Miracle-dz/UniFAR-FTUs huggingface dataset:
Alcohol use disorder‐associated structural and functional characteristics of the insula

Based on our current understanding of insular regions, effects of chronic alcohol use on the insula may affect the integration of sensory‐motor, socio‐emotional, and cognitive function. There is no comprehensive understanding about these differences in individuals with alcohol use disorder that accounts for both structural and functional differences related to chronic alcohol use. The purpose of this study was to investigate these variations in both the anterior and posterior insula in persons with alcohol use disorder. We investigated insula gray matter volume, morphometry, white matter structural connectivity, and resting state functional connectivity in 75 participants with alcohol use disorder (females = 27) and 75 age‐matched healthy control participants (females = 39). Results indicated structural differences mostly in the anterior regions, while functional connectivity differences were observed in both the anterior and posterior insula in those with alcohol use disorder. Differing connectivity was observed with frontal, parietal, occipital, cingulate, cerebellar, and temporal brain regions. While these results align with prior studies showing differences primarily in anterior insular regions, they also contribute to the existing literature suggesting differences in anterior insular connectivity with brain regions shown to be engaged during higher cognitive and emotional tasks.
Mathematical modeling of corona virus (COVID-19) and stability analysis

Abstract In this paper, the mathematical modeling of the novel corona virus (COVID-19) is considered. A brief relationship between the unknown hosts and bats is described. Then the interaction among the seafood market and peoples is studied. After that, the proposed model is reduced by assuming that the seafood market has an adequate source of infection that is capable of spreading infection among the people. The reproductive number is calculated and it is proved that the proposed model is locally asymptotically stable when the reproductive number is less than unity. Then, the stability results of the endemic equilibria are also discussed. To understand the complex dynamical behavior, fractal-fractional derivative is used. Therefore, the proposed model is then converted to fractal-fractional order model in Atangana-Baleanu (AB) derivative and solved numerically by using two different techniques. For numerical simulation Adam-Bash Forth method based on piece-wise Lagrangian interpolation is used. The infection cases for Jan-21, 2020, till Jan-28, 2020 are considered. Then graphical consequences are compared with real reported data of Wuhan city to demonstrate the efficiency of the method proposed by us.
A Retrospective Case Series Study on a Minimally Invasive Ultrasound-Guided First Dorsal Compartment Release Technique for Refractory De Quervain Tenosynovitis

Objective The aim of this study was to evaluate the outcomes of patients treated with a novel minimally invasive complete release of the first dorsal compartment percutaneously under ultrasound guidance using an 18-gauge needle with an 18 blade at the tip. Design This was a retrospective case series. Nine adults (ten wrists) were included in the study of this technique. All patients had failed conservative care and had tenderness to palpation over the first dorsal compartment, a positive Finklestein test, and confirmed tenosynovitis with ultrasound imaging before the procedure. The main outcome measures were reduction in pain as determined by the numeric rating scale and improvement of function determined by the Nirschl Phase scale at both short- and long-term follow-up. Results There was 100% follow-up, with mean (SD) follow-up occurring at 23.1 (9.8) mos (range, 9–42 mos). From preprocedure to follow-up, numeric rating scale pain decreased from 4.1 (SD, 2.5) to 0.0 (SD, 0.0) (P < 0.001), and Nirschl phase improved from 2.5 (SD, 1.9) to 0.2 (SD, 0.4) (P = 0.03). No patients required revision open-release surgery or suffered neurovascular complications. Conclusion This technique resulted in significant improvement of pain and function for all patients and no short- or long-term neurovascular complications were seen.
Knowledge Gaps Regarding Indigenous Health in Occupational Therapy: A Delphi Process

Background. The occupational therapy profession needs to respond to the calls to action from the Truth and Reconciliation Commission (TRC) to engage in the process of reconciliation with Indigenous populations. Purpose. To inform development of a survey intended to determine the knowledge gaps of occupational therapists in relation to Indigenous health. Method. A Delphi process engaging 18 occupational therapists with membership in an Indigenous health network was used to prioritize and refine potential themes identified via literature review. Findings. Results of three consensus rounds and Dunn-Bonferroni post-hoc testing demonstrated three statistically distinct hierarchical tiers of 10 priority themes to inform survey development. Implications. The consensus prioritized themes from the literature to underpin further research on occupational therapists’ knowledge in relation to Indigenous health and can provide a learning scaffold for occupational therapists to support a continued response to the TRC calls to action.
An empirical investigation of the Pathways Model of problem gambling through the conjoint use of self-reports and behavioural tasks

Abstract Background and aims Blaszczynski and Nower (2002) conceptualized their Pathways Model by postulating the existence of three subtypes of problem gamblers who share common characteristics, but also present specific ones. Methods This study investigated how the psychological mechanisms postulated in the Pathways Model predict clinical status in a sample that combined treatment-seeking gamblers (n = 59) and non-problematic community gamblers (n = 107). To test the Pathways Model, we computed a hierarchic logistic regression in which variables associated with each postulated pathway were entered sequentially to predict the status of the treatment-seeking gambler. Self-report questionnaires measured gambling-related cognitions, alexithymia, emotional reactivity, emotion regulation strategies and impulsivity. Behavioural tasks measured gambling persistence (slot machine task), decision-making under uncertainty (Iowa Gambling Task) and decision-making under risk (Game of Dice Task). Results We showed that specific factors theorized as underlying mechanisms for each pathway predicted the status of clinical gambler. For each pathway, significant predictors included gambling-related cognitive distortions and behaviourally measured gambling persistence (behaviourally conditioned pathway), emotional reactivity and emotion regulation strategies (emotionally vulnerable pathway), and lack of premeditation impulsivity facet (impulsivist-antisocial pathway). Discussion and conclusions Our study adds to the body of literature confirming the validity of the Pathways Model and hold important implications in terms of assessment and treatment of problem gambling. In particular, a standardized assessment based on the Pathways Model should promote individualized treatment strategies to allow clinicians to take into account the high heterogeneity that characterizes gambling disorder.
Medication Burden Among Pediatric Cancer Survivors: Analysis of a Population-Wide Electronic Database in Hong Kong

Abstract Background Few studies have evaluated the medication burden borne by survivors of pediatric cancer. This study aimed to describe the drug utilization pattern of chronic medications in a cohort of young pediatric cancer survivors. Methods This was a population-based study of patients diagnosed with cancer at age 18 years or younger between 2000 and 2013 in Hong Kong and who had survived at least 5 years postdiagnosis. The primary outcome is the use of any chronic medication (medications that were prescribed for ≥30 consecutive days within a 6-month period). Multivariable log-binomial models were used to identify factors associated with chronic medication use. Kaplan-Meier analysis was used to present the cumulative proportion of survivors initiated on a chronic medication across time from cancer diagnosis. Results Of the 2444 survivors (median age = 22 years, interquartile range = 16-27 years), 669 (27.4%) required at least 1 chronic medication at least 5 years postdiagnosis. Survivors who developed a chronic health condition (CHC) had a 5.48 (95% confidence interval [CI] = 4.49 to 6.71) times higher risk of taking a chronic medication than those without CHC. At 10 years postdiagnosis, the cumulative proportion of survivors being initiated a chronic medication was 33.4% (95% CI = 31.1% to 35.6%) for the overall cohort. Higher cumulative proportions were observed in survivors with endocrine (74.6%, 95% CI = 68.4% to 79.6%), renal (68.8%, 95% CI = 54.2% to 78.7%), neurological (58.6%, 95% CI = 46.1% to 68.1%), and cardiovascular (54.7%, 95% CI = 44.0% to 63.4%) disorders. Conclusion Survivors with certain CHCs had a higher risk of starting a prescription medication in the early phase of survivorship. Future studies include examining the impact of medication burden on survivors’ functional status.
A hypothesis concerning the role of PPAR family on cardiac energetics in Adriamycin‐induced cardiomyopathy

Adriamycin is an effective anti‐neoplastic drug against a variety of cancer types. However, the drug causes adverse side effects in a number of organ systems. Cardiomyopathy is one of the life‐threatening side effects of Adriamycin. In the current work, we have derived a hypothesis with possible involvement of PPAR family members in the development of Adriamycin‐induced cardiomyopathy. Dysregulation of PPAR family by Adriamycin causes impairment in the transport and β‐oxidation of fatty acids, the key substrate for ATP synthesis in heart. Evidences suggest that dysregulation of PPAR family alters the recruitment of glucose transporters. Furthermore, heme oxygenase‐1 is a crucial enzyme regulating the iron homeostasis in the heart whose expression is regulated by PPAR family. Inverse relationship exists between the expression levels of PPARγ and heme oxygenase‐1. Adriamycin upregulates the expression of heme oxygenase‐1 which in turn disrupts the iron homeostasis in cardiomyocytes. Our molecular docking results show that Adriamycin has a high affinity for iron‐binding sites of heme oxygenase‐1, thereby hindering formation of iron–sulfur complex. The lack of iron–sulfur complex impairs the electron transport chain. In addition, succinate dehydrogenase subunit A is downregulated by Adriamycin. The lack of this subunit uncouples Krebs cycle from ETC. Further, lack of this subunit increases the concentration of succinate, which further alters the mitochondrial membrane potential. Overall, in the present work, we hypothesize that alteration in the expression of PPAR family members is one of the major causes of metabolic chaos and oxidative stress caused by Adriamycin during the development of cardiomyopathy.
Dynamics of an inertially collapsing gas bubble between two parallel, rigid walls

Abstract The collapse of cavitation bubbles in channel flows can give rise to structural damage along neighbouring walls. Although the collapse of a bubble near a single wall has been studied extensively, less is known about bubble collapse between two walls, e.g. as in a channel. We conduct highly resolved, direct simulations of the Navier–Stokes equations to investigate the bubble dynamics and pressures produced by the collapse of a bubble between two parallel rigid walls. We examine the dependence of the dynamics and pressures on the initial bubble location, confinement and driving pressure. For a fixed initial stand-off distance, as the channel width increases the bubble volume, migration distance and re-entrant jet speed approach their single-wall counterparts. We obtain an expression for the minimum channel width at which the confinement does not affect the bubble dynamics depending on the driving pressure difference and initial stand-off distance. For a fixed channel width, varying stand-off distance reduced the maximum wall pressures in the channel relative to the single wall; the trend was consistent for three different driving pressures. Two different jetting behaviours are seen when the bubble is centred in the channel, depending on the channel width. Under significant confinement, wall-parallel re-entrant jets impinge upon each other and further intensify the collapse of the vortex ring.
Deep Learning and Gradient Boosting Ensembles for Classification of Snake Species

This paper describes an deep-learning based ensembling approach for snake species classification. The proposed method employs state-of-the-art models such as ResNet and EfficientNet among others, applies transfer learning and fine-tunes them to the target data domain — snake images, uses them as feature extractors, and finally conjoins the produced representation vectors along with geographic metadata information to train a gradient boosting ensemble classifier to predict the snake species. The authors performed multiple experiments to train individual deep-learning architectures, select effective feature extraction models, and train a gradient boosting classifier using their ensembled features. The approach attained a maximum macro-averaged F1-Score of 51 . 39% on the test data. The corresponding validation F1-Score and Accuracy scores were 52 . 04% and 87 . 13% , respectively.
Overview of the CLEF 2022 SimpleText Task 3: Query Biased Simplification of Scientific Texts

This paper presents an overview of the CLEF 2022 SimpleText Task 3 on query biased simplification of scientific text. After discussing the motivation and general task setup, we detail the exact test collection, consisting of a train of sentences from scientific abstracts paired with human reference simplified sentences, and extensive test corpus of sentences with detailed annotations of lexical and syntactic complexity. We present a detailed analysis of the submitted simplified sentences, and the resulting evaluation scores.
Style Change Detection using Discourse Markers

This article presents our approach for the Style Change Detection Task at PAN 2022 using discourse markers. Discourse markers (such as ‘what’, ‘I have’, etc.) are words or expressions used to connect, organise and manage conversations. We present two different approaches for Style Change Detection at PAN-2022. For Task 1, (Style Change Basic) our approach is based on identifying conversational patterns within the documents between a user and a possible respondent. Then, using classification algorithms, we predict the point of style change detection within each document. For Task 2 (Style Change Advanced) and Task 3 (Style Change Real World), we use an extensive list of frequently occurring discourse markers to identify the number of speakers as the number of authors within the document. This prediction serves as the number of clusters for text segments within the document. Subsequently, using unsupervised clustering we detect clusters of similar text segments such that each cluster comprises of text segment groups corresponding to each author. The resulting F1 scores for our approaches on the test set are: 0.70518 for Task 1, 0.32128 for Task 2 and 0.56360 for Task 3.
Profiling Irony and Stereotype Spreaders with Encoding Dependency Information using Graph Convolutional Network

Figurative language is using words in a way that deviates from the conventional order and meaning in order to ask the reader or listener to understand the meaning by virtue of its relation to some other meaning or concept. It is a rapidly growing area in Natural Language Processing, including the processing of irony, sarcasm, as well as other figures. With irony, language is employed in a figurative and subtle way to mean the opposite of what is literally stated. In this paper, we hypothesize that encoding dependency information allows us to find word associations that capture the author’s style where the main intent is to spread stereotypes in the form of irony. To do this end, we proposed a graph convolutional network that is able to learn the heterogeneous text graphs, representing various dependency information in the text, which enables classifiers to classify ironic and non-ironic spreaders via looking at heterogeneous features. Experimental results are very promising and show the effectiveness of our proposed method.
SSN CSE at ImageCLEFaware 2022: Contextual Job Search Feedback Score based on Photographic Profile using a Random Forest Regression Technique

Social networks have become increasingly popular with millions of users where the digital presence has become more crucial to a person's character judgement. Employers tend to screen through their candidates' profiles on social media to understand their personalities and infer the knowledge about the candidate’s eligibility for a specific job. To address this issue, the ImageCLEF forum is conducting a task to quantify the effect of the photographic profile from 2021 onwards and we participated this year. Therefore, an algorithm was developed to score the images of a user and provide them with comprehensive feedback on the consequences of the images on their selected professions. The approach used to develop the algorithm uses Random Forest Regression which resulted in a Pearson Correlation Coefficient of 0.544.
UniLeiden at LeQua 2022: The first step in understanding the behaviour of the median sweep quantifier using continuous sweep

This paper presents the continuous sweep quantifier, a smoothed adaptation of the median sweep quantifier. Previous research has shown that the median sweep quantifier is a good quantifier. However, it is not well understood why it performs well because it is hard to derive its theoretical properties. The continuous sweep quantifier is a modification of the median sweep quantifier that enables computing theoretical results. The continuous sweep quantifier 1) uses kernel estimates instead of the empirical distribution, 2) constructs decision boundaries instead of applying discrete decision rules, and 3) uses the mean instead of the median. We show that a simplified adaptation of the continuous sweep quantifier performs similarly to the median sweep quantifier in terms of bias and variance on the LeQua 2022 dataset. The continuous sweep quantifier can therefore be used to provide insights into the median sweep quantifier by computing theoretical expressions for bias and variance.
ImageCLEFmedical Caption Task, Concept Detection, Finding Duplicates, SDVA-UCSD Approach

The ImageCLEFmedical Caption task, concept detection goal, was to detect relevant concepts in a large corpus of medical images. For this task, a subset of the extended Radiology Objects in COntext (ROCO) dataset was provided. We utilized 2 approaches to classify images: hashing similarities and convolutional neural networks. Hashing similarity was able to detect duplicate images in the dataset, but did poorly in classifying images. Neural networks did better in classifying images, but our model had problems with low frequency concepts.
CMRE-UoG team at ImageCLEFmedical Caption 2022: Concept Detection and Image Captioning

This work presents the proposed solutions of our team for the ImageCLEFmedical Caption 2022 task [1]. This task is structured as two subtasks: (1) Concept Detection subtask – which consists of detecting Concept Unique Identifiers (CUIs) from the Unified Medical Language System (UMLS) [2] attributed to each image; and (2) the Caption Prediction subtask – which involves generating an accurate description of the content of the image, based on the concepts detected in the first subtask. For both subtasks, the dataset corresponds to a subset of the Radiology Objects in the COntext (ROCO) dataset [3]. In the Concept Detection subtask, we experiment with two different strategies: a) supervised learning – we train a Convolutional Neural Network (CNN) [4, 5] to classify the full set of CUIs; b) image retrieval – we retrieve the top 𝐾 most “similar” images from the training set based on the cosine similarity score between the image representations (extracted from the last average pooling layer), and combine the associated CUIs using a soft majority voting approach, similar to the ImageCLEFmed Caption 2021 winning approach [6]. Our best submission consists of the second image retrieval approach, for which we used an ensemble of five different CNNs. This approach ranked 2nd with an F1 score equal to 0.451, with a margin of approximately 5 × 10 − 4 from the 1st position. In the Caption Prediction subtask, we adopt an image encoder-decoder Transformer model [7], which takes as input the image representation – generated using a CNN image encoder – and generates a text caption describing the image. Furthermore, we considered a multimodal encoder-decoder Trans-former model, which differs from the previous by taking as an additional input the CUIs extracted from the previous subtask alongside an image representation. Our multimodal approach ranked 6th, with a BLEU score [8] of 0.291, and ranked 1st place in terms of ROUGE [9] (the secondary metric for this subtask), with a score of 0.201.
The Pearl Retriever: Two-Stage Retrieval for Pairs of Argumentative Sentences

This paper describes the retrieval approach submitted by Team Pearl to the first Touché shared task at CLEF 2022 [1]. The model combines two retrieval pipelines to obtain pairs of argumentative sentences for a given query that relates to a controversial topic. The first pipeline uses a Dirichlet model to identify relevant arguments while the second applies a DPH model to retrieve relevant sentences. Both sentences and arguments are filtered using pre-calculated scores of argumentative quality and only sentences that belong to one of the remaining arguments are presented as results. We experimented with reranking retrieved arguments using an adapted version of the ArgRank proposed by Wachsmuth et al. [2] but did not find our implementation to improve retrieval performance beyond chance effects. Furthermore, we evaluated different approaches of matching sentences to form coherent pairs and found the naive approach of choosing partners from the immediate neighbourhood of a sentence in its parent argument to outperform more sophisticated solutions.
Style Change Detection: Method Based On Pre-trained Model And Similarity Recognition

For the Style Change Detection task, given a document, the purpose is to determine the number of authors and where authors change. We treat it as a classification problem. For Task1, given a text written by two authors that contains a single style change only, find the position of this change. Task2 is to find all locations where the writing style has changed on given text written by two or more authors. Finally, Task3 is to determine the position of all writing style changes for a text written by two or more authors. Now the style changes occur not only between paragraphs but also at the sentence level. This paper proposes a method of Writing Style Similarity. We treat Task1 and Task3 as binary classification and Task2 as multi-classification. We use ELECTRA, which is a better model than BERT in discriminating tasks, and we choose different versions of models for various tasks. Our approach offers several opportunities for further research. The F1 scores of Task1, Task2, and Task3 are 0. 7346, 0. 4687 and 0. 6720 respectively. Among them, Task2 added two evaluation indicators this year: Diarization Error Rate (DER) and Jaccard Error Rate (JER). Our scores are 0. 2380 and 0. 3138.