Miracle-dz/UniFAR-FTUs huggingface dataset:
Three‐dimensional kinematics of the canine carpal bones imaged with computed tomography after ex vivo axial limb loading and palmar ligament transection

Abstract Objective To describe normal antebrachiocarpal joint kinematic motion during axial loading and to describe the effect of palmar radiocarpal ligament (PRL) and palmar ulnocarpal ligament (PUL) transection on this motion. Sample population Ten forelimbs from 5 adult greyhound cadavers. Methods Limbs were placed in a custom jig and computed tomography images of limbs were obtained in neutral and extended positions. The translation and rotation of the intermedioradiocarpal bone (RCB), ulnar carpal bone, and accessory carpal bone were described relative to the radius through rigid body motion analysis. Kinematic and load analysis was repeated after sequential transection of the PRL and the PUL. Results Sagittal plane extension with a lesser component of valgus motion was found in all evaluated carpal bones. RCB supination was also detected during extension. Compared with the normal intact limb, transection of either or both the PRL and the PUL did not influence mean translation or rotation data or limb load. However, the transection of the PRL and the PUL increased the variance in rotation data compared with intact limb. Conclusion This study describes normal antebrachiocarpal kinematics as a foundation for determining carpal functional units. During axial loading, the PRL and the PUL may function to guide consistent motion in extension and flexion as well as pronation and supination. Clinical significance Three‐dimensional carpal kinematic analyses may improve our understanding of carpal injury and facilitate the development of novel treatments techniques.
End-to-end Speech Recognition With Word-Based Rnn Language Models

This paper investigates the impact of word-based RNN language models (RNN-LMs) on the performance of end-to-end automatic speech recognition (ASR). In our prior work, we have proposed a multi-level LM, in which character-based and word-based RNN-LMs are combined in hybrid CTC/attention-based ASR. Although this multi-level approach achieves significant error reduction in the Wall Street Journal (WSJ) task, two different LMs need to be trained and used for decoding, which increase the computational cost and memory usage. In this paper, we further propose a novel word-based RNN-LM, which allows us to decode with only the word-based LM, where it provides look-ahead word probabilities to predict next characters instead of the character-based LM, leading competitive accuracy with less computation compared to the multi-level LM. We demonstrate the efficacy of the word-based RNN-LMs using a larger corpus, LibriSpeech, in addition to WSJ we used in the prior work. Furthermore, we show that the proposed model achieves 5.1 %WER for WSJ Eval’92 test set when the vocabulary size is increased, which is the best WER reported for end-to-end ASR systems on this benchmark.
Author Profiling using Word Embeddings with Subword Information: Notebook for PAN at CLEF 2018

We present a simple experiment on multilingual author profiling as proposed by the PAN-CLEF 2018 shared task, focusing on the issue of gender identification from Twitter text in English, Spanish and Arabic. Our proposal makes use of word embeddings enriched with char n-gram information, and outperforms a majority class baseline.
Printing the Future—Updates in 3D Printing for Surgical Applications

Three-dimensional (3D) printing is based on additive technology in which layers of materials are gradually placed to create 3D objects. The world of 3D printing is a rapidly evolving field in the medical industry as well as in most sectors of our lives. In this report we present current technological possibilities for 3D printing in the surgical field. There are different 3D printing modalities and much confusion among clinicians regarding the differences between them. Three-dimensional printing technologies can be classified based on the basic material used: solid, liquid, and powder. We describe the main printing methods from each modality and present their advantages while focusing on their applications in different fields of surgery, starting from 3D printing of models for preoperative planning up to patient-specific implants (PSI). We present the workflow of 3D printing for the different applications and our experience in 3D printing surgical guides as well as PSI. We include examples of 3D planning as well as clinical and radiological imaging of cases. Three-dimensional printing of models for preoperative planning enhances the 3D perception of the planned operation and allows for preadaptation of surgical instruments, thus shortening operation duration and improving precision. Three-dimensional printed PSI allow for accurate reconstruction of anatomic relations as well as efficiently restoring function. The application of PSI is expanding rapidly, and we will see many more innovative treatment modalities in the near future based on this technology.
Clinical Outcomes of Transepithelial Photorefractive Keratectomy According to Epithelial Thickness.

PURPOSE To investigate the clinical outcomes, vector parameters, and corneal aberrations of corneal wavefront-guided (CWFG) transepithelial photorefractive keratectomy (PRK), according to epithelial thickness. METHODS This retrospective, comparative case series study included 91 eyes (91 patients) that underwent CWFG transepithelial PRK for myopic astigmatism. Epithelial thickness was less than 50 μm in 48 patients and 60 μm or greater in 43 patients. Clinical outcomes, including visual acuity, manifest refraction, vector parameters, and corneal wavefront aberration, were compared between the two groups. RESULTS The mean uncorrected distance visual acuity, safety and efficacy indices, and aberrometric values were comparable between the two groups at 6 months after transepithelial PRK. The postoperative spherical equivalent was significantly different between the two groups: 0.05 ± 0.19 diopters (D) in the less than 50 μm group and -0.05 ± 0.18 D in the 60 μm or greater group (P = .009). The difference between the two groups was 0.10 D, which is less than the theoretical difference because the epithelial remodeling pattern was different. There was a slight difference in slope between target induced astigmatism vector and surgically induced astigmatism vector (0.9979 in the less than 50 μm group and 0.9145 in the 60 μm or greater group; P = .025). CONCLUSIONS Transepithelial PRK is an effective and safe treatment modality regardless of epithelial thickness. However, a difference in postoperative refraction is present between the two groups, and astigmatic correction may be less in patients with thick epithelium; hence, a new algorithm is needed that can be tailored in accordance with individual epithelial thickness. [J Refract Surg. 2018;34(8):533-540. ].
Collaborative Planning for Mixed-Autonomy Lane Merging

Driving is a social activity: drivers often indicate their intent to change lanes via motion cues. We consider mixed-autonomy traffic where a Human-driven Vehicle (HV) and an Autonomous Vehicle (AV) drive together. We propose a planning framework where the degree to which the AV considers the other agent's reward is controlled by a selfishness factor. We test our approach on a simulated two-lane highway where the AV and HV merge into each other's lanes. In a user study with 21 subjects and 6 different selfishness factors, we found that our planning approach was sound and that both agents had less merging times when a factor that balances the rewards for the two agents was chosen. Our results on double lane merging suggest it to be a non-zero-sum game and encourage further investigation on collaborative decision making algorithms for mixed-autonomy traffic.
Migration, Household Tasks, and Gender: Evidence from the Republic of Georgia 1

We examine whether migration affects the gender division of household tasks and participation in leisure within origin-country households using survey data from the Republic of Georgia. Our theoretical framework identifies two sets of mechanisms whereby migration might influence gender differences in home activities: migrant experience effects and migrant absence effects. We test for both types of effects on the probability that men and women perform gender atypical household tasks and engage in leisure activities by comparing households with and without currently absent and return migrants using probit regressions. We find evidence for both migration absence and migration experience effects on gender differences in housework and leisure. However, these effects are complex and contradictory: Generally, male migration tends to exacerbate gender differences in the sending household while female migration tends to ameliorate them.
A Framework for Creative Visualization-Opportunities Workshops

Applied visualization researchers often work closely with domain collaborators to explore new and useful applications of visualization. The early stages of collaborations are typically time consuming for all stakeholders as researchers piece together an understanding of domain challenges from disparate discussions and meetings. A number of recent projects, however, report on the use of creative visualization-opportunities (CVO) workshops to accelerate the early stages of applied work, eliciting a wealth of requirements in a few days of focused work. Yet, there is no established guidance for how to use such workshops effectively. In this paper, we present the results of a 2-year collaboration in which we analyzed the use of 17 workshops in 10 visualization contexts. Its primary contribution is a framework for CVO workshops that: 1) identifies a process model for using workshops; 2) describes a structure of what happens within effective workshops; 3) recommends 25 actionable guidelines for future workshops; and 4) presents an example workshop and workshop methods. The creation of this framework exemplifies the use of critical reflection to learn about visualization in practice from diverse studies and experience.
Effects of Oncostatin M on Invasion of Primary Trophoblasts under Normoxia and Hypoxia Conditions

Purpose To investigate the effect of oncostatin M (OSM) on protein expression levels and enzymatic activities of matrix metalloprotainase (MMP)-2 and MMP-9 in primary trophoblasts and the invasiveness thereof under normoxia and hypoxia conditions. Materials and Methods Protein expression levels and enzymatic activities of MMP-2 and MMP-9 in primary trophoblasts under normoxia and hypoxia conditions were examined by Western blot and zymography, respectively. Effects of exogenous OSM on the in vitro invasion activity of trophoblasts according to oxygen concentration were also determined. Signal transducer and activator of transcription 3 (STAT3) siRNA was used to determine whether STAT3 activation in primary trophoblasts was involved in the effect of OSM. Results OSM enhanced protein expression levels and enzymatic activities of MMP-2 and MMP-9 in term trophoblasts under hypoxia condition, compared to normoxia control (p<0.05). OSM-induced MMP-2 and MMP-9 enzymatic activities were significantly suppressed by STAT3 siRNA silencing under normoxia and hypoxia conditions (p<0.05). Hypoxia alone or OSM alone did not significantly increase the invasiveness of term trophoblasts. However, the invasion activity of term trophoblasts was significantly increased by OSM under hypoxia, compared to that without OSM treatment under normoxia. Conclusion OSM might be involved in the invasiveness of extravillous trophoblasts under hypoxia conditions via increasing MMP-2 and MMP-9 enzymatic activities through STAT3 signaling. Increased MMP-9 activity by OSM seems to be more important in primary trophoblasts.
Gender Identification through Multi-modal Tweet Analysis using MicroTC and Bag of Visual Words: Notebook for PAN at CLEF 2018

This manuscript describes our solution to solve the Author Profiling task at PAN’18. In this edition, the task asks for identifying the user’s gender using both their Tweets containing texts and images. We used our MicroTC (μTC) text classification framework to cope with the text problem, and a novel approach to Bag of Visual Words to solve the image classification, designed for this task to solve the image classification. Finally, we tried to improve the final prediction using a combination of both approaches.
A quantitative approach for analyzing the spatio-temporal distribution of 3D intracellular events in fluorescence microscopy

Analysis of the spatial distribution of endomembrane trafficking is fundamental to understand the mechanisms controlling cellular dynamics, cell homeostasy, and cell interaction with its external environment in normal and pathological situations. We present a semi-parametric framework to quantitatively analyze and visualize the spatio-temporal distribution of intracellular events from different conditions. From the spatial coordinates of intracellular features such as segmented subcellular structures or vesicle trajectories, QuantEv automatically estimates weighted densities that are easy to interpret and performs a comprehensive statistical analysis from distribution distances. We apply this approach to study the spatio-temporal distribution of moving Rab6 fluorescently labeled membranes with respect to their direction of movement in crossbow- and disk-shaped cells. We also investigate the position of the generating hub of Rab11-positive membranes and the effect of actin disruption on Rab11 trafficking in coordination with cell shape.
Mitochondrial Pharmacology of Dimebon (Latrepirdine) Calls for a New Look at its Possible Therapeutic Potential in Alzheimer’s Disease

Dimebon (latrepirdine), an old antihistaminic drug, showed divergent results in two large clinical trials in Alzheimer disease (AD), which according to our review might be related to the specific pharmacological properties of the drug and the different patient populations included in both studies. Out of the many pharmacological effects of Dimebon, improvement of impaired mitochondrial function seeems to be most relevant for the substantial effects on cognition and behaviour reported in one of the studies, as these effects are already present at the low concentrations of dimebon measured in plasma and tissues of patients and experimental animals. Since impaired mitochondrial function seems to be the major driving force for the progression of the clinical symptoms and since most of the clinical benefits of dimebon originate from an effect on the symptomatic deterioration, mitochondrial improvement can also explain the lack of efficacy of this drug in another clinical trial where symptoms of the patiets remained stable for the time of the study. Accordingly, it seems worthwhile to reevaluate the clinical data to proof that clinical response is correlated with high levels of Neuropsychiatric Symptoms as these show a good relationship to the individual speed of symptomatic decline in AD patients related to mitochondrial dysfunction.
Candidate Labeling for Crowd Learning

Crowdsourcing has become very popular among the machine learning community as a way to obtain labels that allow a ground truth to be estimated for a given dataset. In most of the approaches that use crowdsourced labels, annotators are asked to provide, for each presented instance, a single class label. Such a request could be inefficient, that is, considering that the labelers may not be experts, that way to proceed could fail to take real advantage of the knowledge of the labelers. In this paper, the use of candidate labeling for crowd learning is proposed, where the annotators may provide more than a single label per instance to try not to miss the real label. The main hypothesis is that, by allowing candidate labeling, knowledge can be extracted from the labelers more efficiently by than in the standard crowd learning scenario. Empirical evidence which supports that hypothesis is presented.
Using Translated Data to Improve Deep Learning Author Profiling Models: Notebook for PAN at CLEF 2018

In this report on our participation in the PAN shared task on author profiling, we describe our attempt to identify the gender of authors using their posted tweets and images. The data of interest are tweets in the English, Spanish and Arabic languages as well as images. Included in our report is our final submitted system, a bi-LSTM model with attention, as well as an explanation on the less effective solutions we explored. We also detail an approach to obtain more training data, by simply translating the gold standard data of other languages to the language of interest. This proved to be a cheap and robust method for increasing the accuracy of all three languages. Official test accuracy scores are 79.3, 80.4 and 74.9 for English, Spanish and Arabic respectively.
Stacked Gender Prediction from Tweet Texts and Images: Notebook for PAN at CLEF 2018

This paper describes our participation at the PAN 2018 Author Profiling shared task. Given texts and images from some Twitter's authors, the goal is to estimate their genders. We considered all the languages (Arabic, English and Spanish) and all the prediction types (only from texts, only from images and combined). The final submitted system is a stacked classifier composed of two main parts. The first one, based on previous PAN Author Profiling editions, concerns gender prediction from texts. It consists in a pipeline of preprocessing, word n-grams from 1 to 2, TF-IDF with sublinear weighting, Linear Support Vector classification and probability calibration. The second part is formed by different layers of classifiers used for gender estimation from images: four base classifiers (object detection, face recognition, colour histograms, local binary patterns) in the first layer, a meta classifier in the second layer and an aggregation classifier as third layer. Finally, the two gender predictions, from texts and images, feed into the last layer classifier that provides the combined gender predictions.
The Roc‐COR tandem domain of leucine‐rich repeat kinase 2 forms dimers and exhibits conventional Ras‐like GTPase properties

The Parkinson's disease (PD)‐causative leucine‐rich repeat kinase 2 (LRRK2) belongs to the Roco family of G‐proteins comprising a Ras‐of‐complex (Roc) domain followed by a C‐terminal of Roc (COR) domain in tandem (called Roc‐COR domain). Two prokaryotic Roc‐COR domains have been characterized as ‘G proteins activated by guanine nucleotide‐dependent dimerization’ (GADs), which require dimerization for activation of their GTPase activity and bind guanine nucleotides with relatively low affinities. Additionally, LRRK2 Roc domain in isolation binds guanine nucleotides with relatively low affinities. As such, LRRK2 GTPase domain was predicted to be a GAD. Herein, we describe the design and high‐level expression of human LRRK2 Roc‐COR domain (LRRK2 Roc‐COR). Biochemical analyses of LRRK2 Roc‐COR reveal that it forms homodimers, with the C‐terminal portion of COR mediating its dimerization. Furthermore, it co‐purifies and binds Mg2+GTP/GDP at 1 : 1 stoichiometry, and it hydrolyzes GTP with Km and kcat of 22 nM and 4.70 × 10−4 min−1, respectively. Thus, even though LRRK2 Roc‐COR forms GAD‐like homodimers, it exhibits conventional Ras‐like GTPase properties, with high‐affinity binding of Mg2+‐GTP/GDP and low intrinsic catalytic activity. The PD‐causative Y1699C mutation mapped to the COR domain was previously reported to reduce the GTPase activity of full‐length LRRK2. In contrast, this mutation induces no change in the GTPase activity, and only slight perturbations in the secondary structure contents of LRRK2 Roc‐COR. As this mutation does not directly affect the GTPase activity of the isolated Roc‐COR tandem, it is possible that the effects of this mutation on full‐length LRRK2 occur via other functional domains.
Essencery - A Tool for Essentializing Software Engineering Practices

Software Engineering practitioners work using highly diverse methods and practices, and general theories in software engineering are lacking. One attempt at creating a common ground in the area of software engineering methodologies has been the Essence Theory of Software Engineering, which can be considered a method-agnostic project management tool for software engineering. Essence supports the use of any development practices and provides a framework for building a suitable method for any software engineering context. However, Essence presently suffers from low practitioner adoption that is partially considered to be caused by a lack of proper tooling. In this paper, we present Essencery, a tool for essentializing software engineering methods and practices using the Essence graphical syntax. Essencery aims to facilitate adoption of Essence among potential future users. We present an empirical evaluation of the tool by means of a qualitative, quasi-formal experiment and, based on the experiment, confirm that the tool is easy to use and useful for its intended purpose.
ECSTRA-APHP @ CLEF eHealth2018-task 1: ICD10 Code Extraction from Death Certificates

This paper describes the participation of ECSTRA-APHP team at CLEF eHealth 2018, task 1. The task involved extracting ICD10 codes from death certificates, mainly described with short plain texts. We casted the task as a machine learning problem the prediction of the ICD-10 codes (categorical variable) from the raw text transformed into word embeddings. We relied on probabilistic convolutional neural network for classification. Due to inbalanced representation of the ICD codes, we completed the prediction with dictionary-based lexical matching classifier for cases were there was less than 1,000 documents per code. Our best F1-score was 80.0% on a test set and 69.1% on the validate set (gold standard delivered by the organizers at the end of the challenge). This was the first time convolutional neural net were used for this multilabel classification task. The performance of our models were under the best neural predictor (recurrent network) described last year on the same task at CLEF eHealth (F1-score around 85%).
Multilingual SMS-based author profiling: Data and methods

Abstract In the recent years, many benchmark author profiling corpora have been developed for various genres including Twitter, social media, blogs, hotel reviews and e-mail, etc. However, no such standard evaluation resource has been developed for Short Messaging Service (SMS), a popular medium of communication, which is very useful for author profiling. The primary aim of this study is to develop a large multilingual (English and Roman Urdu) benchmark SMS-based author profiling corpus. The proposed corpus contains 810 author profiles, wherein each profile consists of an aggregation of SMS messages as a single document of an author, along with seven demographic traits associated with each author profile: gender, age, native language, native city, qualification, occupation and personality type (introvert/extrovert). The secondary aims of this study include the following: (1) annotating the proposed corpus for code-switching annotations at the lexical level (approximately 0.69 million tokens are manually annotated for code-switching) and (2) applying the stylometry-based method (groups of sixty-four features) and the content-based method (twelve features) for gender identification in order to demonstrate how our proposed corpus can be used for the development and evaluation of various author profiling methods. The results show that the content-based character 5-gram feature outperformed all the other features by obtaining the accuracy score of 0.975 and F1 score of 0.947 for gender identification while using the entire corpus. Furthermore, our proposed corpora (SMS–AP–18 and code-switched SMS–AP–18) are freely and publicly available for research purpose.