Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of infrapatellar fat pad preservation versus resection on clinical outcomes after total knee arthroplasty in patients with knee osteoarthritis (IPAKA): study protocol for a multicentre, randomised, controlled clinical trial

Introduction The infrapatellar fat pad (IPFP) is commonly resected during total knee arthroplasty (TKA) for better exposure. However, our previous studies have suggested that IPFP size was protective against, while IPFP signal intensity alteration was detrimental on knee symptoms and structural abnormalities. We hypothesise that an IPFP with normal qualities, rather than abnormal qualities, should be preserved during TKA. The aim of this study is to compare, over a 1-year period, the postoperative clinical outcomes of IPFP preservation versus resection after TKA in patients with normal or abnormal IPFP signal intensity alteration on MRI. Methods and analysis Three hundred and sixty people with end-stage knee osteoarthritis and on the waiting list for TKA will be recruited and identified as normal IPFP quality (signal intensity alteration score ≤1) or abnormal IPFP quality (signal intensity alteration score ≥2). Patients in each hospital will then be randomly allocated to IPFP resection group or preservation group. The primary outcomes are the summed score of self-reported Knee Injury and Osteoarthritis Outcome Score (KOOS), KOOS subscales assessing function in daily activities and function in sport and recreation. Secondary endpoints will be included: KOOS subscales (pain, symptoms and quality of life), Knee Society Score, 100 mm Visual Analogue Scale (VAS) Pain, timed up-and-go test, patellar tendon shortening, 100 mm VAS self-reported efficacy of reduced pain and increased quality of life, and Insall-Salvati index assessed on plain X-ray. Adverse events will be recorded. Intention-to-treat analyses will be used. Ethics and dissemination The study is approved by the local Medical Ethics Committee (Zhujiang Hospital Ethics Committee, reference number 2017-GJGBK-001) and will be conducted according to the principle of the Declaration of Helsinki (64th, 2013) and the Good Clinical Practice standard, and in compliance with the Medical Research Involving Human Subjects Act . Data will be published in peer-reviewed journals and presented at conferences, both nationally and internationally. Trial registration number This trial was registered at Clinicaltrial.gov website on 19 October 2018 with identify number NCT03763448.
Extended Overview of CLEF HIPE 2020: Named Entity Processing on Historical Newspapers

This paper presents an extended overview of the first edition of HIPE (Identifying Historical People, Places and other Entities), a pioneering shared task dedicated to the evaluation of named entity processing on historical newspapers in French, German and English. Since its introduction some twenty years ago, named entity (NE) processing has become an essential component of virtually any text mining application and has undergone major changes. Recently, two main trends characterise its developments: the adoption of deep learning architectures and the consideration of textual material originating from historical and cultural heritage collections. While the former opens up new opportunities, the latter introduces new challenges with heterogeneous, historical and noisy inputs. In this context, the objective of HIPE, run as part of the CLEF 2020 conference, is threefold: strengthening the robustness of existing approaches on non-standard inputs, enabling performance comparison of NE processing on historical texts, and, in the long run, fostering efficient semantic indexing of historical documents. Tasks, corpora, and results of 13 participating teams are presented. Compared to the condensed overview [31], this paper includes further details about data generation and statistics, additional information on participating systems, and the presentation of complementary results.
High-school students' mastery of basic flow-control constructs through the lens of reversibility

High-school students specialising in computing fields need to develop the abstraction skills required to understand and create programs. Novices' difficulties at high-school level, ranging from mastery of the "notional machine" to recognition of a program's purpose, have not been investigated as extensively as at tertiary level. This work explores high-school students' code comprehension by asking to reason about reversing conditional and iteration constructs. A sample of 205 K11--13 students from different institutions were asked to engage in a set of "reversibility tasklets". For each code fragment, they need to identify if its computation is reversible and either provide the code to reverse or an example of a value that cannot be reversed. For 4 such items, after extracting the recurrent patterns in students' answers, we have carried out an analysis within the framework of the SOLO taxonomy. Overall, 74% of answers correctly identified if the code was reversible but only 42% could provide the full explanation/code. The rate of relational answers varies from 51% down to 21%, the poorest performance arising for a small array-processing loop (and although 65% of the subjects had correctly identified the loop as reversible). The instruction level did not have a strong impact on performance, indicating such tasks are suitable for K11, when the basic flow-control constructs are usually introduced. In particular, the reversibility concept could be a useful pedagogical instrument both to assess and to help develop students' program comprehension.
Movement and habitat use of headstarted Blanding's turtles in Michigan

Captive breeding or headstarting is a management option to increase population numbers in threatened and endangered animals. The success of these programs depends on increasing population numbers whilemaintainingfitnesswithinpopulations that contain somecaptive-reared individuals.Aspart of anongoing conservation project with Shiawassee National Wildlife Refuge (SNWR) in Saginaw, Michigan, USA, the DetroitZoologicalSociety (DZS)headstartedBlanding’s turtles (Emydoidea blandingii) to18monthsof age.To determine survival, movements, and habitat use of these headstarted Blanding’s turtles, we externally attached radio-transmitters to 24 turtles and released them at 4 sites in release groups of 6 along a transect in a wetland within SNWR during June 2014. We located turtles weekly during the active season (May–Sep) and every 2 weeks during the inactive season (Oct–Apr) for 18 months, starting immediately after release and ending November 2015. We calculated straight-line distances between release sites and home range centers over the study period.We examined habitat use of the turtles in relation to habitat availability. To predict movement of headstartedBlanding’s turtles away from release sites, we used geographicallyweighted regression (GWR)with measured habitat factors and release sites as independent variables. There were differences in home range sizes across release groups but not between years. Headstarted juvenile Blanding’s turtles in this study have home range sizes similar to those measured in other studies for wild-hatched juveniles but smaller home ranges than thosemeasured forwild-hatchedadults.Our study showed thatheadstartedBlanding’s turtlesusedhabitatswith muskrat (Ondatra zibethica) dens and cattails (Typha spp.) more than proportionally available, and used open water, willows (Salix spp.), and lowland forest less than proportionally available. The GWRmodel was able to predict habitat characteristics (water depth and temperature, duckweed [Lemna minor], cattails, muskrat dens andfields, buttonbush [Cephalanthus occidentalis]) that influenced themovement of turtles away from the release sites.Thehabitat coefficient’s influenceonmovementvaried in relation to thecurrent locationof the turtle andas habitat characteristics increased or decreased. Turtle distance from release sites decreased as water depth, water temperature, and duckweed increased, and increased as cattails, muskrat dens and fields, and buttonbush increased. Habitat in the small spatial extent at release sites may affect movement of headstarted Blanding’s turtles. Furthermore, this study uses novel methodologies for assessing headstart programs that can aid future conservation and management efforts by providing information on habitat use and movement patterns of headstarted Blanding’s turtles after release. 2018 The Wildlife Society.
Upregulation of Cellular Palmitoylation Mitigates α‐Synuclein Accumulation and Neurotoxicity

Synucleinopathies, including Parkinson's disease (PD), are characterized by α‐synuclein (αS) cytoplasmic inclusions. αS‐dependent vesicle‐trafficking defects are important in PD pathogenesis, but their mechanisms are not well understood. Protein palmitoylation, post‐translational addition of the fatty acid palmitate to cysteines, promotes trafficking by anchoring specific proteins to the vesicle membrane. αS itself cannot be palmitoylated as it lacks cysteines, but it binds to membranes, where palmitoylation occurs, via an amphipathic helix. We hypothesized that abnormal αS membrane‐binding impairs trafficking by disrupting palmitoylation. Accordingly, we investigated the therapeutic potential of increasing cellular palmitoylation.
Image-to-image translation for automatic ink removal in whole slide images

Abstract. Purpose: Deep learning models are showing promise in digital pathology to aid diagnoses. Training complex models requires a significant amount and diversity of well-annotated data, typically housed in institutional archives. These slides often contain clinically meaningful markings to indicate regions of interest. If slides are scanned with the ink present, then the downstream model may end up looking for regions with ink before making a classification. If scanned without the markings, the information regarding where the relevant regions are located is lost. A compromise solution is to scan the slide with the annotations present but digitally remove them. Approach: We proposed a straightforward framework to digitally remove ink markings from whole slide images using a conditional generative adversarial network based on Pix2Pix. Results: The peak signal-to-noise ratio increased 30%, structural similarity index increased 20%, and visual information fidelity increased 200% relative to previous methods. Conclusions: When comparing our digital removal of marked images with rescans of clean slides, our method qualitatively and quantitatively exceeds current benchmarks, opening the possibility of using archived clinical samples as resources to fuel the next generation of deep learning models for digital pathology.
Long‐duration spaceflight alters estimated intracranial pressure and cerebral blood velocity

During long‐duration spaceflights, some astronauts develop structural ocular changes including optic disc oedema that resemble signs of intracranial hypertension. In the present study, intracranial pressure was estimated non‐invasively (nICP) using a model‐based analysis of cerebral blood velocity and arterial blood pressure waveforms in 11 astronauts before and after long‐duration spaceflights. Our results show that group‐averaged estimates of nICP decreased significantly in nine astronauts without optic disc oedema, suggesting that the cephalad fluid shift during long‐duration spaceflight rarely increased postflight intracranial pressure. The results of the two astronauts with optic disc oedema suggest that both increases and decreases in nICP are observed post‐flight in astronauts with ocular alterations, arguing against a primary causal relationship between elevated ICP and spaceflight associated optical changes. Cerebral blood velocity increased independently of nICP and spaceflight‐associated ocular alterations. This increase may be caused by the reduced haemoglobin concentration after long‐duration spaceflight.
Excellent suppression of physiological myocardial FDG activity in patients with cardiac sarcoidosis

Suppression of physiological myocardial FDG activity is vital in patients undergoing PET/CT for assessment of known or suspected cardiac sarcoidosis. This study aims to evaluate the efficacy of physiological myocardial FDG suppression following a protocol change to a 24‐h high fat very low carbohydrate (HFVLC) diet and prolonged fast.
Few bacterial co-infections but frequent empiric antibiotic use in the early phase of hospitalized patients with COVID-19: results from a multicentre retrospective cohort study in The Netherlands

Abstract Background Knowledge on bacterial co-infections in COVID-19 is crucial to use antibiotics appropriately. Therefore, we aimed to determine the incidence of bacterial co-infections, antibiotic use and application of antimicrobial stewardship principles in hospitalized patients with COVID-19. Methods We performed a retrospective observational study in four hospitals (1 university, 2 non-university teaching, 1 non-teaching hospital) in the Netherlands from March to May 2020 including consecutive patients with PCR-confirmed COVID-19. Data on first microbiological investigations obtained at the discretion of the physician and antibiotic use in the first week of hospital admission were collected. Results Twelve (1.2%) of the 925 patients included had a documented bacterial co-infection (75.0% pneumonia) within the first week. Microbiological testing was performed in 749 (81%) patients: sputum cultures in 105 (11.4%), blood cultures in 711 (76.9%), pneumococcal urinary antigen testing in 202 (21.8%), and Legionella urinary antigen testing in 199 (21.5%) patients, with clear variation between hospitals. On presentation 556 (60.1%; range 33.3–73.4%) patients received antibiotics for a median duration of 2 days (IQR 1–4). Intravenous to oral switch was performed in 41 of 413 (9.9%) patients who received intravenous treatment >48 h. Mean adherence to the local guideline on empiric antibiotic therapy on day 1 was on average 60.3% (range 45.3%–74.7%). Conclusions On presentation to the hospital bacterial co-infections are rare, while empiric antibiotic use is abundant. This implies that in patients with COVID-19 empiric antibiotic should be withheld. This has the potential to dramatically reduce the current overuse of antibiotics in the COVID-19 pandemic.
Transfer Learning for Named Entity Recognition in Historical Corpora

We report on our participation to the 2020 CLEF HIPE shared task as team Ehrmama , focusing on bundle 3: Named Entity Recognition and Classiﬁcation (NERC) on coarse and ﬁne-grained tags. Motivated by an interest to assess the added value of transfer learning for NERC on historical corpora, we propose an architecture made of two components: (i) a modular embedding layer where we combine newly trained and pre-trained embeddings, and (ii) a task-speciﬁc Bi-LSTM-CRF layer. We ﬁnd that character-level embeddings, BERT, and a document-level data split are the most important factors in improving our results. We also ﬁnd that using in-domain FastText embeddings and a single-task as opposed to multi-task approach yields minor gains. Our results conﬁrm that pre-trained language models can be beneﬁcial for NERC on low-resourced historical corpora.
Time-travel Investigation: Toward Building a Scalable Attack Detection Framework on Ethereum

Ethereum has been attracting lots of attacks, hence there is a pressing need to perform timely investigation and detect more attack instances. However, existing systems suffer from the scalability issue due to the following reasons. First, the tight coupling between malicious contract detection and blockchain data importing makes them infeasible to repeatedly detect different attacks. Second, the coarse-grained archive data makes them inefficient to replay transactions. Third, the separation between malicious contract detection and runtime state recovery consumes lots of storage. In this article, we propose a scalable attack detection framework named EthScope, which overcomes the scalability issue by neatly re-organizing the Ethereum state and efficiently locating suspicious transactions. It leverages the fine-grained state to support the replay of arbitrary transactions and proposes a well-designed schema to optimize the storage consumption. The performance evaluation shows that EthScope can solve the scalability issue, i.e., efficiently performing a large-scale analysis on billions of transactions, and a speedup of around \( \text{2,300}\times \) when replaying transactions. It also has lower storage consumption compared with existing systems. Further analysis shows that EthScope can help analysts understand attack behaviors and detect more attack instances.
ICB-UMA at CLEF e-Health 2020 Task 1: Automatic ICD-10 coding in Spanish with BERT

This working notes paper presents our contribution to the CLEF eHealth 2020 Task 1. Our team has participated in the CodiEsp-D subtask, the ﬁrst shared task consisted in the automatic clinical coding of medical cases in Spanish, annotated with ICD-10-CM codes. We tackled the task as a multi-label classiﬁcation problem using BERT model [4]. With the aim of leveraging all the language modeling capacities of the deep bidirectional encoder architecture of BERT, we developed a tailored approach to annotate short fragments of text extracted from the long clinical cases present in the CodiEsp corpus and use them as input to the model. Two publicly available Spanish versions of BERT, namely BETO [3] and BERT-SciELO [1], were ﬁne-tuned on the CodiEsp-D corpus extended by a set of abstracts annotated with ICD-10 codes, following our fragment-based classiﬁcation approach. BERT-SciELO, a BERT-Base model pre-trained from scratch on an unlabeled corpus of biomedical articles in Spanish, achieved the best results among our three submitted systems, obtaining a ﬁnal Mean Average Precision (MAP) metric score of 0.482 on the evaluation set.
Deep Learning for UI Element Detection: DrawnUI 2020

As the ﬁeld of software development is rapidly growing, it becomes vital to show steady progress in the application development sector to maintain the pace. Constantly tweaking the front-end of an application becomes cumbersome for developers. Detection tools that help design UI from hand-drawn sketches can aid developers and non-developers in building applications with great ease. The recent advancements in deep learning techniques and the availability of computational power facilitates training eﬃcient models for object detection. Two such models, the YOLOv4 and Cascade RCNN are implemented for detecting UI elements from hand-drawn sketches and are detailed in this paper. These models are observed to have an average mAP@IoU 0.5 improvement of 31.85% over the baseline Faster RCNN.
ULMFiT for Twitter Fake News Spreader Profiling

21 st century is named as the age of information technologies. Social applications such as Facebook, Twitter, Instagram, etc. have become a quick and huge media for spreading news over the internet. At the same time, the ability for the wide spread of news that is of low quality with intentionally false information is creating havocs causing damage to the extent of losing lives in the society. Such news is termed as fake news and detecting the fake news spreader is drawing more attention these days as fake news can manipulate communities’ minds and also social trust. Until date, many studies have been done in this area and most of them are based on Machine Learning and Deep Learning approaches. In this paper, we have proposed a Universal Language Model Fine-Tuning model based on Transfer Learning to detect potential fake news spreaders on Twitter. The proposed model collects wiki text data to train the Language Model to capture general features of the language and this knowledge is transferred to build a classifier using fake news spreaders dataset provided by PAN 2020 to identify the fake news spreader. The results obtained on PAN 2020 fake news dataset are encouraging.
Overview of CheckThat! 2020 English: Automatic Identification and Verification of Claims in Social Media

We present an overview of the third edition of the Check-That! Lab at CLEF 2020. The lab featured ﬁve tasks in Arabic and English, and here we focus on the three English tasks. Task 1 challenged the participants to predict which tweets from a stream of tweets about COVID-19 are worth fact-checking. Task 2 asked to retrieve ver-iﬁed claims from a set of previously fact-checked claims, which could help fact-check the claims made in an input tweet. Task 5 asked to pro-pose which claims in a political debate or a speech should be prioritized for fact-checking. A total of 18 teams participated in the English tasks, and most submissions managed to achieve sizable improvements over the baselines using models based on BERT, LSTMs, and CNNs. In this paper, we describe the process of data collection and the task setup, including the evaluation measures used, and we give a brief overview of the participating systems. Last but not least, we release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research in the important tasks of check-worthiness estimation and detecting previously fact-checked claims.
An Ensemble Model Using N-grams and Statistical Features to Identify Fake News Spreaders on Twitter

In this notebook, we summarize our work process of preparing a software for the PAN 2020 Profiling Fake News Spreaders on Twitter task. Our final software was a stacking ensemble classifier of five different machine learning models; four of them use word n-grams as features, while the fifth one was based on statistical features extracted from the Twitter feeds. Our software uploaded to the TIRA platform achieved an accuracy of 75% in English and 80.5% in Spanish. Our overall accuracy of 77.75% turned out to be a tie for the first place in the competition.
NLP&IR@UNED at CheckThat! 2020: A Preliminary Approach for Check-Worthiness and Claim Retrieval Tasks using Neural Networks and Graphs

Check-Worthiness and Claim Retrieval are two of the ﬁrst tasks to be performed on the Fake News detection pipeline. In this article we present our approach to these tasks presented in the 2020 edition of the CheckThat! Lab. In the task 1, Tweet Check-Worthiness English, we propose a Bi-LSTM model with Glove Twitter embeddings where the number of inputs has been increased with a graph generated from the additional information provided for each tweet. In task 1 Arabic we have followed a similar approach but using a feed forward neural network model with Arabic embeddings. For the task 5, Debate Check-Worthiness, we propose a naive Bi-LSTM model with Glove embeddings. Finally, our approach to the task 2, Claim Retrieval, is based in a feed forward neural network model with features such as cosine similarity over Universal Sentence Encoder embeddings of tweets and claims, and other linguistic features extracted from both elements.
BOUN-REX at CLEF-2020 ChEMU Task 2: Evaluating Pretrained Transformers for Event Extraction

In this paper, we describe our models and results designated for CLEF-2020 ChEMU Task 2 [4], event extraction for chemical patent documents. We make use of the recent advances in pretrained trans-former architectures such as BERT and BioBERT. We compare several transformers with diﬀerent settings in order to improve performance. Our best performing model with BioBERT transformer architecture and AdamW optimizer achieves 0.7234 exact F1 score on the test dataset.