LLukas22/scidocs huggingface dataset:
Hyponatremia as predictor of worse outcome in real world patients admitted with acute heart failure.

BACKGROUND
Our aim was to determine if hyponatremia, defined as serum sodium level < 135 mmol/L, is a predictor of worse outcome in a cohort of real-world patients with heartfailure (HF).


METHODS
We used data of the National registry of HF (RICA) from Spain, an ongoing multicenter, prospective cohort study. The patients were assigned to two groups regarding sodium levels. Primary end-point was first all-cause readmission, or death by any cause. Secondary end-points were the number of days hospitalized, and the presence of complications.


RESULTS
We identified 973 patients, 147 (15.11%) with hyponatremia. The median age of patients enrolled was 77.25 ± 8.79 years-old, the global comorbidity measured by Charlson comorbidity index (CCI) was upper 3 points and preserved ejection fraction was present in67.1% of them. Clinical complications during admission were significantly higher in the patients with hyponatremia (35.41%, p < 0.001) and this remained as significant predictor after logistic regression adjustment (OR 1.08, p < 0.01). Also mortality and readmissions were more frequent in patients with hyponatremia (20.69% and 22.41%, respectively) but after Cox regression adjustment hyponatremia in our cohort was not associated with increase in 90-day all-cause mortality and readmissions, and only CCI remained significant for primaryend-point (HR 1.08, p < 0.001).


CONCLUSIONS
Hyponatremia is an independent predictor of complications during hospitalization in our real-world cohort, but was not associated with 90 days mortality or readmissions. Global comorbidity, however, played an important role, and could influence the mortality and readmissions of our patients.
Prediction of Employee Turnover in Organizations using Machine Learning Algorithms A case for Extreme Gradient Boosting

Employee turnover has been identified as a key issue for organizations because of its adverse impact on work place productivity and long term growth strategies. To solve this problem, organizations use machine learning techniques to predict employee turnover. Accurate predictions enable organizations to take action for retention or succession planning of employees. However, the data for this modeling problem comes from HR Information Systems (HRIS); these are typically under-funded compared to the Information Systems of other domains in the organization which are directly related to its priorities. This leads to the prevalence of noise in the data that renders predictive models prone to over-fitting and hence inaccurate. This is the key challenge that is the focus of this paper, and one that has not been addressed historically. The novel contribution of this paper is to explore the application of Extreme Gradient Boosting (XGBoost) technique which is more robust because of its regularization formulation. Data from the HRIS of a global retailer is used to compare XGBoost against six historically used supervised classifiers and demonstrate its significantly higher accuracy for predicting employee turnover. Keywords—turnover prediction; machine learning; extreme gradient boosting; supervised classification; regularization
An Investigation of the Interrelationships between Motivation, Engagement, and Complex Problem Solving in Game-based Learning

Digital game-based learning, especially massively multiplayer online games, has been touted for its potential to promote student motivation and complex problem-solving competency development. However, current evidence is limited to anecdotal studies. The purpose of this empirical investigation is to examine the complex interplay between learners’ motivation, engagement, and complex problem-solving outcomes during game-based learning. A theoretical model is offered that explicates the dynamic interrelationships among learners’ problem representation, motivation (i.e., interest, competence, autonomy, relatedness, self-determination, and selfefficacy), and engagement. Findings of this study suggest that learners’ motivation determine their engagement during gameplay, which in turn determines their development of complex problem-solving competencies. Findings also suggest that learner’s motivation, engagement, and problem-solving performance are greatly impacted by the nature and the design of game tasks. The implications of this study are discussed in detail for designing effective game-based learning environments to facilitate learner engagement and complex problemsolving competencies.
Combinational Method for Face Recognition: Wavelet, PCA and ANN

This work presents a method to increase the face recognition accuracy using a combination of Wavelet, PCA, and Neural Networks. Preprocessing, feature extraction and classification rules are three crucial issues for face recognition. This paper presents a hybrid approach to employ these issues. For preprocessing and feature extraction steps, we apply a combination of wavelet transform and PCA. During the classification stage, the Neural Network (MLP) is explored to achieve a robust decision in presence of wide facial variations. The computational load of the proposed method is greatly reduced as comparing with the original PCA based method on the Yale and ORL face databases. Moreover, the accuracy of the proposed method is improved.
Morpheme-based feature-rich language models using Deep Neural Networks for LVCSR of Egyptian Arabic

Egyptian Arabic (EA) is a colloquial version of Arabic. It is a low-resource morphologically rich language that causes problems in Large Vocabulary Continuous Speech Recognition (LVCSR). Building LMs on morpheme level is considered a better choice to achieve higher lexical coverage and better LM probabilities. Another approach is to utilize information from additional features such as morphological tags. On the other hand, LMs based on Neural Networks (NNs) with a single hidden layer have shown superiority over the conventional n-gram LMs. Recently, Deep Neural Networks (DNNs) with multiple hidden layers have achieved better performance in various tasks. In this paper, we explore the use of feature-rich DNN-LMs, where the inputs to the network are a mixture of words and morphemes along with their features. Significant Word Error Rate (WER) reductions are achieved compared to the traditional word-based LMs.
Defining immunological dysfunction in sepsis: A requisite tool for precision medicine.

OBJECTIVES
Immunological dysregulation is now recognised as a major pathogenic event in sepsis. Stimulation of immune response and immuno-modulation are emerging approaches for the treatment of this disease. Defining the underlying immunological alterations in sepsis is important for the design of future therapies with immuno-modulatory drugs.


METHODS
Clinical studies evaluating the immunological response in adult patients with Sepsis and published in PubMed were reviewed to identify features of immunological dysfunction. For this study we used key words related with innate and adaptive immunity.


RESULTS
Ten major features of immunological dysfunction (FID) were identified involving quantitative and qualitative alterations of [antigen presentation](FID1), [T and B lymphocytes] (FID2), [natural killer cells] (FID3), [relative increase in T regulatory cells] (FID4), [increased expression of PD-1 and PD-ligand1](FID5), [low levels of immunoglobulins](FID6), [low circulating counts of neutrophils and/or increased immature forms in non survivors](FID7), [hyper-cytokinemia] (FID8), [complement consumption] (FID9), [defective bacterial killing by neutrophil extracellular traps](FID10).


CONCLUSIONS
This review article identified ten major features associated with immunosuppression and immunological dysregulation in sepsis. Assessment of these features could help in utilizing precision medicine for the treatment of sepsis with immuno-modulatory drugs.
The Multi Centre Canadian Acellular Dermal Matrix Trial (MCCAT): study protocol for a randomized controlled trial in implant-based breast reconstruction

BACKGROUND
The two-stage tissue expander/implant (TE/I) reconstruction is currently the gold standard method of implant-based immediate breast reconstruction in North America. Recently, however, there have been numerous case series describing the use of one-stage direct to implant reconstruction with the aid of acellular dermal matrix (ADM). In order to rigorously investigate the novel application of ADM in one-stage implant reconstruction, we are currently conducting a multicentre randomized controlled trial (RCT) designed to evaluate the impact on patient satisfaction and quality of life (QOL) compared to the two-stage TE/I technique.


METHODS/DESIGNS
The MCCAT study is a multicenter Canadian ADM trial designed as a two-arm parallel superiority trial that will compare ADM-facilitated one-stage implant reconstruction compared to two-stage TE/I reconstruction following skin-sparing mastectomy (SSM) or nipple-sparing mastectomy (NSM) at 2 weeks, 6 months, and 12 months. The source population will be members of the mastectomy cohort with stage T0 to TII disease, proficient in English, over the age of 18 years, and planning to undergo SSM or NSM with immediate implant breast reconstruction. Stratified randomization will maintain a balanced distribution of important prognostic factors (study site and unilateral versus bilateral procedures). The primary outcome is patient satisfaction and QOL as measured by the validated and procedure-specific BREAST-Q. Secondary outcomes include short- and long-term complications, long-term aesthetic outcomes using five standardized photographs graded by three independent blinded observers, and a cost effectiveness analysis.


DISCUSSION
There is tremendous interest in using ADM in implant breast reconstruction, particularly in the setting of one-stage direct to implant reconstruction where it was previously not possible without the intermediary use of a temporary tissue expander (TE). This unique advantage has led many patients and surgeons alike to believe that one-stage ADM-assisted implant reconstruction should be the procedure of choice and should be offered to patients as the first-line treatment. We argue that it is crucial that this technique be scientifically evaluated in terms of patient selection, surgical technique, complications, aesthetic outcomes, cost-effectiveness, and most importantly patient-reported outcomes before it is promoted as the new gold standard in implant-based breast reconstruction.


TRIAL REGISTRATION
ClinicalTrials.gov: NCT00956384.
CRISPR/Cas9-mediated correction of human genetic disease

The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) protein 9 system (CRISPR/Cas9) provides a powerful tool for targeted genetic editing. Directed by programmable sequence-specific RNAs, this system introduces cleavage and double-stranded breaks at target sites precisely. Compared to previously developed targeted nucleases, the CRISPR/Cas9 system demonstrates several promising advantages, including simplicity, high specificity, and efficiency. Several broad genome-editing studies with the CRISPR/Cas9 system in different species in vivo and ex vivo have indicated its strong potential, raising hopes for therapeutic genome editing in clinical settings. Taking advantage of non-homologous end-joining (NHEJ) and homology directed repair (HDR)-mediated DNA repair, several studies have recently reported the use of CRISPR/Cas9 to successfully correct disease-causing alleles ranging from single base mutations to large insertions. In this review, we summarize and discuss recent preclinical studies involving the CRISPR/Cas9-mediated correction of human genetic diseases.
Effect of postural insoles on static and functional balance in children
with cerebral palsy: A randomized controlled study

BACKGROUND
Improved gait efficiency is one of the goals of therapy for children with cerebral palsy (CP). Postural insoles can allow more efficient gait by improving biomechanical alignment.


OBJECTIVE
The aim of the present study was to determine the effect of the combination of postural insoles and ankle-foot orthoses on static and functional balance in children with CP.


METHOD
A randomized, controlled, double-blind, clinical trial. After meeting legal requirements and the eligibility criteria, 20 children between four and 12 years of age were randomly allocated either to the control group (CG) (n=10) or the experimental group (EG) (n=10). The CG used placebo insoles and the EG used postural insoles. The Berg Balance Scale, Timed Up-and-Go Test, Six-Minute Walk Test, and Gross Motor Function Measure-88 were used to assess balance as well as the determination of oscillations from the center of pressure in the anteroposterior and mediolateral directions with eyes open and closed. Three evaluations were carried out: 1) immediately following placement of the insoles; 2) after three months of insole use; and 3) one month after suspending insole use.


RESULTS
The EG achieved significantly better results in comparison to the CG on the Timed Up-and-Go Test as well as body sway in the anteroposterior and mediolateral directions.


CONCLUSION
Postural insoles led to an improvement in static balance among children with cerebral palsy, as demonstrated by the reduction in body sway in the anteroposterior and mediolateral directions. Postural insole use also led to a better performance on the Timed Up-and-Go Test.
Evaluation of a resilience intervention to enhance coping strategies and protective factors and decrease symptomatology.

OBJECTIVE
In this pilot study, the authors examined the effectiveness of a 4-week resilience intervention to enhance resilience, coping strategies, and protective factors, as well as decrease symptomatology during a period of increased academic stress.


PARTICIPANTS AND METHODS
College students were randomly assigned to experimental (n = 30) and wait-list control (n = 27) groups. The experimental group received a psychoeducational intervention in 4 two-hour weekly sessions. Measures of resilience, coping strategies, protective factors, and symptomatology were administered pre- and postintervention to both groups.


RESULTS
Analyses indicated that the experimental group had significantly higher resilience scores, more effective coping strategies (i.e., higher problem solving, lower avoidant), higher scores on protective factors (i.e., positive affect, self-esteem, self-leadership), and lower scores on symptomatology (i.e., depressive symptoms, negative affect, perceived stress) postintervention than did the wait-list control group.


CONCLUSIONS
These findings indicate that this resilience program may be useful as a stress-management and stress-prevention intervention for college students.
What does your selfie say about you?

Selfies refer to self-portraits taken by oneself using a digital camera or a smartphone. They become increasingly popular in social media. However, little is known about how selfies reflect their owners’ personality traits and how people judge others’ personality from selfies. In this study, we examined the association between selfies and personality by measuring participants’ Big Five personality and coding their selfies posted on a social networking site. We found specific cues in selfies related to agreeableness, conscientiousness, neuroticism, and openness. We also examined zero-acquaintance personality judgment and found that observers had moderate to strong agreement in their ratings of Big Five personality based on selfies. However, they could only accurately predict selfie owners’ degree of openness. This study is the first to reveal personality-related cues in selfies and provide a picture-coding scheme that can be used to analyze selfies. We discussed the difference between personality expression in selfies and other types of photos, and its possible relationship with impression management of social media users. 2015 Elsevier Ltd. All rights reserved.
Effect of dependent errors in the assessment of diagnostic or screening test accuracy when the reference standard is imperfect.

When no gold standard is available to evaluate a diagnostic or screening test, as is often the case, an imperfect reference standard test must be used instead. Furthermore, the errors of the test and its reference standard may not be independent. Some authors have opined that positively dependent errors will lead to overestimation of test performance. Although positive dependence does increase agreement between the test and the reference standard, it is not clear if test accuracy will necessarily be overestimated in this situation, and the case of negatively associated test errors is even less clear. To examine this issue in more detail, we derive the apparent sensitivity, specificity, and overall accuracy of a test relative to an imperfect reference standard and the bias in these parameters. We demonstrate that either positive or negative bias can occur if the reference standard is imperfect. The type and magnitude of bias depend on several components: the disease prevalence, the true test sensitivity and specificity, the covariance between the false-negative test errors among the true disease cases, and the covariance between the false-positive test errors among the true noncases. If, for example, sensitivity and specificity are 0.8 for both the test and reference standard and the errors have a moderate positive dependence, test sensitivity is then underestimated at low prevalence but overestimated at high prevalence, while the opposite occurs for specificity. We illustrate these ideas through general numerical calculations and an empirical example of screening for breast cancer with magnetic resonance imaging and mammography.
Sentiment Analysis by Capsules

In this paper, we propose RNN-Capsule, a capsule model based on Recurrent Neural Network (RNN) for sentiment analysis. For a given problem, one capsule is built for each sentiment category e.g., ‘positive’ and ‘negative’. Each capsule has an attribute, a state, and three modules: representation module, probability module, and reconstruction module. The attribute of a capsule is the assigned sentiment category. Given an instance encoded in hidden vectors by a typical RNN, the representation module builds capsule representation by the attention mechanism. Based on capsule representation, the probability module computes the capsule’s state probability. A capsule’s state is active if its state probability is the largest among all capsules for the given instance, and inactive otherwise. On two benchmark datasets (i.e., Movie Review and Stanford Sentiment Treebank) and one proprietary dataset (i.e., Hospital Feedback), we show that RNN-Capsule achieves state-of-the-art performance on sentiment classification. More importantly, without using any linguistic knowledge, RNN-Capsule is capable of outputting words with sentiment tendencies reflecting capsules’ attributes. The words well reflect the domain specificity of the dataset. ACM Reference Format: Yequan Wang1 Aixin Sun2 Jialong Han3 Ying Liu4 Xiaoyan Zhu1. 2018. Sentiment Analysis by Capsules. InWWW 2018: The 2018 Web Conference, April 23–27, 2018, Lyon, France. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3178876.3186015
The impact of personality traits on users' information-seeking behavior

Although personality traits may influence information-seeking behavior, little is known about this topic. This study explored the impact of the Big Five personality traits on human online information seeking. For this purpose, it examined changes in eye-movement behavior in a sample of 75 participants (36 male and 39 female; age: 22–39 years; experience conducting online searches: 5–12 years) across three types of information-seeking tasks – factual, exploratory, and interpretive. The International Personality Item Pool Representation of the NEO PI-R TM (IPIP-NEO) was used to assess the participants’ personality profile. Hierarchical cluster analysis was used to categorize participants based on their personality traits. A three cluster solution was found (cluster one consists of participants who scored high in conscientiousness; cluster two consists of participants who scored high in agreeableness; and cluster three consists of participants who scored high in extraversion). Results revealed that individuals high in conscientiousness performed fastest in most information-seeking tasks, followed by those high in agreeableness and extraversion. This study has important practical implications for intelligent human – computer interfaces, personalization, and related applications. © 2016 Elsevier Ltd. All rights reserved.
Science and technology of fast ion conductors

Fundamentals.- Computer Modeling of Superionics.- Materials Systems.- Crystalline Anionic Fast Ion Conduction.- Amorphous Fast Ion Conductors.- Multiphase and Polycrystalline Fast Ion Conductors.- Characterization Techniques.- Spectroscopic Investigations of Glasses.- X-ray and Neutron Scattering Studies of Superionics.- Electrochemical Measurement Techniques.- Chemical and Electronic Stability.- Phase Stability of Crystalline Fast Ion Conductors.- Degradation of Ceramics in Alkali-Metal Environments.- Mixed Ionic-Electronic Conduction in Fast Ion Conductors and Certain Semiconductors.- Applications.- Novel Solid State Galvanic Cell Gas Sensors.- Remarks on Application of Fast Ion Conductors.- Some Future Trends in Solid State Ionics.- Contributed Papers.- Fast Ion Conduction in the Gd2 (ZrxTi1-x)2 O7 Pyrochlore System.- Plasma Sprayed Zirconia Electrolytes.- Free Lithium Ion Conduction in Lithium Borate Glasses Doped with Li2SO4.- Effects of Halide Substitutions in Potassium Haloborates.- Evidence of Boron-Oxygen Network Modifications in Alkali Borate Glasses.- Characterization of Alkali-Oxide Electrolyte Glass in Thin Films.- Glass Solid Electrolyte Thin Films.- Electronic Structure, Bonding, and Lithium Migration Effects Involving the Surface of the Mixed Conductor ss-LiAl.- Defects with Variable Charges: Influence on Chemical Diffusion and on the Evaluation of Electrochemical Experiments.- Attendees and Lecturers.
Peripartum hysterectomy in Taiwan.

OBJECTIVE
To investigate the incidence and associated risk factors for peripartum hysterectomy in singleton pregnancies.


METHODS
A retrospective cohort study of all women with singleton pregnancies admitted for delivery in 2002 taken from the National Healthcare Insurance database. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for maternal and hospital characteristics using logistic regression.


RESULTS
There were 287 peripartum hysterectomies in 214 237 singleton pregnancies (0.13%). Cesarean delivery, vaginal birth after cesarean (VBAC), and repeat cesarean delivery had higher hysterectomy rates than vaginal delivery, with adjusted ORs of 12.13 (95% CI 8.30-17.74), 5.12 (95% CI 1.19-21.92), and 3.84 (95% CI 2.52-5.86), respectively. Pregnancies complicated with placenta previa, gestational diabetes mellitus (GDM), and premature labor were associated with significantly increased risks for peripartum hysterectomy (P<0.05).


CONCLUSION
Risk factors for peripartum hysterectomy included cesarean delivery, VBAC, repeat cesarean, placenta previa, GDM, and premature labor. VBAC and repeat cesarean had a similar risk.