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The following text sets out a realistic way for decolonisation to move forward in three key areas: collection development, classification and information literacy. These decolonising initiatives are necessary to ensure the continuation of the Polar Library’s high standard of practice.

This database is the supplementary material of Tasseron et al., (2022): 'Towards Robust River Plastic Detection: Combining Lab and Field-based Hyperspectral Imagery' [Submitted and currently under review], preprint available online at https://doi.org/10.31223/X5RW7V. The dataset contains raw images, MATLAB scripts used for training classifier algorithms, trained pipelines, required toolboxes and labelled training datasets used in subsequent analyses.

Introduction To be a successful academic writer, we need a strong awareness of the circumstances that support our writing productivity. Commentary First and foremost, writing requires time-on-task. To obtain focused, effective writing time, we may need to increase our accountability related to writing. In addition, each writer needs a writing system - a set of processes that helps them to reliably complete their writing work. To build an effective writing system, we advocate for observation, reflection, and experimentation related to our writing processes. In particular, examination of the what, where, when, and how of writing is critical. Strategies for optimizing productivity during writing sessions are also needed. Implications Strengthening writing productivity requires mechanisms for accountability, awareness of our writing process, and strategies to enhance the effectiveness of each writing session.

This dissertation examines how in eighteenth-century Europe, naturalists sought to study, grasp and capture the world of fish. Working on the intersection of the history of science and book history, this research aims to shed light on how naturalists came to present themselves as authorities in an emerging field. It does so by focussing on a set of ‘fish books’, i.e., natural historical works that describe and depict fish. The first is Francis Willughby and John Ray’s "Historia piscium" (Oxford, 1686); the second Peter Artedi’s "Ichthyologia sive opera omnia de piscibus" (Leiden, 1738), and the third Marcus Élieser Bloch’s twelve volume series "Allgemeine Naturgeschichte der Fische" (Berlin, 1782–1795). These works are analysed alongside correspondences, manuscripts and natural historical collections. Together, these sources show that the development of the study of fish in this period can be best be understood as a process of continuous demarcation. This dissertation argues that the study of fish was subject to recurrent debates on subject, method and practitioner, and that such discussions were of both epistemological and social nature. In presenting their fish books, naturalists leveraged such discussions as to secure a place for themselves in the capricious environment of early modern natural history.

Uncertainty is unavoidable when dealing with data. The errors in measurements, limitations of measuring tools, or imprecise definition of linguistic variables may result in different types of uncertainty. These ambiguities may be due to vagueness in data which results from the imprecise boundaries of data sets; inconsistency that reflects conflict and contradiction between sets; qualitative description of data which sometimes taken by expertise; or some other type. Ignoring dealing with these types of uncertainty affects the reliability of research and the validity of the results. This article presents three approaches to treat uncertainty using fuzzy logic, intuitionistic logic, and neutrosophic logic and their methodologies in treating these kinds of ambiguity. Fuzzy logic and neutrosophic logic are used in building Rule-based Classification Systems. Different comparisons are presented to illustrate the importance of choosing the suitable logic to tackle the uncertainty in different data sets. These approaches are applied on six real world data sets; Iris, Wine, Wisconsin Diagnostic Breast Cancer, Seeds, Pima, and Statlog (Heart); which are available on UCI Machine Learning Repository web site. The results show that the type of uncertainty in the data set plays a great role in choosing the appropriate logic.

Seizures are the most common neurological emergency in the neonatal period and in contrast to those in infancy and childhood, are often provoked seizures with an acute cause and may be electrographic-only. Hence, neonatal seizures may not fit easily into classification schemes for seizures and epilepsies primarily developed for older children and adults. A Neonatal Seizures Task Force was established by the International League Against Epilepsy (ILAE) to develop a modification of the 2017 ILAE Classification of Seizures and Epilepsies, relevant to neonates. The neonatal classification framework emphasizes the role of electroencephalography (EEG) in the diagnosis of seizures in the neonate and includes a classification of seizure types relevant to this age group. The seizure type is determined by the predominant clinical feature. Many neonatal seizures are electrographic-only with no evident clinical features; therefore, these are included in the proposed classification. Clinical events without an EEG correlate are not included. Because seizures in the neonatal period have been shown to have a focal onset, a division into focal and generalized is unnecessary. Seizures can have a motor (automatisms, clonic, epileptic spasms, myoclonic, tonic), non-motor (autonomic, behavior arrest), or sequential presentation. The classification allows the user to choose the level of detail when classifying seizures in this age group.

Citation analysis does not tell the whole story about the innovativeness of scientific papers. Works by prominent authors tend to receive disproportionately many citations, while publications by less well-known researchers covering the same topics may not attract as much attention. In this paper we address the shortcomings of traditional scientometric approaches by proposing a novel method that utilizes a classifier for predicting publication years based on latent topic distributions. We then calculate real-number innovation scores used to identify potential breakthrough papers and turnaround years. The proposed approach can complement existing citation-based measures of article importance and author contribution analysis; it opens as well novel research direction for time-based, innovation-centered research scientific output evaluation. In our experiments, we focus on two corpora of research papers published over several decades at two well-established conferences: The World Wide Web Conference (WWW) and the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), containing around 3500 documents in total. We indicate significant years and demonstrate examples of highly-ranked papers, thus providing a novel insight on the evolution of the two conferences. Finally, we compare our results to citation analysis and discuss how our approach may complement traditional scientometrics.

Background Functional somatic symptoms and disorders are common and complex phenomena involving both bodily and brain processes. They pose major challenges across medical specialties. These disorders are common and have significant impacts on patients’ quality of life and healthcare costs. Main body We outline five problems pointing to the need for a new classification: (1) developments in understanding aetiological mechanisms; (2) the current division of disorders according to the treating specialist; (3) failure of current classifications to cover the variety of disorders and their severity (for example, patients with symptoms from multiple organs systems); (4) the need to find acceptable categories and labels for patients that promote therapeutic partnership; and (5) the need to develop clinical services and research for people with severe disorders. We propose ‘functional somatic disorders’ (FSD) as an umbrella term for various conditions characterised by persistent and troublesome physical symptoms. FSDs are diagnosed clinically, on the basis of characteristic symptom patterns. As with all diagnoses, a diagnosis of FSD should be made after considering other possible somatic and mental differential diagnoses. We propose that FSD should occupy a neutral space within disease classifications, favouring neither somatic disease aetiology, nor mental disorder. FSD should be subclassified as (a) multisystem, (b) single system, or (c) single symptom. While additional specifiers may be added to take account of psychological features or co-occurring diseases, neither of these is sufficient or necessary to make the diagnosis. We recommend that FSD criteria are written so as to harmonise with existing syndrome diagnoses. Where currently defined syndromes fall within the FSD spectrum – and also within organ system-specific chapters of a classification – they should be afforded dual parentage (for example, irritable bowel syndrome can belong to both gastrointestinal disorders and FSD). Conclusion We propose a new classification, ‘functional somatic disorder’, which is neither purely somatic nor purely mental, but occupies a neutral space between these two historical poles. This classification reflects both emerging aetiological evidence of the complex interactions between brain and body and the need to resolve the historical split between somatic and mental disorders.

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.

Objectives The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. Method A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. Results The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding. Conclusion Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.

Objectives: To apply the Classi cation of Service Or- ganization in Rehabilitation (ICSO-R) classi cation of services to different target groups, include the user perspective, identify missing categories, and propose standardized descriptors for the categories from a Norwegian perspective. Design: Expert-based consensus conferences with user involvement. Subjects: Health professionals, stakeholders and users. Methods: Participants were divided into 5 panels, which applied the ICSO-R to describe the habilitation and rehabilitation services provided to children with cerebral palsy and people with Huntington’s disease, acquired brain injuries (traumatic brain injuries and stroke) and painful musculoskeletal conditions. Ba- sed on the Problem/Population, Intervention, Com- parison, Outcome (PICO) framework, the services were described according to the ICSO-R. Missing ca- tegories were identi ed. Results: The ICSO-R was found to be feasible and applicable for describing a variety of services pro- vided to different target groups in Norway, but the user perspective was lacking, categories were missing, and a need for standardized description of the categories was identi ed. Conclusion: The present work supports the need to produce an updated version of the ICSO-R and to en- courage national and international discussion of the framework. The ICSO-R has the potential to become a tool for the standardized assessment of rehabilita- tion services. For such purposes, more standardized descriptions of subcategories are necessary.

Paper currency recognition (PCR) is an important area of pattern recognition. A system for the recognition of paper currency is one kind of intelligent system which is a very important need of the current automation systems in the modern world of today. It has various potential applications including electronic banking, currency monitoring systems, money exchange machines, etc. This paper proposes an automatic paper currency recognition system for paper currency. A method of recognizing paper currencies has been introduced. This is based on interesting features and correlation between images. It uses Radial Basis Function Network for classification. The method uses the case of Saudi Arabian paper currency as a model. The method is quite reasonable in terms of accuracy. The system deals with 110 images, 10 of which are tilted with an angle less than 15o. The rest of the currency images consist of mixed including noisy and normal images 50 each. It uses fourth series (1984–2007) of currency issued by Saudi Arabian Monetary Agency (SAMA) as a model currency under consideration. The system produces accuracy of recognition as 95.37%, 91.65%, and 87.5%, for the Normal Non-Tilted Images, Noisy Non-Tilted Images, and Tilted Images respectively. The overall Average Recognition Rate for the data of 110 images is computed as 91.51%. The proposed algorithm is fully automatic and requires no human intervention. The proposed technique produces quite satisfactory results in terms of recognition and efficiency.

In order for decision-makers to adjust environmental policy appropriately, it is essential that they utilize reliable data. Now, on a global scale, mangroves are considered endangered, yet there is a general consensus among recent scholars that mangroves in Saloum and Casamance estuaries are currently experiencing regeneration. In contrast with the results of these papers, Carney et al. (2014) published a paper in Geoforum mapping and quantifying a massive mangrove loss. However, remote sensing and mapping shortcomings have been identified. The 2010 map used in the Carney et al. (2014) paper is accurate, however, as a result of errors in georeferencing, window extraction and classification, their 1986 map is not. Mangrove and mudflats are grouped in the same class. The addition of a stacked classification to the image processing used by Carney et al. (2014) enables the realization of a correction of the map, and thus the production of results similar to other studies. The supposed loss of mangroves appears to have been the consequence of this classification error. In fact, we observe a progression of 8804 ha instead of the regression of 37,196 ha assessed by the authors. This published discrepancy, which is in opposition to any other study of this area, must be discussed in order to clarify for scholars and policymakers the actual dynamics of mangrove change in the studied region.

Gevaert, Philippe/0000-0002-1629-8468; Hox, Valerie/0000-0003-2390-294X; Demoly, Pascal/0000-0001-7827-7964; Hellings, Peter W/0000-0001-6898-688X; N.G., Papadopoulos/0000-0002-4448-3468; Van Gerven, Laura/0000-0002-5325-7956; Cingi, Cemal/0000-0003-3934-5092; Seys, Sven/0000-0002-4399-9892 WOS: 000413151200004 PubMed: 28474799 This EAACI position paper aims at providing a state-of-the-art overview on nonallergic rhinitis (NAR). A significant number of patients suffering from persistent rhinitis are defined as nonallergic noninfectious rhinitis (NANIR) patients, often denominated in short as having NAR. NAR is defined as a symptomatic inflammation of the nasal mucosa with the presence of a minimum of two nasal symptoms such as nasal obstruction, rhinorrhea, sneezing, and/or itchy nose, without clinical evidence of endonasal infection and without systemic signs of sensitization to inhalant allergens. Symptoms of NAR may have a wide range of severity and be either continuously present and/or induced by exposure to unspecific triggers, also called nasal hyperresponsiveness (NHR). NHR represents a clinical feature of both AR and NAR patients. NAR involves different subgroups: drug-induced rhinitis, (nonallergic) occupational rhinitis, hormonal rhinitis (including pregnancy rhinitis), gustatory rhinitis, senile rhinitis, and idiopathic rhinitis (IR). NAR should be distinguished from those rhinitis patients with an allergic reaction confined to the nasal mucosa, also called entopy or local allergic rhinitis (LAR). We here provide an overview of the current consensus on phenotypes of NAR, recommendations for diagnosis, a treatment algorithm, and defining the unmet needs in this neglected area of research.

High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the analytical challenge exists in double. The use of effective sampling technique in classification algorithms always yields good prediction accuracy. The SEER public use cancer database provides various prominent class labels for prognosis prediction. The main objective of this paper is to find the effect of sampling techniques in classifying the prognosis variable and propose an ideal sampling method based on the outcome of the experimentation. Inthe first phase of this work the traditional random sampling and stratified sampling techniques have been used. At the next level the balanced stratified sampling with variations as per the choice of the prognosis class labels have been tested. Much of the initial time has been focused on performing the pre-processing of the SEER data set. The classification model for experimentation has been built using the breast cancer, respiratory cancer and mixed cancer data sets with three traditional classifiers namely Decision Tree, Naïve Bayes and K-Nearest Neighbour. The three prognosis factors survival, stage and metastasis have been used as classlabels for experimental comparisons. The results shows a steady increase in the prediction accuracy of balanced stratified model as the sample size increases, but the traditional approach fluctuates before the optimum results.

With the rapid development of the decorative papers industry on a worldwide scale, the aesthetic assessment of decorative papers has evolved as one of the major fields for industrial production. This study was performed to investigate the ability of visible spectroscopy and NIR spectroscopy coupled with the soft independent modeling of class analogy (SIMCA) to reflect the surface characteristics of decorative paper and to classify decorative papers with different visual characteristics. The results showed that visible spectroscopy has a higher relationship with the surface characteristics of decorative papers than the NIR data during PCA analysis due to larger variations. Additionally, when using visible spectroscopy (400 to 780 nm), the classification accuracy reached 94% to 100%, a more accurate result than could be achieved based on color data. In the results of the NIR spectroscopy (780 to 2500 nm), the classification accuracy decreased to the range 1% to 56%, except for a value of 95% for the samples that were grained with a slightly dark color, and a greater number of samples were assigned to more than one class. There were significant differences in the performance of the models built with visible spectroscopy and NIR spectroscopy, so it can be concluded that visible spectroscopy coupled with SIMCA is more useful to classify the different types of decorative papers than NIR spectroscopy.

Data-based analyses are becoming more and more common in the Digital Humanities and tools are needed that focus human efforts on the most interesting and important aspects of exploration, analysis and annotation by using active machine learning techniques. We present our ongoing work on a tool that supports classification tasks for spoken documents (in our case: read-out post-modern poetry) using a neural networks-based classification backend and a web-based exploration and classification environment.

The International League Against Epilepsy (ILAE) Classification of the Epilepsies has been updated to reflect our gain in understanding of the epilepsies and their underlying mechanisms following the major scientific advances that have taken place since the last ratified classification in 1989. As a critical tool for the practicing clinician, epilepsy classification must be relevant and dynamic to changes in thinking, yet robust and translatable to all areas of the globe. Its primary purpose is for diagnosis of patients, but it is also critical for epilepsy research, development of antiepileptic therapies, and communication around the world. The new classification originates from a draft document submitted for public comments in 2013, which was revised to incorporate extensive feedback from the international epilepsy community over several rounds of consultation. It presents three levels, starting with seizure type, where it assumes that the patient is having epileptic seizures as defined by the new 2017 ILAE Seizure Classification. After diagnosis of the seizure type, the next step is diagnosis of epilepsy type, including focal epilepsy, generalized epilepsy, combined generalized, and focal epilepsy, and also an unknown epilepsy group. The third level is that of epilepsy syndrome, where a specific syndromic diagnosis can be made. The new classification incorporates etiology along each stage, emphasizing the need to consider etiology at each step of diagnosis, as it often carries significant treatment implications. Etiology is broken into six subgroups, selected because of their potential therapeutic consequences. New terminology is introduced such as developmental and epileptic encephalopathy. The term benign is replaced by the terms self-limited and pharmacoresponsive, to be used where appropriate. It is hoped that this new framework will assist in improving epilepsy care and research in the 21st century.

BACKGROUND/AIMS: As epilepsy is a complex disease group, it is difficult to diagnose and classify. Oxidative stress plays a vital role in the pathogenesis of epilepsy. This study investigated the total oxidant/antioxidant status levels in patients with focal onset and generalized onset seizures. The results we obtained may help find the etiological cause in patients with seizure complaints and may guide their treatment. In addition, knowing the seizure type of the patient can give an idea about the prognosis of their disease.MATERIALS AND METHODS: The total number of patients included in this prospective study was 58. There were also 57 people in a control group. The patients were classified according to their type of seizure: focal or generalized onset. The serum oxidative stress index (OSI) and total oxidant/antioxidant status values of all patients and control group members were measured. The patients (focal/generalized groups) and control group members were compared.RESULTS: This prospective study was completed with 58 eligible patients who met the inclusion criteria. There were 57 people in the control group. Total oxidant status (TOS) and OSI levels were higher in the seizure groups compared to the control group (p

International audience; The work presented in this paper deals with the use of ontologies to compare scientific texts. It particularly deals with scientific papers, specifically their abstracts, short texts that are relatively well structured and normally provide enough knowledge to allow a community of readers to assess the content of the associated scientific papers. The problem is, therefore, to determine how to assess the semantic proximity/similarity of two papers by examining their respective abstracts. Given that a domain ontology provides a useful way to represent knowledge relative to a given domain, this work considers ontologies relative to scientific domains. Our process begins by defining the relevant domain for an abstract through an automatic classification that makes it possible to associate this abstract to its relevant scientific domain, chosen from several candidate domains. The content of an abstract is represented in the form of a conceptual graph which is enriched to construct its semantic perimeter. As presented below, this notion of semantic perimeter usefully allows us to assess the similarity between the texts by matching their graphs. Detecting plagiarism is the main application field addressed in this paper, among the many possible application fields of our approach.

Measurements of process variations and particle morphology are widely employed in the pulp and paper industry. Two techniques with high potential, infrared thermography and microparticle characterisation, are mainly used qualitatively. Quantitative thermography requires knowledge of the emittance, a material property which has not been measured under many process-relevant conditions. Quantitative characterisation of microparticles, e.g. pulp fines and mineral fillers, requires the analysis of a large number of particles, which can be accomplished using flow microscopes. Flow microscopes for pulp analysis have had insufficient spatial resolution to resolve fines and fillers. Additionally, there has been a lack of methods which can differentiate between fines and fillers in a mixed suspension. State-of-the-art instruments for particle image analysis were evaluated and compared to laser diffractometry, a measurement method based on scattering by diffraction. Laser diffractometry was found to be highly sensitive to the complex refractive index of the particles, and especially to its change due to moisture absorption. A high-resolution imaging flow cytometer and a high-resolution fibre analyser were found to be complementary for characterisation of pure fines and fines/filler mixtures, and superior to laser diffractometry. A method for differentiating between fines and fillers in a suspension based on their autofluorescence and side-scattering was proposed and qualitatively evaluated. Furthermore, a method for measuring the directional and integrated emittance of paper was developed and its accuracy was determined. Measurements on a wide range of samples showed that the emittance of fibre-based materials vary significantly with wavelength, pulp type, observation angle, and moisture content. By applying measured quantitative values of the emittance, the thermal energy emitted by sack paper samples during mechanical deformation could be quantitatively calculated. The increase in thermal energy at the time of rupture was found to correlate well with the elastic share of the mechanical energy that was stored in the sample during its elongation. In summary, the results of this work have facilitated the use of quantitative microparticle analysis and infrared thermography for pulp and paper applications. Mätningar av processvariationer och partiklars form och storlek utförs i stor skala inom massa- och pappersindustrin. Två mättekniker med stor potential, infraröd termografi och mikropartikel-karaktärisering, används mest kvalitativt idag. Kvantitativ termografi kräver att provets emittans är känd. Emittansen är en materialegenskap som inte har mätts för många förhållanden som är relevanta inom papperstillverkning. Kvantitativ karaktärisering av partiklar kräver att ett tillräckligt stort antal partiklar analyseras, något som kan göras med flödesmikroskop. Flödesmikroskop för mäldanalys har haft otillräcklig upplösning för att karaktärisera mikrometerstora partiklar, t.ex. fines och fyllmedel. Det har heller inte funnits någon metod som kan särskilja mellan fines och fyllmedel i en blandning. Högupplösta mätinstrument för bildbaserad mikropartikelkaraktärisering utvärderades och jämfördes med en laserdiffraktometer, en mätmetod baserad på ljusspridning genom diffraktion. Laserdiffraktometerns mätresultat påverkades starkt av det brytningsindex som antogs för provet, och hur brytningsindexet ändrades med fukthalt. En högupplöst bildbaserad flödescytometer och en högupplöst fibermätare konstaterades komplettera varandra vid mätningar av mäldens finmaterial. De var även pålitligare än laserdiffraktometern vid mätningar av organiskt finmaterial. En metod för att skilja mellan organiskt och oorganiskt finmaterial i en mäld baserat på deras autofluorescens och ljusspridning presenterades och utvärderades kvalitativt. En metod för att mäta den vinkelberoende och våglängdsintegrerade emittansen hos fiberbaserade material utvecklades och dess mätnoggrannhet utvärderades. Mätningar på ett stort antal prover visade att emittansen varierade betydligt med våglängd, mäldtyp, observationsvinkel, och fukthalt. Genom att använda den uppmätta emittansen kunde den termiska energin som frigjordes av ett säckpappersprov vid brottögonblicket beräknas. Denna energi korrelerade väl med den elastiska energi som lagrades i provet medan det töjdes, fram till tidpunkten för brottet. Sammanfattningsvis har resultaten av detta arbete möjliggjort kvantitativ användning av mikropartikel-karaktärisering och infraröd termografi i massa- och papperstillämpningar. QC 20160122

Our strategy to support filtering the descriptive texts and the transcriptions in Monasterium.net seeks to assign semantic categories for the legal acts they record, such as confiscation, donation, or property sale.

To cope with the growing performance needs of appliances in datacenters or the network edge, current middle-box functionalities such as firewalls, NAT, DPI, content-aware optimizers or load-balancers are often implemented on multiple (perhaps virtual) machines. In this work, we design a system able to run a pipeline of VNFs with a high level of parallelism to handle many flows. We provide the user facilities to define the traffic class of interest for the VNF, a definition of session to group the packets such as the TCP 4-tuples, and the amount of space per sessions. The system will then synthesize the classification and build a unique, efficient flow table. We build an abstract view of flows and use it to implement support for seamless inspection and modification of the content of any flow (such as TCP or HTTP), automatically reflecting a consistent view, across layers, of flows modified on-the-fly. Our prototype gives rise to a user-space software NFV data-plane enabling easy implementation of middlebox functionalities, as well as the deployment of complex scenarios. Our prototype implementation is able to handle our testbed limit of -34 Gbps of HTTP requests (for 8-KB files) through a service chain of multiples stateful VNFs, on a single Xeon core.

Trees are a canonical structure for representing evolutionary histories. Many popular criteria used to infer optimal trees are computationally hard, and the number of possible tree shapes grows super-exponentially in the number of taxa. The underlying structure of the spaces of trees yields rich insights that can improve the search for optimal trees, both in accuracy and in running time, and the analysis and visualization of results. We review the past work on analyzing and comparing trees by their shape as well as recent work that incorporates trees with weighted branch lengths.

The 1982 ACR classification criteria have become de facto diagnostic criteria for systemic lupus erythematosus (SLE), but a review of the criteria is necessary to include recent diagnostic tests. The criteria were not developed with the help of dermatologists, and assign too much weight to the skin as one expression of a multiorgan disease. Consequently, patients with skin diseases are classified as SLE based mostly on skin symptoms. We discuss specific problems with each dermatologic criterion, but changes must await a new study. We suggest the following guidelines for such a study, aimed at revision of the criteria. 1) The SLE patient group should be recruited in part by dermatologists. 2) The study should evaluate an appropriate international ethnic/racial mix, including late onset SLE as well as pediatric patients. 3) All patients should have current laboratory and clinical evaluations, as suggested in the paper, to assure the criteria can be up-to-date. This includes anti-SS-A and anti-SS-B antibodies and skin biopsies for suspected cutaneous lupus erythematosus except for nonscarring alopecia and oral ulcers. 4) The study should be based on a series of transparent power calculations. 5) The control groups should represent relevant differential diagnoses in numbers large enough to assess diagnostic problems that might be specific to these differential diagnoses. In order to demonstrate specificity of the criteria with a 95% confidence interval between 90 and 100%, each control group of the above should have at least 73 patients.

Purpose International uniformity of definition and classification are crucial for diagnosis and management of cauda equina syndrome (CES). They are also useful for clinicians when discussing CES with patients and relatives, and for medicolegal purposes. Methods We reviewed published literature using PubMed on definition and classification of cauda equina syndrome since 2000 (21 years). Using the search terms ‘cauda equina’ and ‘definition’ or ‘classification’, we found and reviewed 212 papers. Results There were 17 different definitions of CES used in the literature. There were three well-defined methods of classification of CES. The two-stage system of incomplete CES (CESI) versus CES with retention (CESR) is the most commonly used classification, and has prognostic value although the details of this continue to be debated. Conclusion We used the existing literature to propose a clear definition of CES. We also drew on peer-reviewed published literature that has helped to amplify and expand the CESI/CESR dichotomy, adding categories that are both less severe than CESI, and more severe than CESR, and we propose clear definitions in a table form to assist current and future discussion and management of CES.

With the improvement of living standards, everybody is very much conscious about the environmental protection and health safety. Natural dyes have attracted more attention to the industry due to exhibiting better biodegradability and more compatibility with the environment. Characteristic colours that are gathered from common assets can be categorized as either plant, creature, mineral, or microbial colours and can be used for colouring a wide range of regular filaments. Late examination shows that it can likewise be utilized to colour a portion of the manufactured filaments too. Normal colours are not just utilized in the shading of material filaments, they are also utilized for food, prescriptions, handiwork articles, and leather preparing. Extraction and purification play a vital role in the processing of natural dyes. There are different types of extraction process currently available for these natural dyes, such as solvent extraction, aqueous extraction, enzymatic extraction and fermentation, extraction with microwave or ultrasonic energy, supercritical fluid extraction, and alkaline or acid extraction. All these extraction processes have their own advantages as well as some drawbacks depending on the parameters that need to be maintained during the extraction process. Appropriate extraction can be beneficial for specific types of such dyes. In this paper, the classification, characteristics, extraction methods, and the application of natural dyes are introduced in an organized manner.

The material is a continuation of the discussion initiated in this journal No. 2, 2014 and caused by the paper «Early diagnosis of ankylosing spondylitis» (No. 4, 2013). Opponents' commentaries on the elaboration and modification of classification criteria are successively considered and commented; much attention is given to the problem of terminology.

Concerns about problematic gaming behaviors deserve our full attention. However, we claim that it is far from clear that these problems can or should be attributed to a new disorder. The empirical basis for a Gaming Disorder proposal, such as in the new ICD-11, suffers from fundamental issues. Our main concerns are the low quality of the research base, the fact that the current operationalization leans too heavily on substance use and gambling criteria, and the lack of consensus on symptomatology and assessment of problematic gaming. The act of formalizing this disorder, even as a proposal, has negative medical, scientific, public-health, societal, and human rights fallout that should be considered. Of particular concern are moral panics around the harm of video gaming. They might result in premature application of diagnosis in the medical community and the treatment of abundant false-positive cases, especially for children and adolescents. Second, research will be locked into a confirmatory approach, rather than an exploration of the boundaries of normal versus pathological. Third, the healthy majority of gamers will be affected negatively. We expect that the premature inclusion of Gaming Disorder as a diagnosis in ICD-11 will cause significant stigma to the millions of children who play video games as a part of a normal, healthy life. At this point, suggesting formal diagnoses and categories is premature: the ICD-11 proposal for Gaming Disorder should be removed to avoid a waste of public health resources as well as to avoid causing harm to healthy video gamers around the world.

BACKGROUND:Since their inception, Twitter and related microblogging systems have provided a rich source of information for researchers and have attracted interest in their affordances and use. Since 2009 PubMed has included 123 journal articles on medicine and Twitter, but no overview exists as to how the field uses Twitter in research.OBJECTIVE:This paper aims to identify published work relating to Twitter within the fields indexed by PubMed, and then to classify it. This classification will provide a framework in which future researchers will be able to position their work, and to provide an understanding of the current reach of research using Twitter in medical disciplines.METHODS:Papers on Twitter and related topics were identified and reviewed. The papers were then qualitatively classified based on the paper's title and abstract to determine their focus. The work that was Twitter focused was studied in detail to determine what data, if any, it was based on, and from this a categorization of the data set size used in the studies was developed. Using open coded content analysis additional important categories were also identified, relating to the primary methodology, domain, and aspect.RESULTS:As of 2012, PubMed comprises more than 21 million citations from biomedical literature, and from these a corpus of 134 potentially Twitter related papers were identified, eleven of which were subsequently found not to be relevant. There were no papers prior to 2009 relating to microblogging, a term first used in 2006. Of the remaining 123 papers which mentioned Twitter, thirty were focused on Twitter (the others referring to it tangentially). The early Twitter focused papers introduced the topic and highlighted the potential, not carrying out any form of data analysis. The majority of published papers used analytic techniques to sort through thousands, if not millions, of individual tweets, often depending on automated tools to do so. Our analysis demonstrates that researchers are starting to use knowledge discovery methods and data mining techniques to understand vast quantities of tweets: the study of Twitter is becoming quantitative research.CONCLUSIONS:This work is to the best of our knowledge the first overview study of medical related research based on Twitter and related microblogging. We have used 5 dimensions to categorize published medical related research on Twitter. This classification provides a framework within which researchers studying development and use of Twitter within medical related research, and those undertaking comparative studies of research, relating to Twitter in the area of medicine and beyond, can position and ground their work.

PurposeSince its introduction in 2006, messages posted to the microblogging system Twitter have provided a rich dataset for researchers, leading to the publication of over a thousand academic papers. This paper aims to identify this published work and to classify it in order to understand Twitter based research.Design/methodology/approachFirstly the papers on Twitter were identified. Secondly, following a review of the literature, a classification of the dimensions of microblogging research was established. Thirdly, papers were qualitatively classified using open coded content analysis, based on the paper's title and abstract, in order to analyze method, subject, and approach.FindingsThe majority of published work relating to Twitter concentrates on aspects of the messages sent and details of the users. A variety of methodological approaches is used across a range of identified domains.Research limitations/implicationsThis work reviewed the abstracts of all papers available via database search on the term “Twitter” and this has two major implications: the full papers are not considered and so works may be misclassified if their abstract is not clear; publications not indexed by the databases, such as book chapters, are not included. The study is focussed on microblogging, the applicability of the approach to other media is not considered.Originality/valueTo date there has not been an overarching study to look at the methods and purpose of those using Twitter as a research subject. The paper's major contribution is to scope out papers published on Twitter until the close of 2011. The classification derived here will provide a framework within which researchers studying Twitter related topics will be able to position and ground their work.

Background: \ud Since their inception, Twitter and related microblogging systems have provided a rich source of information for researchers and have attracted interest in their affordances and use. Since 2009 PubMed has included 123 journal articles on medicine and Twitter, but no overview exists as to how the field uses Twitter in research. // \ud \ud Objective: \ud This paper aims to identify published work relating to Twitter indexed by PubMed, and then to classify it. This classification will provide a framework in which future researchers will be able to position their work, and to provide an understanding of the current reach of research using Twitter in medical disciplines. Limiting the study to papers indexed by PubMed ensures the work provides a reproducible benchmark. // \ud \ud Methods: \ud Papers, indexed by PubMed, on Twitter and related topics were identified and reviewed. The papers were then qualitatively classified based on the paper’s title and abstract to determine their focus. The work that was Twitter focused was studied in detail to determine what data, if any, it was based on, and from this a categorization of the data set size used in the studies was developed. Using open coded content analysis additional important categories were also identified, relating to the primary methodology, domain and aspect. // \ud \ud Results: \ud As of 2012, PubMed comprises more than 21 million citations from biomedical literature, and from these a corpus of 134 potentially Twitter related papers were identified, eleven of which were subsequently found not to be relevant. There were no papers prior to 2009 relating to microblogging, a term first used in 2006. Of the remaining 123 papers which mentioned Twitter, thirty were focussed on Twitter (the others referring to it tangentially). The early Twitter focussed papers introduced the topic and highlighted the potential, not carrying out any form of data analysis. The majority of published papers used analytic techniques to sort through thousands, if not millions, of individual tweets, often depending on automated tools to do so. Our analysis demonstrates that researchers are starting to use knowledge discovery methods and data mining techniques to understand vast quantities of tweets: the study of Twitter is becoming quantitative research. // \ud \ud Conclusions: \ud This work is to the best of our knowledge the first overview study of medical related research based on Twitter and related microblogging. We have used five dimensions to categorise published medical related research on Twitter. This classification provides a framework within which researchers studying development and use of Twitter within medical related research, and those undertaking comparative studies of research relating to Twitter in the area of medicine and beyond, can position and ground their work.

According to the World Health Organization (WHO), novel coronavirus (COVID-19) is an infectious disease and has a significant social and economic impact. The main challenge in fighting against this disease is its scale. Due to the outbreak, medical facilities are under pressure due to case numbers. A quick diagnosis system is required to address these challenges. To this end, a stochastic deep learning model is proposed. The main idea is to constrain the deep-representations over a Gaussian prior to reinforce the discriminability in feature space. The model can work on chest X-ray or CT-scan images. It provides a fast diagnosis of COVID-19 and can scale seamlessly. The work presents a comprehensive evaluation of previously proposed approaches for X-ray based disease diagnosis. The approach works by learning a latent space over X-ray image distribution from the ensemble of state-of-the-art convolutional-nets, and then linearly regressing the predictions from an ensemble of classifiers which take the latent vector as input. We experimented with publicly available datasets having three classes: COVID-19, normal and pneumonia yielding an overall accuracy and AUC of 0.91 and 0.97, respectively. Moreover, for robust evaluation, experiments were performed on a large chest X-ray dataset to classify among Atelectasis, Effusion, Infiltration, Nodule, and Pneumonia classes. The results demonstrate that the proposed model has better understanding of the X-ray images which make the network more generic to be later used with other domains of medical image analysis.

BACKGROUND Surveillance of multiple congenital anomalies is considered to be more sensitive for the detection of new teratogens than surveillance of all or isolated congenital anomalies. Current literature proposes the manual review of all cases for classification into isolated or multiple congenital anomalies. METHODS Multiple anomalies were defined as two or more major congenital anomalies, excluding sequences and syndromes. A computer algorithm for classification of major congenital anomaly cases in the EUROCAT database according to International Classification of Diseases (ICD)v10 codes was programmed, further developed, and implemented for 1 year's data (2004) from 25 registries. The group of cases classified with potential multiple congenital anomalies were manually reviewed by three geneticists to reach a final agreement of classification as “multiple congenital anomaly” cases. RESULTS A total of 17,733 cases with major congenital anomalies were reported giving an overall prevalence of major congenital anomalies at 2.17%. The computer algorithm classified 10.5% of all cases as “potentially multiple congenital anomalies”. After manual review of these cases, 7% were agreed to have true multiple congenital anomalies. Furthermore, the algorithm classified 15% of all cases as having chromosomal anomalies, 2% as monogenic syndromes, and 76% as isolated congenital anomalies. The proportion of multiple anomalies varies by congenital anomaly subgroup with up to 35% of cases with bilateral renal agenesis. CONCLUSIONS The implementation of the EUROCAT computer algorithm is a feasible, efficient, and transparent way to improve classification of congenital anomalies for surveillance and research. Birth Defects Research (Part A), 2011. © 2011 Wiley-Liss, Inc.

Almost all gynecological and general-surgical operations are - or can be - performed laparoscopically. In comparison to an abdominal approach, the minimally invasive access offers several advantages; however, laparoscopy (both conventional and robotic-assisted) can be associated with a number of approach-specific complications. Although the majority of them are related to the laparoscopic entry, adverse events may also occur due to the presence of pneumoperitoneum or the use of laparoscopic instruments. Unfortunately, a high proportion of complications (especially affecting the bowel and ureter) remain unrecognized during surgery. This narrative review provides comprehensive up-to-date information about definitions, classifications, risk factors and incidence of surgical complications in conventional and robotic-assisted laparoscopy, with a special focus on gynecology. The topic is discussed from various perspectives, e.g. in the context of stage of surgery, injured organs, involved instruments, and in relation to malpractice claims.

Objectives To develop and test the performance of computerized ultrasound image analysis using deep neural networks (DNNs) in discriminating between benign and malignant ovarian tumors and to compare its diagnostic accuracy with that of subjective assessment (SA) by an ultrasound expert. Methods We included 3077 (grayscale, n = 1927; power Doppler, n = 1150) ultrasound images from 758 women with ovarian tumors, who were classified prospectively by expert ultrasound examiners according to IOTA (International Ovarian Tumor Analysis) terms and definitions. Histological outcome from surgery (n = 634) or long‐term (≥ 3 years) follow‐up (n = 124) served as the gold standard. The dataset was split into a training set (n = 508; 314 benign and 194 malignant), a validation set (n = 100; 60 benign and 40 malignant) and a test set (n = 150; 75 benign and 75 malignant). We used transfer learning on three pre‐trained DNNs: VGG16, ResNet50 and MobileNet. Each model was trained, and the outputs calibrated, using temperature scaling. An ensemble of the three models was then used to estimate the probability of malignancy based on all images from a given case. The DNN ensemble classified the tumors as benign or malignant (Ovry‐Dx1 model); or as benign, inconclusive or malignant (Ovry‐Dx2 model). The diagnostic performance of the DNN models, in terms of sensitivity and specificity, was compared to that of SA for classifying ovarian tumors in the test set. Results At a sensitivity of 96.0%, Ovry‐Dx1 had a specificity similar to that of SA (86.7% vs 88.0%; P = 1.0). Ovry‐Dx2 had a sensitivity of 97.1% and a specificity of 93.7%, when designating 12.7% of the lesions as inconclusive. By complimenting Ovry‐Dx2 with SA in inconclusive cases, the overall sensitivity (96.0%) and specificity (89.3%) were not significantly different from using SA in all cases (P = 1.0). Conclusion Ultrasound image analysis using DNNs can predict ovarian malignancy with a diagnostic accuracy comparable to that of human expert examiners, indicating that these models may have a role in the triage of women with an ovarian tumor. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology., This article's abstract has been translated into Spanish and Chinese. Follow the links from the abstract to view the translations.

BACKGROUND: Surveillance of multiple congenital anomalies is considered to be more sensitive for the detection of new teratogens than surveillance of all or isolated congenital anomalies. Current literature proposes the manual review of all cases for classification into isolated or multiple congenital anomalies. METHODS: Multiple anomalies were defined as two or more major congenital anomalies, excluding sequences and syndromes. A computer algorithm for classification of major congenital anomaly cases in the EUROCAT database according to International Classification of Diseases (ICD)v10 codes was programmed, further developed, and implemented for 1 year's data (2004) from 25 registries. The group of cases classified with potential multiple congenital anomalies were manually reviewed by three geneticists to reach a final agreement of classification as "multiple congenital anomaly'' cases. RESULTS: A total of 17,733 cases with major congenital anomalies were reported giving an overall prevalence of major congenital anomalies at 2.17%. The computer algorithm classified 10.5% of all cases as "potentially multiple congenital anomalies''. After manual review of these cases, 7% were agreed to have true multiple congenital anomalies. Furthermore, the algorithm classified 15% of all cases as having chromosomal anomalies, 2% as monogenic syndromes, and 76% as isolated congenital anomalies. The proportion of multiple anomalies varies by congenital anomaly subgroup with up to 35% of cases with bilateral renal agenesis. CONCLUSIONS: The implementation of the EUROCAT computer algorithm is a feasible, efficient, and transparent way to improve classification of congenital anomalies for surveillance and research. Birth Defects Research (Part A) 91: S44-S50, 2011. (C) 2011 Wiley-Liss, Inc.

Finite mixtures of generalized linear models are commonly fitted by maximum likelihood and the EM algorithm. The estimation process and subsequent inferential and classification procedures can be badly affected by the occurrence of outliers. Actually, contamination in the sample at hand may lead to severely biased fitted components and poor classification accuracy. In order to take into account the potential presence of outliers, a robust fitting strategy is proposed that is based on the weighted likelihood methodology. The technique exhibits a satisfactory behavior in terms of both fitting and classification accuracy, as confirmed by some numerical studies and real data examples.

Supplemental Digital Content is Available in the Text. The official WHO field tests of the International Classification of Diseases 11 classification of chronic pain demonstrate an excellent diagnostic coding performance and clinical utility of the new classification., Because chronic pain has been poorly represented in the International Statistical Classification of Diseases and Related Health Problems (ICD) despite its significant contribution to the burden of disease worldwide, the International Association for the Study of Pain (IASP) developed a classification of chronic pain that was included in the ICD-11 version as “MG30” and approved by the World Health Assembly in 2019. The objective of this field test was to determine how well the classification of chronic pain works in the context of the ICD-11. A web-based survey using the WHO-FiT platform recruited 177 healthcare professionals from all WHO regions. After a training on coding chronic pain hosted by the IASP Web site, participants evaluated 18 diagnostic codes (lines) of the 2017 frozen version of the ICD-11 and 12 vignettes (cases) describing chronic pain conditions. Correctness, ambiguity, and perceived difficulty of the coding were compared between the ICD-11 and the ICD-10 and the applicability of the morbidity rules for the ICD-11 verified. In the line coding, 43.0% of correct chronic pain diagnoses assigned with the ICD-10 contrasted with 63.2% with the ICD-11. Especially in cases in which the chronic pain is regarded as the symptom of an underlying disease, the ICD-11 (63.5%) commanded more correct diagnoses than the ICD-10 (26.8%). The case coding was on average 83.9% accurate, only in 1.6% of cases any difficulty was perceived. The morbidity rules were applied correctly in 74.1% of cases. From a coding perspective, the ICD-11 is superior to the ICD-10 in every respect, offering better accuracy, difficulty, and ambiguity in coding chronic pain conditions.

The Department of Business Management and Analytics in Arcada University of Applied Sciences arranged a Workshop on Analytics in May 25, 2015. Four Working Papers presented in this workshop are published in this report.

A large portion of Canada is covered by wetlands; mapping and monitoring them isof great importance for various applications. In this regard, Remote Sensing (RS) technology hasbeen widely employed for wetland studies in Canada over the past 45 years. This study evaluatesmeta-data to investigate the status and trends of wetland studies in Canada using RS technologyby reviewing the scientific papers published between 1976 and the end of 2020 (300 papers in total).Initially, a meta-analysis was conducted to analyze the status of RS-based wetland studies in terms ofthe wetland classification systems, methods, classes, RS data usage, publication details (e.g., authors,keywords, citations, and publications time), geographic information, and level of classificationaccuracies. The deep systematic review of 128 peer-reviewed articles illustrated the rising trendin using multi-source RS datasets along with advanced machine learning algorithms for wetlandmapping in Canada. It was also observed that most of the studies were implemented over theprovince of Ontario. Pixel-based supervised classifiers were the most popular wetland classificationalgorithms. This review summarizes different RS systems and methodologies for wetland mappingin Canada to outline how RS has been utilized for the generation of wetland inventories. The resultsof this review paper provide the current state-of-the-art methods and datasets for wetland studies inCanada and will provide direction for future wetland mapping research., We would like to thank reviewers for their so-called insights.

Saving textual data and accessing them in many fields have become one of the basic problems nowadays. The usage of these data effectively is directly related to the development of storage and access tools that will be used. Therefore, software programs using different methods have been developed. One of the points that need to be taken into account is data classifying. Because using raw data in these classifying processes is harmful, finding the stem of the texts is useful. In this study, the successes of two different stemming algorithms in the text classifying are comparatively examined.

In the article it is offered to rationalize work of installations on processing of hydrocarbonic raw materials, at the expense of its classification on types, using criterion of forecasting (KP) of the direction of processing. This approach will generally contribute to the rational use of technological equipment, reducing the metal consumption of devices and processing schemes, reducing energy costs by recovering excess heat and reducing heat exchange with the environment, efficient use of pumping equipment. At the same time, the general culture of production will increase and the harmful load on the environment will be reduced. Experimental studies have shown that the relative dielectric constant (ε), kinematic viscosity (ν20, mm2 / s) and Conradson coking (хk,%) of hydrocarbons, significantly depend on its chemical and fractional composition. In view of this, the proposed CP should be based on the above indicators. Experimental studies have allowed determining certain limit values of KP in accordance with which, hydrocarbons can be attributed to a certain type: type 0 –; type 1, 2 –; type 3 –; type 4 –. On the basis of the calculated values of KP, in the future, it is possible to develop rational schemes of technological processing of hydrocarbons, which will be related to the fuel, oil and combined direction (option). Depending on the needs of certain types of petroleum products, the target components obtained in the implementation of these schemes are hydrocarbon gases, motor and boiler fuels, lubricating oils, petroleum coke, bitumen, by-products – degradation gases, paraffin, resins and asphaltenes., В статті запропоновано раціоналізувати роботу установок з переробки вуглеводневої сировини, за рахунок її класифікації за типами, використовуючи критерій прогнозування (КП) напрямку переробки. Такий підхід у загальному випадку буде сприяти раціональному використанню технологічного обладнання, зниженню металоємності апаратів та схем переробки, зниженню енергетичних витрат за рахунок рекуперації надлишкового тепла та зниження теплообміну з навколишнім середовищем, ефективному використанню насосного обладнання. При цьому, також буде підвищуватися загальна культура виробництва та буде спостерігатися зменшенням шкідливого навантаження на довкілля. Експериментальні дослідження показали, що показники відносної діелектричної проникності (ε), кінематичною в’язкістю (ν20, мм2/с) та коксівністю за Конрадсоном (хк, %) вуглеводневої сировини, суттєво залежать від її хімічного та фракційного складу. Зважаючи на це,запропонований КП повинен базується на урахуванні означених вище показників. Експериментальні дослідження дозволили визначити певні граничні значення КП у відповідності до яких, вуглеводневу сировину можна віднести до певного типу: тип 0 – ; тип 1, 2 – ; тип 3 – ; тип 4 – . На підставі розрахованих значень КП, в подальшій перспективі, можна розробити раціональні схеми технологічної переробки вуглеводневої сировини, які будуть відноситися до паливного, оливного та комбінованого напрямку (варіанту). В залежності від потреби у певних видів нафтопродуктів, цільовими компонентами, які отримують при реалізації даних схем є вуглеводневі гази, моторні і котельні палива, змащувальні оливи, нафтовий кокс, бітуми, побічні продукти – гази деструкції, парафін, смоли і асфальтени.

2nd International Behcet Uz Children's Congress -- MAR 04-07, 2020 -- Izmir, TURKEY Background. We aimed to investigate the effectiveness of ketogenic diet (KD) in children with various types of refractory epilepsy. Methods. A total of 91 children (49 females) aged 3 to 193 months (median, 52 months) with drug resistant epilepsy who received KD treatment for at least 12 months were enrolled in the study. Seizure frequency, adherence to diet, reason for discontinuation of KD, and adverse effects were recorded. Response was defined as >= 50% improvement in seizure frequency compared to baseline. We also searched for influences of different variables on the outcome. Results. Intent-to-treat analysis revealed an improvement in seizure frequency for >= 50% in 73.6%, 80.2%, 75.8%, 73.6%, and 70.3% of patients at month-1,-3,-6,-9, and month-12, respectively. Overall, 32 (35.2%) patients remained seizure-free at month-12. There was no significant differences between responders and non responders in terms of age at onset of epilepsy, age at onset of KD, gender, or etiology. Mild hyperlipidemia was associated with a higher response rate. At the last follow-up (median: 20 months), 38 (41.8%) patients were still maintained on KD. While 15.4% of patients completed the diet with a success in seizure control, remainder discontinued KD due to lack of efficacy (23.1%), non-adharence to diet (11%), intercurrent infection (4.4%), adverse effects (3.3%), and death (1.1%). Conclusion. Ketogenic diet treatment appears to be effective in about two-thirds of children with various types of drug-resistant epilepsy, including one-third remaining seizure free. Mild hyperlipidemia seems to be associated with a higher response rate. Discontinuation of KD is mostly due to lack of efficacy or nonadherence, and rarely side effects.

Vaaratapahtumien raportointia pidetään potilasturvallisuutta parantavien toimien käynnistämisen kulmakivenä. Korkean kehitysasteen potilastietojärjestelmiin liittyvissä vaaratapahtumissa on tunnistettu tarve kehittää toimintaympäristöömme soveltuva luokitus, joka hyödyttäisi tapahtumien tunnistamista, rakenteista raportoimista ja analysointia. Tutkimuksella pyrittiin vaaratapahtumien raportointijärjestelmän käytön ja tiedon hyödynnettävyyden edistämiseen potilastietojärjestelmäturvallisuuden osa-alueella. Tavoitteena oli kuvata vaaratapahtumaluokituksen sisältöä ja perustella luokituskehitystyössä tehtyjä ratkaisuja, jotta raportointia voidaan toteuttaa kehittyneimpien potilastietojärjestelmien ongelmatilanteissa ja vaaratapahtumissa. Luokituskehityksen lähtökohtana olivat potilastietojärjestelmien virhetyyppien aiemmat tutkimustulokset, joissa vaaratapahtumailmoitusten kuvauksia analysoitiin ja luokiteltiin koskien välittömästi uuden potilastietojärjestelmän käyttöönoton jälkeistä kuutta kuukautta. Luokittelutyön ohjaavat periaatteet perustuivat luokituksen ominaispiirteisiin, käyttötarkoitukseen ja käytettävyyteen. Luokituksen 13 pääluokkaa kuvaavat korkean kehitysasteen potilastietojärjestelmän kliinisen käytön aikana syntyviä vaaratapahtumia, joista suurimmat luokat aineistossa olivat rajapinta-, käytettävyys-, työnkulku-, lääkitysosio- ja kirjaamisongelmat. Puolta luokituksen pääluokkaa täydentää 2-6 alaluokkaa, jolloin lopputuloksena oli 35 luokkaa. Tulokset kuvastavat sekä teoreettisia ja metodologisia tavoitteita luokituksen sisällölliseksi ja laadulliseksi kehittämiseksi että käytännöllisiä tavoitteita raportoinnin kehittämiseksi. Kliinisestä näkökulmasta laadituilla ongelmatyypin kuvauksilla on tarkoituksena ohjata tapahtumien luokittelemista käytännössä. Tutkimus tuotti teoreettis-käytännöllisen tuloksen luokitustutkimuksen alueella. Kehitettyä luokitusta voidaan soveltaa korkean kehitysasteen potilastietojärjestelmiin liittyvien vaaratapahtumien raportoinnissa ja analysoinnissa. Tuloksissa kuvataan luokkien erityispiirteet ja erottavat tekijät suhteessa muihin luokkiin perustellen tehtyjä ratkaisuja. Luokituksen käytön oletetaan tukevan vaaratapahtumien raportointia ja tiedon käyttämistä. Jatkossa edellytetään luokituksen systemaattista ylläpitoa ja kehittämistä empiiriseen aineistoon pohjautuen, jotta tiedon laatu kehittyisi edelleen tukemaan potilasturvallisuutta potilastietojärjestelmien käytössä., Patient safety incident reporting is currently considered a cornerstone of efforts to improve patient safety. For incidents related to high-maturity electronic health record systems (EHRs), there is a need to develop a classification appropriate to clinical operating environment that would benefit the identification of incidents and enhance structured reporting and analysis. The overall aim of the study was to advance use of a voluntary patient safety incident reporting system and to develop the usability of reports in the field of EHR safety. The aim was to categorize and reason patient safety incidents related to high-maturity EHRs so that the classification responds to the characteristics of errors in these systems. Previous research results on the error types in EHRs and incident reports were analyzed and classified for a six-month period immediately after the implementation of the EHRs. The guiding principles of the classification work were based on the features, usage and usability of the classification. The 13 main classes describe the incidents that occur during the clinical use of the advanced EHRs. The largest instance of classes in this dataset were interface, usability, workflow, medication section, and documentation problems. Half of the main classes are supplemented by 2 to 6 subclasses, resulting in 35 classes. The results reflect both theoretical and methodological objectives for the qualitative and contentual development of the classification and practical objectives for the development of reporting. From a clinical point of view, the problem type descriptions are intended to guide the classification of incidents in practice. The study produced a theoretical-practical result in the field of classification research. The classification can be applied to the reporting and analysis of incidents related to high-maturity EHRs. The results highlight the specific features of the category and the distinguishing factors in relation to the other categories, and reasoning for these outcomes. The use of classification is assumed to support incident reporting and use of data. In the future, systematic maintenance and development of the classification based on empirical data will be required in order to further develop the quality of data to support patient safety in the use of EHRs.

Rationale: The endemic of peri-implantitis affects over 25% of dental implants. Current treatment depends on empirical patient and site-based stratifications and lacks a consistent risk grading system. Methods: We investigated a unique cohort of peri-implantitis patients undergoing regenerative therapy with comprehensive clinical, immune, and microbial profiling. We utilized a robust outlier-resistant machine learning algorithm for immune deconvolution. Results: Unsupervised clustering identified risk groups with distinct immune profiles, microbial colonization dynamics, and regenerative outcomes. Low-risk patients exhibited elevated M1/M2-like macrophage ratios and lower B-cell infiltration. The low-risk immune profile was characterized by enhanced complement signaling and higher levels of Th1 and Th17 cytokines. Fusobacterium nucleatum and Prevotella intermedia were significantly enriched in high-risk individuals. Although surgery reduced microbial burden at the peri-implant interface in all groups, only low-risk individuals exhibited suppression of keystone pathogen re-colonization. Conclusion: Peri-implant immune microenvironment shapes microbial composition and the course of regeneration. Immune signatures show untapped potential in improving the risk-grading for peri-implantitis.

Background: Intrathoracic ribs are very rare congenital anomalies, and often discovered incidentally on chest X-ray. Since its first description by Lutz in 1947, approximately 50 cases have been reported in the literature till date. The aim is to review the all reported intrathoracic ribs, summarize their clinical features, and propose a potential classification. Methods: All relevant literatures were searched and reviewed. The terms include intrathoracic rib, intrathoracic bifid rib, trans-thoracic rib and intrathoracic rib anomaly. We have summarized the first finding events, origination, distribution, related anomalies and imaging features of intrathoracic rib, and propose an updated classification. Results: The patients' age at initial finding was from six weeks to 79 years old. Of all, sixty percent was less than 30 years old. There was no difference in gender. Most of them were reported by authors in western countries (85.3%, 58/68), and incidental findings by radiologist and respiratory physician. The intrathoracic rib occurs more frequently on the right side, and is usually single and unilateral. According to the new classification, type I and II was account for 45.6% and 35.3%, respectively. Conclusion: Intrathoracic rib is rare findings in clinical practice. It is useful that radiologists or clinician are familiarized with the imaging appearances of these malformations. These anomalies reflect some disturbances during the embryo development, leading us to propose a potential classification that could contribute to a better understanding of this rib anomaly.

Contains fulltext : 231688.pdf (Publisher’s version ) (Closed access) PURPOSE: Pathogenic variants in the X-linked gene NEXMIF (previously KIAA2022) are associated with intellectual disability (ID), autism spectrum disorder, and epilepsy. We aimed to delineate the female and male phenotypic spectrum of NEXMIF encephalopathy. METHODS: Through an international collaboration, we analyzed the phenotypes and genotypes of 87 patients with NEXMIF encephalopathy. RESULTS: Sixty-three females and 24 males (46 new patients) with NEXMIF encephalopathy were studied, with 30 novel variants. Phenotypic features included developmental delay/ID in 86/87 (99%), seizures in 71/86 (83%) and multiple comorbidities. Generalized seizures predominated including myoclonic seizures and absence seizures (both 46/70, 66%), absence with eyelid myoclonia (17/70, 24%), and atonic seizures (30/70, 43%). Males had more severe developmental impairment; females had epilepsy more frequently, and varied from unaffected to severely affected. All NEXMIF pathogenic variants led to a premature stop codon or were deleterious structural variants. Most arose de novo, although X-linked segregation occurred for both sexes. Somatic mosaicism occurred in two males and a family with suspected parental mosaicism. CONCLUSION: NEXMIF encephalopathy is an X-linked, generalized developmental and epileptic encephalopathy characterized by myoclonic-atonic epilepsy overlapping with eyelid myoclonia with absence. Some patients have developmental encephalopathy without epilepsy. Males have more severe developmental impairment. NEXMIF encephalopathy arises due to loss-of-function variants.

The authenticity of tea has become more important to the industry while the supply chains become complex. The quality and price of tea produced in different regions varies greatly. Currently, a rapid analytical method for testing the geographical origin of tea is missing. XRF is emerging as a screening technique for mineral and elemental analysis with applications in the traceability of foodstuffs, including tea. This study aims to develop a reliable multivariate classification model using XRF spectroscopy to obtain the mineral content. A total of 75 tea samples from tea producing countries throughout the world were analysed. After variable shortlisting, 18 elements were used to construct the multivariate models. Tea origin was determined by classifying the tea into 5 major geographical regions producing most of the global tea. PCA showed initial clustering in some regions, although the types of teas included in the study (black, green, white, herbal) showed no discrete cluster membership. The prediction power of each classification model developed was determined by using two multivariate classifiers, SIMCA and PLS-DA, against an independent validation set. The average overall correct classification rates of PLS-DA models were between 54-85% while the results of SIMCA models were between 70-84% resolving the poor clustering initially shown by PCA. This study demonstrated the potential of geographical origin of tea prediction using elemental contents of tea. Naturally, the classification can be linked not only to origin but to the type of tea as well. Practical application Wholesalers and retailers need a rapid and robust screening tool to confirm the origin and type of tea they sell to consumers. X-Ray fluorescence spectroscopy proved a good technique for achieving this in commercial teas sourced worldwide. Building on multivariate models, broad classification was accomplished both in terms of origin (Asian vs non-Asian) and in tea type with zero sample preparation and low cost of analysis., Graphical abstract Image 1, Highlights • Investigated if elemental content (XRF) can indicate origin of commercial teas. • Only 18 elements were selected for analysis based on their repeatability performance. • Multivariate classification was used to classify in five or two global regions. • Asian vs non-Asian classification reached 85% correct prediction rate. • Classification linked not only with origin but with type of tea as well.