Controlling the formation of high aspect ratio void channels inside glass is important for applications like the high-speed dicing of glass. Here, we investigate void formation using ultrafast Bessel beams in the single shot illumination regime. We characterize the morphology of the damages as a function of pulse energy, pulse duration, and position of the beam inside fused silica, Corning Eagle XG, and Corning Gorilla glass. While a large set of parameters allow for void formation inside fused silica, the operating window is much more restricted for Eagle XG and Gorilla glass. The transient formation of a molten layer around voids enables us interpreting the evolution of the morphology with pulse energy and duration. Cutting and drilling glass is an important technological problem, particularly when the width over which the material has to be modified is much smaller than the length of the modification. This is high aspect ratio structuring. In this context, ultrafast laser processing is very attractive because the infrared wavelength and the very high intensity of the pulses enable energy deposition in three dimensions, including inside the bulk of glass . The lasBecause point-by-point scanning is relatively slow, a number of techniques were developed to improve the overall speed for the micro-structuring. In addition, elongated voids cannot be formed by scanning, since a nano-void produced by a first pulse would be erased by the microexplosion produced by the second pulse next to it . Bessel Laser structuring with high aspect ratio is particularly useful in the context of stealth dicing, a concept in which a series of pulses under high-speed translation of the sample is used to create an entire modification plane . A mechaHere, we show that, depending on the material, voids do not form for the same parameters. We compare three glass types: fused silica, CorningOur experimental setup see  is compoAfter processing, the laser-modified regions were characterized by scanning the sample with white light, brightfield illumination, in an optical microscope arrangement with a numerical aperture of 0.8 and aberration-correction. Stitching the microscope images provided high resolution images of the whole high aspect ratio structure. In Importantly, the morphology of the material transformation can vary along the beam. An example is shown in We further explored, in a parametric study, the evolution of the laser-induced structures as a function of pulse energy, pulse duration, and beam position. We summarized our results in In In The operating window for void formation is restricted to a small energy range before bubbling sets in. This window is even smaller in the case of Gorilla glass. For most of the pulse durations in Gorilla glass, above the energy for the bubbling regime, only faint structures are found, whereas this phenomenon occurs in Eagle XG only for 1.5 ps and never in fused silica. It could be attributed to a slower relaxation in Gorilla glass than in that of the other media.We attribute the differences between the different glasses in terms of void opening to the difference in softening point. Indeed, it exceeds 1500 In Eagle XG and Gorilla glass, beam positioning close to the entrance and exit surfaces yield to similar tendencies as in the case of fused silica. While no major differences could be observed with We observed that void formation in transparent glasses strongly depends on energy and pulse duration. In fused silica, voids can be formed for a relatively large set of parameters. By increasing pulse energy and pulse duration, the high aspect ratio void channel splits into bubbles with a diameter of approximately 1 We interpret the evolution of the morphology of the damages induced by ultrashort Bessel beams in these glasses in the following way. For sufficiently high energies, a void can form inside glass, either after a micro-explosion ,33 or byThese results shed light on the formation of high aspect ratio void channels inside different glasses. We believe they will impact on future technologies for ultrafast laser material processing of glass.


As autonomous navigation is being implemented in several areas including the maritime domain, the need for robust tracking is becoming more important for traffic situation awareness, assessment and monitoring. We present an online repository comprising three designated marine radar datasets from real-world measurement campaigns to be employed for target detection and tracking research purposes. The datasets have their respective reference positions on the basis of the Automatic Identification System (AIS). Together with the methods used for target detection and clustering, a novel baseline algorithm for an extended centroid-based multiple target tracking is introduced and explained. We compare the performance of our algorithm to its standard version on the datasets using the AIS references. The results obtained and some initial dataset specific analysis are presented. The datasets, under the German Aerospace Centre (DLR)’s terms and agreements, can be procured from the company website’s URL provided in the article. The need for a safe, collision-free and ecologically clean maritime navigation keeps getting more pronounced with increasing global trade demand ,2,3. OveThe use of benchmark datasets has supported much development into various autonomous navigation applications through the recent years. For instance, the KITTI  and nuScIn addition to the datasets, we demonstrate the application of a tailored multi-target tracker on the same. Multiple target tracking (MTT) is the problem of jointly estimating the individual states of targets alongside the targets’ cardinality at each observation step . Two maiThere are three steps involved in the processing chain implemented in this work and are as summarised. In the first one, potential targets are extracted from radar images as point clouds. These point clouds are then subject to the approach ,25 in thapproach ,26 filteapproach ,23, wher(1)We first introduce the DLR radar repository comprising three individual datasets along with the participating vessels’ reference positions as recorded from the AIS data.(2)We then adapt the standard JPDA filter as a baseline algorithm for tracking centroids on the datasets in the repository to highlight the overall blockchain performance, compare the adapted version to the standard one and discuss the associated challenges.Our two-fold contribution is therefore as follows: The article is thusly organized: In https://doi.org/10.26090/rwmr. It is to be noted that the datasets are open for requests from the EU and NATO member states; requests from other countries are subject to individual assessment by the approval board. The aim of using these selected trajectories from some measurement campaigns was to capture real-world situations as closely as possible on common navigation routes. Some basic details of each dataset, that is, the number of targets and frames captured, the dataset duration and the sensors involved, are summarised in The DLR radar repository, under the own company’s export control principles, can be acquired as a zipped file (dataset-wise) from the following DOI: In each campaign, either an own vessel or a participating vessel (as for MANV) was on course. Frame grabbers were used to capture the radar (X-band) screen visuals at a frequency of 1 Hz, during which some random data loss occurred as seen in Selected AIS information that were recorded have been included, such as the position, dimensions, course over ground, speed over ground and true heading of the vessels equipped with transponders. Due to security and privacy related reasons, information divulging the identity of the vessels (from the unique maritime mobile service identification referred to as the MMSI), and the date and time of campaigns have been omitted. Furthermore, we note that the regions of interest have been more or less constrained to a coverage of their own vessels’ radar ranges, and that different (own) vessels were part of the specific campaigns. The dimensions of AIS-equipped targets involved are summarised in referenceMeas.mat. The initial AIS messages received were decoded and filtered based on the vessels’ MMSIs to eliminate unwanted data and outliers. The filtered information initially contained vessel positions in geodetic coordinates, that is, in latitude and longitude . In case more than one message were received at a specific time, the most recent message was considered. The positions were then converted to some local East North Up (ENU) coordinate system using an appropriate reference point location pivoting a tangent plane approximating the Earth’s surface. In general, AIS information that is transmitted is dependent on the speed of the vessel: higher speeds result in more frequent updates. The concerned AIS positional measurements were linearly interpolated to match the standard radar-grabber frequency of 1 Hz. For the datasets, a linear interpolator was enough to obtain the missing points. The AIS data can be found in radarImages.As for the radar images, they have been processed by cropping out the user-interface functions so that only the region of interest is visible together with the sensor range values. This step was vital to remove real-world identifiable data such as the date and time of the experiment. The processed images are stored in the folder called polarMeas.json and cartesianMeas.json files, both in the polar and Cartesian coordinates format. A sample code  for visualizing the detections is also incorporated.The detected point clouds (obtained from the approach in the next section) are stored in the In this section, we describe the methods used on the datasets from detecting to tracking the targets. Our overall processing chain at observation step (a)Each image is masked, hence eliminating further interface information  and converted to grayscale.(b)Using specific thresholds for intensity values, much of the weaker blobs have been filtered out. This reduces clutter from within the observation region.(c)Determinant of Hessians (DoH) from Python’s skimage library (Blob Detection. Available online: https://scikit-image.org/docs/dev/auto_examples/features_detection/plot_blob.html (accessed on 2 July 2021)), is applied next for automatically detecting potential targets based on their distributions.The blob detection algorithm, (d)The cloud of points (or measurements) that fall under the detected blob’s radius are extracted and stored in range (metres) and bearing (radians).The captured radar images were each processed sequentially in chronological order and the positions of the potential targets stored in appropriate files were then fed to the baseline tracker. Steps followed for the image processing are as described:This method has been improved upon from the manual target versions in the previous works ,23. The On the basis of the AIS-retrieved own vessel’s positions, the point clouds’ coordinates have also been converted to a Cartesian ENU coordinate representation for both processing and visualisation simplicity . At thisTo calculate the centroids from the potential targets that have been extracted as point clouds, we perform ustering ,30, this minimum ,25. As tGiven that a cluster consists of At observation step The JPDA method, which associates validated measurements to targets based on their specific probabilistic weights, is employed. An extended Kalman filter (EKF)  is then A single target state ented in .For an efficient measurement processing, multiple stages of gating were carried out to eliminate measurements away from predicted target position: a rectangular one for l gating ,26. The In this part, we briefly go over the main steps for calculating the joint association probabilities of centroid measurements to targets. More elaborate explanations of the same can be found in the basic works ,14,19. At first, a binary validation matrix Under the 1-1 mapping constraint between a centroid and a target, the association matrix The marginal association probability These values are then used as weights for state estimation of each track from the standard EKF equations .In this work, the tracks have been initialised from some initial guesses around the first AIS reference positions of the tracks wherever available  and otherwise around the radar measurements (as it is in the case of the AIS transponder-unequipped craft in MANV). The Euclidean distance-based positional errors are used to evaluate the filter estimates against the reference. In this section, we demonstrate the tracking results and carry out some analysis on all three datasets. Two versions of JPDA are considered. The first one is the standard JPDA filter as conceived in , and theWhile they both consider the centroids as measurements, the basic difference between the two filters is in the gating and accounting the dispersion matrices of the centroids considered for the adapted version, as explained in the previous section. The estimated tracks for DAAN show continuous and properly associated measurement to track, in particular around the point of closest approach of Target 2 to the aid to navigation which is around In the case of DARC, despite having the RACON-originated measurements often interfering with those of the true target, the track was well estimated as seen in A note here is to also consider the dimensions , that was most likely from a leisure craft not equipped with any AIS transponder. The dataset has a relatively finer resolution and smaller vessels, hence the overall error in comparison to the other two datasets is lower. There are however more fluctuations here because of the targets’ manoeuvres and brisk speed and course changes throughout the whole duration. The peak between While the errors of the two filter versions for the targets in MANV and Target 2 in DAAN were almost overlapping, the errors in DARC and Target 3 in DAAN were disparate. There is a higher improvement particularly in DARC throughout the entire trajectory, the most challenging dataset considered. For Target 3 in DAAN, the improvement occurs during the first half where the visibility is rather poor, with occasional moments of occlusion. As soon as visibility enhances, the errors overlap. The dispersion matrix in EC-JPDA takes the measurements distribution into account thereby making the filter more robust than its counterpart in the noisier surroundings by retaining information related to the target’s underlying body. This also implies that there is also a relevant correlation of the range of the own vessel with respect to the target to the estimate’s accuracy: closer and undisturbed measurements from close ranges offer similar results for both versions, in contrast to the distant and highly noisy measurements obtained from farther ranges. In the latter case, the EC-JPDA filter could provide more accurate results.The onboard marine radars have limited sensor resolution, which when considered together with the typical radar imperfections (smearing and reflections for instance) make solving applications such as ETT more challenging. In general, the precision is decreased for both the kinematics and the perceived size of the vessel, which somehow distorts the values of the dispersion matrix and leads to inaccuracies during tracking. Furthermore, the tracking results are heavily dependent on the clustering results and tend to vary between runs. As In this article, we have presented the DLR image-based marine radar datasets—that are available to the community for research purposes. The datasets aim at advancing the state of the arts methods in particularly maritime research, as they capture very maritime route-specific challenges such as aids to navigations and RACONs.Altogether, we also present a blockchain to explain our target detection, measurement clustering and MTT approaches respectively. As some first step, a new adaptation for MTT that basically uses a centroid-based JPDA, called EC-JPDA, was proposed as a baseline algorithm. The results of EC-JPDA tracker and a standard JPDA tracker run on the dataset was shown and the results were discussed. Overall, it was deduced that EC-JPDA outperformed the standard one on a very noisy and challenging dataset. The performance was similar to the standard one under some more ideal conditions .


Breast cancer is a serious threat to women's physical and mental health. In recent years, its incidence has been on the rise and it has become the top female malignant tumor in China. At present, adjuvant chemotherapy for breast cancer has become the standard mode of breast cancer treatment, but the response results usually need to be completed after the implementation of adjuvant chemotherapy, and the optimization of the treatment plan and the implementation of breast-conserving therapy need to be based on accurate estimation of the pathological response. Therefore, to predict the efficacy of adjuvant chemotherapy for breast cancer patients is to find a predictive method that is conducive to individualized choice of chemotherapy regimens. This article introduces the research of DCE-MRI images based on deep transfer learning in breast cancer adjuvant curative effect prediction. Deep transfer learning algorithms are used to process images, and then, the features of breast cancer after adjuvant chemotherapy are collected through image feature collection. Predictions are made, and the research results show that the accuracy of the prediction reaches 70%. In recent years, technology has brought people not only the changes in living environment and lifestyle, but also some health diseases. For example, breast cancer has now occupied the first place among female malignant tumors in the world and has become the main culprit in the soaring mortality rate of female tumor patients , 2. AdjuFor breast cancer assisted evaluation, although pathological biopsy is the most accurate, evidence-based medicine shows that patients with malignant tumors should minimize the number of biopsies to prevent the spread and metastasis of cancer cells. Therefore, pathological examinations often occur before chemotherapy. After the operation and the later period of treatment, there is a lag effect to some extent , 6. AlthThe main content of this article is the study of DCE-MRI image based on deep transfer learning in breast cancer adjuvant curative effect prediction. Firstly, the deep transfer learning algorithm is applied to breast cancer image processing, and secondly, the curative effect of breast cancer adjuvant chemotherapy is adopted. For evaluation and research, and finally through the feature data collection of images before and after adjuvant chemotherapy for breast cancer, the accuracy of DCE-MRI images in predicting the adjuvant curative effect of breast cancer is obtained. The innovation of this article lies in the application of deep migration learning algorithms to image processing before and after adjuvant chemotherapy for breast cancer, which achieves good results in image processing, detection, and classification.The background analysis of breast cancer can describe the progress and status of the tumor, which is based on the segmentation of the entire breast based on MRI images. By using MRI image sequences to construct a pipeline for breast region segmentation, the possibility of automatic breast cancer diagnosis can be realized. Research on breast region segmentation based on traditional and deep learning methods has been carried out for several years, but for the following background analysis, most of the studies have not achieved satisfactory results. Sui D proposed a new method of whole breast region segmentation method based on U-net++ . CompareLu proposed a new method of information extraction based on deep Convolutional Neural Networks and transfer learning . First, Deep learning methods have been widely used in many patterns recognition tasks and have achieved good results. Among them, the more prominent algorithm is the Convolutional Neural Network (CNN) algorithm. Generally, the input of CNN is image data, so the structure of CNN can be designed according to the characteristics of the image, so that it has higher efficiency and better performance than general structure neural network , 22. Thex, but the degree of difference between the obtained probability score and the category to which the actual x belongs is not intuitively [x and the real value y, and it can also be used to measure the parameters w, b of the predicted value of x in the hypothetical function. When the loss function is high, the current parameters W and b perform poorly, and when the loss function is low, it means that the current parameters W and b perform better. In mathematical principle, the loss function of softmax classifier can be obtained by maximum likelihood estimation [The loss function is that after a given hypothesis function, we can calculate the probability score of each category for each input data uitively . This intimation , 25. TheJ  is the loss function; for good  parameters, the loss function can be made to obtain a smaller value. The specific operation method is to achieve corresponding matrix norm F through the loss function Add regular term (W) [err is called cross entropy, and term (W) , 27. Theλ is an actual number, and its function is to adjust the degree of influence of regularization on the loss function. The size of the λ value setting determines the degree of influence. The above is the derivation process of the loss function and the final expression formula.In recent years, with the rapid development of computer vision, machine vision, and other technologies, deep learning has achieved better results than human eye recognition in some areas of processing natural images, such as image classification and target detection and behavior recognition , 29. TheConvolutional layer: compared with the general neural network layer, the CNN network layer has two different characteristics. One is convolution based on the principle of sparse connection, and the other is weight sharing . (1) ConS = 2, and the Max operation is used in each local space. 75% of the information is discarded from the input information, and the output result is obtained. The principle is shown in Pooling layer: the operation of the pooling layer is performed independently on each depth component, and the most common one is maximum convergence . What isLinear rectification layer and fully connected layer: the linear rectification layer has the same operation as the above loss function, which activates the elements one by one without changing the data dimension , 38. AftThis part of the content mainly introduces the different structural layers in CNN, including convolutional layer, pooling layer, linear rectification layer, and fully connected layer. The general structure of CNN is shown in In the clinical treatment of breast cancer, with the promotion and popularization of adjuvant chemotherapy, it has become the standard treatment mode for locally advanced breast cancer. In clinical applications, if an accurate evaluation of the efficacy of neoadjuvant chemotherapy is made before surgery, it is helpful to timely discover intractable tumors that do not respond to treatment or only have a small response and adjust the chemotherapy regimen for breast cancer patients in real time to reduce the risk of failure. Necessary treatments and toxic side effects caused by chemotherapeutic drugs increase the possibility of complete pathological remission for patients and at the same time help select the timing of surgery and determine the patients who are suitable for breast-conserving treatment. Therefore, it is necessary to make an objective and accurate evaluation of the expected curative effect of breast cancer patients during adjuvant chemotherapy. The efficacy of adjuvant chemotherapy is generally evaluated in both clinical and pathological aspects. Clinical evaluation is generally based on changes in tumor number and tumor volume to evaluate the efficacy of chemotherapy. Oncologists from anticancer organizations around the world have formulated the WHO evaluation criteria for the efficacy of solid tumors. With the continuous improvement of the treatment level and tumor diagnosis, the evaluation criteria for the efficacy of solid tumors have also undergone several revisions. The cooperation has established the Response Evaluation Criteria in Solid Tumors (RECIST) that is currently recognized by the international tumor community. Target lesions refer to measurable lesions used for comparative evaluation of efficacy before and after treatment in the RECIST1.1 standard. The difference between the efficacy evaluation standard and the WHO standard is shown in Pathological examination is the gold standard for diagnosing the efficacy of neoadjuvant chemotherapy. Compared with clinical evaluation, it has more important meaning for treatment guidance and prognostic judgment. However, it requires case puncture during surgery, which will cause problems for the patient. At the same time, the secondary injury has the problem of not timely evaluation results, also limiting its clinical application. This study was carried out based on clinical evaluation standards, and the gold standard of pathological examination was used as a reference. X-axis, Y-axis, and Z-axis represent the three-dimensional structure of the image, and the T-axis represents the enhancement of the image before and after the injection of the contrast agent. DCE-MRI images reflect the internal structure and characteristics of the analyzed tissue through the above-mentioned methods, and the data studied in this article are obtained from these image data.According to the operating principle of DCE-MRI image acquisition, the image data is composed of four-dimensional data. By combining the absorption and release of intravenous contrast agent in the analyzed tissue, three-dimensional images of the tissue before and after the injection of the contrast agent are obtained , so the data structure of DCE-MRI images is four-dimensional, that is, three spatial dimensions plus one time dimension (enhanced information at different times). Usually, the four-dimensional data structure of breast DCE-MRI scan is shown in P < 0.05).The case data of this experiment was collected from data of 60 malignant breast cancer patients confirmed by surgical cases in a certain regional hospital during a certain period. All cases were female patients, ranging in age from 29 to 61 years, with an average age of 47.35 years. Among the 60 patients, the pathological diagnosis showed 30 cases of invasive ductal carcinoma, 22 cases of invasive carcinoma, 4 cases of intraductal carcinoma, and 4 cases of invasive poorly differentiated adenocarcinoma. All patients underwent DCE-MRI scans before and 2 cycles after chemotherapy. The clinicopathological information of breast cancer patients was listed, including age, menopause, tumor size, and cancer type. The specific results are shown in The examination equipment used for DCE-MRI image acquisition is a dedicated breast phased array coil for acquisition. During the examination and scanning process, the patient lies prone on the rigid testing table with the hands flat on both sides of the body, and the breasts on both sides naturally droop and enter the breast phased array coil.The object analyzed in this study is T1-weighted dynamic enhanced imaging (DCE-MRI), the scan sequence is l3d1, and the detection time of a dynamic enhanced MRI of the breast is about 15 to 18 minutes. The first sequence of the dynamic enhancement scan is the mask plain scan sequence. For the sequence scan, the mask sequence is scanned before the injection of the contrast agent. At the end of the sequence, the paramagnetic contrast agent is placed on the patient's arm at a dose of 0.2 mmol/kg. A high-pressure syringe is used to inject intravenously at an average flow rate of 4 ml/s. In the dynamic enhancement scan process, the scan sequence of 3-time phases is set, after 1 minute 20 seconds and 2 minutes 25 seconds, respectively, and the image acquisition work of the S1 sequence and the S2 sequence is performed, respectively. Starting from 5 minutes to 30 seconds after S2 imaging is completed, the MRI scanner will change the parameters to perform high-resolution sequence image acquisition of the breast. The reconstruction layer thickness of this mode is reduced to 0.8 mm. It is possible to obtain a more detailed breast image than the first three sequences, so that the doctor can make an accurate diagnosis. Since the three plain scan sequences use the same scan parameters, the image set specifications of each sequence are the same. Therefore, the data is stored as a four-dimensional matrix to facilitate subsequent research. Dynamic contrast-enhanced MRI (DCE-MRI) is the introduction of contrast agent, tracing the TIC curve of the area of interest. And then the paper selects the appropriate pharmacokinetic model analysis to measure various quantitative parameters reflecting the microcirculation state of tumor foci and the penetration of contrast agent, including volume transfer constant Ktrans, rate constant Kep, and extravascular extracellular space volume ratio etc. In fact, the contrast agent itself does not directly have biological characteristics, but the penetration effect tends to condense in the tissue area with high vascular permeability, thus presenting an enhancement effect. The degree of enhancement depends on the microvessel density, blood flow, and vascular permeability of the lesion. In the case of sexual conditions, DCE-MRI can indirectly capture this microscopic change by analyzing the corresponding quantitative parameters. Moreover, neovascularization is the key to the development of breast cancer, while neoadjuvant chemotherapy drugs focus on destroying the formed tumor blood vessels and antagonizing them. The tumor is about to generate new blood vessels. Therefore, DCE-MRI can objectively and indirectly evaluate the effect of chemotherapy drugs on the tumor.In the research process of this part, firstly, the correlation analysis of breast DCE-MRI image features before adjuvant chemotherapy and after adjuvant chemotherapy for 2 cycles was performed to screen out some features, and then CNN was used to further select the feature set, and then the support vector machine was used to analyze the final features. The feature set is used for classification prediction. The specific process is shown in (1)Sensitivity:(2)Specificity:(3)Accuracy:(4)True positive rate:(5)False positive rate:When distributing the training set and test set data, the case is the basic unit to separate, to avoid the images in the same case sequence from appearing in training and testing. The area under the experimenter's characteristic curve (AUC) is evaluated by the following formulas about specificity, accuracy, and sensitivity to evaluate the experimental prediction results. When evaluating the classification effect of a classifier, the formula for each evaluation index used is as follows:A set of sensitivity and specificity will change with the change of the decision threshold, so only using SEN and SPE can only evaluate the classification effect of a specific threshold. The ROC curve can obtain continuously varying sensitivity and specificity under different thresholds. The closer the AUC is to 1, the better the performance of the classifier is.In deep transfer learning, the trained model parameters are assigned to the convolutional layer of CNN. At the same time, the basic learning rate of transfer learning needs to be adjusted appropriately before training. First, initialize the parameters to the convolutional layer before and after chemotherapy, and then lower the learning rate to the minus 4th power of 10. Second, input the training data to the model for training. Finally, the actual test results are obtained.It can be seen from According to Based on the research of deep transfer learning algorithm, this paper first applies the deep transfer learning algorithm to the detection and classification of images. Secondly, it establishes the evaluation characteristics through the evaluation criteria of the curative effect and finally uses DCE-MRI to conduct breast cancer before and after chemotherapy. The image analysis finally reached an accurate prediction of the curative effect of adjuvant chemotherapy for breast cancer. The research in this article has achieved a certain effect in the prediction of adjuvant chemotherapy for breast cancer. It is helpful to the treatment plan of adjuvant chemotherapy for breast cancer. It also relieves certain trial and error for patients with adjuvant chemotherapy for breast cancer. The process has certain practical guiding significance.


Observational data over 15 years of rotavirus vaccine introduction in Belgium have indicated that rotavirus hospitalisations in children aged <5 years plateaued at a higher level than expected, and was followed by biennial disease peaks. The research objective was to identify factors influencing these real-world vaccine impact data. We constructed mathematical models simulating rotavirus-related hospitalisations by age group and year for those children. Two periods were defined using different model constructs. First, the vaccine uptake period encompassed the years required to cover the whole at-risk population. Second, the post-uptake period covered the years in which a new infection/disease equilibrium was reached. The models were fitted to the observational data using optimisation programmes with regression and differential equations. Modifying parameter values identified factors affecting the pattern of hospitalisations. Results indicated that starting vaccination well before the peak disease season in the first year and rapidly achieving high coverage was critical in maximising early herd effect and minimising secondary sources of infection. This, in turn, would maximise the reduction in hospitalisations and minimise the size and frequency of subsequent disease peaks. The analysis and results identified key elements to consider for countries initiating an optimal rotavirus vaccine launch programme. Development and implementation of an appropriate vaccination programme with high vaccine coverage should help to control transmissible infections in a population . VaccinaStreptococcus pneumoniae, human papillomavirus or meningococci were also subject to similar issues around optimum implementation to reach maximum disease control benefit as quickly as possible [Newer vaccines developed against diseases caused by rotavirus, possible ,6. Recenpossible . When thpossible ,9, as prpossible ,11. Howepossible ,13,14. Upossible ,16,17. Tpossible , based opossible . In the possible ,22,23,24This research should help HICs that have not yet introduced rotavirus vaccination but now wish to begin a vaccination campaign such as Switzerland, France, the Netherlands, or Denmark  to identTo understand the potential impact of vaccination over time, some background on rotavirus disease and the vaccine is needed. Rotavirus disease is mainly seen in very young children aged less than two years, often accounting for close to 80% of all the child rotavirus hospital disease events in the at-risk population aged under five years. The hospital disease age spread followed a Weibull distribution up to the age of 60 months  and a 3-dose live attenuated human-bovine assorted vaccine with five strains  [There are two commercially available oral rotavirus vaccines commonly used in HICs, a 2-dose live attenuated vaccine derived from a single human strain  . The vacNJ, USA) . As a reNJ, USA) .The level of vaccine coverage during the first peak season after introduction has additional consequences for the level of indirect  effect of the vaccination. Indirect effects can lead to important added protection of non-vaccinated at-risk children. Once the vaccine coverage has reached the 5-year implementation in the at-risk population, a new situation of infection spread and disease manifestation appears, depending on the initial coverage achieved. This is no longer a situation of coverage build-up, but a post-vaccine uptake period. A new state of infection equilibrium is achieved between susceptible, infected/infectious, and recovered children, exposed to a much lower rate of infection risk because of the vaccination .RotaBIS is a database study initiated in 2007 and conducted in 11 hospitals in Belgium . The centres had 546 paediatric beds, representing 30.6% of a total of 1793 paediatric beds in Belgium . All chiWe compared the absolute numbers of rotavirus-positive test results between the pre-vaccination and post-vaccination study seasons with the number of positive tests in the pre-vaccination period as the reference. The underlying assumption is that the coverage area for each of the hospitals participating in the study remained the same across the whole study period. Therefore, the most relevant value for the comparison of pre-and post-vaccination is the average absolute number of positive tests observed per time unit. Vaccine sales data in Belgium showed that vaccine coverage was low before reimbursement. We therefore assumed that no children were vaccinated prior to reimbursement in November 2006.Ethical approval was obtained annually from 2007 when the study was initiated and from 2018 for a 3-year contract period instead of annual data. Combining these two different periods into a single model construct is challenging. The vaccine impact has a different dynamic, and as a consequence, the infection spread is also different in each period. We therefore modelled the vaccine uptake period and post-uptake periods separately, using different model types for each period. The vaccine uptake period used a linear regression equation with different parameters affecting the hospitalisation rate per unit time, and is described in detail elsewhere . The preH0) was that the effect of vaccination observed during the vaccine uptake period resulted from a process that includes vaccine waning, which may reduce the vaccination effect, as often claimed in the literature [aH) is that the vaccine impact seen is mainly the result of a combination of direct vaccine effect, indirect herd effect, and secondary sources of infection, with vaccine waning playing a much smaller role.The null hypothesis  is that there is no further decline to be observed early in the post-uptake period, but that regular peaks may appear with smaller height and lower frequency than in the pre-vaccination period if the vaccine coverage at the start was not optimal. This pattern results from the influence of secondary sources of infection, and should be characterised by a change in the age distribution of hospitalised cases among the unvaccinated children in the post-uptake peaks compared with pre-vaccination.Expectations for changes in hospitalisation over time with a vaccine of high efficacy and coverage are a continuous, proportional decline across the at-risk age groups, resulting in smaller hospitalisation peaks and time intervals between the peaks . The firTo model the vaccine uptake period, we used a regression equation with known input variables with values estimated based on the RotaBIS data , in whicOther codes are explained in As shown, the equation had two groups of independent variables affecting the outcome. The first group was related to the vaccination programme. Some variables had a positive direct impact on hospitalisations (vaccine efficacy and coverage), but others had a negative direct impact by reducing the vaccine effect (waning). The second group was related to the indirect impact of the vaccine, again, with positive effects in reducing the hospitalisations in unvaccinated children (the herd effect) and negative effects . The value estimates of the variables were obtained from the RotaBIS study examining the impact of vaccination on hospitalisation in Belgium since the vaccine introduction in late 2006 ,33,34,35The post-uptake period, as above-mentioned, is characterised by a new dynamic infection equilibrium in the at-risk population that may show hospitalisation peaks, as seen in the pre-vaccination period but at a much reduced height and frequency. The model must therefore simulate the observed hospitalisation peaks and be able to demonstrate that marginal changes in vaccine coverage affect the hospitalisation numbers. We selected a dynamic Susceptible–Infectious (SI) model, constructed using the Hamer–Soper (H–S) model type in which model predictions could remain stable over time ,36. ThisA H–S model assumes constant replenishment of susceptible individuals via new-borns. The rate of new events  is jointly proportional to the number of susceptibles and the number of infectious individuals. The number of recoveries was also proportional to the number of events. Those not recovering were assumed to be equally contagious over the duration of an event. Those recovered were assumed to be immune and no longer contributed to the disease spread. The model design is presented in The H–S equations were:The data input for the second model  was alsoThe overall aim was to obtain three output results with the two models developed. The first one was to identify datasets for each model that achieved a best fit with the observed data for each period. This should help to identify the key factors affecting the results. A second aim was to evaluate the effect of improving vaccine coverage by 10 percentage points during the post-uptake-period, in a situation resembling the RotaBIS study data. The third area to explore was the effect on hospitalisations in the post-uptake period of improving vaccine coverage at the start of the first year in the vaccine uptake period to an ideal situation.To fit the model-generated data as closely as possible to the observed hospitalisation data at each annual time point, we used constrained optimisation, in which the objective function was to minimise the difference in hospitalisations between the observed and modelled data accumulated over the first eight years of the vaccine introduction. A best-fit software programme in Microsoft Excel, called Solver, was used to identify the value for each variable selected to produce the optimal match with the observational data while complying with specific constraints. These constraints were introduced at two levels. The first level specified the minimum and maximum range within which the values of the variables may vary in the regression equation (the four direct vaccine effect variables). The second level was related to the annual time- and age-specific fit of the data (the four indirect vaccine effect variables). Scenario analysis was conducted to explore the effect on outcomes of vaccine coverage achieved in the first year. We introduced for that effect, a linear relationship between the herd effect and first-year vaccine coverage, in which a one percentage point rise in vaccine coverage in the first year resulted in a 1.64 percentage point increase in herd effect. This was estimated based on RotaBIS data. Vaccine coverage achieved during the peak season in the first year was around 52% and the maximum coverage that could be obtained by starting the vaccination earlier was 85%, while the herd effect measured in the first year was 31% and in an ideal scenario could reach 85%. A second linear relationship was introduced between this change in herd effect and the effect of secondary sources of infection in subsequent years in the same unvaccinated age groups. For each percentage point increase in herd effect, the reduction in the cannibalising effect of the secondary infection sources was 1.06 percentage points. The cannibalising effect of secondary sources of infection on herd effect in RotaBIS was estimated at 35% in the second year of observation, reaching 0% when the maximum herd effect was attained .Sensitivity analysis evaluated four conditions separately: no herd effect; no secondary sources of infection; no waning present in the regression analysis; and a simulation of the compensation needed in waning to substitute the effect of removing secondary sources of infection to reach the observed hospitalisation result after eight years. We called this simulation the adjusted waning process.The first analysis evaluated the changes in age distribution in the hospitalisations between the pre-vaccination period and the peaks in the post-uptake period. We split hospitalisations into 13 age-groups and compared the distribution of hospitalisations between the pre-vaccination period and the first peak in the post-uptake period.To run the dynamic model, data on estimated susceptibles at year 9 post-vaccination, when the stabilising dynamic infectious disease processes of the post-uptake period were operational, were needed. These were not available in the RotaBIS dataset, and were estimated using the following calculations. The pre-vaccination period in the RotaBIS study had around 1385 rotavirus hospitalisations per year in the child population up to five years old. This number represented about 2.5% of the at-risk child population, and therefore, a stable annual population number of 55,000 children was estimated as the input data for the population aged up to five years . Each yeReplicating the observed biennial peaks in the model, we identified the drivers of their appearance and how those peaks could be reduced by changing their input values. It should be noted that the observed data reported in Sensitivity analysis investigated the effect of changing the pool of susceptibles by 10 percentage points by increasing vaccine coverage by 10 percentage points on the number of hospitalisations in the post-uptake period.Finally, a scenario evaluation assessed whether increasing the vaccine coverage from the first year in the vaccine uptake period affected the hospitalisation peaks during the post-uptake period. This was simulated by reducing the number of susceptibles in the H–S model by increasing vaccine coverage by 15 percentage points, 30 percentage points, or to a maximum coverage rate of 95%.We used @Risk from Palisade to simulate the distributions and STATA software to statistically evaluate the datasets.To bring the modelled curve to the level of the observed data, we had to include the impact of secondary sources of infection . This isThe model output identified the time-point at which the hospitalisation curve levelled off to a plateau, instead of continuing to decrease at the same rate as previous years. This plateau occurred in year 3 after the first two years of vaccination had reduced the bulk of the disease burden. The hospitalisation level at which the plateau appeared was determined by the coverage rate obtained during the peak season in the first year, as explored in the scenario analysis.aH), that secondary sources of infection affect vaccine effect during the vaccine uptake period.The fact that the model needed to include secondary sources of infection to obtain the best match to the observed data is consistent with our alternative hypothesis . This is consistent with our alternative hypothesis (aH). Conversely, the group aged 15–23 months increased in relative size, from 19% pre-vaccination to 35% post-vaccination. Both age-groups together (3 to 23 months) formed an important susceptible population group during the disease peak season in the post-uptake period, causing the hospitalisation peaks every two years. In absolute terms, the peaks were small (n = 305) compared with the primary source in the pre-vaccination period (n = 828). It therefore takes a longer time (two years) before the susceptible group is sufficiently large during the peak season to induce a hospitalisation peak. This age-redistribution results in the peaks occurring regularly in frequency and in size every two years instead of annually as in the pre-vaccination period. As shown in The With differential equations, changing one variable input often affects more than one result at the same time. If the number of newborn susceptibles was changed by a higher vaccine coverage, it had an impact on the number of susceptibles in the group, but also on the reproductive number. This reduction in the number of susceptibles extended the time taken to increase the number of susceptibles, which in turn resulted in smaller hospital peaks over time, proportional to the number of susceptibles available to cause the peak .Decreasing the number of susceptibles by increasing first-year vaccination coverage by 15 percentage points, 30 percentage points and to 95%, concentrated in the group aged 3–12 months, systematically reduced the frequency and height of the peaks in the post-uptake period. Frequency reduced from biennial disease peaks to every four years, every eight years, and every 10 years as the number of susceptibles was reduced. There was also a dramatic reduction in the height of the peaks, resulting from the lack of susceptibles present in the older age-groups, which caused the greater height in the simulation fitted to the observed data. The analysis illustrates that if the vaccination had been introduced at higher coverage from the first year of vaccination, thereby minimising susceptibility in the group aged 13–23 months, the likelihood is that repetitive disease peaks would be much reduced in size and frequency for a considerable time into the post-uptake period.The mathematical modelling analysis presented here suggests an explanation for the higher than expected plateau and subsequent biennial peaks in rotavirus hospitalisations observed in the RotaBIS study after the introduction of rotavirus vaccination in Belgium. Our findings indicate that the most important determinant of rotavirus vaccination impact is the coverage rate in the first year. Achieving a high vaccine coverage rate as quickly as possible before the first rotavirus disease seasonal peak maximises the impact of vaccination on reducing rotavirus hospitalisations in at-risk children for many years after vaccine introduction. Conversely, increasing vaccine coverage in later years has relatively little effect. Our model findings indicate that secondary sources of infection are consistent with the observed RotaBIS data, while vaccine waning is not a satisfactory explanation. The analysis presented here extends our previous study on the vaccine uptake period  by addinThis study could help decision-makers to design vaccine implementation strategies to achieve better rotavirus vaccination outcomes than those obtained in Belgium. It should be noted that the RotaBIS study results were locally interpreted as a vaccination success, with the reduction in disease-specific hospitalisations of around 73% in the at-risk population ,39. The The analysis also shows the importance of understanding and identifying the primary sources of infection together with the presence of secondary sources. Therefore, the objective of a new vaccination programme against rotavirus should be to maximise the elimination of the primary source of infection in the first year as a necessary condition for subsequent reduction in the effect of secondary sources. Not achieving that goal immediately allows the virus to remain in the at-risk population in the older age group acting as secondary sources of infection, with the appearance of regular small disease peaks over time. These new peaks are conditional on the remaining susceptible group, with their contact network and transmission risks defining the reproductive number, resulting in regular peaks. This new disease pattern, appearing 7–8 years after the vaccine introduction in the RotaBIS data, is little affected by an increase in vaccination coverage  in the vaccinated group when coverage is already above 80% during the post-uptake period. Our analysis showed that this is because the primary infection source shifted to an older age group when vaccine coverage was not maximised in the first year. This older age group was larger than the increase in the vaccinated group resulting from the 10 percentage point increase in coverage, and as they are too old for vaccination, the vaccine has no immediate impact on it. Data (not yet disclosed) from the period of the lockdown measures taken to reduce the COVID-19 pandemic in Belgium in 2020 indicated that rotavirus hospitalisations were significantly reduced for that year, although a new disease peak would have been expected. Vaccination coverage rates remained high, and the lockdown measures may have effectively been an opportunistic measure to obtain long-term maximum control of rotavirus infection spread and disease events in Belgium. This may indicate that the biennial disease peaks arising from low vaccine coverage in the first year can be subsequently reduced by drastic measures such as lockdown. If the biennial rotavirus peaks do not reappear in the future, we may never know whether the biennial peaks seen in the RotaBIS study would have been maintained over time.Compared with the previous reporting of the RotaBIS results , the folComparison with other countries may also be of interest to identify similarities and differences compared with our results. We were able to collect data from four countries in Europe that initiated a UMV against rotavirus disease during the past decades, although results were not reported or published beyond the first four to six years of their vaccination programme. These countries are Austria 2007) ,17,40, F07 ,17,40The published analyses from the USA and Australia were mainly concerned with obtaining the right numbers of rotavirus disease cases with the diagnosis corrected from the databases available. In Australia, the latest epidemiologic study on the rotavirus vaccine effect was mainly on acute gastro-enteritis data, in which for the first five years after vaccine introduction (2007–2012), the peaks by age groups were more attenuated, except for the group aged more than three years old, four years after the introduction of the vaccine . It coulThe USA data, recently reported in 2019, have some other interesting features . First, This evaluation of the Belgian data has some limitations. Over a 15-year period, there could have been changes in disease management among the participating centres, of which we were unaware, which could have affected the number of disease events hospitalised such as less severe cases being hospitalised because beds were available. We also made assumptions that the catchment area was considered the same during the whole observation period for each participating hospital, which can be questioned because there is a known overall decrease in newborns noted over the years in Belgium. On the other hand, the study was simple to implement, and no extra effort was requested from the personnel in each of the participating centres as we only used data that were already collected in the hospital databases. Rotavirus tests are reimbursed in Belgium until the age of two years, which means that the data collected under that age limit could be considered quite reliable. There was a strong incentive among the participating centres to continue the study if regular updates about the results were provided. The post-uptake model did not include an age-structured design and therefore could not replicate the change in age distribution observed in the RotaBIS data. However, the model was helpful in indicating the direction of the movements caused by changes in the values of the variables in the model.n ≈ >4); the seasonality of the infection is established but its mechanism remains a mystery; and the vaccine has a limited age-indication (2 to <8 months old). These features need to be considered to construct and organise a vaccination programme to obtain optimal disease control from the start to the long-term. To better understand the results of a vaccination programme, it is critical to initiate at launch an adequate monitoring programme and evaluation scheme, provided that sufficient pre-vaccination data have been assembled. This should not be an expensive extra investment and would greatly help in the assessment of the real value of this vaccination.In summary, our models indicate that the process of reduction in rotavirus-related hospitalisations through vaccination was driven most strongly by selecting the optimal starting date of the vaccination and rapidly achieving high vaccine coverage before the first peak disease season. In this respect, Finland and the UK followed the best approach among the countries for which data are available, although it is unknown whether this was a lucky decision or an intentional choice. The coverage rate of their vaccination programme in children is high in both countries as they have special vaccination programmes in place for young children. Belgium may have recently received a second chance for better control of rotavirus disease in the at-risk population with the non-pharmaceutical intervention measures taken against COVID-19, in addition to maintaining a high rotavirus vaccine coverage. The USA is an interesting example of how vaccination can deviate from optimal implementation and the potential difficulty of re-adjusting once a new infection equilibrium has been reached if vaccine coverage is not high enough at the start. Rotavirus infection and its vaccine differ from other vaccine-preventable infectious diseases in children in important ways. The disease burden is concentrated in a very small age group, very young children aged up to two years; the infection is highly contagious (R


Currently there is insufficient evidence to support the routine administration of nitric oxide donors in the treatment of threatened preterm labor. An understanding of the role that nitric oxide plays in the management of threatened preterm labor may lead to more effective treatment and prevention. This is why the aim of our study was to examine the involvement of exogenous nitric oxide release in regulating responses of the human pregnant myometrium to oxytocin. Biopsies of human myometrial tissue during pregnancy were obtained from 8 pregnant women, aged 21-35 years. The responses of the specimens to oxytocin in the absence and presence of a DETA/NO were recorded under isometric conditions. Preincubation with exogenous nitric oxide significantly (p<0.001) attenuated the contractile response of the uterine strips to oxytocin in concentrations higher than 10-8mol/L. The inhibition of nitric oxide synthesis alone or in combination with DETA/NO incubation did not significantly change the oxytocin contractile effect in the concentration-response curve. Moreover, there was no significant variation in the mean value for log EC50for oxytocin between the group with oxytocin alone and other groups. We present evidence in support of the hypothesis that continuous nitric oxide supply to the human pregnant myometrium environment attenuates its response to oxytocin but only when endogenous production of nitric oxide is not impaired. However, when NO synthesis was inhibited, the myometrium response to OXT did not change considerably, even in the presence of DETA/NO. This fact may indicate that the reaction of the pregnant myometrium to OXT changes only when endogenous NO is produced. In the animal model, administering the NOS-inhibiting agent virtually produced all the symptoms of preeclampsia, suggesting a direct relationship between NO production and vascular changes in a normal pregnancy ,30. InteUndoubtedly, problems and questions, like many other previous studies on the pathophysiology of preeclampsia, still remain to be elucidated. Nevertheless, our study is part of the abundance of research on the basic science and physiological changes in this syndrome. Based on this research, we provide evidence to support the hypothesis that a continuous supply of NO to the human pregnant myometrium environment attenuates its response to OXT, but only when endogenous NO production is not impaired. Complicated NO-mediated physiology involving NO requires advanced biochemical and biomolecular research that can support or counter both our findings and those reported above.


Background: The congenital tarsal kink syndrome is a rare form of congenital upper eyelid entropion associated with cardiovascular, musculoskeletal or central nervous system disorders. This syndrome must be recognized and surgically treated as a perinatal emergency to avoid associated complications—corneal ulcer, corneal leucoma, secondary amblyopia and decreased vision among children. Methods: A literature review was conducted to clarify the diagnosis particularities and the corrective surgery options of the congenital entropion on the upper eyelid. Results: Four relevant studies were found by researching the Web of Science and PubMed databases up to November 2021 for “congenital tarsal kink syndrome” and “congenital upper eyelid entropion”. Conclusions: In this paper, we present a case of congenital unilateral entropion of the upper left eyelid in the context of a tarsal kink syndrome in a one-month old infant, manifested by the absence of eyelashes on the upper eyelid of the left eye, hyperlacrimation and conjunctival hyperemia. Essential in managing the upper eyelid entropion is protecting the cornea. Furthermore, correcting a tarsal kink is eminently surgical, choosing between open or closed procedures. Herein, we address the difficulty in the timely diagnosis of this uncommon condition and make formal recommendations based on all reported cases. The congenital tarsal kink syndrome is a very rare form of congenital upper eyelid entropion ,2. PhysiThis paper aims to highlight diagnostic and treatment approaches for the uncommon congenital tarsal kink syndrome and to offer arguments for universal ophthalmologic evaluation of newborns at birth.Systematic research of the literature between 1984 and 2021 was conducted in the PubMed, Web of Science, Embase and MEDLINE  databases to select full-length articles published in peer-reviewed journals up to November 2021. The keywords included in the search strategy were “congenital tarsal kink syndrome” and “congenital upper eyelid entropion”. We limited inclusion to clinically reported cases or series of cases that fulfill the diagnosis of congenital eyelid entropion related to congenital tarsal kink syndrome, isolated or associated with a genetic or chromosomal abnormality. Full-text articles were included. We excluded articles related to other causes of congenital or noncongenital entropion, such as disinsertion of the eyelid retractors, insufficiency of verticality of the posterior lamella, spastic, involutional or cicatricial entropion.The study was conducted in accordance with the Declaration of Helsinki, and the mother signed the informed consent for the publication of this case.n = 21) of cases are unilateral and 22.23% (n = 6) are bilateral. The referral diagnosis was congenital entropion for thirteen cases, corneal ulceration for six cases, corneal opacity for five cases, corneal erosion for two patients and conjunctivitis for one patient. Surgical techniques included closed procedures for nine patients and open procedures for sixteen patients. An ocular patch was used in two cases.We found a total of 20 relevant articles. We selected only nine full-text articles. We excluded 11 articles because no full text was available. Twenty-seven cases of congenital entropion have been identified in the context of tarsal kink syndrome, 16 male and 11 female . The meaWe present the case of a male two-month old patient admitted for corneal ulceration on their left eye. The pregnancy and childbirth were uneventful, the child was born at term by the vaginal way, and no associated maternal systemic diseases were reported. The mother noticed the static eyelid anomaly. The neonatologist did not consider the condition an emergency and assured the parents it would spontaneously remit. At the age of 1 month, the mother brought the child to the ophthalmologist with the following symptoms: absence of eyelashes on the upper eyelid of the left eye, hyperlacrimation and conjunctival congestion. After an examination conducted under general anesthesia, the diagnosis was corneal erosion and spastic entropion in the left eye. The recommended treatment included antibiotic and cycloplegic eye drops, a therapeutic contact lens was applied, and the patient was scheduled for a check-up after 14 days. However, as the mother noticed the spontaneous expulsion of the therapeutic contact lens a few hours after being released, the recurrence and worsening of photophobia and hyperlacrimation, associated with changes in the transparency of the cornea, the patient returned to the clinic after seven days. As a result, the decision was made to have the patient examined by the oculoplasty department.Physical examination revealed spastic entropion of the upper eyelid on the entire eyelid length, accentuated in the inner two-third, hypertrophy of the pretarsal orbicularis muscle and the absence of the upper eyelid fold in the left eye. No static or dynamic eyelid anomalies were revealed in the right eye or in the lower left eyelid.Examination under general anesthesia allowed for a difficult eversion of the upper eyelid, using the Desmarres retractor, which revealed a horizontal tarsal scar band located at approximately 2–3 mm from the edge of the eyelid, in the middle of the tarsus, accompanied by a shortening of the posterior upper eyelid lamella and the inward twisting of the ciliary margin. It also revealed a central corneal leucoma measuring approximately 6 mm in diameter and a central corneal ulceration measuring approximately 2 mm in diameter A,B. ApplUpon clinical examination, the diagnosis was set—tarsal kink syndrome—and the recommendation was surgical correction. The preanesthetic pediatric visit, however, revealed an upper airways infection, so the surgery was postponed until the infection had been cleared. In the meantime, the patient underwent thorough investigations in order to exclude associated systemic pathologies, including a conjunctival smear test that later showed no pathological germs. Moreover, topical treatment was carried out at home during this time, consisting of frequent administration of corneal trophic and epithelial regeneration substances.After 14 days, the patient returned to the clinic and underwent corrective surgery of the congenital entropion on the upper eyelid, which consisted of making a cutaneous-muscular incision 2–3 mm from the ciliary margin, revealing the tarsal plate and excising the fibrotic band on the entire length and width of the tarsal plate. Since there was no resulting relaxation of the eyelid, a partial disinsertion of the levator aponeurosis was performed, at which time the eyelid returned to a normal position. Three absorbable 5/0 sutures were made on the trajectory: skin (ciliary margin)—upper tarsal plate—skin (ciliary margin) in the inner, median and outer third of the upper eyelid .The intervention was completed with the excision of a hypertrophied orbicular pretarsal muscle extension, a therapeutic contact lens placement and the execution of a blepharorrhaphy A–F.The post-op evolution was favorable. Four days after surgery, the blepharorrhaphy sutures were removed and the upper eyelid retained its normal position with a slight eyelid ptosis. The recommendation was to apply a light topical anti-inflammatory steroid treatment, corneal trophic and epithelial regeneration substances with emulsified vitamin A and artificial tears with hyaluronic acid. At the one-week check-up, the upper eyelid maintained its normal position, the corneal ulceration was healed and the corneal leucoma was in remission.At the one-month check-up, the evolution was spectacular; the eyelid maintained its normal position, the eyelid slit was symmetric to the one of the congener eye, and the cornea regained its transparency almost completely, with a persisting central corneal macula. The therapeutic contact lens was removed, and the recommendation was made to continue the topical treatment with emulsified vitamin A, artificial tears with hyaluronic acid and light anti-inflammatory steroid treatment to recover corneal transparency.One month and a half after surgery, the evolution continues to be very good, with no subjective symptomatology .Our surgical technique for congenital tarsal kink syndrome is innovative because the sutures do not include the lower edge of the tarsal plate, only the ciliary skin edge and the upper tarsal edge were associated with the excision of a hypertrophied orbicular pretarsal muscle extension.The congenital upper eyelid entropion is rare, and, in most cases, it is associated with a shortening of the posterior eyelid lamella of different etiologies ,2. AlthoThe surgical goal in patients with tarsal kink syndrome is to obtain a normal eyelid statics. Regardless of the surgical technique used—closed or open procedures for entropion correction—the results reported in the reviewed articles are good in terms of eyelid statics. The patient’s visual prognosis is related to the timeline of diagnosis and treatment initiation, as well as the associated corneal changes .Most patients with tarsal kink syndrome are isolated cases that do not have another associated pathology. There are the situations when this condition can be found in some syndromes or other pathologies.In comparison to our surgical technique, some of the techniques described in Correcting a tarsal kink is eminently surgical—whether we are talking about open or closed procedures ,3. The gThe prognosis of the patients is very good in the case of early diagnosis and treatment, and irreversible corneal damage is thus avoided ,2,6. In The congenital tarsal kink syndrome is a serious and rare disease that can have severe consequences for the patient if it is not treated properly and in time. The “invisibility” of the ciliary edge and the absence of the upper eyelid fold can be important diagnostic indicators for the neonatologist and pediatrician. A simple maneuver involving the eversion of the upper eyelid, revealing the posterior side of the eyelid, allows for early diagnosis. The tarsal kink must be taken into consideration when making the etiologic differential diagnosis of congenital corneal opacities and corneal ulcerations. Surgical treatment applied in time is effective and reduces the risk of corneal leucomas.


Iconography studies the visual content of artworks by considering the themes portrayed in them and their representation. Computer Vision has been used to identify iconographic subjects in paintings and Convolutional Neural Networks enabled the effective classification of characters in Christian art paintings. However, it still has to be demonstrated if the classification results obtained by CNNs rely on the same iconographic properties that human experts exploit when studying iconography and if the architecture of a classifier trained on whole artwork images can be exploited to support the much harder task of object detection. A suitable approach for exposing the process of classification by neural models relies on Class Activation Maps, which emphasize the areas of an image contributing the most to the classification. This work compares state-of-the-art algorithms  in terms of their capacity of identifying the iconographic attributes that determine the classification of characters in Christian art paintings. Quantitative and qualitative analyses show that Grad-CAM, Grad-CAM++, and Smooth Grad-CAM++ have similar performances while CAM has lower efficacy. Smooth Grad-CAM++ isolates multiple disconnected image regions that identify small iconographic symbols well. Grad-CAM produces wider and more contiguous areas that cover large iconographic symbols better. The salient image areas computed by the CAM algorithms have been used to estimate object-level bounding boxes and a quantitative analysis shows that the boxes estimated with Grad-CAM reach 55% average IoU, 61% GT-known localization and 31% mAP. The obtained results are a step towards the computer-aided study of the variations of iconographic elements positioning and mutual relations in artworks and open the way to the automatic creation of bounding boxes for training detectors of iconographic symbols in Christian art images. It is shttps://en.wikipedia.org/wiki/Saint_symbolism—accessed on 15 May 2021) lists 257 characters with 791 attributes. This makes the manual attribution of iconographic classes to image collections challenging, due to the tension between the available amount of expert work and the high number of items to be annotated.With the advent of digital art collections, iconographic class attribution has acquired further importance, as a way to provide a significant index on top of digital repositories of art images, supporting both students and experts in finding and comparing works by their iconographic attributes. However, the analysis of iconography requires specialized skills, based on the deep knowledge of the symbolic meaning of a very high number of elements and of their evolution in space and time. The WikiPedia page on Christian Saint symbolism  has already been used for artwork analysis tasks, such as genre identification , author Applying CV to the analysis of art iconographic poses challenges, in part, general and, in part, specific to the art iconography field. As in general-purpose image classification and object detection, the availability of large high quality training data is essential. The natural image dataset in use today are very large and provided with huge numbers of annotations. Conversely, in the narrower art domain, image datasets are less abundant, smaller, and with less high-quality annotations. Furthermore, unlike natural images, painting images are characterized by less discriminative features than natural ones. The color palette is more restricted and subject to artificial effects, such as colored shadows and chiaroscuro. Images of paintings may also portray partially deteriorated subjects  and belong to historical archives of black and white photos.Despite the encouraging results of applying Convolutional Neural Networks (CNNs) for iconography classification , it remaAre CAMs an effective tool for understanding how a CNN classifier recognizes the iconographic classes of a painting?Are there significant differences in the state-of-the-art CAM algorithms with respect to their ability to support the explanation of iconography classification by CNNs?Are the image areas highlighted by CAMs a good starting point for creating semi-automatically the bounding boxes necessary for training iconography detectors?This paper addresses the following research questions:http://www.artdl.org—accessed on 15 May 2021) consisting of 42,479 images of artworks portraying Christian Saints divided into 10 classes. Note that, in order to avoid ambiguity, we refer to the specific algorithm as “CAM” and to the generic output as “class activation maps”.We apply four state-of-the-art class activation map algorithms  followed by a sequence of convolutional residual blocks (conv2_x …conv5_x). A Global Average Pooling (GAP) module computes the average value for each feature map obtained as an output of the last layer (conv5_x). The probability estimates are computed by the last component, which is typically a fully connected (FC) layer [C) layer .c and a position k in the last convolutional layer at position k and with class c. Intuitively, a high CAM value at position CAMs  are baseDifferently from the original approach, we compute the CAM output not only for the predominant class, but for all the classes. The ArtDL dataset contains multi-class multi-label images and this formulation allows us to analyze which regions of the artwork are associated with which classes, also in the case of wrong classification.c and k-th feature map. The Grad-CAM for a class c at position Grad-CAM  is a varc with a score Grad-CAM++  is a genThe parameter As in the other CAMs, it holds thatz and exploits the visual sharpening of the class activation maps by averaging random samples taken from a feature map close to the input. The value of the activation map n is the number of samples, Smooth Grad-CAM++  is a varThe evaluation exploits the ArtDL dataset , an exisThe ArtDL dataset  compriseThe ArtDL images are associated with high-level annotations specifying which Iconclass categories appear in them (from a minimum of 1 to a maximum of 7). Whole-image labels are not sufficient to assess the different ways in which the class activation maps methods focus on the image content. For this purpose, it is necessary to annotate the dataset with bounding boxes that localize the symbols listed in h and w are the height and width of the conv5_x layer and c is the number of classes. Since the output size  is smaller than the input size, due to the convolution operations performed by the ResNet architecture, each class activation map is upsampled with bilinear interpolation to match the input image size. Min-max scaling is applied to the upsampled class activation maps to normalize them in the The class activation maps are generated by feeding the image to the ResNet50 model and applying the computation explained in t, it is possible to separate the class activation map into background  and foreground . The choice of the threshold value aims at making foreground areas concentrate on the Saints’ figure and symbols. A class activation map contains values in the range from 0 to 1. Given a threshold Intersection Over Union (IoU) is a standard metric used to compute the overlap between two different areas. It is defined as:This section presents the results of comparing quantitatively the effectiveness of the class activation maps algorithms in the localization of iconography classes and their symbols.s. To set the hyper-parameter values, a grid-search was executed in the following space: Smooth Grad-CAM++ is the only method that requires hyper-parameters: the standard deviation Component IoUThis metric evaluates how well the class activation map focuses on the individual Saints’ symbols. First, the class activation map foreground area is divided into connected components, i.e., groups of pixels connected to each other. The IoU value is calculated between each ground-truth bounding box and the connected components that intersect it. Then, the average IoU across all symbol classes is taken. This procedure is repeated for all threshold values.Global IoUx axis does not change across methods, because the influence of the number of components is less relevant when the global metric is computed. An alternative metric is the IoU between the union of all the bounding boxes in the image and the entire foreground area of the class activation map taken at a given threshold. This metric is calculated for all threshold values and assesses how the class activation map focuses on the whole representation of the Saint, favoring those class activation maps methods that generate wider and more connected areas rather than separated components. Bounding box coverage When analyzing the class activation map algorithms, a factor to consider is also how many bounding boxes are covered by each class activation map. This metric alone is not enough to characterize the performance because a trivial class activation map that covers the entire image would have Irrelevant attentionWhen evaluating the global IoU, a low value can occur for two reasons: (1) the two areas have a very small intersection or (2) the two areas overlap well but one is much larger than the other. Thus, an analysis on how much the class activation maps focus on irrelevant parts of the image helps characterizing low global IoU values. Irrelevant attention corresponds to the percentage of class activation map area outside any bounding box. This section presents a qualitative analysis of the results obtained by the different class activation map algorithms highlighting their capabilities and limitations. Each example shows the original image, the class activation maps generated by each algorithm (with background in black and foreground in white) and the ground-truth bounding boxes.Positive examplesA counterexample of the difficulty of detecting such generic attributes as the vest is illustrated in Negative examplesClass activation maps algorithms tend to fail consistently in two cases: when multiple symbols are too close or have a substantial overlap and when the representation of a symbol is rather generic and covers a wide area of the image. Relevant irrelevant regionsAn interesting case occurs when the class activation map algorithms focus on an apparently irrelevant area, which instead contains a relevant iconographic attribute not present in the ground truth. Confusion with unknown co-occurring classThe goal of the presented work is to compare the effectiveness of alternative class activation map algorithms in isolating the salient regions of artwork images that have the greatest impact for the attribution of a specific iconography class. The capacity of a class activation map algorithm to identify precisely the areas of an image that correspond to the whole Saint or to one of the iconographic symbols that characterize him/her can help build a training set for the object detection task. The class activation map can be used as a replacement of the manual annotations necessary for creating a detection training set by computing the smallest bounding boxes that comprise the foreground area and using such automatically generated annotations for training an object detector. This approach is known as weakly supervised object detection and is an active research area . To inveCollect the images on which all the four methods satisfy a minimum quality criterion: for symbol bounding boxes component IoU greater than 0.165 at threshold 0.1 see  and for Compute the Grad-CAM class activation map of the selected images and apply the corresponding threshold: 0.1 for symbol bounding boxes and 0.05 for whole Saint bounding boxes;Only for symbol boxes: split the class activation maps into connected components. Remove the components whose average activation value is less than half of the average activation value of all components. This step filters out all the foreground pixels with low activation that usually correspond to irrelevant areas ;For each Iconclass category, draw one bounding box surrounding each component (symbol bounding boxes) and one bounding box surrounding the entire class activation map (whole Saint bounding boxes).In the procedure above, Grad-CAM is chosen to compute the candidate symbol and whole Saint bounding boxes, because it has the highest value of the bounding box coverage metrics (together with Smooth Grad-CAM++ ) and covers wider areas, at the same time, focusing on the correct details.Symbol bounding boxes Whole Saint bounding boxes Quantitative evaluation of whole Saint bounding boxesFor the whole Saint case, each estimated bounding box can be labeled with the iconography class of the corresponding Saint portrayed in the image. In this way, it is possible to quantify the coincidence between the bounding box of the ground truth and the bounding box computed from the class activation map. For this purpose, three object detection metrics have been computed: the average IoU value between the GT and the estimated bounding boxes, mean Average Precision and GT-known Loc. The latter is used in several works ,50,51) t,51 t50,5The whole Saint estimated bounding boxes appear to be suitable for creating the pseudo ground truth for training an object detector with the weakly supervised approach. Two observations motivate the viability of Grad-CAM for this purpose. As in the GT-known Loc metrics, the goodness of an object detection is usually evaluated with a minimal IoU threshold of 0.5 and the boxes generated automatically with Grad-CAM obtain 0.55 IoU on average, which suggests that the automatically estimated bounding boxes have a quality similar to the bounding boxes produced by a fully supervised object detector, albeit inferior to the quality of the bounding boxes created by humans. Grad-CAM, which is designed to be an interpretability technique, can be used also to estimate bounding boxes that reach to ArtDL ,52. For This work has presented a comparative study about the effectiveness of class activation maps as a tool for explaining of how a CNN-based classifier recognizes the Iconclass categories present in images portraying Christian Saints. The symbols relevant to the identification of the Saints were annotated with bounding boxes and the output of the class activation maps algorithms were compared to the ground truth using four metrics. The analysis shows that Grad-CAM achieves better results in terms of global IoU and covered bounding boxes and Smooth Grad- CAM++ scores best in the component IoU thanks to its precision in delineating individual small size symbols. The irrelevant attention metric promotes the original CAM algorithm as the best approach, but the low component IoU and box coverage complement such an evaluation showing that CAM covers too small areas. While for natural images Smooth Grad-CAM++ outperforms the other three algorithms , in our Future work will concentrate on the comparison of other activation mapping techniques ,51,53,54


Given the better prediction performance of the BPNN, it is recommended as a more reliable tool for the spatial mapping of the groundwater As concentration. Subsequently, the As concentrations estimated obtained using the BPNN are applied to develop a spatial map illustrating the risk to human health associated with the ingestion of As-containing groundwater based on the noncarcinogenic hazard quotient (HQ) and carcinogenic target risk (TR) standards established by the U.S. Environmental Protection Agency. Such maps can be used to demarcate the areas where residents are at higher risk due to the ingestion of As-containing groundwater, and prioritize the areas where more intensive monitoring of groundwater quality is required. The spatial mapping of As concentrations from the BPNN was also used to demarcate the regions where the groundwater is suitable for farmland and fishponds based on the water quality standards for As for irrigation and aquaculture.Groundwater resources are abundant and widely used in Taiwan’s Lanyang Plain. However, in some places the groundwater arsenic (As) concentrations far exceed the World Health Organization’s standards for drinking water quality. Measurements of the As concentrations in groundwater show considerable spatial variability, which means that the associated risk to human health would also vary from region to region. This study aims to adapt a back-propagation neural network (BPNN) method to carry out more reliable spatial mapping of the As concentrations in the groundwater for comparison with the geostatistical ordinary kriging (OK) method results. Cross validation is performed to evaluate the prediction performance by dividing the As monitoring data into three sets. The cross-validation results show that the average determination coefficients (R Groundwater accounts for a substantial portion of the freshwater supply in the Lanyang Plain, Taiwan. To resolve the problem of a lack of reservoirs for the storage of seasonal rainfall and the poor quality of the surface water, area residents are heavily reliant upon the groundwater for agricultural irrigation, aquaculture, domestic and drinking purposes. Groundwater quality monitoring for the Lanyang Plain conducted by the Environmental Protection Bureau (EPB) of Yilan County ,2,3 has Conventional spatial mapping methods, such as kriging, which is based on geostatistical theory, have been widely used for modeling the spatial variability of groundwater quality variables with limited field data. Lee et al.  and LianIn contrast, data-driven machine learning techniques, such as artificial neural network (ANN) or random forest (RF) methods, can facilitate the process by resolving a spectrum of nonlinearity problems. Purkait et al.  developeThe aforementioned studies illustrate that the machine learning approach has the potential to act as a spatial mapping tool with high prediction performance for several groundwater quality issues. This study is thus designed to develop the ANN as a spatial mapping tool for estimation of the geographical variability of As concentrations in the Lanyang Plain. We also make a comparison between the prediction performance of ANN and the conventional OK method. The predicted As geographical distribution is further used to calculate the noncarcinogenic hazard quotient (HQ) and carcinogenic target risk (TR) and demarcate the regions where people are at a higher health risk. The yielded health risk can be used for improving the decision-making process for health risk management associated with ingestion of As-containing groundwater in the Lanyang Plain.The Lanyang Plain is an alluvial fan on the Lanyang River bound by the Snow Mountains on the northwestern side, the Central Range to the southwest and the Pacific Ocean on the east . The lanThe Lanyang alluvial fan is composed of recent alluvial deposits, including gravel, sand and silt, and clay comprised of detrital slates, quartz sandstone and crystallized gneiss . The bedMost recently, Liu and Wu  performeith input variable; n is the number of input data.As a data-driven method, ANNs can learn the complex mapping between the input and the output given sufficient data, and their flexible structure can also provide a good estimation for various problems. ANNs are designed to simulate the process of the transport of electric potentials by neural cells in living creatures. The single neuron operates along the following functions:The structure of an ANN includes three main layers. First, there is an input layer, which is responsible for receiving the input variables and transporting the signal to the next layer without any artificial neurons being used in the computation. Second, there is at least one hidden layer, which is composed of artificial neurons for the computation operation and which is used to extract the patterns associated with the process or system being analyzed. The role of this layer (or these layers) is to perform most of the internal processing in the network. The last output layer is also composed of neurons and is responsible for producing the final network outputs with the same format as the real output value set in the training process .A feed-forward back-propagation neural network (BPNN) was chosen for use in this study. The feedforward BPNN training procedure is a supervised learning method and is divided into two main parts. The Levenberg–Marquardt (LM) algorithm, used for training in this study, is a blend of the gradient descent and Gauss–Newton iterations, and is probably the most widely used optimization method, since its hyper-spherical trust region has proven to provide a better solution in searching for the minima .The actual spatial data were mostly messy and scattered, with adjacent data usually having a higher degree of similarity and correlation than those far away. The core of the geostatistical kriging technique is the regionalized variable theory, which states that the variables in an area exhibit both random and spatially structured properties and a second-order stationary process is assumed . A geostThe experimental semi-variograms were calculated pair-by-pair using Equation (3) and subsequently fitted against a theoretical semi-variogram model ofSpherical semi-variogram model:Exponential semi-variogram model:Gaussian semi-variogram model:M is the total number of the sampled locations; and Ordinary kriging is a spatial interpolation estimator that is applied to find the best linear unbiased estimate at a non-sampled locationThis study assesses the health risk, specifically the carcinogenic and non-carcinogenic risks, associated with the drinking of As-contaminated (inorganic) groundwater using the methods recommended by the USEPA ,26.TR) index, which is used to quantify the cancer risk caused by those substances classified as definite or probable human carcinogens. Thus, an estimated TR value equal to TR (life time risk index) is formulated as follows:C is the As concentration (µg/L); IR is the daily water intake (L/day); ED is the exposure duration (year); EF is the exposure frequency (day/year), which is how many days an individual is exposed to As over the course of a year; BW is the body weight (kg); AT is the average life time for carcinogenic exposure (days); CSF is the cancer slope factor (mg/L) obtained from the Integrated Risk Information System (IRIS) database; and 10−3 is a conversion factor. The cancer slope factor (CSF), which is used for characterizing the relationship between dose and response, is a key parameter in the TR model. The CSF is an upper-bound estimate of the probability that a person will develop cancer when exposed to a chemical over a lifetime of 70 years.The carcinogenic risk is evaluated based on the target risk (HQ) index which is defined as the ratio of potential exposure to a reference magnitude for which there are no expected adverse effects. If the HQ value is greater than 1, an adverse non-carcinogenic effect is regarded as possible. The HQ is calculated byDI is the daily intake of As (µg/kg/day); C is the As concentration (µg/L); IR is the daily water intake (L/day); and RfD is the oral reference dose derived by the USEPA [The non-carcinogenic risk is evaluated based on the hazard quotient  . The As −2 of the mean squared error. The structure of the developed model is shown in An exponential semi-variogram model (Equation (3)) was applied to fit the experimental semi-variograms data for each individual training dataset of the OK method. 2) and RMSEs for each different BPNN model tried in this study. Based on the highest average R2 value and the lowest average RMSE value, model  was chosen to apply for spatial mapping and for comparison of the mapping performance with that obtained from the OK method. 2 and RSME values for BPNN and OK. The results show that the average R2 values for cross validation of the As concentrations obtained with BPNN and OK are 0.55 and 0.49, whereas the average RMSE values are 0.49 and 0.54, respectively. Based on the average R2 and RSMEs, we can conclude that the BPNN provides better performance than the OK.The BPNN was then used to predict the geographical distribution of As concentrations in the Lanyang Plain. First, the area of the Lanyang Plain was spatially discretized into a grid system of 1 km × 1 km grids. The As concentrations were calculated at each grid center from the output of the BPNN model. TRs and HQs (using Equations (8) and (10)) with which to demarcate the regions of unacceptable carcinogenic and non-carcinogenic risk. The values of the parameters required for assessment of the health risk calculated with Equations (8)–(10) are shown in The As concentrations at each grid center obtained using BPNN were then used to calculate the TRs are classified into three levels: Level 1, with a TR value of less than TR value is between TR value of greater than HQ values were classified into two levels: Level 1, in which the HQ value is greater than 1, which is considered to cause an adverse non-carcinogenic outcome; and Level 2, where the HQ values are lower than 1, which means an acceptable adverse non-carcinogenic outcome. TRs and HQs, which could result in carcinogenic and non-carcinogenic risk. Together with the distribution of population density, the map can define the areas of high-risk groundwater usage. According to The Agriculture and aquaculture are the most common types of land usage in the Lan-yang Plain and they are heavily dependent upon groundwater to meet their demands. According to the Council of Agriculture, Taiwan, the acceptable limit for As concentration in irrigation and aquaculture is 50 µg/L. We performed spatial mapping of the As concentration in groundwater and made a comparison between two distinct approaches: backward propagation neural network (BPNN) and ordinary kriging (OK). The findings show that the BPNN has better prediction performance than the OK method. Subsequently, the BPNN was used to develop spatial maps showing the geographical distribution of As contaminations in the groundwater. The As concentrations obtained using the BPNN approach were then used to develop a spatial map for carcinogenic and noncarcinogenic health risk associated with exposure to arsenic through the drinking of groundwater. For zones with unaccepted HQs and TRs, the promising measures include supply of safe tap water and public education to raise community awareness. The spatial mapping shows the regions unsuitable for farmland and fishponds, as defined by the estimated groundwater As concentrations, exceed the water quality standards for irrigation and aquaculture. Groundwater as a water source should be replaced with the supply of treated, safe surface water, or by using groundwater collected from other areas in regions where the groundwater quality is unsafe for irrigation or aquaculture but is currently being used for farmlands and fishponds. Alternatively, improved land management practices offer promising possibilities to ensure the availability and quality of water for farmlands and fishponds