Miracle-dz/UniFAR-FTUs huggingface dataset:
Treatment of bipolar depression: making sensible decisions

A major challenge in the treatment of major depressive episodes associated with bipolar disorder is differentiating this illness from major depressive episodes associated with major depressive disorder. Mistaking the former for the latter will lead to incorrect treatment and poor outcomes. None of the classic antidepressants, serotonin specific reuptake inhibitors, or serotonin-norepinephrine reuptake inhibitors have ever received regulatory approval as monotherapies for the treatment of bipolar depression. At present, there are only 3 approved medication treatments for bipolar depression: olanzapine/fluoxetine combination, quetiapine (immediate or extended release), and lurasidone (monotherapy or adjunctive to lithium or valproate). All 3 have similar efficacy profiles, but they differ in terms of tolerability. Number needed to treat (NNT) and number needed to harm (NNH) can be used to quantify these similarities and differences. The NNTs for response and remission for each of these interventions vs placebo range from 4 to 7, and 5 to 7, respectively, with overlap in terms of their 95% confidence intervals. NNH values less than 10 (vs placebo) were observed for the spontaneously reported adverse events of weight gain and diarrhea for olanzapine/fluoxetine combination (7 and 9, respectively) and somnolence and dry mouth for quetiapine (3 and 4, respectively). There were no NNH values less than 10 (vs placebo) observed with lurasidone treatment. NNH values vs placebo for weight gain of at least 7% from baseline were 6, 16, 58, and 36, for olanzapine/fluoxetine combination, quetiapine, lurasidone monotherapy, and lurasidone combined with lithium or valproate, respectively. Individualizing treatment decisions will require consideration of the different potential adverse events that are more likely to occur with each medication. The metric of the likelihood to be helped or harmed (LHH) is the ratio of NNH to NNT and can illustrate the tradeoffs inherent in selecting medications. A more favorable LHH was noted for treatment with lurasidone. However, OFC and quetiapine monotherapy may still have utility in high urgency situations, particularly in persons who have demonstrated good outcomes with these interventions in the past, and where a pressing clinical need for efficacy mitigates their potential tolerability shortcomings. In terms of maintenance therapy, adjunctive quetiapine is the only agent where the NNT vs lithium or valproate alone is less than 10 for both the prevention of mania and the prevention of depression.
Noninvasive Diagnosis of Venous Thromboembolism

Venous thromboembolism (VTE), although relatively easy to treat, can often be difficult to diagnose noninvasively. Many different noninvasive tests are currently available to diagnose VTE, including both pulmonary embolism and deep venous thrombosis. Choosing a sensible diagnostic strategy requires an understanding of the each test’s strengths and drawbacks, as well as its applicability to the particular clinical situation. In this article, the principles of each test are discussed, and the diagnostic criteria are explained and critiqued. Specific algorithms are suggested for common clinical situations. In addition, the areas in which noninvasive testing does not yield accurate diagnoses are described. In those circumstances, invasive contrast examinations (venography or pulmonary angiography) may still be necessary. Finally, promising newer strategies are described that may, in the future, enable VTE to be diagnosed faster, easier, and less expensively.
Expanding the definition of addiction: DSM-5 vs. ICD-11

While considerable efforts have been made to understand the neurobiological basis of substance addiction, the potentially “addictive” qualities of repetitive behaviors, and whether such behaviors constitute “behavioral addictions,” is relatively neglected. It has been suggested that some conditions, such as gambling disorder, compulsive stealing, compulsive buying, compulsive sexual behavior, and problem Internet use, have phenomenological and neurobiological parallels with substance use disorders. This review considers how the issue of “behavioral addictions” has been handled by latest revisions of the Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Classification of Diseases (ICD), leading to somewhat divergent approaches. We also consider key areas for future research in order to address optimal diagnostic classification and treatments for such repetitive, debilitating behaviors.
Neurodevelopmental outcomes in infants exposed in utero to antipsychotics: a systematic review of published data

The proportion of pregnancies exposed to either second-generation antipsychotics (SGAs) or first-generation antipsychotics (FGAs) varies between 0.3%–2% of all pregnancies, but, until now, little is known about the potential neurobehavioral teratogenicity of antipsychotics. Assessing this safety facet is the aim of this article. PubMed, Scopus, and Google Scholar were searched for eligible articles. PubMed (1954 to May 2016) was searched using several medical subject headings, variously combined. PubMed search results were also limited using the search filter for human studies published in English. Scopus and Google Scholar searches were filtered for article title (antipsychotics/neuroleptics, pregnancy). After excluding duplicates, 9,250 articles were identified and 29 met the following inclusion criteria: only articles that provided original/primary data on neurodevelopmental outcome in human offspring older than 4 months of age, independently of the study design, were selected for review. Indeed, some relevant neurodevelopmental milestones are achieved at this time. Length of study and neurodevelopmental assessment methodology did not influence the study selection. Unfortunately, published data on neurodevelopmental teratogenicity of SGAs mainly derive from case reports and small case-series studies. Even findings emerging from case-control and prospective/retrospective studies are of limited clinical relevance because of their small sample sizes. Limited data are also available on FGAs. Hence, we have to conclude that the long-term neurodevelopmental outcomes for children exposed in utero remain unclear. Low to very low quality evidence of retrieved data makes impossible to confirm or exclude potential long-lasting untoward effects on infant neurocognitive development associate with antenatal exposure to either SGAs or FGAs.
On the np-completeness of certain network testing problems

Let G(V, E) be an undirected graph which describes the structure of a communication network. During the maintenance period every line must be tested in each of the two possible directions. A line is tested by assigning one of its endpoints to be a transmitter, the other to be a receiver, and sending a message from the transmitter to the receiver through the line. We define several different models for communication networks, all subject to the two following axioms: a vertex cannot act as a transmitter and as a receiver simultaneously and a vertex cannot receive through two lines simultaneously. In each of the models, two problems arise: What is the maximum number of lines one can test simultaneously? and What is the minimum number of phases necessary for testing the entire network? , where, by “phase” we mean a period in which some tests are conducted simultaneously. We show that in most models, including the “natural” model of radio communication, both problems are NP-hard. In some models the problems can be solved by reducing them to either a maximum matching problem or an edge coloring problem for which polynomial algorithms are known. One model remains for which the complexity of the minimization problem is unknown.
High-grade malignant glioma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up.

High-grade malignant glioma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up R. Stupp, J.-C. Tonn, M. Brada & G. Pentheroudakis On behalf of the ESMO Guidelines Working Group* Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne, Lausanne, and Department of Oncology-Hematology Riveria-Chablais, Vevey, Switzerland; Department of Neurosurgery, Universitatsklinikum Grosshadern, Ludwig-Maximilians-University Munich, Munich, Germany; Institute of Cancer Research and the Royal Marsden NHS Foundation Trust, Sutton, UK; Department of Medical Oncology, Ioannina University Hospital, Ioannina, Greece
Minimum augmentation of a tree to a K-edge-connected graph

This paper solves the minimum augmentation problem for a given tree and positive integer k, that is, to make a tree k-edge-connected by adding the minimum number of edges. It is shown that the minimum number of edges is the least integer not less than a half of the deficiency of the tree which is defined as the sum of k-(degree) over all the vertices whose degrees are less than k. The proof is constructive and gives a polynomial-time algorithm for constructing such an augmentation.
Multilingual pronunciation by analogy

Abstract Automatic pronunciation of unknown words (i.e., those not in the system dictionary) is a difficult problem in text-to-speech (TTS) synthesis. Currently, many data-driven approaches have been applied to the problem, as a backup strategy for those cases where dictionary matching fails. The difficulty of the problem depends on the complexity of spelling-to-sound mappings according to the particular writing system of the language. Hence, the degree of success achieved varies widely across languages but also across dictionaries, even for the same language with the same method. Further, the sizes of the training and test sets are an important consideration in data-driven approaches. In this paper, we study the variation of letter-to-phoneme transcription accuracy across seven European languages with twelve different lexicons. We also study the relationship between the size of dictionary and the accuracy obtained. The largest dictionaries of each language have been partitioned into ten approximately equal-sized subsets and combined to give ten different-sized test sets. In view of its superior performance in previous work, the transcription method used is pronunciation by analogy (PbA). Best results are obtained for Spanish, generally believed to have a very regular (‘shallow’) orthography, and poorest results for English, a language whose irregular spelling system is legendary. For those languages for which multiple dictionaries were available (i.e., French and English), results were found to vary across dictionaries. For the relationship between dictionary size and transcription accuracy, we find that as dictionary size grows, so performance grows monotonically. However, the performance gain decelerates (tends to saturate) as the dictionary increases in size; the relation can simply be described by a logarithmic regression, one parameter of which (α) can be taken as quantifying the depth of orthography of a language. We find that α for a language is significantly correlated with transcription performance on a small dictionary (approximately 10,000 words) for that language, but less so for asymptotic performance. This may be because our measure of asymptotic performance is unreliable, being extrapolated from the fitted logarithmic regression.
Edge-vulnerability and mean distance

The mean distance of a simple connected graph G of order n is defined by We answer a problem of Plesnik about the edge-vulnerability of G related to μ, i.e., we find a bound for μ(G − e) − μ(G) and for where e is an edge of G such that G − e is still conencted. This question is of interest in interconnection La distance moyenne d'un graphe G, simple et connexe, d'ordre n, est definie par Nous repondons ici a un probleme de Plesnik sur l'arěte-vulnerabilite de G par rapport a μ, i.e. nous trouvons une borne pour μ(G −e) − μ(G) et pour ou e est une arěte de G telle que G − e soit encore connexe.
Metabolic Parameters and Nonalcoholic Fatty Liver Disease in Hypopituitary Men

Patients with hypopituitarism have the feature of metabolic syndrome, including central obesity, insulin resistance, and dyslipidemia. Because metabolic syndrome, including insulin resistance, is the main pathogenesis of the development of nonalcoholic fatty liver disease (NAFLD), we considered that patients diagnosed with hypopituitarism have an increased risk of developing NAFLD. We compared control subjects and hypopituitary men in metabolic parameters and the frequency of fatty liver on abdominal ultrasonography, and analyzed associating factors with the severity of the fatty liver in patients with hypopituitarism. 34 male patients with hypopituitarism and 40 age and sex-matched control subjects were included. The frequency of fatty liver on abdominal ultrasonography was significantly higher in hypopituitary men compared to control subjects (32.5% vs. 70.6%, p=0.001). Ln CRP and free fatty acids were significantly elevated in hypopituitary patients with fatty liver compared to patients without fatty liver. Ln GH was significantly lower in hypopituitary patients with fatty liver. The severity of fatty liver on abdominal ultrasonography correlated with negatively Ln GH, after adjusting for the BMI effect (p=0.020). There is a difference only between the severe fatty liver group and normal liver group in the analysis of the mean Ln GH level between 4 groups according to the severity of fatty liver (p=0.036). In conclusion, NAFLD is more common in hypopituitary patients than control subject. Severe growth hormone deficiency in hypopituitarism was associated with the severe degree of hepatic steatosis in NAFLD.
Pathotypic and genotypic characterization of two Bangladeshi isolates of Newcastle disease virus of chicken and pigeon origin.

Two Bangladeshi isolates of Newcastle disease virus (NDV), one from a chicken and one from a pigeon, were characterized in this study. Pathogenicity of the isolates was evaluated on the basis of intracerebral pathogenicity index (ICPI). Both the isolates were found to be of velogenic pathotype having ICPI of 1.83 and 1.51 for the chicken and pigeon isolate, respectively. Genotype of the isolates was determined by phylogenetic analysis based on partial F gene sequences. A 766-bp genome fragment spanning partial M and F gene was amplified by RT-PCR and sequenced. The first 354 bp of the coding region of F gene and corresponding deduced amino acid sequences (residues 1-118) of these two NDV isolates were aligned with that of other NDV strains retrieved from GenBank. A phylogenetic tree constructed from the alignment showed that the chicken isolate (BD-C162) belonged to the newly described genotype XIII and the pigeon isolate (BD-P01) to genotype VI. Both the chicken and pigeon isolates possessed a virulent-like fusion protein cleavage site (112) RRQKRF(117) .
Datasets for generic relation extraction*

Abstract A vast amount of usable electronic data is in the form of unstructured text. The relation extraction task aims to identify useful information in text (e.g. PersonW works for OrganisationX, GeneY encodes ProteinZ) and recode it in a format such as a relational database or RDF triplestore that can be more effectively used for querying and automated reasoning. A number of resources have been developed for training and evaluating automatic systems for relation extraction in different domains. However, comparative evaluation is impeded by the fact that these corpora use different markup formats and notions of what constitutes a relation. We describe the preparation of corpora for comparative evaluation of relation extraction across domains based on the publicly available ACE 2004, ACE 2005 and BioInfer data sets. We present a common document type using token standoff and including detailed linguistic markup, while maintaining all information in the original annotation. The subsequent reannotation process normalises the two data sets so that they comply with a notion of relation that is intuitive, simple and informed by the semantic web. For the ACE data, we describe an automatic process that automatically converts many relations involving nested, nominal entity mentions to relations involving non-nested, named or pronominal entity mentions. For example, the first entity is mapped from ‘one’ to ‘Amidu Berry’ in the membership relation described in ‘Amidu Berry, one half of PBS’. Moreover, we describe a comparably reannotated version of the BioInfer corpus that flattens nested relations, maps part-whole to part-part relations and maps n-ary to binary relations. Finally, we summarise experiments that compare approaches to generic relation extraction, a knowledge discovery task that uses minimally supervised techniques to achieve maximally portable extractors. These experiments illustrate the utility of the corpora.1
Special issue on statistical learning of natural language structured input and output

Abstract During last decade, machine learning and, in particular, statistical approaches have become more and more important for research in Natural Language Processing (NLP) and Computational Linguistics. Nowadays, most stakeholders of the field use machine learning, as it can significantly enhance both system design and performance. However, machine learning requires careful parameter tuning and feature engineering for representing language phenomena. The latter becomes more complex when the system input/output data is structured, since the designer has both to (i) engineer features for representing structure and model interdependent layers of information, which is usually a non-trivial task; and (ii) generate a structured output using classifiers, which, in their original form, were developed only for classification or regression. Research in empirical NLP has been tackling this problem by constructing output structures as a combination of the predictions of independent local classifiers, eventually applying post-processing heuristics to correct incompatible outputs by enforcing global properties. More recently, some advances of the statistical learning theory, namely structured output spaces and kernel methods, have brought techniques for directly encoding dependencies between data items in a learning algorithm that performs global optimization. Within this framework, this special issue aims at studying, comparing, and reconciling the typical domain/task-specific NLP approaches to structured data with the most advanced machine learning methods. In particular, the selected papers analyze the use of diverse structured input/output approaches, ranging from re-ranking to joint constraint-based global models, for diverse natural language tasks, i.e., document ranking, syntactic parsing, sequence supertagging, and relation extraction between terms and entities. Overall, the experience with this special issue shows that, although a definitive unifying theory for encoding and generating structured information in NLP applications is still far from being shaped, some interesting and effective best practice can be defined to guide practitioners in modeling their own natural language application on complex data.
Hiperendemicidad de Hepatitis viral B y Delta en pueblos indìgenas de la Amazonía Peruana

Objective: To determine the prevalence of serological markers for viral hepatitis B and delta in indigenous communities in the Peruvian Amazon jungle, as well as their distribution in the different river basins and what are the associated risk factors. Materials and methods: A cross-sectional study was performed in 837 inhabitants from 37 native communities distributed alongside 12 river basins in the Peruvian Amazon jungle. Epidemiological data were obtained with respect to the presence of viral hepatitis B and delta, and a venous blood sample was taken in order to determine HBsAg, HBeAg, total anti-HBcAg anti-HBcAg IgM, and anti-delta antibodies using an ELISA technique. Results: Average age in the studied population was 22,7 years (range 1–94 years), and 50,/% were male. Previous infection caused by hepatitis B virus was determined in 519 (59,7%) subjects, and a recent infection was detected in 16 (1,8%) subjects. 82 HBsAg carriers were found (9,4%); of them, 18 (21,9%) were positive for HBeAg, and 15 (3,3%) were male. 44,2% of subjects less than ten years old had past history of infection. 39% (32/82) of HBsAg carriers had also hepatitis delta virus infection. The prevalence of HBV was higher in natives (64,3) compared to that in mestizos (50,6%); as well in northern jungle
On dependence and reliability computations

When computing the reliability of a system consisting of several components, it is usually assumed that the components are statistically independent of each other. In case the components are associated, it is known that this leads to underestimation if the system is a series, whereas the converse holds for parallel systems. In this paper, we consider general monotone systems and study the error resulting from the independence assumption when the component states are, in fact, distributed according to certain dependence models. We also consider some applications of the results to network systems.
MaltOptimizer: Fast and effective parser optimization

Abstract Statistical parsers often require careful parameter tuning and feature selection. This is a nontrivial task for application developers who are not interested in parsing for its own sake, and it can be time-consuming even for experienced researchers. In this paper we present MaltOptimizer, a tool developed to automatically explore parameters and features for MaltParser, a transition-based dependency parsing system that can be used to train parser's given treebank data. MaltParser provides a wide range of parameters for optimization, including nine different parsing algorithms, an expressive feature specification language that can be used to define arbitrarily rich feature models, and two machine learning libraries, each with their own parameters. MaltOptimizer is an interactive system that performs parser optimization in three stages. First, it performs an analysis of the training set in order to select a suitable starting point for optimization. Second, it selects the best parsing algorithm and tunes the parameters of this algorithm. Finally, it performs feature selection and tunes machine learning parameters. Experiments on a wide range of data sets show that MaltOptimizer quickly produces models that consistently outperform default settings and often approach the accuracy achieved through careful manual optimization.
Today's lifestyles, tomorrow's cancers: trends in lifestyle risk factors for cancer in low- and middle-income countries.

BACKGROUND The global burden of cancer is projected to increase from 13.3 to 21.4 million incident cases between 2010 and 2030 due to demographic changes alone, dominated by a growing burden in low- and middle-income countries (LMICs). Lifestyle risk factors for cancer are also changing in these countries and may further influence this burden. DESIGN We consider examples of changes already occurring in population-level distributions of tobacco and alcohol consumption, body weight, and reproductive lives of women to gauge the magnitude of their projected impact on cancer incidence in future decades. RESULTS Trends in lifestyle factors vary greatly between settings and by sex. Some common trends point to considerable increases in cancers of the (i) lung in men due to tobacco smoking; (ii) upper aerodigestive tract (UADT) due to increasing tobacco and alcohol consumption, worse in men; (iii) colon from increasing body mass index, and alcohol and tobacco consumption; and (iv) in women, breast due particularly to consistent international trends of younger age at menarche, smaller family size, and, at postmenopausal ages, increasing body weight. CONCLUSIONS In many LMICs, the future cancer burden will be worsened by changing lifestyles. Affected common cancer sites likely to experience the largest increases are lung, colon, UADT, and breast.