Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanical principles of nuclear shaping and positioning

Positioning and shaping the nucleus represents a mechanical challenge for the migrating cell because of its large size and resistance to deformation. Cells shape and position the nucleus by transmitting forces from the cytoskeleton onto the nuclear surface. This force transfer can occur through specialized linkages between the nuclear envelope and the cytoskeleton. In response, the nucleus can deform and/or it can move. Nuclear movement will occur when there is a net differential in mechanical force across the nucleus, while nuclear deformation will occur when mechanical forces overcome the mechanical resistance of the various structures that comprise the nucleus. In this perspective, we review current literature on the sources and magnitude of cellular forces exerted on the nucleus, the nuclear envelope proteins involved in transferring cellular forces, and the contribution of different nuclear structural components to the mechanical response of the nucleus to these forces.
Trends in surgical treatment of femoral neck fractures in the elderly

ABSTRACT Objective To analyze recent demographic and medical billing trends in treatment of femoral neck fracture of American elderly patients. Methods The American College of Surgeons National Surgical Quality Improvement Program database was analyzed from 2006 to 2015, for patients aged 65 years and older, using the Current Procedural Terminology codes 27130, 27125, 27235, and 27236. Patient demographics, postoperative complications, and frequency of codes were compared and analyzed over time. Our sample had 17,122 elderly patients, in that, 70% were female, mean age of 80.1 years (standard deviation±6.6 years). Results The number of cases increased, but age, gender, body mass index, rates of diabetes and smoking did not change over time. Open reduction internal fixation was the most commonly billed code, with 9,169 patients (53.6%), followed by hemiarthroplasty with 5,861 (34.2%) patients. Combined estimated probability of morbidity was 9.8% (standard deviation±5.2%), and did not change significantly over time. Postoperative complication rates were similar between treatments. Conclusion Demographics and morbidity rates in femoral neck fractures of elderly patients did not change significantly from 2006 to 2015. Open reduction internal fixation was the most common treatment followed by hemiarthroplasty.
Fracture risk assessment in home care patients using the FRAX® tool

ABSTRACT Objective To assess the ten-year risk of hip and osteoporotic fracture in home care patients using the FRAX® tool. Methods A retrospective, cross-sectional observational study including patients aged ≥ 40 and ≤ 90 years and receiving home care from a private provider. The risk of fracture was calculated using an online calculator. High risk was defined as risk of hip fracture greater than 3% or risk of osteoporotic fracture greater than 20%. Data were expressed as absolute number (n), relative frequency (%), mean, standard deviation (±) and probability value (p). Results Eighty-three (37.7%) out of 222 patients were at high risk of fracture. Of these, 81 (36.7%) were at high risk of hip fracture, as follows: 18 patients aged 70-80 years (17 female) and 63 patients aged 80-90 years (51 female). High risk of osteoporotic fracture was limited to two female patients (0.1%) aged over 80 years. Conclusion FRAX® analysis revealed similar fracture risks in the sample and the older adult population overall. Prospective investigation of fracture rates in home care patients, identification of true risk factors and construction of a home care patient-specific clinical score are warranted.
Clustering the Learning Patterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System

A common goal of Intelligent Tutoring Systems (ITS) is to provide learning environments that adapt to the varying abilities and characteristics of users. To do this, researchers must identify the learning patterns exhibited by those interacting with the system. In the present work, we use clustering analysis to capture learning patterns in over 250 adults who used the ITS, CSAL (Center for the Study of Adult Literacy) AutoTutor , to gain reading comprehension skills. AutoTutor has conversational agentsth at teach literacy adults with low literacy skills comprehension strategies in 35 lessons. These comprehension strategies align with one or more of the following levels specified in the Graesser-McNamara theoretical framework of comprehension: word, textbase, situation model and rhetorical structure . We used the adult learners’ average response times per question and performance across lessons to cluster the students’ learning behavior. Performance was measured as the proportion of 3-alternative-response questions answered correctly. Lessons were coded on one of the four theoretical levels of comprehension. Results of the cluster analyses converged on four types of learners: proficient readers, struggling readers, conscientious readers and disengaged readers. Proficient readers were fast and accurate; struggling readers worked slowly but were not accurate; conscientious readers worked slowly and performed comparatively well; disengaged readers were fast but did not perform well. Interestingly, the behaviors of learners in different clusters varied across the four theoretical levels. Identifying types of readers can enhance the adaptivity of AutoTutor by allowing for more personalized feedback and interventions designed for particular learning behaviors.
Estimation of adjusted expected excess length‐of‐stay associated with ventilation‐acquired pneumonia in intensive care: A multistate approach accounting for time‐dependent mechanical ventilation

The expected excess length‐of‐stay is an established concept to assess the health and economic impact of nosocomial, that is, hospital‐acquired infections such as ventilation‐acquired pneumonia in intensive care. Estimation must account for the timing of infection as in a multistate perspective, because common retrospective comparisons yield inflated estimates due to time‐dependent bias. Since occurrence of ventilation‐acquired pneumonia is closely linked to ventilation status, we suggest a multistate model incorporating time‐dependent mechanical ventilation as additional states. The appeal is that the expected excess length‐of‐stay decomposes into extra days spent under ventilation and not under ventilation. This is not only highly relevant from a patient's perspective regarding quality of life, but also from an economic point of view, because ventilation is a major cost driver. The challenge is that estimation involves complex functionals of the matrix of transition probabilities, which in turn are based on the transition hazards. To address heterogeneity between patients, which is a common phenomenon in observational hospital epidemiology, we apply pseudovalue regression to adjust the ventilation‐specific quantities for baseline confounding. The performance of our proposal is assessed by simulation and the methods are illustrated on data provided by 12 French intensive care units. Preliminary results indicate that the expected excess length‐of‐stay associated with ventilation‐acquired pneumonia is mainly triggered by extra days spent under mechanical ventilation, and that the excess is most pronounced for intensive care patients with fewer comorbidities at baseline. We also find that such a decomposition is challenging for early times. Example code is provided.
82 Treebanks, 34 Models: Universal Dependency Parsing with Multi-Treebank Models

We present the Uppsala system for the CoNLL 2018 Shared Task on universal dependency parsing. Our system is a pipeline consisting of three components: the first performs joint word and sentence segmentation; the second predicts part-of-speech tags and morphological features; the third predicts dependency trees from words and tags. Instead of training a single parsing model for each treebank, we trained models with multiple treebanks for one language or closely related languages, greatly reducing the number of models. On the official test run, we ranked 7th of 27 teams for the LAS and MLAS metrics. Our system obtained the best scores overall for word segmentation, universal POS tagging, and morphological features.
Deep Neural Net with Attention for Multi-channel Multi-touch Attribution

Customers are usually exposed to online digital advertisement channels, such as email marketing, display advertising, paid search engine marketing, along their way to purchase or subscribe products( aka. conversion). The marketers track all the customer journey data and try to measure the effectiveness of each advertising channel. The inference about the influence of each channel plays an important role in budget allocation and inventory pricing decisions. Several simplistic rule-based strategies and data-driven algorithmic strategies have been widely used in marketing field, but they do not address the issues, such as channel interaction, time dependency, user characteristics. In this paper, we propose a novel attribution algorithm based on deep learning to assess the impact of each advertising channel. We present Deep Neural Net With Attention multi-touch attribution model (DNAMTA) model in a supervised learning fashion of predicting if a series of events leads to conversion, and it leads us to have a deep understanding of the dynamic interaction effects between media channels. DNAMTA also incorporates user-context information, such as user demographics and behavior, as control variables to reduce the estimation biases of media effects. We used computational experiment of large real world marketing dataset to demonstrate that our proposed model is superior to existing methods in both conversion prediction and media channel influence evaluation.
Interacting Timescales in Perspective-Taking

Through theoretical discussion, literature review, and a computational model, this paper poses a challenge to the notion that perspective-taking involves a fixed architecture in which particular processes have priority. For example, some research suggests that egocentric perspectives can arise more quickly, with other perspectives (such as of task partners) emerging only secondarily. This theoretical dichotomy–between fast egocentric and slow other-centric processes–is challenged here. We propose a general view of perspective-taking as an emergent phenomenon governed by the interplay among cognitive mechanisms that accumulate information at different timescales. We first describe the pervasive relevance of perspective-taking to cognitive science. A dynamical systems model is then introduced that explicitly formulates the timescale interaction proposed. This model illustrates that, rather than having a rigid time course, perspective-taking can be fast or slow depending on factors such as task context. Implications are discussed, with ideas for future empirical research.
A two‐wave panel study on teachers' emotions and emotional‐labour strategies

The aim of this study was to examine the reciprocal relations between discrete emotions that teachers typically experience while teaching and interacting with students (i.e., joy, love, anger, and hopelessness) and emotional labour strategies (i.e., deep acting and two forms of surface acting-hiding feelings and faking emotions) over time. To address these research aims, two-wave panel design and cross-lagged structural equation modeling were implemented. An initial sample of 2,022 teachers filled in questionnaires with a time lag of 6 months. The results showed that teachers' emotions and emotional labour are related to each other over time. Love positively predicted deep acting and anger positively predicted hiding feelings and faking emotion over time. The opposite direction of association was also established-deep acting positively predicted joy, whereas hiding feelings positively predicted hopelessness. The results highlight the importance of taking a discrete approach to emotions and triadic approach to emotional labour when examining their reciprocal relations and may have important practical implications in promoting teachers' emotional well-being.
HyperGCN: Hypergraph Convolutional Networks for Semi-Supervised Classification

Graph-based semi-supervised learning (SSL) is an important learning problem where the goal is to assign labels to initially unlabeled nodes in a graph. Graph Convolutional Networks (GCNs) have recently been shown to be effective for graph-based SSL problems. GCNs inherently assume existence of pairwise relationships in the graph-structured data. However, in many real-world problems, relationships go beyond pairwise connections and hence are more complex. Hypergraphs provide a natural modeling tool to capture such complex relationships. In this work, we explore the use of GCNs for hypergraph-based SSL. In particular, we propose HyperGCN, an SSL method which uses a layer-wise propagation rule for convolutional neural networks operating directly on hypergraphs. To the best of our knowledge, this is the first principled adaptation of GCNs to hypergraphs. HyperGCN is able to encode both the hypergraph structure and hypernode features in an effective manner. Through detailed experimentation, we demonstrate HyperGCN's effectiveness at hypergraph-based SSL.
High-through identification of T cell-specific phage-exposed mimotopes using PBMCs from tegumentary leishmaniasis patients and their use as vaccine candidates against Leishmania amazonensis infection

Abstract In the current study, phage-exposed mimotopes as targets against tegumentary leishmaniasis (TL) were selected by means of bio-panning cycles employing sera of TL patients and healthy subjects, besides the immune stimulation of peripheral blood mononuclear cells (PBMCs) collected from untreated and treated TL patients and healthy subjects. The clones were evaluated regarding their specific interferon-γ (IFN-γ) and interleukin-4 (IL-4) production in the in vitro cultures, and selectivity and specificity values were calculated, and those presenting the best results were selected for the in vivo experiments. Two clones, namely A4 and A8, were identified and used in immunization protocols from BALB/c mice to protect against Leishmania amazonensis infection. Results showed a polarized Th1 response generated after vaccination, being based on significantly higher levels of IFN-γ, IL-2, IL-12, tumour necrosis factor-α (TNF-α) and granulocyte-macrophage colony-stimulating factor (GM-CSF); which were associated with lower production of specific IL-4, IL-10 and immunoglobulin G1 (IgG1) antibodies. Vaccinated mice presented significant reductions in the parasite load in the infected tissue and distinct organs, when compared with controls. In conclusion, we presented a strategy to identify new mimotopes able to induce Th1 response in PBMCs from TL patients and healthy subjects, and that were successfully used to protect against L. amazonensis infection.
Fall Risk Program for Oncology Inpatients: Addition of the “Traffic Light” Fall Risk Assessment Tool

Background: The incidence of falls on inpatient oncology units indicated the need for quality improvement. This project aimed to reduce falls by implementing a fall reduction plan including the “Traffic Light” Fall Risk Assessment Tool (TL-FRAT). Local Problem: We retrospectively reviewed the oncology unit fall data from January 2013 to September 2014 and found that the average fall incidence was high. Methods: The project used a program evaluation design, and the process was guided by Kotter's 8-step change model. Interventions: We implemented the TL-FRAT to classify oncology inpatients at a high risk of falling in advance. Results: The average fall incidence and falls with injury during the project were reduced. Conclusions: Adding the TL-FRAT to the fall protocol on the units effectively reduced the incidence of falls related to impaired mobility. The TL-FRAT can improve nurses' sensitivity to falls related to impaired mobility and, subsequently, guide corresponding fall prevention strategies.
Foal meat volatile compounds: effect of vacuum ageing on semimembranosus muscle.

BACKGROUND Cooked meat flavor arises through a combination of thermally generated aroma volatile and nonvolatile compounds in a matrix of muscle fiber, connective tissue, and fat. Ageing could affect meat odor, taste, and flavor by the development of odor compounds in the raw product. The aim of the work is to assess the ageing effect on the volatile compounds profile by solid-phase microextraction and gas chromatography-mass spectrometry analysis of foal meat vacuum packaged for storage at 4 °C for a period of 14 days. RESULTS Only pentane and 3,7-dimethylnonane were significantly affected by ageing time (P < 0.01). Octanal and nonanal presented an increasing trend with higher values at 14 ageing days compared with six ageing days (P < 0.01). CONCLUSIONS Ageing poorly affects the volatile compounds production of foal meat. Probably, 14 days is considered to be a short maturation time in vacuum packaging for foal meat. © 2018 Society of Chemical Industry.
Adipoinductive effect of extracellular matrix involves cytoskeleton changes and SIRT1 activity in adipose tissue stem/stromal cells

Abstract Adipose tissue (AT) homeostasis and expansion are dependent on complex crosstalk between resident adipose stromal/stem cells (ASCs) and AT extracellular matrix (ECM). Although adipose tissue ECM (atECM) is one of the key players in the stem cell niche, data on bidirectional interaction of ASCs and atECM are still scarce. Here, we investigated how atECM guides ASCs’ differentiation. atECM altered shape and cytoskeleton organization of ASCs without changing their proliferation, β-galactosidase activity and adhesion. Cytoskeleton modifications occurred due to fostered parallel organization of F-actin and elevated expression of Vimentin in ASCs. After seven-day cultivation, atECM impaired osteogenesis of ASCs, simultaneously decreasing expression of Runx2. In addition, atECM accelerated early adipogenesis concomitantly with altered Vimentin organization in ASCs, slightly increasing PPARγ, while elevated Adiponectin and Vimentin mRNA expression. Early adipogenesis triggered by atECM was followed by upregulated mitochondrial activity and Sirtuin 1 (SIRT1) expression in ASCs. Proadipogenic events induced by atECM were mediated by SIRT1, indicating the supportive role of atECM in adipogenesis-related metabolic state of ASCs. These results provide a closer look at the effects of atECM on ASC physiology and may support the advancement of engineering design in soft tissue reconstruction and fundamental research of AT.
Framing genetically modified mosquitoes in the online news and Twitter: Intermedia frame setting in the issue-attention cycle

We investigate how the online news and Twitter framed the discussion about genetically modified mosquitoes, and the interplay between the two media platforms. The study is grounded in the theoretical frameworks of intermedia agenda setting, framing, and the issue-attention cycle and combines methods of manual and computational content analysis, and time series analysis. The findings show that the Twitter discussion was more benefit-oriented, while the news coverage was more balanced. Initially, Twitter played a leading role in framing the discussion about genetically modified mosquitoes. When the public learned about the issue, online news gained momentum and led the Twitter publics to discuss the risks of genetically modified mosquitoes. Based on the findings, we argue that the intermedia frame setting may change its direction over time, and different media outlets may be influential in leading different aspects of the conversation.
VOS: a Method for Variational Oversampling of Imbalanced Data

Class imbalanced datasets are common in real-world applications that range from credit card fraud detection to rare disease diagnostics. Several popular classification algorithms assume that classes are approximately balanced, and hence build the accompanying objective function to maximize an overall accuracy rate. In these situations, optimizing the overall accuracy will lead to highly skewed predictions towards the majority class. Moreover, the negative business impact resulting from false positives (positive samples incorrectly classified as negative) can be detrimental. Many methods have been proposed to address the class imbalance problem, including methods such as over-sampling, under-sampling and cost-sensitive methods. In this paper, we consider the over-sampling method, where the aim is to augment the original dataset with synthetically created observations of the minority classes. In particular, inspired by the recent advances in generative modelling techniques (e.g., Variational Inference and Generative Adversarial Networks), we introduce a new oversampling technique based on variational autoencoders. Our experiments show that the new method is superior in augmenting datasets for downstream classification tasks when compared to traditional oversampling methods.
Rotational Thromboelastometry for Assessing Bleeding Complications and Factor XIII Deficiency in Cardiac Surgery Patients

We aimed to detect alterations and deficits in hemostasis during cardiac surgery with cardiopulmonary bypass (CPB) using point-of-care-supported coagulation analysis (rotational thromboelastometry, impedance aggregometry), in addition to single factor assays for the measurement of fibrinogen (FI) and factor XIII (FXIII) levels. Forty-one patients scheduled for elective cardiac surgery with CPB were enrolled in this observational study. Perioperative measurement (pre-, postheparin, 30-minutes before the end of bypass, 1-hourpostoperatively) of standard laboratory variables, additional rotational thromboelastometry (ROTEM; International GmbH, Munich, Germany), Multiplate analysis (Roche, Switzerland), and an assay of FXIII activity were performed as well as the collection of epidemiological data and blood loss. The FI and FXIII levels as well as the measured ROTEM and Multiplate parameters correlated weakly with the blood loss. Clotting time and maximum clot firmness (MCF) of the intrinsically activated ROTEM showed a good correlation (rCT-INTEM = 0.378; P < .05, rMCF-INTEM = 0.305; P < .05) with postoperative drainage loss, suggesting a dependence of blood loss on the initial intrinsic activity. Additionally, perioperative FI or FIBTEM levels and the FXIII levels correlated with each other. Intrinsically activated ROTEM showed a good correlation with postoperative drainage loss, thus suggesting a dependence of blood loss on the initial intrinsic activity and therefore facilitating clinicians to assess postoperative bleeding complications. Based on the FI level or the MCFFIBTEM measured by ROTEM, it may also be possible to assess the FXIII concentration. Especially in chronically ill and massive bleeding cardiac surgery patients with significantly decreased FXIII levels, the knowledge of FXIII deficiency may help clinicians to treat coagulation disorders more adequately.