Second, in order to obtain an accurate BAA, the extracted critical bone regions are fed into the gender-assisted bone age estimation network. The results showed that the proposed method achieved a mean absolute error (MAE) of 5.45 months on the public dataset Radiological Society of North America (RSNA) and 3.34 months on our private dataset.Bone age assessment (BAA) from hand radiographs is crucial for diagnosing endocrinology disorders in adolescents and supplying therapeutic investigation. In practice, due to the conventional clinical assessment being a subjective estimation, the accuracy of BAA relies highly on the pediatrician's professionalism and experience. Recently, many deep learning methods have been proposed for the automatic estimation of bone age and had good results. However, these methods do not exploit sufficient discriminative information or require additional manual annotations of critical bone regions that are important biological identifiers in skeletal maturity, which may restrict the clinical application of these approaches. In this research, we propose a novel two-stage deep learning method for BAA without any manual region annotation, which consists of a cascaded critical bone region extraction network and a gender-assisted bone age estimation network. First, the cascaded critical bone region extraction network automatically and sequentially locates two discriminative bone regions  Bone age assessment refers to a clinical application that is widely used in pediatric radiology, therapeutic estimation of endocrinology disorders, and judgment of children's growth  playing important roles in bone age estimation, the previous deep learning approaches for BAA could be separated into two classes, including annotation-free methods and annotation-based methods. The former annotation-free methods employ the whole hand radiographs as input and usually fed them into the end-to-end and single-stage model for estimated bone age directly. Spampinato et al.  built BoThe latter annotation-based methods use the processed images with manual annotations of bounding boxes as input. These strategies could extract features in specific regions according to prior knowledge and then produce the estimated age. Iglovikov et al.  employedThe annotation-based methods with the additional manual annotations generally show better performance and higher accuracy than the annotation-free methods. The methods that use the original image as input do not fully exploit the discriminative local information and ignore the fine-grained analysis of specific regions, so the accuracy and interpretability of these methods tend to be poor. Manual annotation is also time-consuming and has hindered the translation of experimental methods into clinical applications.Given the problems existing in existing research methods and the shortcomings of the most commonly used G&P and TW methods, our two-stage bone age evaluation method of positioning + prediction not only considers the weight of different bone areas but also does not require physicians to spend a lot of time on bone age analysis as the TW method does, which also reduces the impact of subjective differences among different evaluators. At the same time, we used Grad-CAM to visually present the imaging content of the bone region in the positioning stage and compared the extracted hand bone region with the observation area of the traditional atlas method, which has better clinical acceptance for physicians.via visualized heat maps. In the second step, the Xception and ResNet50 networks were used to extract the bone region features of the carpal and metacarpophalangeal regions, respectively. The modal fusion information was then obtained by dynamically exchanging feature mappings. We feed the gender information into the age estimation network as an additional input, which helps to optimize the deep learning network and improve the estimation performance.We have proposed a two-step method for fully automatic bone age estimation based on X-ray images of hand bones without any annotation. The architecture consists of a key bone region localization network and a bone age estimation network. In the first step, an enhanced Inception V3 network with the Convolutional Block Attention Module (CBAM) is implemented to automatically identify and localize the critical regions of hand bone images The contributions of this paper are summarized as follows: (1) This method proposes an effective strategy to identify the critical bone regions for bone age estimation, which could encourage the model to focus on the features of critical regions and weaken the influence of irrelevant regions. (2) The proposed method feeds the gender information into the age assessment network as additional input, which improves the generalization of the bone age assessment network. (3) This two-stage structure, including critical bone region localization and bone age estimation, is more interpretable and increases the clinical acceptance of the proposed method.https://www.kaggle.com/kmader/rsna-bone-age.The RSNA dataset is public and collected by the Radiological Society of North America (RSNA), it consists of 14,236 hand bone radiographs, including 12,611 training datasets, 1,425 validation datasets, and 200 test datasets  was cropped and preserved. Then the original image was masked by a black rectangle on the critical bone area. The masked image went through the critical region extraction network again, and another critical bone region  could be extracted. For the bone age recognition network, automatically cropped images are input into the Xception model and ResNet50 model for feature learning. Then, according to the mid-fusion method, the carpal bone features extracted from the Xception model and the metacarpus&phalanx bone features extracted from the Res-Net50 model are exchanged and concatenated. The gender information is then entered into the second network as additional input. Finally, the concatenate feature vectors were sent into the fully connected layer for bone age estimation. The BAA network architecture is shown in via the peak attention area of the generated visual heat map. To better extract the important bone regions of the hand bone images and achieve an accurate assessment, we embedded the Convolutional Block Attention Module (CBAM) attention mechanism module into the Inception V3 architecture. This specific model can fully automatically detect and extract the critical bone regions The Inception V3 architecture, proposed by Szegedy, is an advanced pre-trained CNN model  represents 7 × 7 convolution layer.where F represents the output feature map for each layer of the model, MLP is the fully connected layer, AvG&Pool is the global average pooling layer, MaxPool is the global maximum pooling layer, and σ is the sigmoid activation function. The spatial attention weights are calculated in a similar way, but the pooling layer is changed to be a channel domain pooling, and the MLP layer is changed to be a convolution layer, where In the critical bone region network, we omit the top layer of the Inception V3 network, then the remaining architecture is adopted as the backbone network for feature extraction, and the model is loaded with pre-trained weights on the ImageNet dataset. Applying a modified CAM method, the grad-CAM method, visualized heat map of the hand bone image is generated. In light of the peak attention of visualized heat maps, the important bone region  could be localized.In this assessment module, the backbone network for feature extracting was built using the mid-term fusion method, which refers to the first transformation of different modal data into high-dimensional feature expression, and then fusion in the middle layer of the model , the size of the learning rate is set to 0.0003, and the number of epochs for training is set to 30.Due to the proposed model regards the bone age assessment as a regression task, mean square error (MSE) is adopted as the loss function in this task,yi is the ground-truth age and gi is the predicted value of bone age.where n represents the number of the training sets, In this study, bone age assessment was treated as a regression task, the evaluation indicator for network performance is the mean absolute error (MAE) between the output of the model and the ground-truth age, which is represented asyi is the true value of bone age, and where N represents the number of input samples and In this study, we present a deep neural network incorporating CBAM to address the limitations of neglecting important regions in hand bone images and the costly manual annotation process. The results of the ROI extraction and feature activation heat map are presented in Meanwhile, we evaluated the visual interpretation of the feature-extracting network through the grad-CAM method and examined the activated heat maps on the original images. The third and fourth rows in After obtaining the critical bone areas, we fed the critical bone images into the backbone network for feature learning and age prediction. The correlations between the true age and the estimated age are shown in We also compare the assessment performance of our proposed method with other existing advanced networks, as illustrated in To reinforce the efficacy of the proposed method and emphasize the importance of incorporating gender information, we conducted a comparative study on the RSNA dataset. The results of the experiment, which compared the impact of attention mechanisms and gender information on prediction outcomes, are presented in The results presented in In this study, we introduce a novel two-stage, fully automatic method for evaluating bone age without the requirement for manual annotations. Our results demonstrate that the localization of critical bone regions can significantly improve the accuracy of bone age evaluation, and gender information plays a crucial role in this process. The proposed method addresses the issue of extracting key hand bones and reduces the dependence on costly and subjective manual annotations. Consequently, it achieves an acceptable level of accuracy and interpretability and has the potential for eventual clinical implementation. We believe that this proposed method holds great promise for future clinical applications. However, the research in the field of bone age assessment can be expanded and improved from the following aspects: This study demonstrated the influence of key bone regions on the prediction results but did not further discuss the influence of key areas such as carpal bone and metacarpus&phalanx bone on the accuracy of bone age prediction. In future studies, specific regions can be extracted by positioning network for network model training to further demonstrate the influence of different bone regions in the field of bone age prediction. At the same time, the experimental effect achieved in this study is the two-stage separation mode of positioning and prediction. In the later stage, it can be considered to integrate positioning and prediction into an end-to-end network. This end-to-end network not only has strong clinical acceptance but also can be better applied in the field of bone age evaluation, helping physicians realize accurate bone age evaluation.https://www.kaggle.com/kmader/rsna-bone-age.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin. Written informed consent was obtained from the individual(s), and minor(s)' legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.ZL was responsible for providing the ideas and expenditure in the project. WC mainly completed the work of locating key bone areas and constructing the network for predicting the age of hand bone. YC completed the work of data preprocessing. YJ and ZH helped WC to build the network. XL and YJ provided the modification of the thought to increase the degree of innovation in the research. All authors contributed to the article and approved the submitted version.


Objective: Endoscopy is a medical diagnostic procedure used to see inside the human body with the help of a camera-attached system called the endoscope. Endoscopic images and videos suffer from specular reflections (or highlight) and can have an adverse impact on the diagnostic quality of images. These scattered white regions severely affect the visual appearance of images for both endoscopists and the computer-aided diagnosis of diseases. Methods & Results: We introduce a new parameter-free matrix decomposition technique to remove the specular reflections. The proposed method decomposes the original image into a highlight-free pseudo-low-rank component and a highlight component. Along with the highlight removal, the approach also removes the boundary artifacts present around the highlight regions, unlike the previous works based on family of Robust Principal Component Analysis (RPCA). The approach is evaluated on three publicly available endoscopy datasets: Kvasir Polyp, Kvasir Normal-Pylorus and Kvasir Capsule datasets. Our evaluation is benchmarked against 4 different state-of-the-art approaches using three different well-used metrics such as Structural Similarity Index Measure (SSIM), Percentage of highlights remaining and Coefficient of Variation (CoV). Conclusions: The results show significant improvements over the compared methods on all three metrics. The approach is further validated for statistical significance where it emerges better than other state-of-the-art approaches.Clinical and Translational Impact Statement—The mathematical concepts of low rank and rank decomposition in matrix algebra are translated to remove specularities in the endoscopic images The result shows the impact of the proposed method in removing specular reflections from endoscopic images indicating improved diagnosis efficiency for both endoscopists and computer-aided diagnosis systems I.Endoscopic procedures are used for the diagnosis of various pathologies in the internal human body with the help of a camera system. The images and videos obtained from the endoscopic procedure are used to detect any kind of abnormalities present in the examined organ through visual interpretation by the endoscopist or sometimes by a computer-aided diagnosis (CAD) system. If the images captured by the camera system contain undesired artifacts, the identification of abnormalities becomes difficult and challenging. Robust and reliable identification of abnormalities has become a fundamental medical imaging problem and is extensively studied by researchers Six artifacts from image acquisition Such presence of highlights has been reported to result in failure for feature extraction, especially in surgical navigation systems that use augmented reality (AR) In this paper, we present a novel approach for medical imaging applications that utilize mathematical principles of matrix decomposition and low-rank structure. Specifically, we propose the use of low-rank decomposition and singular value thresholding operations to effectively remove specular reflections from endoscopic images. Our method offers a promising solution for improving the quality and visibility of endoscopic imaging in medical diagnosis and treatment.In the rest of the paper, previous works related to specular reflection removal are discussed in II.Different works have been proposed to remove specular reflections from medical images. Many of them consider the reflection removal as a two-stage problem The highlight areas are scattered/dispersed throughout the entire image, as shown in In Arnold et al. The deep learning, supervised and semi-supervised methods are also investigated for specular reflection removal from both natural and medical images when labeled datasets are available to train the models In the absence of labelled datasets , specular reflection removal is carried out using the family of classical matrix decomposition methods such as Robust Principal Component Analysis (RPCA) A.1)We propose a new approach which exploits the characteristics of highlight pixels with an iterative decomposition procedure to generate a pseudo-low-rank component and a highlight component irrespective of the degree of sparsity. Unlike normal RPCA-based algorithms that fail to eliminate the highlight efficiently, our approach can remove the highlights even when the highlight is distributed sparsely in the image.2)We propose to exploit human vision-based Hue-Saturation-Value (HSV) 3)Our approach eliminates parameter estimation by exploiting the characteristics of highlight pixels making it a parameter-free approach unlike previous approaches. Previous unsupervised methods of highlight detection rely highly on the setting up of its parameters according to the dataset or even according to the lighting conditions of individual images 4)We further provide a benchmark evaluation of the proposed approach on three publicly available datasets using four different state-of-the-art methods to demonstrate better performance. We supplement our qualitative and quantitative analysis with statistical analysis to establish the benefits of our proposed method.We propose an approach based on the matrix decomposition method to address the drawbacks of the existing techniques for specular reflection removal in endoscopic images. Our major contributions are listed below:III.Identifying and removing the highlight pixels requires adequate knowledge of the characteristics and distribution of highlight pixels. As inspired by A.1We refrain from presenting the singular values from the tail end for the sake of illustration.1In order to obtain a highlight-free image, the singular values may be modified by removing the lower singular values and reducing the magnitudes of the upper singular values. Since the distribution varies from image to image  and fromB.The gastrointestinal tract is usually covered by watery surfaces. When light is incident on a normal tissue surface, the reflected light contains the spectral component corresponding to the body surface. However, when the tissues are covered in a watery surface, almost all the spectral components are reflected back by this smooth Lambertian surface A low absorption rate at the surface results in high-intensity values for specular reflections compared to the diffuse reflection rate. This reveals the next prominent characteristic of the specular reflection: high-intensity value. These two characteristics, low saturation, and high intensity, can be used to identify the highlight pixels supporting our hypothesis behind the proposed approach of using human vision-based processing of images.IV.Our proposed approach is based on the idea of extracting the low-rank component embedded in the image, which is asserted to be highlight-free. We, therefore, intend to decompose the original image with highlight into a highlight-free pseudo-low-rank component and a sparse highlight component. However, the highlight component, which is generally sparse in nature may contain some useful information, some of which are vital for the identification of various pathologies. Conventional RPCA •Initially, a mask of highlight pixels is approximated by utilizing the properties of highlight pixels discussed in •The low-rank component of the original image is then computed using the SVD approach. The remaining component is thus sparse, as the lower singular values correspond to them •At this point, the highlight component contains some required information and some highlight. The sparse component contains the distributed highlight and useful information in the form of sparse. We thus need to remove highlights from the low-rank component and add to the sparse component and useful information from the sparse component and re-insert them into the low-rank component, respectively.•Hence, to distinguish the useful information and highlight in the sparse component, the characteristics of the highlight and the mask created are utilized. In order to remove the highlight further from the low-rank component, the extraction parameters are changed in the singular value decomposition.•These two processes are repeated until no highlight is present in the low-rank component and no useful information is retained in the sparse component. The convergence of the algorithm is declared when the low-rank component is diffuse enough, at which point the sparse component provides the highlight component.Our proposed method can be summarized in the following steps:A detailed discussion of the various steps to obtain a Pseudo-Low-Rank component, which is the highlight-free image, is provided in the next section.A.As argued earlier, the conventional RPCA framework fails to decompose the highlight image into highlight-free component and highlight component without losing the details. Hence this work proposes a modification in the decomposition process of the conventional RPCA framework. Let 1)A pixel is said to be a highlight pixel if its saturation value is less than a certain saturation threshold and its intensity value is greater than a certain threshold. Setting a hard threshold is challenging as the illumination differs for different images. To circumvent the problem, we adopt a soft threshold based on Now, a binary mask In addition to the highlight components, the presence of dark regions around the boundary of highlight pixels also need to be eliminated to avoid boundary inconsistencies. Close observation of the endoscopic images with highlight reveals that there is a small dark boundary associated with highlight regions. An illustration of this artifact is provided in 2We have used a structuring element of In order to remove these boundaries which have different characteristics than that of highlights, the mask is to be recalculated. A morphological dilation operation is performed on the available mask to obtain a new mask with a suitable structuring element2)Once the mask is created, iterative decomposition procedure can be initiated. Initially the Singular Value Thresholding (SVT) operator where, The value of 3)Step 1 can be used. Since the positions of the useful information are mutually exclusive with that of the highlight pixels, multiplying the sparse matrix with the mask will provide highlight component only.The sparse matrix The remaining part of the sparse matrix, which is the residue matrix, represents the useful information.4)step 2 and step 3 are repeated until this quantity becomes less than a threshold as per the convergence criteria. Each iteration begins with an augmented image If the residue matrix Algorithm 11Input: X 2Output: L 1Initialize: L, H = 0, 4X.Read Image to 5Calculate 6M using Generate the binary Mask 7L & H according to Update 8L gives the highlight-free image. Else repeat from Step 7.Check for convergence. The condition is The overall process used to obtain the highlight-free image from the original endoscopic image is described in V.The proposed algorithm is tested on three different datasets to check for the generalizability of the algorithm. Our aim is to assess the proposed algorithm’s generalizability by applying it to diverse datasets containing different types of modalities and including both normal and pathology sets. To achieve this, the Kvasir dataset from Simula Research Laboratory 3All the algorithms were run on Python 3.9 on Windows 11 platform with intel(R) Core(TM) i7-10700K CPU @3.79GHz and 32GB RAM.The Kvasir dataset is an annotated collection of images used for identifying pathology, comprising eight different image classes. For evaluation of the proposed work, two image classes from the Kvasir dataset are selected. The algorithm is tested on the ‘Polyp’ class, as polyp detection and segmentation are currently prominent areas of research in endoscopy. To verify its effectiveness on normal and pathology images alike, the ‘Normal-Pylorus’ class is randomly selected from the three available normal classes. These classes will be referred to as the Kvasir Polyp dataset and Kvasir Normal-Pylorus dataset from now onwards. 500 images with specular reflections from both classes are selected randomly and are used in the evaluation process. We also use the Kvasir Capsule dataset To holistically evaluate the proposed approach, we compute Structural Similarity Index Measure (SSIM) Before delving into quantitative analysis, we present a qualitative illustration of the proposed approach as shown in A.A visual comparison of the results is provided in The results of the inpainting method B.To further quantify the comparison results, three metrics such as the Structural Similarity Index Measure (SSIM), the percentage of highlights remaining and the Coefficient of Variation (CoV) are computed whose analysis is presented further.1)To evaluate the efficiency of the proposed method, SSIM is calculated between the initial mask generated for the proposed method and the highlight component obtained after the decomposition for various methods taken for comparison. The initial mask is considered the required highlight component. The higher the SSIM, the better the quality of the highlight removed. As evident from the results in 2)The percentage of highlights remaining is calculated as, The statistical significance of the percentage of highlight removed is deduced by performing a one-way ANOVA test 3)Although the highlights are removed completely, the reconstructed regions should blend with their surroundings to not compromise the quality of the image. We, therefore, employ the Coefficient of Variation (CoV) to measure the image quality in the vicinity of the highlight regions. It is the ratio between the standard deviation CoV is calculated for the neighborhood of all the highlight regions as shown in The CoV values are calculated on all three datasets, Kvasir Polyp, Kvasir Capsule and Kvasir Normal-Pylorus, and similar values are computed for state-of-the-art methods as presented in C.Although the method works efficiently on various datasets, it suffers from some demerits. Because of the global nature of the singular value thresholding operation and low rank-based extraction, sometimes it is impossible to blend perfectly with the near neighborhood of the reconstructed region as shown in Further, if the overexposed regions cover a large portion of the image, as shown in column one in D.Conditional GANs can be used to remove highlights from endoscopic images by conditioning the generator network on the input image with highlights as well as an additional condition that describes the highlight regions in the image. To train the conditional GAN for highlight removal, a dataset of endoscopic images with corresponding highlight region conditions should be used. The network is trained to minimize the difference between the generated output image and the ground truth image without highlights, while also ensuring that the discriminator can distinguish between real and fake images. Once the network is trained, it can remove highlights from new endoscopic images by conditioning on the highlight region condition. However, the availability of large-scale data for training a cGAN still remains an open problem.VI.The proposed algorithm decomposes the original highlight image into a pseudo-low-rank component and a highlight component. The highlight-free image, which is the desired output, will then be the pseudo-low-rank component. The paper exploits the non-sparse nature of the highlight component to propose a generalizable method. The method does not require any parameter setting, and thereby no fine-tuning is involved making it a parameter-free approach. Our results on three different public datasets suggest the promising nature of the proposed algorithm. Even though the proposed algorithm works well for various datasets, the reconstructed regions do not completely blend with their surroundings. We hypothesize this to be a result of the global nature of the low-rank decomposition. Further, this method does not recover the actual information behind the highlight region. In future works, the idea proposed in the paper can be extended to include the information from temporal frames to recover the actual information behind the highlight regions.


Drug-induced nephrotoxicity is a major cause of kidney dysfunction with potentially fatal consequences. The poor prediction of clinical responses based on preclinical research hampers the development of new pharmaceuticals. This emphasises the need for new methods for earlier and more accurate diagnosis to avoid drug-induced kidney injuries. Computational predictions of drug-induced nephrotoxicity are an attractive approach to facilitate such an assessment and such models could serve as robust and reliable replacements for animal testing. To provide the chemical information for computational prediction, we used the convenient and common SMILES format. We examined several versions of so-called optimal SMILES-based descriptors. We obtained the highest statistical values, considering the specificity, sensitivity and accuracy of the prediction, by applying recently suggested atoms pairs proportions vectors and the index of ideality of correlation, which is a special statistical measure of the predictive potential. Implementation of this tool in the drug development process might lead to safer drugs in the future. Nephrotoxicity refers to the harmful effects that occur in the kidneys due to chemicals and medicines, known as nephrotoxicants, often resulting in their rapid deterioration. The kidneys are uniquely susceptible to drug-induced injury due to their high cardiac output and their role in the excretion of waste compounds from the body. Due to their pivotal role in concentrating and reabsorbing the glomerular filtrate, the kidney proximal tubular cells are particularly prone to elevated levels of circulating toxicants. Drug-induced nephrotoxicity (DIN) has been identified as a major contributor to both acute kidney injury (AKI) and chronic kidney disease (CKD). Prospective cohort studies of AKI have shown the estimated incidence of DIN to be between 14 and 26% in adult populations . AKI ariThe ability to discern nephrotoxic structures during the initial stages of drug development presents an opportunity to improve patient health outcomes. However, the mechanisms of DIN are intricate and can vary between drug classes. These distinctions are typically generalised based on the histological component of the kidney that is primarily affected. Several processes can cause nephrotoxicity involving diverse segments, such as glomerular damage, glomerulonephritis and interstitial nephritis, renal tubular injury and cytotoxicity, leading to necrosis and tubular obstructions due to drug-induced crystallopathy ,6. Drug Experimental and clinical testing of drugs of their toxic potential are expensive and time-consuming actions. Computational analyses of available databases on drug-induced toxicity is an attractive alternative. Some models have been developed on nephrotoxicity, addressing general nephrotoxicity and/or specific endpoints, such as tubular necrosis ,19,20,21http://www.insilico.eu/coral, accessed on 15 March 2023) is one of the ways to solve this task. The most common approach to computational modelling is to calculate molecular descriptors first, and then, using them, to develop an in silico model. Compared to the traditional approach, CORAL has the advantage that it does not require the calculation of the molecular descriptors; instead, it simply uses the molecular formula of the potential toxicant represented as SMILES [The use of CORAL software  were taken from the literature . Table 1The endpoint addressed contained 565 diverse chemical structures, including real world data on 287 nephrotoxic drugs in humans and 278 non-nephrotoxic approved drugs. These compounds were randomly distributed in the active training (≈25%), passive training (≈25%), calibration (≈25%) and validation sets (≈25%). Each of these sets has a special task: (i) the active training set provides the foundation of the model, i.e., compounds of this set are used for building up the predictive model; (ii) the passive training set is the inspector of the model, i.e., compounds of this set are used to verify whether the model is satisfactory for substances which are absent in the active training set; (iii) the aim of the calibration set is to detect the start of the overtraining; and (iv) the validation set is used for the final validation of the predictive potential of the model, using substances that were not used to develop the model.Two optimal descriptors calculated with attributes of SMILES are examined here:Two descriptors which are the sum of the so-called correlation weights are examined here. The correlation weights are coefficients calculated with the Monte Carlo method. The second version of the optimal SMILES-based descriptor was calculated with atoms pairs proportions (APP) correlation weights . Using descriptor values, one can calculate y  using the so-called semi-correlation technique :(3)y=C0+kS is a SMILES atom, i.e., one symbol  or group of symbols which cannot be examined separately . kSS and kSSS combines two and three SMILES atoms, respectively. CW(kS), CW(kSS) and CW(kSSS) are the correlation weights of the above SMILES fragments. CIIC is the index of ideality of correlation calculated with data on the calibration set as follows [The optimal SMILES-based descriptor requires numerical data on the correlation weights. Monte Carlo optimisation is a tool to calculate these correlation weights. Here, two target functions for the Monte Carlo optimisation were examined: follows :(7)IIC=rThe calculations were carried out in fifteen epochs. An epoch is a step-by-step modification of all correlation weights. The sequences of these modifications are random and different for each epoch.DCW1 and the Monte Carlo optimisation with target function calculated with Equation (5). This approach is the classical CORAL approach. DCW2 and target function calculated with Equation (5). In this case, a more sophisticated algorithm is used, adopting Equation (2) and not Equation (1). Comparing the results shown in DCW2 and the target function calculated with Equation (6). In this case, we applied a further improvement in the algorithm, using the index of ideality of correlation. Considering the results for the total set, we observe that all statistical parameters are better . There are other preferable aspects that can be interpreted from MCC) values range from 0.28 to 0.71 in We tried different approaches to develop in silico models. Alternative  methods for testing chemicals involve an entire arsenal of tools developed for QSAR analyses. This includes gradient machine learning methods , artificIncorporating data on drug molecular operating environment (MOE) descriptors allowed the construction of highly predictive models, as characterised by values of sensitivity of 0.87, specificity of 0.87 and MCC of 0.74. Thus, the described approach results in a model comparable with related models in the literature . Comparewww.vegahub.eu, accessed on 15 March 2023), and in this way the model will be freely and openly available. The availability of this model will help industry to screen new substances they want to develop, anticipating possible critical effects. It must be noted that the model is suitable only if the compounds fall within the applicability domain of the model. This is the usual limitation of any in silico model, since the model learns from the available experimental data. Modern software tools, such as the one we have developed, can cope with this considering the chemical structure of the substance to be evaluated. In this particular case, the model refers to pharmaceutical substances. Since there are multiple pharmaceutical classes, it is likely that the model does not cover all pharmaceuticals, and for this the implementation within VEGAHUB will be useful, since in this way the applicability domain will be measured automatically. We are also developing further models for kidney toxicity at different levels, addressing adverse outcome pathways and the no observed adverse effect level.We will implement this model for nephrotoxicity within the VEGAHUB platform , i.e., with application of the index of ideality of correlation. Thus, (i) the suggested APP vector correlated with the nephrotoxicity and (ii) the index of ideality of correlation as a measure of the predictive potential of a model improves the model performance and is an effective component of the target function for the Monte Carlo optimisation used to develop the model.In the final analysis, we present a novel deployment of CORAL software to predict drug-induced nephrotoxicity. Our model was optimised using previously published and widely accessible SMILES data and is comparable to similar techniques for classifying drugs as toxic or non-toxic. Thus, this in silico model may prove useful for predicting nephrotoxicity for novel substances as a stand-alone method or as part of an integrated approach at a much earlier stage of clinical development, therefore saving resources in terms of animal models, human resources and indeed sponsor investment.


Epilepsy is a neurological disorder characterized by spontaneous recurrent seizures. While 20% to 30% of epilepsy cases are untreatable with Anti-Epileptic Drugs, some of these cases can be addressed through surgical intervention. The success of such interventions greatly depends on accurately locating the epileptogenic tissue, a task achieved using diagnostic techniques like Stereotactic Electroencephalography (SEEG). SEEG utilizes multi-modal fusion to aid in electrode localization, using pre-surgical resonance and post-surgical computer tomography images as inputs. To ensure the absence of artifacts or misregistrations in the resultant images, a fusion method that accounts for electrode presence is required. We proposed an image fusion method in SEEG that incorporates electrode segmentation from computed tomography as a sampling mask during registration to address the fusion problem in SEEG. The method was validated using eight image pairs from the Retrospective Image Registration Evaluation Project (RIRE). After establishing a reference registration for the MRI and identifying eight points, we assessed the method’s efficacy by comparing the Euclidean distances between these reference points and those derived using registration with a sampling mask. The results showed that the proposed method yielded a similar average error to the registration without a sampling mask, but reduced the dispersion of the error, with a standard deviation of 0.86 when a mask was used and 5.25 when no mask was used. Epilepsy is a neurological disorder with a worldwide prevalence of 0.8% to 1.2%, where 20% to 30% of cases are untreatable with Anti-Epileptic Drugs (AED) .The success of the surgical intervention depends on a precise localization of the epileptogenic tissue. Diagnostic techniques, including but not limited to Stereotactic Electroencephalography (SEEG), play a crucial role in achieving this accuracy ,5,6. TheImage fusion is a processing technique that involves mapping images into a common coordinate system and merging the aligned results into a single output. Numerous methods are available for image fusion; however, the performance of each technique is influenced by characteristics related to the acquisition and image type ,11. WhenBased on the challenges associated with image registration, we conducted a systematic review, using the methodology outlined by Kitchenham  for liteThese challenges were evident in SEEG examinations conducted at Clinica Imbanaco Grupo Quironsalud in Cali, Colombia, where we identified registration errors in the fusion of MRI and CT images primarily attributed to the presence of electrodes. These inconsistencies required manual adjustments to correctly align the misregistered MRI images. In response to these challenges, we introduce an image fusion method that accounts for external elements, primarily in exams like SEEG. It is crucial to note that while our method is designed to mitigate the impact of external objects in the images and enhance the spatial accuracy of electrode localization, it does not explore into the analysis of electric signals from the deep electrodes. Such an analysis falls outside the scope of this study and would demand a distinct analytical framework.The general procedure shown in Given that our literature search did not identify any fusion methods that take into account the electrode and utilize segmentation for registration , we deviTo extract the electrodes from the CT, we employed simple thresholding with a window of 1500 HU to 3000 HU (i). Subsequently, we computed a mask of the head tissue to remove the skull from the segmented image (viii). Next, we generated a brain mask for the MRI, which had been aligned with the CT, utilizing the ROBEX (Robust Brain Extraction) tool, which is a stripping method based on the work of Iglesias et al. . FinallyTo segment the skull, we employed a simple threshold with a window ranging from 300 HU to 1900 HU (ii). Subsequently, we employed a morphological eroding technique with a cross kernel of size We generate a head mask using Otsu’s thresholding (v) to exclude any object external to the head . After tFor MRI registration, we employed an affine rigid transformation combined with a gradient descent algorithm, using Mutual Information (MI) as the similarity metric. We opted for this registration approach because MI is based on the normal probability distribution between images ,11, whicThe preliminary electrode segmentation employs both thresholding and morphological operations. However, this approach might segment some bone tissue alongside the electrodes, which must be excluded from the final image. To address this, we use the aligned MRI to generate a brain mask with the aid of the ROBEX tool, as depicted Finally, we add the segmented electrodes to the aligned MRI to produce the fused image .Given the lack of a standardized validation methodology for multi-modal image fusion, as highlighted in our 2021 literature review , we opteWe selected eight images from the RIRE dataset containing both MRI and CT images. To simulate the presence of electrodes, we introduced cylinders into the CT images. Subsequently, we performed a rigid registration on the images without electrodes. The transformation obtained from this registration served as a reference for further analysis.Furthermore, to measure the performance, we compared the location of brain structures in the registered images. This comparison was conducted using the Euclidean distance between the reference structure in the CT and the corresponding structure in the registered MRI.To obtain a CT image with electrodes, we used the RIRE dataset and added cylinders to the images to simulate SEEG electrodes. These simulated electrodes were designed with a diameter of 3 mm and a length of 80 mm, mirroring the specifications of deep electrodes that feature eight contacts with a 10 mm spacing between them . The graTo evaluate the performance of our method, we applied a rigid registration on the RIRE dataset without electrodes. This registration procedure employed an affine transformation with a gradient descent algorithm. We used the eight points defined in the RIRE dataset to calculate the reference point using the reference registration. We computed the error using the resulting points in e points , and Px,We measure the performance of the fusion method by using the subsequent brain structures in the CT image as reference points: (i) the Sylvian aqueduct; (ii) the anterior commissure; (iii) the pineal gland; (iv) the right lens; (v) the left lens.With the guidance of a medical expert, we manually localized the structures of interest, obtaining their positions in both the CT and registered MRI images using the 3D Slicer version 4.11. Upon identifying these structures, we measured the error as the Euclidean distance between the reference structure point in the CT image and the corresponding point in the registered MRI using MI):Mutual Information :Structural Similarity Index SSIMRMSE):Root Mean Square Error RMSEPSNR):Peak Signal-to-Noise Ratio PSNR=We validated the performance of the fusion method following the two methodologies in For the second validation, we use the methodology described in After the structure localization, we compute the Euclidean distance between the points from the registered MRI against the points of the CT images. This process is applied the resulting images from our proposed method and the method that uses no sampling mask in the registration. The resulting Euclidean distances are shown in The validation results, displayed in While the validation with the limited dataset showed some promising results, it still requires further refinement. While we were able to reduce the Euclidean distances for all structures in patient 2’s images and for some structures, such as the anterior commissure and the pineal gland, in patient 3’s images, our method displayed a higher Euclidean distance in images for patient 1 and patient 4. We employed global fusion metrics from While the application of the mask did induce an increase in error for certain images, the implementation of our method with the mask notably reduced the average error and overall dispersion, as depicted in Our proposed image fusion method between MRI and CT, which considers the electrodes, is useful in addressing the identified problem, where the presence of external objects produced a registration error. This approach improves the registration in the images using the RIRE dataset. However, in the second validation stage, our method demonstrated a lower average error, yet we observed instances where performance was lower when the sampling mask was applied. This could be attributed to the potential importance of information proximal to the electrode for the calculation of similarity metrics during the registration procedure. However, even in these cases, the error increase was minimal compared to scenarios where the error was lower.Another challenge was the absence of a standardized validation method for multimodal fusion, prompting us to develop our own method using available data. This included the limited dataset from Imbanaco and the deprecated RIRE dataset.We also found a lack of research on methods for electrode segmentation in SEEG. This necessitated the development of our own registration method, a task that was not initially included in the project scope.All objectives of the project were achieved. The project successfully identified primary techniques for image registration and fusion between MRI and CT images, developed a method to fuse these images when external objects were present, and conducted an evaluation to measure and compare the performance of the designed method. In the evaluations, the method outperformed other existing state-of-the-art techniques in certain scenarios.We have developed and presented an image fusion method for combining CT and MRI from SEEG exams. Our approach aims to minimize misregistration errors between pre-surgical MRI and post-surgical CT images, by the use of a sampling mask of all voxels that are not electrode in the post-surgical CT image.We acknowledged the lack of a standard validation method for image fusion and registration in brain images, mainly when external objects are presented in one of the images. We addressed this by employing two evaluation approaches: (i) a simulation-based evaluation method with synthetic electrodes generated from the RIRE dataset; and (ii) an evaluation using four image pairs acquired from patients at Clinica Imbanaco Grupo Quirón Salud, where we measured the error using five anatomical structures that can be localized in the pre-surgical MRI and post-surgical CT images.Our findings indicate that the proposed method outperforms the existing state-of-the-art techniques in the simulation-based evaluation using the RIRE dataset. In the evaluation using clinical images, we observed that our method demonstrated superior performance in some cases, while showing a slight decrease in performance in others. Despite this variability, the overall average Euclidean distance was lower for our method, suggesting an improvement in registration accuracy.We recommend enhancing the second validation methodology by increasing the number of images and refining the localization of brain structures to further reduce bias in the evaluation results. This would enable a more comprehensive assessment of the proposed fusion method’s performance for clinical scenarios.In conclusion, our proposed image fusion method shows promise for improving the accuracy of the registration in SEEG. With further development and refinement, this approach has the potential to significantly impact the field of epilepsy treatment, offering further aid in the localization of epileptogenic tissue when SEEG is employed.


Hematopoietic cancers (HCs) are a heterogeneous group of malignancies that affect blood, bone marrow and lymphatic system. Here, by analyzing 1960 RNA-Seq samples from three independent datasets, we explored the co-expression landscape in HCs, by inferring gene co-expression networks (GCNs) with four cancer phenotypes  as well as non-cancer bone marrow. We characterized their structure  and function . We found that, as in other types of cancer, the highest co-expression interactions are intra-chromosomal, which is not the case for control GCNs. We also detected a highly co-expressed group of overexpressed pseudogenes in HC networks. The four GCNs present only a small fraction of common interactions, related to canonical functions, like immune response or erythrocyte differentiation. With this approach, we were able to reveal cancer-specific features useful for detection of disease manifestations. Leukemias are malignancies from precursors of leukocytes that accumulate in blood or in the bone marrow. They can arise from myeloid or lymphoid linage. According to the velocity with which leukemias progress they can be acute, with a fast development, or chronic, if the disease progresses slowly. Lymphomas are tumors from lymphocytes that affect the lymph nodes, thymus or spleen, and can be broadly categorized into Hodgkin and Non-Hodgkin lymphomas. Multiple myeloma is a tumor of differentiated plasma cells, which unlike normal plasma cells, can proliferate and accumulate in the bone marrow. It is also characterized by the secretion of large amounts of a monoclonal immunoglobulin protein.Hematological cancers (HC) are a heterogeneous group of malignancies from immune cells or their precursors. They can be categorized into three major groups: leukemias, lymphomas and multiple myeloma2.The malignant transformation of normal cells entails alterations in their regulatory circuits and molecular processes that have consequences at multiple scales of the organism. The interactions between the different components in each of these scales makes difficult to fully understand the global deregulation that produce and sustains the cancerous phenotype. Contemporary biology has developed computational approaches, able to analyze large data from next generation sequencing (NGS) technologies. These implementations have proven to be useful to unveil cancer-associated features, as well as structural or functional differences with normal tissues3. Although the importance of genetic expression in cancer is out of discussion, it is also clear that the gene regulation during the carcinogenic process is strongly altered by several factors. Additionally, the gene expression landscape often does not provide information on how those genes are regulated4.Next generation sequencing, in particular gene expression profiles, have been broadly used to discover key features that may alter the transcriptional program, triggering different outcomes8.To overcome the latter challenge, a common approach used for high-throughout-derived datasets, is the gene co-expression network (GCN). These networks are commonly inferred by correlating the expression profile of gene couples within multiple samples. GCNs offer a framework that allows the analysis of global changes in a given phenotype, such as cancer. With this approach, it can be quantified the statistical dependence of any pair of genes’ expression9.In a nutshell, GCNs are theoretical objects composed of genes, which correspond to nodes in the network. Those genes are connected to other genes by means of a given kind of correlation between them. To analyze cancer NGS-derived data, GCNs have become one of the most broadly used tools, given that several implementations are freely available, the obtained results are reproducible and they can be easily contrasted with the phenotypic counterpart14. From these analysis we have identified two main common phenomena between the different cancer GCNs: (1) an elevated proportion of strong interactions between genes from the same chromosomes  in comparison to the network of their respective normal tissue, and (2) the size of cancer-derived networks connected components is much smaller than those observed in a normal-derived GCN. We refer to this phenomenon as loss of inter-chromosomal co-expression and it has been observed in each cancer GCN that we have analyzed15.Previous works from our group have used GCNs to analyze the changes on the transcriptional regulation profiles from several carcinomas and their effects on the regulation of biological processes (BPs) in the diseaseTo the extent of our knowledge, no analysis using this approach has been reported regarding hematological malignancies. Hence, we sought to construct GCNs of these diseases employing RNA-Seq data of 1495 bone marrow samples from The Cancer Genome Atlas consortium (TCGA). We analyzed samples from patients diagnosed with acute lymphocytic leukemia from B and T cells , we wondered whether the different cancer phenotypes had a common topological structure. To this purpose, we inferred the network of the shared interactions between the four HCs. Furthermore, we implemented a community detection algorithm on this intersection network and a functional enrichment analysis (FEA) on the resulting communities in order to identify the biological processes (BPs) associated to each community. A differential expression analysis of the genes present in those communities was also included in order to observe whether the BPs had a trend of differential gene expression, thus indicating an increase or depletion for such processes in a specific phenotype. Finally, we performed the same workflow of community detection and functional enrichment analysis on the individual HCs networks in order to determine additional functional features. This allowed us to identify the common BPs among the four HCs and compare them by the differential expression trend of the genes responsible of the enrichment of the communities.16)), containing BALL-only samples; the other dataset was obtained from Saint Jude cloud (17).For validation purposes, we also constructed HC networks from two different datasets: on from the Royal Children Hospital  test. Distribution of intra-chromosomal fractions in HCs and control phenotypes was compared by taking the values from top 10,000 to entclass1pt{minimaTo explore the co-expression between different types of genes other than protein coding, we did not apply any filter regarding RNA biotype. We constructed networks with all the available biotypes in the samples. In the AML network, more than 30% of the interactions occur between pseudogenes  the gene co-expression landscape in HCs (as in carcinomas) is topologically different from normal GCNs, which is mainly manifested in the the loss of inter-chromosomal co-expression and the smaller connected components in HCs; and (2) that the co-expression interactions between pseudogenes is increased in the HCs networks, which is a feature that has not been explored by our group. As far as the authors are aware of, this has not been reported in the current literature.A differential expression analysis was carried out to detect DEGs in each cancer phenotype in comparison the normal bone marrow group. Figure 18, although their exact role in cancer is still unknown.The top overexpressed and the top underexpressed genes are unique in each phenotype, except for DSC3, a member of the desmocollin subfamily that is overexpressed in both BALL and AML. BALL and TALL have members of the olfactory receptor family overexpressed, namely OR5P2 and OR10T2, respectively. OR10R3P, a pseudogene of the aforementioned family is also in the top overexpressed genes in TALL. The olfactory receptor family genes encode for G-protein coupled receptors related with the sense of smell, and have been reported to be overexpressed in several cancer cell linesOn the top underexpressed genes of BALL, TALL and MM are many genes related with the immune system, and in AML this top is conformed by four small nucleolar RNAs and a pseudogene of a keratin protein. Notwithstanding, the biological insights that we could obtain from these mere list of DEGs is limited; however, integrated with the topological analyses of GCNs and their functional enrichment analysis, we identified specific molecular processes that were exacerbated or diminished in HCs.In order to identify the main similarities in the architecture of the GCNs of the HCs, we inferred a network of the common interactions between the top-100,000 interactions of our four cancer networks. This network has 1,995 nodes and 4,140 edges.19) to identify robust structures across different scales, followed by a functional enrichment analysis using the clusterProfiler20 R package  and translation elongation factors. Several of these pseudogenes are also present in the intersection network, and are mainly overexpressed in all HCs. A large set of co-expressed riboprotein inter-chromosome genes has been also reported in networks from other 15 cancer tissuesTwo communities in the intersection network (C12455 and C12508) are enriched for "immunoglobulin production". C12455 is conformed by genes from the human leukocyte antigen (HLA) complex overexpressed in BALL and AML, and underexpressed in TALL and MM. C12508 is a community conformed by immunoglobulin light chain genes that are underexpressed on the three leukemias and overexpressed in the case of multiple myeloma, which could be associated with the elevated secretion of monoclonal immunoglobulin proteins by myeloma cells.Another community in our intersection network (C12428) is enriched for "erythrocyte homeostasis" and "erythrocyte" "differentiation", although with a little over The intersection network of the four HCs allowed us to identify which processes were represented by the same interactions in the four GCNs. Nevertheless, this analysis does not account for the shared BPs that are represented by different interactions in each phenotype, which could bias our comparison between the phenotypes. To have a better sense of the expression trends of the BPs in each phenotype, we performed the same community detection and functional enrichment analysis workflow on the individual cancer networks and identified the processes that were commonly present in the four phenotypes. We integrated this information in a heatmap Fig.  that shoThis analysis shows a contrast in the differential expression trends of the BPs among HCs. For example, several communities are enriched for processes related to cell cycle and regulation of the immune system, but they have a clear different expression trend among the four phenotypes.The main similarity in the figure are those BPs related with the assembly of ribonucleoprotein complexes, whose genes are mainly overexpressed. Meanwhile, the processes related with cell division present a mixture of differential expression trend, which is also depicted in the four networks at the left part of the figure.22. For BALL, the majority of the most significant differentially expressed genes are overexpressed and also related with cell proliferation. For example, MYBL2 is known to be ubiquitously expressed on proliferating cells23. In TALL, almost every gene related with cell cycle is overexpressed. The most relevant underexpressed genes are E2F8  are underexpressed. In AML we observed a similar underexpression trend in cell cycle processes. However, in this case there are some overexpressed genes that have an active role in the regulation of cell cycle, like CKS1B, that is crucial for the function of cyclin dependent kinases and for progression through cell cycle in cancer cellsAn important amount of common biological processes of the four cancer GCNs are related to the adaptive immune system. This was expected given the origin of these malignancies. The processes of B cell activation are under-regulated in every phenotype except for MM. On the other hand, the processes related with T cell regulation are exacerbated in TALL and MM, but diminished in BALL and AML.These shared BPs are very similar to those enriched on the intersection network. This indicates that hematological malignancies have a shared set of affected BPs, and that these processes are probably deregulated through different mechanisms in each HCs, given their unique expression pattern in each phenotype.16) containing only samples from BALL patients, and another from Saint Jude’s Hospital .It is worth noticing that the results presented here have been obtained from calculations using the TCGA hematological cancer dataset. To validate these results we decided to perform all the calculations using two different datasets: one from the Royal Children Hospital , we constructed GCNs for each cancer phenotype and analyzed their architecture and functional enrichments, and compared them against the normal bone marrow.24, lung25, and clear cell renal carcinoma27. In these studies, the cancer networks have a diminished amount of inter-chromosomal interactions. With these results, we show that this phenomenon is not restricted to carcinomas. Hence, we could argue that the loss of inter-chromosomal interactions is a common deregulated feature of the co-expression landscape in cancer.As expected, the architecture of the cancer networks are different from the normal bone marrow in two main aspects: (1) the number and size of network components, and (2) the chromosomal location of genes that composed them. As mentioned before, the cancerous networks have several small components, many of them made up of genes from the same chromosome. This result coincide with the previous GCNs analyzed from breast cancerThe topological differences between the four HC networks are noteworthy, especially regarding the augmented intra-chromosomal interactions in comparison with the normal bone marrow. In Fig. 15. Furthermore, these networks consistently exhibit a smaller proportion of intra-chromosomal interactions compared to their counterparts in cancer networks.Concerning the control networks, it is worth noting that they were constructed using a relatively modest dataset of only 37 samples. Despite the limited sample size, the inferred gene-gene interactions exhibited statistical significance . According to this, the transcripts of pseudogenes regulate their parental gene expression through competition for its regulatory elements, such as miRNAsIt could be possible that a deregulation on the transcription of RPs is allowing their co-expression, and that this serves as a mechanism used by cancer cells to sustain high rates of protein translation that contribute to their proliferation. A similar mechanism could be affecting the expression pattern of their pseudogenes, since they retain some characteristics of their parental genes, which could prompted their co-expression. Importantly, pseudogene complexes observed in HC networks are mostly overexpressed Figs. C and 3. Interestingly, this pattern in the pseudogenes co-expression could be associated with the loss of inter-chromosomal regulation, since the network with the most amount of relationships between pseudogenes (AML) has the least amount of cis-chromosomal interactions. Furthermore, the opposite occurs in MM, which has the least amount of relationships between pseudogenes and the highest proportion of cis-chromosomal edges. Further studies on the co-expression landscape of non-protein coding genes should be carried out to address this possibility.The mechanism regulating these protein coding genes and their pseudogenes should be similar, but not exactly the same, since otherwise one would detect interactions between pseudogenes and protein coding genes in the top interactions. Further research on the subject of the transcription regulation of RPs and their pseudogenes in cancer is necessary to propose a more robust hypothesis regarding their co-expression patterns.Given the common site of origin of the samples (the bone marrow) we investigated the interactions shared between the four HCs networks. Most of these shared interactions are inter-chromosomal, and are enriched for specific biological processes. Nevertheless, the genes in this intersection network have a phenotype-specific differential expression trend. This suggests differences on their regulation in each disease.34.To better assess these differences, we analyzed the functional enrichment on the individual networks and identified the common processes between them. These shared BPs are similar to those enriched on the intersection network. In this regard, the finding that every gene in the cell division-associated communities for MM (with the exception of ZWILCH and STAG1) is underexpressed, could mislead to the conclusion that the myeloma cells are not proliferating. We believe that this underexpression pattern must be retained on myeloma cells from their normal cell of origin, the long-lived plasma cell, which no longer divides after its differentiation from naive B cells35. Given these relatively slow rates of progression, we could assume that the proliferation of malignant plasma cells occurs slowly and in a lower rate than the proliferation of normal cells inside the normal bone marrow. The low proliferation rates would also explain the underexpression of genes related with cell cycle, and the differences of this in comparison with the acute leukemias, which are malignancies of rapid evolution. Since the RNA-Seq samples for these analyses were performed in bulk, we are not able to dissect the specific expression profile of the cells into the sample. Given that the BM has a relatively small amount of plasma cells , we believe that further studies involving myeloma and plasma cells could better asses the cell cycle regulation in the disease.It is worth noting that MM is a progressive disease, which typically arises after monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). The progression from these stages to MM is approximately 1% per year for MGUS and 10% per year for SMM22. PCLAF and PCNA genes are involved in DNA synthesis and the G1/S transition37. However, we also detected several underexpressed genes that regulate the mitotic spindle and chromosome segregation . This suggests that, although there is an active proliferation in these phenotypes, the processes responsible for an adequate division are downregulated, which could contribute to (or generate) the genomic instability in theses diseases.The analysis of cell cycle related communities in AML and BALL clearly reflects an ongoing proliferation, with the overexpression of genes like CKS1B, PCLAF or PCNA Fig. . CKS1B iAn important amount of common biological processes of the four cancer GCNs are related to the adaptive immune system. This was expected given the origin of these malignancies. The processes of B cell activation are downregulated in every phenotype except for MM. On the other hand, the processes related with T cell regulation are diminished in TALL and MM, but exacerbated in BALL and AML. We believe that this expression trend is linked to the cells of origin of the malignancies. B cells and members of the myeloid lineage are professional antigen presenting cells (APCs) which activate T cells through MHC molecules. Hence, the overexpression of genes related with this processes (mainly HLA genes) could be a legacy from the normal cell of origin that are specialized in the presentation of antigens to T cells.The genes enriched for B cell regulation are mainly immunoglobulins, and their overexpression in MM is expected as its normal cell of origin, the plasma cell, produces antibodies and do not have the ability to present antigens to T cells (which explains the underexpression of HLA genes in this phenotype).Together, our results on the validation datasets show that different datasets of the same HCs reflects the underlying deregulation of the co-expression landscape manifested through the loss of inter-chromosomal interactions in these phenotypes. Furthermore, and despite the validation networks have not the exact same architecture of the TCGA networks, most of the biological processes represented by the interactions in the validation networks are also found in the enrichment of the TCGA GCNs.40. However, none of those omics have been determinant to explain the significant decrease of long-range gene co-expression. Further studies complementary to the gene co-expression analysis, such as the methylome profile, or Hi-C data in different cancer tissues are also ongoing.In order to unveil the mechanisms behind the loss of inter-chromosome gene co-expression in cancer, other omic technologies have been evaluated. For instance, copy number variation data, transcription factor binding sites, or micro-RNA co-expression41. So far, the loss of long-range co-expression has only been seen at the transcriptomic level of regulation.Complementary to the last, a recent study on the protein co-expression landscape of breast cancer, showed that the loss of long distance co-expression is not observed at the proteome level. The most relevant protein co-expression interactions occur between members of the same protein family or between proteins with similar functions42. Therefore, the co-expression method does not influence the phenomenon.Despite the lack of a mechanistic explanation for the bias of cancer GCNs to aggregate gene clusters from the same chromosome, we argue that the different phenomena described here must be part of the broad deregulation that occurs in HCs at the transcriptional level. The significant coincidence between the TCGA-derived GCNs with the Saint Jude and Royal Children Hospital datasets, together with the statistical assessment of network topologies and enrichment analyses, allows to suggest that this phenomenon is not an artefact of the datasets nor of the methods that determine the gene correlation. We observed a high similarity between networks constructed with MI and Spearman correlation See ". FurtherIn this work, we performed a global analysis of the transcriptional landscape from four hematopoietic cancers. Through the use of GCNs we were able to identify several gene interactions that are shared by the four HC networks, which translates in biological processes common to these malignancies. Nevertheless, the genes in these common interactions have a unique differential expression profile that reflects differences in their regulation in each phenotype. It is necessary to further analyze the topology of the individual GCNs of HCs to understand how the circuits of normal hematopoietic cells are deregulated to promote these diseases.An aspect that also needs to be further explored is the topology of normal bone marrow network. Here, the data set used was made up of 37 RNA-Seq samples, which was the total number of normal bone marrow samples present in the TCGA dataset at the moment of the analysis. Given that we performed a normalization workflow for each HC data set with the same normal group, we obtained a total of four networks for the normal bone marrow. However, our approach can be highly sensitive to the number of samples analyzed, which could represent a limitation to detect co-expression interactions with a high confidence. Further studies using a larger group size could provide more robust networks to further compare the topology of the normal and cancerous phenotypes and identify interactions deregulated in the disease. Nevertheless, our results show that our network theory approach to investigate cancer phenotypes is able to elucidate relevant clinical features. For example, the communities associated with erythrocyte development that are downregulated, evidence the anemia suffered by patients of these diseases. To clarify whether these biological features of HCs are a consequence of the deregulation of the normal co-expression landscape, additional research is necessary.An important aspect of the GCNs of hematologic cancers should be taken into account: the effect of the ages of the patients whose samples are included in these type of studies. Multiple myeloma, the network with the more pronounced loss of inter-chromosomal co-expression, was inferred with all patients in advanced edges, meanwhile the rest of HCs were obtained from pediatric patients.Future investigation should be done involving lymphomas samples, which were neglected here given the lack of an appropriate control group to compare these samples. Single-cell RNA-Seq data will certainly improve the accuracy of the gene co-expression landscape. To analyze other -omics data could add molecular interactions that may help to disentangle the molecular mechanisms that make different these hematopoietic malignancies. However, with this approach we emphasize the importance of this kind of analyses to better understand the co-expression landscape in cancer and its associated functional features.https://portal.gdc.cancer.gov, The Cancer Genome Atlas consortium. The cancer samples included those categorized as “Recurrent Blood Derived Cancer - Bone Marrow” and “Primary Blood Derived Cancer - Bone Marrow” of patients with a primary diagnosis of “acute myeloid leukemia, nos”, “precursor b-cell lymphoblastic leukemia”, “t lymphoblastic leukemia/lymphoma” and “multiple myeloma”. Also, normal bone marrow samples used were those categorized as “bone marrow normal”.The samples used in this worked were retrieved from 16 (RCH dataset) and from https://www.stjude.cloud/research-domains/pediatric-cancer (St. Jude dataset). The St. Jude data downloaded corresponded to the samples of diagnoses of “B cell Acute Lymphoblastic Leukemia, NOS”, “Acute Myeloid Leukemia” and “T cell Acute Lymphoblastic Leukemia, NOS”.The validation datasets were retrieved fromA complementary annotation file was obtained from BioMart in order to obtain the chromosome name, gene type, GC content, gene start and gene end. The gene length was inferred by the rest of the gene end minus the gene start.43, as follows: Genes on the 0.25 quantileGenes with an expression value of 0 across the majority of the samples in the datasetsGenes with a mean expression less than 10 were removed.After filtering the genes, we performed normalization procedures for several biases of the sequencing technology using "EDASeq"44 and "NOISeq"45 in R: Normalization for the length transcript and the GC content of the genes.Between-lane normalization followed by Trimmed Mean of M values (TMM).Lastly, we apply the multidimensional noise reduction algorithm "ARSyN" to assure that the cancer and normal samples were separate and clustered correctly, PCA visual exploration was used to confirm this  is a good estimator of dependency between variables, it can be strongly affected by the number of samples in the analysis. Considering the small amount of samples in our control group, we constructed networks with both MI and Spearman correlation, which is less dependent on the number of samples, although it specifically detects monotonic relationships. To decide with which type of network (MI or Spearman correlation) we should continue our analysis, we identified the gene interactions that are shared between each network in different cut-off points  test on the fraction of inter-chromosome interactions at different cut-off points on the networks , refers to a subset of nodes within a network, that are more densely connected to each other than to nodes outside the subset.In order to correlate topological structures inside our networks with biological processes, we first performed the community detection algorithm "HiDeF" through the "CyCommunityDetection" app in CytoscapeThe "HiDeF" algorithm integrates the concept of persistent homology with existing algorithms for community detection in order to identify robust structures intrinsic to a dataset. We applied "HiDeF" with its default setting in Cytoscape using the "Louvain" algorithm. The communities detected were then enriched using the R package "clusterProfiler" to correlate the genes inside the communities to biological processes of Gene Ontology. We used the EnsemblID of each gene in this analysis and considered that a community was enriched if the correlation to a biological process had an adjusted p-value less than 1We calculated the percentage of over and underexpressed genes on the communities enriched for each BP on the individual cancer networks. We rested the proportions and called the result "differential expression trend" for a BP on a specific phenotype. A value of 1 depicts that every gene enriched for a BP has an overexpression tendency, a value of -1 means that every gene has an underexpression tendency, and a value of 0 depicts that there are the same proportion of over and underexpressed genes enriched for the process.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Legends.


Due to their ability to achieve higher DOA estimation accuracy and larger degrees of freedom (DOF) using a fixed number of antennas, sparse arrays, etc., nested and coprime arrays have attracted a lot of attention in relation to research into direction of arrival (DOA) estimation. However, the usage of the sparse array is based on the assumption that the signals are independent of each other, which is hard to guarantee in practice due to the complex propagation environment. To address the challenge of sparse arrays struggling to handle coherent wideband signals, we propose the following method. Firstly, we exploit the coherent signal subspace method (CSSM) to focus the wideband signals on the reference frequency and assist in the decorrelation process, which can be implemented without any pre-estimations. Then, we virtualize the covariance matrix of sparse array due to the decorrelation operation. Next, an enhanced spatial smoothing algorithm is applied to make full use of the information available in the data covariance matrix, as well as to improve the decorrelation effect, after which stage the multiple signal classification (MUSIC) algorithm is used to obtain DOA estimations. In the simulation, with reference to the root mean square error (RMSE) that varies in tandem with the signal-to-noise ratio (SNR), the algorithm achieves satisfactory results compared to other state-of-the-art algorithms, including sparse arrays using the traditional incoherent signal subspace method (ISSM), the coherent signal subspace method (CSSM), spatial smoothing algorithms, etc. Furthermore, the proposed method is also validated via real data tests, and the error value is only 0.2 degrees in real data tests, which is lower than those of the other methods in real data tests. In recent years, DOA estimations ,2,3 haveCurrently, incoherent narrowband signal DOA estimation techniques have been extensively used. However, due to the complex propagation environment ,20, coheFor wideband signals, many types of studies have been conducted regarding wideband DOA estimation algorithms. Usually, the wideband signal is first decomposed into several narrowband signals with multiple frequencies via time-to-frequency conversion. Then, two widely used classes of wideband DOA estimation algorithms are used. One is the incoherent signal subspace method (ISSM) ,29,30, wIn this paper, firstly, we choose a sparse array in order to obtain a higher spectral estimation accuracy and a larger DOF using a fixed number of antennas. Then, we choose the CSSM method to assist in the process of decorrelating the coherent signal. In order to avoid the final estimation result relying on the pre-estimation, we propose a focusing algorithm without pre-estimation. Then, we virtualize the covariance matrix of sparse array due to the decorrelation operation. To more effectively counteract the effects of coherence, we adopt an enhanced spatial smoothing algorithm to make full use of the information in the covariance matrix of individual subarrays and the mutual covariance matrix of different subarrays. Finally, the MUSIC algorithm is applied to obtain the final estimated values.The paper is organized as follows: In the Considering that the We transformed the output of each sensor into the frequency domain and partitioned the frequency band into The data covariance matrix can be written as follows:The traditional CSSM algorithm was used to focus information derived from different frequency bins on a reference frequency bin, and narrowband algorithms were then used to obtain DOA estimation values. The key purpose of this algorithm was to construct a focusing matrix The data covariance matrix was generated as follows:The focusing matrices at different frequencies are non-unique, and the methods used to construct this matrix have been extensively explored. The rotational signal subspace (RSS) algorithm is now the approach most widely used to construct unitary focusing matrices by minimizing the Frobenius norm of the array manifold mismatches.However, for most existing methods of constructing focus matrices, initial estimations were critical, and poor initial estimations could have a significant impact on the final DOA estimations.In order to avoid excessive dependence on initial estimations for the final accuracy, we adopted a focusing algorithm that did not require pre-estimation. It can be observed that the unitary matrix with columns that are the eigenvectors of the data covariance matrix spanned the same subspace across each frequency as the array manifold, meaning that we can use them to construct the focusing matrix. This method does not require initial estimations compared to the approach of building a focusing matrix based on the array steering vector.Firstly, we define the focusing matrix as follows:In practical cases, we multiply The covariance matrix can be presented as follows:Summing up the matrices at By constructing a focusing matrix to focus all frequency components to the reference frequency and then averaging the covariance matrix, the correlation coefficients between the signals were reduced, meaning that the rank of the covariance matrix was equal to the number of sources used for decorrelation.Finally, we constructed the focusing matrix without the need for initial estimations, focused the covariance matrices at different frequencies to the reference frequency Since the array we used was a two-level nested array, it needed to be virtualized before it could be used to perform DOA estimation. Thus, we vectorized the above covariance matrix and foundWe sorted As the received signal is coherent, subspace-based and propagator-based algorithms cannot be used directly. And one of the most famous decorrelation algorithms is the spatial smoothing algorithm. The entire array is divided into Therefore, the cross-covariance matrix And the backward cross-covariance matrix can be expressed as follows:One of the drawbacks of many spatial smoothing methods is that they do not make full use of the information in the subspace, while the spatial smoothing method used in this paper utilizes information including the cross-covariance matrix Furthermore, we apply the MUSIC algorithm to the covariance matrix The MUSIC spectrum is generated as follows:Algorithm 1: The Proposed MethodOutput: DOA estimation valuesConstruct the focus matrix Obtain the covariance matrix Vectorize the above covariance matrix Apply an enhanced spatial smoothing method to obtain the rank-restored covariance matrix Apply the MUSIC algorithm to obtain the estimation results according to Equations (17) and (18)Input: The received data Then, DOA estimation values are obtained through a spectrum search. The main steps of the proposed method are shown in Algorithm 1.To prove the superiority of the proposed method, we compared it to several other methods. The four methods used are as follows: (i) Using the method in , which uIn this section, the root mean square error (RMSE) was used to verify algorithm performance. The expression of RMSE is as follows:We design two wideband sources impinging on a two-level nested array from 20° and 45°, and the number of sensors used for both subarrays is 10. The more frequency bins there are present, the slower the processing speed will be, and the accuracy of the results will raise. When the number of frequency bins reaches a certain threshold, the change in result accuracy will become negligible or even non-existent. As a result, between 280 and 320 MHz, a total of 40 frequency bins is uniformly sampled for wideband DOA estimation.As shown in As shown in To demonstrate the effectiveness of the proposed method, we conducted a set of experiments inside of the room, in which a wideband signal with a bandwidth of 10 Mhz impinged on the sparse array from 12°. A total of 32 frequency bins were used for wideband DOA estimation, the experimental equipment and scene are shown in In this paper, we propose a DOA estimation algorithm for wideband coherent signals using the nested array. Firstly, a two-level nested array is used to ensure large DOF in the virtual array. Then, the focusing matrix is constructed without initial estimation to ensure that the initial estimation’s influence on the final estimation in the conventional algorithm is avoided. Furthermore, an enhanced spatial smoothing algorithm is used for decorrelation. Finally, the MUSIC algorithm is applied to perform high-accuracy DOA estimation. The proposed method has better decorrelation and higher accuracy than the ISMM-ss, CSMM-enss and IEF-CSSM methods. In our experimental tests, the proposed method is effective for coherent wideband signal processing, even in indoor environments, and we have demonstrated the advantage of the proposed method in relation to accuracy compared to other methods. In the future, we will investigate low-rank matrix recovery and denoising using the obtained covariance matrix before angle estimation, which could help to reduce residual interference on the covariance matrix.