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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.

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.

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.

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.

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.

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.

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.

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.

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 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.

Background. Over the past decade, chronic kidney disease (CKD) has become an area of intensive clinical and epidemiological research. Despite the clarity provided by the Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines, there appears to be within the CKD research literature significant disagreement on how to define CKD and measure kidney function. Methods. The objectives of this study were to investigate the variety of methods used to define CKD and to measure kidney function in original research papers as well as to investigate whether the quality of the journal had any effect on the quality of the methodology used. This was a descriptive review and not a meta-analysis. Information was extracted from each article including publication details (including the journal's impact factor), definition of CKD, method used to estimate kidney function and quantity of serum creatinine readings used to define CKD. An electronic search of MEDLINE through OVID was completed using the search term CKD. The search was limited to articles in English published in 2009. Studies were included in the review only if they were original research articles including patients with CKD. Articles were excluded if they reported data from a paediatric population, a population solely on dialysis or if there was no full-text access through OVID. Each article was assessed for quality with respect to using KDOQI CKD definition criteria. A description of the pooled data was completed and chi-square tests were used to investigate the relation between article quality and journal quality. Analysis was carried out using SPSS (15.0) and a P-value of < 0.05 was considered to indicate statistical significance. Results. The final review included 301 articles. There were a variety of methods used to define CKD in original research articles. Less than 20% (n = 59) of the articles adhered to the established international criteria for defining CKD. The majority of articles (52.1%) did not indicate the quantity of serum creatinine measurements used to define CKD. The impact factor or specialist nature of the scientific journal appears to have no bearing on whether or not published articles use the gold standard KDOQI guidelines for labelling a patient with a diagnosis of CKD. Conclusions. This review of literature found that a variety of definitions are being used in original research articles to define CKD and measure kidney function which calls into question the validity and reliability of such research findings and associated clinical guidelines. International consensus is urgently required to improve validity and generalizability of CKD research findings.

BACKGROUND: EUROCAT is a network of population-based congenital anomaly registries providing standardized epidemiologic information on congenital anomalies in Europe. There are three types of EUROCAT membership: full, associate, or affiliate. Full member registries send individual records of all congenital anomalies covered by their region. Associate members transmit aggregate case counts for each EUROCAT anomaly subgroup by year and by type of birth. This article describes the organization and activities of each of the current 29 full member and 6 associate member registries of EUROCAT. METHODS: Each registry description provides information on the history and funding of the registry, population coverage including any changes in coverage over time, sources for ascertaining cases of congenital anomalies, and upper age limit for registering cases of congenital anomalies. It also details the legal requirements relating to termination of pregnancy for fetal anomalies, the definition of stillbirths and fetal deaths, and the prenatal screening policy within the registry. Information on availability of exposure information and denominators is provided. The registry description describes how each registry conforms to the laws and guidelines regarding ethics, consent, and confidentiality issues within their own jurisdiction. Finally, information on electronic and web-based data capture, recent registry activities, and publications relating to congenital anomalies, along with the contact details of the registry leader, are provided. CONCLUSIONS: The registry description gives a detailed account of the organizational and operational aspects of each registry and is an invaluable resource that aids interpretation and evaluation of registry prevalence data. Birth Defects Research (Part A) 91: S51-S100, 2011. (C) 2011 Wiley-Liss, Inc.

Adult observers have surprisingly low calculation efficiencies for letter recognition (see, e.g., Pelli, Burns, Farell, & Moore-Page, 2006). Here, we examine the possibility that this is partly due to observers' neglecting paper features (e.g., the absence of ascenders and descenders in 'o'). Each of 16 observers completed 5,000 trials of a single-letter two-alternative forced-choice detection task. Using a combination of classification image analyses and Bayesian statistical analyses, we argue that between 60% and 75% of our participants indeed neglected paper features.

We commend Pleijel et al. (2008) for advocating forcefully for inclusion of specimen vouchers in all molecular systematic studies. Unfortunately, the paper contains significant factual errors that have the potential of spreading serious misinformation about GenBank among the scientific community. [GenBank is used here to denote the International Nucleotide Sequence Database, comprising GenBank, EMBL & DDBJ]. GenBank is an archival database, and our submitters are responsible for providing the taxonomic identifications for their entries – as a result, there are certainly misidentified sequence entries in GenBank. We also agree that “vouchers constitute an essential link between data and taxa”, and we have worked very hard to support and encourage that kind of annotation in GenBank entries. But they go on to state: “Furthermore, at GenBank there is currently no dedicated field for specification of vouchers.” This is incorrect. GenBank introduced the /specimen_voucher qualifier in 1998, and have actively promoted its use since then – GenBank currently has more than 600,000 entries from systematic and phylogenetic studies annotated with specimen vouchers. We also note that one the authors (Oxelman) actually used the /specimen_voucher field in one of their own entries ({"type":"entrez-nucleotide","attrs":{"text":"EF061375","term_id":"119654765","term_text":"EF061375"}}EF061375) which appears in Table 1 of the paper. To provide the background overlooked by Pleijel et al. (2008), specimen vouchers may be simply annotated as follows: /specimen_voucher="Oxelman 1234 (K)" [for specimens deposited in curated natural history collections] When we get large submissions (>10 entries) that lack adequate specimen annotation, we routinely send a form letter asking for it. We don’t (and can’t) require it. As Pleijel et al. point out, most journals don’t require voucher annotation, though most do require GenBank accessions. We can’t set the bar higher for GenBank submissions without jeopardizing this arrangement. A free-text formatted field for specimen vouchers was a significant improvement in our annotation palette, but after several years it became clear that we could do better by adding some structure to this qualifier. We adopted the Darwin Core triplet format of : : and introduced two new qualifiers, so as not to dilute the meaning of already popular /specimen_voucher. /specimen_voucher – for specimens vouchered in natural history collections /culture_collection – for live cultures, cell lines &c. /bio_material – for other kinds of collections: stock centers, seed banks, zoos & aquaria, DNA banks &c. In the best cases, when a collection is digitized and accessible on the web, this structured format allows us to generate hotlinks directly from the qualifier in the sequence entry to the specimen record itself. Many culture collections have this capability, but very few museums or herbaria to date – most notably the Museum of Vertebrate Zoology at Berkeley, and the University of Alaska Museum of the North. For example: {"type":"entrez-nucleotide","attrs":{"text":"FJ151112","term_id":"217388507","term_text":"FJ151112"}}FJ151112 – /specimen_voucher="MVZ:Herp:244898" This entry is hotlinked to the corresponding specimen page at the MVZ, and was published in Mol. Phylogenet. Evol. 49 (3), 806–826, within a month of Pleijel et al. Table 1 deserves more discussion – each of the accessions listed there were submitted by one of the authors of Pleijel et al. (Thollesson, Jondelius & Oxelman), and each is instructive in its own way. Thollesson did not see fit to submit any specimen data with the first two entries in Table 1. Accession {"type":"entrez-nucleotide","attrs":{"text":"AJ225185","term_id":"4127760","term_text":"AJ225185"}}AJ225185, from Dendronotus frondosus, is linked to Mol. Phylogenet. Evol. 16 (2), 161–172 (2000). There is no voucher information published in this paper, nor in the previous paper, which reported the 16S sequences from the same set of specimens. The accession cited in the second entry in Table 1 is apparently incorrect, and the genus is misspelled. Thollesson has submitted four entries from Protopelagonemertes sp. 544 ({"type":"entrez-nucleotide","attrs":{"text":"AJ436817","term_id":"30140125","term_text":"AJ436817"}}AJ436817, {"type":"entrez-nucleotide","attrs":{"text":"AJ436872","term_id":"30140163","term_text":"AJ436872"}}AJ436872, {"type":"entrez-nucleotide","attrs":{"text":"AJ436927","term_id":"30140223","term_text":"AJ436927"}}AJ436927, {"type":"entrez-nucleotide","attrs":{"text":"AJ436975","term_id":"30140425","term_text":"AJ436975"}}AJ436975). Neither these entries nor the associated publication [Proc. Biol. Sci. 270 (1513), 407–415] contain any voucher data. This is not GenBank’s fault. Likewise, Jondelius did not see fit to submit any specimen data with the third entry, {"type":"entrez-nucleotide","attrs":{"text":"AF167423","term_id":"9622130","term_text":"AF167423"}}AF167423. If he had, we would have annotated this with: /bio_material="SMNH :99999" /note="SMNH 99999: illustrations" The annotation ‘SMNH ’ deserves an aside. We were surprised to find that a catalog of natural history collection codes did not exist – so we built one, starting with the Index Herbariorum, the World Data Centre for Microorganisms, and specialty lists associated with resources like the Catalog of Fishes, the World Spider Catalog &c. This resource allows us to recognize that SMNH (also from Table 1) is used by three natural history museums. SMNH – Swedish Museum of Natural History SMNH – Saskatchewan Museum of Natural History (aka Royal Saskatchewan Museum) SMNH – Schmidt Museum of Natural History, Emporia State University The tag ‘ ’ indicates that SMNH in this case refers to the Swedish Museum of Natural History. The fourth entry, from Oxelman, is properly annotated with a specimen voucher, and we commend him for this. His only fault was to put his name on a paper that claimed that it was not possible to do this. The final entry, {"type":"entrez-nucleotide","attrs":{"text":"AJ511670","term_id":"39104379","term_text":"AJ511670"}}AJ511670, also from Thollesson is also well annotated, with /strain="ATCC 51973 = CCUG 35103", which was the best that we could do in 2004. We could enhance this annotation today with /culture_collection="CCUG:35103", which would hotlink directly to the CCUG page for this strain. Pleijel et al. close by contrasting the poor example of GenBank with the Barcoding of Life Database “which demands not just voucher specimens, but also trace files from the actual sequencing.” GenBank pioneered the archiving of trace files to support the Human Genome Project, and we have worked to support voucher annotation and linkages to specimen records in natural history collections long before the Barcoding initiative existed. The GenBank tools exist, but in the end the onus is on the submitters to include relevant sequence annotation. It is never too late to update your entries, and we encourage authors everywhere to update theirs – in most cases a simple table of accessions and field values is all it takes.

Filamentous bacteria belonging to the genus Thiothrix were detected in activated sludge samples using the fluorescent in situ hybridisation (FISH) technique. A 16S rRNA-targeted oligonucleotide probe was developed for the detection of members of the T. fructosivorans group, and the performance of probe TNI for the detection of Thiothrix nivea group was enhanced by using an unlabeled competitor. A set of 5 probes covering all phylogenetic groups of Thiothrix were used to examine samples taken from selected activated sludge plants treating paper and board mill wastes. Members of the T. eikelboomii group formed the predominant filamentous bacterial population in plants experiencing poor sludge settleability, whereas members of the T. nivea group were commonly found but not dominantly in the remaining plants. Members of the T. fructosivorans group were not detected at any significant level in any of the samples. The distribution of the main Thiothrix types remained unchanged throughout the investigation period. It was evident that mixed populations of Thiothrix spp. were present in all activated sludge samples investigated, the observed differences were in the relative abundance of the various groups. These findings were supported by the results obtained using conventional microscopy.

Throughout the social sciences, tests have been used for two primary - and different - purposes: a) to estimate where an examinee is located on an ability/trait continuum (e.g., intelligence tests), or b) to classify an examinee as either above or below a particular point on the ability continuum (e.g., criterion-referenced tests). From a psychometric perspective, the scoring procedure of the test should reflect the purpose for which the test is being used. From a practical perspective, the administration procedure should be as efficient as possible. The Myers-Briggs Type Indicator (MBTI; Briggs & Myers, 1976) is a personality inventory designed to classify examinees according to four bipolar dimensions. Although the MBTI is quite popular within corporate America, critics have threatened the validity of the MBTI with two seemingly contradictory faults: the test is too unreliable and too long. The purpose of this study was to examine the degree to which using an item response theory (IRT) Ph. D.

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

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.

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.

Aim To determine neurocognitive performance and behavioural problems in children with Panayiotopoulos syndrome. Method All 18 children (10 females, 8 males; mean age 4y 7mo; SD 1y 10mo) diagnosed with Panayiotopoulos syndrome at the Kempenhaeghe Epilepsy Center in the Netherlands between 2010 and 2017 were analysed retrospectively. All underwent a neuropsychological/behavioural assessment, an academic assessment, and a 24-hour electroencephalogram. Results Mean full-scale IQ (93.5; range 76-123; p=0.04) and performance IQ (93.2; range 76-126; p=0.04) were within the normal range, although significantly lower compared to the normative mean. Verbal IQ (96.3; range 76-118) and processing speed (96.1; range 74-114) were not significantly lower. Simple auditory/visual reaction times, visual attention, visual-motor integration, and verbal memory were significantly lower compared to normative values. On average, patients with Panayiotopoulos syndrome were 8 months behind in arithmetic speed and 11 months behind in reading speed for the number of months in school. Behavioural questionnaires revealed significantly higher scores on reported internalizing behavioural problems. Interpretation Children with Panayiotopoulos syndrome demonstrated diffuse cognitive dysfunction in full-scale IQ, performance IQ, visual attention, visual-motor integration, and verbal memory. A high incidence of internalizing behavioural problems was reported. This strongly suggests neuropsychological and behavioural comorbidity in children with Panayiotopoulos syndrome. What this paper addsChildren with Panayiotopoulos syndrome are at risk for cognitive deficits in various cognitive domains. Children with Panayiotopoulos syndrome are also prone to internalizing behavioural problems. Mild-to-severe academic underachievement was present in more than half of the children with Panayiotopoulos syndrome.

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.

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.

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.

Borderline Personality Disorder (BPD) is characterized by an increased emotional sensitivity and dysfunctional capacity to regulate emotions. While amygdala and prefrontal cortex interactions are regarded as the critical neural mechanisms underlying these problems, the empirical evidence hereof is inconsistent. In the current study, we aimed to systematically test different properties of brain connectivity and evaluate the predictive power to detect borderline personality disorder. Patients with borderline personality disorder (n = 51), cluster C personality disorder (n = 26) and non-patient controls (n = 44), performed an fMRI emotion regulation task. Brain network analyses focused on two properties of task-related connectivity: phasic refers to task-event dependent changes in connectivity, while tonic was defined as task-stable background connectivity. Three different network measures were estimated (strength, local efficiency, and participation coefficient) and entered as separate models in a nested cross-validated linear support vector machine classification analysis. Borderline personality disorder vs. non-patient controls classification showed a balanced accuracy of 55%, which was not significant under a permutation null-model, p = 0.23. Exploratory analyses did indicate that the tonic strength model was the highest performing model (balanced accuracy 62%), and the amygdala was one of the most important features. Despite being one of the largest data-sets in the field of BPD fMRI research, the sample size may have been limited for this type of classification analysis. The results and analytic procedures do provide starting points for future research, focusing on network measures of tonic connectivity, and potentially focusing on subgroups of BPD. Electronic supplementary material The online version of this article (10.1007/s00406-020-01201-3) contains supplementary material, which is available to authorized users.

The EULAR/ACR 2019 classification criteria for SLE constitute a current and optimized clinical approach to SLE classification. Classification is still not based on molecular approaches and the results from large studies using polyomics may be interpreted as demonstrating the relevance of the genetic and environmental background rather than splitting SLE into several entities. In fact, an association study within the EULAR/ACR classification criteria project found associations between manifestations only within organ domains. This independency of various organ manifestations argues for SLE as one disease entity. The current review article will therefore concentrate on the clinical and immunological manifestations of SLE and on what we have already learned in this century. Moreover, the structure and essential rules of the EULAR/ACR 2019 classification criteria will be discussed. While classification and diagnosis are distinct concepts, which have to remain clearly separated, information derived from the process towards the classification criteria is also useful for diagnostic purposes. Therefore this article also tries to delineate what classification can teach us for diagnosis, covering a wide variety of SLE manifestations.

Lupus nephritis (LN) is a frequent and severe manifestation of SLE. Along the decades, the epidemiology of LN and its clinical presentation have been changing. However, even though retrospective cohort studies report a decreased mortality rate and an improvement in the disease prognosis, the percentage of patients progressing into end stage renal disease (ESRD) keeps steady despite the improvements in therapeutic strategies. Current in-use medications have been available for decades now, yet over the years, regimens for optimizing their efficacy and minimizing toxicity have been developed. Therapeutic research is now moving towards the direction of precision medicine and several new drugs, targeting selectively different pathogenetic pathways, are currently under evaluation with promising results. In this review, we address the main changes and persistent unmet needs in LN management throughout the past decades, with a focus on prognosis and upcoming treatments.

A downside of next-generation sequencing technology is the high technical error rate. We built a tool, which uses array-based genotype information to classify next-generation sequencing–based SNPs into the correct and the incorrect calls. The deep learning algorithms were implemented via Keras. Several algorithms were tested: (i) the basic, naïve algorithm, (ii) the naïve algorithm modified by pre-imposing different weights on incorrect and correct SNP class in calculating the loss metric and (iii)–(v) the naïve algorithm modified by random re-sampling (with replacement) of the incorrect SNPs to match 30%/60%/100% of the number of correct SNPs. The training data set was composed of data from three bulls and consisted of 2,227,995 correct (97.94%) and 46,920 incorrect SNPs, while the validation data set consisted of data from one bull with 749,506 correct (98.05%) and 14,908 incorrect SNPs. The results showed that for a rare event classification problem, like incorrect SNP detection in NGS data, the most parsimonious naïve model and a model with the weighting of SNP classes provided the best results for the classification of the validation data set. Both classified 19% of truly incorrect SNPs as incorrect and 99% of truly correct SNPs as correct and resulted in the F1 score of 0.21 — the highest among the compared algorithms. We conclude the basic models were less adapted to the specificity of a training data set and thus resulted in better classification of the independent, validation data set, than the other tested models. Electronic supplementary material The online version of this article (10.1007/s13353-020-00586-0) contains supplementary material, which is available to authorized users.

Background Different disease stages of arrhythmogenic right ventricular cardiomyopathy (ARVC) can be identified by right ventricle (RV) longitudinal deformation (strain) patterns. This requires assessment of the onset of shortening, (systolic) peak strain, and postsystolic index, which is time‐consuming and prone to inter‐ and intra‐observer variability. The aim of this study was to design and validate an algorithm to automatically classify RV deformation patterns. Methods We developed an algorithm based on specific local characteristics from the strain curves to detect the parameters required for classification. Determination of the onset of shortening by the algorithm was compared to manual determination by an experienced operator in a dataset containing 186 RV strain curves from 26 subjects carrying a pathogenic plakophilin‐2 (PKP2) mutation and 36 healthy subjects. Classification agreement between operator and algorithm was solely based on differences in onset shortening, as the remaining parameters required for classification of RV deformation patterns could be directly obtained from the strain curves. Results The median difference between the onset of shortening determined by the experienced operator and by the automatic detector was 5.3 ms [inter‐quartile range (IQR) 2.7–8.6 ms]. 96% of the differences were within 1 time frame. Both methods correlated significantly with ρ = 0.97 (P

We aimed to develop an HCC classification model based on the integrated gene expression and methylation data of methylation-driven genes. Genome, methylome, transcriptome, proteomics and clinical data of 369 HCC patients from The Cancer Genome Atlas Network were retrieved and analyzed. Consensus clustering of the integrated gene expression and methylation data from methylation-driven genes identified 4 HCC subclasses with significant prognosis difference. HS1 was well differentiated with a favorable prognosis. HS2 had high serum α-fetoprotein level that was correlated with its poor outcome. High percentage of CTNNB1 mutations corresponded with its activation in WNT signaling pathway. HS3 was well differentiated with low serum α-fetoprotein level and enriched in metabolism signatures, but was barely involved in immune signatures. HS3 also had high percentage of CTNNB1 mutations and therefore enriched in WNT activation signature. HS4 was poorly differentiated with the worst prognosis and enriched in immune-related signatures, but was barely involved in metabolism signatures. Subsequently, a prediction model was developed. The prediction model had high sensitivity and specificity in distributing potential HCC samples into groups identical with the training cohort. In conclusion, this work sheds light on HCC patient prognostication and prediction of response to targeted therapy.

Background: Multiple myeloma (MM) is the second most common hematologic malignancy worldwide and does not have sufficient prognostic indicators. FCER1G (Fc fragment Of IgE receptor Ig) is located on chromosome 1q23.3 and is involved in the innate immunity. Early studies have shown that FCER1G participates in many immune-related pathways encompassing multiple cell types. Meanwhile, it is associated with many malignancies. However, the relationship between MM and FCER1G has not been studied.Methods: In this study, we integrated nine independent gene expression omnibus (GEO) datasets and analyzed the associations of FCER1G expression and myeloma progression, ISS stage, 1q21 amplification and survival in 2296 myeloma patients and 48 healthy donors.Results: The expression of FCER1G showed a decreasing trend with the advance of myeloma. As ISS stage and 1q21 amplification level increased, the expression of FCER1G decreased (P = 0.0012 and 0.0036, respectively). MM patients with high FCER1G expression consistently had longer EFS and OS across three large sample datasets (EFS: P = 0.0057, 0.0049, OS: P = 0.0014, 0.00065, 0.0019 and 0.0029, respectively). Meanwhile, univariate and multivariate analysis indicated that high FCER1G expression was an independent favorable prognostic factor for EFS and OS in MM patients (EFS: P = 0.006, 0.027, OS: P =0.002,0.025, respectively).Conclusions: The expression level of FCER1G negatively correlated with myeloma progression, and high FCER1G expression may be applied as a favorable biomarker in MM patients.

The year 2020 will certainly be remembered in human history as the year in which humans faced a global pandemic that drastically affected every living soul on planet earth. The COVID-19 pandemic certainly had a massive impact on human’s social and daily lives. The economy and relations of all countries were also radically impacted. Due to such unexpected situations, healthcare systems either collapsed or failed under colossal pressure to cope with the overwhelming numbers of patients arriving at emergency rooms and intensive care units. The COVID -19 tests used for diagnosis were expensive, slow, and gave indecisive results. Unfortunately, such a hindered diagnosis of the infection prevented abrupt isolation of the infected people which, in turn, caused the rapid spread of the virus. In this paper, we proposed the use of cost-effective X-ray images in diagnosing COVID-19 patients. Compared to other imaging modalities, X-ray imaging is available in most healthcare units. Deep learning was used for feature extraction and classification by implementing a multi-stream convolutional neural network model. The model extracts and concatenates features from its three inputs, namely; grayscale, local binary patterns, and histograms of oriented gradients images. Extensive experiments using fivefold cross-validation were carried out on a publicly available X-ray database with 3886 images of three classes. Obtained results outperform the results of other algorithms with an accuracy of 97.76%. The results also show that the proposed model can make a significant contribution to the rapidly increasing workload in health systems with an artificial intelligence-based automatic diagnosis tool.

Summary COVID‐19 is a quickly spreading over 10 million persons globally. The overall number of infected patients worldwide is estimated to be around 133,381,413 people. Infection rate is being increased on daily basis. It has also caused a devastating effect on the world economy and public health. Early stage detection of this disease is mandatory to reduce the mortality rate. Artificial intelligence performs a vital role for COVID‐19 detection at an initial stage using chest radiographs. The proposed methods comprise of the two phases. Deep features (DFs) are derived from its last fully connected layers of pre‐trained models like AlexNet and MobileNet in phase‐I. Later these feature vectors are fused serially. Best features are selected through feature selection method of PCA and passed to the SVM and KNN for classification. In phase‐II, quantum transfer learning model is utilized, in which a pre‐trained ResNet‐18 model is applied for DF collection and then these features are supplied as an input to the 4‐qubit quantum circuit for model training with the tuned hyperparameters. The proposed technique is evaluated on two publicly available x‐ray imaging datasets. The proposed methodology achieved an accuracy index of 99.0% with three classes including corona virus‐positive images, normal images, and pneumonia radiographs. In comparison to other recently published work, the experimental findings show that the proposed approach outperforms it.

For manufacturing companies, quality management is a key feature for increasing the competitiveness, productivity, profitability, and sustainability of their systems. Quality improvement (QI) methods aim to achieve high-quality parts without reducing performance. The Industry 4.0 framework brought technological developments that cannot be used by traditional QI methods, such as Six Sigma, Lean, Lean Six, the Theory of Constraints, and Total Quality Management, which are widely used in manufacturing companies. The need for higher manufacturing sustainability and market requirements has led to the search for alternative QI methods with superior performance to traditional QI methods such as Zero-Defect Manufacturing (ZDM). The current paper is a position paper with a goal to present the ZDM approach and providing a clear definition about ZDM to align everyone in one common understanding of ZDM. Many researchers and manufactures are skeptical about ZDM, therefore, numerous argumentative questions have been created and answered, to convince them why they should migrate from traditional QI methods to ZDM. The migration to ZDM has already started, to support this statement numerous facts from the literature have been presented. Finally, several directions were identified, demonstrating that there is still plenty of room for research in several domains.

This paper presents results of investigations of the possibility of using 'Gigovići'-Ulcinj limestone (Republic Montenegro) as filler in various industry branches. Micronization methods, granulometric composition, oil and water absorption and degree of whiteness were investigated, and chemical and thermal analyses (DT/TG) were performed. Physico-chemical properties of this limestone classify it among high quality carbonate raw materials with relatively high CaCO3 content of 98.16 %, as well as low MgCO3 content of 1.53 % and low silicate content (SiO2 0.30 %). Its quality satisfies requirements of standards on using of calcium carbonate as filler in industry of paints and coatings; paper industry, rubber and PVC industry; glass industry; production of mineral fertilizers; foundry industry; sugar industry and metallurgy. Due to the low degree of whiteness (85.25 %) 'Gigovići' limestone cannot be used in pharmaceutical and cosmetics industry. Due to relatively high content of MgO (0.73 %) and Fe (340 ppm), as well as high content of heavy metals, Cu (18 ppm), Ni (24 ppm) and Cd (9 ppm), 'Gigovići' limestone cannot be used, in production of cattle feed and for neutralization of acidic soils.

Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Neural Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets.In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examines key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms.

Reports are the standard way of communication between the radiologist and the referring clinician. Efforts are made to improve this communication by, for instance, introducing standardization and structured reporting. Natural Language Processing (NLP) is another promising tool which can improve and enhance the radiological report by processing free text. NLP as such adds structure to the report and exposes the information, which in turn can be used for further analysis. This paper describes pre-processing and processing steps and highlights important challenges to overcome in order to successfully implement a free text mining algorithm using NLP tools and machine learning in a small language area, like Dutch. A rule-based algorithm was constructed to classify T-stage of pulmonary oncology from the original free text radiological report, based on the items tumor size, presence and involvement according to the 8th TNM classification system. PyContextNLP, spaCy and regular expressions were used as tools to extract the correct information and process the free text. Overall accuracy of the algorithm for evaluating T-stage was 0,83 in the training set and 0,87 in the validation set, which shows that the approach in this pilot study is promising. Future research with larger datasets and external validation is needed to be able to introduce more machine learning approaches and perhaps to reduce required input efforts of domain-specific knowledge. However, a hybrid NLP approach will probably achieve the best results. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10278-020-00327-z) contains supplementary material, which is available to authorized users.

Decision tree is one of the best expressive classifiers in data mining. A decision tree is popular due to its simplicity and straightforward visualization capability for all types of datasets. Decision tree forest is an ensemble of decision trees. The prediction accuracy of the decision tree forest is more than a decision tree algorithm. Constant efforts are going on to create accurate and diverse trees in the decision tree forest. In this paper, we propose Tangent Weighted Decision Tree Forest (TWDForest), which is more accurate and diverse than random forest. The strength of this technique is that it uses a more accurate and uniform tangent weighting function to create a weighted decision tree forest. It also improves performance by taking opinions from previous trees to best fit the successor tree and avoids the toggling of the root node. Due to this novel approach, the decision trees from the forest are more accurate and diverse as compared to other decision forest algorithms. Experiments of this novel method are performed on 15 well known, publicly available UCI machine learning repository datasets of various sizes. The results of the TWDForest method demonstrate that the entire forest and decision trees produced in TWDForest have high prediction accuracy of 1–7% more than existing methods. TWDForest also creates more diverse trees than other forest algorithms.

Significance: The Mueller matrix decomposition method is widely used for the analysis of biological samples. However, its presumed sequential appearance of the basic optical effects (e.g., dichroism, retardance, and depolarization) limits its accuracy and application. Aim: An approach is proposed for detecting and classifying human melanoma and non-melanoma skin cancer lesions based on the characteristics of the Mueller matrix elements and a random forest (RF) algorithm. Approach: In the proposal technique, 669 data points corresponding to the 16 elements of the Mueller matrices obtained from 32 tissue samples with squamous cell carcinoma (SCC), basal cell carcinoma (BCC), melanoma, and normal features are input into an RF classifier as predictors. Results: The results show that the proposed model yields an average precision of 93%. Furthermore, the classification results show that for biological tissues, the circular polarization properties (i.e., elements m44, m34, m24, and m14 of the Mueller matrix) dominate the linear polarization properties (i.e., elements m13, m31, m22, and m41 of the Mueller matrix) in determining the classification outcome of the trained classifier. Conclusions: Overall, our study provides a simple, accurate, and cost-effective solution for developing a technique for classification and diagnosis of human skin cancer.

Standard triplicate blood pressure (BP) measurements pose time barriers to hypertension screening, especially in resource‐limited settings. We assessed the implications of simplified approaches using fewer measurements with adults (≥18 years old) not using anti‐hypertensive medications from the US National Health and Nutrition Examination Survey 1999‐2016 (n = 30 614), and two datasets from May Measurement Month 2017‐2018 (n = 14 795 for Nepal and n = 6 771 for India). We evaluated the proportion of misclassification of hypertension when employing the following simplified approaches: using only 1st BP, only 2nd BP, 2nd if 1st BP in a given range (otherwise using 1st), and average of 1st and 2nd BP. Hypertension was defined as average of 2nd and 3rd systolic BP ≥140 and/or diastolic BP ≥90 mm Hg. Using only the 1st BP, the proportion of missed hypertension ranged from 8.2%–12.1% and overidentified hypertension from 4.3%–9.1%. Using only 2nd BP reduced the misclassification considerably (corresponding estimates, 4.9%–6.4% for missed hypertension and 2.0%–4.4% for overidentified hypertension) but needed 2nd BP in all participants. Using 2nd BP if 1st BP ≥130/80 demonstrated similar estimates of missed hypertension (3.8%–8.1%) and overidentified hypertension (2.0%–3.9%), but only required a 2nd BP in 33.8%–59.8% of participants. In conclusion, a simplified approach utilizing 1st BP supplemented by 2nd BP in some individuals has low misclassification rates and requires approximately half of the total number of measurements compared to the standard approach, and thus can facilitate screening in resource‐constrained settings.

Background Many people suffer from insomnia, a sleep disorder characterized by difficulty falling and staying asleep during the night. As social media have become a ubiquitous platform to share users’ thoughts, opinions, activities, and preferences with their friends and acquaintances, the shared content across these platforms can be used to diagnose different health problems, including insomnia. Only a few recent studies have examined the prediction of insomnia from Twitter data, and we found research gaps in predicting insomnia from word usage patterns and correlations between users’ insomnia and their Big 5 personality traits as derived from social media interactions. Objective The purpose of this study is to build an insomnia prediction model from users’ psycholinguistic patterns, including the elements of word usage, semantics, and their Big 5 personality traits as derived from tweets. Methods In this paper, we exploited both psycholinguistic and personality traits derived from tweets to identify insomnia patients. First, we built psycholinguistic profiles of the users from their word choices and the semantic relationships between the words of their tweets. We then determined the relationship between a users’ personality traits and insomnia. Finally, we built a double-weighted ensemble classification model to predict insomnia from both psycholinguistic and personality traits as derived from user tweets. Results Our classification model showed strong prediction potential (78.8%) to predict insomnia from tweets. As insomniacs are generally ill-tempered and feel more stress and mental exhaustion, we observed significant correlations of certain word usage patterns among them. They tend to use negative words (eg, “no,” “not,” “never”). Some people frequently use swear words (eg, “damn,” “piss,” “fuck”) with strong temperament. They also use anxious (eg, “worried,” “fearful,” “nervous”) and sad (eg, “crying,” “grief,” “sad”) words in their tweets. We also found that the users with high neuroticism and conscientiousness scores for the Big 5 personality traits likely have strong correlations with insomnia. Additionally, we observed that users with high conscientiousness scores have strong correlations with insomnia patterns, while negative correlation between extraversion and insomnia was also found. Conclusions Our model can help predict insomnia from users’ social media interactions. Thus, incorporating our model into a software system can help family members detect insomnia problems in individuals before they become worse. The software system can also help doctors to diagnose possible insomnia in patients.

Since tumor is seriously harmful to human health, effective diagnosis measures are in urgent need for tumor therapy. Early detection of tumor is particularly important for better treatment of patients. A notable issue is how to effectively discriminate tumor samples from normal ones. Many classification methods, such as Support Vector Machines (SVMs), have been proposed for tumor classification. Recently, deep learning has achieved satisfactory performance in the classification task of many areas. However, the application of deep learning is rare in tumor classification due to insufficient training samples of gene expression data. In this paper, a Sample Expansion method is proposed to address the problem. Inspired by the idea of Denoising Autoencoder (DAE), a large number of samples are obtained by randomly cleaning partially corrupted input many times. The expanded samples can not only maintain the merits of corrupted data in DAE but also deal with the problem of insufficient training samples of gene expression data to a certain extent. Since Stacked Autoencoder (SAE) and Convolutional Neural Network (CNN) models show excellent performance in classification task, the applicability of SAE and 1-dimensional CNN (1DCNN) on gene expression data is analyzed. Finally, two deep learning models, Sample Expansion-Based SAE (SESAE) and Sample Expansion-Based 1DCNN (SE1DCNN), are designed to carry out tumor gene expression data classification by using the expanded samples. Experimental studies indicate that SESAE and SE1DCNN are very effective in tumor classification.

The Inorganic Crystal Structure Database (ICSD) is the world's largest database of fully evaluated and published crystal structure data, mostly obtained from experimental results. However, the purely experimental approach is no longer the only route to discover new compounds and structures. In the past few decades, numerous computational methods for simulating and predicting structures of inorganic solids have emerged, creating large numbers of theoretical crystal data. In order to take account of these new developments the scope of the ICSD was extended in 2017 to include theoretical structures which are published in peer‐reviewed journals. Each theoretical structure has been carefully evaluated, and the resulting CIF has been extended and standardized. Furthermore, a first classification of theoretical data in the ICSD is presented, including additional categories used for comparison of experimental and theoretical information., The article discusses how theoretical crystal data are supplementing experimental data for simulation and prediction of structures of inorganic solids in the Inorganic Crystal Structure Database.

Melanoma is the most dangerous type of skin cancer and has highly heterogeneous features. Despite progress in melanoma classification, interpatient heterogeneity remains difficult to predict, especially in terms of long-term survival. Here, based on mRNA-seq, miRNA-seq and DNA methylation data from 447 cutaneous melanoma patients in the Cancer Genome Atlas, we performed integrative and single-dataset clustering analyses. A novel group of patients was identified, including 301 with better, 55 with poorer and 91 with intermediate prognoses. Immune genes were upregulated in the better prognostic group, and higher immune scores (representing a greater extent of immune cell infiltration into tumor tissues) were associated with better prognoses. Higher expression of 115 genes was determined to predict better outcomes. The better prognostic group also exhibited DNA hypomethylation, and immune pathways were enriched among the hypomethylated genes. Using exome-seq data from the same patients, we observed that the better prognostic group harbored the highest number of mutations. The mutational signature in the better prognostic group was associated with ultraviolet light exposure. These integrated investigations have potential therapeutic significance, as they clarify the molecular heterogeneity of cutaneous melanoma and enhance its classification.

There is currently no accepted classification of autosomal recessive cerebellar ataxias, a group of disorders characterized by important genetic heterogeneity and complex phenotypes. The objective of this task force was to build a consensus on the classification of autosomal recessive ataxias in order to develop a general approach to a patient presenting with ataxia, organize disorders according to clinical presentation, and define this field of research by identifying common pathogenic molecular mechanisms in these disorders. The work of this task force was based on a previously published systematic scoping review of the literature that identified autosomal recessive disorders characterized primarily by cerebellar motor dysfunction and cerebellar degeneration. The task force regrouped 12 international ataxia experts who decided on general orientation and specific issues. We identified 59 disorders that are classified as primary autosomal recessive cerebellar ataxias. For each of these disorders, we present geographical and ethnical specificities along with distinctive clinical and imagery features. These primary recessive ataxias were organized in a clinical and a pathophysiological classification, and we present a general clinical approach to the patient presenting with ataxia. We also identified a list of 48 complex multisystem disorders that are associated with ataxia and should be included in the differential diagnosis of autosomal recessive ataxias. This classification is the result of a consensus among a panel of international experts, and it promotes a unified understanding of autosomal recessive cerebellar disorders for clinicians and researchers.