Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to balance efficiency and innovation for optimal adaptive expertise

Abstract It is critical for health professionals to continue to learn and this must be supported by health professions education (HPE). Adaptive expert clinicians are not only expert in their work but have the additional capacity to learn and improve in their practices. The authors review a selective aspect of learning to become an adaptive expert: the capacity to optimally balance routine approaches that maximize efficiency with innovative ones where energy and resources are used to customize actions for novel or difficult situations. Optimal transfer of learning, and hence the design of instruction, differs depending on whether the goal is efficient or innovative practice. However, the task is necessarily further complicated when the aspiration is an adaptive expert practitioner who can fluidly balance innovation with efficiency as the situation requires. Using HPE examples at both the individual and organizational level, the authors explore the instructional implications of learning to shift from efficient to innovative expert functioning, and back. They argue that the efficiency-innovation tension is likely to endure deep into the future and therefore warrants important consideration in HPE.
High frequency of valganciclovir underdosing for cytomegalovirus prophylaxis after renal transplantation

Abstract Background The correct valganciclovir dose for cytomegalovirus (CMV) prophylaxis depends on renal function estimated by the Cockcroft–Gault (CG) estimated creatinine clearance (CG-CrCl) formula. Patients with delayed or rapidly changing graft function after transplantation (tx) will need dose adjustments. Methods We performed a retrospective investigation of valganciclovir dosing in renal transplant patients receiving CMV prophylaxis between August 2003 and August 2011, and analysed valganciclovir dosing, CG-CrCl, CMV viraemia (CMV-PCR <750 copies/mL), leucopenia (<3500/µL) and neutropenia (<1500/µL) in the first year post-transplant. On Days 30 and 60 post-transplant, dosing pattern in relation to estimated creatinine clearance was analysed regarding CMV viraemia, leucopenia and neutropenia. Results Six hundred and thirty-five patients received valganciclovir prophylaxis that lasted 129 ± 68 days with a mean dose of 248 ± 152 mg/day of whom 112/635 (17.7%) developed CMV viraemia, 166/635 (26.1%) leucopenia and 48/635 (7.6%) neutropenia. CMV resistance within 1 year post-transplant was detected in three patients. Only 137/609 (22.6%) patients received the recommended dose, while n = 426 (70.3%) were underdosed and n = 43 (7.1%) were overdosed at Day 30 post-tx. Risk factors for CMV viraemia were donor positive D (+)/receptor negative R (−) status and short prophylaxis duration, but not low valganciclovir dose. Risk factors for developing leucopenia were D+/R− status and low renal function. No significant differences in dosing frequency were observed in patients developing neutropenia or not (P = 0.584). Conclusion Most patients do not receive the recommended valganciclovir dose. Despite obvious underdosing in a large proportion of patients, effective prophylaxis was maintained and it was not associated as a risk factor for CMV viraemia or leucopenia.
Texture-based Graph Model of the Lungs for Drug Resistance Detection, Tuberculosis Type Classification, and Severity Scoring: Participation in ImageCLEF 2018 Tuberculosis Task

In 2018, ImageCLEF proposed a task using CT (Computed Tomography) scans of patients with tuberculosis (TB). The task was divided into three subtasks: multi–drug resistance detection, TB type classification, and severity scoring. In this work we present a graph model of the lungs capable of characterizing TB patients with different lung problems. The graph contains a fixed number of nodes with weighted edges based on dissimilarity measures between texture descriptors computed in the nodes. This model encodes the texture distribution along the lungs, making it suitable for describing patients with different TB types. The results show the strength of the technique, leading to high results in the three subtasks.
Identification and Classification of Misogynous Tweets Using Multi-classifier Fusion

For this study, we used the Doc2Vec embedding approach for feature extraction, with the context window size of 2, minimum word frequency of 2, sampling rate of 0.001, learning rate of 0.025, minimum learning rate of 1.0E-4, 200 layers, batch size of 10000 and 40 epochs. Distributed Memory (DM) is used as the embedding learning algorithm with the negative sampling rate of 5.0. Before feature extraction, all the tweets were pre-processed by converting the characters to their lower case, removing stop words, numbers, punctuations and words that contain no more than 3 characters as well as stemming all the kept words by Snowball Stemmer. Additionally, three classifiers are trained by using SVM with a linear kernel, random forests (RF) and gradient boosted trees (GBT). In the testing stage, the same way of text pre-processing and feature extraction is applied to test instances separately, and each pair of two out of the three trained classifiers (SVM+RF, SVM+GBT and RF+GBT) are fused by combining the probabilities for each class by averaging.
Clinical significance of carcinoembryonic antigen in peritoneal fluid detected during operation in stage I–III colorectal cancer patients

Background/Aims Early diagnosis of peritoneal metastases in patients with colorectal cancer (CRC) can influence patient prognosis. The aim of this study was to identify the clinical significance of carcinoembryonic antigen (CEA) in peritoneal fluid detected during operation in stage I–III CRC patients. Methods Between April 2009 and April 2015, we reviewed medical records from a total of 60 stage I–III CRC patients who had peritoneal fluid collected during operation. Patients who had positive cytology in the assessment of peritoneal fluid were excluded. We evaluated the values of CEA in peritoneal fluid (pCEA) to predict the long-term outcomes of these patients using Kaplan-Meier curves and Cox regression models. Results The median follow-up duration was 37 months (interquartile range, 21–50 months). On receiver operating characteristic analysis, pCEA had the largest area under the curve (0.793; 95% confidence interval, 0.635–0.950; P=0.001) with an optimal cutoff value of 26.84 (sensitivity, 80.0%; specificity, 76.6%) for predicting recurrence. The recurrence rate was 8.1% in patients with low pCEA (<26.84 ng/mL, n=37), and 52.2% in patients with high pCEA (≥26.84 ng/mL, n=23). In multivariate Cox regression analysis, high pCEA (≥26.84 ng/mL) was a risk factor for poor cancer-free survival (CFS) in stage I–III patients. Conclusions In this study, we determined that high pCEA (≥26.84 ng/mL) detected during operation was helpful for the prediction of poor CFS in patients with stage I–III CRC.
UNSL's participation at eRisk 2018 Lab

In this paper we describe the participation of the LIDIC Research Group of Universidad Nacional de San Luis (UNSL) Argentina at CLEF eRisk 2018 Lab. The main goal of this Lab is considering early risk detection scenarios where the issue of getting timely predictions with a reasonable confidence level becomes critical. Two completely different approaches were used, that we will refer as flexible temporal variation of terms (FTVT) and sequential incremental classification (SIC). FTVT is a semantic representation of documents that explicitly considers the partial information that is made available in the different “chunks” to the early risk detection systems along the time. FTVT is an improvement on the TVT method [1] that allows varying the number of chunks considered in the representation according to the “level of urgency” required in the classification. SIC is a novel approach for text categorization that incrementally estimates the level of belonging of a piece of text to the different categories based on an accumulative process of evidence. In the test stage, FTVT obtained the lowest ERDE5 error in both pilot tasks and SIC achieved the highest precision for the anorexia detection task providing strong evidence that both approaches used by our team are interesting alternatives to deal with early risk detection tasks.
Clostridium difficile exosporium cysteine-rich proteins are essential for the morphogenesis of the exosporium layer, spore resistance, and affect C. difficile pathogenesis

Clostridium difficile is a Gram-positive spore-former bacterium and the leading cause of nosocomial antibiotic-associated diarrhea that can culminate in fatal colitis. During the infection, C. difficile produces metabolically dormant spores, which persist in the host and can cause recurrence of the infection. The surface of C. difficile spores seems to be the key in spore-host interactions and persistence. The proteome of the outermost exosporium layer of C. difficile spores has been determined, identifying two cysteine-rich exosporium proteins, CdeC and CdeM. In this work, we explore the contribution of both cysteine-rich proteins in exosporium integrity, spore biology and pathogenesis. Using targeted mutagenesis coupled with transmission electron microscopy we demonstrate that both cysteine rich proteins, CdeC and CdeM, are morphogenetic factors of the exosporium layer of C. difficile spores. Notably, cdeC, but not cdeM spores, exhibited defective spore coat, and were more sensitive to ethanol, heat and phagocytic cells. In a healthy colonic mucosa (mouse ileal loop assay), cdeC and cdeM spore adherence was lower than that of wild-type spores; while in a mouse model of recurrence of the disease, cdeC mutant exhibited an increased infection and persistence during recurrence. In a competitive infection mouse model, cdeC mutant had increased fitness over wild-type. Through complementation analysis with FLAG fusion of known exosporium and coat proteins, we demonstrate that CdeC and CdeM are required for the recruitment of several exosporium proteins to the surface of C. difficile spores. CdeC appears to be conserved exclusively in related Peptostreptococcaeace family members, while CdeM is unique to C. difficile. Our results sheds light on how CdeC and CdeM affect the biology of C. difficile spores and the assembly of the exosporium layer and, demonstrate that CdeC affect C. difficile pathogenesis.
Semimechanistic Population Pharmacokinetic Model to Predict the Drug–Drug Interaction Between S‐ketamine and Ticlopidine in Healthy Human Volunteers

Low‐dose oral S‐ketamine is increasingly used in chronic pain therapy, but extensive cytochrome P450 (CYP) mediated metabolism makes it prone to pharmacokinetic drug‐drug interactions (DDIs). In our study, concentration‐time data from five studies were used to develop a semimechanistic model that describes the ticlopidine‐mediated inhibition of S‐ketamine biotransformation. A mechanistic model was implemented to account for reversible and time‐dependent hepatic CYP2B6 inactivation by ticlopidine, which causes elevated S‐ketamine exposure in vivo. A pharmacokinetic model was developed with gut wall and hepatic clearances for S‐ketamine, its primary metabolite norketamine, and ticlopidine. Nonlinear mixed effects modeling approach was used (NONMEM version 7.3.0), and the final model was evaluated with visual predictive checks and the sampling‐importance‐resampling procedure. Our final model produces biologically plausible output and demonstrates that ticlopidine is a strong inhibitor of CYP2B6 mediated S‐ketamine metabolism. Simulations from our model may be used to evaluate chronic pain therapy with S‐ketamine.
Face-Inversion Effect on Disgust Evoked by a Cluster of Dots

A cluster of dots such as lotus seed pods evokes extremely strong disgust when it is placed on human and animal skins. However, few empirical studies have examined the role of the background image, such as skin, in the generation of disgust. In this study, we investigated whether the orientation of background faces influences the disgust evoked by the dot pattern. The participants were asked to evaluate disgust to an upright, inverted, or phase-scrambled face image with or without a cluster of dots on it and then complete a questionnaire measuring trypophobia proneness (Trypophobia Questionnaire). The results suggested that disgust was intensified by the background faces, especially by the upright faces. The intensification of disgust in the upright face was correlated positively with the Trypophobia Questionnaire scores. The results indicated a face-inversion effect on the disgust to the dot pattern, suggesting a significant role of the background image.
Visual Concept Selection with Textual Knowledge for Understanding Activities of Daily Living and Life Moment Retrieval

This paper presents our approach to the task of ImageCLEFlifelog 2018. Two subtasks, activities of daily living understanding (ADLT) and life moment retrieval (LMRT) are addressed. We attempt to reduce the user involvement during the retrieval stage by using natural language processing technologies. The two subtasks are conducted with dedicated pipelines, while similar methodology is shared. We first obtain visual concepts from the images with a wide range of computer vision tools and propose a concept selection method to prune the noisy concepts with word embeddings in which textual knowledge is inherent. For ADLT, the retrieved images of a given topic are sorted by time, and the frequency and duration are further calculated. For LMRT, the retrieval is based on the ranking of similarity between image concepts and user queries. In terms of the performance, our systems achieve 47.87% of percentage dissimilarity in ADLT and 39.5% of F1@10 in LMRT.
White-matter integrity in patients with systemic lupus erythematosus and memory deficits

Purpose Cognitive dysfunction is common in neuropsychiatric systemic lupus erythematosus (SLE). Memory is a commonly affected cognitive domain. Clinically, however, it is difficult to detect memory deficits. The objective of this study is to evaluate whether normal controls and SLE patients with and without memory deficit differ in terms of white-matter integrity. Methods Twenty SLE patients with memory deficit were compared to 47 SLE patients without memory deficit and 22 sex-, age-, and education-matched control individuals. Diffusion tensor imaging (DTI) was performed in a 1.5-Tesla scanner. For tract-based spatial statistics analysis, a white-matter skeleton was created. A permutation-based inference with 5000 permutations with a threshold of p < 0.05 was used to identify abnormalities in fractional anisotropy (FA). The mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD) were also projected onto the mean FA skeleton. Results Compared to controls, SLE patients with and without memory deficit had decreased FA in: bilateral anterior thalamic radiation, inferior fronto-occipital fasciculus, superior longitudinal fasciculus, uncinate fasciculus, corticospinal tract, genu, and body of the corpus callosum. SLE patients with and without memory deficit also presented increased MD and RD values compared to controls in these areas. Comparison between SLE patients with and without memory deficit did not present significant differences in DTI parameters. Conclusion DTI can detect extensive abnormalities in the normal-appearing white matter of SLE patients with and without memory deficit, compared to controls. However, there was no difference, in terms of white-matter integrity, between the groups of SLE patients.
Multi-Language Neural Network Model with Advance Preprocessor for Gender Classification over Social Media: Notebook for PAN at CLEF 2018

This paper describes approaches for the Author Proﬁling Shared Task at PAN 2018. The goal was to classify the gender of a Twitter user solely by their tweets. Paper explores a simple and efﬁcient Multi-Language model for gender classiﬁcation. The approach consists of tweet preprocessing, text representation and classiﬁcation model construction. The model achieved the best results on the English language with an accuracy of 72.79%; for the Spanish and Arabic languages the accuracy was 72.20% and 64.36%, respectively.
Neuropathy following spinal nerve injury shares features with the irritable nociceptor phenotype: A back‐translational study of oxcarbazepine

The term ‘irritable nociceptor’ was coined to describe neuropathic patients characterized by evoked hypersensitivity and preservation of primary afferent fibres. Oxcarbazepine is largely ineffectual in an overall patient population, but has clear efficacy in a subgroup with the irritable nociceptor profile. We examine whether neuropathy in rats induced by spinal nerve injury shares overlapping pharmacological sensitivity with the irritable nociceptor phenotype using drugs that target sodium channels.
The impact of foreign accent on irony interpretation

In modern multi-cultural societies, conversations between foreign speakers and native listeners have become very common. These exchanges often include the use of figurative language. The present study examines, for the first time, whether native listeners’ non-literal interpretation of discourse is influenced by indexical cues such as speaker accent. Native listeners were presented with ironic and literal Spanish stories uttered in a native or foreign accent (Spanish and British English accents, respectively). Two types of irony were considered: ironic criticism (frequently used) and ironic praise (less frequently used). Participants were asked to rate stories on their level of irony. Results showed an impact of foreign accent on natives’ non-literal interpretation. The effect was evident in the less frequent ironic constructions (ironic praise), with foreign accented utterances considered less ironic than native accented utterances. These findings revealed that native listeners’ figurative interpretation of ironic praise can change depending on indexical cues, with a reduction of pragmatic inferences in the case of foreign accent.
Delay of Initial Feeding of Zebrafish Larvae Until 8 Days Postfertilization Has No Impact on Survival or Growth Through the Juvenile Stage

Abstract The use of early-stage zebrafish for biomedical research spans early organogenesis to free-swimming larva. A key benefit of this model organism is that repeated assessments spanning several days can be performed of individual larvae within a single experiment, often in conjunction with administered drugs. However, the initiation of feeding, typically at 5 days postfertilization (dpf), can make serial assessments challenging. Therefore, delayed feeding would increase the utility of the model. To ask whether feeding could be delayed without adversely affecting larval growth and development up to 39 dpf, we systematically raised zebrafish and introduced feeding at 5 dpf or delayed initial feeding up to 9 dpf. We assessed survival into the juvenile stage (39 dpf) and anterior-posterior length at this age as proxies for growth and development. Delaying feeding initiation up to 8 dpf did not decrease baseline survival of greater than 90%; survival decreased to 66% only when delayed to 9 dpf. Larval length was no different under any of these conditions. Our findings define 9 dpf as the critical age before which larval zebrafish must be fed when raising to 39 dpf. The option to delay feeding to 8 dpf will broaden experimental applications for the zebrafish larval model.
Debugging Neural Machine Translations

In this paper, we describe a tool for debugging the output and attention weights of neural machine translation (NMT) systems and for improved estimations of confidence about the output based on the attention. The purpose of the tool is to help researchers and developers find weak and faulty example translations that their NMT systems produce without the need for reference translations. Our tool also includes an option to directly compare translation outputs from two different NMT engines or experiments. In addition, we present a demo website of our tool with examples of good and bad translations: this http URL
Experience of Domestic Violence and Psychological Morbidity in Spouses of Alcohol-Dependent Males

Background: Prevalence of both domestic violence (DV) and alcohol use is reported to be high in Kerala. The prevalence of DV and psychological morbidity in spouses of alcohol-dependent males has not been studied objectively. Methods: This cross-sectional study was undertaken to study the occurrence of DV and psychological morbidity–major depressive disorder (MDD), anxiety disorders, and adjustment disorders–in spouses of alcohol-dependent males attending the de-addiction center of a tertiary care hospital in South India. Sixty consecutive cases, aged 18–55 years, were recruited after getting informed consent. They were assessed using Domestic Violence Questionnaire (DVQ), Mini International Neuropsychiatric Interview Schedule, and a questionnaire to assess adjustment disorder. The association of DV with psychological morbidities was also studied. Results: DV was reported by 41 (68.3%, 95% confidence interval [CI] = 55.0–79.7) cases. At least one psychiatric morbidity was observed in 51 (85.0%, 95% CI = 72.9–92.5) cases–MDD in 15 (25.0%, 95% CI = 15.1–38.1), anxiety disorders in 6 (10%, 95% CI = 4.1–21.2), and adjustment disorder in 32 (53.3%, 95% CI = 40.1–66.1) cases each. No statistically significant association was observed between DV and any of the psychiatric disorders. However, DVQ scores showed significant correlation with years of marriage (Pearson's r = 0.268, P < 0.05) and with stressful life events over the past 1 year (Pearson's r = 0.424, P < 0.05). Conclusions: High rates of DV and psychological morbidity were seen in spouses of alcohol-dependent males.
Overview of the 6th Author Profiling Task at PAN 2018: Multimodal Gender Identification in Twitter

This overview presents the framework and the results of the Author Profiling shared task at PAN 2018. The objective of this year’s task is to address gender identification from a multimodal perspective, where not only texts but also images are given. For this purpose a corpus with Twitter data has been provided, covering the languages Arabic, English, and Spanish. Altogether, the approaches of 23 participants are evaluated.
Adversarial Geometry and Lighting using a Differentiable Renderer

Many machine learning classifiers are vulnerable to adversarial attacks, inputs with perturbations designed to intentionally trigger misclassification. Modern adversarial methods either directly alter pixel colors, or "paint" colors onto a 3D shapes. We propose novel adversarial attacks that directly alter the geometry of 3D objects and/or manipulate the lighting in a virtual scene. We leverage a novel differentiable renderer that is efficient to evaluate and analytically differentiate. Our renderer generates images realistic enough for correct classification by common pre-trained models, and we use it to design physical adversarial examples that consistently fool these models. We conduct qualitative and quantitate experiments to validate our adversarial geometry and adversarial lighting attack capabilities.