Miracle-dz/UniFAR-FTUs huggingface dataset:
Multispecies coalescent analysis confirms standing phylogenetic instability in Hexapoda

The multispecies coalescent (MSC) has been increasingly used in phylogenomic analyses due to the accommodation of gene tree topological heterogeneity by taking into account population‐level processes, such as incomplete lineage sorting. In this sense, the phylogeny of insect species, which are characterized by their large effective population sizes, is suitable for a coalescent‐based analysis. Furthermore, studies so far recovered short internal branches at early divergences of the insect tree of life, indicating fast evolutionary radiations that increase the probability of incomplete lineage sorting in deep time. Here, we investigated the performance of the MSC for a phylogenomic data set of hexapods compiled by Misof et al. (2014, Science 346:763). Our analysis recovered the monophyly of most insect orders, and major phylogenetic relationships were in agreement with current insect systematics. We identified, however, some evolutionary associations that were consistently problematic. Most noticeable, Hexapod monophyly was disrupted by the sister group relationship between the remiped crustacean and Insecta. Additionally, the interordinal relationships within Polyneoptera and Neuropteroidea were found to be phylogenetically unstable. We show that these controversial phylogenetic arrangements were also poorly supported by previous analyses, and therefore, we evaluated their robustness to stochastic errors from sampling sites and terminals, confirming standing problems in hexapod phylogeny in the genomics age.
Is psychotherapy effective? A re-analysis of treatments for depression

Abstract Aims The aim of this study was to reanalyse the data from Cuijpers et al.'s (2018) meta-analysis, to examine Eysenck's claim that psychotherapy is not effective. Cuijpers et al., after correcting for bias, concluded that the effect of psychotherapy for depression was small (standardised mean difference, SMD, between 0.20 and 0.30), providing evidence that psychotherapy is not as effective as generally accepted. Methods The data for this study were the effect sizes included in Cuijpers et al. (2018). We removed outliers from the data set of effects, corrected for publication bias and segregated psychotherapy from other interventions. In our study, we considered wait-list (WL) controls as the most appropriate estimate of the natural history of depression without intervention. Results The SMD for all interventions and for psychotherapy compared to WL controls was approximately 0.70, a value consistent with past estimates of the effectiveness of psychotherapy. Psychotherapy was also more effective than care-as-usual (SMD = 0.31) and other control groups (SMD = 0.43). Conclusions The re-analysis reveals that psychotherapy for adult patients diagnosed with depression is effective.
Antiretroviral resistance among HIV-1 patients on first-line therapy attending a comprehensive care clinic in Kenyatta National Hospital, Kenya: a retrospective analysis

Introduction Antiretroviral therapy plays a major role in reducing the impact of Human Immunodeficiency Virus/Acquired Immune Disease Syndrome, especially in resource-limited settings. However, without proper infrastructure, it has resulted in emergence of drug resistance mutations in infected populations. To determine drug resistance mutations among patients attending a comprehensive care facility in Nairobi, 65 blood samples were successfully sequenced. Methods Whole blood samples were also tested for CD4+T-cell count and plasma HIV-1 RNA Viral load. Drug-resistance testing targeting the HIV-1 RT gene was determined. Patients were on first line ART that consisted of two NRTIs, and one NNRTI. Results Females were younger (mean 42) than males (mean 45) and lower median CD4+ counts (139 cells/μl) than males (152 cells/μl). The prevalence of drug resistance mutations (any major mutation) in this population was 23.1% (15/65). Major NRTI mutations were detected in 11 patient samples, which included M184V (n = 6), M41L (n=3), D67N (n=2), K219Q (n=3) and T215F (n=2). Major NNRTI mutations were detected in 14 patient samples. They included K103N (n = 10), G190A (n = 1), Y181C (n = 1) and Y188L (n = 1). Conclusion Presence of major mutations in this study calls for proper laboratory infrastructure to monitor treatment as well as regular appraisals of available regimens.
Study of the Impact of PHY and MAC Parameters in 3GPP C-V2V Mode 4

In the latest years, 3GPP has added short-range cellular-V2X (C-V2X) to the features of LTE and 5G to allow vehicles, roadside devices, and vulnerable users to directly exchange information using the same chipset as for classical long-range connections. C-V2X is based on the use of advanced physical layer techniques and orthogonal resources, and one of the main aspects affecting its performance is the way resources are allocated. Allocations can be either managed by the network or in a distributed way, directly by the nodes. The latter case, called Mode 4, is required in those situations where the network cannot be involved in the scheduling process, for example, due to a lack of coverage, but could also be adopted in order to reduce the processing burden of eNodeB. An algorithm, defined in the standards, makes nodes sense the medium, and identify the best time-frequency combination to allocate their messages. Focusing on C-V2X Mode 4, in this paper, we analyze the parameters of the algorithm designed by 3GPP and their impact on the system performance. Through simulations in different large-scale scenarios, we show that modifying some parameters have negligible effect, that the proper choice of others can indeed improve the quality of service, and that a group of parameters allows to tradeoff reliability with update delay. The provided results can also be exploited to guide the future work.
Judging a Book by its Description : Analyzing Gender Stereotypes in the Man Bookers Prize Winning Fiction

The presence of gender stereotypes in many aspects of society is a well-known phenomenon. In this paper, we focus on studying and quantifying such stereotypes and bias in the Man Bookers Prize winning fiction. We consider 275 books shortlisted for Man Bookers Prize between 1969 and 2017. The gender bias is analyzed by semantic modeling of book descriptions on Goodreads. This reveals the pervasiveness of gender bias and stereotype in the books on different features like occupation, introductions and actions associated to the characters in the book.
Who and How Should Prescribe and Conduct Exercise Programs for Pregnant Women? Recommendations Based on the European Educational Standards for Pregnancy and Postnatal Exercise Specialists

Abstract Extensive scientific evidence has shown that prenatal physical activity is a prerequisite for the proper course of pregnancy, fetal development, labour and delivery, and the postpartum period. In 2015, the American College of Obstetricians and Gynecologists published a breakthrough statement that inactivity in pregnancy is risky behaviour. The aim of this paper is to provide answers to the questions concerning who and how should prescribe and conduct exercise programs for pregnant women. Close cooperation between the woman, her obstetric care provider and exercise specialists is necessary to promote exercise in pregnancy. Obstetricians should carefully evaluate women with medical or obstetric complications before starting the exercises. They should also provide women with general information about the benefits of prenatal exercise and the risks of physical inactivity. On the other hand, the design and implementation of the exercise program are tasks for exercise professionals, preferably ones qualified according to the European educational standards for Pregnancy and Postnatal Exercise Specialists. Both empirical observation and scientific research confirm the need to educate obstetric care providers, exercise professionals, and both pregnant women and their families about prenatal physical activity. They all require constantly updated information on how to use exercise to stimulate the positive development of pregnancy, ensure the greatest possible well-being for the future mother, and prepare her for childbirth and the postpartum period. These educational activities should be evidence-based. They must not perpetuate unfounded opinions, often harmful to the woman’s health, on what she should or should not do while exercising during pregnancy. The presented update underlines some pertinent recommendations in this area.
TAFRO Syndrome – A Specific Subtype of Castleman's Disease in China

To the Editor: Castleman’s disease (CD) describes a group of lymphoproliferative disorders with heterogeneous clinical manifestations. [1] CD comprises at least two distinct diseases (unicentric CD [UCD] and multicentric CD [MCD]) with respective clinical features and prognoses: UCD often involves one lymph node area with favorable outcome; MCD is a systemic disease with relatively poor prognosis which involves multiple lymph node areas with constitutional symptoms (e.g. fever, night sweats, and weakness) and organ dysfunction. According to pathogenesis, MCD can be further divided into different subtypes based on the status of human herpes virus‐8 (HHV‐8) infection. Of the HHV‐8 negative MCD (also named as idiopathic MCD [iMCD]), there is a peculiar subtype named TAFRO syndrome which constitutes a constellation of unique clinical manifestations (T: Thrombocytopenia, A: Anasarca, F: myelofibrosis, R: Renal dysfunction, and O: Organomegaly). This syndrome, first reported in Japan,[2] has been reported mostly in the Japanese population and occasionally in Caucasians. There is no such case reported in China. We herein report the first patient with TAFRO syndrome in China with confirmed myelofibrosis.
A literature review and meta‐analysis of outcomes in microsurgical reconstruction using vasopressors

There is a hesitancy to utilize vasopressors in microsurgical reconstruction due to fear of vessel spasm and subsequent flap compromise. Although there are large literature reviews analyzing vasopressor usage in head and neck reconstruction, this has not been extrapolated to all regions of the body. The goal of this study was to perform a meta‐analysis examining vasopressor usage and risk of complications in microsurgical reconstruction for all recipient sites.
Myoepithelial cells are a dynamic barrier to epithelial dissemination

The mammary epithelium is composed of an inner luminal and surrounding myoepithelial cell layer. The presence of cancer cells beyond the myoepithelium defines invasive breast cancer, yet the role of the myoepithelium during invasion remains unclear. We developed a 3D organotypic culture assay to model this process through lineage-specific expression of the prometastatic transcription factor Twist1. We sought to distinguish the functional role of the myoepithelium in regulating invasion and local dissemination. Myoepithelial-specific Twist1 expression induced cell-autonomous myoepithelial cell escape. Remarkably, luminal-specific Twist1 expression was rarely sufficient for escape. Time-lapse microscopy revealed that myoepithelial cells collectively restrain and reinternalize invading Twist1+ luminal cells. Barrier function correlated with myoepithelial abundance and required the expression of &agr;-smooth muscle actin and P-cadherin. We next demonstrated that myoepithelial cells can restrain and recapture invasive cancer cells. Our data establish the concept of the myoepithelium as a dynamic barrier to luminal dissemination and implicate both smooth muscle contractility and intercellular adhesion in barrier function.
Explicit Retrofitting of Distributional Word Vectors

Semantic specialization of distributional word vectors, referred to as retrofitting, is a process of fine-tuning word vectors using external lexical knowledge in order to better embed some semantic relation. Existing retrofitting models integrate linguistic constraints directly into learning objectives and, consequently, specialize only the vectors of words from the constraints. In this work, in contrast, we transform external lexico-semantic relations into training examples which we use to learn an explicit retrofitting model (ER). The ER model allows us to learn a global specialization function and specialize the vectors of words unobserved in the training data as well. We report large gains over original distributional vector spaces in (1) intrinsic word similarity evaluation and on (2) two downstream tasks − lexical simplification and dialog state tracking. Finally, we also successfully specialize vector spaces of new languages (i.e., unseen in the training data) by coupling ER with shared multilingual distributional vector spaces.
Connecting Supervised and Unsupervised Sentence Embeddings

Representing sentences as numerical vectors while capturing their semantic context is an important and useful intermediate step in natural language processing. Representations that are both general and discriminative can serve as a tool for tackling various NLP tasks. While common sentence representation methods are unsupervised in nature, recently, an approach for learning universal sentence representation in a supervised setting was presented in (Conneau et al.,2017). We argue that although promising results were obtained, an improvement can be reached by adding various unsupervised constraints that are motivated by auto-encoders and by language models. We show that by adding such constraints, superior sentence embeddings can be achieved. We compare our method with the original implementation and show improvements in several tasks.
Large Scale Online Brand Networks to Study Brand Effects

Mining consumer perceptions of brands has been a dominant research area in marketing. The marketing literature provides a well-developed rationale for proposing brands as intangible assets that significantly contribute to firm performance. Con-sumer-brand perceptions typically collected through surveys or focus groups, require recruitment and interaction with a large set of participants; leading to cost, feasibility and validity issues. The advent of web 2.0 opens the door to the application of a wide range of data-centric approaches which can automate and scale beyond the traditional methods used in marketing science. We address this knowledge area by exploiting social media based brand communities to generate a brand network, incorporating consumer perceptions across a broad ecosystem of brands. A brand network is one in which individual nodes represent brands, and a weighted link be-tween two nodes represents the strength of consumer co-in-terest in these two brands. The implicit brand-brand network is used to examine two branding effects, in particular, positioning and performance. We use hard and soft clustering algorithms, Walktrap Clustering and Stochastic Block Model-ling respectively, to identify subsets of closely related brands; and this provides the basis for examining brand positioning. We also examine how a focal brand ’ s location in the brand network relates to performance, measured in terms of relative market share. For this, a hierarchical regression analysis is conducted between brand network variables and brand performance. While the size of brand community on Twitter does relate to brand performance, the brand network variables like degree, eigenvector centrality and between-industry links help improve the model fit considerably.
Identifying Risk Factors For Heart Disease in Electronic Medical Records: A Deep Learning Approach

Automatic identification of heart disease risk factors in clinical narratives can expedite disease progression modelling and support clinical decisions. Existing practical solutions for cardiovascular risk detection are mostly hybrid systems entailing the integration of knowledge-driven and data-driven methods, relying on dictionaries, rules and machine learning methods that require a substantial amount of human effort. This paper proposes a comparative analysis on the applicability of deep learning, a re-emerged data-driven technique, in the context of clinical text classification. Various deep learning architectures were devised and evaluated for extracting heart disease risk factors from clinical documents. The data provided for the 2014 i2b2/UTHealth shared task focusing on identifying risk factors for heart disease was used for system development and evaluation. Results have shown that a relatively simple deep learning model can achieve a high micro-averaged F-measure of 0.9081, which is comparable to the best systems from the shared task. This is highly encouraging given the simplicity of the deep learning approach compared to the heavily feature-engineered hybrid approaches that were required to achieve state-of-the-art performances.
Learning to combine multiple string similarity metrics for effective toponym matching

ABSTRACT Several tasks related to geographical information retrieval and to the geographical information sciences involve toponym matching, that is, the problem of matching place names that share a common referent. In this article, we present the results of a wide-ranging evaluation on the performance of different string similarity metrics over the toponym matching task. We also report on experiments involving the usage of supervised machine learning for combining multiple similarity metrics, which has the natural advantage of avoiding the manual tuning of similarity thresholds. Experiments with a very large dataset show that the performance differences for the individual similarity metrics are relatively small, and that carefully tuning the similarity threshold is important for achieving good results. The methods based on supervised machine learning, particularly when considering ensembles of decision trees, can achieve good results on this task, significantly outperforming the individual similarity metrics.
Tofacitinib for refractory interstitial lung diseases in anti-melanoma differentiation-associated 5 gene antibody-positive dermatomyositis

Objective We aimed to determine the outcome of combination therapy with tofacitinib (TOF) in a case series of refractory rapidly progressive interstitial lung disease (ILD) associated with anti-melanoma differentiation-associated 5 gene (MDA5) antibody-positive (Ab+) DM. Patients who had poor prognostic factors and failed to respond to immunosuppressive therapy were selected for TOF treatment. Methods Five patients with anti-MDA5 Ab+ DM-ILD who failed to respond to triple therapy with high dose glucocorticoids, CSA and CYC were given additional TOF (10 mg/day). To identify the poor prognostic factors, data from 15 consecutive patients (seven survived and eight died) with anti-MDA5 Ab+ DM-ILD before induction of TOF were analysed. Results Three poor prognostic factors were identified: serum ferritin level >1000 ng/ml before therapy; ground-glass opacities in all six lung fields before therapy; and worsening of pulmonary infiltrates during therapy. All six patients who had all of the three factors and received triple therapy died before TOF therapy. There were five patients who had all of the three prognostic factors and failed to respond to triple therapy, but were able to receive the combination therapy with TOF; among them, three survived and two died. The survival rate of patients who received TOF was significantly better than that of the historical controls with immunosuppressive therapy before TOF. The patients who received TOF experienced complicated adverse events, particularly viral infection. Conclusion Combination therapy with TOF might have the potential to control refractory anti-MDA5 Ab+ DM-ILD.
A Review of Dynamic Modeling Approaches and Their Application in Computational Strain Optimization for Metabolic Engineering

Mathematical modeling is a key process to describe the behavior of biological networks. One of the most difficult challenges is to build models that allow quantitative predictions of the cells' states along time. Recently, this issue started to be tackled through novel in silico approaches, such as the reconstruction of dynamic models, the use of phenotype prediction methods, and pathway design via efficient strain optimization algorithms. The use of dynamic models, which include detailed kinetic information of the biological systems, potentially increases the scope of the applications and the accuracy of the phenotype predictions. New efforts in metabolic engineering aim at bridging the gap between this approach and other different paradigms of mathematical modeling, as constraint-based approaches. These strategies take advantage of the best features of each method, and deal with the most remarkable limitation—the lack of available experimental information—which affects the accuracy and feasibility of solutions. Parameter estimation helps to solve this problem, but adding more computational cost to the overall process. Moreover, the existing approaches include limitations such as their scalability, flexibility, convergence time of the simulations, among others. The aim is to establish a trade-off between the size of the model and the level of accuracy of the solutions. In this work, we review the state of the art of dynamic modeling and related methods used for metabolic engineering applications, including approaches based on hybrid modeling. We describe approaches developed to undertake issues regarding the mathematical formulation and the underlying optimization algorithms, and that address the phenotype prediction by including available kinetic rate laws of metabolic processes. Then, we discuss how these have been used and combined as the basis to build computational strain optimization methods for metabolic engineering purposes, how they lead to bi-level schemes that can be used in the industry, including a consideration of their limitations.
On the Complexity of Optimal Correlated Auctions and Reverse Auctions

We investigate the problem of finding a revenue-optimal auction with correlated bidders. We give an algorithm for the exact solution for two bidders, and for a $\frac5 3 $-approximation for many bidders, improving from $\mathcalO (n^6)$ runtime to $\mathcalO (n^3)$ for both problems by exploiting structural properties of this problem directly. We show that for correlated bidders, reverse auctions behave differently from auctions. For two bidders we discuss a constant-factor reduction in complexity. For $k \geq 3$ bidders, we show that the optimal reverse auction must sometimes buy k copies of the item.