For earthquake disaster assessment using remote sensing (RS), multisource image registration is an important step. However, severe earthquakes will increase the deformation between the remote sensing images acquired before and after the earthquakes on different platforms. Traditional image registration methods can hardly meet the requirements of accuracy and efficiency of image registration of post-earthquake RS images used for disaster assessment. Therefore, an improved image registration method was proposed for the registration of multisource high-resolution remote sensing images. The proposed method used the combination of the Shi_Tomasi corner detection algorithm and scale-invariant feature transform (SIFT) to detect tie points from image patches obtained by an image partition strategy considering geographic information constraints. Then, the random sample consensus (RANSAC) and greedy algorithms were employed to remove outliers and redundant matched tie points. Additionally, a pre-earthquake RS image database was constructed using pre-earthquake high-resolution RS images and used as the references for image registration. The performance of the proposed method was evaluated using three image pairs covering regions affected by severe earthquakes. It was shown that the proposed method provided higher accuracy, less running time, and more tie points with a more even distribution than the classic SIFT method and the SIFT method using the same image partitioning strategy. Disasters caused by severe earthquakes, such as collapsed buildings, road damage, dammed lakes, and secondary geological disasters, pose a threat to people’s lives and property safety worldwide. In recent years, major earthquakes that occurred in China, such as the Wenchuan earthquake on 12 May 2018, and the Yushu earthquake on 14 April 2010, attracted considerable attention from the government and society. Obtaining exact disaster information immediately after severe earthquakes is important for disaster rescue and relief and can effectively reduce the damage caused by severe earthquakes . Since tThe data processing for extracting earthquake disaster information from RS images includes image preprocessing, disaster information extraction, and disaster assessment . The accWith the development of RS technology, different satellite sensors can provide multispectral, multitemporal, and multiplatform RS images. Additionally, the spectral resolution of RS images is also continually improving, and image registration research has gradually shifted from medium-low resolution to high resolution . The regImage registration is the process of the geometric registration of two or more images covering the same area and acquired at different times, from different viewpoints, or by different sensors . The majThe performances of several different algorithms, including SIFT, shape context , steerabDue to surface deformations caused by severe earthquakes, post-earthquake RS images usually show surface damage such as building collapse, ground displacements, road damage, and landslides, making it more challenging for the coregistration of pre- and post-earthquake RS images. Many algorithms work well for RS image registration, but when used for the registration of pre-earthquake and post-earthquake RS images, especially for mountain areas with complex terrain, two problems arise, including the lack of tie points and the uneven distribution of matched tie points. To solve these two problems, a fast automatic registration method for multisource high-resolution RS image registration in earthquake damage assessment was proposed in this study. In this method, the Shi_Tomasi and SIFT algorithms were combined to obtain tie points using an image partition matching strategy considering geographic information. The proposed method was compared with the classic SIFT method and the SIFT method using the same image partitioning strategy using three pairs of post- and pre-earthquake images that have complex terrain and significant differences in color tone. The rest of this paper is organized as follows. The proposed method is introduced in (1)Constructing a pre-earthquake image database;(2)Image enhancement;(3)Image partitioning strategy based on geographic information constraints;(4)Image patch matching using the combination of Shi_Tomasi and SIFT;(5)Removing the outliers;(6)Homogenizing the even spatial distribution of matched tie points;(7)Image transformation and resampling.The proposed method employs an improved SIFT algorithm using the Shi_Tomasi approach to detect feature points on enhanced images and a partition matching strategy considering geographic information to search for tie points. Seven steps are involved in the method , as follTo improve the efficiency and the degree of automation of image processing, a pre-earthquake image database can be first constructed using high-resolution optical remote sensing images covering the study area. The construction of the database mainly consists of three steps. First, radiometric calibration, atmospheric correction, and projection coordinate conversion are used to preprocess the pre-earthquake high-resolution satellite images. Then, the preprocessed images are geometrically corrected according to ground control points (GCPs) if GCPs are available. If no GCPs are available in the case of emergency, high-resolution pre-earthquake RS images with high quality and strict orthorectification can be used without geometric correction. Finally, the identity number (ID) and the coordinates of the corresponding high-resolution satellite image are recorded in a coordinate file, as shown in Usually, image denoising is employed to reduce image noise before image matching. However, the denoised images may suffer from relatively low image contrast when the image has only a single channel or the color change is not obvious . This mag  is the value of pixel  after image stretching, f  is the original value of pixel , and a and b are the values at 2% and 98%, respectively, of the cumulative frequency of the original image. c and d are the minimum and the maximum of the stretched image, respectively. In this work, c = 0, and d = 255.The 2% linear stretch method is usually performed by defining a transfer function in the following form:To test the advantage of the 2% linear stretch method, an experiment was performed using the GaoFen-1 (GF-1) multispectral image (with an 8-m resolution) and the SIFT algorithm. The original image yielded 16 matched tie points, as shown in For large high-resolution remote sensing images, the extraction and matching of feature points usually have high computational complexity, which requires considerable memory and is time-consuming. In addition, it yields a low accuracy of registration when enough feature points are extracted from only some of the regions of the image. To solve this problem, an image partitioning strategy based on geographic information constraints was adopted to divide remote sensing images into several image patches. For each image patch, the corresponding reference image can be obtained according to the projected coordinates of the image patch. The extraction and matching of feature points were applied to each image patch pair.i, JiI) are the image coordinates of the ith pixel, and  are the corresponding projected coordinates of ;  are the projected coordinates of the top left corner in the original whole scene input image, and G1, G2, G4, and G5, are the parameters of the transformation model. With respect to the geometric information of RS images, the mapping relationship  between n×n patches, each of which has a size of M×N pixels. Each patch is numbered, and the coordinates of the four corners of the patch are recorded. For example, the corners of patch 2NF are recorded as A1, A2, A3, and A4. The image coordinates of corners A1 and A3 are then calculated and recorded as  and , respectively.First, the input image is divided into 0, Y0X) are the projected coordinates of the top left corner in the original whole scene input image; , i = 1 or 3 are the image coordinates of corners A1 and A3 of the image patch 2NF; , i = 1 or 3 are the projected coordinates of A1 and A3; G1 is the pixel size of the input image; G5 is the negative value of pixel size of the input image; and the values of G2 and G4 are equal to 0.Second, the image coordinates are transformed using Equation (2) into the projected coordinates according to the geometric information of the input image. Specifically, , ), which correspond to the two corners (B1 and B3) of the reference image patch shown in Third, according to the projected coordinates B2 and B4) of the reference image patch can be obtained according to those of corners B1 and B3. The image coordinates of the four corners B1, B2, B3, and B4 are then used to obtain the corresponding reference image patch from the pre-earthquake satellite image.Finally, the image coordinates of the other two corners  produced by a shift  of the local window is provided by Equation (3):w  is a window function at position .Denoting the image intensity of the image as E , I  can be expanded using the Taylor series:xI and yI are image derivatives in the x and y directions, respectively. Combining Equations (3) and (4), we obtain Equation (5) as:M is defined using Equation (6),To search for the windows that produce a large R is calculated using Equation (7) and used to justify whether the window contains a corner:detM =λ1λ2; trace(M) = λ1 + λ2; and λ1 and λ2 are the eigenvalues of M. K is an empirical value, and the general value of K is 0.04 [R is greater than a threshold value (T), the window in which R is treated as a corner point is a good tracking point.Finally, a score  is 0.04 . If the R for the Shi_Tomasi algorithm is calculated using Equation (8).Different from the Harris algorithm, the score R is greater than T, the point can be marked as a Shi_Tomasi corner. The schematic of the Shi_Tomasi algorithm is shown in maxN) for each image patch should be set in advance, usually to 1500.If L, the gradient magnitude m  and orientation θ  can be calculated using Equations (9) and (10), respectively.The SIFT descriptor was employed for feature description of the feature points detected in this work. The SIFT descriptor proposed by Lowe in 2004 is a local feature descriptor based on the gradient distribution in the detected regions. By assigning one or more consistent orientations to each feature point based on the gradient directions of a local image, the feature descriptor of the feature point can be represented relative to the orientations and therefore achieve invariance to image rotation. For each feature point  of the scale image An orientation histogram is formed from the gradient orientations of the sample points within a region around the feature point. It has 36 bins covering the 360° range of orientations. Each bin covers 10 degrees. As shown in After determining the orientation of a feature point, the SIFT descriptor of the feature point is calculated using three steps, as illustrated in First, the coordinates of the descriptor and the gradient orientations are rotated relative to the orientation of the feature point to achieve orientation invariance. Then, the orientation histograms over 4×4 sample regions centered with the feature point are created. There are eight directions for each orientation histogram, in which the length of each arrow represents the size of that histogram entry. Therefore, Lowe suggested using a 4 × 4 × 8 = 128 element feature vector for each feature point. Finally, the feature vector is normalized to unit length to reduce the effects of illumination change.SND) may be very close to that of the nearest neighbor (ND). To reduce the impact of noise, the proposed method calculates the distance ratio, denoted as R, of ND to SN (R = DN/DSND). The nearest neighbor point is accepted as the match point of the feature point if R is higher than a default value of 0.8.The BBF algorithm, based on a multidimensional space segmentation tree (K-D tree) , was adoIt is worth noting that the image coordinates of the matched points within each image patch need to be converted to the coordinates of the entire image scene. The random sample consensus (RANSAC) algorithm is commonly used to remove incorrect matches obtained using the SIFT algorithm . The RANw, ywx), }, where  are the coordinates of the feature point in the input image to be registered, and  are the coordinates of the corresponding feature point in the reference image. The incorrectly matched tie points were excluded using the following steps.The matched tie points are denoted as {.w, yw) is the transformed coordinates of w, yw) of the ith tie point were obtained using Equations (13) and (14), respectively,ix and Δiy are the residual errors of the ith feature point and ix and iy are the coordinates of the ith feature point in the reference image. Tie point pairs that have y and iRMSE values higher than an initial threshold, which is denoted as RT, are considered outliers and removed.After that, the residual error and root-mean-square error H and iRMSE were estimated iteratively based on the remaining tie points and the outliers were removed until either the Δi,x Δiy and iRMSE values were lower than the threshold RT or the number of iterations was higher than a set value denoted as iterN.Finally, The uneven spatial distribution of the matched tie points may lead to local geometric distortions of the rectified images. In addition, redundant tie points covering some local regions may increase the number of computations and therefore reduce the efficiency of image processing. To solve this problem, a greedy algorithm  was usedNtp tie point pairs were obtained after the elimination of the outliers, the following steps were performed to achieve the even distribution of the tie points.Given that a number of RMSE value of each tie point pair was calculated, and all the tie point pairs were then sorted in ascending order according to the RMSE values.Step 1: The perspective transform model between the input image and the reference image was estimated using all the matched tie points. The RMSE values. The first item of the sorted tie point pairs, which is numbered as 1 , was selected as the first node.Step 2: The sorted tie point pairs were numbered according to the i and the other tie points j  were calculated using the Euclidean distance. If the distance of a tie point j was less than a threshold (LT), this tie point was considered to be too close to the tie point i. Then, this tie point was removed, and let i = i+1.Step 3: The distances between tie point i = Ntp.Step 4: Step 3 was repeated until LT) of 30 pixels, 108 tie points with a model error of 0.75 pixels remained, as shown in To test the employed approach for the even distribution of tie points, an experiment was performed using an 800 × 800 RS image ,In this section, three groups of postearthquake RS images were used to compare the proposed method with two other methods, including the classical SIFT (referred to as SIFT henceforth) and the SIFT method using the same patch matching approach (referred to as Patch-SIFT) used by the proposed method. The details of the three datasets  are introduced in The experiments were performed on a computer with an Intel Core i7-4770 CPU 3.40-GHz processor and 14.0 GB of physical memory, using Visual Studio 2013 (C#) as the programming environment.Three sets of high-resolution RS images after earthquakes were used in the experiment. All the images contain different secondary disasters caused by severe earthquakes. The first image is the QuickBird multispectral image (2.4 m) of the Wenchuan area that suffered from the Wenchuan earthquake (magnitude 8.2) on 12 May 2018. The image, with a size of 2427 × 2569 pixels, was acquired on 26 December 2008. The elevation of the image ranges from 1346 to 2960 m, as shown in The details of the three post-disaster images and the corresponding reference images obtained from the pre-earthquake image database are presented in According to the characteristics of the three datasets, the parameter settings of the proposed method are presented in cpN), the used time for image registration (T), the accuracy of the transformation model obtained using the final tie points (MRMSE), and the geometric accuracy of the registered image using some verification points (TRMSE).The performance of the proposed method was evaluated using four criteria, including the number of matched tie points (MRMSE and TRMSE are computed using Equation (16),i, yi) and cubic (Nd = 3) polynomial models were considered in the experiments.cpN = 45) but significantly more than that of the SIFT method (cpN = 21). The proposed method yielded very similar performances when using the quadratic and cubic polynomial models. Using the cubic polynomial model, the proposed method yielded an TRMSE value of 1.40 pixels, which is significantly lower than those of the Patch-SIFT (TRMSE = 2.01 pixels) and SIFT (TRMSE = 9.18 pixels) methods. Although the SIFT method provided the lowest TRMSE value (0.81 pixels), the TRMSE value of 9.18 pixels is extremely high. Using the cubic polynomial model, the running time of the proposed method was 2.20 s, which is only 6.47% of the SIFT (33.98 s) method and 44.27% of the Patch-SIFT (4.97 s) method. Similarly, the proposed method using the quadratic polynomial model gave a lower TRMSE value and a shorter running time than the other two methods.In the experiment using the first image pair, the proposed method obtained 52 matched tie points, which is slightly more than that of the Patch-SIFT method  and SIFT (cpN = 4) methods. Since the numbers of tie points extracted by the Patch-SIFT method were less than the minimum number required for the cubic polynomial model, only the quadratic polynomial model was used for the Patch-SIFT method. For a similar reason, only the linear polynomial model was used for the SIFT method. Using the cubic polynomial model, the proposed method yielded a running time of 1.56 s, an MRMSE value of 1.81 pixels, and an TRMSE value of 1.75 pixels. The running time is only 34.29% of the SIFT (4.55 s) and 48.30% of the Patch-SIFT (3.23 s). The MRMSE and TRMSE values are also significantly lower than those of the other two methods. Similarly to the first image pair, the running time, MRMSE, and TRMSE of the proposed method obtained using the quadratic polynomial model were very close to those obtained using the cubic polynomial model. Similarly, the distribution of the matched tie points extracted by the proposed method , whereas the Patch-SIFT and SIFT methods yielded 94 and 46 tie points, respectively. The proposed method has a significant advantage in the running time over the other two methods. The running times of the proposed method were 2.05 and 1.93 s for the cubic and quadratic polynomial models, respectively. The former is only 7.36% of the SIFT method (27.85 s) and 32.23% of the Patch-SIFT method (6.36 s) using the same model. The proposed method provided MRMSE values of 1.37 and 1.39 pixels, respectively, which are higher than those of the Patch-SIFT  and SIFT  methods. Although the SIFT method yielded the lowest model error, the validation error  was extremely high. The main reason is that the number of matched tie points was small, and the spatial distribution of tie points was uneven. The spatial distribution of matched tie points is shown in The third image pair shows significant differences in temporal and hue. In the experiment, the proposed method also provided the largest number of tie points . Topographic relief in the images increased the difficulty of image registration. However, in this case, the proposed method still achieved better registration results.Nd = 2) and cubic (Nd = 3) polynomial models were used in the experiments to generate registered images. The proposed method yielded very similar performances when using the two models. Using the quadratic polynomial model (Nd = 2), the proposed method also provided shorter running times and lower TRMSE values than the Patch-SIFT and SIFT methods using the same model. The proposed method using the cubic polynomial model yielded slightly longer running times and lower TRMSE values than the proposed method using the quadratic polynomial model. Consequently, we think that both the quadratic and cubic polynomial models can be selected for the images used in the experiments.Both the quadratic  and 36.7% of the Patch-SIFT (46.76 s). The experimental results also show that the registration results of the proposed method are better than those of the other two methods. This demonstrates that the proposed method also works for RS images covering urban areas and shows advantages in running time on larger RS images.The proposed method can be also used for satellite images (such as GF-1 and GF-2 images) covering plains, according to the results of the other experiments. For example, a GF-1 multispectral image covering Chengdu City, Sichuan province, China, was also tested. The image was recorded on March 9, 2018 and had a relatively large size (5354 × 5354 pixels) and a spatial resolution of 8 m. A total of 175 matched tie points were extracted by the proposed method, with an The GE images were used in this work to construct the pre-earthquake image database and then used as the reference for the co-registration of post-earthquake RS images. If higher quality high-resolution satellite remote sensing images were available in the study area, the images could be used as the reference for image registration. In this case, the proposed method is also applicable and is expected to yield better performance than the other two methods.In general, the proposed method can provide geometrically accurate images for disaster information extraction, and is helpful for the assessment of disasters, such as collapsed buildings, damaged roads, and landslides. The proposed method also has potential applications. For instance, the registered images obtained by the proposed method can be integrated with big data, such as the Floating Car Data , which iTo improve the efficiency and accuracy of multisource remote sensing image registration in the process of earthquake disaster assessment, a new automatic registration method for multisource RS images was proposed in this paper. Based on the construction of a pre-earthquake database, the proposed method employed a combination of the Shi_Tomasi and SIFT methods to extract tie points using the image partitioning strategy based on geographic information constraints. Additionally, a greedy algorithm was introduced to eliminate redundant tie points and thus to achieve even spatial distribution of matched tie points. The accuracy and robustness of the proposed method were compared with the traditional SIFT method and the Patch-SIFT method using three pairs of high-resolution images covering regions affected by severe earthquakes. The experimental results show that the proposed method outperformed the other two methods in terms of the accuracy of the registered image, running time, and number and distribution of matched tie points. The larger number and more even distribution of the tie points extracted by the proposed method contribute to the improvement in accuracy. The experimental results indicate that the proposed method is more suitable than the Patch-SIFT and SIFT methods in the case of the registration of post-earthquake high-resolution RS images used for earthquake damage assessment. According to the experimental results of a test using a GF-1 image covering urban areas (with a size of 5354 × 5354 pixels), the proposed method can also be used for satellite images covering plains (including urban areas), and shows significant advantages in running time over the two comparison methods for large-size RS images.


In general, DGA is applied in monitoring systems based upon an autoregressive model; the current value of a time series is regressed on past values of the same series, as well as present and past values of some exogenous variables. The main difficulty is to decide the order of the autoregressive model; this means determining the number of past values to be used. This study proposes a wavelet-like transform to optimize the order of the variables in a nonlinear autoregressive neural network to predict the in oil dissolved gas concentration (DGC) from sensor data. Daubechies wavelets of different lengths are used to create representations with different time delays of ten DGC, which are then subjected to a procedure based on principal components analysis (PCA) and Pearson’s correlation to find out the order of an autoregressive model. The representations with optimal time delays for each DGC are applied as input in a multi-layer perceptron (MLP) network with backpropagation algorithm to predict the gas at the present and future times. This approach produces better results than choosing the same time delay for all inputs, as usual. The forecasts reached an average mean absolute percentage error (MAPE) of 5.763%, 1.525%, 1.831%, 2.869%, and 5.069% for C The transformer is one of the most important devices in the electricity distribution process, and reliable power distribution depends largely on the failure-free operation of this equipment. The failure of the transformer during operation can bring a significant loss of revenue to the utility, possible environmental damage, explosion and fire risks, and expensive costs of repair or replacement ,2. In th2), methane (CH4), acetylene (C2H2), ethylene (C2H4), ethane (C2H6), carbon monoxide (CO), and carbon dioxide (CO2). On condition of internal transformer failure occurring, the emergence speed of these gases is accelerated 33]. In , the autHowever, approaches based on autoregressive models apply the same order for all input variables and do not take into account the time delay relationship between gas concentrations. Notwithstanding, we have seen that the optimal selection of the time delay for each concentration of gas affects the output.Regarding forecast accuracy, this approach shows some better predictions than ,38,39 s.2H2 concentration, instead of input 12, 6, 10, and 8 passed values of the gases C2H4, C2H6, CH4, and H2, respectively; according to in Equation (4), we simply use the corresponding four approximations created by the wavelet-like transform for each exogenous gas.It is important to highlight the low computational cost of the proposed model, because it takes a matter of seconds to run. In the example above regarding the prediction of the CThis work presented an approach based on the wavelet transform to determine the ideal time delay for each gas concentration and use it as a regression model in an MLP network. The application of a wavelet-like transform creates sparse approximations of gas concentrations with different time delays, and enables us to define an accurate and computationally efficient prediction model. The prediction model has high precision as it focuses on the trend of the input signals from the noise-free approximations calculated by the wavelet transform. Moreover, it reduces of the number of input variables in the autoregression model when using the approximations resulting from transformations with wavelets of different lengths, which already consider the time delay determined for each variable.In addition to simply assessing the relationship of different gases, the proposal makes it possible to identify the relationship between a specific gas and delayed approximations of all other gases. This is a fundamental result for monitoring systems, as it indicates that variations in different gases take different times to be reflected in the concentrations of other gases under observation. As an example, it was observed that variations in ethane affect acetylene concentrations more quickly than variations in ethylene. So, the approach extends the discussion of the temporal relation between gas concentrations, providing an expansion of knowledge of the temporal relationship between gases underlying degradation process of the insulating oil and solid insulating material.Thus, wavelet-like approximations of the external variables were an effective tool to incorporate temporal memory of the autoregressive model and to determine the ideal delay for each input. As future research, we suggest investigating other feature selection techniques to evaluate the results of Pearson’s correlation and PCA.


Cystadenocarcinoma papillare serosum IIIc and Cystadenocarcinoma mucinosum IIIc. Concentrations of parameters were measured by ELISA. Analysis of the obtained results showed a statistically significantly higher concentration of sTRAIL and OPG in the serum of women with ovarian serous and mucinous cancer compared to the control group (p < 0.0001). Statistical significance was found between sTRAIL and OPG concentration in G1 and G3 serous cancer (p < 0.01) and in OPG mucinous cancer between G1 and G3 (p < 0.01) and G2 and G3 (p < 0.01). An important role in the pathogenesis of ovarian cancer is played by disorders of the apoptosis process involving the sTRAIL/OPG system, which are associated with the histological type and the degree of histological differentiation of the tumour. Determining the concentration of tested parameters in combination with other markers may be useful in the future in the diagnosis of ovarian cancer, but that requires further research.Due to the ability to selectively induce apoptosis in cancer cells, the most interesting target for clinical research is the tumour necrosis factor ligand inducing apoptosis (TRAIL), which binds specific receptors, including osteoprotegerin (OPG). The aim of the study was to analyse the concentration of soluble TRAIL (sTRAIL) and OPG in the serum of women with serous or mucinous ovarian cancer, taking into account different levels of cancer histological differentiation. The group included 97 women with the diagnosed  Ovarian cancer is still one of the worst-prognosis gynaecological cancers with the highest mortality in most countries in the world, and unfortunately, statistical incidence studies predict that by 2040 mortality will increase significantly ,2. It isLiterature shows that in the formation and development of ovarian tumours, an important role is played by disorders of the apoptosis process, which may be regulated by soluble ligands and their receptors belonging to the superfamily of tumour necrosis factor molecules (TNF), and the ability to monitor them by measuring them in blood serum might find possible clinical use.Studies conducted so far have shown that ovarian tumours are accompanied by changes in the concentration of markers monitoring the process of apoptosis in the following ligand/receptor systems: TNF/sTNF-R, sCD30/sCD30L, sCD40/sCD40L, and soluble tumour necrosis factor ligand inducing apoptosis (sTRAIL)/sTRAIL-R1 and R2. These changes indicate disturbances in the extrinsic pathway of apoptosis induction and may constitute one of the immune mechanisms supporting the development of ovarian cancer. In addition, sTNF-R1, sTNF-R2, sCD30, sCD30L, and sCD40 concentrations have been shown to be useful in the early diagnosis of ovarian cancer, and sCD40L, sTRAIL-R1, and sTRAIL-R2 in the differential diagnosis of benign tumours ,6,7.Among the molecules belonging to the apoptosis receptor pathway, the most interesting target of clinical research is the tumour necrosis factor ligand that induces TRAIL (TNF-related apoptosis inducing ligand) ,9,10,11.As shown by the studies conducted so far, TRAIL may also show nonapoptotic functions supporting tumour development in cancer cells resistant to apoptosis ,14. TRAIThere are five types of TRAIL receptors. They are four membrane receptors TRAIL-R1 (DR4), TRAIL-R2 (DR5), TRAIL-R3 (DcR1), and TRAIL-R4 (DcR1), and one soluble receptor called osteoprotegerin (OPG) ,20. DeatDue to the demonstrated anti-apoptotic effect of osteoprotegerin in patients with ovarian cancer , and theTherefore, the aim of the study was to analyse the concentration of soluble tumour necrosis factor ligand inducing apoptosis (sTRAIL) and its osteoeprotegerine receptor (OPG) in the serum of women with serous or mucinous ovarian cancer, taking into account the histological differentiation of the cancer.Cystadenocarcinoma papillare serosum III c  and 45 women with the diagnosed Cystadenocarcinoma mucinosum III c . Staging employed the criteria recommended by the International Federation of Gynaecology and Obstetrics (FIGO). The assessment of the degree of histological differentiation of cancer (grading) is presented according to the following scale: G1—highly differentiated, G2—moderately differentiated, G3—poorly differentiated. The diagnosis of tumours was done on the basis of clinical symptoms, results of gynaecological and histopathological examination, and laboratory tests. The women qualified to the studied group were clinically diagnosed with ovarian tumour confirmed with a histopathological examination. Other coexisting disorders of the reproductive organs were excluded. Research material came from the Clinical Ward of Obstetrics and Gynecology, Medical University of Silesia. In addition, 42 healthy women aged between 28–67 (mean age: 47.74 ± 10.64 years) were included in the control group. These women did not have any gynaecological diseases.In the study group, blood was taken from the women before surgery. Blood was collected from these women to obtain serum, and then centrifuged at 1500× g for 15 min and stored for testing at −80 °C in small portions.The study group included 97 women, aged 19 to 78 (mean age: 55.32 ± 14.61 years) with the diagnosed ovarian cancer. The group included 52 women with the diagnosed However, blood in women from the control group was collected while they reported for control tests. The collected biological material was handled in the same way as for the study group.TM MEIA kits, test sensitivity was 2 U/mL.Quantitative immunoenzymatic ELISA was performed in both the test and control groups using the following tests: TRAIL/Apo2L Diaclone and MicroVue OPG EIA Kit from Qudiel Corporation. Tests sensitivity amounted to 64 pg/mL and 0.04 pmol/L. Tumour CA 125 (cancer antigen-125) antigen concentration was determined by microparticulate immunoenzymatic method (MEIA) using Abbott Diagnostics Tumour Markers CA-125 The conduct of these studies was approved by the ethics committee of the Medical University of Silesia in Katowice and all women participating in the study.The obtained test results were subjected to statistical analysis using the Statistica 13.3 program. The normality of the distribution of tested variables was checked using the Shapiro–Wilk test. For the parameters tested, the median and quartile range were determined, and the results obtained were compared using the Mann–Whitney test.p < 0.05.The Spearman’s rank test was used to perform the correlation, giving the correlation coefficient (r), and the statistically significant level was assumed to be p < 0.0001). Next, the serum sTRAIL concentration in women with serous and mucinous ovarian cancer was analysed. In the serum of women with serous ovarian cancer Q1 and Q3 were, respectively, 2213.50 and 3839.00, and the median value was 3020.50 pg/mL, while in the group of women with mucinous ovarian cancer, Q1 and Q3 were 1711.00 and 3200.00, with a median of 2450.80 pg/mL. Statistically significantly higher serum sTRAIL concentration was found in both women with serous and mucinous histological types of cancer compared to those in the control group (p < 0.0001). In addition, the analysis of the research results showed statistical significance between the concentration of the studied parameter depending on the degree of histological differentiation of G1 (highly differentiated) and G3 (poorly differentiated) serous and mucous carcinomas (p < 0.01). However, no significant differences were found between the serum concentration of sTRAIL in women with serous and mucinous ovarian carcinoma moderately differentiated (G2) and poorly differentiated (G3).In the serum of healthy women belonging to the control group and women with ovarian cancer, sTRAIL concentration was determined. Due to the fact that the values obtained did not correspond to the normal distribution, the results were presented in the form of median and lower and upper interquartile range (Q1 and Q3). In the group of healthy women, Q1 and Q3 were, respectively, 490.00 and 822.67, while the median value was 640.00 pg/mL. In the group of women with ovarian cancer, Q1 and Q3 were, respectively, 2001.00 and 3700.00, with the median equal to 2733.00 pg/mL. A statistically significantly higher concentration of the studied parameter in the serum of women with ovarian cancer was found compared to that in the control group (The obtained results are presented in p < 0.0001) correlation was found between variables.In the next stage of the statistical analysis, a correlation test was performed, which showed the existence of a relationship between the TRAIL concentration and the CA 125 marker concentration in the blood serum of the examined women. A positive (r = 0.63) statistically significant  curves were drawn, which are illustrated in The diagnostic value of TRAIL and CA 125 antigen was assessed by determining the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for these parameters, as shown in p < 0.0001). Next, the OPG concentration in the serum of the examined women was analysed depending on the histological type of ovarian cancer. In the serum of women with serous ovarian cancer, Q1 and Q3 were, respectively, 19.01 and 40.00, and the median value was 29.45 pmol/L. In the group of women with mucinous ovarian cancer, Q1 and Q3 were 15.46 and 28.90, with a median of 21.00 pmol/L. Statistically significantly higher serum OPG concentration was found in both women with serous and mucinous histological types of cancer compared to those in the control group (p < 0.0001). In addition, the analysis showed statistical significance between OPG concentration depending on the degree of histological differentiation in women with serous ovarian cancer between G1 and G3 (p < 0.01), and in women with mucinous cancer between G1 and G3 (p < 0.01) and G2 and G3 (p < 0.01). The obtained results are shown in Further analysis included assessment of osteoprotegerin (OPG) serum concentration in healthy women in the control group and women with ovarian cancer. The obtained values did not correspond to the normal distribution, therefore, the results were presented in the form of median and lower and upper interquartile range (Q1 and Q3). In the group of healthy women, Q1 and Q3 were, respectively, 2.76 and 8.70, while the median value was 4.70 pmol/L, and in the group of women with ovarian cancer, Q1 and Q3 were 16.90 and 37.00, with a median equal to 26.00 pmol/L. A statistically significantly higher concentration of the studied parameter in the serum of women with ovarian cancer was found compared to the concentration in women from the control group (p < 0.0001) correlation was found between the variables. The linear regression curve showing this relationship is presented in Further analysis showed the relationship between the OPG concentration and the CA 125 marker concentration in the blood of the examined women. A positive (r = 0.51) and statistically significant  and TRAIL-R2 (DR5). Receptors may also exist in soluble forms. Studies in the serum of women with ovarian cancer have shown an increased serum concentration of sTRAIL in women with ovarian serous cancer and a benign tumour—serous adenocarcinoma and mature teratoma of the ovary. It has also been demonstrated that the determination of sTRAIL-R1 and sTRAIL-R2 may prove useful in the differential diagnosis of benign ovarian tumours. According to the authors, changes in the concentration of the studied parameters testified to apoptosis disorders in women with ovarian cancer involving the sTRAIL/sTRAIL-R1 and sTRAIL/sTRAIL-R2 systems .Cystadenocarcinoma papillare serosum IIIc according to FIGO) and mucinous (Cystadenocarcinoma mucinosum IIIc according to FIGO). A significantly increased sTRAIL concentration in the serum of women with both serous and mucinous cancer compared to the control group was demonstrated, which indicates the disturbance of the extrinsic apoptosis process dependent on the histological type of ovarian cancer, which might be one of the immunological mechanisms involved in pathogenesis of these cancers. Interesting observations were also provided by the analysis of sTRAIL concentration, taking into account the degree of histological differentiation of cancer. The demonstrated statistical significance between the concentration of the studied parameter in the serum of women with serous and highly differentiated G1 and poorly differentiated G3 mucinous carcinomas shows that apoptosis disorders involving sTRAIL are also associated with the degree of histological differentiation of ovarian cancer, which could be useful in developing new methods of targeted ovarian cancer therapy. In addition, the analysis showed that there is a correlation between the concentration of sTRAIL and the decimal logarithm of the concentration of CA 125 antigen in the blood serum of women from the study group, which was statistically significant , and the assessment of the diagnostic value of the tests showed that the determination of the concentration of sTRAIL and CA 125 in the blood serum is characterised by high sensitivity for sTRAIL and the CA 125 antigen, and the simultaneous determination of the concentration of these parameters in the serum of women may be useful in the diagnosis of ovarian cancer, but requires further research.The promising results obtained induced us to further analysis, which included assessing the serum concentration of sTRAIL ligands in women diagnosed with different types of cancer, including serous , according to FIGO, or primary peritoneal carcinoma (PPC) eligible for an interval debulking surgery (IDS) were conducted by Gasowska-Bodnar et al. . The obtResearch conducted so far has shown that TRAIL might participate in both the selective induction of apoptosis in some cancer cells and may perform nonapoptotic functions, including participation in the development of certain cancers and metastasis. For this reason, it still remains the subject of intensive research for possible clinical use.Literature shows that TRAIL is a promising therapeutic tool in cancerous diseases, including ovarian cancer ,28,29,30The research showed that TRAIL had a synergistic effect, leading to increased apoptosis after incubation of TRAIL-resistant cell lines with cytotoxic mediators.According to the authors, the demonstrated ability of TRAIL to induce apoptosis in ovarian cancer cells, as well as to increase the activity of chemotherapeutic agents even in cell lines resistant to TRAIL-induced cytotoxicity, may be of great importance in the fight against this disease .Also, Cuello et al.  assessedNumerous studies are still underway to understand the mechanisms underlying TRAIL resistance in ovarian cancer, including combination therapy with cytotoxic chemotherapy, retinoids, proteasome inhibitors, demethylating agents, Akt inhibitors, and EGFR inhibitors. Many of them have already demonstrated efficacy in restoring TRAIL sensitivity in preclinical studies, and developing new strategies might in the future improve the effectiveness of existing therapies .Understanding these mechanisms also involves TRAIL-specific receptors, including osteoprotegerin, and an assessment of their possible diagnostic and therapeutic use. Osteoprotegerin not only inhibits apoptosis, but it might also induce proliferation by binding to various cell surface receptors, including canonical cell survival and proliferative pathways. OPG also participates in the induction of angiogenesis, facilitating tumour growth .Cross et al.  were theAccording to the authors, the results obtained indicate a significant contribution to OPG expression of endothelium between malignant tumours and benign tissues, and confirm the potential biological role of this molecule in the development and/or maintenance of the vascular system of the tumour.A clear separation in endothelial OPG expression between malignant tumours and nonmalignant tissues is presented in our data, which confirms the potential biological role of the molecule in the development and/or maintenance of the vascular system of the tumour .In contrast, Lane et al.  assessedp < 0.0001) correlation between the OPG concentration and the decimal logarithm of the CA 125 antigen concentration in the blood serum of the test group of women, and the assessment of the diagnostic value of tests showed that the determination of the concentration of OPG and the marker CA 125 in blood serum is characterised by high sensitivity for OPG and the CA 125 antigen, which indicates that the simultaneous determination of the concentration of these parameters in women’s serum may be useful in the diagnosis of ovarian tumours, but that requires further research.Literature shows that determination of OPG concentration together with other parameters may also be useful in the diagnosis and monitoring of therapy for some cancers, including ovarian cancer. However, no analysis of the OPG concentration in the serum of women with ovarian cancer has been conducted so far, taking into account the histological type of cancer and the degree of histological differentiation of cancer. Such studies were conducted as a part of this work. The analysis showed a significantly increased concentration of OPG in the serum of women with both serous and mucinous cancer compared to the concentration in the control group, which indicates disturbances in the process of extrinsic apoptosis occurring with the participation of OPG, depending on the histological type of ovarian cancer, and may also indicate the autocrine secretion of this receptor by cancer cells. Interesting observations were also provided by the analysis of OPG concentration taking into account the degree of histological differentiation of cancer, which showed statistical significance between the degree of G1 and G3 differentiation in serous cancer, and in mucinous cancer between G1 and G3 as well as G2 and G3, which might be used in the creation of new therapeutic strategies. In addition, it has been shown that there is a positive significant (r = 0.51, Similar studies, but in a different biological material, were conducted by Chudecka-Głaz et al. , who evaInteresting observations are also provided by studies on the assessment of the importance of determining osteoprotegerin concentration as a possible prognostic and diagnostic marker in other cancers. Such studies were conducted by Mizutani et al. , who werAn important role in the pathogenesis of ovarian cancer is played by receptor-mediated apoptosis disorders involving soluble particles sTRAIL and osteoeprotegerine, which are not only one of the important immunological mechanisms of immunosuppression associated with ovarian cancer, but may also promote the development of this tumour.The observed changes in the sTRAIL/OPG system concentration in the serum of women with ovarian cancer are related to the histological type of ovarian cancer and the degree of histological differentiation of this cancer, which may be used in the development of new immunotherapy methods.The determination of sTRAIL and OPG in combination with other markers may be useful in the future in the diagnosis of ovarian cancer, but that is requires further research.To sum up, based on the research we concluded that:


Two techniques were employed to incorporate Pd nanoparticles on the P25 surface: photodeposition (series Pd-P) and impregnation (series Pd-I). Both series were characterized in depth using a variety of instrumental techniques: BET, DRS, XRD, XPS, TEM, FTIR and FESEM. The modified series exhibited a significant change in pore size distribution, but no differences compared to the original P25 with respect to crystalline phase ratio or particle size were observed. The Pd0 oxidation state was predominant in the Pd-P series, while the presence of the Pd2+ oxidation state was additionally observed in the Pd-I series. The photoactivity tests were performed in a continuous photoreactor with the photocatalysts deposited, by dip-coating, on borosilicate glass plates. A total of 500 ppb of NO was used as input flow at a volumetric flow rate of 1.2 L·min−1, and different relative humidities from 0 to 65% were tested. The results obtained show that under UV-vis or Vis radiation, the presence of Pd nanoparticles favors NO removal independently of the Pd incorporation method employed and independently of the tested relative humidity conditions. This improvement seems to be related to the different interaction of the water with the surface of the photocatalysts in the presence or absence of Pd. It was found in the catalyst without Pd that disproportionation of NO2 is favored through its reaction with water, with faster surface saturation. In contrast, in the catalysts with Pd, disproportionation took place through nitro-chelates and adsorbed NO2 formed from the photocatalytic oxidation of the NO. This different mechanism explains the greater efficiency in NOx removal in the catalysts with Pd. Comparing the two series of catalysts with Pd, Pd-P and Pd-I, greater activity of the Pd-P series was observed under both UV-vis and Vis radiation. It was shown that the Pd0 oxidation state is responsible for this greater activity as the Pd-I series improves its activity in successive cycles due to a reduction in Pd2+ species during the photoactivity tests.Two series of new photocatalysts were synthesized based on modification with Pd of the commercial P25 photocatalyst (EVONIK The 5002 crystalline phases present in all the catalysts are anatase (A) and rutile (R), identified by the Crystallography Open Database (COD) as COD9015929 and COD9015662, respectively. No presence of metal is observed, as expected for the weight percentage of 0.5% of Pd, an amount which is difficult to detect using X-ray diffraction. Differences are observed in the colors of the Pd-modified photocatalysts . However, in the pore size distribution represented in The TEM study enabled the identification of the deposits of Pd particles incorporated on the P25. It can be seen for both samples that the deposits are very small at the nanometer scale .4+ signal of the TiO2 (9). The O signal appears at values close to 529.5 ± 0.5 eV, attributable principally to the oxygen of the crystalline lattice of the TiO2 [lattice/Ti ratio remained close to the theoretical value at around 2 in the modified photocatalysts. This indicates that the modification carried out in this work by means of the deposition of palladium particles is only superficial. An analysis of the oxidation state of the Pd can be made from The samples were analyzed by XPS in order to determine the oxidation states of the Pd incorporated on the surface of the titanium oxide. the TiO2 . A slighthe TiO2 . The Ola3d5/2)) [2+, as described in other samples obtained by photodeposition [2+ (336.4 eV (3d5/2)) is clearly defined, apparently in the form of PdO according to its value [3d5/2)) . Despiteposition ,14,21. Fts value .−2 of P25 with Pd, independently of the modification method, display a continuous profile irregular in thickness, ranging between 198-1690 nm and 150–1437 nm, for Pd-P and Pd-I, respectively . Therefore, the nitrocompounds and the adsorbed NO2 observed in the FTIR studies . Hence, during the NO elimination studies in the catalysts with Pd, the reaction expressed in (6) does not take place until the catalysts are left in an air atmosphere, and the NO concentration decreases. However, in the case of the P25, the formation of nitrates is favored through another mechanism in which surface hydration of the catalyst is influential. This will be discussed below.The reaction of the NO with oxygen surface ions gives rise to nitrites (4), and the reaction of nitrites with oxygen surface ions gives rise to NO studies  are gene−1 attributed to solvated nitrates. These bands were more intense in the catalysts with Pd.The FTIR spectra of the catalysts after 15 h of NO elimination at 65% RH are shown in 2 can be disproportionated through the following reaction:In the presence of humidity, the NO2 with water requires the formation of a prior isomer [2 can be conditioned by the characteristics of the adsorbed water. −1 and another at 1620 cm−1 attributed to the stretching and bending modes of adsorbed water, respectively [−1, another at 2134 cm−1 and a third at 1643 cm−1, which are typical of liquid water. That is, the adsorption of water in these catalysts seems to be less intense than in the P25. The intense interaction of water in the P25 appears to favor the disproportionation of NO2 through reaction (4).It has been reported that the disproportionation of NOr isomer . The forectively . In cont2 is not photocatalytically degraded but rather adsorbed and disproportionated on the surface of the TiO2.These results are in agreement with those of previous studies made by our research team  in which2 in the catalysts with Pd. This explains the greater eliminating capacity of this compound with the Pd-modified catalysts.In short, the absence of high surface hydration favors the formation of nitro-chelates and the adsorption of NO2. The increase of NO2 generated with humidity is greater for the Pd-I series. This means that, in 5 h, NOx removal is lower for Pd-I. Hence, in the absence of humidity, Pd-I and Pd-P function practically the same, but in the presence of humidity, the Pd-P series has greater photocatalytic efficiency. Although it was found for 65% RH that they have the same overall NOx removal capacity, the oxidation kinetics are faster for the Pd-P series . The formation of these species is favored by the presence of humidity from the oxidation of Pd2+. The formation of these species and the consequences for the adsorption of NOx species may explain the changes in growth and decay in the NO2 profile that takes place in the first hour of irradiation for the catalysts of the Pd-I series, which are the catalysts which have a higher amount of oxidized palladium Pd2x oxidation kinetics in Pd-I and Pd-P, an XPS analysis was undertaken of the evolution of the Pd-I sample for different test times of 5 h and 24 h (In order to clarify the results and the different evolution in the NOand 24 h a.2+ to Pd0. 0 (grayish color). For Pd-I at 5 h, Pd presents a quite complex set of peaks, which indicates more species than Pd0 and PdO, which may be due to the Pd4+ (R5 and R6).No appreciable differences were observed in the Ti and O signals compared to the initial sample. 2 and removed NOx for various cycles of photoactivity with intermediate washes for Pd-I. Enhancement of the activity after each radiation cycle is confirmed as the Pd is reduced. In the second cycle, similar values to those of the Pd-P sample are obtained, and in the third cycle, the results are even bettered as the Pd-P sample also initially contained some oxidized Pd.x is removed and less NO2 generated. Activity in the visible is attributable to the presence of Pd2+ and Pd0 on the surface of the modified photocatalysts. Under visible radiation, the presence of Pd0 gives rise to energy transitions between the TiO2 valence band and localized energy levels due to metal clusters [Activity was also tested with Vis radiation b,d,f. Itclusters ,43. In aclusters , which cclusters .2 surface improves NOx removal. This improvement appears to be related to the presence of Pd, giving rise to a different interaction of the water with the TiO2 surface.The presence of Pd on the TiO2 is not photocatalytically degraded but rather is adsorbed and disproportionated on the TiO2 surface. In the present work, it was found that in the catalysts with Pd, the NO2- and NO2 generated from the photocatalytic oxidation of the NO in the absence of humidity are adsorbed on the surface of the catalyst, and there is no disproportionation to give nitrates until the NO is eliminated from the reaction medium. However, in the P25, there is some NO2 disproportionation due to the high surface hydration of the catalyst and less NO2 adsorption.It is shown, as also indicated in previous studies, that NO0 than impregnation. It seems to be that the Pd0 form is beneficial for the activity of the catalyst under both ultraviolet and visible radiation.The photodeposition method produces a higher quantity of Pd


Sudden cardiac death is the most common cause of mortality worldwide. Most cases occur at home and the individualsmost likely witnessed are family members. Spouses play a significant role in the recovery of such patients.We aimed to investigate the effect of Cardiopulmonary Resuscitation (CPR) and cardiac chest pain managementtrainings on the perceived control, depression, stress and anxiety in the spouses of the patients with Myocardial Infarction (MI). The present randomized controlled trial study was performed on 78 spouses of the patients admitted to Cardiac Care Unitof the hospitals affiliated to Shiraz University of Medical Sciences from August 2013 to April 2014. The subjects wererandomly assigned into intervention (n=40) and control groups (n=38). The intervention group took part in educationalworkshop on CPR and cardiac chest pain management and followed-up for six weeks. Data were collected using DepressionAnxiety Stress (DASS) Scale and Rotter’s internal-external locus of Control Scale. Statistical methods of data analysisincluded descriptive statistics, Chi-squared test, paired sample t-test, independent sample t-test, one way analysis of covariance, and multivariate analysis of covariance. There were significant differences between intervention and control groups in perceived control and DASS subscales.After the intervention, perceived control scores reduced from 9.42±3.33 to 8.15±3.65 (P=0.001); depressionfrom 28.85±11.99 to 21.65±8.64 (P=0.001); anxiety from 26.6±9.46 to 20.70±6.29 (P=0.02); and stress from 30.20±10.91 to 24.32±10.23 (P=0.01) in the intervention group. CPR and cardiac chest pain management trainings could effectively improve the perceived control, depression, anxiety, and stress in thespouses of the patients with MI. Trial Registration Number: IRCT201310128124N2. CVDs are predicted to be the first leading cause of mortality and morbidity worldwide, accounting for over 17 million deaths annually. 6According to the studies conducted in several countries, it is estimated that every 9 seconds someone in the world dies suddenly and 370.000 to 750.000 of these deaths are caused by cardiac arrest. This rate is also increasing annually. 7Evidence shows that almost 70 to 77% of out-of-hospital deaths and most interventions of cardiac deaths occur at home. However, the survival rate of people afflicted with cardiac arrest at home is lower than those who experience it at public places. 8Psychological issues of spouses and supporting them is crucial due to some reasons. Firstly, many of the spouses commonly suffer from psychological stresses which last longer than those of the patients. Secondly, the spouses’ stress and, especially depression, might be transferred to the patients with MI and also manifested in them. 7Besides, psychological stresses, anxiety and depression are significantly associated with increased morbidity and mortality in the patients. The spouses’ stress can harm their ability to protect and support their patients. One of the effective key points in enhancing the spouses’ protection ability is helping them to control their stress. Psychological stresses occurring after a serious illness follows decreased sense of perceived control. Perceived control is the individuals’ beliefs in an internal locus, which affects the events and environment, so that they will be able to achieve their desired goals. 9Higher levels of internal perceived control can reduce the spouses’ psychological stress. Such a trait can be modified or reinforced using appropriate interventions such as cardiopulmonary resuscitation (CPR). 9The actions taken during the initial minutes of an emergency, including basic life support (BLS), are critical for the patient’s survival. 10CPR as a public skill is one of the greatest inventions of medical history and prompt delivery of CPR is a crucial determinant of survival for many victims of sudden cardiac arrest. 11Among the family members of the patients with myocardial infarction (MI), their spouses play a significant role in the support and recovery of such patients. The patients’ spouses have commonly reported unpleasant symptoms and feelings such as anxiety, depression, fear, loss of appetite, and inability to concentrate. Such psychological symptoms often last for months after the heart attack. 9On the other hand, 3/4 of SCDs occur at home and in the presence of the victim’s family. Therefore, the family members are most available and important people to the patient. 13Evidence also shows that learning critical measures to be taken to save the patients’ lives in the case of cardiac arrest is one of the most important needs of the spouses of the patients who have had an MI. CPR training has met such a need effectively. 14Resuscitation performed by bystanders on patients who are in cardiac arrest and prompt arrival of emergency medical services improves the survival rates and enhances the quality of life in the survivors. 16Besides, receiving CPR from bystanders at the scene increases the survival rate by 2 to 3 times. 13In this field, several national and international studies have been conducted. In other countries, the studies have focused on the impact of CPR training on perceived control in spouses, and assessed attitudes towards CPR training in family members of patients. 9In Iran, the studies published up to now assessed the effect of education of CPR on knowledge and skills of health care team such as nursing staff and physician. 18However, a study focusing on the effect of CPR and cardiac chest pain management training in the family members of patients has not been done. However, the family members are the most suitable target group to receive such training. In addition, one of the important concerns about saving the patient’s life is the lack of knowledge and sufficient skills in patients and their relatives. 19Due to the important role of the family and spouses in providing support and care for patients and lack of supportive systems and appropriate group training of caregivers in Iran, interventional research on the inclusion of chest pain management and BLS programs is strongly suggested. Therefore, we aimed to investigate the effect of CPR and cardiac chest pain management training on the perceived control, depression, stress, and anxiety in the spouses of the patients with MIOn the other hand, the findings of a study on the family members’ knowledge and performance in dealing with a patient’s heart attack in Iran showed that the patients’ family and companions take them to emergency department without performing appropriate action for heart attack. The results of this study approved the need for education to community members in Iran in order to raise the level of performance and awareness in confrontation with of a patient’s heart attack. 20and the following formula using Med Calc statistical software with a power of 95%; α of 0.05; mean difference of 12.56; Zα of 1.96; Zβof 1.64; σ of 15.18; and loss rate of 10%. The present randomized controlled trial study was performed on the spouses of all 84 patients admitted to Cardiac Care Unit(CCU) of the hospitals affiliated to Shiraz University of Medical Sciences from August 2013 to April 2014.The sample size was calculated as 42 in each group based on the data of similar studies n=[(Zα/2+Zβ)2×2δ2](μ1-μ2)2A random number table was used to assign 84 participants, who had the inclusion criteria. Inclusion criteria included ability tounderstand the information presented (which is confirmed by the researcher), ability to participate in research, lack of participation in similartraining programs simultaneously or since 6 months before the study, age under 65 years old, and willingnessto participate in the research. We assigned the individuals to enter numbers 1 to 84 respectively and then usethe random number table. We selected 42 numbers from 1 to 84; then, by throwing the coin, we assigned the individualscorresponding to the numbers selected from the random numbers table to the control or intervention groups. 42 participants (from 1 to 84)were also replaced in the other group. Six participants were excluded from the study as they withdrew from participationor had to undergo coronary artery bypass graft. We continued the study with 78 participants in the intervention (N=40)and control (N=38) groups , 40 part9According to Moser et al.’s study on perceived control of spouses, which was based on the follow-up period of 1 month the follow-up period lasted six weeks. 21Internal consistencies for depression, anxiety, and stress scales of DASS in the normative sample were 0.91, 0.81 and 0.89, respectiely. 22To measure the rate of stress, anxiety, depression, and perceived control, we administered two questionnaires to the participants of the intervention and control groups before and after six weeks of the research period. Data were collected using Depression, Anxiety and Stress Scale (DASS) and Internal-external Locus of Control scale. DASS is a 42-item self-reported questionnaire designed to measure the severity of depression, anxiety, and stress. Thus, the following cut-off points were developed for defining it. The levels of depression included 0-9 normal, 10-13 mild, 14-20 moderate, 21-27 severe, and 28+ extremely severe. The levels of anxiety included 0-7 normal, 8-9 mild, 10-14 moderate, 15-19 severe, and 20+ extremely severe. Also, the level of stress included 0-14 normal, 15-18 mild, 19-25 moderate, 26-33 severe, and 34+ extremely severe. Each of the three scales contained 14 items and the total score in each scale was calculated by summing the scores for the relevant items. The validity and reliability of DASS were well confirmed by Lovibond and Lovibond. 22Szabo approved the construct validity of DASS. 23In Iran, the reliability of DASS scales was confirmed by examining the coincidence coefficients and test re-test coefficients. In addition, the construct validity of the two scales of depression and anxiety was confirmed by using the correlation coefficient between the scores of the two scales with those of the subjects in the Beck Depression Scale and the four-system anxiety questionnaire. Concurrent validity of sub-scales was determined by calculating the scores of a sub-sample from the general population (n=315) with a group of patients who suffered from psychological disorders. According to the results of this study, DASS scales have the necessary requirements for application in clinical situations with Iranian people. 24In this study, the reliability of this scale was assessed by testing its stability in a group of 30 spouses of the patients suffering from MI from a baseline measurement to six weeks later in the same subjects who received no intervention. The interclass correlation for baseline scores and six week’s scores was 0.96, which confirms the reliability of this scale . 25The first three factors together accounted for 41.3% of the item variance. The correlation between the factors were as follows: Depression-Anxiety r=0.42; Anxiety-Stress r=0.46; and Depression-Stress r=0.39 which confirmed the satisfactory reliability of the three scales. The factor structure of DASS was confirmed with two different approaches and findings providing support for the psychometric properties of the DASS scales and their convergent and discriminant validity with other instruments developed on clinical populations. 26In Iran, various studies have been conducted on the reliability and validity of the scale. The reliability coefficient of this scale has been calculated 0.70 using two-half-way and Kooder-Richardson method . Also, the reliability coefficient of this scale has been at the same level with a test-retest method within one to two months. The reliability coefficient obtained from the short form of this scale was 0.72 for the students at Noor University, as shown in the Noforsati’s study. 27In the present study, Cronbach’s alpha of Internal Locus of Control scale was 0.75.Locus of Control scale is designed to measure generalized expectancies for internal versus external locus of control. In this scale, social learning is considered as a theoretical framework. Also, it is a 29-item forced-choice test and each item has two options (A and B). The scale includes six filler items and 23 items scored as 0 or 1. Opposite responses receive no score. It has a minimum score of 0 and a maximum of 23 with the high scores representing an external locus of control and a low score representing an internal locus of control. Rotter describes the development of the tests of individual differences in a generalized belief in internal-external and provides reliability, discriminant validity and normative data for one test, along with a description of the results of several studies of construct validity. The reliability of the Internal Locus of Control Rotter scale has been calculated by the test-retest method; Rotter reported their level from 0.49 to 0.83. Discriminant validity is indicated by the low relationships with such variables as intelligence, social desirability, and political liberalness. Most significant evidence of the construct validity of the Internal-External (I-E) scale comes from the predicted difference in behavior for individuals above and below the median of the scale or from correlations with behavior criteria. This study was approved by the Ethics Committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1392.S6680). The participants were provided with sufficient information about the study procedure and objectives, benefits, nature, and duration of the study. They also signed written informed consents after being ascertained about the confidentiality of their information. They were also ensured that they could withdraw from the study whenever they desired. Statistical methods of data analysis included descriptive statistics , Chi-squared test (for demographic variables) , One way analysis of covariance (for perceived control), and multivariate analysis of covariance  that were performed using SPSS/21 software. Demographic variables in the intervention and control groups are reported in To examine the effect of CPR and cardiac chest pain management training on Perceived Control, Depression, Stress and Anxiety,we used the analysis of covariance, so that the subjects’ pretest and posttest scores were used as covariate and dependent variables,respectively and the intervention variables were used as independent variable. The result of one way analysis of covariance forexamination of the effectiveness of CPR and Cardiac Chest Pain Management Trainings on Perceived Control is presented in As shown in As seen in According to the results, it can be concluded that CPR trainings and chest pain management has a positive effect on the level of anxiety in the spouses of the patients with MI, reducing it in the intervention group. One reason for this decrease can be the presence of telephone follow-up, contact with spouses and answering their questions about the training during the study.19Providing information can reduce the psychological stresses by enhancing perceived control. 26So far, the studies conducted in Iran on perceived control were on asthma patients 28and students, 27indicating the impact of education on internalization of the perceived control. Considering the matching results of other studies on other groups, we should take this point into account in managing emergency situations in the family of heart patients.The results of the present study reported that the mean scores of perceived control significantly decreased after the intervention in the intervention group. This decline indicated the positive effects of training CPR and chest pain management for spouses in the intervention group. Decreased level of perceived control score in the intervention group represents the inner perceived control orientation and increases the sense of perceived control in the spouses of the intervention group after the training. Other studies have shown that CPR training and chest pain management can also increase the spouses’ knowledge, and such an increase is one of the important factors in increasing internal perceived control. 29and mentally retarded children’s parents 30provide evidence that supportive educational interventions such as CPR trainings and social supports could reduce anxiety. A study was conducted on diabetic patients showed that coping skills training reduced anxiety. 20Another study also demonstrated that the counseling and training of the spouses as a simple intervention can reduce anxiety. 8The present study showed that the anxiety scores in the intervention group improved after the intervention. Previous studies for caregivers of hospitalized patients 30family caregivers of patients with cancer, 31and diabetic patients. 20Studies have also shown that anxiety and depression reduction in spouses may also be a cost-effective way to improve the psychosocial status, decrease mortality and morbidity in the patient groups. 8Therefore, considering the important role of education in reducing anxiety and depression, it is essential that educating the spouses and family members of patients is considered as an important topic in the curative treatment program, especially for nurses.Our results also confirmed that the participants of both groups experienced the severe and extremely severe level of depression before the intervention. However, the depression mean scores in the intervention group decreased after six weeks. This reflects the significant effect of training on the depression level of spouses in such groups. Other researchers in Iran have proven that consultation and trainings could reduce the depression level in mentally retarded children’s parents, 30and cancer 31) confirmed the significant effects of consultation and trainings on reduction of stress.We observed that stress mean scores significantly decreased in the intervention group after the intervention. This confirms the significant effect of CPR and chest pain management trainings on the stress level of spouses. Many similar studies on other disease . The outcomes were evaluated only six weeks after the educational intervention. Thus, future studies with longer follow-up periods are recommended for better evaluation. The low sample size of the participants was another limitation of the study.Pain management and CPR trainings could effectively increase the perceived internal control and decrease depression, stress, and anxiety in the spouses of patients with MI. Considering the importance and positive effects of CPR training, we can use in rehabilitation and training programs for the patients and their families. The role of nurses in implementing and promoting Cardiac Chest Pain Management and CPR Trainings should be considered.


Rp2) and the ratio of prediction to deviation (RPD) for the prediction of SOM from moist soil have achieved Rp2 = 0.92 and the RPD = 3.19, both of which are about 5% better than that of using all bands for building the PLSR model. The very high RPD (=3.19) obtained in this study may suggest that the 2D-COS technique is effective for the analysis of complex system like the prediction of SOM from moist soil.This paper illustrates a simple yet effective spectroscopic technique for the prediction of soil organic matter (SOM) from moist soil through the synchronous 2D correlation spectroscopy (2D-COS) analysis. In the moist soil system, the strong overlap between the water absorption peaks and the SOM characteristic features in the visible-near infrared (Vis-NIR) spectral region have long been recognised as one of the main factors that causes significant errors in the prediction of the SOM content. The aim of the paper is to illustrate how the tangling effects due to the moisture and the SOM can be unveiled under 2D-COS through a sequential correlogram analysis of the two perturbation variables  independently. The main outcome from the 2D-COS analysis is the discovery of SOM-related bands at the 597 nm, 1646 nm and 2138 nm, together with the predominant water absorbance feature at the 1934 nm and the relatively less important ones at 1447 nm and 2210 nm. This information is then utilised to build partial least square regression (PLSR) models for the prediction of the SOM content. The experiment has shown that by discarding noisy bands adjacent to the SOM features, and the removal of the water absorption bands, the determination coefficient of prediction ( Earth soil consists of complex mixture of substances, including organic carbon (OC), organic matter (OM), minerals, water and air. In the perspective of plant growth, the essence of the soil is the soil organic matter (SOM) and its content within a unit volume of soil quantifies its fertility for the growth of crops. SOM is mostly the crop residues added to the soil after their decompositions by soil macro-fauna and micro-organisms. As such the SOM consists of carbon, hydrogen, oxygen . Thus, tR2) from almost zero when no moisture correction is made, to over 0.50 after the correction is applied [R2) and the ratio of prediction to deviation (RPD) for assessing the SOM in moist soil (<35% w/w moisture) were about 0.86–0.88 [R2 and RPD of 0.892 ± 0.004 and 3.053 ± 0.056 respectively [There are many approaches and methodologies reported in the literature to minimise the effects due to moisture in the soil for improving the assessment accuracy of SOM via spectroscopy techniques. One approach adopts the model transfer methodology that utilises data of standard samples, such as the external parameter orthogonalization method (EPO)  which pr applied . Amongst applied ,31,32,40.86–0.88  and 2.66.86–0.88  respectiectively .R2 ~ 0.90. Recent study also expressed that new wavebands between 550–700 nm and 800–850 nm, 1700–1800 nm [Apart from the correction approach as outlined in the previous paragraph, alternative methodology has been the discovery of the SOM related spectral bands ,57,58,59–1800 nm , narrow –1800 nm ,56 were –1800 nm ,54,56,59While there are numerous different sets of SOM-related bands reported in the literature that offers extremely useful information to workers in this field, it also introduces somewhat confusions to the users/researchers without knowing how to choose specific set of bands for their applications. This is particularly true when one deals with real world soil sample which may compose of numerous minerals and ingredients in the soil. This paper serves the sole purpose of introducing a simple, yet efficient method, to allow the researchers/users to visualise their data vividly and to allow crucial bands to be selected for their applications. The paper adopts the synchronous 2D correlation spectroscopy (2D-COS) method ,64 from This paper is structured to highlight and to demonstrate the effectiveness of the 2D-COS technique for solving real-world problems. As such the present paper has employed a few arbitrarily selected moisture contents of soil samples to illustrate the working principles and methodology of the 2D-COS. The emphasis of the present work is to demonstrate the capability of the 2D-COS for unveiling subtle SOM features, and to allow their contents to be assessed more faithfully, despite of the presence of the overwhelmingly interfering spectral peaks from the moisture in the soil. The methodology can be applied for field trials when several batches of samples are collected locally from a field, the SOM contents of these samples can be deduced more faithfully through the few selected bands given by the 2D-COS analysis over these samples. The 2D-COS is a technique to extract the essence of the spectral band feature for further analysis.The basis of the two-dimensional correlation spectroscopy (2D-COS) is the formulation of a 2D matrix of the band-to-band covariance of all spectral vectors in the data set ,64,65. Tp represents a measure of physical disturbances such as temperature, concentration, pressure, or moisture, etc. The spectral variable v can be any spectroscopic quantity such as wavelength in the infrared or NIR spectral range [The external perturbation(s) al range ,67,68, Ral range  or Terahal range ,70, etc.ν, p) of the system under perturbation can be formally defined as:p is bound within minp and maxp and The dynamic spectrum minp, maxp) is then transformed into 2D-COS by using the correlation analysis method [ν1 and ν2 over finite observation interval p between minp and maxp:. > represents the covariance operator. Thus, the synchronous 2D correlation intensity ν1 and ν2, as the value of the perturbation p is changed.The fluctuation of the spectral properties between the perturbations  in magnitude simultaneously. However, the cross peaks at the coordinates B and D is negative indicating that the intensity change of one component is increasing while the other is decreasing.A schematic example of the synchronous 2D correlation spectrum in the form of a contour map is shown in w/w) according to the mass ratio:w/w) in the loam soils [Total of 50 loam soil samples had been collected from the central of Shanxi Province  in China and all samples were sampled from the top 5–15 cm of the soil surface for this study. The samples were first prepared by air-drying for a few days, then they were sieved through a 2.5-mm mess filter and were finally oven-dried at 106 °C for 6 h to eliminate the moisture in the soil samples. Subsequently all soil samples were dosed by five different moisture contents of approximately ; and the other part was designated for experiment. The GT measurement of the SOM contents was found ranging from 0.40% to 7.92%, with the mean value of 2.22% and standard deviation of 1.25%. The other parts of the soil samples were then dosed by various moisture concentrations.Each of the 50 soil samples was divided into two equal parts: One was subject to destructive ground truth (GT) measurement for assessing the SOM content by using the conventional potassium dichromate method  within the spectral range of 350–2500 nm under the same experimental environment in the laboratory. The spectral resolution of the ASD was about 3 nm full-width-at-half-maxima (F WHM) at 700 nm, and 10 nm FWHM at 1400 nm and 2100 nm. Experimental condition consists of an external halogen light source illuminated at a few degrees off the normal plane of the samples. Each of the soil samples were spread out thinly (~5 mm thick) over the entire Petri dishes and three spectra were taken from one spot of the soil sample. The spectral acquisition was then repeated for another three different spots of the same soil sample. The mean of these nine spectra represented the vis-NIR spectrum of the sample. Each sample spectrum contains 2051 spectral bands, thus there are 50 × 2051 spectral features available for the characterisation of <50 discrete SOM contents over the entire sample size in this study. A splice correction had been applied for the correction of the step change of sensor response function at 1000 nm wavelength; and noisy data in the 350–400 nm and 2451–2450 nm wavebands were discarded prior to the data analysis.Note that the objective of this study is to assess the SOM content in the soil samples that contain high level of moistures which induce interference in the spectral analysis. Assessment of moisture level is not the goal of this work, the main emphasis of the current work is to choose the spectral features  that are associated with the SOM, but they are not greatly affected by the moisture spectral bands. To fulfil this aim, the proposed 2D-COS method is used. Once the most appropriate spectral bands have been established, the validity of these bands for SOM assessment is testified through the PLSR method. The SOM contents of all 50 samples were assessed using PLSR method. All samples were divided into five equal set and the SOM assessment was conducted under the five-fold cross-correlation scheme. The raw vis-NIR spectra is firstly analysed through the 2D-COS method as outlined in To demonstrate the effectiveness of the 2D-COS for enhancing the accuracy of the SOM content detection from moist soil, the experiment has been proceed under the following four schemes: (1) all spectral bands of the moist soil sample between 400–2450 nm have been utilised for building the PLSR model; (2) all spectral bands except the moisture bands have been employed; (3) all spectral bands except the moisture and weak noisy bands; and (4) just the SOM related bands  are used as the input of the PLSR model.The goodness of the prediction was assessed by comparing the predicted result with respect to the ground truth (GT) data via statistical indices:R2):N is the number of prediction data. The R2 measures how tight are the data points to the regression line, i.e., it gives the overall goodness of the regression model. However, a model depends on how reliable the calibration data is, hence there are three variants of R2: the coefficient of determination for the calibration (Rc2), the coefficient of determination for the cross validation (Rcv2) and the coefficient of determination for the prediction/verification (Rp2). The Rc2 utilises the calibration data for the analysis and it measures the goodness (or the integrity) of the calibration data for the development of the regression model, while the Rp2 shows the goodness of the verification (test) data. Hence the goodness of the Rc2 has direct impact on the goodness of the Rp2. Other assessment metrics such as the root mean square error of calibration (RMSEC), the root mean square error of cross validation (RMSECV), the root mean square error of prediction (RMSEP) and the ratio of prediction to deviation (RPD) are defined as:Coefficient of determination (Rc2), (b) root mean square error of calibration (RMSEC), (c) determination coefficient of cross validation (Rcv2), (d) root mean square error of cross validation (RMSECV), (e) determination coefficient of prediction (Rp2), (f) root mean square error of prediction (RMSEP) and (g) the ratio of prediction to deviation (RPD). Generally, models with high Rc2, Rcv2 and Rp2, and also high RPD with low RMSEC, RMSECV and RMSEP are indicative of good models. Since the RMSEP measures the mean of the sum of squares due to errors, i.e., the mean of the squared residual of the regressions, the RPD and Rp2 together give a much better indication about the goodness and robustness of the model. When the RPD < 1.4 it is generally regarded as unacceptable predictions and when 1.4 ≤ RPD < 1.8 the prediction is considered as fair. Good predictions are generally in the range of 1.8 ≤ RPD < 2.0, and higher values of RPD 2.0 ≤ RPD < 2.5 generally indicative of very good predictions. When exceptionally high RPD ≥ 2.5 is obtained this implies the model gives excellent predictions [In this study the goodness of the prediction models has been assessed through the following assessment indices: (a) determination coefficient of calibration . All absorbance spectra in the figure exhibits very similar shape just like that shown in Conventional spectroscopic analysis for SOM assessments routinely employs 1-D spectral data for building regression models ,54,56,59It is a technical challenge to analyse minute/subtle variations of features, particularly when they are convolved with other information due to various external perturbations. As mentioned previously, the synchronous 2D-COS technique is dedicated for unveiling subtle features due to external perturbations without the need of filtering or derivative operations. The main objective of this work is to demonstrate how the moisture, and the SOM, can be treated as perturbation variables within the context of correlation synchronous spectroscopy, and subsequently the SOM content can be revealed more accurately even when high level of moisture is present in the soil. First, it is necessary to establish the influence of the moisture on the spectroscopic property of the moist soil. Since we have two perturbation variables  in this dataset, the analysis is proceeded with one variable to be kept constant; e.g., the SOM content is kept constant in order to study the effect of moisture as perturbation variable. The spectral data of all 50 samples has been regrouped into six sets, and each set contains a fixed amount of SOM content. According to the correlation intensities given by the contour plots, the influence of the auto-peak at around 1934 nm due to the moisture effect is seen to be the strongest. Second affected is the feature at around 1447 nm and the peak at 2210 nm is the least affected by moisture. It is known that the spectral features at 1934 nm originated from the interlayer water disturbances, while the one at 1447 nm is related to the hydroxyl groups and the peak at 2210 nm may be related to the O-H stretching vibrations and minerals. As depicted in Once the effects of perturbation due to moisture in the moist soil are understood, it is intuitive to investigate the influence of the SOM content on the spectral characteristics of moist soil. Similar to the last section, the spectral of all soil samples have been grouped into five sets and each has moisture content of (a) oven-dry, (b) 5%, (c) 10%, (d) 15% and (e) 17%. In this case we consider the SOM content in the soil as the perturbation parameter here. Two distinct auto-peaks at around 597 nm and 1646 nm, have been seen clearly from the contour plot of the correlation synchronisation map of the oven-dried soil samples see a. A weakIt is also observed that these SOM-related peaks seem to disappear when the moisture content is increased from 10% to 17% see c–e. As mThe 2D correlation synchronisation analysis presented in previous sections have shown that, the three soil moisture characteristic bands at around 1934 nm, 1447 nm and 2210 nm wavelengths have imposed significant interference to the SOM features which are predominately located at approximately 597 nm, 1646 nm and 2138 nm wavelengths. The moisture features have induced strong spectroscopic distortions thereby affecting the accuracy of the SOM prediction severely. As mentioned in Note that the Exp (A)–(D) use all 50 samples under the five-fold cross validation configuration, in which some of the samples have been randomly chosen for calibration of the PLSR model, and the other samples for validation. Since all 50 samples have been ground trothed by destructive methods  for building the PLSR model. The much improved Rc2 in Exp (B) reduces the RMSEP by 30%  to 0.47 in Exp (B)) which boosts the RPD by 24%  to 2.89 in Exp (B)). The Rp2 of Exp (B) is ~0.89 and with the RPD of 2.89, this result is compared very favourably with respect to previous studies which have achieved good Rp2 of 0.86–0.88 [Rc2 of 0.93 (cf. the Rc2 of 0.92 in Exp (B)) which helps to reduce the RMSEP by ~15%  to the RMSEP = 0.40 in Exp (A)). The reduction of the RMSEP in Exp (A) is seen as an instantaneously positive boost of the RPD.The very poor PLSR result by using only the SOM bands to build the model in Exp (D) may suggest that the moisture bands should be removed for the analysis. The removal of three moisture bands that have been found as according to the 2D–COS analysis in .86–0.88 ,68 and R.86–0.88  by utiliRp2 has been enhanced by ~3%, and the RMSEP is reduced by ~10%  to RMSEP = 0.42 in Exp (C)). The dramatic reduction of the RMSEP by ~10% upon removal of noisy bands between 400 and 500 nm may suggest that the SOM band at about 597 nm is rather important for assessing the SOM content from moist soil. The RPD in Exp (C) is seen to increase by also ~10%, presumably it is the consequence of the large reduction in RMSEP, giving the best RPD of 3.19 over the four experiments! From the literature it is seen that the best RPD and the Rp2 reported for assessing SOM from moist soil have been RPD < 2.66 and Rp2 < 0.9 [Rp2) of ~5% better than that using all 2051 spectral bands for assessing the SOM content as performed in the Exp(A).Recall the 2D-COS analysis in p2 < 0.9 ,66,68,78As mentioned in the introduction section, the main purpose of this work is to introduce the concept and to illustrate the effectiveness of the 2D-COS for the analysis of complex system. Hence other techniques, such as pre-processing using filters and spectral derivatives have not been applied here. Because of the broad intrinsic absorption line width in the vis-NIR spectral region, which is further broadened by the optics and instrumental noise of the sensor, the assessment of small features like the SOM will be enhanced if the spectral resolution can be improved. Current effort in our lab is working towards the enhancement of the spectral resolution by using data processing technique. It is believed that the higher resolution data will help to enhance the prediction of the SOM in the moist soil significantly. In our next paper we would like to explore how 2D-COS can be further exploited in conjunction with spectral dimension reduction, wavelet denoising and spectral derivative for the assessment of SOM.This paper is aimed at introducing a simple yet effective technique for the analysis of complex systems more accurately and effortlessly. The system chosen in this paper is the assessment of soil organic matter (SOM) from moist soil samples by using spectroscopic technique. It is well-known that there are a few water absorption peaks overlapping with the SOM characteristic features in the visible-near infrared (Vis-NIR) spectral range. This makes the assessment of SOM from moist soil using spectroscopic technique very difficult. Through a simple synchronous 2D correlation spectroscopy (2D-COS) method, the interplay between the perturbation variables within a complex system can be untangled through a sequential analysis of the system when it is subjected to one perturbation at a time. By using the assessment of the SOM from moist soil system as an illustrative example, the contents of the SOM and the moisture level have been treated as perturbations to the soil system. The effects of the moisture, and the addition of SOM into the soil, have been analysed independently. The analysis is performed on the calibration data set that has known information of moisture and SOM contents in the samples. As demonstrated in Rp2 of Exp (C) is 0.92, which is seen to be the best over the four experiments performed in this study. It seems that the RPD = 3.19 and Rp2 = 0.92 may be one of the best results for assessing SOM from moist soil ever been reported in the open domain as far as the authors are aware. The present result may suggest that the 2D-COS technique is effective for the analysis of complex system like the prediction of SOM from moist soil.As soon as the characteristic spectroscopic features of the SOM and the water bands have been revealed (by 2D-COS), this information is then utilised to build partial least square regression (PLSR) models for the prediction of the SOM content. In the present study, the effectiveness of SOM assessment by using information given by 2D-COS is assessed through the PLSR under a five-fold cross validation scheme. Four PLSR models has been established by using different sets of spectral bands to build the model: (A) all spectral bands has been used, (B) all the SOM bands but without water absorption bands, (C) all SOM bands but without water bands and also discards noisy bands between 400 and 500 nm and (D) only the SOM feature bands are used. Note that all the SOM and water bands utilised here are those revealed by the 2D-COS analysis. The PLSR result has revealed the worst prediction of SOM with RPD of 2.34, when the SOM bands alone ) have been used for building the PLSR model. However, the use of the SOM band without water bands as performed in Exp (B) gives almost 24% improvement of the RPD, i.e., increase the RPD = 2.34 from Exp (D) to RPD = 2.89 in Exp (B). This indicates that the water absorption peaks impose severe interference to the SOM features. Further enhancement of the SOM assessment has been found by discarding noisy bands adjacent to the SOM feature as demonstrated in Exp (C), which achieves the best RPD of 3.19, almost 10% better than that Exp (B). The Since this work is aimed at introducing the 2D-COS technique for the analysis of complex system, pre-processing techniques have not been applied in the present paper. In our forthcoming paper we would like to exploit the 2D-COS in conjunction with spectral dimension reduction , wavelet denoising and spectral derivative for enhancing the prediction accuracy of SOM.