimentsAll the experiments were performed in the classical statement of the slider model  were excited by impacts over its edge. The impacts were produced by a plane rough striker attached to the diaphragm of the coil driven loudspeaker, which was fed by the signal from the white noise generator. Elastic vibrations were measured with accelerometers Bruel & Kjaer 4344 mounted on the rod and on the block (no. 6 in Supplementary Fig. For a detailed analysis, we chose the section of stress-deformation diagram, where the strength of the model fault reached its residual value of τin situ.To investigate the complete spectrum of the frictional slip behaviors, we altered the gouge content in the model fault. We chose granular quartz and clay because they are common minerals in natural fault gouges and are well-studied materials with reproducible friction behavior. In the experiments, we controlled only those mechanical and kinematic parameters of the system that could be measured 58.Despite the low level of normal stresses and temperatures we believe that it is possible to draw an analogy to the nature, based on the similarity of rheological diagrams of laboratory and natural faults. The stick-slip mode corresponds to normal earthquakes under field conditions and the stable sliding corresponds to the aseismic creep. The tests clearly identified transient deformation events with low displacement amplitudes and very low slip velocities. The intermediate deformation modes are the analogues of such natural phenomena as low-frequency earthquakes, very low-frequency earthquakes and slow slip eventsAll the data that support findings of this work were collected on geomechanical test bench of the Institute of Geosphere Dynamics of Russian Academy of Sciences. All the data presented in this study are available from corresponding author on request.Supplementary materials


The water filtration results indicated that permeability of the membranes changed from 288 to 3275 L/m2hbar. The successful preparation of such an interesting material may provide a new approach for the design and development of electrospun filter membranes.Membranes are considered as a promising technology for separation and filtration processes. Here, novel polyvinylidene fluoride (PVDF) nanofibrous multilayer membranes were fabricated by wire-based industrial electrospinning equipment following by a lamination process. The lamination process was optimised under various applied temperature, force of lamination, and lamination time. Air permeability and burst-pressure tests were run to determine the optimum membranes for filtration application. The structures of the prepared membranes were characterised by scanning electron microscopy and pore-size analysis. The hydrophilic properties of the membranes were evaluated using water contact angle measurement, and the mechanical strength of the membranes was analysed. Air and water filtration tests were run to find the possible application of prepared membranes. The air filtration results showed that membranes had high filtration efficiencies: Over 99.00% for PM Air and water pollution has become a major environmental concern due to the growing global population, increasing economic expansion, and amount of consumption. Many developing countries face an increase in particulate matter (PM) as an air pollutant and amounts wastewater ,2,3,4. OThe versatility of nanotechnology could greatly help to overcome the current issues of cleaning of air and water using novel nanostructured membranes produced by the electrospinning process. Electrospinning, a simple and cost-effective method, has been used extensively to produce nanofibres with high pore volumes, small pore size, fibre diameters of nano size with tightly controlled size distribution, and high surface area to volume ratio. Nanofibrous membranes are made of randomly laid fibres oriented anisotropically that can effectively filter out particles by a size-exclusion mechanism. Electrospun polyvinylidene fluoride (PVDF) nanofibrous membranes find applications in many areas such as membrane distillation , microfiHere, we present a novel lamination method to use nanofibres in various applications. The aim of this work was to conduct the nanofibres into a filtration application using lamination technology and to measure their performance for both air and water filtration. Firstly, various lamination conditions were tested in terms of their effect on the mechanical and surface morphology properties of the nanofibres. Lamination of the fibre surfaces could significantly enhance the mechanical performance of the nanofibrous membrane. Later, the prepared membranes were used as filters and their performance was measured.Furthermore, characterisation and evaluation of the nanofibrous membranes were carried out to relate their structural properties to their separation performance and properties. To the best of our knowledge, no such strategy has been reported on the preparation and characterisation of various laminated PVDF nanofibre membranes for both air and water filtration applications.N,N-dimethylformamide (DMF)  and mixed overnight. The prepared solution was electrospun using production-line needle-free electrospinning equipment . In this technology, two types of wire electrode are placed upward (connected to a negatively charged high voltage supply) and downward (connected to a positively charged high voltage supply). The downward wire, called the feeding wire, is placed from one side to the other of the equipment in a horizontal direction and passes along the feeding unit, as shown in PVDF  was dissolved in 2.In our work, the downward wire electrode was maintained at +55 kV while the upward electrode was maintained at −15 kV. The distance between the electrodes was 188 mm. The distance between the upward electrode and the supporting backing material was 2 mm. The speed of the backing material was 20 mm/min. The temperature and humidity of the input air were set to 23 °C and 20% by the air-conditioning system. The nanofibre layer was collected on a backing paper at 3 g/m2 PVDF nanofibre layer, a 12 g/m2 co-polyamide adhesive layer  and a 100 g/m2 polyethylene terephthalate spunbond nonwoven layer . Firstly, the prepared PVDF nanofibres were cut into A4 size and then placed on the adhesive web. The nonwoven supporting layer was placed on the other side of the adhesive web to create a nanofibre-adhesive web-nonwoven sandwich structure. Heat-press equipment  was used for the lamination process  with an extension rate of 10 mm/min at room temperature in dry conditions. The samples were 100 mm long, 25 mm wide, and the distance between the two clamps was 50 mm. Both machine direction (MD), and the counter direction (CD) were tested. At least three measurements were taken for each sample.The delamination of the nanofibrous membrane was tested, and the maximum burst pressure was recorded. The testing device was developed in our laboratory, and is shown in The maximum, average and the minimum pore sizes are determined by a bubble point measurement device (developed in our laboratory), which was working with capillary flow porometry theory. Bubble point method has been explained in detail ,32,33. TSurface morphology of the membranes was examined using a scanning electron microscope  after the lamination process. At least 50 fibres were measured for each sample. Image-J (free online software) was used to determine fibre diameters.The water contact angle of the samples was measured using a Krüss Drop Shape Analyzer DS4 , at five different points, using deionised water  on the clean and dry samples at room temperature.The maximum, average and the minimum pore sizes were determined by a bubble-point measurement device working with capillary-flow porometry theory, and which was developed in our laboratory.2, South Carolina, USA). At least three measurements were taken for each sample. The nanofibre surface of the membrane was placed upstream.The air permeability of all multilayer nanofibrous membranes was tested using an SDL ATLAS Air Permeability Tester  for air filtration. The filtration efficiency for PM2), t is the filtration time (h), V is the total volume of the permeate (L), p is the transmembrane pressure (bar), and t is the filtration time. The nanofibre surface of the membrane was placed upstream.A cross-flow filtration unit developed in our laboratory, as shown in In water filtration test, the nanofibre surface of the membranes was in contact with feed solution.The mechanical strength of a single nanofibre layer is not suitable for application in air or water filtration. The main reason to laminate the nanofibre layer is to improve its strength against tearing or bursting, using a supporting layer. The lamination process is quite challenging. An improper lamination can cause burning of the nanofibre layer, blocking of the pores with adhesive or easy delamination from the surface of the supporting material. To determine the best lamination condition, various system parameters were changed, such as applied temperature, force of lamination or time of lamination. Laminated samples were subjected to air permeability and burst-pressure tests to select the most suitable membranes for further applications. The main reason for selecting these two tests can only be explained as: (a) The air permeability test shows whether all the pores or almost all pores of the nanofibre layer are blocked with adhesive. Membranes with the highest permeability have advantages in air and water filtration, and (b) burst-pressure tests indicate the maximum pressure that the membrane can withstand without delaminating and separating the nanofibres from the supporting layer. The air permeability and the maximum burst pressure of the membranes are given in The density of the nanofibrous membranes can be one of the biggest problems in the air permeability test. The high density means a thicker sample, which reduces the air flow.−2s−1 were selected as promising candidates for filtration application. Only seven membranes showed reasonable air permeability with high burst pressure. The results of air permeability and burst pressure of all samples are given in Hence, almost all the samples showed very low air permeability; their burst pressure was considered as the criterion for the selection of membranes. Since there is no previous work dealing with the effect of lamination on membrane delamination and bursting, the degree of acceptable air permeability and the burst pressure was determined by us, based on previous experiences in our laboratory. From each temperature series, at least one membrane was selected. In this case, the membranes with burst pressures over 175 kPa and air permeability ≥7 LmResults indicated that applied force of lamination is the most effective lamination parameter compared to temperature and lamination time. Based on the results, one can understand that a force over 100 kN or less than 40 kN was not suitable for the lamination process of PVDF nanofibres. When the applied force was lower  membranes delaminated and burst easily. Most of the selected samples were prepared below 110 °C, which leads to energy saving for real applications.Tensile testing is one of the most efficient methods to measure tensile properties of nanofibrous membranes. Using a single nanofibre layer, it is not possible to measure the tensile strength due to the low mechanical properties of nanofibres. For this reason, the tensile property of the nanofibrous membranes depends on the tensile properties of the supporting layer used for lamination of the nanofibre layers. The tensile strength of the membranes shows the resistance of the membranes to tearing under pressure or load. The results indicate that the nonwoven supporting layer had an anisotropic structure, which resulted in different tensile strength and elongation at break for the MD and CD of the membranes. The lamination parameters did not show an important effect on the tensile properties. The results indicate that the membranes had a very high tensile strength that could withstand external forces or pressures.The surface morphology of the nanofibrous membranes was investigated using SEM images . The fibAccording to The average pore size of the selected membranes was measured and shown in (1)Membranes which were laminated under the same temperature and force of lamination but different lamination duration indicated that higher lamination duration decreased the pore size.(2)Membranes which were laminated under the same temperature and duration but different force of lamination showed that the pore size of the membrane did not change significantly.(3)Membranes which were laminated under the same force of lamination and duration but different temperature showed that the pore size of the membrane slightly decreased with increasing applied temperature.(4)All the membranes showed pore size below one µm.The measurement of pore size showed that:Water affinity is one of the important criteria for the permeability and wettability of the membranes for water domain filtration. It was found that both hydrophobic and super hydrophilic membranes were disadvantageous for use in the separation of wastewater . The watBased on the results of the contact angle, the lamination process showed the large effect on the hydrophilicity of the membranes. The neat nanofibre with no lamination showed a contact angle of around 115°. After lamination with a co-polyamide adhesive web, the wettability of the total membrane changed. It was found that the heat-press treatment slightly decreased the contact angle of the electrospun polyvinylidene fluoride-co-hexafluoropropylene membrane due to changes in the physical structure . Our resThe selected membranes from 2), caused a decrease in the permeability of the total membrane, while increasing the filtration efficiency. In the literature, it was found that using a high density of nanofibre web and a multilayer system, the filtration efficiency of the material increased while air permeability decreased [2.5 and PM0.1, are given in Nanofibre membranes are suitable for air filtration, catching fine particles due to the narrow pore size of the nanofibres. Using a neat nanofibre web, it is possible to achieve high filtration efficiency with low air permeability that is demanded. However, a nanofibre web by itself is soft and fragile and cannot be used as an air filter due to its poor mechanical properties. At least a second layer is required to support the nanofibre web to improve its mechanical weakness. Herein, a high-density PET spun bond was used as supporting material. The lamination process, with a high-density supporting layer and the high-density nanofibre web  on laminated nanofibrous membrane properties were investigated to increase the possible application of the membranes for both air and water filtration.The surface morphology of the nanofibre web after lamination was investigated by using scanning electron microscopy, water contact angle, and average pore size in order to consider morphology changes. It was observed that the fibre diameter of the membranes remained unchanged under various lamination conditions. On the other hand, the water contact angle changed depending on the lamination conditions. It was found that lamination time played a major role in the pore size of the membranes. In addition, to compare breathability and the bursting strength of laminates, air permeability, and burst-pressure tests were run to select the best lamination conditions. It was found that all the membranes showed very low air permeability with high delamination resistance. Furthermore, air and water filtration tests were run to examine membrane performance and efficiency.These results indicate that laminating temperature is an effective parameter for water permeability of the membranes. Thus, varying lamination parameters could lead to the development of membranes with different levels of air and water filtration efficiencies depending on need and use.


Imaging genetics deals with relationships between genetic variation and imaging variables, often in a disease context. The complex relationships between brain volumes and genetic variants have been explored with both dimension reduction methods and model-based approaches. However, these models usually do not make use of the extensive knowledge of the spatio-anatomical patterns of gene activity. We present a method for integrating genetic markers (single nucleotide polymorphisms) and imaging features, which is based on a causal model and, at the same time, uses the power of dimension reduction. We use structural equation models to find latent variables that explain brain volume changes in a disease context, and which are in turn affected by genetic variants. We make use of publicly available spatial transcriptome data from the Allen Human Brain Atlas to specify the model structure, which reduces noise and improves interpretability. The model is tested in a simulation setting and applied on a case study of the Alzheimer’s Disease Neuroimaging Initiative.The online version of this article (10.1186/s40708-018-0091-0) contains supplementary material, which is available to authorized users. The aim of imaging genetics studies is to find associations between genetic variants and imaging features, often in a disease context . This scSo far, imaging genetics studies have been largely focused on the brain , 3–6, deOne of the largest challenges facing imaging genetics studies is the statistical power needed to identify reliable associations. In a typical GWAS, researchers have to correct for the number of independent tests performed (i.e. number of independent SNPs tested) in order to limit the number of false-positive discoveries. However, a genome-wide brain-wide imaging genetic study will not only have to correct for the number of independent SNPs, but also for the number of independent imaging features tested. As a result, many studies are underpowered to identify reliable associations. One of the largest imaging genetics studies  analysedDespite the high dimensionality of the imaging data (millions of voxels), the actual number of independent tests for which we need to correct in an imaging genetics study is far smaller than the number of voxels. Due to the spatial relationships between voxels, measurements from neighbouring voxels are usually highly correlated. A common approach is to test genetic associations for anatomically defined brain regions . SeveralSeveral methods have been proposed to identify associations between genetic variants and imaging features by applying dimension reduction, such as variations of canonical correlation analysis , and indIn this work, we propose a method to identify associations between candidate genetic variants and imaging features allowing for the incorporation of prior knowledge. The proposed method combines a graphical model with dimension reduction to model the effect of SNPs on brain imaging features through a set of latent variables. We use a maximum likelihood structural equation modelling (SEM) approach to find the edge weights of our model . By perfOur model uses the transcriptional profiles for grouping because we consider gene expression to be an intermediate phenotype, that links SNPs to brain imaging features. Most disease-associated SNPs are located near regulatory regions of the genome , and theThe interplay between genetic variation, brain anatomy, and disease symptoms is complex. We use a structural equation model with latent variables  to modelin areas . Large-sIn our graphical model, we define groups of brain regions, based on the transcriptional profiles of these areas in the healthy brain. Areas that share patterns of gene expression in a normal brain may be similarly affected by genetic variations. For each of the region groups, we introduce one latent variable. This latent variable is affected by the genetic variations and causes changes in relevant brain regions. This makes our model similar to principal component analysis (PCA) on sets of brain regions, combined with a regression for the latent variables. However, in our model the weights are estimated together, and the latent variables reflect not only the correlations between the regions , but also those between regions and SNPs and among the SNPs.i. The reason both types of measurements are centred is to eliminate intercepts from the model. The brain measurements are, in addition, scaled to unit variance to compensate for the considerably larger variance in thickness or volume for larger brain areas. The genetic variants and brain measurements are connected in the model by a set of latent variables, We model the relationship between single nucleotide polymorphisms (SNPs) and brain region measurements. Let In addition to the variables included in the model, we have two other sources of information. In defining the model structure, we make use of external information on the brain region measurements, in the form of brain region groups with a shared transcriptional profile. These groups are defined based on the spatial gene expression data of the healthy adult brain. Finally, the goal is to understand disease-related phenotypes. The disease labels are not used in the modelling stage. However, we hypothesize that if the variation in the data is related to a disease state, the latent variables will reflect this. After model fitting, we therefore associate each individual’s estimated latent variable score with his or her disease status.We model the relationship between brain SNP values and brain region measurements in a structural equation model (SEM). It consists of two parts. The first part is a linear model for brain region measurements as a function of the latent variables,The number of latent variables is equal to the number of brain region groups, which are defined based on external spatial gene expression data. A region group contains the brain regions with a similar transcriptional profile, as these may react similarly to differences in genetic background. We restrict each latent variable to only predict the brain region measurements for its own region group. This results in a restriction on the weight matrix mentclass2pt{minimIn linear Gaussian structural equation modelling, we learn the parameters of a model by optimizing the correspondence between the observed covariance The elements of the implied covariance can be parameterized in terms of the model coefficients. The first element isThe next element of the implied covariance matrix isSome elements of the implied covariance are often assumed to be zero. These assumptions lead to a strong simplification of the implied covariance. It is common in a regression setting to pose that the predictor variables and error variables are independent. In our case, the error independence assumption leads to mentclass2pt{minimAs a result of these assumptions, the full implied covariance matrix of the model reduces tolavaan package in R [Model fitting is performed in the age in R . For idelavaan yields estimates for p values , when using the MLM estimation procedure [The model fit in rocedure . Using trocedure .Simulated data The model is evaluated on both simulated and real data. In the simulation, we first generated SNP values  to 5. We randomly selected 10 SNP-to-latent weights  database (adni.loni.usc.edu) [www.adni-info.org.usc.edu) . The ADNThe ADNI database contains measurements on a large number of cognitive normal (CN) controls, individuals with late mild cognitive impairment (LMCI), and individuals with Alzheimer’s disease (AD). The measurements in the database include patient demographics, raw and processed MRI data, biomarker data, and SNP data. For the brain volumes we made use of the UCSF cross-sectional FreeSurfer (version 4.3) cortical thickness and white matter parcellation measurements. For the SNPs we made use of the ADNI 1 Illumina Human 610-Quad BeadChip data, with imputation as previously described . In the Our methodology is not suited to genome-wide analysis. Instead, it tries to find the effects of specific SNPs on a set of latent variables. As candidate SNPs we selected a set of 35 polymorphisms associated with Alzheimer’s disease according to the International Genomics of Alzheimer’s Project (IGAP) study results . IGAP isThe volume data were present for 112 regions. We corrected it for individual age, gender, and whole brain volume (using linear regression), with the goal of maintaining all meaningful variation in brain region volumes, possibly related to the disease phenotype. For our latent variable model, the brain region volumes were linked to region groups. We defined these region groups based on the transcriptional profiles in the healthy adult human brain, as provided by the Allen Atlas . We thenTo evaluate the performance of our model, the SEM was fitted to each of the simulated data sets. We considered two measures for model comparison. First, we set out to assess the prediction of phenotypes from the latent variables, with a logistic regression. In each of the 100 simulated data sets, we estimated the latent variable scores and used only those to predict the phenotype. For each of these 100 models, we obtained an Akaike information criterion (AIC) value. We compared our model to several logistic regression models that use only the simulated data, instead of the SEM estimated latent scores. The first alternative model uses only all the brain region measurements, the second only all the SNP measurements, and the third a combination of all regions and SNP measurements. As a fourth alternative model, we performed a PCA on the volume measurements and extracted the first five principal components. Figure p values for predicting the latent variables . All SNPs that affect either of these two latent variables effectively impact the phenotype. We consider those SNPs to be the SNPs with a true effect. We now consider how these SNPs are ranked for importance in our SEM analysis, and two alternative approaches. From our SEM fit, we extracted the robust SNP p values in two ways: (1) for each SNP we took the maximum p value of the two per latent variable and then the minimum p value over the five latent variables; or (2) for each SNP we used Fisher’s method [p values per latent variable  for SNP importance. We varied a threshold for this score from 0 to 1 and compared the set of SNPs with values below this threshold to the set of SNPs with a known true effect. In this way, we constructed a receiver operating characteristic curve for SNP retrieval and calculated the corresponding area under the curve (AUC).We considered combining these s method  to combip value per SNP–latent combination (SEM max) or using Fisher’s method (SEM Fisher), and of the GWAS-like approaches. The SEM max method has the highest average AUC, indicating that it is best able to rank the SNPs on their importance for the phenotype. Note that the SEM Fisher method has the disadvantage that either a strong SNP-to-latent or a strong latent-to-phenotype effect can lead to a low combined p value, regardless of the other value. The observed difference between the univariate and multivariate approach is very small, which is to be expected since the simulated SNP values are independent.We compared the performance of our methodology to a straightforward modelling approach: a logistic regression to predict the disease status phenotype from the SNPs. This was performed both in a univariate way (as in a GWAS) and in a multivariate way. Figure To test the robustness of our model, we also compared the models for a range of alternative simulation settings. Additional file SubCort2) are negatively correlated to the latent variable scores, reflecting a more heterogeneous signal in this group.We apply our methodology to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data . We seleAPOE, on the hippocampal region group  to alternative approaches with varying simulation parameters. We simulated a range of noise on latent variables, noise on volumes, number of SNPs, and number of misspecified latent to volume links.Additional file 3: Fig. S2. Weights (loadings) from the latent variables to the region measurements. The rows correspond to latent variables (region groups), and the columns to brain regions. Each first loading per region group was set to 1, for model identifiability. The loadings show the strength of the relationship between each latent variable and the thickness/volume of its corresponding brain regions. See Additional file