In this work, we propose the software library PyPore3D, an open source solution for data processing of large 3D/4D tomographic data sets. PyPore3D is based on the Pore3D core library, developed thanks to the collaboration between Elettra Sincrotrone (Trieste) and the University of Trieste . The Pore3D core library is built with a distinction between the User Interface and the backend filtering, segmentation, morphological processing, skeletonisation and analysis functions. The current Pore3D version relies on the closed source IDL framework to call the backend functions and enables simple scripting procedures for streamlined data processing. PyPore3D addresses this limitation by proposing a full open source solution which provides Python wrappers to the the Pore3D C library functions. The PyPore3D library allows the users to fully use the Pore3D Core Library as an open source solution under Python and Jupyter Notebooks PyPore3D is both getting rid of all the intrinsic limitations of licensed platforms  and adding, when needed, the flexibility of being able to integrate scientific libraries available for Python . Recently, the production of three dimensional (3D) images obtained by X-ray computed tomography (CT) experiments is gaining increasing interest by the scientific community. This is due to the big potential of this technique for the nondestructive characterisation of the internal microstructure of opaque objects.Over the last decade, the advances in both synchrotron light-based sources, as well as desktop CT imaging equipment, have lead to producing large 3D data sets while the acquisition time of synchrotron-based CT scans has significantly been reduced . This isAs far as data analysis software is concerned, performance optimisation requires working on three aspects: usability, efficiency and availability. From a software development perspective, till now, low-level languages, such as C, are the most favoured when it comes to performance. Furthermore, GPU computing  in recent years has enabled faster processing. However, being based on low-level programming languages, it is still challenging to program. Both approaches need sophisticated programming skills, which are obviously not suitable for the scientific community who prefer to rather focus on fast prototyping and testing of their highly evolving scientific ideas.This is one of the reasons that Python language has become popular among researchers since the user operates with high-level abstractions, while hiding most of the low-level commands . Hence, However, Python still cannot fit into many applications since, unlike C language, it has insufficient computational performance in many use cases. Recently, a trend has been adopted to mix the benefits of Python and C languages through hybrid frameworks. The design of these solutions keeps Python in the user front end, while modules that optimise the performance are implemented in C and work in the background. For this purpose, binding libraries have been introduced that wrap low-level codes and use them with Python code such as SWIG  and CythSeveral commercial software offering analysis tools for volume images have been available since many years, such as, e.g., VGStudio , Avizo aIn this paper, we introduce PyPore3D, an open source software framework developed by the SYRMEP collaboration  at ElettThe goal of the proposed software code is to provide an efficient solution to 3D morpho-textural analyses that can be easily performed on commodity hardware. This solution is designed such that it combines the simplicity of Python language and the performance of the C one, while being easy to customise. Besides, it can also be integrated with the analytical approaches that are highly adopted by the current scientific community, including data science, machine learning and visual analysis tools. It is worth mentioning that the current version of PyPore3D does not include built-in 2D/3D visualisation modules. However, PyPore3D, in principle, can be integrated with a third party visualisation and analysis software that supports python ;Providing easy means for pipeline customisation through a modular architecture.The PyPore3D package was developed for Linux and tested on Windows. In the following application examples, the reported results were generated on an Ubuntu 20.04.1 operating system (OS) installed on a workstation with an 8-Core processor and 16 GB of RAM. The performance results are shown for scripts that run on the Linux OS. The design of PyPore3D is based on two concepts:https://gitlab.elettra.eu/aboulhassan.amal/PyPore3D (accessed on 13 June 2022).PyPore3D source code can be found in: The architecture of PyPore3D is illustrated in The current package is customised by the specific know-how of the SYRMEP users collaboration while being highly modular. Therefore, it can inspire further customisation as well. As shown in The lowest layer represents the C code that is integrated with the PyPore3D pacakge. This layer currently includes the Pore3D C Core library proposed by Brun et al. ,27, as tThe C and Python packages are bound together through the C-Python interface. This interface is composed of cross platform scripts that transform the C code into binaries. The binaries are then called in intermediate Python files using the SWIG tool . SWIG isThe Python wrappers involve pure Python code developed specifically to wrap Pore3D in Python. These wrappers call the C-Python functions generated by SWIG. Besides, new code is developed to encapsulate them into more concise Python libraries that are easy to use and extend. For example, the new code includes help functions and function argument validations.Once the Python wrappers are called, they can be integrated with other Python toolkits. In the current library, we use intermediate files to communicate with different state-of-the-art packages such as Simple ITK  for multThe C code binaries are encapsulated into newly developed wrappers, which can be called easily from any Python editor or GUI, such as Jupyter Notebooks used in the current case studies.In this section, the main functionalities of the PyPore3D Framework are applied on examples of image analysis of different materials. There is no unique protocol for the extraction of quantitative information from 3D images since it highly depends on the data type and specific application.The PyPore3D design takes into account the need for flexibility in data processing and analysis by providing simple and flexible scripting tools for the required protocols. The user can follow his own strategies having a full control of the parameters of the applied algorithm guided by the documentation. The main functions used in the current examples are illustrated in https://gitlab.elettra.eu/aboulhassan.amal/PyPore3D (accessed on 13 June 2022).Jupyter Notebooks that implement the examples in this section can be downloaded from: The parametrisation of the morphology of objects in the 3D domain that constitute natural and artificial materials  is a key factor for their description, study and interpretation. Applications encompass biomedical and materials science studies and all the fields of geology including petrology, volcanology, sedimentology and related fields. In fact, retrieving the real morphology of objects reduces the uncertainty inherent to the extension of the 2D measurements to the third dimension by means of stereological methods  in propagation-based phase-contrast mode  of four Sample SSK36 is a basaltic andesite lava rock from the effusive activity of Skaros volcano . SampleAll the SRl., 2017 ). The exl., 2017 ,42,43. AThe adopted processing and analysis protocol is illustrated in https://gitlab.elettra.eu/aboulhassan.amal/PyPore3D (accessed on 13 June 2022).As shown in First, a 3D Median filter was applied to the selected volume of interest (700 × 700 × 700 voxels). Then, the pores within this volume were segmented using the 3D automatic Otsu method  followedIn Volume density (VV): quantifies the sample porosity. This is computed by the percentage of object voxels with respect to the considered volume.Specific surface area (SV): is a measure of the surface of the object with respect to the total volume.Mean curvature (MV): is an index of the dominance of convex or concave shapes.Euler characteristics  and Elongation (EA) indices. The I index is computed according to the mean intercept length method , while EThe results of the Basic Analysis are illustrated in Blob Analysis) module. This analysis is conducted on the same data sets presented in In this section, we illustrate an example application of the  data sets, the skeleton structure is computed. As shown in In this analysis, we applied the LKC skeletonisation algorithm, which was proposed in . The resIn this section, we show an example of the protocol applied to characterise a multi- phase material. Multiphase materials are commonly studied by the scientific community. They target phases corresponding to components with different chemical compositions, including mass density, structural or morphological properties. These components can correspond to different gray levels in the X-ray tomograms. In many applications, it is required to separate these phases in order to quantitatively analyse each of them.The sample used in the current case study is an Ottawa sand in brine at 0 degrees C. SRIn the current example, we show how the use of the Simple ITK functions—integrated with PyPore3D—can generate correct segmentation. The Simple ITK library implements several state-of-the-art segmentation methods such as k-means and multiple thresholding methods. We integrated these functions with the current PyPore3D wrappers of the Pore3D C code to allow users to integrate the C code in a simple script whose applied protocol is illustrated in Multiphase segmentation is explored in the current application using the k-means clustering algorithm . The impThe detailed adopted protocol is illustrated in In In this section, we investigate the implementation of the PyPore3D Morphometric Analysis module through an example of applications in biomedical data. This module can extract a series of analytical parameters, directly in the 3D domain, suitable for trabecular-like porous media such as the bone data depicted in the current case study.2). The sample was cut using a bandsaw along the axial direction right below the lesser trochanter  and the specimen were then stored at −25 °C until scanning. The central femoral neck core acquired using the SR3, in which the trabecular phase was present only, and thanks to the small isotropic voxel size . The donor was a female  distance was set at 150 mm. For each scan, a set of 1200 projections was recorded with continuous sample rotation over a 180-degree scan angle and an exposure time per projection of 1.0 s. Each set of acquired raw images was processed using the STP software suite. A single-distance phase-retrieval algorithm was applied to projection images before CT reconstruction [Propagation-based phase-contrast SRtruction , settingAn example of data processing results are displayed in BvTv: The ratio between bone volume and total volume;BsBv: The ratio between bone surface and bone volume;TbN: The trabecular thickness;TbTh: The trabecular separation of the solid phase objects;TbSp: Trabecular number measuring the number of traversals across a solid structure.The parameters computed in the Morphometric Analysis ,59 modulThe results of the analysis are reported in In the previous sections, we have reported several examples of tomographic image processing and analysis modules that are commonly used in materials science characterisation through X-ray imaging techniques. We also presented different domains of application such as geological and biomedical samples. The examples explored several processing functions such as filtering, segmentation and skeletonisation. In parallel, we also reported the computational time needed for the different operations that imply various levels of complexity and, based on the hardware used, limitations in data size.The corresponding performance in terms of computational time and data sizes, denotes the feasibility of running PyPore3D on modern commodity hardware with no need for special equipment. Some computations are more time-consuming such as skeletonisation for some data sets. However, there is room for further optimisation using parallel computing, for example, which benefits from mixing C and Python languages. Another aspect of performance is usability. The users can develop relatively complex analysis scripts with minimal programming effort. As we have discussed in the previous examples, the software is currently being tested on tomographic data sets of different specimens. For each data set, the user created a Python Jupyter notebook for every analysis protocol involving different modules from PyPore3D, SimpleITK Python wrappers, and plugins from other state-of-the-art Python packages such as Plotly.PyPore3D gives the users full control on the analytical protocol to be adopted allowing mixing of various types of functions in one script with no special licenses or setups as well as making use of many Python packages to reduce the analysis workflow by including several functions in one script. This solution demonstrates a substantial improvement in increasing the beamtime efficiency in terms of obtained results, and its highly modular concept could inspire further customisation and extensions from developers. It also provides an easy way to exchange analysis solutions and foster collaborations within the user community by sharing Jupyter Notebooks.In the future we plan to integrate advanced methods in the fields of machine learning and computer vision. These approaches can be employed to automate and fasten the segmentation and feature detection in large and complex data sets such as, for instance, in the case of dynamic tomographic acquisition . These domains are getting high importance in the scientific fields due to their power in automating the analysis and processing of big data. The related common tools currently developed by the scientific community are based on Python and PyPore3D enables the involvement of these tools in the relevant data reduction pipelines.


Mastitis is one of the greatest issues for the global dairy industry and controlling these infections by vaccination is a long-sought ambition that has remained unfulfilled so far. In fact, gaps in knowledge of cell-mediated immunity in the mammary gland (MG) have hampered progress in the rational design of immunization strategies targeting this organ, as current mastitis vaccines are unable to elicit a strong protective immunity. The objectives of this article are, from a comprehensive and critical review of available literature, to identify what characterizes adaptive immunity in the MG of ruminants, and to derive from this analysis research directions for the design of an optimal vaccination strategy. A peculiarity of the MG of ruminants is that it does not belong to the common mucosal immune system that links the gut immune system to the MG of rodents, swine or humans. Indeed, the MG of ruminants is not seeded by lymphocytes educated in mucosal epithelia of the digestive or respiratory tracts, because the mammary tissue does not express the vascular addressins and chemokines that would allow the homing of memory T cells. However, it is possible to elicit an adaptive immune response in the MG of ruminants by local immunization because the mammary tissue is provided with antigen-presenting cells and is linked to systemic mechanisms. The optimal immune response is obtained by luminal exposure to antigens in a non-lactating MG. The mammary gland can be sensitized to antigens so that a local recall elicits neutrophilic inflammation and enhanced defenses locally, resulting from the activation of resident memory lymphocytes producing IFN-γ and/or IL-17 in the mammary tissue. The rational exploitation of this immunity by vaccination will need a better understanding of MG cell-mediated immunity. The phenotypic and functional characterization of mammary antigen-presenting cells and memory T cells are amongst research priorities. Based on current knowledge, rekindling research on the immune cells that populate the healthy, infected, or immunized MG appears to be a most promising approach to designing efficacious mastitis vaccines. The MG is an ectodermal appendage giving rise to an epithelial organ the main function of which is to secrete a nutritious liquid food for the offspring. In ruminants, another important function is to provide the newborn, which is agammaglobulinemic at birth, with a crucial humoral immune defense through antibodies to relevant pathogens. As an organ that communicates with the body environment, the MG is exposed to invasion by microbial agents. The lactating MG is prone to infection by bacteria that can thrive in milk and during involution at cessation of milking. We can thus expect that evolutionary pressure has endowed the MG with adapted defenses against infections. Environment-exposed organs such as mucosa, the skin, and exocrine glands are protected from infections by the combined action of the innate and adaptive immune systems. The immune responses that protect epithelial surfaces present specialized characteristics that define the common mucosal immune system (CMIS) . Early iOver the last 50 years, a number of articles have dealt with the subject of adaptive immunity in the MG of ruminants. They used a variety of approaches: investigations of natural infections, intramammary inoculation of mammary pathogens, systemic or local immunization regimens, analysis of the humoral response and less frequently of the cellular response, or phenotypic and functional characterization of the cells in MG secretion or tissue. A synthesis of these sources of information is not easy due to the disparity of experimental approaches, the constant evolution of knowledge and concepts in a fast-evolving scientific field, sometimes contradictory results, and our limited knowledge of mammary immunity. Despite these shortcomings, it is possible to identify some clear features characterizing adaptive immunity in the MG of ruminants. These characteristics, interpreted and assembled according to current concepts in adaptive immunology, allowed us to propose a schematic vision of acquired immunity to MG infections. However, we did not address in this review the interactions of the adaptive immunity with the innate immune system. In particular, the interactions of lymphocytes with mammary epithelial cells have not been considered, although these interactions are crucial for the inductive and effector arms of cell-mediated immunity. Therefore, we have deliberately focused on immune responses dependent on memory T cells.S. aureus showed an increased reactivity of the MG upon repeated infections  than infusion with the soluble monomer  may not be able to induce antibodies because, contrary to soluble antigens , they are eliminated during the milking that follows the infusion  were removed prior to immunization , 85% were CD44pos and 80% LFA-1pos  have been found in the ductal epithelium of pre-pregnancy lambs with increased density with age , possibly dendritic cells, was present almost exclusively in the bilayer epithelium at the level of the Fürstenberg's rosette. DCs identified based on morphology, on low expression of CD14, and high expression of MHC II and CD11c or CD205 were found in alveolar and ductal epithelia and in the connective tissue (A few studies show that antigen-presenting cells (APCs) are present in the MG epithelium, suggesting that the MG provides a suitable environment to initiate an immune response, in particular during the dry period. Many stellate or spindle-shaped MHC IIing ewes . Numerou of ewes . In all e tissue . Most ofwith age . Infusio rosette . At thise tissue . Scarce e tissue , 49. Twoe tissue .Brucella into the lactiferous sinuses  of dry MGs in ewes induced higher titers of antibodies in milk than the injection of bacteria into the mammary tissue  near the mammary LN, although titers of circulating antibodies were similar  or APCs in a different state of maturation. Antigen processing by epithelium-associated APCs (dendritic cells or macrophages) likely differs from the handling by APCs like inflammatory monocytes attracted by the local inflammation caused by the injection of a vaccine in the mammary tissue. From this perspective, the APCs conditioned by the epithelium microenvironment would instruct the naïve T lymphocytes in a way characteristic of the transepithelial presentation of antigen. Transepithelial antigen exposure would induce both a systemic and local immune response, while interstitial antigen exposure would mainly induce a systemic response.It is therefore likely that the MG is equipped to sample antigens from the cisternal, ductal, or alveolar lumen by specialized APCs and to transfer the loaded cells to the draining LNs, although this has not been formally demonstrated. The presence of APCs, together with the local inflammatory response triggered by infection, fulfills the conditions for the initiation of an immune response. This is supported by studies showing consistently that immune responses are induced by intramammary immunizations. Of note, there are two ways the MG immune system can be stimulated, resulting in different immune responses. Infusion of killed  similar . MoreoveE. coli during the dry period elicited antibodies in colostrum and milk  into another quarter, before collection of mammary tissue and draining LNs for evaluation of antigen retrieval  but no local response was detected whereas bacteria induced mainly a local antibody response  into a quarter and particulate antigen  in the teat canal of lactating ewes . These structures, also known as ectopic lymphoid tissue, are induced in non-lymphoid organs by lasting infections and play a role in local immunity  in the milk from healthy MGs. Their numbers are higher in foremilk than in other fractions . The propos T cells in milk may increase with parity (pos CD8pos T cells (cytotoxic/suppressor T cells) are the most numerous, followed by CD3pos CD4pos helper T cells. Most of these cells have a memory phenotype (CD45RO+) and a majority an activated phenotype (CD2pos)  , 73, 79.pos cells that are CD8neg and CD2neg and WC1neg cells that express CD8 αβ heterodimer and CD2 . In uninfected quarters, the proportion of neutrophils is initially as high as the proportion of macrophages, then macrophages predominate and lymphocytes represent one third of the cells in the secretion of involuted glands , more in samples from E. coli infection than in samples from streptococcal infections. By RT-PCR analysis on whole cell population IL6 and IFNG mRNA were found in all samples , but not IL2 or IL4 mRNA. Another study of naturally occurring mastitis showed that αβ and γδ T lymphocytes were recruited in milk along with the dominant population of neutrophils  from the blood and milk of cows with mastitis compared to cells from blood and milk of healthy cows. After infusion of E. coli into the MG of lactating cows, there was an early influx of CD8pos T cells, followed by an increase in CD4pos and B cells  started at 30 hpi and peaked at 48 hpi, mainly with neutrophils , then CD4pos. In milk samples with elevated SCC, the proportion of CD8pos increased, and also the proportion of CD4neg-CD8neg (supposed γδ T cells) , suggesting to the authors that CD4pos cells were associated with low bacterial shedding, i.e., a more efficient immune response.In the course of natural intramammary infections of cows by d glands . The numtrophils . The CD4 B cells . Followitrophils . Influx  B cells . Among TT cells) . The proS. aureus mastitis. Following infection by S. aureus, most of the lymphocytes that infiltrated the mammary tissue were located in close apposition to the alveolar and ductal epithelium  and activated (CD2pos or ACT2pos) phenotype. Some of their functions have been identified, but we lack information on their antigen repertoire, and importantly, we need information on the functions of the T cells that populate the MG tissue. Recently, mucosal-associated invariant T cells (MAIT) have been found in human and bovine milk  when compared to blood lymphocytes , 107, whactation . The red T cells . Another T cells . Little ith IL-2 . T cellspathogen . The T cIFNG but not IL2 or IL4 mRNA  receptor, produced by mammary epithelial cells. As SCF acts synergistically with IL-2, IL-7 and IL-15 to induce proliferation and cytokine production by intraepithelial lymphocytes, c-kit production suggests a possible interaction between epithelial cells and γδ T cells in the bovine MG. CD4pos and CD8pos cells expressed TGF-β mRNA, but γδ T cells had the highest level of expression. The authors suggested that the activation of γδ T cells via the SCF receptor exerted a suppressive effect on the production of cytokines by other T cells during lactation.Some information on the potential activities of milk lymphocytes was derived from transcriptomic studies. Milk cells from natural mammary infections expressed IL4 mRNA . Milk cettle IL2 . Milk lere found . Interes sorting . CD4 andpos T cells were present in the lactating MG, but their numbers increased during the post-weaning involution. A proportion of these cells were RORγ Tpos (Th17 cells) during lactation, in increasing numbers at the beginning of involution, whereas FoxP3pos T cells (Treg cells) were very few during lactation but increased markedly in numbers at the late phase of involution. Gata3pos cells (Th2 lymphocytes), RORγTpos Foxp3pos (Th17-Treg lymphocytes), and RORγ Tpos Gata3pos cells (Th17-Th2 lymphocytes) were also present. The authors concluded that some form of immune tolerance was present to prevent the lactating and involuting MG from developing self-reactive immunity against milk components. Mouse mastitis models were used to characterize the lymphocytes recruited in the MG by infection. Inoculation of S. aureus into the MG through the teat canal induced the recruitment of IL-17-producing γδ T cells, IL-17pos (Th17) and IFN-γpos (Th1) lymphocytes, followed 5 days post-infection by an influx of CD4pos CD25pos (Treg) cells (E. coli (pos (Th17) cells. Of note, Th17 cells were also present in control glands, although in lower numbers than in inoculated glands, and IL-17 was detected, in accord with the reported influx of Th17 lymphocytes early in involuting glands after weaning  cells , 115. Si than WC1pos γδ T cells in milk before and after challenge, but the proportion did not change after challenge. Most milk lymphocytes were CD45ROpos. Intriguingly, no TNF-α or IL-1β was detected in milk by ELISA, and there was very little increase in milk serumalbumin concentration even at peak leukocytosis, showing a disconnection between blood/milk barrier leakage and diapedesis and transepithelial migration of leukocytes. More recently, the enrichment of the bovine immunology toolbox enabled a more in-depth analysis. IL-17A and IFN-γ were detected at the onset of the neutrophilic inflammatory response in the milk of ovalbumin-induced immune response of sensitized cows, whereas IL-1β and IL-6 were found in only a few samples , and during lactation . The response is systemic, including the skin and the MG. The mammary response can be induced in lactating MGs with small amounts of antigens that are inert in the MG of naïve animals. This means that recognition by the innate immune system of the MG is dispensable to trigger the reaction. As epithelial cell junctions are tight during lactation, this supposes that the luminal antigen is taken up by APCs able to sample the lumen, processed, and presented to antigen-specific lymphocytes. These antigens trigger the response in the absence of MAMPs, which could stimulate the APCs. The reaction of lymphocytes in the absence of strong co-stimulation by APCs supposes that these responding lymphocytes are not naïve. The promptitude of the milk leukocytosis and absence of pre-existing inflammation indicate that these lymphocytes are present in the mammary tissue before antigen exposure. The type of response (neutrophilic inflammation) strongly suggests that these memory lymphocytes are effectors of the type 3 immunity. The inferred sequence of events is schematized in The current model of adaptive immunosurveillance proposes that several memory T cell subsets perform distinct roles. These subsets are defined based first on trafficking patterns, and second on other features such as cytokine, surface markers, and transcriptional signatures , 129. Twpos Trm are found primarily in epithelial layers, while CD4pos Trm are found in the lamina propria and parenchyma  secretion, activation of DCs and recruitment of B and T cells  immunization is more efficient because the local inflammation will attract more circulating T cells than in non-infused uninflamed glands. Transepithelial antigen exposure is more efficient than interstitial antigen exposure because the antigen is taken over by epithelial or sub-epithelial APCs that are conditioned by the mammary microenvironment. In turn, the mammary APCs will instruct the antigen-specific naïve T cells encountered in the MG draining lymph nodes. This will orient the development of the CD4ne types , 132.pos and CD8pos Trm cells could be elicited, as cross-presentation by mucosal APCs might present soluble antigens through MHC class I  that are at the frontline of MG infections. The same question applies to the role of ductal macrophages during infection or local immunization. The phenotype of milk and mammary tissue lymphocytes and APCs deserves better characterization with the newly available reagents. Above all, the functional capacity of mammary lymphocytes need to be investigated and compared to their blood counterparts. Gaps in our knowledge of adaptive immunity to the MG are numerous  in milk corresponds to a physical loss of memory as it equates to a loss of MG memory T cells, which would result in short-lived adaptive immunity in the MG? The new powerful tools that are available to investigate the immune pathways and mechanisms make the search for answers to these questions feasible. Based on current knowledge, rekindling research on the immune cells that populate the healthy, infected, or immunized MG appears to be a most promising approach for the design of efficacious mastitis vaccines. However, this requires the mobilization of significant resources that are not easy to implement in the field of mastitis research. It is regrettable that during the last decades the funding of research on mammary immunity has been so parsimonious, which has discouraged researchers and hindered collaborations. We hope that this enticing field of research will attract new researchers and funding.The orientation to be given to the immune response for the best possible protection is still open to question, even though arguments in favor of Th17 cells and type 3 immunity for the defense of the MG against infections have been put forward . SeveralPR, GF, and RPM: conceptualization, writing-review and editing, and funding acquisition. PR: writing-original draft preparation. All authors contributed to the article and approved the submitted version.This work was supported by Agence Nationale de la Recherche (ANR-20-CE20-0023); INRAE.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Corticosteroids such as Dexamethasone (DEX) are commonly licensed for therapy in meat animals due to their known pharmacological properties. However, their misuse aimed to achieve anabolic effects is often found by National Residues Control Plans. The setup of a complementary “biomarker based” methods to unveil such illicit practices is encouraged by current European legislation. In this study, the combined use of molecular and histological quantitative techniques was applied on formalin fixed paraffin embedded (FFPE) muscle samples to assess the effects of illicit DEX treatment on veal calves. A PCR array, including 28 transcriptional biomarkers related to DEX exposure, was combined with a histochemical analysis of muscle fiber. An analysis based on unsupervised (PCA) and supervised (PLS-DA and Kohonen’s SOM) methods, was applied in order to define multivariate models able to classify animals suspected of illicit treatment by DEX. According to the conventional univariate approach, a not-significant reduction in type I fibres was recorded in the DEX-treated group, and only 12 out of 28 targeted genes maintained their expected differential expression, confirming the technical limitations of a quantitative analysis on FFPE samples. However, the multivariate models developed highlighted the possibility to establish complementary screening strategies, particularly when based on transcriptional biomarkers characterised by low expression profiles. Official controls on the residues of illicit growth promoters and other misused authorised drugs in meat products, both marketed within the European Community (EU) and imported from Third Countries, are strictly regulated to preserve consumer safety. Nevertheless, the illicit misuse of these compounds to increase animal production has still been detected by National Residues Control Plans (NRCPs) and Europol investigations, often revealing how easily steroids are accessed in illegal markets by easy-profit prone breeders.Indeed, a broad range of illicit practices have been unveiled during the years by the cited control activities, but, unfortunately, similarly to sports doping, novel formulations based on both new designer drugs and already known performance enhancers are also continuously developed for farm animals ,2. ConseIn the list of misused substances found by NRCP surveys, authorised drugs such as corticosteroids are also often reported , being aIn Italy, in 2008, the Italian Ministry of Health introduced the histological plan as a complementary strategy for the control of growth-promoting molecules misuse. The objective of this monitoring activity was to verify whether the samples from the slaughterhouses exceeded a predefined prevalence threshold level for illicit treatment with corticosteroids at the national level ,5.Indeed, as remarked by EFSA since 2013, the establishment of complementary diagnostic methods, based on biological effects and biomarkers of exposure to anabolic substances, could be useful to update current tests available in NRCPs . In thisAdditionally, regarding DEX effects on cattle, different transcriptomics and proteomic studies have been proposed to firstly identify candidate biomarkers of exposure ,12,13,14The aim of the work was therefore to combine the output of a Real Time PCR array, designed to quantify expression levels of multiple transcriptional biomarkers related to DEX exposure, with a histochemical measurement of muscle fibres. This coupled molecular/histological quantitative approach was applied on the same muscle samples that were formalin-fixed, paraffin-embedded (FFPE) and collected during an animal trial performed to assess the effects of illicit treatment with DEX on veal calves. The use of a multivariate analysis was tested to identify an optimal set of histological and/or transcriptional biomarkers that are able to classify animals suspected of illicit treatment by steroids, i.e., DEX.Archived paraffin blocks of bovine biceps brachii muscle samples, from a previous animal trial to study the effects of DEX illicit administration , were reBriefly, 2 month old Friesians male calves were recruited, randomly divided into two groups and reared for about 4 months. One month before slaughtering, one group was orally treated with dexamethasone 21-phosphate disodium salt at a dose of 0.4 mg/day per animal for 20 days, according to a presumed anabolic protocol of treatment, and the other group was kept as control and treated with a placebo. The animals were all slaughtered at about 7 months in an EC certified slaughterhouse approximately 10 days after the last drug administration; control animals were slaughtered after the treated ones. The experiment was carried out in accordance with the European Council Directive 2010/63/EU 86/609 (D.Lgs 26/2014) and was authorised by the Italian Ministry of Health and the Ethics Committee of the University of Turin. At the end of the sampling procedure, the carcasses of the treated animals were destroyed according to the relevant legislation (Directive 2003/74/EC). The study was a randomised controlled blind clinical trial. The biceps brachii muscle samples were collected at the slaughterhouse and immediately fixed in 10% neutral buffered formaldehyde and transferred to the laboratory.Further and comprehensive information regarding the animal trial is available in the work of Richelmi et al., 2017 .Forty FFPE muscle samples (15 controls + 25 DEX treated) from the cited animal trial were randomly chosen for microscopy and transcriptomics investigations, plus two additional FFPE samples from calves treated with a therapeutic dose of DEX. The authorised therapeutic schedule foreseen was dexamethasone 21-phosphate disodium salt  to verify the correct orientation of the fibres. Only the samples correctly orientated and without alterations were selected for the morphometric analysis. The selected samples were then sectioned in 3–5 μm slices for the immunohistochemistry analysis. Sections were incubated in a buffer solution at pH 6 at 97 °C for 30 min to reduce the nonspecific binding of the secondary antibodies; then, they were incubated at room temperature in a humid chamber for 1 h with a 1:50 solution of a monoclonal antibody specific for the slow myosin heavy chain type 1 (MHC1) to detect type 1 fibres . After washing, the EnVision System Kit  for polyclonal and monoclonal antibodies was used as the detection system. The immune reactions were visualised by applying a 3,3′-diaminobenzidine (DAB) chromogen solution (Dako) for 4′ .All sections were analysed using optical microscopy and images were captured using a Nikon DS-Fi1 colour digital camera . For each slide, 25 muscle fibre diameters were measured after manual selection in 5 randomly selected 200× fields digital images (5 fibres in each image) . The NISThe total RNA was isolated from the same FFPE muscle samples used for the histological analysis and optimised procedures for long-term archived FFPE tissues were applied . BrieflyA custom Taqman Real Time PCR Array  was designed on 96-wells optical plates to quantify the expression levels of the 28 genes that were identified in previous studies as related to the DEX exposure ,14,15. TThe total RNA from the extracted samples (2.5 µg) was reverse transcribed to a final volume of 20 µL with the SuperScript-IV Vilo kit (Thermo Fisher Scientific). The cDNA from each sample was then preamplified with Taqman Preamp Master Mix and Custom preamp primers pool (Thermo Fisher Scientific) according the manufacturer’s guidelines, which were as follows: hold stage of 10 min at 95 °C, 14 cycles of 15 s at 95 °C and 4 min at 60 °C, followed by final denaturation and Taq polymerase inactivation step (99 °C for 10 min). The conclusive Real Time PCR step on preamplified cDNAs (tenfold diluted in TE buffer) was performed using the Taqman Fast Advance Master Mix (Thermo Fisher Scientific) and a StepOne Plus Real-Time PCR System (Thermo Fisher Scientific) with the following thermal cycling conditions (fast ramp mode): two consecutive holding stages (2 min at 50 °C and 2 min at 95 °C), followed by 40 cycles of 1 s at 95 °C and 20 s at 60 °C .t-test and p-values < 0.05 were considered statistically significant. Relative quantification (RQ) of the 28 selected transcripts was performed using the ∆∆Cq method [The collected histological data were analysed using Prism software 6.01 . The Shapiro–Wilk test was used to test the normality of the data distribution before the statistical analyses. The data were described by mean and standard deviation (SD). Morphometry analysis-related differences were also assessed by unpaired q method . Prelimiq method ,23.p < 0.05).The fold changes of all biomarkers for both the control and treated groups of muscle samples were reported using the log2 scale ± confidence interval (CI) at 95%. The significance of recorded fold changes was assessed by one-way ANOVA with Tukey’s post hoc test  dataset, since suitable and well-oriented muscular fibres from the respective paraffin-embedded blocks used for the microscopy analysis with complete gene expression profiles were not available for all samples. The final RQhisto dataset therefore consisted of 35 samples described by 33 variables. This dataset comprised 12 controls, 22 treated samples and just 1 sample treated with a therapeutic dose of dexamethasone, that was exploited for external prediction and was not included in the model calculation.A multivariate statistical approach was then applied to all the collected data, consisting of a pattern recognition approach, followed by a classification analysis, to verify the ability of the measured variables in classifying control vs. DEX-treated samples. The analysis was carried out on the following two datasets:Gene expression data in both datasets were firstly normalised according to Genorm and NormFinder results, then treated using Principal Component Analysis , PartialPCA  is a mulPartial Least Squares (PLS) ,25,26 isKohonen’s SOMs  are artiKohonen’s networks can also be trained in a supervised manner to provide sample classification (Supervised Kohonen networks—SKN) , wherebyPCA and PLS-DA were carried out with Matlab R2014a , utilizing the in-house-developed routines and the Classification Toolbox from Milano Chemometrics ; Kohonenp > 0.05). Descriptive statistics of the morphometry analysis are reported in Only thirty five out of forty two samples selected for the gene expression study were also suitable for the morphometry analysis, four samples showed an incorrect fibre orientation, and one further sample was excluded since it presented eosinophilic myositis. No significant differences were identified between the diameter measure of the fibres in the canonical univariate analysis  in some samples had Cq values over the cut-off for pre-amplified cDNA (>32 Cq).Genorm and NormFinder analyses revealed RPLP0 and HSP8A as the most stable genes for data normalisation.p-values in The differentially expressed genes (DEG), defined by the comparison of the normalised expression levels from DEX-treated and control groups, are reported with the associated Of the 28 biomarkers originally selected for array design, only 12 targets plus two genes tested as reference genes were found to be still differentially expressed (DE) in the analysed FFPE muscle samples .PCA was applied to both the RQ and RQhisto datasets after autoscaling . In both cases, the first four PCs explain more than 60% of the overall information see , showingLooking at the corresponding loading plots, both datasets reveal variables at positive loadings on PC1  to be characterised by larger signals in illicitly treated samples and lower signals in controls, while variables at negative loadings on PC1  show an opposite behavior. The subsequent PCs do not show a clear separation of the samples into groups and are not shown.PCA highlighted the possibility of identifying the two groups of samples (controls and DEX treated ones) by means of the most significant PCs in an unsupervised approach. PLS-DA was therefore applied to both RQ and RQhisto datasets separately to identify biomarkers of the illicit treatment with a supervised approach. Notwithstanding the reduced size of the datasets, the supervised approach, coupled to cross-validation procedures, was able to identify the candidate biomarkers more reliably.PLS-DA was therefore applied on both datasets, using a cross-validation procedure with the elimination of 20% of the samples from the training set at each validation step, with 1000 repetitions. For both datasets, the best results in cross-validation were obtained with 2 LVs in the final model. In both cases, the class of the samples corresponding to the therapeutic dose were not included in model calculations but were exploited for an external prediction.The two models show satisfactory performances in fitting (NER% about 90%) and a slightly decreased level of performance in cross-validation (about 86–88%)  accordinThe score plot of the first two LVs  shows thThe samples characterised by a therapeutic dose, not included in the model calculations, were predicted by both models as belonging to the control class. In both cases, positive coefficients correspond to variables with higher values in samples treated by illicit doses of dexamethasone and lower values in control samples; however, variables characterised by negative coefficients show instead an opposite behaviour. The plot of the coefficients for the RQhisto dataset reveals that the three variables related to histology do not show high coefficients and seem not to play a relevant role in the final model. Among the variables characterised by a relevant coefficient in the final models, CYP1A1, CRISPLD2, MT2A, GAPDH and PPIA showed the highest negative coefficients and therefore appear to be more expressed in the control samples, while GALNT9, MYOG, MYF6 and MMP2 showed the highest positive coefficients and were therefore more expressed in the illicit treatment. The only difference between the two models consists of the variables characterised by the highest positive coefficients that, in the case of the RQhisto dataset, do not include MYF6 and MMP2.Kohonen’s supervised SOMs were then applied to both datasets separately, with the following setup: a top map of 8 × 8 hexagonal neurons, toroidal boundary, batch algorithm, random initialization of weights, learning rate decreasing linearly from 0.5 to 0.01 and 200 training epochs . The datFor both datasets, the results in fitting are better than PLS-DA, thereby providing the perfect classification of all the samples, while the models show acceptable predictive abilities in cross-validation, slightly better than PLS-DA. In both cases, the samples with therapeutic doses were predicted in the control class.The top maps obtained for both datasets are represented in The results of the PCA applied to the weights calculated for each neuron of the top map and each variable for RQ dataset and RQhisto dataset, respectively, are reported in For the RQhisto dataset C,D, contThe detection and quantification of transcriptional biomarkers in FFPE samples is known to be biased by the fragmentation and degradation grade of nucleic acids induced by formalin . MoreoveIn our study, the choice of as-short-as-possible amplification regions for all the considered transcripts see  and cDNAp < 0.05) differential expression levels  and classification methods (PLS-DA and Kohonen’s supervised SOMs), all the biological information coming from the whole RQ dataset was considered, providing a promising classification of the untreated (control) and DEX therapeutic treated samples (predicted by both models as belonging to the control group), with only a few misclassified samples. In particular, during cross-validation with Kohonen’s SOMs, six samples appeared to be misclassified in the group of anabolic DEX-treated samples, while the fitting of the models was perfect.The application of PLS-DA helped to identify the candidate biomarkers in a more reliable way with respect to PCA, through the exploitation of a supervised approach coupled to cross-validation strategies which helped in the identification of candidate biomarkers. This is also highlighted by the fact that some of the variables with significant loadings on PC1 in the PCA analysis . For the variables characterised as discriminating, a certain overlapping between the two methods was observed, since the variables identified as the most relevant in PLS-DA  were also identified as relevant by Kohonen’s SOMs; however, ANNs  identified some additional variables as relevant for classification, e.g., CCL24, GAPDH, RASD1, FGL2, MYOD1, MEDAG, FKBP5.The applied tools were indeed limited by the reduced number of analysed samples; consequently, to increase the prediction capabilities of the described multivariate models, additional biological information on the analysed samples was added by means of the histological characterisation of the muscle fibres, for which it is known that adaptive changes can occur in response to variations in the pattern of neural stimulation, loading conditions, availability of substrates, and hormonal signals .Our results indicate a not-significant reduction in type I fibres; these data can be justified because it was previously reported that type 2 muscle fibres are more pronounced than type 1 fibres due to a chronic exposure to excess glucocorticoid .The choice to study only type 1 fibres was made because the biceps brachii of bovines contains a large percentage of type I myofibers . MoreoveAccordingly, the fusion of different datasets , for the same DEX-treated and control FFPE muscle samples, revealed only a small improvement in the developed models in terms of fitting and cross-validation performances, with a slight reduction in the number of misclassified samples (from six to four samples) when RQ and RQhisto dataset performances were compared . HoweverThe present study confirmed that a gene expression analysis can be successfully applied on FFPE muscle samples, and that it can, in the future, be used to update the current Italian histological NRCP, to contrast both the illicit administration of unauthorised drugs  and misuse of authorised drugs such as corticosteroids . The mul


Conducting routine inspections for compliance with age-of-sale laws is essential to reducing underage access to tobacco. We seek to develop a multilevel propensity score model (PSM) to predict retail violation of sales to minors (RVSM).The Food and Drug Administration compliance check of tobacco retailers with minor-involved inspections from 2015 to 2019 (n=683 741) was linked with multilevel data for demographics and policies. Generalised estimating equation was used to develop the PSM using 2015–2016 data to predict the 2017 RVSM. The prediction accuracy of the PSM was validated by contrasting PSM deciles against 2018–2019 actual violation data.In 2017, 44.3% of 26 150 zip codes with ≥1 tobacco retailer had 0 FDA underage sales inspections, 11.0% had 1 inspection, 13.5% had 2–3, 15.3% had 4–9, and 15.9% had 10 or more. The likelihood of having an RVSM in 2017 was higher in zip codes with a lower number of inspections (adjusted OR (aOR)=0.988, 95% CI (0.987 to 0.990)) and penalties ) and a higher number of violations ) in the previous 2 years. Urbanicity, socioeconomic status, smoking prevalence and tobacco control policies at multilevels also predicted retail violations. Prediction accuracy was validated with zip codes with the highest 10% of the PSM 3.4 times more likely to have retail violations in 2019 than zip codes in the bottom decile.The multilevel PSM predicts the RVSM with a good rank order of retail violations. The model-based approach can be used to identify hot spots of retail violations and improve the sampling plan for future inspections. Tobacco use by youth and young adults can harm brain development and increases the likelihood of nicotine addiction.Under the Family Smoking Prevention and Tobacco Control Act, the US Food and Drug Administration (FDA) works through state contractors to inspect tobacco retailers for marketing violations and underage sales.According to recent studies of FDA inspections, retail violations for sales to minors were more likely to occur in neighbourhoods of lower socioeconomic status (SES) with a higher percentage of minority residents and weaker tobacco control policies.To fill this critical gap, we harnessed multilevel and longitudinal data that link 2015–2019 FDA compliance inspections with zip code-level SES and other contextual factors, such as county-level smoking prevalence, state and local adoption of Tobacco 21, and other state tobacco control policies. We then developed and validated a multilevel PSM to predict whether the inspection resulted in an RVSM and further demonstrated the opportunity to identify areas with a high risk of violations in applying the PSM for future compliance inspections.FDA’s undercover buy inspections involve the use of a minor under the supervision of the inspectors to evaluate whether a retailer sells tobacco products to individuals who are under 18 years old.Each inspection result was classified as a binary variable: no violation (coded as 0) and violation . We also classified the inspection results based on whether there was a penalty  or not . We aggregated inspection results  from each zip code over 2 years to serve as predictors .Data from 5-year estimates (2014–2018) of the American Community Survey (ACS) were used to investigate demographic correlates of RVSM. We selected zip code-level variables that are known to be associated with retail violations of tobacco sales to minors,Data were obtained from ReferenceUSAData for Tobacco 21 ordinances at both local and state levels were obtained from the University of Missouri Tobacco Control Research Center,Data for state cigarette tax and tobacco retailer licensing requirements across all 50 states and the District of Columbia for each year from 2014 to 2019 were obtained from the Centers for Disease Control and Prevention’s State Tobacco Activities Tracking and Evaluation System.The multilevel data from different sources were linked at the zip code, county and state levels using ArcGIS V.10.5 see .The PSM for RVSM  is constructed as follows:where all predictive variables are listed in the first column of The propensity score was then calculated as the probability of RVSM at each inspection by applying the corresponding regression coefficient to each predictor with the formula:where We validated the PSM prediction accuracy between the development sample (2017 inspection data) and two separate validation samples (2018 or 2019 inspection data) based on the following steps: (1) we calculated the predicted PSM score at each inspection in 2017, 2018 or 2019 based on previous 2-year inspection results at the zip code level  and other multilevel contextual factors as predictors (see equation 2); (2) we ranked individual inspection data from the lowest to the highest PSM score  at each inspection and then split up the ranked data into 10 equal subsections ; (3) we plotted the actual retail violation rates at each decile (the number of violations/the number of inspections) in each year by the decile of PSM score in In order to demonstrate potential opportunities in applying the PSM score, we further reported the distribution of US zip codes with ≥1 tobacco retailer and the number of tobacco retailers by the PSM score deciles, and the number of FDA compliance inspections in 2017 in Of 26 131 zip codes with tobacco retailers in 2017, 44.3% (11 589) had no FDA compliance inspections involving minors, 11.0% (2875) had 1 inspection, 13.5% (3517) had 2–3 inspections, 15.3% (4009) had 4–9 inspections, and 15.9% (4160) had 10 or more inspections. The multilevel PSM outcomes from the development sample (2017) and validation samples (2018 or 2019) are presented in 10.1136/tobaccocontrol-2021-056742.supp1Supplementary dataGiven a large number of tobacco retailers (n>350 000) in the USA and the high cost of retail inspections,To our knowledge, this is the first study to incorporate inspection history, compliance outcomes, and penalties in conjunction with SES and the number of tobacco retailers at the zip code level, smoking prevalence rate at the county level and tobacco control policies at multiple levels to predict future tobacco retail compliance of underage sales laws. Although previous studies have reported that neighbourhood characteristics and tobacco control policies are associated with retail violations of underage sales,The current study provides further evidence of the role of strong tobacco control policies in reducing youth access to tobacco products. For instance, state-level Tobacco 21 policies were associated with 50% lower odds of a sales violation in 2019 and local-level Tobacco 21 policies were associated with 30% lower odds of a sales violation. Notably, tobacco retail licensing requirements were associated with a 19%–33% reduction in the odds of a sales to minor violation. However, there were still 13 states without any tobacco retail licensing requirements as of December 2020.The PSM identified opportunities to improve sampling designs for retail inspections. For instance, 44% of zip codes with a tobacco retailer were not inspected in 2017, over a half of these zip codes are located in the non-metropolitan areas and had an average of 9.0 tobacco retailers at each zip code. The PSM analysis shows that these zip codes were not less risky than other zip codes: 31.0% of non-inspection zip codes were located in the top 30% of the PSM vs 30.5% of those with 10+ compliance inspections in 2017. Furthermore, over 43 000 tobacco retailers with a high propensity score for violations were not inspected in 2017. Since the FDA is spending hundreds of millions of dollars each year in compliance inspection and the Substance Abuse and Mental Health Services requires states to hit a target retail violation rate in order to receive block funding,This study is subject to limitations. First, there might be other confounding factors affecting the retail violations, which are not accounted for in this study. However, this study sought to include a variety of multilevel factors that have been shown to associate with tobacco retail compliance.This study developed a multilevel propensity score to predict the retail violations of underage sales to minors, which can be used to identify hot spots of retail violations and improve the sampling plans in the future. This study also adds to the literature by investigating associations between time-variant variables such as previous inspections, violations, penalties, and future inspection and violation outcomes. The data-driven propensity can provide state and local authorities rich information to prioritise regions with compliance disparities and provide resources to reduce tobacco-related disparities due to youth access to tobacco. Although the study focuses on age-of-sale inspections, the finding can be extended to determine priority regions that need tobacco retail training, signage and other intervention programmes.Previous research has shown that restricting the supply of tobacco products through age-of-sale restrictions is an essential component in reducing youth smoking.Retail violations of underage sales can be predicted with previous inspection results, in conjunction with social-ecological and contextual factors at the community level.The propensity score model can be used to identify regions with a high risk of retail violations and improve the sampling plan in future compliance inspections.


FBN1) gene. Cardinal features of MFS include ectopia lentis (EL), musculoskeletal features and aortic root aneurysm and dissection. Although dissection of the ascending aorta is the main cause of mortality in MFS, the clinical course differs considerably in age of onset and severity, even among individuals who share the same causative variant, suggesting the existence of additional genetic variants that modify the severity of the cardiovascular phenotype in MFS. We recruited MFS patients and classified them into severe (n = 8) or mild aortic phenotype (n = 14) according to age of presentation of the first aorta-related incident. We used Exome Sequencing to identify the genetic variants associated with the severity of aortic manifestations and we performed linkage analysis where suitable. We found five genes associated with severe aortic phenotype and three genes that could be protective for this phenotype in MFS. These genes regulate components of the extracellular matrix, TGFβ pathway and other signaling pathways that are involved in the maintenance of the ECM or angiogenesis. Further studies will be required to understand the functional effect of these variants and explore novel, personalized risk management and, potentially, therapies for these patients.Marfan Syndrome (MFS) is an autosomal dominant condition caused by variants in the fibrillin-1 ( FBN1, the gene encoding for fibrillin-1 an extracellular matrix (ECM) protein that is the major structural component of microfibrils [Marfan syndrome  is a rare connective tissue disorder with a reported incidence of 1 in 3000 to 5000 individuals [ofibrils ,3. The cFBN1, including intrafamilial variability [FBN1 produce among other effects, variations in its expression and suggest that different MFS-causing genotypes could modify the severity of the disease [Clinical manifestations of MFS are highly variable with significant phenotypic heterogeneity even among individuals harboring the same pathogenic variant in iability ,6. Progniability , althoug disease ,9,10,11. disease ,13,14.FBN1 gene [The main mechanisms by which aortic aneurysms (AAs) occur in MFS are largely unknown. However, there is significant evidence that aneurysms are a consequence of changes in the ECM integrity of the aortic wall and that these would be mediated by perturbations in the transforming growth factor-β (TGFβ) signaling pathway . This peBN1 gene .FBN1 on the frequency and progression of the cardiovascular phenotype in patients with MFS is very limited; therefore, deepening our understanding of this phenomenon is very important to improve the clinical management/risk stratification of patients.Knowledge of the effects of genetic variants elsewhere outside of In the present study, we evaluated genetic variants that could modify the severity of the clinical course of aortic aneurysm or dissection in patients with MFS using exome sequencing and linkage analysis.Chilean patients of all ages with a confirmed diagnosis of MFS were identified and recruited for this study. Among those, we selected 5 families with both affected and unaffected members as well as 5 non-familial cases with MFS. We focused on the most life-threatening aspect of MFS: aortic aneurysm and dissection. In order to test the hypothesis that variants in modifying genes may mitigate or exacerbate the severity of the aortic phenotype, we classified our subjects in “mild” and “severe” aortic phenotype. MFS patients were clinically diagnosed according to the criteria published in the revised version of the Ghent criteria for MFS . Since tInstitutional Ethics Committee approved the study registry protocol at both recruitment sites  and written informed consent was obtained from all participants.We used Exome Sequencing to identify genetic variations that may be associated with the severity of the cardiovascular phenotype in MFS. Patients with MFS belonging to the 5 families studied, their informative healthy relatives and all non-familial cases with MFS were sequenced for further analysis.http://broadinstitute.github.io/picard (accessed on 12 April 2022) [Exome Sequencing was performed at BGI using their proprietary DNBseq™ NGS Platform as well as Agilent V6 + UTR library kit with depth of coverage 100×. Variant analysis was performed following the best practice recommendations of the Genome Analysis Toolkit (GATK) . Brieflyil 2022) . Variantil 2022) . Before We performed variant prioritization analysis using the Variant Annotation, Analysis, and Search Tool (VAAST 2.0) package . VAAST uIn this work we generated Exome Sequencing data from small families and also from non-familial cases, and consequently, we analyzed the data in two ways: (1) the familial approach, in which we analyzed the families that had both members affected with severe phenotype and affected with mild phenotype; and (2) the case-control approach, in which we used the exome data of all patients  classified as either severe or mild phenotype and performed a case-control analysis using VAAST .In the first approach , we performed VAAST analysis in families 1, 3 and 5. Because no data of any affected individual from generation I was available that could link affected patients from generation II by ancestry, we could not do linkage analysis for the entire family 1. However, family 1 was partitioned in two distinct nuclei and analyzed considering mild and severe patients with the possibility of identifying alleles shared by IBD  segments, as shown in In the case-control approach, Exome Sequencing data of all MFS patients classified as severe and all MFS patients classified as mild were analyzed. Because we could not estimate the effect size of the putative modifier variants/genes, i.e., big effect size under a “monogenic-like” model or a small effect size under a “polygenic-like” model , we perfFBN1), could explain changes in the function of a molecular pathway associated with the pathophysiology of MFS.We filtered for and selected genes that met any of the following criteria: (1) Gene Ontology (GO) for biological processes distinctively associated with pathophysiology of MFS; (2) genes whose products interact with proteins that play a role in the homeostasis of the ECM or in the TGFβ signaling pathway (selected using STRING ); or (3)Raw sequencing data will be available under Bio Project accession number PRJNA795044 upon publication of this manuscript.p-value = 0.0743). A total of 50% of severe cases already required aortic surgery (4/8) while approximately 30% of mild cases have required this surgery (4/14). Our first analysis was to confirm the presence of an MFS-causing variant on the FBN1 gene in all recruited patients, shown in According to the age of presentation of the first cardiovascular manifestation and/or accident related to the aorta, we classified 8 patients as severe phenotype and 14 as mild . WES wasp value < 0.05 in each analysis setup after performing the Bonferroni correction for multiple comparisons. The complete list of genes with statistical significance identified after performing each of the analyzes using VAAST is shown in The primary list of genes obtained from the different analyses mentioned above is large, and, in some analyses, it is composed of hundreds of genes. In order to situate these candidate genes in a biological context, several avenues were explored. Considering all the selection criteria established in this study, we selected eight genes that met those criteria. A graphical description of the selection process is shown in PPARD (Peroxisome Proliferator-Activated Receptor δ). We found a missense variant (c.140G>A) with an autosomal dominant model of inheritance (PPARD gene could be decreasing its protective function of the vascular structure and promoting the severe aortic phenotype in these patients.We identified five candidate genes associated with a severe aortic phenotype: the first is eritance  in exon eritance . This vaFBN1 gene (c.1240C>A) (FBN1 that she shares with her mother (MFS-causing variant) and a second variant in FBN1 that is shared with her father (Non-pathogenic variant). This variant substitutes the conserved, non-polar amino acid Proline by the polar amino acid Threonine in position 414 of the protein, and it is located in a domain where it has been predicted that folding that contributes to tertiary structure of this protein occurs [FBN1 cause variation in the expression levels of the gene and, in light of this, this second FBN1 variant in this patient could be considered as a modifier gene that favors the severity of MFS [When a recessive inheritance model was used to perform the analysis of family 1.1 in VAAST, a second missense variant in the 1240C>A) , additio1240C>A)  was clasy of MFS ,14,42.JAG1 (c.3355G>C and c.3354T>A) , resulting in the release of the Notch Intracellular Domain (NICD) by proteolytic cleavage and subsequent translocation of NICD to the nucleus, where it interacts with the Recombinant Binding Protein Suppressor (RBPJ) and other transcription factors to regulate the activation and suppression of target genes that initiate a signaling cascade involved in vascular wall homeostasis and remodeling [JAG1 could downregulate the Jagged1-Notch signaling and therefore impede proper restoration of vascular wall homeostasis.Interestingly, another candidate gene that could modulate the cardiovascular phenotype was prominent in our results. Two missense variants in 3354T>A)  segregatmodeling ,44,45. Jmodeling . We hypoMAP3K1. We identified variants associated with the severe phenotype in this gene both in the familial approach (family 3) and in the non-familial, case control one . One consequence of ECM degradation in the pathophysiology of MFS is the increase in MMPs [ in MMPs , which l in MMPs  and deta in MMPs . This ap in MMPs ,51.PTPRJ is another gene that showed variants that segregated in patients with severe phenotype but not in patients with mild phenotype . Two of these variants (c.66_68del and c.2182G>A) were predicted to have a damaging or deleterious effect that modifies the structure and function of the protein by SIFT  is expressed in vascular endothelial cells. CD148 has the capacity to modulate cell proliferation, cell migration and is involved in vascular development through negative regulation of VEGF receptor 2 (VEGFR2) signaling [PTPRJ have been associated with coarctation of the aorta [PTPRJ is upregulated in the aortic tissue of patients with thoracic aortic aneurysm (TAA) and could predict the presentation of TAA [PTPRJ due to the presence of gene variants could be associated with TAA produced by the uncontrolled activation of the TGFβ signaling pathway in patients with variants in FBN1.ignaling ,54. Varihe aorta . A recenn of TAA . AlthougKIAA1462, TNFSF18, and TGFBR3L)  modifies the reading frame and generates the loss of a serine in the amino acid sequence. We also identified an in-frame variant (c.2577_2578insACTGCTGCT), which produced an insertion of the amino acids Thr-Ala-Ala in the protein. Finally, we found a missense variant (c.424C>T) that changes the conserved amino acid Ala142 to Thr. KIAA1462, also known as JCAD , is a cell–cell binding protein expressed primarily in endothelial cells [Our VAAST analysis showed three more candidate genes (TGFBR3L)  that segriant c.24_2576delal cells .VCAM1 [JCAD gene could decrease its expression and, in turn, generate an increase in YAP/TAZ that, as mentioned above, could produce a protective effect for the appearance of aortic dissection. On the other hand, we identified two missense variants (c.361G>A and c.451A>G) in the gene TNFSF18 that change amino acids that are largely conserved: Gly121Ser and Asn151Asp, respectively. It is worth noting that TNFSF18 was highly prioritized on our VAAST analysis despite not being expressed in aortic tissue. However paradoxical, this only highlights the necessity of further investigating the biological role of this gene in aortic homeostasis.Recent studies have shown that the depletion of KIAA1462 decreased the expression of genes such as VCAM1 , which iVCAM1 . Jones eVCAM1 . It has VCAM1 . We beliAdditionally, we found a frameshift insertion (c.455_456insTTG) that incorporates a premature termination codon (PTC) and generates a shorter, aberrant mRNA. All the variants found in this gene are in an extracellular domain and using pDomTHREADER  was recognized how folk domain; therefore, these variants could produce a conformational change of the protein. TNFSF18 (member 18 of the tumor necrosis factor ligand superfamily) activates the expression of MMP-2 and MMP-9, but by phosphorylation of p-STAT1, and positively regulates the expression of VCAM1 and ICAM1 .TGFBR3L is an important paralog of the TGFBR3 gene that encodes for a coreceptor for TGF-β and has a role in the regulation of the transforming growth factor receptor beta signaling pathway superfamily by promoting the binding of TGFβ2 to the TGFβR1/TGFβR2 receptor complex [FBN1 and is involved in the over-activation observed in patients with MFS [TGFBR3L (c.418_420del) that generates a loss of a codon in the mRNA that encodes for amino acid Pro140 and that is located in the zona pellucida (ZP) [ complex . One of  complex ,66. In twith MFS . We idenida (ZP)  domain oida (ZP) , suggestWhen crystallization data was available, we decided to perform 3D modeling of proteins to assess whether the variant identified in MFS patients with extreme aortic phenotype had a major effect in structure that could lead us to hypothesize a functional consequence of the genetic variant. We were able to retrieve PDB data for MAP3K1, PTPRJ, TNFSF18 and TGFBR3L that we then modeled using Swiss-PDBViewer . We thenOur results show that there are variants in genes that regulate components of the extracellular matrix, TGFβ pathway and in genes that function in other signaling pathways that are involved in the maintenance of the ECM or angiogenesis and that could modify the cardiovascular phenotype in MFS. We found five candidate genes from the VAAST analysis associated with severe aortic phenotype in MFS patients and three genes that could be protective for this phenotype.In the present study, we recruited patients with a clinical diagnosis of MFS in agreement with the Ghent criteria for MFS. Based on these, we stratified all MFS patients as “severe” or “mild” depending on the age at which they underwent their first surgical repair of the aorta or the age at which they presented their first event of aortic dissection. With this approach, we sought to differentiate patients according to the point in time at which their aortic wall failed to sustain a normal architecture as a measure of how severe perturbations in the underlying molecular mechanisms present in each of our study participants.Fibrillin 1 plays a pivotal role in regulating the bioavailability of TGF-β ,72,73, aThis study pinpoints several genes that play a role in the complex architecture of aortic homeostasis and genetic variants that may significantly affect their function to the extent of possibly causing a major shift in the progression of aortic phenotype in these patients. In an attempt to have a first glimpse of the mechanisms by which the identified variants cause the extreme aortic phenotypes observed in MFS patients, we modeled the proteins with the amino acid substitutions and insertions/deletions where suitable to assess whether overt changes in protein structure could be observed. For most proteins modeled, modest changes are observed. However, this observation must be correlated with complementary studies such as eQTL analysis and functional testing of the variants reported here in appropriate in vitro and in vivo systems in order to deepen our understanding of this biological phenomenon.Patients with MFS present a high variability in the severity of the aortic phenotype. Understanding this phenotypic variability would allow predicting clinical outcomes in these patients, taking more effective measures for proper clinical management and avoidance of life risk situations. In this study, we proposed a novel strategy to stratify MFS patients into mild or severe aortic phenotype, which made it possible to estimate risk profiles through the identification of variants associated with these extreme phenotypes.Although Exome Sequencing has become an important tool for the discovery of genetic variants, its use has some limitations in relation to the loss of important information in genomic regions with insufficient coverage . The search for both pathogenic and modifier variants with Exome Sequencing data is therefore done at risk of the loss of information in non-coding regions of the human genome. We hope to further our studies using Genome Sequencing data in the future to overcome this liability.Finally, future studies are needed to determine the molecular mechanisms by which these genetic variants could act to modify the severity of the aortic phenotype in these patients, and whether these genes are involved in the development of aortic aneurysm using in vitro and in vivo experiment models.


Analysis of P-wave amplitudes extracted from these downhole gathers shows a consistent amplitude anomaly at the injection level, visible in all events that occurred after the start of injection. This indicates that the anomaly is caused by changes in elastic properties in the reservoir caused by CO2 saturation. However, extracted amplitudes show significant variability between earthquakes even without subsurface changes; thus, multiple events are required to distinguish the time-lapse anomaly from time-lapse noise. Ubiquity of these events even in a tectonically quiet region  makes this technique a viable and cost-effective option for downhole monitoring.Monitoring changes of formation properties along the well bore associated with the presence of carbon dioxide can be important for both tracking the plume inside of the primary containment and detecting leakage into the zone located above the reservoir. This can be achieved with time lapse wireline logging, but this approach requires well intervention and is not always possible. If the well is permanently instrumented with an optical fibre, it can be used as a distributed seismic receiver array to detect gas behind the casing by monitoring changes in amplitude of the seismic waves generated by active or passive seismic sources. Previous research showed the efficacy of this technique using continuous seismic sources. The Stage 3 Otway Project presented an opportunity to test this technique using passive seismic recording, as downhole fibre-optic arrays recorded numerous regional earthquakes over the period of nearly 2 years before, during, and after CO Some of these projects, e.g., monitoring CO2 storage, require seismic surveys to be acquired as frequently as possible to ensure timely detection of unwanted subsurface changes such as CO2 leakage from the target reservoir. However, performing 3D seismic surveys every few months may be prohibitively expensive and disruptive for other land users. Both of these factors can be partly mitigated by the use of permanent receivers such as distributed acoustic sensors or DAS [2 plume, producing an image of the subsurface as frequently as every 2 days. Time-lapse or 4D reflection seismic monitoring is an important technology for monitoring COs or DAS ,2,3,4,5 s or DAS . Recentls or DAS  demonstrIn addition to signals from controlled sources, permanent receivers such as DAS record an abundance of seismic energy from various natural sources and human activities  ,9,10. HaAnother powerful technology for subsurface characterisation is ambient noise tomography (ANT) ,18,19,202 in a monitoring well by using active seismic source data from the CO2CRC Otway Stage 3 experiment, where 15 kt of supercritical CO2-rich mixture was injected into a saline aquifer 1.5 km beneath the surface. During the experiment, the DAS array also recorded many regional earthquakes. This provides an opportunity to test the performance of the technique in a purely passive mode by using body waves from these earthquakes.Recent studies suggest that subsurface changes can be also monitored by tracking changes in seismic amplitudes based on a simple principle that formation softening causes an increase of displacement or strain ,22,23. P, et al.  expanded, et al.  demonstrIn the method of Pevzner, et al. , the usempedance ; k is a mpedance . In active seismic, sources are approximately stable over time; thus, measured strain (or strain rate) over time is directly proportional to the changes in the DAS impedance Y . However2-rich mixture into a saline aquifer at a depth of 1.5 km, known as the Paaratte formation. For this purpose, five wells, including one injector  and four deviated monitoring wells (CRC-4-7), were drilled at the Otway International Test Centre in the Australian state of Victoria, in an area of approximately 1 km2. All wells are instrumented with fibre-optic sensing cables deployed behind the casing used for continuous DAS (and distributed temperature sensing (DTS)) recording. All the wells were drilled to a total depth of approximately 100 m below the injection interval. Well trajectories and the simplified interpreted CO2 plume contour are shown on The CO2CRC Otway Project Stage 3 is focused on downhole monitoring of a small-scale injection of CO2 injection period (4 months), and at least 8 months after the end of injection, apart for a few operational gaps .DAS data have been acquired continuously in all of these wells and used for active  and passAll of the fibre-optic cables are connected to three DAS interrogators, having either a pair of wells or a well and a 1 km long trenched helically wound cable. Composition of the ambient seismic energy recorded by DAS in the downhole environment was studied previously in a number of Australian wells instrumented with fibre-optic cables, including CRC-2 and CRC-3 ,27, a trIn particular, oceanic microseisms are located within the 0.1–1 Hz frequency band and mostly propagate as surface waves. Distant large earthquakes have a significant amount of energy in frequencies below 1–2 Hz. These events are easily detectable but not particularly suitable for amplitude analysis as their direct body-wave arrivals are usually severely scattered over the long raypath. On the other hand, the event must be sufficiently distant from the observation site to ensure that the waves are refracted sufficiently deep, emerge at steep angles, and propagate along the wellbore as plane waves. For most of the shallow crustal earthquakes occurring few tens of kilometres away, this condition is likely to be automatically fulfilled. From observations of many DAS seismograms, it appears that refracted waves from regional events with epicentral distances of 50 to 200 km are most suitable for amplitude analysis. Detect all events around the time corresponding to the injection using a modified short time average over long time average (STA/LTA) algorithm .Manually sort these events and pick travel time curves for P and S waves for several target events.Scan the entire dataset using a semblance-based algorithm similar to the one applied to the pilot data acquired in CRC-3 ahead of drilling the most recent Otway wells (2018–2019)  using onThe first stage in the analysis is detection of these events. The detection workflow comprised several steps:Well: CRC-3 and CRC-4, all traces below 200 m,Date start: 01/12/2020,Date end: 18/2/2021 (CRC-4); 16/03/2021 (CRC-3),STA window: 64 ms,LTA window: 800 ms,STA/LTA detection threshold: 60 .For the STA/LTA scan, we used the following parameters:Prior to calculation of STA/LTA values, the data are filtered with bandpass Ormsby filter (2–3–100–240 Hz) to exclude the influence of the oceanic microseisms and large distant earthquakes. This procedure produces several hundred events, with a large proportion of false alarms. Signals were then manually inspected, and some of the events were used to build the travel time curve library for the semblance-based detector.To detect the events using the semblance-based algorithm, we selected two boreholes, one from each drill pad, namely, CRC-4 and CRC-7.Semblance calculation time window: 30 ms,Combined weighted semblance (70% for P-wave and 30% for S-wave) threshold: 0.3,Minimal event separation: 30 s,Bandpass filter: Ormsby, 10–20–100–240 Hz.The parameters were selected as follows:All detected signals were manually verified and only events having similarity to natural regional earthquakes were added to the catalogue. In total, this workflow detected 174 events originated away from the Otway site. Out of these 174 events, 23 are present in the catalogue provided by Geoscience Australia  with magThe 174 earthquakes can either be natural seismic events or originate from human activity, such as mine site blasting. Unlike the situation close to Perth , where most events are attributed to mine blasting ,9, in Vi2. While having such a densely instrumented site is ideal for location of local induced seismicity, this is far from optimal for regional seismic events. The system is too small to get the locations reliably but still gives some approximate estimates.The DAS-instrumented wells employed to detect the events are located within an area of ~1 kmTo this end, we need to estimate the distance and azimuth to the event. Approximate epicentral distance can be obtained from the difference between P and S wave arrival times . The distance re C=7.5 .Out of all 174 events, 119 had identifiable P and S waves. Using data from multiple wells corresponding to the same vertical depth, it is possible to search for azimuth and apparent horizontal velocity, which would minimise the misfit between the observed and theoretical arrival times for the P waves. This implies that the wave is a locally plane wave, and the velocity in the vicinity of the site is laterally uniform.Azimuths to the epicentre were obtained by picking and analysing first breaks of P wave arrivals. We used the closest receiver to 1300 m TVD for each well and only those events which have the valid picks from at least three wells. The result of the procedure is shown in In general, more earthquakes were detected closer to the site. This demonstrates the decay of sensitivity with the distance from the site. The distance to the epicentre of the furthermost event we were able to locate was ~284 km.Surprisingly, a large portion of the events originated from two distinct clusters. One was located offshore in the vicinity of Cape Otway and the town of Apollo Bay (at least 31 events). The other was near the Jancourt East (11 events). Note that the Apollo Bay cluster was active over the entire observation period. This clustering may have important practical significance for reservoir monitoring using earthquakes. More or less permanent location of the epicentres can improve repeatability of the observations and can perhaps be considered in the design of receiver arrays.While the accuracy of the location is not easy to estimate, it is possible to get some information about it by comparing these estimates against catalogue locations. ST–PT delay (most probably from the Apollo Bay Area). The data were pre-processed with a bandpass filter (2–5–20–30 Hz). The lower cut-off frequency was selected as frequencies below 2 Hz are usually dominated by ocean microseisms . FrequeAmplitudes were estimated as the root-mean-square values in the 1 s time window starting 200 ms before the strongest arrival for each trace corresponding to each depth in the well. Next, the values from different events were normalised to a common level, using the algorithm for decomposition of amplitude scalars for surface-consistent amplitude correction in reflection seismology (treating earthquakes as sources and DAS channels as receivers). After decomposition, only the source part of coefficients was applied to the amplitudes. The results are presented in the next section.2 injection. The gap at the 1355–1380 m depth interval corresponds to DAS channels in CRC-3 that were faulty for the duration of the acquisition. This gap is present in all CRC-3 DAS seismograms and amplitude profiles shown in this section. 2 saturation. The strength and clarity of the amplitude anomaly varied between events, probably owning to variations of the signal-to-noise ratio, their angle to the vertical, and lateral heterogeneity of the subsurface.2. The strength (and shape) of the amplitude trough varied between events but is broadly consistent with the results obtained from active seismic analysis and forward modelling [The inverse of the extracted amplitude for each of the events shown in odelling . More spodelling ; this di2 injection causes not only changes in the reservoir properties but also changes of the borehole environment such as temperature, which can also affect the DAS response [Note that COresponse . However2 can be detected though amplitude analysis of regional earthquake records on permanently installed downhole DAS receivers. We used the data from the injector well as it was the only well which had a CO2 accumulation around it at the target reservoir level. However, the value of the approach is in the ability to detect the changes in the elastic properties anywhere along the wellbore. This can be especially important for tasks such as monitoring of the unwanted migration into the so-called “above zone”, part of the subsurface located above the primary containment section. Whereas for the Otway case, the approach used natural regional earthquakes, other passive sources of body waves can be used. These include mine site blasts, which would produce similar wavefield to relatively low-magnitude earthquakes [We demonstrated that injected COthquakes . SurfaceDetection of the amplitude anomaly associated with the change in elastic properties using plane P waves is likely to be extended to the analysis of shear wave amplitudes. However, this is likely to be more complicated as those waves come after the first arrivals and interfere with other components of the wavefield. At this stage, we have not explored the possibility to separate P and S waves using some 2D filtering techniques. It is also possible that a waveform inversion technique could be developed instead of the separation. We speculate that the inversion technique can also be designed to analyse both amplitude changes and variations in travel time caused by the changes in body wave velocities.It may also be possible to extend the technique to very-low-frequency sources of energy, such as teleseismic events. Multiple detectable events occur every day; thus, the reliability of this source is very high. Another advantage of such events is an easy separation from other noise sources in frequency domain, such as all surface events and even oceanic microseisms, which have most of the energy concentrated between 0.1 and 1 Hz. Oceanic microseisms propagate along the continental crust as Rayleigh waves with several modes , which aLike all other methods involving DAS, the proposed technique requires stability of the DAS system over monitoring time. This stability is best analysed using controlled seismic sources rather than earthquakes. The Otway project is ideal for such a test, as it operates nine permanent sources of seismic energy known as surface orbital vibrators (SOVs). Analysis of SOV/DAS data over 2 years confirms the stability of this system ,25. LongIn addition to elastic properties, there are other factors which might trigger time-lapse amplitude changes. One of these is coupling of the fibre-optic receivers to the formation. Sidenko, et al.  reported2 monitoring.Nearly 2 years of passive seismic recording at the multi-well array of downhole receivers at the Otway International Test Centre revealed numerous regional earthquakes . Most of this regional seismicity originated from several clusters. This ubiquity of the events  and their spatial clustering allowed the use of their P-wave amplitudes for time-lapse monitoring of formation stiffness, thus validating the method proposed in the previous studies and confirming its applicability for CO2 injection. This indicates that the anomaly was caused by a reduction in the elastic properties (DAS impedance) in the reservoir caused by CO2 saturation. However, due to variability of the extracted amplitudes between earthquakes even without an injection, multiple events are required to distinguish the time-lapse anomaly from time-lapse noise.Analysis of P-wave amplitudes extracted from downhole DAS gathers of the earthquakes recorded over nearly 2 years of observations showed a consistent-amplitude anomaly at the injection level, visible in all events that occurred after the start of CO