title: Multidisciplinary team approach in acute myocardial infarction patients undergoing veno-arterial extracorporeal membrane oxygenation

text: BACKGROUND: Limited data are available on the impact of a specialized extracorporeal membrane oxygenation (ECMO) team on clinical outcomes in patients with acute myocardial infarction (AMI) complicated by cardiogenic shock (CS). This study evaluated whether specialized ECMO team is associated with improved in-hospital mortality in AMI patients undergoing veno-arterial (VA) ECMO. METHODS: A total of 255 AMI patients who underwent VA-ECMO were included. In January 2014, a multidisciplinary ECMO team was founded at our institution. Eligible patients were classified into a pre-ECMO team group (n = 131) and a post-ECMO team group (n = 124). The primary outcome was in-hospital mortality. RESULTS: In-hospital mortality (pre-ECMO team vs. post-ECMO team, 54.2% vs. 33.9%; p = 0.002) and cardiac intensive care unit mortality (pre-ECMO team vs. post-ECMO team, 51.9% vs. 30.6%; p = 0.001) were significantly lower after the implementation of a multidisciplinary ECMO team. On multivariable logistic regression model, implementation of the multidisciplinary ECMO team was associated with reduction of in-hospital mortality [odds ratio: 0.37, 95% confidence interval (CI) 0.20–0.67; p = 0.001]. Incidence of all-cause mortality [58.3% vs. 35.2%; hazard ratio (HR): 0.49, 95% CI 0.34–0.72; p < 0.001) and readmission due to heart failure (28.2% vs. 6.4%; HR: 0.21, 95% CI 0.08–0.58; p = 0.003) at 6 months of follow-up were also significantly lower in the post-ECMO team group than in the pre-ECMO team group. CONCLUSIONS: Implementation of a multidisciplinary ECMO team was associated with improved clinical outcomes in AMI patients complicated by CS. Our data support that a specialized ECMO team is indispensable for improving outcomes in patients with AMI complicated by CS.


title: Girls and women in the educational system: The curricular challenge

text: This Viewpoint argues that, while efforts must continue to achieve universal primary and secondary education at the global level for both boys and girls, the concern with access and thus enrollment and completion parity has blinded many governments from seeing the crucial need to examine what is actually learned in school. Stronger concern with curriculum would bring a stronger focus on the formal knowledge conveyed in schools and on the ways in which this knowledge might (or might not) facilitate a substantial change in the social relations of gender.


title: Genome re-sequencing and reannotation of the Escherichia coli ER2566 strain and transcriptome sequencing under overexpression conditions

text: BACKGROUND: The Escherichia coli ER2566 strain (NC_CP014268.2) was developed as a BL21 (DE3) derivative strain and had been widely used in recombinant protein expression. However, like many other current RefSeq annotations, the annotation of the ER2566 strain was incomplete, with missing gene names and miscellaneous RNAs, as well as uncorrected annotations of some pseudogenes. Here, we performed a systematic reannotation of the ER2566 genome by combining multiple annotation tools with manual revision to provide a comprehensive understanding of the E. coli ER2566 strain, and used high-throughput sequencing to explore how the strain adapted under external pressure. RESULTS: The reannotation included noteworthy corrections to all protein-coding genes, led to the exclusion of 190 hypothetical genes or pseudogenes, and resulted in the addition of 237 coding sequences and 230 miscellaneous noncoding RNAs and 2 tRNAs. In addition, we further manually examined all 194 pseudogenes in the Ref-seq annotation and directly identified 123 (63%) as coding genes. We then used whole-genome sequencing and high-throughput RNA sequencing to assess mutational adaptations under consecutive subculture or overexpression burden. Whereas no mutations were detected in response to consecutive subculture, overexpression of the human papillomavirus 16 type capsid led to the identification of a mutation (position 1,094,824 within the 3′ non-coding region) positioned 19-bp away from the lacI gene in the transcribed RNA, which was not detected at the genomic level by Sanger sequencing. CONCLUSION: The ER2566 strain was used by both the general scientific community and the biotechnology industry. Reannotation of the E. coli ER2566 strain not only improved the RefSeq data but uncovered a key site that might be involved in the transcription and translation of genes encoding the lactose operon repressor. We proposed that our pipeline might offer a universal method for the reannotation of other bacterial genomes with high speed and accuracy. This study might facilitate a better understanding of gene function for the ER2566 strain under external burden and provided more clues to engineer bacteria for biotechnological applications.


Building power through crisis


title: Successful Factors for Adoption of Synchronous Tools in Online Teaching at Scale

text: With the advancement of technology in recent decades, online learning is becoming increasingly popular.


title: Designing Authentic Assessments: Engaging Business Students in Flow Experience with Digital Technologies

text: Embracing technology in higher education has become a necessity, rather than a desired value-added addition to educational delivery.


Guidance for Maintaining Skin Health When Utilizing Protective Masks for Prolonged Time Intervals


Mitigating Pressure Injury Challenges When Placing Patients in a Prone Position: A View From Here


title: Applying Telehealth Technologies and Strategies to Provide Acute Care Consultation and Treatment of Patients With Confirmed or Possible COVID-19

text: BACKGROUND AND PURPOSE: The COVID-19 pandemic continues to grow, with 19% of total confirmed patients classified as severe or critical experiencing complications such as dyspnea, hypoxia, acute respiratory distress syndrome, or multiorgan failure. These complications require rehabilitative care. Considering the contagious nature of COVID-19 and the necessity to decrease the volume of health care professionals entering confirmed COVID-19 patient rooms and becoming a potential disease vector, can audiovisual technologies employed by telehealth and telerehabilitation help? CASE DESCRIPTION: This case discusses the Baylor Scott and White Institute for Rehab (BSWIR) Physical Medicine and Rehabilitation (PMR) department COVID-19 acute care therapy team's creation of a telehealth strategy to provide early rehabilitative intervention without increasing the odds of disease transmission. OUTCOMES: The COVID-19 therapy team created a simple process for identifying and triaging care for patients with possible or confirmed COVID-19. These patients were evaluated and treated by the dedicated team using telehealth strategies. A structured risk-benefit analysis was used to determine when in-room care was indicated. DISCUSSION: Acute care physical therapy, occupational therapy, and speech-language pathology telehealth strategies can add value by mitigating COVID-19–related harm and influencing recovery, while not unnecessarily becoming additional disease vectors consuming personal protective equipment. COVID-19 is not only an aggressive respiratory illness similar to acute respiratory distress syndrome but also highly contagious and a risk for health care providers. Telehealth strategies allow therapists to intervene early, opening the possibility to maximize recovery and prevent harm or decompensation. Telehealth strategies can be more prevention-focused while the patient is experiencing relatively good health with goals to maximize strength and endurance before the disease process evolves to critical illness. As COVID-19 progresses, therapy can help mitigate potential complications associated with prolonged intensive care unit stay and ventilator management.


WOCN Society President's Message: Supporting Our Members Through a Pandemic


Personal Protective Equipment–Related Equipment Dermatitis: A View From Here


Recommendations for Wound Assessment and Photodocumentation in Isolation


Editorial: Summon the Heroes


title: Voices of the new generation: science in a state of benign confusion

text: Building a research group in China (when you have not mastered the language) means you face the same problems as elsewhere, with an extra bit of confusion.


title: Speaking up against inequity and racism

text: Inequalities are prevalent across the spectrum of cancer research and patient care, with destructive repercussions for people and society. We cannot ignore them and must act against the social injustices that perpetuate them.


Mitteilungen der Österreichischen IVF-Gesellschaft


Single-fraction magnetic resonance guided stereotactic radiotherapy – A game changer?


How to Be a Positive Radiology Leader in Times of Crisis


Nurses as political knowledge brokers, opportunities for growth in the Spanish context


Special issue on the 52nd Heyrovský discussion - Electrochemistry of Organic Compounds and Biopolymers


Crisis (Pandemic) management playbook for assisted living


title: Interne Strukturen und Prozesse öffentlicher Organisationen

text: am Ende dieses Kapitels sollten Sie: einen Überblick über die zentralen Elemente, Vorteile und Probleme bürokratischer Organisationen und Verfahren haben; deren Ausgestaltung auf der Ebene der Ministerien und Kommunen in der Bundesrepublik sowie die wichtigsten Kritikpunkte kennen; unterschiedliche Möglichkeiten von Planung und Entscheidung in der öffentlichen Verwaltung und wichtige klassische Entscheidungstheorien kennen; die vielfältigen Beziehungen zwischen Politik und Verwaltung und die dabei relevanten normativen Vorstellungen verstehen; sowie; erkennen, dass es in der Politikberatung meistens um Verwaltungsberatung geht.


Kawasaki Disease and Invasive Pneumococcal Infection


title: Accurate Identification of Tomograms of Lung Nodules Using CNN: Influence of the Optimizer, Preprocessing and Segmentation

text: The diagnosis of pulmonary nodules plays an important role in the treatment of lung cancer, thus improving the diagnosis is the primary concern. This article shows a comparison of the results in the identification of computed tomography scans with pulmonary nodules, through the use of different optimizers (Adam and Nadam); the effect of the use of pre-processing and segmentation techniques using CNNs is also thoroughly explored. The dataset employed was Lung TIME which is publicly available. When no preprocessing or segmentation was applied, training accuracy above 90.24% and test accuracy above 86.8% were obtained. In contrast, when segmentation was applied without preprocessing, a training accuracy above 97.19% and test accuracy above 95.07% were reached. On the other hand, when preprocessing and segmentation was applied, a training accuracy above 96.41% and test accuracy above 94.71% were achieved. On average, the Adam optimizer scored a training accuracy of 96.17% and a test accuracy of 95.23%. Whereas, the Nadam optimizer obtained 96.25% and 95.2%, respectively. It is concluded that CNN has a good performance even when working with images with noise. The performance of the network was similar when working with preprocessing and segmentation than when using only segmentation. Also, it can be inferred that, the application of preprocessing and segmentation is an excellent option when it is required to improve accuracy in CNNs.


title: Machine Learning Techniques for Identity Document Verification in Uncontrolled Environments: A Case Study

text: Distributed (i.e. mobile) enrollment to services such as banking is gaining popularity. In such processes, users are often asked to provide proof of identity by taking a picture of an ID. For this to work securely, it is critical to automatically check basic document features, perform text recognition, among others. Furthermore, challenging contexts might arise, such as various backgrounds, diverse light quality, angles, perspectives, etc. In this paper we present a machine-learning based pipeline to process pictures of documents in such scenarios, that relies on various analysis modules and visual features for verification of document type and legitimacy. We evaluate our approach using identity documents from the Republic of Colombia. As a result, our machine learning background detection method achieved an accuracy of 98.4%, and our authenticity classifier an accuracy of 97.7% and an F1-score of 0.974.


title: A Preliminary Study on Score-Based Hyper-heuristics for Solving the Bin Packing Problem

text: The bin packing problem is a widespread combinatorial problem. It aims at packing a set of items by using as few bins as possible. Among the many available solving methods, approximation ones such as heuristics have become popular due to their reduced cost and generally acceptable solutions. A further step in this regard is given by hyper-heuristics, which literature usually defines as “high-level heuristics to choose heuristics”. Hyper-heuristics choose one suitable heuristic from a set of available ones, to solve a particular portion of an instance. As the search progresses, heuristics can be exchanged, adapting the solution process to the current problem state under exploration. In this work, we describe how to generate and use hyper-heuristics that keep a record of the scores achieved by individual heuristics on previously solved bin packing problem instances in the form of rules. Then, hyper-heuristics manage those scores to estimate the performance of such heuristics on unseen instances. In this way, the previous actions of the hyper-heuristics determine which heuristic to use on future unseen cases. The experiments conducted under different scenarios yield promising results where some of the hyper-heuristics produced outperform isolated heuristics.


title: Classification of Cattleya Trianae and Its Varieties by Using Colorimetry

text: Orchids in general, like Cattleya trianae, have been characterized mostly by taxonomic and visual studies. However, colour is not used for classification. Here, a new method for identifying and classifying orchids of different varieties of Cattleya trianae is introduced. This method is not subjective and uses the colour information obtained from the central axis of the Cattleya trianae lip. To this end, a new acquisition protocol was established, which uses a new device for image acquisition of the labellum’s central axis from the hippocampus to the epicentre. The colour patterns found between samples of the same variety were adjusted and it was verified by using correlation they can be employed to identify each variety. Finally, a support vector machine was used to classify and identify four Cattleya trianae varieties, finding that a linear kernel was enough to classify them with an accuracy of 100%.


title: Orthogonal Local Image Descriptors with Convolutional Autoencoders

text: This work proposes the use of deep learning architectures, and in particular Convolutional Autencoders (CAE’s), to incorporate an explicit component of orthogonality to the computation of local image descriptors. For this purpose we present a methodology based on the computation of dot products among the hidden outputs of the center-most layer of a convolutional autoencoder. This is, the dot product between the responses of the different kernels of the central layer (sections of a latent representation). We compare this dot product against an indicator of orthogonality, which in the presence of non-orthogonal hidden representations, back-propagates a gradient through the network, adjusting its parameters to produce new representations which will be closer to have orthogonality among them in future iterations. Our results show that the proposed methodology is suitable for the estimation of local image descriptors that are orthogonal to one another, which is often a desirable feature in many patter recognition tasks.


title: Gender Identification in Social Media Using Transfer Learning

text: Social networks have modified the way we communicate. It is now possible to talk to a large number of people we have never met. Knowing the traits of a person from what he/she writes has become a new area of computational linguistics called Author Profiling. In this paper, we introduce a method for applying transfer learning to address the gender identification problem, which is a subtask of Author Profiling. Systems that use transfer learning are trained in a large number of tasks and then tested in their ability to learn new tasks. An example is to classify a new image into different possible classes, giving an example of each class. This differs from the traditional approach of standard machine learning techniques, which are trained in a single task and are evaluated in new examples of that task. The aim is to train a gender identification model on Twitter users using only their text samples in Spanish. The difference with other related works consists in the evaluation of different preprocessing techniques so that the transfer learning-based fine-tuning is more efficient.


title: New Method for Extreme Color Detection in Images

text: In image processing and computer vision, it is common to find applications, in which it is necessary to detect reference points characterized by extreme color, i.e., a primary color RGB or complementary CMY with very high saturation. Thus, there are cases in which a certain class of objects can be distinguished according to their characteristic extreme color, which can be used as landmarks or to identify objects. Therefore, there is an interest in identifying landmarks characterized by extreme colors. In this paper, a new method for detecting objects with an extreme color is introduced and compared with other approaches found in the literature. The methods are analyzed and compared using a color palette in which a transition between R, G, B, C, M and Y colors is generated. The results obtained show that the methods studied allow the specific colors to be adequately discriminated, while the proposed method is the only one that allows the full range of extreme colors R, G, B, C, M and Y to be detected, being more selective than the others, by taking practically the areas corresponding to each color separately .


title: A Simple Methodology for 2D Reconstruction Using a CNN Model

text: In recent years, Deep Learning research have demonstrated their effectiveness in digital image processing, mainly in areas with heavy computational load. Such is the case of aerial photogrammetry, where the principal objective is to generate a 2D map or a 3D model from a specific terrain. In these topics, high-efficiency in visual information processing is demanded. In this work we present a simple methodology to build an orthomosaic, our proposal is focused in replacing traditional digital imagen processing using instead a Convolutional Neuronal Network (CNN) model. The dataset of aerial images is generated from drone photographs of our university campus. The method described in this article uses a CNN model to detect matching points and RANSAC algorithm to correct feature’s correlation. Experimental results show that feature maps and matching points obtained between pair of images through a CNN are comparable with those obtained in traditional artificial vision algorithms.


title: Automatic Estrus Cycle Identification System on Female Dogs Based on Deep Learning

text: Vaginal cytology is a complementary economic method and of simple realization, an indicative to determine in which stage of the estrous cycle the dog is, to achieve a higher fertility and fertility rate. This method is based on determining the type and quantity of cells of the different stages of the estrous cycle, since the hormonal changes that the vaginal mucosa undergoes during the estrous cycle are shown in the morphology of its epithelial cells. The canine female in her reproductive life goes through different phases of activity and hormonal rest that are repeated cyclically. This is called the estrous cycle and consists of 4 stages: proestrus, estrus, diestrus and anestrus. The interpretation of vaginal cytology’s, is a process to which a considerable amount of time is dedicated by its observation in the microscope and the same interpretation by the doctor which can become subjective and poorly performed, causing economic losses for the owners. Therefore, this work proposes an automatic system that will identify six types of cells and the quantity of them in the glass slide, based on a Faster R-CNN to determine in which stage of the estrous cycle the dog is. Our results show an accuracy of 91.6%. The proposed system will improve the efficiency and speed of cytology’s to decreased from 1 h approximately to just a few seconds.


title: ModuleNet: A Convolutional Neural Network for Stereo Vision

text: Convolutional Neural Networks (CNN) has gained much attention for the solution of numerous vision problems including disparities calculation in stereo vision systems. In this paper, we present a CNN based solution for disparities estimation that builds upon a basic module (BM) with limited range of disparities that can be extended using various BM in parallel. Our BM can be understood as a segmentation by disparity and produces an output channel with the memberships for each disparity candidate, additionally the BM computes a channel with the out–of–range disparity regions. This extra channel allows us to parallelize several BM and dealing with their respective responsibilities. We train our model with the MPI Sintel dataset. The results show that ModuleNet, our modular CNN model, outperforms the baseline algorithm Efficient Large-scale Stereo Matching (ELAS) and FlowNetC achieving about a 80% of improvement.


title: Towards Inpainting and Denoising Latent Fingerprints: A Study on the Impact in Latent Fingerprint Identification

text: In this paper, we provide a study about the impact of the most prominent inpainting and denoising solutions on the latent fingerprint identification. From an in-depth analysis, we show how some of the analyzed inpainting and denoising solutions can improve up 63% for Rank-1 and 26% for Rank-20 the fingerprint identification rates when state-of-the-art minutiae extractors are used. Nevertheless, it is necessary to create new denoising and inpainting solutions that are specifically built to deal with latent fingerprints and their associated issues.


title: Experimental Study on Transfer Learning in Denoising Autoencoders for Speech Enhancement

text: The quality of speech signals is affected by a combination of background noise, reverberation, and other distortions in real-life environments. The processing of such signals presents important challenges for tasks such as voice or speaker recognition. To enhance signals in such challenging conditions several deep learning-based methods have been proposed. Those new methods have proven to be effective, in comparison to classical algorithms based on statistical analysis and signal processing. In particular, recurrent neural networks, especially those with long short-term memory (LSTM and BLSTM), have presented surprising results in tasks related to enhancing speech. One of the most challenging aspects of artificial neural networks is to reduce the high computational cost of the training procedure. In this work, we present a comparative study on transfer learning to accelerate and improve traditional training based on random initialization of the internal weights of the networks. The results show the advantage of the proposal in terms of less training time and better results for the task of denoising speech signals at several signal-to-noise ratio levels of white noise.


title: Improvement of the Turajlić Method for the Estimation of Gaussian Noise in Images

text: Gaussian noise estimation is an important step in some of the more recently developed noise removal methods. This is a difficult task and although several estimation techniques have been proposed recently, they generally do not produce good results. In a previous comparative study, among several noise estimation techniques, a method proposed in 2017 by Turajlić was found to give the best results. Although acceptable, they are still far from ideal. Therefore, several changes to this method are introduced in this paper to improve the estimation. Tests on monochromatic images contaminated with different levels of Gaussian noise showed that the modified method produces a significant improvement in the estimation of Gaussian noise, over 35%, at a slightly higher computational cost.


title: Learning Clasiffier Systems with Hebbian Learning for Autonomus Behaviors

text: One of the main characteristics of multi-agent systems is the ability to solve problems achieving objectives. This is possible because of the learning mechanisms that are embedded in the systems and go from neural networks up to vector support machines. Agent-based systems stand out for their autonomy and adaptation of dynamic conditions of the environment. This article presents the Hebbian theory, which is one of the learning methods from the neuroscience field. A particularity presented by the Hebbian theory from the computer since field perspective is the primary mechanism of synaptic plasticity where the value of a synaptic connection increases if neurons on both sides of a said synapse are activated repeatedly, creating a new one simultaneously. This mechanism is integrated into the Learning Classifier Systems (LCS) to validate its effectiveness in the solution task, and can be used in multi-agent systems.


title: Fruit Classification for Retail Stores Using Deep Learning

text: Payment of fruits or vegetables in retail stores normally require them to be manually identified. This paper presents an image classification method, based on lightweight Convolutional Neural Networks (CNN), with the goal of speeding up the checkout process in stores. A new dataset of images is introduced that considers three classes of fruits, inside or without plastic bags. In order to increase the classification accuracy, different input features are added into the CNN architecture. Such inputs are, a single RGB color, the RGB histogram, and the RGB centroid obtained from K-means clustering. The results show an overall 95% classification accuracy for fruits with no plastic bag, and 93% for fruits in a plastic bag .


title: What the Appearance Channel from Two-Stream Architectures for Activity Recognition Is Learning?

text: The automatic recognition of human activities from video data is being led by spatio-temporal Convolutional Neural Networks (3D CNNs), in particular two-stream architectures such as I3D that reports the best accuracy so far. Despite the high performance in accuracy of this kind of architectures, very little is known about what they are really learning from data, resulting therefore in a lack of robustness and explainability. In this work we select the appearance channel from the I3D architecture and create a set of experiments aimed at explaining what this model is learning. Throughout the proposed experiments we provide evidence that this particular model is learning the texture of the largest area (which can be the activity or the background, depending on the distance from the camera to the action performed). In addition, we state several considerations to take into account when selecting the training data to achieve a better generalization of the model for human activity recognition.


title: COUPLED: Calibration of a LiDAR and Camera Rig Using Automatic Plane Detection

text: LiDARs and cameras are two widely used sensors in robotics and computer vision, particularly for navigation and recognition in 3D scenarios. Systems combining both may benefit from the precise depth of the former and the high-density information of the latter, but a calibration process is necessary to relate them spatially. In this paper, we introduce COUPLED, a method that finds the extrinsic parameters to relate information between them. The method implies the use of a setup consisting of three planes with charuco patterns to find the planes in both systems. We obtain corresponding points in both systems through geometric relations between the planes. Afterward, we use these points and the Kabsch algorithm to compute the transformation that merges the planes between both systems. Compared to recent single plane algorithms, we obtain more accurate parameters, and only one pose is required. In the process, we develop a method to automatically find the calibration target using a plane detector instead of manually selecting the target in the LiDAR frame.


title: Spatial [Formula: see text]-Trimmed Fuzzy C-Means Algorithm to Image Segmentation

text: An important aspect should be taken into account, when an image is segmented, the presence of atypical information. In this investigation an algorithm is proposed that is noise tolerant in the segmentation process. A method to image segmentation that combines Fuzzy C-Means (FCM) algorithm and Trimmed Means filter, called Spatial [Formula: see text] Trimmed Fuzzy C-means, using local information to achieve better segmentation. The FCM is very sensitive to noise, and the Trimmed Means filter is used to eliminate outliers with a lower computational cost. Compared to some state-of-the-art algorithms, the proposed is faster and noise tolerant, demonstrating better performance in the metrics considered.


title: Onboard CNN-Based Processing for Target Detection and Autonomous Landing for MAVs

text: In this work, we address the problem of target detection involved in an autonomous landing task for a Micro Aerial Vehicle (MAV). The challenge is to detect a flag located somewhere in the environment. The flag is posed on a pole, and to its right, a landing platform is located. Thus, the MAV has to detect the flag, fly towards it and once it is close enough, locate the landing platform nearby, aiming at centring over it to perform landing; all of this has to be carried out autonomously. In this context, the main problem is the detection of both the flag and the landing platform, whose shapes are known in advanced. Traditional computer vision algorithms could be used; however, the main challenges in this task are the changes in illumination, rotation and scale, and the fact that the flight controller uses the detection to perform the autonomous flight; hence the detection has to be stable and continuous on every camera frame. Motivated by this, we propose to use a Convolutional Neural Network optimised to be run on a small computer with limited computer processing budget. The MAV carries this computer, and it is used to process everything on board. To validate our system, we tested with rotated images, changes in scale and the presence of low illumination. Our method is compared against two conventional computer vision methods, namely, template and feature matching. In addition, we tested our system performance in a wide corridor, executing everything on board the MAV. We achieved a successful detection of the flag with a confidence metric of 0.9386 and 0.9826 for the Landing platform. In total, all the onboard computations ran at an average of 13.01 fps.


title: A Method for Estimating Driving Factors of Illicit Trade Using Node Embeddings and Clustering

text: The trade on illegal goods and services, also known as illicit trade, is expected to drain 4.2 trillion dollars from the world economy and put 5.4 million jobs at risk by 2022. These estimates reflect the importance of combating illicit trade, as it poses a danger to individuals and undermines governments. To do so, however, we have to first understand the factors that influence this type of trade. Therefore, we present in this article a method that uses node embeddings and clustering to compare a country based illicit supply network to other networks that represent other types of country relationships (e.g., free trade agreements, language). The results offer initial clues on the factors that might be driving the illicit trade between countries.


title: Not All Swear Words Are Used Equal: Attention over Word n-grams for Abusive Language Identification

text: The increasing propagation of abusive language in social media is a major concern for supplier companies and governments because of its negative social impact. A large number of methods have been developed for its automatic identification, ranging from dictionary-based methods to sophisticated deep learning approaches. A common problem in all these methods is to distinguish the offensive use of swear words from their everyday and humorous usage. To tackle this particular issue we propose an attention-based neural network architecture that captures the word n-grams importance according to their context. The obtained results in four standard collections from Twitter and Facebook are encouraging, they outperform the [Formula: see text] scores from state-of-the-art methods and allow identifying a set of inherently offensive swear words, and others in which its interpretation depends on its context.


title: Vision-Based Blind Spot Warning System by Deep Neural Networks

text: Traffic accidents represent one of the most serious problems around the world. Many efforts have been concentrated on implementing Advanced Driver Assistance Systems (ADAS) to increase safety by reducing critical tasks faced by the driver. In this paper, a Blind Spot Warning (BSW) system capable of virtualizing cars around the driver’s vehicle is presented. The system is based on deep neural models for car detection and depth estimation using images captured with a camera located on top of the main vehicle, then transformations are applied to the image and to generate the appropriate information format. Finally the cars in the environment are represented in a 3D graphical interface. We present a comparison between car detectors and another one between depth estimators from which we choose the best performance ones to be implemented in the BSW system. In particular, our system offers a more intuitive assistance interface for the driver allowing a better and quicker understanding of the environment from monocular cameras.


title: Structured Pointcloud Segmentation for Individual Mangrove Tree Modeling

text: Tree structure parameters of mangrove forests are hard to measure in the field and therefore inventories of this type of forests are impossible to keep up to date. In this article, we tested a structured pointcloud segmentation method for extracting individual mangrove trees. Structure parameters of individual trees were estimated from the segmented pointcloud and its 3d geometry was generated using revolution surfaces. Estimated parameters were then assessed at both plot and tree levels using field data. It was observed that the number of segments in each test plot agreed well with the number of trees observed in the field. Nonetheless, the estimated parameters exhibited mixed accuracy with top height being the most accurate.


title: Basic Pattern Graphs for the Efficient Computation of Its Number of Independent Sets

text: The problem of counting the number of independent sets of a graph G (denoted as i(G)) is a classic #P-complete problem. We present some patterns on graphs that allows us the polynomial computation of i(G). For example, we show that for a graph G where its set of cycles can be arranged as embedded cycles, i(G) can be computed in polynomial time. Particularly, our proposal counts independent sets on outerplanar graphs.


title: Towards Selecting Reducts for Building Decision Rules for Rule-Based Classifiers

text: In rule-based classifiers, calculating all possible rules of a learning sample consumes many resources due to its exponential complexity. Therefore, finding ways to reduce the number and length of the rules without affecting the efficacy of a classifier remains an interesting problem. Reducts from rough set theory have been used to build rule-based classifiers by their conciseness and understanding. However, the accuracy of the classifiers based on these rules depends on the selected rule subset. In this work, we focus on analyzing three different options for using reducts for building decision rules for rule-based classifiers .


title: Towards Dendrite Spherical Neurons for Pattern Classification

text: This paper introduces the Dendrite Spherical Neuron (DSN) as an alternative to the Dendrite Ellipsoidal Neuron (DEN), in which hyperspheres group the patterns from different classes instead of hyperellipses. The reasoning behind DSN is simplifying the computation of DEN architecture, where a centroid and covariance matrix are two dendritic parameters, whereas, in DSN, the covariance matrix is replaced by a radius. This modification is useful to avoid singular covariance matrices since DEN requires measuring the Mahalanobis distance to classify patterns. The DSN training consists of determining the centroids of dendrites with the k-means algorithm, followed by calculating the radius of dendrites as the mean distance to the two nearest centroids, and finally determining the weights of a softmax function, with Stochastic Gradient Descent, at the output of the neuron. Besides, the Simulated Annealing automatically determines the number of dendrites that maximizes the classification accuracy. The DSN is applied to synthetic and real-world datasets. The experimental results reveal that DSN is competitive with Multilayer Perceptron (MLP) networks, with less complex architectures. Also, DSN tends to outperform the Dendrite Morphological Neuron (DMN), which uses hyperboxes. These findings suggest that the DSN is a potential alternative to MLP and DMN for pattern classification tasks.


title: Analysis of Repair Costs of Scholar Buildings Affected by Earthquakes Using Data Mining. Case Study: Earthquakes of 2017 in Mexico

text: Earthquakes are events that cannot be predicted. However, when they occur, devastating consequences are shown in economic, social and structural areas, among others. In this paper, the mining of association rules is carried out in order to estimate the repair cost required by schools affected during the earthquakes of September 7th and 19th, of 2017 in Mexico. For that, we use the public data collected by the Mexican FONDEN.


title: A Novel Set of Moment Invariants for Pattern Recognition Applications Based on Jacobi Polynomials

text: A novel set of moment invariants for pattern recognition applications, which are based on Jacobi polynomials, are presented. These moment invariants are constructed for digital images by means of a combination with geometric moments, and are invariant in the face of affine geometric transformations such as rotation, translation and scaling, on the image plane. This invariance is tested on a sample of the MPEG-7 CE-Shape-1 dataset. The results presented show that the low-order moment invariants indeed possess low variance between images that are affected by the mentioned geometric transformations.


title: Restoration of Range Images by the Gaussian Pyramid Method, Testing Different Interpolation Techniques to Select the Best Performance

text: The inpainting method implemented in this work was used to estimate the missing information in a range image from a single image, achieved independence of the RGB image of the scene or multiple range images to perform the restoration. The proposal is based on improving the results of restoring range images using the Gaussian Pyramid method. This, finding the best interpolation technique to use in this method to estimate the missing information. Different interpolation techniques were computed and applied in order to know the best option to implement. This is carried out considering the amount of information that can be estimated, processing time and the total of information missing in the image to be restored. The method was tested with five different databases, one of which was created specifically for this work. These databases include different interior scenarios with several objects. A qualitative and quantitative comparative analysis of the obtained results was performed.


title: Digital Assisted Image Correlation for Metal Sheet Strain Measurement

text: Current methods of correlation and point matching between stereoscopic images produce large errors or are completely inefficient when the surface has a repetitive, non-isotropic, low contrast pattern. In this article a new method of Digital Assisted Image Correlation (DAIC) is presented to match specific points in order to estimate the deformation of the surface in the metal sheets used in the automotive industry. To achieve this, it is necessary to stamp the surface to be measured with a regular pattern of points, then a digital image processing is done to obtain the labels of the circles of the pattern. After this, a semi-automatic search is made in the labels of both images to correlate all of them and perform the triangulation. DIC is used to corroborate the correspondence between points and verify the accuracy and efficiency of the developed method. This allows the 3D reconstruction of the sheet with a minimum of information and provides more efficiency and a great benefit in computational cost. Deformation is calculated by two methods, which show similarity between the values obtained with a digital microscope. It is assumed that quality of marks stamping, lighting, and the initial conditions, also contribute for trustworthy effects.


title: Comparison of Recurrent Neural Networks for Wind Power Forecasting

text: Integrating wind power to the electrical grid is complicated due to the stochastic nature of the wind, which makes its prediction a challenging task. Then, it is important to devise forecasting tools to support this task. For example, a network that integrates an Echo State Network architecture and Long Short-Term Memory blocks as hidden units (ESN+LSTM) has been proposed, showing good performance against a physical model. This paper proposes to compare this network versus Echo State Network (ESN) and Long Short-Term Memory (LSTM), to forecast wind power from 1 to 24 h ahead. Results show than the ESN+LSTM model outperforms the performance reached for ESN and LSTM, in terms of MSE, MAE, and the metrics used in the Taylor diagram. In addition, we observe that the advantage of this network is statistically significant during the first moments of the forecast horizon, in terms of T-test and Wilcoxon-test.


ERCP in RYGB-patients: One size fits all?


title: Of tuberculosis and non-tuberculous mycobacterial infections – a comparative analysis of epidemiology, diagnosis and treatment

text: Pulmonary diseases due to mycobacteria cause significant morbidity and mortality to human health. In addition to tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), recent epidemiological studies have shown the emergence of non-tuberculous mycobacteria (NTM) species in causing lung diseases in humans. Although more than 170 NTM species are present in various environmental niches, only a handful, primarily Mycobacterium avium complex and M. abscessus, have been implicated in pulmonary disease. While TB is transmitted through inhalation of aerosol droplets containing Mtb, generated by patients with symptomatic disease, NTM disease is mostly disseminated through aerosols originated from the environment. However, following inhalation, both Mtb and NTM are phagocytosed by alveolar macrophages in the lungs. Subsequently, various immune cells are recruited from the circulation to the site of infection, which leads to granuloma formation. Although the pathophysiology of TB and NTM diseases share several fundamental cellular and molecular events, the host-susceptibility to Mtb and NTM infections are different. Striking differences also exist in the disease presentation between TB and NTM cases. While NTM disease is primarily associated with bronchiectasis, this condition is rarely a predisposing factor for TB. Similarly, in Human Immunodeficiency Virus (HIV)-infected individuals, NTM disease presents as disseminated, extrapulmonary form rather than as a miliary, pulmonary disease, which is seen in Mtb infection. The diagnostic modalities for TB, including molecular diagnosis and drug-susceptibility testing (DST), are more advanced and possess a higher rate of sensitivity and specificity, compared to the tools available for NTM infections. In general, drug-sensitive TB is effectively treated with a standard multi-drug regimen containing well-defined first- and second-line antibiotics. However, the treatment of drug-resistant TB requires the additional, newer class of antibiotics in combination with or without the first and second-line drugs. In contrast, the NTM species display significant heterogeneity in their susceptibility to standard anti-TB drugs. Thus, the treatment for NTM diseases usually involves the use of macrolides and injectable aminoglycosides. Although well-established international guidelines are available, treatment of NTM disease is mostly empirical and not entirely successful. In general, the treatment duration is much longer for NTM diseases, compared to TB, and resection surgery of affected organ(s) is part of treatment for patients with NTM diseases that do not respond to the antibiotics treatment. Here, we discuss the epidemiology, diagnosis, and treatment modalities available for TB and NTM diseases of humans.


Covid-19: An urgent need for a psychoneuroendocrine perspective


title: An extensive analysis of the presence of altmetric data for Web of Science publications across subject fields and research topics

text: Sufficient data presence is one of the key preconditions for applying metrics in practice. Based on both Altmetric.com data and Mendeley data collected up to 2019, this paper presents a state-of-the-art analysis of the presence of 12 kinds of altmetric events for nearly 12.3 million Web of Science publications published between 2012 and 2018. Results show that even though an upward trend of data presence can be observed over time, except for Mendeley readers and Twitter mentions, the overall presence of most altmetric data is still low. The majority of altmetric events go to publications in the fields of Biomedical and Health Sciences, Social Sciences and Humanities, and Life and Earth Sciences. As to research topics, the level of attention received by research topics varies across altmetric data, and specific altmetric data show different preferences for research topics, on the basis of which a framework for identifying hot research topics is proposed and applied to detect research topics with higher levels of attention garnered on certain altmetric data source. Twitter mentions and policy document citations were selected as two examples to identify hot research topics of interest of Twitter users and policy-makers, respectively, shedding light on the potential of altmetric data in monitoring research trends of specific social attention.


title: Epilogue: The Language Opportunities for the XXI Century

text: This epilogue to the Special Issue on Language Challenges in the XXI Century offers commentary on the current state of social scientific inquiry in the field of language and social psychology. Inspired by the seven articles that make up this Special Issue, I became curious about what we would find if we sought language opportunities instead of language challenges in the 21st century. I recommend future scholarship at the intersections of global and linguistic diversity include a positive social science approach in order to consider the full spectrum of challenges and assets. I conclude with a note about the direction of future research related to COVID-19.


title: Het raadsel van de immuniteit

text: Hoe komt het dat mensen die geïnfecteerd en ziek worden (hopelijk) herstellen? En waarom zijn ze daarna vaak immuun voor een aanval van dezelfde ziekte, maar niet voor een andere? Dat was eeuwenlang een puzzel.


title: Investigating Query Expansion and Coreference Resolution in Question Answering on BERT

text: The Bidirectional Encoder Representations from Transformers (BERT) model produces state-of-the-art results in many question answering (QA) datasets, including the Stanford Question Answering Dataset (SQuAD). This paper presents a query expansion (QE) method that identifies good terms from input questions, extracts synonyms for the good terms using a widely-used language resource, WordNet, and selects the most relevant synonyms from the list of extracted synonyms. The paper also introduces a novel QE method that produces many alternative sequences for a given input question using same-language machine translation (MT). Furthermore, we use a coreference resolution (CR) technique to identify anaphors or cataphors in paragraphs and substitute them with the original referents. We found that the QA system with this simple CR technique significantly outperforms the BERT baseline in a QA task. We also found that our best-performing QA system is the one that applies these three preprocessing methods (two QE and CR methods) together to BERT, which produces an excellent [Formula: see text] score (89.8 [Formula: see text] points) in a QA task. Further, we present a comparative analysis on the performances of the BERT QA models taking a variety of criteria into account, and demonstrate our findings in the answer span prediction task.


title: Pattern Learning for Detecting Defect Reports and Improvement Requests in App Reviews

text: Online reviews are an important source of feedback for understanding customers. In this study, we follow novel approaches that target the absence of actionable insights by classifying reviews as defect reports and requests for improvement. Unlike traditional classification methods based on expert rules, we reduce the manual labour by employing a supervised system that is capable of learning lexico-semantic patterns through genetic programming. Additionally, we experiment with a distantly-supervised SVM that makes use of the noisy labels generated by patterns. Using a real-world dataset of app reviews, we show that the automatically learned patterns outperform the manually created ones. Also the distantly-supervised SVM models are not far behind the pattern-based solutions, showing the usefulness of this approach when the amount of annotated data is limited.


title: Using BERT and Augmentation in Named Entity Recognition for Cybersecurity Domain

text: The paper presents the results of applying the BERT representation model in the named entity recognition task for the cybersecurity domain in Russian. Several variants of the model were investigated. The best results were obtained using the BERT model, trained on the target collection of information security texts. We also explored a new form of data augmentation for the task of named entity recognition.


title: Movies Emotional Analysis Using Textual Contents

text: In this paper, we use movies and series subtitles and applied text mining and Natural Language Processing methods to evaluate emotions in videos. Three different word lexicons were used and one of the outcomes of this research is the generation of a secondary dataset with more than 3658 records which can be used for other data analysis and data mining research. We used our secondary dataset to find and display correlations between different emotions on the videos and the correlation between emotions on the movies and users’ scores on IMDb using the Pearson correlation method and found some statistically significant correlations.


title: Analysis and Multilabel Classification of Quebec Court Decisions in the Domain of Housing Law

text: The Régie du Logement du Québec (RDL) is a tribunal with exclusive jurisdiction in matters regarding rental leases. Within the framework of the ACT (Autonomy Through Cyberjustice Technologies) project, we processed an original collection of court decisions in French and performed a thorough analysis to reveal biases that may influence prediction experiments. We studied a multilabel classification task that consists in predicting the types of verdict in order to illustrate the importance of prior data analysis. Our best model, based on the FlauBERT language model, achieves F1 score micro averages of 93.7% and 84.9% in Landlord v. Tenant and Tenant v. Landlord cases respectively. However, with the support of our in-depth analysis, we emphasize that these results should be kept in perspective and that some metrics may not be suitable for evaluating systems in sensitive domains such as housing law.


title: KI – Einblicke in die künstliche Intelligenz

text: Nun haben Sie gerade gelesen, dass uns die digitalen Assistenten an der einen oder anderen Stelle auf den Fersen sind, und schon muss ich noch einen drauflegen und in das Thema künstliche Intelligenz (KI) einsteigen.


title: A Sentiwordnet Strategy for Curriculum Learning in Sentiment Analysis

text: Curriculum Learning (CL) is the idea that learning on a training set sequenced or ordered in a manner where samples range from easy to difficult, results in an increment in performance over otherwise random ordering. The idea parallels cognitive science’s theory of how human brains learn, and that learning a difficult task can be made easier by phrasing it as a sequence of easy to difficult tasks. This idea has gained a lot of traction in machine learning and image processing for a while and recently in Natural Language Processing (NLP). In this paper, we apply the ideas of curriculum learning, driven by SentiWordNet in a sentiment analysis setting. In this setting, given a text segment, our aim is to extract its sentiment or polarity. SentiWordNet is a lexical resource with sentiment polarity annotations. By comparing performance with other curriculum strategies and with no curriculum, the effectiveness of the proposed strategy is presented. Convolutional, Recurrence and Attention based architectures are employed to assess this improvement. The models are evaluated on standard sentiment dataset, Stanford Sentiment Treebank.


title: CONQUEST: A Framework for Building Template-Based IQA Chatbots for Enterprise Knowledge Graphs

text: The popularization of Enterprise Knowledge Graphs (EKGs) brings an opportunity to use Question Answering Systems to consult these sources using natural language. We present CONQUEST, a framework that automates much of the process of building chatbots for the Template-Based Interactive Question Answering task on EKGs. The framework automatically handles the processes of construction of the Natural Language Processing engine, construction of the question classification mechanism, definition of the system interaction flow, construction of the EKG query mechanism, and finally, the construction of the user interaction interface. CONQUEST uses a machine learning-based mechanism to classify input questions to known templates extracted from EKGs, utilizing the clarification dialog to resolve inconclusive classifications and request mandatory missing parameters. CONQUEST also evolves with question clarification: these cases define question patterns used as new examples for training.


title: Towards Explainability in Using Deep Learning for the Detection of Anorexia in Social Media

text: Explainability of deep learning models has become increasingly important as neural-based approaches are now prevalent in natural language processing. Explainability is particularly important when dealing with a sensitive domain application such as clinical psychology. This paper focuses on the quantitative assessment of user-level attention mechanism in the task of detecting signs of anorexia in social media users from their posts. The assessment is done through monitoring the performance measures of a neural classifier, with and without user-level attention, when only a limited number of highly-weighted posts are provided. Results show that the weights assigned by the user-level attention strongly correlate with the amount of information that posts provide in showing if their author is at risk of anorexia or not, and hence can be used to explain the decision of the neural classifier.


title: Natural Language Generation Using Transformer Network in an Open-Domain Setting

text: Prior works on dialog generation focus on task-oriented setting and utilize multi-turn conversational utterance-response pairs. However, natural language generation (NLG) in the open-domain environment is more challenging. The conversations in an open-domain chit-chat model are mostly single-turn in nature. Current methods used for modeling single-turn conversations often fail to generate contextually relevant responses for a large dataset. In our work, we develop a transformer-based method for natural language generation (NLG) in an open-domain setting. Experiments on the utterance-response pairs show improvement over the baselines, both in terms of quantitative measures like BLEU and ROUGE and human evaluation metrics like fluency and adequacy.


title: The Role of Personality and Linguistic Patterns in Discriminating Between Fake News Spreaders and Fact Checkers

text: Users play a critical role in the creation and propagation of fake news online by consuming and sharing articles with inaccurate information either intentionally or unintentionally. Fake news are written in a way to confuse readers and therefore understanding which articles contain fabricated information is very challenging for non-experts. Given the difficulty of the task, several fact checking websites have been developed to raise awareness about which articles contain fabricated information. As a result of those platforms, several users are interested to share posts that cite evidence with the aim to refute fake news and warn other users. These users are known as fact checkers. However, there are users who tend to share false information, who can be characterised as potential fake news spreaders. In this paper, we propose the CheckerOrSpreader model that can classify a user as a potential fact checker or a potential fake news spreader. Our model is based on a Convolutional Neural Network (CNN) and combines word embeddings with features that represent users’ personality traits and linguistic patterns used in their tweets. Experimental results show that leveraging linguistic patterns and personality traits can improve the performance in differentiating between checkers and spreaders.


title: Enabling Interactive Answering of Procedural Questions

text: A mechanism to enable task oriented procedural question answering system for user assistance in English is presented in this paper. The primary aim is to create an answering “corpus” in a tree-form from unstructured document passages. This corpus is used to respond to the queries interactively to assist in completing a technical task. Reference manuals, documents or webpages are scraped to identify the sections depicting a “procedure” through machine learning techniques and then an integrated task tree with extracted procedural knowledge from text is generated. The automated mechanism breaks the procedural sections into steps, the appropriate “decision points” are identified, the interactive utterances are generated to gain user inputs and the alternative paths are created to complete the tree. Conventional tree traversal mechanism provides step by step guidance to complete a task. Efficacy of the proposed mechanism is tested on documents collected from three different domains and test results are presented.


title: A Position Aware Decay Weighted Network for Aspect Based Sentiment Analysis

text: Aspect Based Sentiment Analysis (ABSA) is the task of identifying sentiment polarity of a text given another text segment or aspect. In ABSA, a text can have multiple sentiments depending upon each aspect. Aspect Term Sentiment Analysis (ATSA) is a subtask of ABSA, in which aspect terms are contained within the given sentence. Most of the existing approaches proposed for ATSA, incorporate aspect information through a different subnetwork thereby overlooking the advantage of aspect terms’ presence within the sentence. In this paper, we propose a model that leverages the positional information of the aspect. The proposed model introduces a decay mechanism based on position. This decay function mandates the contribution of input words for ABSA. The contribution of a word declines as farther it is positioned from the aspect terms in the sentence. The performance is measured on two standard datasets from SemEval 2014 Task 4. In comparison with recent architectures, the effectiveness of the proposed model is demonstrated.


title: Improving Latent Dirichlet Allocation: On Reliability of the Novel Method LDAPrototype

text: A large number of applications in text data analysis use the Latent Dirichlet Allocation (LDA) as one of the most popular methods in topic modeling. Although the instability of the LDA is mentioned sometimes, it is usually not considered systematically. Instead, an LDA is often selected from a small set of LDAs using heuristic means or human codings. Then, conclusions are often drawn based on the to some extent arbitrarily selected model. We present the novel method LDAPrototype, which takes the instability of the LDA into account, and show that by systematically selecting an LDA it improves the reliability of the conclusions drawn from the result and thus provides better reproducibility. The improvement coming from this selection criterion is unveiled by applying the proposed methods to an example corpus consisting of texts published in a German quality newspaper over one month.


title: Improving Named Entity Recognition for Biomedical and Patent Data Using Bi-LSTM Deep Neural Network Models

text: The daily exponential increase of biomedical information in scientific literature and patents is a main obstacle to foster advances in biomedical research. A fundamental step hereby is to find key information (named entities) inside these publications applying Biomedical Named Entities Recognition (BNER). However, BNER is a complex task compared to traditional NER as biomedical named entities often have irregular expressions, employ complex entity structures, and don’t consider well-defined entity boundaries, etc. In this paper, we propose a deep neural network (NN) architecture, namely the bidirectional Long-Short Term Memory (Bi-LSTM) based model for BNER. We present a detailed neural network architecture showing the different NN layers, their interconnections and transformations. Based on existing gold standard datasets, we evaluated and compared several models for identifying biomedical named entities such as chemicals, diseases, drugs, species and genes/proteins. Our deep NN based Bi-LSTM model using word and character level embeddings outperforms CRF and Bi-LSTM using only word level embeddings significantly.


title: Enhancing Subword Embeddings with Open N-grams

text: Using subword n-grams for training word embeddings makes it possible to subsequently compute vectors for rare and misspelled words. However, we argue that the subword vector qualities can be degraded for words which have a high orthographic neighbourhood; a property of words that has been extensively studied in the Psycholinguistic literature. Empirical findings about lexical neighbourhood effects constrain models of human word encoding, which must also be consistent with what we know about neurophysiological mechanisms in the visual word recognition system. We suggest that the constraints learned from humans provide novel insights to subword encoding schemes. This paper shows that vectors trained with subword properties informed by psycholinguistic evidence are superior to those trained with ad hoc n-grams. It is argued that physiological mechanisms for reading are key factors in the observed distribution of written word forms, and should therefore inform our choice of word encoding.


title: Human-in-the-Loop Conversation Agent for Customer Service

text: This paper describes a prototype system for partial automation of customer service operations of a mobile telecommunications operator with a human-in-the loop conversational agent. The agent consists of an intent detection system for identifying the types of customer requests that it can handle appropriately, a slot filling information extraction system that integrates with the customer service database for a rule-based treatment of the common scenarios, and a template-based language generation system that builds response candidates that can be approved or amended by customer service operators. The main focus of this paper is on the system architecture and machine learning system structure design, and the observations of a limited pilot study performed to evaluate the proposed system on customer messages in Latvian. We also discuss the business requirements and practical application limitations and their influence on the design of the natural language processing components.


title: A User-centred Analysis of Explanations for a Multi-component Semantic Parser

text: This paper shows the preliminary results of an initial effort to analyse whether explanations associated with a semantic parser help users to generalise the system’s mechanisms regardless of their technical background. With the support of a user-centred experiment with 66 participants, we evaluated the user’s mental model by associating the linguistic features from a set of explanations to the system’s behaviour.


title: A Cooking Knowledge Graph and Benchmark for Question Answering Evaluation in Lifelong Learning Scenarios

text: In a long term exploitation environment, a Question Answering (QA) system should maintain or even improve its performance over time, trying to overcome the lacks made evident through the interactions with users. We claim that, in order to make progress in the QA over Knowledge Bases (KBs) research field, we must deal with two problems at the same time: the translation of Natural Language (NL) questions into formal queries, and the detection of missing knowledge that impact the way a question is answered. The research on these two challenges has not been addressed jointly until now, what motivates the main goals of this work: (i) the definition of the problem and (ii) the development of a methodology to create the evaluation resources needed to address this challenge.


title: Improving the Community Question Retrieval Performance Using Attention-Based Siamese LSTM

text: In this paper, we focus on the problem of question retrieval in community Question Answering (cQA) which aims to retrieve from the community archives the previous questions that are semantically equivalent to the new queries. The major challenges in this crucial task are the shortness of the questions as well as the word mismatch problem as users can formulate the same query using different wording. While numerous attempts have been made to address this problem, most existing methods relied on supervised models which significantly depend on large training data sets and manual feature engineering. Such methods are mostly constrained by their specificities that put aside the word order and ignore syntactic and semantic relationships. In this work, we rely on Neural Networks (NNs) which can learn rich dense representations of text data and enable the prediction of the textual similarity between the community questions. We propose a deep learning approach based on a Siamese architecture with LSTM networks, augmented with an attention mechanism. We test different similarity measures to predict the semantic similarity between the community questions. Experiments conducted on real cQA data sets in English and Arabic show that the performance of question retrieval is improved as compared to other competitive methods.


title: An Adaptive Response Matching Network for Ranking Multi-turn Chatbot Responses

text: With the increasing popularity of personal assistant systems, it is crucial to build a chatbot that can communicate with humans and assist them to complete different tasks. A fundamental problem that any chatbots need to address is how to rank candidate responses based on previous utterances in a multi-turn conversation. A previous utterance could be either a past input from the user or a past response from the chatbot. Intuitively, a correct response needs to match well with both past responses and past inputs, but in a different way. Moreover, the matching process should depend on not only the content of the utterances but also domain knowledge. Although various models have been proposed for response matching, few of them studied how to adapt the matching mechanism to utterance types and domain knowledge. To address this limitation, this paper proposes an adaptive response matching network (ARM) to better model the matching relationship in multi-turn conversations. Specifically, the ARM model has separate response matching encoders to adapt to different matching patterns required by different utterance types. It also has a knowledge embedding component to inject domain-specific knowledge in the matching process. Experiments over two public data sets show that the proposed ARM model can significantly outperform the state of the art methods with much fewer parameters.


title: Combining Character and Word Embeddings for Affect in Arabic Informal Social Media Microblogs

text: Word representation models have been successfully applied in many natural language processing tasks, including sentiment analysis. However, these models do not always work effectively in some social media contexts. When considering the use of Arabic in microblogs like Twitter, it is important to note that a variety of different linguistic domains are involved. This is mainly because social media users employ various dialects in their communications. While training word-level models with such informal text can lead to words being captured that have the same meanings, these models cannot capture all words that can be encountered in the real world due to out-of-vocabulary (OOV) words. The inability to identify words is one of the main limitations of this word-level model. In contrast, character-level embeddings can work effectively with this problem through their ability to learn the vectors of character n-grams or parts of words. We take advantage of both character- and word-level models to discover more effective methods to represent Arabic affect words in tweets. We evaluate our embeddings by incorporating them into a supervised learning framework for a range of affect tasks. Our models outperform the state-of-the-art Arabic pre-trained word embeddings in these tasks. Moreover, they offer improved state-of-the-art results for the task of Arabic emotion intensity, outperforming the top-performing systems that employ a combination of deep neural networks and several other features.


title: Enhancement of Short Text Clustering by Iterative Classification

text: Short text clustering is a challenging task due to the lack of signal contained in short texts. In this work, we propose iterative classification as a method to boost the clustering quality of short texts. The idea is to repeatedly reassign (classify) outliers to clusters until the cluster assignment stabilizes. The classifier used in each iteration is trained using the current set of cluster labels of the non-outliers; the input of the first iteration is the output of an arbitrary clustering algorithm. Thus, our method does not require any human-annotated labels for training. Our experimental results show that the proposed clustering enhancement method not only improves the clustering quality of different baseline clustering methods (e.g., k-means, k-means--, and hierarchical clustering) but also outperforms the state-of-the-art short text clustering methods on several short text datasets by a statistically significant margin.


title: Literary Natural Language Generation with Psychological Traits

text: The area of Computational Creativity has received much attention in recent years. In this paper, within this framework, we propose a model for the generation of literary sentences in Spanish, which is based on statistical algorithms, shallow parsing and the automatic detection of personality features of characters of well known literary texts. We present encouraging results of the analysis of sentences generated by our methods obtained with human inspection.


title: Studying Attention Models in Sentiment Attitude Extraction Task

text: In the sentiment attitude extraction task, the aim is to identify «attitudes» – sentiment relations between entities mentioned in text. In this paper, we provide a study on attention-based context encoders in the sentiment attitude extraction task. For this task, we adapt attentive context encoders of two types: (I) feature-based; (II) self-based. Our experiments (https://github.com/nicolay-r/attitude-extraction-with-attention) with a corpus of Russian analytical texts RuSentRel illustrate that the models trained with attentive encoders outperform ones that were trained without them and achieve 1.5–5.9% increase by [Formula: see text]. We also provide the analysis of attention weight distributions in dependence on the term type.


title: Jointly Linking Visual and Textual Entity Mentions with Background Knowledge

text: “A picture is worth a thousand words”, the adage reads. However, pictures cannot replace words in terms of their ability to efficiently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed, reveal different and complementary information that, if combined, result in more information than the sum of that contained in the single media. The combination of visual and textual information can be obtained through linking the entities mentioned in the text with those shown in the pictures. To further integrate this with agent background knowledge, an additional step is necessary. That is, either finding the entities in the agent knowledge base that correspond to those mentioned in the text or shown in the picture or, extending the knowledge base with the newly discovered entities. We call this complex task Visual-Textual-Knowledge Entity Linking (VTKEL). In this paper, after providing a precise definition of the VTKEL task, we present a dataset composed of about 30K commented pictures, annotated with visual and textual entities, and linked to the YAGO ontology. Successively, we develop a purely unsupervised algorithm for the solution of the VTKEL tasks. The evaluation on the VTKEL dataset shows promising results.


Von Zustand und Zukunft des Westens – Ein Bericht zur 56. Münchner Sicherheitskonferenz


title: Revisiting respiratory syncytial virus’s interaction with host immunity, towards novel therapeutics

text: Every year there are > 33 million cases of Respiratory Syncytial Virus (RSV)-related respiratory infection in children under the age of five, making RSV the leading cause of lower respiratory tract infection (LRTI) in infants. RSV is a global infection, but 99% of related mortality is in low/middle-income countries. Unbelievably, 62 years after its identification, there remains no effective treatment nor vaccine for this deadly virus, leaving infants, elderly and immunocompromised patients at high risk. The success of all pathogens depends on their ability to evade and modulate the host immune response. RSV has a complex and intricate relationship with our immune systems, but a clearer understanding of these interactions is essential in the development of effective medicines. Therefore, in a bid to update and focus our research community’s understanding of RSV’s interaction with immune defences, this review aims to discuss how our current knowledgebase could be used to combat this global viral threat.


title: Surgical ligation of patent ductus arteriosus in preterm neonates weighing less than 1500g: a 9-year single center experience

text: BACKGROUND: The aim of this study was to determine the feasibility and outcomes of early surgical ligation in preterm neonates with hemodynamically significant patent ductus arteriosus (HSPDA) and to investigate predictors for surgical treatment after unsuccessful medical management. METHODS: Medical records from the neonatal intensive care unit of Hanyang University Seoul Hospital from January 2010 to December 2018 were retrospectively reviewed. 233 preterm neonates weighing less than 1500g with HSPDA were enrolled in our study. Of these preterm neonates, 134 underwent surgical ligation and were subdivided into the early ligation group (n = 49; within 10 days of age) and the late ligation group (n = 85; after 10 days of age). RESULTS: The mean gestational age and birth weight were significantly lower in the patent ductus arteriosus (PDA) ligation group than in the Non-ligation group (p < 0.001). PDA ductal diameter > 2.0 mm (p < 0.001), low Apgar score at 5 min (p = 0.033), and chorioamnionitis (p = 0.037) were the predictors for receiving surgical treatment for PDA. Early ligation was significantly associated with a low incidence of culture-proven sepsis (p = 0.004), mechanical ventilator time > 4 weeks (p = 0.007), necrotizing enterocolitis stage (NEC) ≥ III (p = 0.022), and intraventricular hemorrhage (IVH) grade ≥ III (p = 0.035). CONCLUSIONS: Early surgical ligation minimizes the adverse effects of HSPDA in predicted preterm neonates who subsequently require surgical treatment for PDA. This result suggests that in preterm neonates weighing less than 1500g with HSPDA that is unresponsive to medical treatment, delayed ductal closure should be avoided to reduce severe NEC, severe IVH, culture-proven sepsis, and facilitate earlier endotracheal extubation.


Thrombozytentransfusion bei hämatologisch-onkologischen Patienten


title: Hospital-Based Case Series Analysis of Road Traffic Trauma Patients in Sri Lanka

text: Road traffic accidents claim many lives each year worldwide and cause significant disability among survivors. Resulting socioeconomic burden is severe in low- and middle-income countries. Global emphasis currently focuses on trauma education and prevention in addition to improving post-injury care. Sri Lankan government recently made compulsory legislation to improve the safety standards of imported motor vehicles. Such regulations would not directly protect vulnerable road users (VRUs) who form the main bulk of Sri Lankan and South Asian automobile trauma casualties. With the objective of reviewing the management outcome of automobile trauma in order to correlate the potential impact of new legislation on injury prevention, data of all admitted road traffic injury victims were audited for 2 months. Out of 473 eligible cases (332 (70.2%)—males; mean age 37.2 years), there were 14 (3%) fatalities. Of 459 (97%) survivors, 77 (16%) suffered major injuries. Twelve out of 14 (85.7%) fatalities, 64 of 77 (83.1%) survivors with major injuries and 263 of 382 (68.8%) cases of lesser injuries were VRUs (p value = 0.02: chi-square). VRUs had a significantly higher mean Injury Severity Score (ISS) of 10.96 ± 8.43 SD than non-VRUs who had a mean ISS of 8.14 ± 6.04 SD (p = 0.003: t test). Among all survivors, 33/39 (84.6%) with permanent disability, 95/110 (86.4%) with temporary disability and 199/310 (64.2%) with no residual disability were VRUs (p value < 0.0001: chi-square). Of 222 drivers/riders, 45 (20.3%) had consumed alcohol prior to the incident and 20 (9%) were driving without a valid licence. Thirty-four out of 162 (21%) motor bike travellers were not wearing a protecting helmet. Results denote that VRUs suffer significant majority of the fatalities and major injuries after road traffic trauma and to have higher residual temporary/permanent disability among survivors. Recent compulsory motor vehicle safety measures are unlikely to directly protect majority of automobile trauma victims in Sri Lanka. To obtain a higher impact on road traffic injury prevention, main emphasis has to be given to improve the safety standards of VRUs in developing countries.


title: Profiling of a large-scale municipal wireless network

text: Pervasive connectivity is an essential underlying substrate for smart cities, leading municipalities to start programs where Wi-Fi is the fundamental building block to develop public Municipal Wireless Networks. Even though hundreds of cities around the world offer some form of Wi-Fi access, there are no widely available results regarding the network Quality of Service (QoS), user Quality of Experience (QoE), and overall utilization profile. The Municipality of São Paulo operates a free Wi-Fi Internet program in 120 public spaces called digital squares. We collected user connection data, network performance, and service availability for more than 2 years from the 120 squares and undertook experiments with video streaming in five squares. We used this unique large dataset to evaluate the impact of current admission control practices in public Wi-Fi networks on the network QoS, user QoE and service availability, also providing insights into the most common QoS/QoE issues and their causes. We also leveraged the data set to establish and verify a correlation between the number of users access in the network and specific events occurring in the area.


Pandemics of People & Plants: Which Is the Greater Threat to Food Security?


COVID-19: What's next?()


COVID-19 with Pulmonary Involvement. An Autoimmune Disease of Known Cause()


title: Predictive Roles of the Repetitive Saliva Swallowing Test (RSST) in Aspiration Pneumonia and Other Respiratory Diseases: Does the RSST Have a Predictive Role in Aspiration Pneumonia and Other Respiratory Diseases?

text: Patients with dysphagia do not always present with subjective symptoms. However, asymptomatic dysphagia can also cause clinical issues, especially in those with respiratory conditions. Therefore, adequate screening is an essential beginning to their care. The repetitive saliva swallowing test (RSST) is one of the safest screening methods for dysphagia; it can be easily performed by nonprofessionals in any setting. There is evidence of its predictive values in aspiration pneumonia, chronic obstructive pulmonary disease (COPD), artificial ventilation, and other conditions. Additionaly, it has recently been found to be a strong predictor of the risk of future COPD exacerbation. The cost-effectiveness, harmlessness, and simplicity make it an optimal screening method for the large population of patients with respiratory conditions, although different cutoff values may be useful in different populations. It also takes into account multiple aspects of the swallowing ability, such as respiration, musculature, cognition, and general well-being.


title: Animal Models of Aspiration Pneumonia

text: Appropriate animal models of aspiration pneumonia may be required for studying the mechanism of aspiration and aspiration-induced pneumonia. Animal models of AP allow us to investigate distinct types of pneumonia at various disease stages, studies that are not possible in patients. AP animal models should have features of bacterial pneumonia and swallowing abnormality. Our animal model of aspiration, using recombinant E1-deleted Ad vectors, may be advantageous relative to earlier models for assessing the development of aspiration pneumonia in association with disturbed upper airway reflexes, since DNA virus infection of bronchiolar epithelial cells in the lower respiratory tract can be assessed by the localization and intensity of LacZ gene expression The other candidate model of aspiration was applied for the experimental stroke in mice induced by occlusion of the middle cerebral artery. Aspiration pneumonia was caused by intranasal application of a small amount of Streptococcus pneumoniae. Acid pneumonitis is a major cause of sterile acute lung injury (ALI), resulting in acute respiratory distress syndrome (ARDS) or Mendelson’s syndrome. Several types of animal models of acid aspiration are available using a wide range of developed transgenic models. Different types of animal models of both aspiration pneumonia and aspiration pneumonitis have considerably aided our understanding of disease pathogenesis and testing and developing of new treatment strategies.


title: Moral Challenges When Suspecting Abuse and Neglect in School Children: A Mixed Method Study

text: The World Health Organization (WHO), concludes that child maltreatment is a global concern calling for a multi sectoral interdisciplinary approach. School professionals, such as social workers, teachers, and health care professionals are in positions to discover and report maltreatment enabling social workers to intervene. However, a variety of reports reveal an evident gap between incidences and frequency of number of cases reported. A review of relevant research indicates that the problem of “not reporting” suggests that moral conflicts are activated in the process of decision-making. The aim was to gain a deeper understanding of school professionals’ experiences of reporting suspected neglect and abuse to the Social Welfare Board. In a mixed method approach 32 school professionals, such as teachers, social workers, nurses and psychologists participated in interviews and responded to questionnaires. Findings from the qualitative content analysis were compared to the quantitative analysis in a meta-analysis. Moral conflicts occur when faced with making decisions about how to best deal with a child’s situation. Thoughts about the child’s best interest and relationship with his/her parents as well as the informants´ own safety, were central. The comparative meta- analysis of both data sets revealed these conflicts commence with a moral sensitivity of possible negative consequences for the child. Moral sensitivity can be viewed as a “good” personal attribute, it paradoxically might lead to moral stress despite an open ethical climate. Based on the results of this study, further research on the interpersonal aspects of dealing with moral conflicts involved in reporting suspected child abuse is indicated.


title: Le régime définitif de TVA de l’UE applicable aux échanges transfrontière : est-il temps de repenser l’approche ?

text: The VAT regime, applicable in the EU to intra-community trade in goods and to a lesser extent services in place on a ‘transitional’ basis is complex, subject to significant attacks by fraudsters, and leaves legitimate business exposed. In 2020 it is time to move forward, to put in place a ‘definitive regime, which is simple, and as fraud and future proof as possible. This article addresses the issues and the Commission’s proposals before ECOFIN, and proposes some concrete solutions.


title: Evaluation of T-activated proteins as recall antigens to monitor Epstein–Barr virus and human cytomegalovirus-specific T cells in a clinical trial setting

text: BACKGROUND: Pools of overlapping synthetic peptides are routinely used for ex vivo monitoring of antigen-specific T-cell responses. However, it is rather unlikely that these peptides match those resulting from naturally processed antigens. T-activated proteins have been described as immunogenic and more natural stimulants, since they have to pass through antigen processing and comprise activation of all clinically relevant effector cell populations. METHODS: We performed comparative analysis of numbers and cytokine expression pattern of CD4 and CD8 T cells after stimulation with recombinant, urea-formulated T-activated EBV-BZLF1, -EBNA3A, and HCMV-IE1, and -pp65 proteins or corresponding overlapping peptide pools. Freshly isolated and cryopreserved PBMC of 30 EBV- and 19 HCMV-seropositive and seven EBV- and HCMV-seronegative subjects were stimulated ex vivo and analysed for IFN-γ, TNF and IL-2 production by flow cytometry-based intracellular cytokine staining. RESULTS: T-activated proteins showed a high specificity of 100% (EBV-BZLF1, HCMV-IE1, and -pp65) and 86% (EBV-EBNA3A), and a high T-cell stimulatory capacity of 73–95% and 67–95% using freshly isolated and cryopreserved PBMC, respectively. The overall CD4 T-cell response rates in both cohorts were comparable after stimulation with either T-activated protein or peptide pools with the exception of lower numbers of CD8 T cells detected after stimulation with T-activated EBV-EBNA3A- (p = 0.038) and HCMV-pp65- (p = 0.0006). Overall, the number of detectable antigen-specific T cells varied strongly between individuals. Cytokine expression patterns in response to T-activated protein and peptide pool-based stimulation were similar for CD4, but significantly different for CD8 T-cell responses. CONCLUSION: EBV and HCMV-derived T-activated proteins represent innovative, highly specific recall antigens suitable for use in immunological endpoint assays to evaluate success or failure in immunotherapy clinical trials (e.g. to assess the risk of EBV and/or HCMV reactivation after allogenic hematopoietic stem cell transplantation). T-activated proteins could be of particular importance, if an impaired antigen processing (e.g. in a post-transplant setting) must be taken into account.


title: Statistical analysis plan for a cluster-randomised trial assessing the effectiveness of implementation of a bedside evidence-based checklist for clinical management of brain-dead potential organ donors in intensive care units: DONORS (Donation Network to Optimise Organ Recovery Study)

text: BACKGROUND: The quality of clinical care of brain-dead potential organ donors may help reduce donor losses caused by irreversible or unreversed cardiac arrest and increase the number of organs donated. We sought to determine whether an evidence-based, goal-directed checklist for donor management in intensive care units (ICUs) can reduce donor losses to cardiac arrest. METHODS/DESIGN: The DONORS study is a multicentre, cluster-randomised controlled trial with a 1:1 allocation ratio designed to compare an intervention group (goal-directed checklist for brain-dead potential organ donor management) with a control group (standard ICU care). The primary outcome is loss of potential donors due to cardiac arrest. Secondary outcomes are the number of actual organ donors and the number of solid organs recovered per actual donor. Exploratory outcomes include the achievement of relevant clinical goals during the management of brain-dead potential organ donors. The present statistical analysis plan (SAP) describes all primary statistical procedures that will be used to evaluate the results and perform exploratory and sensitivity analyses of the trial. DISCUSSION: The SAP of the DONORS study aims to describe its analytic procedures, enhancing the transparency of the study. At the moment of SAP subsmission, 63 institutions have been randomised and were enrolling study participants. Thus, the analyses reported herein have been defined before the end of the study recruitment and database locking. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03179020. Registered on 7 June 2017.


Author Correction: Health and sustainability in post-pandemic economic policies


title: The association between the seasonality of pediatric pandemic influenza virus outbreak and ambient meteorological factors in Shanghai

text: BACKGROUND AND OBJECTIVES: The number of pediatric patients diagnosed with influenza types A and B is increasing annually, especially in temperate regions such as Shanghai (China). The onset of pandemic influenza viruses might be attributed to various ambient meteorological factors including temperature, relative humidity (Rh), and PM(1) concentrations, etc. The study aims to explore the correlation between the seasonality of pandemic influenza and these factors. METHODS: We recruited pediatric patients aged from 0 to 18 years who were diagnosed with influenza A or B from July 1st, 2017 to June 30th, 2019 in Shanghai Children’s Medical Centre (SCMC). Ambient meteorological data were collected from the Shanghai Meteorological Service (SMS) over the same period. The correlation of influenza outbreak and meteorological factors were analyzed through preliminary Pearson’s r correlation test and subsequent time-series Poisson regression analysis using the distributed lag non-linear model (DLNM). RESULTS: Pearson’s r test showed a statistically significant correlation between the weekly number of influenza A outpatients and ambient meteorological factors including weekly mean, maximum, minimum temperature and barometric pressure (P < 0.001), and PM(1) (P < 0.01). While the weekly number of influenza B outpatients was statistically significantly correlated with weekly mean, maximum and minimum temperature (P < 0.001), barometric pressure and PM(1) (P < 0.01), and minimum Rh (P < 0.05). Mean temperature and PM(1) were demonstrated to be the statistically significant variables in the DLNM with influenza A and B outpatients through time-series Poisson regression analysis. A U-shaped curve relationship was noted between the mean temperature and influenza A cases (below 15 °C and above 20 °C), and the risks increased for influenza B with mean temperature below 10 °C. PM(1) posed a risk after a concentration of 23 ppm for both influenza A and B. High PM(1), low and the high temperature had significant effects upon the number of influenza A cases, whereas low temperature and high PM(1) had significant effects upon the number of influenza B cases. CONCLUSION: This study indicated that mean temperature and PM(1) were the primary factors that were continually associated with the seasonality of pediatric pandemic influenza A and B and the recurrence in the transmission and spread of influenza viruses.


title: Synthesis, characterization and applications of poly-aliphatic amine dendrimers and dendrons

text: In the current era, the dendrimers have vast potential applications in the area of electronics, healthcare, pharmaceuticals, biotechnology, engineering products, photonics, drug delivery, catalysis, electronic devices, nanotechnologies and environmental issues. This review recaps the synthesis, characterization and applications of poly-aliphatic amine dendrimers.


title: Diagnosing of Diabetic Retinopathy with Image Dehazing and Capsule Network

text: As it was discussed before in Chap. 10.1007/978-981-15-6325-6_4, the disease of diabetic retinopathy (DR) ensure terrible results such as blindness, it has been a remarkable medical problem examined recently. Here, especially retinal pathologies can be the biggest problem for millions of blindness cases seen world-wide [1]. When all the cases of blindness are examined in detail, it was reported that there are around 2 million diabetic retinopathy cases causing the blindness so that early diagnosis has taken many steps away in terms of having the highest priority in eliminating or at least slowing down disease factors (causing blindness) and so that reducing the rates of blindness at the final [2, 3].


Impact of Population Mask Wearing on Covid-19 Post Lockdown


title: Assessment of Dynamic Intracranial Compliance in Children with Severe Traumatic Brain Injury: Proof-of-Concept

text: BACKGROUND AND AIMS: Intracranial compliance refers to the relationship between a change in intracranial volume and the resultant change in intracranial pressure (ICP). Measurement of compliance is useful in managing cardiovascular and respiratory failure; however, there are no contemporary means to assess intracranial compliance. Knowledge of intracranial compliance could complement ICP and cerebral perfusion pressure (CPP) monitoring in patients with severe traumatic brain injury (TBI) and may enable a proactive approach to ICP management. In this proof-of-concept study, we aimed to capitalize on the physiologic principles of intracranial compliance and vascular reactivity to CO(2), and standard-of-care neurocritical care monitoring, to develop a method to assess dynamic intracranial compliance. METHODS: Continuous ICP and end-tidal CO(2) (ETCO(2)) data from children with severe TBI were collected after obtaining informed consent in this Institutional Review Board-approved study. An intracranial pressure-PCO(2) Compliance Index (PCI) was derived by calculating the moment-to-moment correlation between change in ICP and change in ETCO(2). As such, “good” compliance may be reflected by a lack of correlation between time-synched changes in ICP in response to changes in ETCO(2), and “poor” compliance may be reflected by a positive correlation between changes in ICP in response to changes in ETCO(2). RESULTS: A total of 978 h of ICP and ETCO(2) data were collected and analyzed from eight patients with severe TBI. Demographic and clinical characteristics included patient age 7.1 ± 5.8 years (mean ± SD); 6/8 male; initial Glasgow Coma Scale score 3 [3–7] (median [IQR]); 6/8 had decompressive surgery; 7.1 ± 1.4 ICP monitor days; ICU length of stay (LOS) 16.1 ± 6.8 days; hospital LOS 25.9 ± 8.4 days; and survival 100%. The mean PCI for all patients throughout the monitoring period was 0.18 ± 0.04, where mean ICP was 13.7 ± 2.1 mmHg. In this cohort, PCI was observed to be consistently above 0.18 by 12 h after monitor placement. Percent time spent with PCI thresholds > 0.1, 0.2, and 0.3 were 62% [24], 38% [14], and 23% [15], respectively. The percentage of time spent with an ICP threshold > 20 mmHg was 5.1% [14.6]. CONCLUSIONS: Indirect assessment of dynamic intracranial compliance in TBI patients using standard-of-care monitoring appears feasible and suggests a prolonged period of derangement out to 5 days post-injury. Further study is ongoing to determine if the PCI—a new physiologic index, complements utility of ICP and/or CPP in guiding management of patients with severe TBI. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12028-020-01004-3) contains supplementary material, which is available to authorized users.


Introduction: immunopathology of unresolved tropical diseases


title: Virulence of Helicobacter pylori outer membrane proteins: an updated review

text: Helicobacter pylori (H. pylori) infection is associated with some gastric diseases, such as gastritis, peptic ulcer, and gastric cancer. CagA and VacA are known virulence factors of H. pylori, which play a vital role in severe clinical outcomes. Additionally, the expression of outer membrane proteins (OMPs) helps H. pylori attach to gastric epithelial cells at the primary stage and increases the virulence of H. pylori. In this review, we have summarized the paralogs of H. pylori OMPs, their genomic loci, and the different receptors of OMPs identified so far. We focused on five OMPs, BabA (HopS), SabA (HopP), OipA (HopH), HopQ, and HopZ, and one family of OMPs: Hom. We highlight the coexpression of OMPs with other virulence factors and their relationship with clinical outcomes. In conclusion, OMPs are closely related to the pathogenic processes of adhesion, colonization, persistent infection, and severe clinical consequences. They are potential targets for the prevention and treatment of H. pylori–related diseases.