The vibration of the vocal fold lamina propria is an important factor involved in vocal production and aging may change the amount of hyaluronic acid in the vocal fold leading to dysphonia.This study compares the concentration of hyaluronic acid in vocal folds of aged and young female rats. Study design: experimental.We used the vocal cords of 13 female rats divided into two groups: five aged rats and eight young ones. The tissue concentration of hyaluronic acid was determined using the fluorimetric method with the hyaluronic acid binding-protein coated on plates of enzyme-linked immunosorbent assay (ELISA), conjugated with biotin. Europium-labeled streptavidin was added and, after europium release with the use of enhancement solution, the final fluorescence was measured in a fluorometer.We found the following concentrations of hyaluronic acid in vocal fold according to the group: 581.7 ng/mg in old female rats and 1275.6 ng/ mg in young female rats. Statistical analysis showed differences between groups.The vocal folds of old female rats have a lower concentration of hyaluronic acid when compared to such concentration on the vocal folds of young female rats. Since the first histology studies involving the vocal folds (VF)The VF has a LP rich in glycosaminoglycans (GAG), which are the interstitial proteins responsible for the filling and viscosity of these tissues.Hyaluronic acid (HA), one of the main LP GAG, has a high density of negative charges in its structure. This makes it highly hydrophilic, making it the main responsible for viscosity, fluidity and tissue fillingNumerous types of disorders can change the amount of HA in the VF and compromise phonation quality - and aging is one of the most prevalent. Glottic characteristics indicative of presbylarynx, such as an arched VF and vocal process prominence, have been described by many authorsOther authors describe the effects of aging on the fibrous proteins, showing an atrophy of the elastic fibers and increase in the density of VF collagen fibersAlthough proven a reduction in the gene expression which code vocal fold HA synthase on the vocal fold of elderly ratsThe goal of the present study is to compare the concentration of hyaluronic acid in the VF of female aged rats.This research Project was analyzed and approved by our Ethics Committee, and this study is experimental.We used the VFs of 13 female Wistar-sp rats, weighing about 250 grams. We considered as aged, those rats with ages near 24 months.ad libitum feeding. The larynx was removed around 2 minutes after the slaughter and was preserved in eppendorf micro tubes with acetone.We included eight young and eight old female rats, not pregnant and not breastfeeding, without any prior use of medication and under Both vocal folds from each female rat were removed from the larynx and fragmented in small particles. The tissue was weighed after centrifuging and drying, and it was then submitted to protein digestion under protease . After protease inactivation by boiling, the samples were centrifuged and the floating layer was harvested for HA quantification.enzyme-linked immunosorbent assay (ELISA) plates, similar to a ligation antibody, and also as a biotin-tandem probe, working as a marked secondary antibody.In order to measure the HA, we used the fluorometric method with the hyaluronic acid ligand protein (HALP)The ELISA plates were coated with HALP and incubated with samples of a hyaluronic acid standard-solution (000-500 mg/l), to serve as a fluorescein standard curve, and with the sample solution obtained from the VF, in tripled.enhancement solution, the final fluorescence was measured in a fluorometer.Afterwards, the ELISA plates were washed and the biotin-tandem HALP was added. After a new washing process, we added europium-marked streptavidin. After europium is released from streptavidin with the use of an The data was interpreted and analyzed by means of a Mann-Whitney non-parametric test.The group of young rats had a mean age of 135.5 days and it is formed by sexually mature adults. On the other hand, the group of old rats had a mean age of almost two years.In order to compare the groups, we employed the Mann-Whitney non-parametric test . We may,One of the first studies to detect GAGs on VFs used immunohistochemistry to identify the HA receptor, since the antibodies are not capable of directly ligating to the HAOther authorsAlso using the Muller-Mowry Colloidal Iron dye, other authorsAnother unspecific tissue dying method, which simultaneously dyes different types of GAGs is the Alcian Blue. Some authors have utilized this dying method, without digestion by hyaluronidase, to identify age-related HA changes to the VFDifferently from previous studies, ours utilized a specific quantification method for HA, which provides its concentration in nanograms per milligram of dry weightOther authors utilized this same fluorescence method for the HA quantification in the human vocal foldOur study utilized the vocal folds of rats and, because of the diminutive size of the rat's VF and the impossibility of separating the lamina propria from the adjacent muscles, the entire vocal fold depth was measured. Therefore, our results reflect HA concentration in the lamina propria and in the thyroarytenoid muscle together.Most of the studies which use human VF as a study material remove the larynx many hours after death. However, in our study the mean time between the death and tissue fixation was 2 minutes. Considering that GAGs are substances which degrade fast, a fast VF fixation contributes to enhance the accuracy of our results.Airway manipulation before death and the time elapsed between the death and the vocal fold fixation are factors which have a high potential to cause bias. The possibility of avoiding these errors reinforces the aforementioned advantages of using mice as animal models to study the vocal foldsIt is very difficult to obtain older rats, because of their high mortality. Some authors have shown that Wistar rats live the longest – 34 months, and reach old age at around 24 months, a time when their mortality rates reach 50%It is known that a deep understanding of the LP of vocal folds requires understanding the different functions of each one of the substances involved in VF vibration. Thus, the hyaluronic acid, as one of the main components of the extracellular matrix, is fundamentally important for the vocal fold mucosal wave laxity and for an optimal vocal quality.Therefore, we believe that our study may contribute with important information. With an accurate method which yields the tissue concentration of HA in relation to the animal's dry weight, our study showed that aging reduces the amount of HA in the VF of female rats.The results enable us to conclude that the vocal fold or older female rats have a lower concentration of hyaluronic acid when compared to those from young female rats.


The acceleration of large-scale sequencing and the progress in high-throughput computational analyses, defined as omics, was a hallmark for the comprehension of the biological processes in human health and diseases. In cancerology, the omics approach, initiated by genomics and transcriptomics studies, has revealed an incredible complexity with unsuspected molecular diversity within a same tumor type as well as spatial and temporal heterogeneity of tumors. The integration of multiple biological layers of omics studies brought oncology to a new paradigm, from tumor site classification to pan-cancer molecular classification, offering new therapeutic opportunities for precision medicine. In this review, we will provide a comprehensive overview of the latest innovations for multi-omics integration in oncology and summarize the largest multi-omics dataset available for adult and pediatric cancers. We will present multi-omics techniques for characterizing cancer biology and show how multi-omics data can be combined with clinical data for the identification of prognostic and treatment-specific biomarkers, opening the way to personalized therapy. To conclude, we will detail the newest strategies for dissecting the tumor immune environment and host–tumor interaction. We will explore the advances in immunomics and microbiomics for biomarker identification to guide therapeutic decision in immuno-oncology. Cancer is an important cause of death worldwide, even if its mortality has declined during the last decade . The IntUntil recently, cancer was widely considered as organ dependent and characterized by the site of apparition of the tumor. This hypothesis was slowly abandoned due to the heterogeneity present between two patients with a similar tumor  that wasThe use of single omics, such as genomics and transcriptomics, uncovered many driver genes to better comprehend the genomic landscape of cancer. Interestingly, some of these studies revealed the wide complementarity of genomics and transcriptomics, many of the driver genes being identified by either one or the other modality . This foTo this day, even with the new information that multi-omics approaches can bring, understanding how cancer develops and maintains is puzzling. Indeed, cancer cells interact with many different components including the host immune system . These iIn the first part, due to the growing interest in multi-omics, this review will address the challenges raised by omics and how to overcome them. In the second part, we will provide an overview of different databases that are useful in cancer research. In the third part, this review will attempt to illustrate how to integrate mutli-omics to clarify cancer complexity, and how the use of machine learning can help to predict survival and treatment response. Finally, we will give an overview of new methods to decipher the interaction between the host and the cancer cells and how it can bring opportunity for personalized therapy .To integrate the massive data flow generated through the different biological elements explored by NGS, innovative computational tools are needed for diminishing the data dimension, then making them manipulable by human hands. These tools allow researchers to fulfill the gap of missing knowledges and contribute to the discovery of novel biomarkers, deciphering the complexity of cancers . DespiteOne of these challenges is the heterogeneity that exists across the biological layers. It may differ in type, with numerical or categorical features, discrete or continuous variables with different ranges. The number of features can also vary between the different data sources, creating novel struggles to consider. Another burden, operating during sample collection, are missing values, setting up uninterpretable elements difficult to handle by some algorithms. Also, processing outliers and highly correlated variables might be burdensome to integrate as they might be irrelevant, create noise, or overfeed a system .To face these challenges, different tools have been created that we will summarize . Two maiStarting with exploratory matrices, herein is a brief overview of two algorithms using this approach: matrix factorization and logic data mining. These methods would be preferred for well-defined exhaustive datasets containing extreme values that need to be considered.Matrix factorization: joint and individual variation explained (JIVE), non-negative factorization (NMF), and structural learning and integrative decomposition (SLIDE) are some of the tools based on this approach. This algorithm uses a system of multiplication between columns and rows on a dataset decomposed in multiple matrices of smaller dimension. Each matrices have n columns, referring to n common objects, that will be multiplied by m rows, referring to other common objects. The matrix can contain different biological elements, that is, gene expression and miRNA measurement. The strength of matrix factorization is to cluster different matrices together but requires a dataset with scarce missing values .Logic data mining is a multistep procedure: 1) feature binarization, that assigns a threshold to convert each feature in binary values (0 or 1); 2) feature selection, that uses a machine learning approach to select a subset of data with relevant features introduced in a model; 3) extraction of logic formulas to build the final classification model. As an example, microarray logic analyzer (MALA) is based on this approach and has been used to identify overexpressed interrelated genes and proteins involved in a common pathway .We will now detail four types of algorithms to better conceptualize the probabilistic matrix approach. These models follow the principles of Gaussian probability, linear regression, or Bayesian statistics and are very useful to troubleshoot datasets with missing values.Gaussian latent variable model uses a probabilistic matrix starting with the dimension reduction of the dataset. The matrix composed of N columns and D rows will be decreased to a matrix with a lower dimensionality on D. D will be reduced in Q most relevant data. Relevant data will then be extracted to identify a pattern explaining the results. Because the algorithm is based on Gaussian probability, dataset with a Gaussian distribution perform better with this instrument than expression profiles near 0. Lower outliers close to the null value may cause misinterpretation during the analysis. However, missing values are well managed by the algorithm. A tool using this algorithm, iCluster, will be further explained in the next chapter .Generalized linear regression is one of the subcategories found in the generalized linear model. This algorithm is composed of three components: 1) random component; 2) systematic component; and 3) link function. The first step is the matrix generation, then a linear regression will be applied to the matrix finding the best possible linear relation to fit the expected values. The systematic components are distributed on the line while random components are outside of the line. The link function will identify a relationship between the linear predictor and the distribution of the random components. It aims to explain why some values are following a linear repartition while others are not. This model performs well when using data that are already explained on unexplained data. The accuracy might, however, be hindered by outliers that may introduce misinterpretation. This model is used in the icluster + tools .Z with n x k dimensions, following the rule of multivariate normal distribution. A joint integrative clustering will capture relevant features to individualize distinct clusters across omics datasets. The use of Baye’s theorem enables the analysis of factors with various distributions and correlation among datasets. The probabilistic model is also permissive of missing values  family tools used for multi-omics data integration. This unsupervised method infers principal component-based factors to decompose each matrix of M different omics components. The matrices will be transformed into a Z matrix of factors for each sample and M weight (W) matrices with features in rows and factors in columns, for each omics element. Downstream analysis will identify inference across Z and W matrices. Also, because the algorithm is based on a Bayesian framework, the distributions of the data are placed on the unobserved variables of the models and the algorithm will keep running until all the data have been characterized .To finish, despite the numerous tools that are now available, the perfect tool does not exist. The continual development of computational methods necessitates systematic evaluation (benchmarking) of the omics data analyses tools and methods . The majThe expansion and acceleration of NGS have generated a tremendous amount of data. In a collaborative effort, extensive omics databases have been created to stock and make those data publicly accessible to researchers, allowing for large meta-analysis for a tumor category or across cancer types. These datasets can host the sequencing output of different biological elements from bulk tumor or single-cell sequencing as well as clinical and treatment information. We will provide a summary of the main databases that are available for cancer research and detail the characteristics of each library with tumor types included and the biological and clinical contents provided .The Cancer Genome Atlas (TCGA) is the largest pan-cancer multi-omics database with clinical annotation allowing for large meta-analysis. TCGA is widely used by the research community, promoting new discoveries on tumor biology, evolution, and treatment specific biomarkers validated on meta-analysis across cancer types .The Cancer Immunome Database (TCIA) is a new database of immunogenic analysis of NGS data from 20 different types of solid tumors derived from TCGA database. This database has served for the elaboration of a pan-cancer immunogenomic classification for checkpoint blockade sensitivity .The International Cancer Genomics Consortium (ICGC) is the most ambitious biomedical efforts research since the human genome project. This database has permitted novel observation of cancer biology through the whole genome annotated alterations from 2,800 samples .The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) contains genomic data from breast cancers. This database helped improving the classification and subtyping of breast tumors and already led to the discovery of 10 subgroups .Therapeutically Applicable Research to Generate Effective Treatments (TARGET) is a clinically annotate database hosting genomics and transcriptomics data from pediatric tumors. This dataset empowered the description of the driving process of childhood cancers, and was used, for example, for characterizing the immune environment of pediatric cancers and its prognostic impact .Cancer Research Institute (CRI) iAtlas is a database platform allowing the study of interaction between tumors and immune microenvironment, granting the possibility to explore immune response across genomics and clinical phenotypes. iAtlas (first version) allows researchers to explore these readouts and the relation between tumor types and immune response . CRI iAtThe dramatic acceleration in data production and the policy for data sharing have increased the comprehension of cancer complexity and fostered the integration of interrelated biological elements of omics analysis in more and more complex computational models that we will show in detail in the following.Tumor heterogeneity remains a hurdle to understand tumor biology. For example, for a same tumor type, there is a wide variation in clinical evolution across population of patients, highlighting differences in tumor cells to progress or mutate . These dOne of these tools that allows the sample classification is a microarray logic analyzer (MALA). MALA is based on logic data mining algorithm. Its follows a three-step process:  discreteTo better understand cancer, researchers need to characterize the different cancer subtypes. Subtyping cancer is more complicated and remains one of the major challenges in cancer research. The identification of subtypes can provide an understanding of the underlying molecular mechanisms and thereby help design precise treatment strategies for efficient cancer management. Contrary to classification that is more histologic, the subtypes are influenced by oncogenic alterations and/or modifications in the gene/protein expression . Moleculi.e., gene expression and miRNA), while the latter is intrinsic of one component and unrelated to others  algorithm. This tool aims to individualize two types of structures: joint and individual structures. The former is the biological patterns of the samples that are shared between the different component types  consists in multiplication between the columns and the rows of a matrix. The input data sets are formed by a matrix called mponents .WHI and intNMF considers heterogenous effects . nNMF involves a two-step process with network generation and integration of the network. To generate the network, a consensus matrix is built with a binary value attributed to each sample to reflect the connectivity between samples. Successive cycles are performed attributing a new binary value for every novel entry until the matrix is stabilized. The mean of the consensus matrix is made for each iterative cycle until the last cycle. This generates a consensus matrix for each data type integrated. The larger elements of the consensus matrix reflect the higher similarity between samples. After the networks are generated, samples can be considered as vertices and the consensus values as the edges. The network integration is based on the message passing theory (updating and combining network) that is processed on two ways: strong signals present in any data are conserved and consistent signals in multiple data are added up during an iterative process. Weak signals disappear while filtering out the noise. An advantage of nNMF is its ability to detect true cluster of small size with high reliability. Clinical application of nNMF demonstrated the capacity to establish novel clusters on head and neck squamous cell carcinoma, glioblastoma, and low-grade glioma data sets, suggesting a new comprehensive subtyping that eliminates previously unclassified samples .NMF and its extensions are based on matrix factorization algorithm that are efficient tools to treat a massive amount of data but are underperforming with missing values. To complement this part, iCluster family tools are good alternatives. iCluster is based on probabilistic matrices algorithm and iCluster is based on a Gaussian latent variable model. The basic concept of iCluster is to jointly estimate the link between the data with a dimension reduction principle: data and features are clustered together to maximize the correlation between data types . iClusteCNTN4 and RFTN1) for lung adenocarcinoma (The first extension of iCluster was iCluster+ (or iClusterplus), a model based on a generalized linear regression combined to the basic algorithm of iCluster. Compared to different models, iCluster + produces the best classification when integrating unknown datasets. It has been useful to construct two molecular subtypes and identified two core genes  in ovarian cancers. But it also demonstrated the role of BIN2 in ovarian cancer, and how it can be a favorable survival outcome (GPX2 for smoking induced cancer (lung squamous cell carcinoma), but also identified OAS2 and TRIM22 as modulator of the immune response  of glioblastoma oncogene rognosis . iProFun outcome . Finallyresponse .via supervised or unsupervised learning. Supervised learning relies on a labelled input dataset used to train and develop a function to predict the outcome on an experimental dataset. The algorithm attempts to find patterns relating the features to the given label. The second step is to integrate data test and to classify the validation data in the right label by following the identified patterns. The correlation between the given and predicted labels can be compared to assess the accuracy of the model. Unsupervised learning clusters the samples by identifying and regrouping different features following a similar pattern . It can be used for machine learning and for deep learning. ANN is a simple approach to create an artificial model, composed of neurons organized in layers. This is composed of an input layer, hidden layer, and an output layer. The input layer corresponds to the dataset given to the algorithm and the output layer corresponds to the possible outputs of the algorithm, in this case, a classification of the data. The hidden layer is what defines the complexity of the algorithm, it represents possible pathways linking the input layer to the output layer. For the integration of multi-omics, different blocks, specific to each type of omics data, are created. Depending on the biological question, different machine learning algorithms can be preferred.A vast diversity of algorithms are available for machine learning; random forest classifier and k-nearest neighbor are commonly supervised classifiers that are easily suitable for biomarker discovery. A random forest algorithm builds multiple decision trees that are randomly generated. Each decision tree provides a classification. The classification that is chosen in a majority of cases is used to classify each datapoint of the dataset . K-neareThese different approaches have been successfully applied to biological data to improve prognoses prediction. For example, ANN has already been used to analyze the survival in two breast cancer datasets. The model was able to predict the prognoses (favorable or unfavorable) and relapse probability . In the Deep learning approaches are a subset of machine learning that contains multiple hidden layers organized in a network allowing for progressive learning . Deep leSurvival analysis learning with multi-omics neural network (SALMON) attempts to aggregate and simplify the gene expression data to enable prognosis prediction. Deep learning approaches typically introduce an extensive number of parameters from a limited sample set that can introduce data overfitting rendering the models ineffective. In comparison, SALMON favorizes the use of eigengene matrices of gene co-expression modules to feed the model. This model was compared to other survival prognostic models on breast cancer datasets, including DeepSurv and random survival forest. SALMON predictor reached a higher survival concordance index than other methods and showed that the integration of multi-omics data combined with clinical and demographical features can increase the prediction performance. This algorithm was used to develop an age-specific prognostic score for breast cancer .CNN is very useful for deconvolutional observations of histological images used to classify and predict cancer prognoses. CNN was successful to detect and to predict patient outcomes on haematoxylin-eosin-stained tumor tissue microarray with a higher accuracy than the visual prognostic prediction. For that, the image was cut into spots that were characterized with a pre-trained convolutional neural network . MesoNetDrugCell is a visible neural network for drug response prediction. The model is built with two branches. The first one modulates the hierarchical organization of molecular subsystems in human cells. Each subsystem, involving small protein complexes, is connected to larger pathways and to cellular functions assigned from a bank of artificial neurons. The input layer is the mutation status from genomics data and the output corresponds to the state of the whole cell based on the genotype. The second branch of the system is a CNN assessing the fingerprint of a drug. The output from the two branches of the model is combined in a single layer of neurons integrating the response of a genotype to a certain treatment. To test on the first side of the model, they used two large drug screening resources: the Cancer Therapeutics Response Portal (CTRP) v2 and the Genomics of Drug Sensitivity in Cancer (GDSC) database. Using these two resources, it covers 684 drugs and 1,235 cell lines. Each cell-line genotype was represented with a binary vector (mutated or non-mutated). The chemical structure of the drug was represented by an average of 81 activated bits in the Morgan fingerprint vector. Drug cell was trained to associate each genotype-drug paired with its corresponding drug–response curve. As a last point, drug cell was clinically tested stratify cancer patients depending on their response to treatment. They tested drug cell on clinical trial data from 221 estrogen receptor positive metastatic breast cancer patients and showed that the model was able to predict the response to mTOR and CDK4/6 inhibitors. Drug cell also includes a feature for predicting the sensitivity to drug combination .In a number of studies, DNN was used to predict prognoses. As an example, DeepSurv is a tool defined as a prognostic model. With the integration of time-dependent and treatment-sensitive survival data to the model, it was trained to provide treatment recommendations. DeepSurv was tested on four real-life datasets: Worcester Heart Attack Study (WHAS), the Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (SUPPORT), the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and Rotterdam & the German Breast Cancer Study Group (GBSG). DeepSurv takes input as the patient’s baseline data. The output is composed of one node estimating the log-risk function in the Cox model (estimation of the effect parameters without any consideration of the hazard function). As the second part, it uses a treatment recommender system. This system takes into account a group of patients assigned to a specific treatment group to predict the log-risk of a given treatment. Using the same base line hazard functions, it is possible to calculate the personal risk-ratio of prescribing one treatment and then, determine a treatment recommendation algorithm. To assess the possibility of a treatment recommendation system, DeepSurv was trained on the Rotterdam tumor bank to build the recommendation system that was then tested on GBSG. The treatment recommendation system demonstrated that, on a real clinical dataset, the patient’s survival would be increased by following the treatment recommendation of the algorithm. In comparison, a random survival forest-based recommendation system was not able to improve the patient’s outcome . To concOther examples of machine and deep learning models applicable for biomarker discovery are detailed in a recent review articles devoted to this subject  Zhu et . So far,Novel fields of interest have emerged to unravel host–tumor interactions  through The host-tumor interaction comprises a succession of interrelated steps facilitating or refraining an efficient immune recognition of tumor cells. These steps are dependent of the tumor biology, the host ability to recognize the tumor, and the mechanisms that will promote immune inflammation or immune evasion. Briefly, the somatic mutations of tumors result in the expression of neoantigens that can be exposed to and recognized by the host antigen presenting cells (APCs). This will activate an immune cascade responsible for the activation and recruitment of effector T cells in the tumor . These pIFNγ) or INFγ gene expression profile signature (The identification of biomarkers was first made possible by immunohistochemistry (IHC) assay that has the advantage of spatial annotation but is limited by the sparse number of features that can be assessed. A classification in three IHC immune states for tumor infiltrated lymphocytes (TILs) density is usually admitted with immune inflamed (or “hot”), immune excluded, and immune deserted (or “cold”) tumors . Few biomarkers are so far broadly recognized. Some from the IHC like tumor infiltrated lymphocytes (TILs) density, mostly for CD8, and the protein expression of PD-L1 in the tumor. Other biomarkers derive from NGS test: tumor mutation burden (TMB) and the level of expression of interferon-gamma  method  in threeTumor types were unevenly distributed between the immune groups, and thus highlighting the association of the immune phenotype and the diagnosis. Furthermore, the immune subtyping was correlated with the prognosis, inflammatory subtype having the best clinical outcome while wound healing dominant subtype had the worse survival. The clinical impact was observed across cancers and within a same cancer type. This work demonstrates that access to NGS, well clinically annotated large tumor database, and immunogenomics integration help understand the interplay of host immune system and tumor cells. The immune landscape characterization can also improve tumor classification and prognostication as shown in this pediatric database.A systematic meta-analysis of tumor and microenvironmental biomarkers for ICB sensitivity has investigated the relative impact of independent and combined biomarkers. An immunomics analysis from bulked-tumor transcriptomics and genomics was performed on a dataset from seven cancer types from 1,008 adult patients (CPI1000+) treated with immune checkpoint blockade (ICB) . The goaCXCL9 expression, a CD8 attractive chemokine, were the strongest predictors for ICB response, followed by CD8A expression, T-cell inflamed INFG gene expression, and CD274 expression. Due to the high prevalence of TMB in the literature, researchers decided to subdivide the somatic mutation by mutations fitting in an immunogenic signature. Some signatures like dinucleotide variants, that are source of amino-acid changes and generate immunogenic epitopes, ultraviolet (UV), or tobacco mutation signatures also came out as significant determinant of ICB response. In somatic copy number analysis, two copy number anomalies were identified as positive or negative predictor of response. Surprisingly, host factors like the loss of HLA heterogeneity or HLA subtypes did not show any impact on ICB sensitivity. They also performed single-cell RNA sequencing of a reactive CD8 TILs from patients to identify T-cell intrinsic markers of ICB sensitivity and highlighted CXCL13 and CCR5 gene involvement. The immune cell enumeration or BCR/TCR clonality assay were not assessed in this study.In univariate analysis, clonal and total TMB and CXCL9 expression had a better prediction accuracy than clonal TMB alone, yet inferior to the multivariate model. The integration of multiparameter biomarkers could explain approximatively 60% of the response to ICB in the different cancer types.A machine learning model for multivariate analysis including the significant determinant of response was a better predictor of ICB response than TMB alone in the initial cohort and three external independent validation datasets. Also, a two-parameter biomarker model combining clonal TMB and This study showed that multilayers integration of immunogenomics data and multiparameter models can improve the prediction of biomarker to anticipate the response to immunotherapy. It is open the opportunity to mechanistically solve host-tumor interaction and to understand how ICB remodel the tumor environment.IFNG, MZB1, JCHAIN, and IGLL5) in ICB responders. Using TRUST-algorithm for BCR clonotype enumeration, they observed an increase of clonal counts for heavy and light chains and in BCR diversity in responders. MCP-counter deconvolution algorithm also confirmed the enrichment of B cells in responders compared to non-responders. To confirm the role of B cell in ICB activity, they used IHC to assess B-cell density and to interrogate the spatial repartition of B cell and they performed functional study. The IHC confirmed an increase in B-cell density in responders and revealed a spatial organization in tertiary lymphoid structure (TLS) where CD20+ B cells colocalize with CD4+, CD8+, and FOXP3+ T cells. A previous study similarly showed a correlation between B-cell signature and increased the expression of CD8A and CD8+ T-cell infiltration  that is abundant and specific to each species. The 16 S rRNA genes are isolated and amplified from microbiome containing samples and then sequenced for species characterization. The downside of this technique is the need of high accuracy in the primer for amplification . A seconClostridium species) . In murine model of melanoma, it was shown that the use of different antibiotics decreases the response of PD-1 treatment compromising its tumor effect . The samspecies) . A recenspecies) . They dereatment .The role of microbiomics for modeling the host-tumor interaction and the immune response to cancer is now established, however, microbiomics has not yet been routinely implemented in multi-omics analysis but is probably a key element of cancer biology. Considering the high plasticity of the microbiome constitution, longitudinal analysis of the flora should be preferred . Most imAs we have shown, combining multiple biological elements of the tumor and of the host environment is an extremely powerful way to anatomize the TiME and to discover new biomarkers. However, there is no standardized method for immunomics and most of the computational integration tools are home made. To improve the generalization of the observation and to accelerate the possibility of patient selection for personalized therapy, standardization of the method is urging.In that sense, an available R package for computational tool for immuno-oncology biological research (IOBR) has been developed to comprehensively combine and interpret multi-omics data in the context of immuno-oncology . IOBR haFuture directions to fully characterize the immune environment and its dynamic would be to implement microbiomics data to such model and to construct longitudinal models with the introduction of time dependent models . The incCancer is a complex and highly heterogenous disease that is yet partially solved. Despite the molecular characterization made possible by the advances of NGS technologies, many of the underlying oncogenic mechanisms remain puzzling. The integration of the multilayers of the omics elements is an avenue to further elucidate cancer biology. In this review, we highlighted the challenges of computational modelization of large multi-omics datasets and we provided some clues for how to overcome these barriers. We presented some innovative bioinformatic tools developed to enlighten the implication of all the interconnected biological elements of cancer and showed how determinant they are to refine the disease subtyping and classification, and to discover biomarkers. Multi-omics integration has also heightened the field of immunomics and microbiomics, and thus has dramatically accelerated the identification of robust biomarkers for ICB efficacy toward the development of tailored immunotherapy.The constant modernization of the models endows analyses of increasingly larger datasets with a growing number of components. Collaborative effort for high-quality and clinically well-annotated databases, combining all the elements of high-throughput sequencing, is crucial to feed the models. Finally, the standardization of the methods would aid in replicating and confirming the results to increase the global knowledge and, ultimately, improve cancer treatments.


Teaching quality evaluation (TQE) is an important link in the process of school teaching management. Evaluation indicators and teaching quality have a complicated and nonlinear connection that is influenced by several variables. Some of these drawbacks include too much subjectivity and unpredictability, difficulty in defining index weights, sluggish convergence, and weak computer capacity. The current assessment techniques and models have these issues as well. This research uses an ANN model to assess the quality of mathematics instruction at colleges and universities (CAU) in order to address the challenging nonlinear issue of TQE and completes the following tasks. (1) The background and significance of TQE research are analyzed, and the domestic and foreign research status of TQE and neural network is systematically expounded. (2) The technical principle of DNN is introduced and the DDAE-SVR DNN model is constructed, and then, the evaluation index system of mathematics teaching quality is constructed. (3) The DDAE-SVR DNN model is put out as a potential alternative. The Adam method is used in the unsupervised training process to dynamically modify the learning step size for each training parameter. The spatial properties of the original data may be modified several times such that the reconstruction can be completed after many hidden layers have been applied. Data essentials such as precision, accuracy, and consistency are prioritized above all other considerations when generating the final product. Unsupervised prediction uses SVR and maps the complicated nonlinear connection into high-dimensional space in order to attain linearity in low-dimensional space, which is the goal of the supervised prediction process. The usefulness and benefits of the model provided in this research in the mathematics TQE may be verified by tests including the use of the TQE dataset and comparisons with other shallow models. On the other hand, the excitation function of the BPNN is a sigmoid function, and the derivative of the function changes in  in a large definition domain. The normalization of the sample data helps the network to converge as soon as possible, thereby improving the computational efficiency of BPNN. The method used in this article is as follows:This research uses data from a university's educational administration system to compile an assessment dataset for math courses taught between 2012 and 2018, totaling 1685 samples. Data gathered from students' evaluations of a teacher's teaching process is utilized as an input value in the assessment model. In order to ensure model verification, the comprehensive score is employed as the model's goal predicted output value based on the numerous lecture recordings of teaching supervision group instructors. A total of 1520 sample data were collected after analyzing the dataset and removing the data with high, low, and inconsistent evaluations. The resultant data samples are then subjected to normalization preprocessing.n), actual value of the data (pi), and its model's anticipated value (ri). As part of the DDAE, a new method known as the Adam algorithm is implemented, which minimizes output data error while preserving as many of the original data's key properties as possible. In order to increase the model's accuracy in making predictions, the key SVR parameters are tweaked in the supervised output layer. Performance comparison measurements are used to compare this model to other shallow models. Model assessment and prediction accuracy are evaluated using MAPE, MSE, SMAPE, and RMSE in this chapter's DDAE-SVR DNN model, which gives more advantages than other models in terms of mathematical quality evaluation. These are the formulas they use. This includes test data's sample size . The distribution of sample data in high-dimensional space is controlled by the kernel function type. As a result, improving SVR's prediction efficiency and accuracy requires tweaking these three key parameters. Kernel function types include linear, polynomial, sigmoid, and radial basis functions; the error penalty coefficient range is set at , v = 0, in light of the sample dataset's size. Unsupervised training is the only way to uncover the last layer of information. As part of the DDAE-SVR DNN model's prediction assessment, the output feature vector is utilized as an input feature vector for DDAE-SVR DNN model's output feature layer. The SVR is then used to evaluate prediction accuracy. (2) Final hidden layer neurons may be acquired using DDAE-SVR DNN model's unsupervised learning and training approach, which reduces the error between the output data reconstructed from final hidden layer and original input data. For the model prediction output layer, a feature vector is utilized as an input. The model's final output prediction layer evaluates and predicts the TQE using the SVR prediction period. In terms of SVR performance, error penalty coefficient The DDAE-SVR DNN model uses a range of 21 to 25 neurons in each hidden layer to arrive at the final number. Unsupervised learning is used in conjunction with two hidden layers and an optimization strategy in the Adam approach to build the model. Each time an evaluation sample dataset is introduced into the model during training, the hidden layer's number of neurons is modified to account for the changes. By training the DDAE unsupervised, the DDAE is able to rebuild the error curve between output data and the original input data, as illustrated in v, the training error parameter, is controlled by a polynomial kernel function, and an error penalty coefficient of 1 is used in this DNN model. The optimal value for  may be used to regulate the number of support vectors and training error. We use a feature vector from the model's final hidden layer neuron that was trained without supervision to acquire the input feature vector for SVR. MAPE and MSE values both exhibit a decreasing and subsequently increasing pattern with respect to time as seen in v is 0.3, this chapter's model has the least prediction error and the highest accuracy.According to this chapter's hypothesis, the DDAE-SVR DNN model provided in this chapter has a better TQE than other models; hence, this chapter optimizes the model's parameters. For comparison, three models of the conventional BPNN, SVM, and adaptive BPNN were built. To train and evaluate the model, input the evaluation sample dataset and denormalize the prediction and evaluation outcomes. Comparisons are made using performance measures like MAPE and MSE. High-quality talent is the primary aim of higher education, and teaching quality is an essential way of achieving this goal. The TQE at CAU is a crucial strand in the web of educational administration. Every day, college professors are evaluated on their ability to teach in an objective and fair manner using objective, scientific methods. It is a useful tool for assessing how well the school's teachers are doing their jobs. In order to increase the quality of instruction, managers may learn from teachers' classroom activities how well their teaching strategies are working in practice. However, TQE is mostly a nonlinear classification problem. It is a multifactor complex system with qualitative and quantitative indicators. Because the indicators are multilevel and complex, it is hard to put a finger on it using a predetermined mathematical model. TQE makes use of a number of well-established assessment techniques, including the weighted mean method, AHP, and the fuzzy comprehensive judgment approach, among others. Although some achievements have been achieved, there are still many defects, such as lack of self-learning ability, and it is difficult to make accurate evaluations. When determining the weight of each evaluation index, it is often estimated by experience, which leads to the subjective evaluation and cannot solve the problem well. Therefore, this paper builds a DDAE-SVR DNN model to evaluate the quality of mathematics teaching and completes the following tasks. (1) The background and significance of TQE research are analyzed, and the domestic and foreign research status of TQE and neural network is systematically expounded. (2) The technical principle of DNN is introduced and the DDAE-SVR DNN model is constructed, and then, the evaluation index system of mathematics teaching quality is constructed. (3) The DDAE-SVR DNN model is put out as a potential alternative. The Adam method is used in the unsupervised training process to dynamically modify the learning step size for each training parameter. The spatial properties of the original data may be modified several times such that the reconstruction can be completed after many hidden layers have been applied. Data essentials such as precision, accuracy, and consistency are prioritized above all other considerations when generating the final product. Unsupervised prediction uses SVR and maps the complicated nonlinear connection into high-dimensional space in order to attain linearity in low-dimensional space, which is the goal of the supervised prediction process. The usefulness and benefits of the model provided in this research in the mathematics TQE may be verified by tests including the use of the TQE dataset and comparisons with other shallow models.Лысенко


A 44-year-old male presented to the department of physiotherapy with the primary concern of inability to fully straighten his right elbow. As per the history given by the patient, he had sustained a fall on an outstretched hand 10 months ago. He had sustained a fracture around the elbow, which was managed conservatively with plaster cast for six weeks by a local practitioner. He had pain and swelling around the elbow after the injury, but it had subsided two months ago. On examination, terminal 30 degrees of the range of motion (ROM) of right elbow was limited. Although, forearm supination-pronation was full. The X-ray revealed bone chip at the anterior aspect of the distal humerus, along with supracondylar fracture of the humerus that was not completely healed. Bone chip can be differentiated from myositis ossificans, which is a common complication of fractures around the elbow, by its well-defined margins. As a part of physiotherapy intervention, gentle active ROM of right elbow and functional use of right hand for light activities of daily living was emphasized. The patient was advised to avoid use of heat, massage and jerky movements of elbow as these can lead to myositis ossification. He was assured that he had functional ROM of 30 degrees to 130 degrees elbow flexion, so lack of terminal extension should not bother him in his lifestyle.


The aim of this article is to describe a novel approach combining muco-gingival, regenerative and prosthetics concepts for immediate implant insertion that overcomes the limits traditionally considered as contraindications for Type 1 flapless implant positioning, simultaneously obtaining soft- and hard-tissue augmentation. After pre-surgical CBCT evaluation, the surgical technique consisted in the execution of a lateral-approach coronally advanced envelope flap, with oblique submarginal interproximal incisions directed towards the flap’s center of rotation (the tooth to be extracted); after buccal-flap elevation, the atraumatic extraction of the tooth was performed. Following guided implant insertion, a mixture of biomaterial and autologous bone was placed, stabilized by a pericardium membrane and a connective-tissue graft sutured in the inner aspect of the buccal flap. The peri-implant soft tissues were conditioned with a provisional crown until the shape and position for the mucosal scallop to resemble the gingival margin of the adjacent corresponding tooth were obtained; then, the definitive screw-retained restoration was placed. Within the limitations of this case report, the proposed immediate implant placement approach combining CTG application and buccal bone regeneration showed the possibility of obtaining 1-year-follow-up implant success, stable bone level, good esthetic results and high patient satisfaction. Implant placement into the socket during the same surgical session as tooth removal can be classified as immediate . While mThis approach is described as Type 1 and is considered a complex procedure according to the SAC Classification (straightforward (S), advanced (A) and complex (C)) ; therefoTherefore, despite the survival rate of post-extraction implants in the esthetic area is high, there is also a very high risk of mucosal recession, which recommends a careful case selection. The ITI  stated that IIP Type 1 is considered the treatment of choice as a flapless procedure in sites with ideal anatomical conditions, such as an intact facial bone wall with a thick wall phenotype (>1 mm) and a thick gingival biotype . Under tThe aim of this article is to describe a novel approach combining mucogingival, regenerative and prosthetics concepts for immediate implant insertion that overcomes the aforementioned limits traditionally considered as contraindications for Type 1 flapless implant positioning.A 40-year-old patient presented with an endodontic fistula on the maxillary right canine a–c. RadiPrior to the surgery, CBCT was requested in order to plan a fully guided implant insertion.The mean axis of the implant coincided with the most palatal extension of the cuspid of the canine of the predetermined final restoration. During planning, it was decided to use a Prama implant with a short neck of 1.8 mm, a diameter of 3.8 mm and a length of 11.5 mm. The apical-coronal planning of implant placement was chosen in relation to the esthetically ideal position of the buccal soft-tissue margin of the final restoration; the smooth implant collar was 2 mm apical with respect to the ideal position of the buccal soft-tissue margin of the final restoration. In fact, in an apico-coronal direction, the endosseous implant portion is positioned 3.5–4 mm apical with respect to an imaginary horizontal line that is placed at the same level of the gingival margin of the natural homologous tooth, which always represents the reference point for implant positioning. The use of transmucosal implants  is useful for displacing the implant–abutment connection away from the bone crest. This reduces bone resorption and makes hygiene maintenance easier both for the patient and the hygienist.The buccal bone wall was extremely thin but partially maintained with 2 mm of buccal bone dehiscence a,b.The surgical technique consisted in the execution of a lateral-approach coronally advanced envelope flap, which requires oblique submarginal interproximal incisions directed towards the flap’s center of rotation  a,b; the The implant site was prepared in a fully guided manner, using drills of progressively larger diameter, so that at the moment of guided implant insertion, the fixture could reach the pre-established position with enough primary stability to consent immediate provisionalization (35 Ncm) a,b. The Cerabone; Botiss) and autologous bone was placed to cover the exposed implant threads and the thin layer of remaining buccal bone and to fill the gap between the implant surface and the socket wall  placed between the bone particles and a connective-tissue graft . First, we sutured the weakest papilla  before placing the provisional crown ; this siAfter the complete closure of the papillae, a final sling suture suspended around the cingulum of the implant-supported crown  was performed to obtain a tight adaptation of the flap’s keratinized tissue into the convex, smooth surface of the provisional, a prerequisite for the stability of the surgical wound and for minimizing the risk of flap shrinkage. The absence of blood seeping from the margins is a sign of surgical wound stability, and this can minimize the contraction of the coronally advanced flap. At the end of surgery, an X-ray scan was performed .As shown in One year after final restoration placement, the clinical outcome remained stable and fully satisfied the patient’s esthetic demands a–c. The A novel muco-gingival approach for immediate implant placement (MIIP) was adopted in the present study to simultaneously obtain soft- and hard-tissue augmentation. The described case, due to the thin phenotype, the presence of a vestibular bone dehiscence and a residual wall thickness <1 mm, represents a contraindication for IIP , and traRecently, a paper evaluating the esthetic outcomes of immediate implant placement with immediate provisionalization in the anterior maxilla with buccal dehiscence  concludeMoreover, a recent systematic review concluded that when an elevated risk of mid-facial recession is expected in the aesthetic zone following IIP , the adjunctive use of a CTG contributes to mid-facial soft-tissue stability .Bearing in mind these new concepts, the presented muco-gingival approach aimed to overcome the factors traditionally considered as contraindications for IIP and immediate provisionalization.IIP is usually intended in a flapless way, aiming at reducing the surgical trauma and the risk of buccal soft-tissue dehiscence occurrence.In the MIIP approach, flap elevation is crucial and associated with a variety of advantages.After raising a flap, tooth extraction is easier and can be performed with less risk of trauma to interproximal soft tissues. Better visibility makes it easier to distinguish between areas in need of bone reconstruction or soft-tissue augmentation and areas that do not need any intervention. In the presented case, a small buccal dehiscence (≤3 mm) was treated with a mixture of biomaterial and autologous bone with a resorbable pericardium membrane whose main objective was to give stability to the bone graft in the absence of the buccal plate. Excessive amounts of biomaterial/autologous bone, which could impinge on the supracrestal implant portion, should be removed, leaving the bone graft only at a level where buccal bone reconstruction is predictable before placement of the connective-tissue graft.Moreover, flap elevation is followed by a coronal advancement that makes it possible to achieve the complete coverage of the most coronal-correct positioning of the connective-tissue graft and compensate for flap shrinkage.The most coronal placement of the graft  was chosen to compensate for future tissue contraction and to have, once the tissues healed, buccal soft-tissue margin that was coronally positioned in relation to the gingival margin of the contralateral tooth and that could be prosthetically conditioned in order to obtain a mucosal margin of the implant crown that was at the same height and had the same morphology of the gingival margin of the homologous tooth. The graft was sutured to the flap’s internal surface at the base of the surgical papillae in a paramarginal position with internal horizontal mattress sutures, keeping the anatomical de-epithelialized papillae free from any interference, making first intention wound healing with the surgical papillae of the coronally advanced flap possible. Furthermore, this graft stabilization allowed us to extend the mesial distal dimension of the graft that could be extended to include the width of both papillae. This allowed us to increase the buccal lingual thickness of the interproximal soft tissue, which prevented early papilla shrinkage and furthered papilla growth in height during the conditioning phase with the temporary crown. Suturing the graft to the flap’s internal surface does not preclude the graft from acting as a membrane for the stabilization of biomaterial/autologous bone whenever said bone graft is not completely protected by the alveolar bone due to the presence of bone dehiscence. Guided implant placement plays another fundamental role in MIIP. Firstly, it limits possible error in implant positioning, which represents the main predisposing factor for soft-tissue dehiscence occurrence ,24; secoMoreover, a provisional crown rather precise provided by technicians based on pre-surgical planning can be easily relined and be ready at the end of surgery before suturing the flap, adapting it to the convex buccal surface of the temporary crown, thus allowing flap stability to be obtained.Finally, it should be taken into consideration that post-extractive implants are the first-choice treatment for patients that require tooth extraction in the esthetic area only when the clinical conditions are suitable for the immediate provisionalization of the implant. In fact, the esthetic advantages are mainly related to the ability of the provisional crown to support the soft tissues, preventing their collapse and shrinkage. In the presence of tooth extraction for periodontal reasons, this approach should not be taken into consideration for two main reasons: firstly, periodontal disease must be treated prior to implant tooth replacement, and secondly, even in the rare case of a localized form of periodontitis that affects only one tooth (sporadic loss of attachment), if the extent of bone loss excludes the possibility of performing regenerative periodontal therapy, this is a significant reason not to place a post-extractive implant with immediate provisionalization .Within the limitations of this case report, the proposed MIIP, immediately temporized through combining CTG application and buccal bone regeneration, shows the possibility of obtaining 1-year-follow-up implant success, stable bone level, good esthetic results and high patient satisfaction. Controlled randomized clinical trials with longer follow-ups are advocated to confirm the efficacy of this approach.


The broad availability and low cost of smartphones have justified their use for structural health monitoring (SHM) of bridges. This paper presents a smartphone application called App4SHM, as a customized SHM process for damage detection. App4SHM interrogates the phone’s internal accelerometer to measure accelerations, estimates the natural frequencies, and compares them with a reference data set through a machine learning algorithm properly trained to detect damage in almost real time. The application is tested on data sets from a laboratory beam structure and two twin post-tensioned concrete bridges. The results show that App4SHM retrieves the natural frequencies with reliable precision and performs accurate damage detection, promising to be a low-cost solution for long-term SHM. It can also be used in the context of scheduled bridge inspections or to assess bridges’ condition after catastrophic events. Investment in road and rail infrastructure has been extensive in most developed countries, and bridges have proved to be among the most vulnerable parts of these assets, as observed by the number of collapses and their dire consequences.Bridge owners use bridge management systems (BMS) to ensure a coordinated approach for optimizing costs, risks, serviceability, and sustainability of bridges. Initially, BMSs were simply inventories of basic information about bridges, such as construction date, location, and some structural details . Then, tFollowing the increased awareness of the limitations of visual inspection, in the last three decades, the non-destructive evaluation (NDE) and the structural health monitoring (SHM) fields have emerged to add more quantitative information into BMSs. NDE concerns the health assessment of a structure, or its components, through offline non-damaging procedures . On the In order to facilitate the automatic interpretation of monitoring data and damage identification, SHM has been posed in the context of a statistical pattern recognition (SPR), which is traditionally split into four stages: (i) operational evaluation, (ii) data acquisition, (iii) feature extraction and generation, and (iv) statistical modeling for feature classification. This paradigm has been rooted in the artificial intelligence field, where machine learning algorithms play an essential role for pattern recognition . Damage Despite the potential benefits of SHM for the authorities, it is widely recognized that bridge SHM is still not fully capable of producing reliable information on the presence of damage in a full-field monitoring strategy . AccordiIn line with the general digitalization trend, the broad availability and low cost of smartphones have justified the recent investment in their use for SHM of bridges. Smartphones contain built-in sensors, storage capabilities, processors, and broadband communication networks, such as the cellular 4/5G network, Wi-Fi, and Bluetooth. Built-in sensors include accelerometers, cameras, microphones, gyroscopes, magnetometers, Global Positioning System (GPS) systems, LiDAR sensors, etc. A summary of the smartphone-related sensor technology and its application in SHM is made in reference . This tyTwo main sensing strategies have been pursued, namely the use of a smartphone’s camera for displacement, deformation, and crack measurements through digital image correlation ,10, and Since 2015, several publications have reported the applicability of smartphones for SHM of bridges using vibrations measured from the internal accelerometers. Zhao et al.  showed tFeng et al.  performeUsing two smartphones in the laboratory, Ozer et al.  showed hMei and Gul  proposedShrestha et al.  implemenRecently, Ozer et al.  used mulIn conclusion, early studies assessed the applicability, precision, and resolution of smartphone vibration measurements and concluded that they are reliable in the frequency range of interest for bridges (<20 Hz).Previous attempts to harvest the capability of the smartphones to recognize and measure frequencies that lay in the effective frequency range have been reported. An effort which is relatively similar to App4SHM was reported by Ozer et al. in 2015 .This paper proposes a novel smartphone application for damage detection in bridges under ambient vibration conditions, making use of the built-in accelerometer and following the SPR paradigm. The application is not limited to cables, nor to frequency extraction, as was the case of the applications cited above. Instead, it includes a machine learning algorithm capable of recognizing damage after being trained with data from the undamaged structure. The application is first tested on data sets from a laboratory steel beam to verify the sensing and damage detection capabilities and then it is tested on two twin post-tensioned concrete bridges for damage detection. In both cases, the natural frequencies obtained from the application are compared with those estimated using data sets from a conventional data acquisition system.App4SHM is particularly well-suited to periodically monitor bridges under operational and environmental conditions. It was developed in a joint effort between the Civil Research Group and the Computer Science Department from the Lusófona University in Portugal. It can be applied to perform SHM or NDE during scheduled inspections, or to assess the structural condition after a catastrophic event, or when required by authorities and stakeholders. It may also be regarded as NDE technology integrated into the SHM of bridges. In the damage identification hierarchy, this application only addresses the first level, related to damage detection.App4SHM is an Android application already available in the Google Play Store to a limited group of testers, selected upon request after the application was presented at the 10th European Workshop on Structural Health Monitoring . We are The organization of this article is as follows: besides this section, App4SHM  is an apStructure Identification (Step 1)—identification of the structure from a structural database. A new structure can be added via server;Data Acquisition (Step 2)—measurement of acceleration time series in the direction orthogonal to the screen of the smartphone  for a usFeature Extraction (Step 3)—estimation of the first three natural frequencies from the power spectral density of the accelerogram (using the Welch method). They are stored into a feature vector (observation);Damage Detection (Step 4)—for each new observation, a damage indicator is computed based on the Mahalanobis-squared distance . SubThe application is composed of two software components: a mobile application and a computational platform hosted by the server. The mobile application is developed in Kotlin and is compatible with devices running Android 10 operating system (or higher). It implements a navigation mechanism with four tabs  since the backoffice algorithms implemented on the server need not be changed.The application is linked with a Python/Django server through REST webservices to get existing structures, perform calculations, and save the measured structural frequencies in a MySQL database . As smartphones have imperfect sampling frequencies , it is nThe mobile application sends the acceleration data to the server, using a webservice, to extract frequencies. The frequency extraction process is comprised of two parts. First, a power spectral density analysis is done in the server using the Welch algorithm. For this purpose, the measurement interval is divided into a number of overlapping intervals. Both the number of intervals (default = 3) and the overlapping time  can be controlled programmatically. The power spectral density returned to the user is the average of the spectra for each of the intervals. Then, the power spectral density plot is sent back to the smartphone and shown in the mobile application see c. The spDI), which is transmitted to and shown in the mobile application  that that27])As proposed by Figueiredo et al. ,25 in thz is obtained from data collected on the damaged system, the observation will be further away from the mean of the normal condition. On the other hand, if an observation is obtained from a system within its undamaged condition, even with operational and environmental variability, it will be closer to the mean of the normal condition.The assumption is that if a new observation z is extracted from an undamaged condition and it is multi-dimensional Gaussian distributed, then its m degrees of freedom, DIs above a certain level of confidence.Herein, for feature classification, it is assumed that if an observation Two key questions regarding the use of smartphones for SHM can be posed as follows: is the (poor) sensitivity of their accelerometers enough to endorse the correct calculation of the frequencies needed for damage detection? And how large should the damage be to be recognizable by the application? This section firstly assesses the quality of the smartphone’s measurements in terms of accelerations, power spectral density, and natural frequencies. Subsequently, the damage detection capabilities of App4SHM are evaluated.A simply supported steel beam was used to compare both the time series and the power spectral densities obtained using App4SHM and a conventional data acquisition system.The steel beam, illustrated in The steel beam was subjected to non-destructive forced vibration tests using an impact hammer, a conventional sensing system composed of two piezoelectric uniaxial accelerometers from PCB Piezotronics, Inc. , and a smartphone. The two piezoelectric accelerometers, with a sensitivity of 10 and 0.5 V/g and a measurement range of ±0.5 and ±10 g, were fixed on the underside of the steel plate by means of an auxiliary base, which was glued to the steel structure using a quick bonding gel. The comparative study was first carried out in terms of the first three natural frequencies, which were computed resorting to a peak-picking method on a power spectral density determined using the Welch algorithm. The accuracy of the natural frequencies is instrumental for the functioning of the damage detection algorithms. Next, in order to conduct the remainder of the comparative study, two of the 30 non-destructive tests were chosen. These tests correspond to those that deviate the most and the least from the average in terms of natural frequencies. The time series and power spectral densities, determined using the two accelerometers and the smartphone, were compared both qualitatively and by means of time-lagged cross-correlation. The latter allows one to automatically synchronize the conventional sensing system and smartphone’s time series and determine the maximum cross-correlation between them. It is noted that, unlike the correct estimation of the natural frequencies, the good correlations between the power spectral densities and (even more so) between the acceleration time series are not instrumental for the damage detection algorithms. However, the analysis of the correlation between the measurements obtained using the conventional sensing system and the smartphone supports a more complete perspective of the limitations of the latter as compared to the former.The first three natural frequencies for the 30 time series are estimated and presented in Additionally, a correlation test was made to verify if the natural frequencies computed resorting to the conventional accelerometers and smartphone are linearly related to each other. Due to the strong positive correlation observed for the same frequencies, one may conclude that an excellent agreement between the natural frequencies computed using the smartphone and those determined using conventional sensing systems was attained.p-values of 0.77, 0.99 and 0.68, respectively. Therefore, since a p-value larger than 0.05 indicates a valid null hypothesis , one may conclude that the frequencies computed using the reported sensing systems are statistically similar.The ANOVA test is commonly used to analyze the differences between the means of several populations based on their variances. It was used in the present study to verify the similarity between the average frequencies attained with different accelerometers. Through the comparison of the values attained for the first, second, and third natural frequencies, the ANOVA test determined Although the main output of App4SHM is the indication of damage based on the first three natural frequencies of a given structure, a more in-depth analysis is made in terms of the recorded time series and power spectral densities. The following two subsections present the results.Due to the impracticality of testing all the available time series, and due to their similarity in terms of natural frequencies, only two sets of time series were chosen for further analysis. These sets, which are highlighted in Therefore, the time series recorded by the smartphone is only compared with one of the conventional sensing systems, namely the 333B40 accelerometer, due to its larger measurement range.The entire set of time series obtained during the experimental study was used to compute their respective power spectra, which were determined using the Welch algorithm. Additionally, An additional experimental campaign was performed to evaluate the damage detection capabilities of App4SHM. One hundred dynamic impulse tests were conducted on the simply supported steel beam presented in Subsequently, three different masses , defined by Equation (1), as a function of the added mass and its location is presented in DI, and the vertical lines indicate the ± standard deviation range. The DI threshold that separates undamaged from damaged data is shown as a horizontal line. The lowest added mass tends to yield higher DIs than the training data, but they always remain below the threshold. The 50 g mass is recognized as damage in most cases, especially when the mass is located at mid-span. However, the threshold is located at less than one standard deviation from the mean, so it is expected that the Mahalanobis-based algorithm would yield a considerable number of false negatives. Finally, the highest mass has a marked impact on the DI, with the measurements always located more than two standard deviations higher than the threshold. This means that false negatives should very seldomly occur for this level of added mass. Lastly, as expected, placing the mass at mid-span leads to larger DIs as compared to those attained with the mass at one quarter of the span.The evolution of the damage index  were estimated through ARTeMIS Modal using the enhanced frequency domain decomposition (EFDD). A comparative study was conducted resorting to measurements in section 2 see  of the nAs reference, three time series with 15 min duration each were obtained using the traditional sensing system (GeoSIG). For later comparison, three natural frequencies were estimated and are summarized in The natural frequencies estimated using App4SHM are based on six time series of 20 s duration each obtained in the same one-hour time window . The acqComparing the means of both sensing systems and taking GeoSIG as reference, one can conclude that the App4SHM outputs consistent results in terms of natural frequencies . new bridge over the course of one year  were included in the training data set of the Mahalanobis-based machine learning algorithm. Each observation is composed of three natural frequencies  was found. Comparing the average frequencies obtained resorting to the smartphone with those determined using the conventional sensing system, a maximum difference of 0.54% was obtained. The lab beam was also used to assess the sensitivity of App4SHM to damage, simulated through the addition of three different weights at two locations on the structure. It was concluded that damage becomes detectable when at least one of the monitored frequencies differs in more than two standard variations from its mean value under undamaged conditions.The analyses conducted on two twin concrete bridges show that in terms of feature extraction, App4SHM outputs natural frequencies that are consistent with those obtained using conventional accelerometers (differences < 2.42%), even using short time-series durations, and within the variability observed with the traditional sensing system. Moreover, App4SHM correctly identifies the existence of outliers when observations from one of the bridges are fed into machine learning algorithms trained with data from the other  bridge.The results from the two case studies show that App4SHM is reliable for both vibration measurement and damage detection in the context of SHM. However, as is the case with all individual components of the SHM, App4SHM is not meant to be used unaccompanied, but integrated into a Bridge Management System, along with complementary sensors and NDE.The app and the sensing technology of smartphones still have some limitations. However, the laboratory and field tests have shown certain applications where we can obtain a reasonable trade-off between the potential lack of sensitivity and the advantages provided by the ease of installation and data acquisition as well as wireless transmission.


In this work, a framework is proposed for decision fusion utilizing features extracted from vehicle images and their detected wheels. Siamese networks are exploited to extract key signatures from pairs of vehicle images. Our approach then examines the extent of reliance between signatures generated from vehicle images to robustly integrate different similarity scores and provide a more informed decision for vehicle matching. To that end, a dataset was collected that contains hundreds of thousands of side-view vehicle images under different illumination conditions and elevation angles. Experiments show that our approach could achieve better matching accuracy by taking into account the decisions made by a whole-vehicle or wheels-only matching network. Data fusion is a very challenging topic in machine learning that can provide complimentary information and enhance the understanding of data structures. Multi-modal data have become more widely available due to advances in sensor development. While data fusion is not a new topic, it has recently witnessed a considerable increase in the demand for foundational principles for different applications. There are three levels of data fusion that can be considered, namely, data fusion, feature fusion, and decision fusion. Data fusion involves integrating unprocessed data generated by each sensor. In feature fusion, features extracted from raw data, collected from various sensors, and combined to construct a better feature. Decision fusion, on the other hand, optimally combines the decisions reached by different algorithms or sensors to yield a more informed decision.The objective of this work was to develop a principled decision fusion framework for vehicle matching using convolutional neural networks (CNNs). The topic of vehicle re-identification has been studied extensively in the literature. In , the autIn this work, key features from vehicle side-view images and their corresponding wheels are extracted using Siamese networks. Pattern information specific to the wheels can often provide supplementary information about the vehicles in question; for example, two otherwise identical vehicles of the same make, model, and color, could potentially be distinguished if their wheels or hubcaps are different. Our dataset was collected under various illumination conditions and elevation angles. Individual similarity scores are combined, which are derived from features extracted from either the whole-vehicle images or from cropped images of their wheels. A principled integration of the individual scores is expected to enhance overall matching accuracy; thus, an overall similarity score is reached by a joint aggregation of the whole-vehicle and wheel similarity scores.The balance of the paper is organized as follows. In The topic of multi-modal data fusion has been extensively studied in computer vision. Laying out the fundamentals for data fusion has become crucial for many applications, including target recognition ,8,9, hanDecision fusion techniques can be classified on the basis of the fusion type. The most popular fusion type is voting-based, which includes majority voting, weighted voting, and Borda count, which sums the reverse ranks to perform decision fusion . Other vDeveloping real-time transportation technologies remains a crucial topic for safety, surveillance, security, and robotics. Decisions regarding traffic flow and surveillance need to be performed in real time to detect potential threats and act accordingly. Computer vision systems can be employed to automatically match and track vehicles of interest. In , the autThe primary contribution of this paper is a decision fusion framework for vehicle matching which aggregates decisions from two Siamese networks handling a pair of vehicle images and their detected wheels. Integrating the decisions helps reinforce the consistency between the outputs of the matching networks. In our evaluation, a recently collected dataset was used called Profile Images and Annotations for Vehicle Re-identification Algorithms (PRIMAVERA) , which hTo substantiate the validation of our proposed approach, a dataset that contains hundreds of thousands of side-view vehicle images was collected. As mentioned above, this dataset has been made publicly available . The datLicense plate readers were collocated with the sensors to provide a ground-truth identity for each collected vehicle. While license plates were used for ground truth, this approach to vehicle re-identification does not rely on a license plate but only a profile view of the vehicle. Actual license plate numbers were obfuscated by replacing each plate number with an arbitrary number that was subsequently used as the vehicle ID. Sample images from the dataset are depicted in The dataset contains 636,246 images representing 13,963 vehicles. Each vehicle has a different number of images depending on the number of collected video frames and how many times it passed by one of the sensors. Our dataset was divided into training and validation sets. The training set contains 543,926 images representing 11,918 vehicles, and the validation set contains 92,320 images representing 2045 vehicles.Our vehicle matching algorithm consists of two main stages. The first stage includes a whole-vehicle matching network, which compares a pair of vehicle images and generates a similarity score. The first stage also contains a wheel detector, which detects the outermost two wheels in each vehicle image and feeds them into the wheel matching network. This network compares the detected wheels and outputs wheel similarity scores. The second stage of the proposed framework is the decision fusion network, which combines the vehicle and wheel matching scores toward the goal of obtaining a more accurate and robust overall similarity score. The diagram of the proposed framework is depicted in i and T different modalities such that t denotes the modality index and t, a decision f is the matching function and zIn the following, our problem formulation is explained. Assume having two data points, The following section elaborates on how the neural networks are constructed for the proposed framework.The objective of the whole-vehicle matching network is to provide a similarity score between any pair of vehicle images. For this purpose, a Siamese network  was traiTo explore the contribution of leveraging finer features from the vehicle wheels to the performance of vehicle matching, a wheel detector was trained using a portion of the training data. In particular, the wheels were manually labeled and bounded for 4077 images using LabelImg program . AfterwaAs discussed earlier, the wheels can be utilized as an additional source of information; specifically, the aim is to successfully integrate the finer details from the wheel patterns into the overall vehicle matching task and thus achieve better performance in comparison to only using the whole-vehicle images by themselves. In order to match the wheels of each pair of vehicles, another Siamese network was trained to generate wheel similarity scores. The wheel detector that was developed was utilized to estimate the bounding boxes and crop the wheels from the vehicle images. The detected pairs of wheels were saved and later used to train this Siamese network. Our framework is evaluated and experimental results are shown in In this section, the efficacy of our proposed fusion method is investigated, and its performance is evaluated on the dataset.As explained in The second part of the network is the differencing phase, in which the output of one branch is subtracted from the other. The third and last component of the network is the matching network. This part of the network consists of six layers, and it operates on the differenced input to output a similarity score between 0 and 1. The structure of the matching network is listed in In our experiments, a binary cross-entropy loss function was utilized for training the network. A batch size of 256 was used for the training stage of the experiments. The network was trained for 100,000 steps, and the performance on the validation set was computed every 100 steps. In order to assess algorithm performance over the widest variety of conditions possible, a diverse subset of the validation set was constructed and used for validation. This subset contains more than 12,000 pairs of vehicle images that were collected at different locations at different times of the day and at different angles of elevation. Since the similarity score has a range of 0 to 1, a threshold of 0.5 was applied to evaluate matching accuracy. If the matching score is more than 0.5, it indicates that the two images belong to the same vehicle, and vice versa. The training and validation performance of the vehicle matching network is depicted in As the threshold is varied, the accuracy value can change; thus, picking an appropriate threshold for this metric is important. In order to evaluate the performance of the vehicle matching network with different threshold values, the true positive rate (TPR), true negative rate (TNR), and matching accuracy were computed as the threshold value was varied for the test set. Accuracy here is defined as the percent of total matches performed (including any number of positive and negative pairs) for which the algorithm produced a correct answer. The resulting curves as functions of threshold value are depicted in It may be of interest to note that the PRIMAVERA dataset consists of vehicle images that have been flipped, rotated, scaled, and shifted such that the front-most and rear-most wheels are always centered on the same two pixel locations in the image. Specifically, an image of size 234 × 234 × 3 was created such that the vehicle is facing to the right  and the rear and front wheels are centered at pixels An alternative to wheel locking that does not require a wheel detection step is to simply form an input image based on the bounding box returned by the initial vehicle detector algorithm. Due to the variability in how these bounding boxes are constructed, detected instances of a given vehicle may be shifted and scaled relative to one another, and any rotation of the vehicle due to sensor orientation will not be corrected.To investigate the utility of the wheel-locking preprocessing step, the validation set performance of the whole-vehicle neural network using wheel-locked images as input was compared to when non-wheel-locked images were used as input. In both cases, image intensities were normalized to have pixel values between 0 and 1. A comparison of training and validation set accuracy between the two preprocessing methods is shown in In this subsection, the results of the wheel matching network are shown. The wheel detector described in All the wheel images were resized to 100 × 100 × 3. Similarly to the vehicle matching network described in The network was trained for 40,000 steps with a learning rate of 0.002. The same dataset described in In From the figure, it can be seen that using a threshold value equal to 0.47 leads to an equal compromise between the error rates.In the following, the results of our decision fusion approach are shown. In this approach, first, all pairs of images go through the whole-vehicle matching network, which generates a whole-vehicle similarity score. Afterwards, the wheel detector described in As a baseline for decision fusion, the average of the three matching scores is computed. Taking the average of the scores has a reasonable chance of providing good results and is the basis of comparison for our deep fusion approach. In In addition, in In order to substantiate our reasoning, some illustrative matching instances are shown in To better explore the potential contribution of decision fusion in this scenario, a deep decision fusion network was constructed. Using both the vehicle and the wheel similarity scores, a multi-layered, fully connected network was trained that combines the scores and provides a final decision. This can be seen as combining the three scores by performing a smart weighted averaging of the decisions from the wheel and vehicle matching networks. Matching scores from the original validation set were split into training (70%) and testing (30%). The new training set had 6265 pairs of vehicles, and the new testing set had 2685 pairs. The network was trained for 500 epochs. The performance of the deep fusion network was tested on the new testing set that contained 2685 pairs of vehicles. The training accuracy was 98.47%. The performance of the decision fusion network was subsequently compared to the baseline, soft voting, and majority voting performances on the testing set. The 95% confidence interval was also calculated, and the results are depicted in In In this paper, a deep neural network approach was proposed for decision fusion to match pairs of vehicles through recovering feature signatures from vehicle imagery using Siamese networks. After training the vehicle and the wheel matching networks, the learned networks were then utilized to match new observed data. It was shown that leveraging pattern information specific to the wheels provided supplementary information about the vehicles in question. Experimental results showed a significant improvement in the matching accuracy after fusing the similarity scores from pairs of vehicles and their corresponding wheels’ signatures. In addition, our model performed well under diverse illumination conditions. Proposed future work includes investigating the use of vehicle imagery collected by drones for vehicle matching. Drone images may be able to provide supplementary information about the vehicles in question; for example, two similar vehicles could potentially be distinguished if their angled or elevated views reveal distinguishing differences. Moreover, on-demand data acquisition by drones would be more flexible than other traditional methods. In addition, additional sources of information could be fused, such as those describing other distinctive regions of the vehicle.