Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of head impacts and chemical irritation on elite athletes’ olfaction

ABSTRACT Limited research exists on athletes’ olfaction. As diet is an elementary part of an athlete’s lifestyle and training, it is important to know whether the sport discipline itself carries a risk for olfactory impairment. The aim of this study was to evaluate whether elite swimmers (chemical irritation), boxers (head impact) or soccer players (head impact) are at risk for impaired olfaction. Elite-level male swimmers (n = 30), boxers (n = 35) and soccer players (n = 30) aged 18–40 years were recruited from Finnish sport clubs. Floorball players (n = 30) were recruited as a control group. All participants filled in a questionnaire about their training history, nasal and sinonasal diseases, asthma, nasal operations and traumas, smoking and self-evaluation of olfaction and taste function. Sniffin’ Sticks odour identification test with 12 different odorants and anterior rhinoscopy were performed on all participants. The mean score from the smelling test did not differ between the sport groups. Sinonasal diseases and bronchial asthma were more common among swimmers than among the other athletes. Rhinitis symptoms were common among all athletes. Boxing, soccer or swimming does not seem to affect sense of smell. The majority of our participating elite athletes had normal olfaction, even if they had had a long history of active sports. Highlights Boxing, soccer or swimming does not seem to affect sense of smell. Rhinitis symptoms were prevalent among all elite athlete groups in our study. Athletes with hyposmia can perceive their own decreased olfaction. Swimmers have more bronchial asthma and sinus diseases than other athlete groups.
Effectiveness of mobile health interventions to improve nasal corticosteroid adherence in allergic rhinitis: A systematic review

Abstract Background Mobile health interventions (MHI) offer the potential to help improve nasal corticosteroid (NCS) adherence in allergic rhinitis (AR). The aim of this systematic review was to summarise the current evidence on the effectiveness of MHI for improving NCS adherence in AR. Methods We systematically searched MEDLINE, Embase and the Cochrane Central register of Controlled Trials (CENTRAL) for randomised controlled trials filtered for publication dates between 2010 and 2021. We evaluated the effects of MHI aiming to improve NCS adherence on self‐management outcomes in AR and comorbid conditions. Two reviewers independently screened potential studies, extracted study characteristics and outcomes from eligible papers and assessed risk of bias using the Cochrane Risk of Bias tool 2.0. High heterogeneity precluded meta‐analysis. Data were descriptively and narratively synthesised. Results Our searches identified 776 individual studies of which 4 met the inclusion criteria. These studies were heterogeneous with respect to participant, intervention and outcome characteristics. We considered all outcome‐specific overall risk of bias assessments to be of high risk of bias except for two studies examining NCS adherence which received ‘some concern’ grades. The three studies which reported on NCS adherence found that MHI were associated with improvement in NCS adherence. Significant MHI‐associated improvement in symptoms or disease‐specific quality of life was found in one study each, whilst no study reported significant differences in nasal patency. Conclusions Whilst MHI showed potential to improve NCS adherence, their effect on clinical outcomes varied. Furthermore, robust studies with longer intervention durations are needed to adequately assess effects of MHI and their individual features on NCS adherence and clinical outcomes.
2.2.2 Harmonizing Systems and Software Cost Estimation

The objective of this paper is to examine the gaps and overlaps between software engineering and systems engineering cost models with intent to harmonize the estimates in engineering estimation. In particular, we evaluate the central assumptions of the COSYSMO and COCOMO II models and propose an approach to identify gaps and overlaps between them. We provide guidelines on how to reconcile and resolve the identified gaps and overlaps. The ultimate purpose of this work is to develop effective techniques for accurately estimating the combined systems and software engineering effort for software‐intensive systems.
Automated Detection of Patients in Hospital Video Recordings

In a clinical setting, epilepsy patients are monitored via video electroencephalogram (EEG) tests. A video EEG records what the patient experiences on videotape while an EEG device records their brainwaves. Currently, there are no existing automated methods for tracking the patient’s location during a seizure, and video recordings of hospital patients are substantially different from publicly available video benchmark datasets. For example, the camera angle can be unusual, and patients can be partially covered with bedding sheets and electrode sets. Being able to track a patient in real-time with video EEG would be a promising innovation towards improving the quality of healthcare. Specifically, an automated patient detection system could supplement clinical oversight and reduce the resource-intensive efforts of nurses and doctors who need to continuously monitor patients. We evaluate an ImageNet pre-trained Mask R-CNN, a standard deep learning model for object detection, on the task of patient detection using our own curated dataset of 45 videos of hospital patients. The dataset was aggregated and curated for this work. We show that without fine-tuning, ImageNet pre-trained Mask R-CNN models perform poorly on such data. By fine-tuning the models with a subset of our dataset, we observe a substantial improvement in patient detection performance, with a mean average precision of 0.64. We show that the results vary substantially depending on the video clip.
Efficacy and safety of therapeutic use of cannabis derivatives and their synthetic analogs: Overview of systematic reviews

The debate on the use of cannabinoids for therapeutic purposes is constantly on the rise. This overview aimed to map the evidence on the therapeutic effects of cannabis derivatives and their synthetic analogs. Systematic reviews (SRs) of randomized trials were identified through a comprehensive search in several databases, and their methodological quality were evaluated with AMSTAR‐2. The results for main outcomes are presented, prioritizing those from updated and better quality SRs. Finally, 68 SRs, addressing 37 different health conditions, were included. The methodological quality was high for eight SRs. The evidence certainty (GRADE) for the effects of cannabinoids is not high for any of the outcomes identified. Evidence certainty was moderate for the following: (a) cannabidiol appears to be beneficial for quality of life but increases the risk of adverse events in ulcerative colitis; (b) cannabinoids in general appear to have no clinically important benefit for chronic non‐oncologic pain, spasticity‐related pain in multiple sclerosis, or for acute post‐operative pain; (c) cannabinoids in general appear to have a benefit in reducing chemotherapy‐related nausea and vomiting. For all other outcomes from remaining comparisons, the evidence certainty was low, very low, or not evaluated, which prevents recommendations for or against their routine use.
Effects of quercetin on ovarian function and regulation of the ovarian PI3K/Akt/FoxO3a signalling pathway and oxidative stress in a rat model of cyclophosphamide‐induced premature ovarian failure

To investigate the ability of quercetin to improve ovarian function and inhibit ovarian oxidative stress through the PI3K/Akt/FoxO3a signalling pathway in a rat model of premature ovarian failure (POF), we constructed a POF rat model with cyclophosphamide (CTX) and treated it with quercetin. Haematoxylin and eosin staining (H&E staining) was used to observe the morphological changes of the ovaries. The serum levels of AMH, E2, FSH, SOD, GSH‐Px and MDA were determined by enzyme‐linked immunosorbent assays. The expression of phosphatidylinositol 3‐kinase (PI3K), protein kinase B (Akt), forkhead box O3a (FoxO3a) and their phosphorylated forms AMH, FSH and their receptors in the ovary were detected by western blots. The mRNA expression of PI3K, Akt, FOXO3a, AMH, FSH and their receptors was detected by qRT‐PCR. Our results showed that quercetin could significantly increase the expression of AMH, E2, SOD and GSH‐Px, upregulate the protein expression of AMH, FSH and its receptor and decrease the expression ratio of phosphorylated PI3K, Akt, FOXO3a and the unphosphorylated forms. Moreover, quercetin inhibited the mRNA expression of PI3K, Akt and FOXO3a. These results suggest that quercetin can restore ovarian function and inhibit oxidative stress by regulating the PI3K/Akt/FoxO3a signalling pathway.
Clinical application of three-dimensional transvaginal ultrasonography in the diagnosis of intrauterine adhesions

Objective The present study aimed to evaluate the diagnostic accuracy of three-dimensional transvaginal ultrasonography (3D-TVS) for intrauterine adhesions (IUA). Methods We performed a retrospective cohort study. A total of 500 women aged 19 to 46 years with uterine lesions who received treatment from the Department of Obstetrics and Gynecology were enrolled. Endometrial 3D imaging was conducted to obtain the display plane and 3D-TVS parameters. Patients also underwent hysteroscopy for a definitive diagnosis. Results For diagnosing IUA, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy of 3D-TVS were 98.8%, 90.8%, 91.4%, 98.7%, and 94.8%, respectively. For diagnosing a submucosal myoma, the sensitivity, specificity, PPV, NPV, and overall accuracy of 3D-TVS were 88.2%, 97.9%, 88.2%, 97.9%, and 96.4%, respectively. For diagnosing endometrial polyps, the sensitivity, specificity, PPV, NPV, and overall accuracy of 3D-TVS were 94.7%, 96.8%, 92.9%, 97.7%, and 96.2%, respectively. Conclusions Before hysteroscopy, 3D-TVS, as a method of screening, has great value for comparing differences before and after treatment, and for evaluating therapeutic effects.
Moving and Handling Children After Death: An Inductive Thematic Analysis of the Factors That Influence Decision Making by Children's Hospice Staff

Hospices for children and adolescents in the United Kingdom provide care to the bodies of deceased children in specially designed chilled bedrooms called “cool rooms. ” In an effort to develop resources to support hospice practitioners to provide this specialist area of care, this study aimed to identify the factors that influence decision making when moving and handling children's bodies after death in a hospice cool bedroom. An internet-based survey was sent to all practitioners employed by 1 children's hospice. A total of 94.9% of eligible staff responded (n = 56). An inductive approach to thematic analysis was undertaken, using a 6-phase methodological framework. Three core themes were identified that inform practitioners' perception of the appropriateness of moving and handling decisions: care of the body, stages of care, and method of handling. The complexity of decision making and variation in practice was identified. Practitioners relied on both analytical and initiative decision making, with more experienced practitioners using an intuitive approach. Evidence-based policy and training influence the perception of appropriateness and the decisions and behavior of practitioners. The development of a policy and education framework would support practitioners in caring for children's bodies after death, standardizing expectations and measures of competence in relation to moving and handling tasks.
Code Clone Detection based on Event Embedding and Event Dependency

The code clone detection method based on semantic similarity has important value in software engineering tasks (e.g., software evolution, software reuse). Traditional code clone detection technologies pay more attention to the similarity of code at the syntax level, and less attention to the semantic similarity of the code. As a result, candidate codes similar in semantics are ignored. To address this issue, we propose a code clone detection method based on semantic similarity. By treating code as a series of interdependent events that occur continuously, we design a model namely EDAM to encode code semantic information based on event embedding and event dependency. The EDAM model uses the event embedding method to model the execution characteristics of program statements and the data dependence information between all statements. In this way, we can embed the program semantic information into a vector and use the vector to detect codes similar in semantics. Experimental results show that the performance of our EDAM model is superior to state-of-the-art open source models for code clone detection.
Early-time searches for coherent radio emission from short GRBs with the Murchison Widefield Array

Abstract Many short gamma-ray bursts (GRBs) originate from binary neutron star mergers, and there are several theories that predict the production of coherent, prompt radio signals either prior, during, or shortly following the merger, as well as persistent pulsar-like emission from the spin-down of a magnetar remnant. Here we present a low frequency (170–200 MHz) search for coherent radio emission associated with nine short GRBs detected by the Swift and/or Fermi satellites using the Murchison Widefield Array (MWA) rapid-response observing mode. The MWA began observing these events within 30–60 s of their high-energy detection, enabling us to capture any dispersion delayed signals emitted by short GRBs for a typical range of redshifts. We conducted transient searches at the GRB positions on timescales of 5 s, 30 s, and 2 min, resulting in the most constraining flux density limits on any associated transient of 0.42, 0.29, and 0.084 Jy, respectively. We also searched for dispersed signals at a temporal and spectral resolution of 0.5 s and 1.28 MHz, but none were detected. However, the fluence limit of 80–100 Jy ms derived for GRB 190627A is the most stringent to date for a short GRB. Assuming the formation of a stable magnetar for this GRB, we compared the fluence and persistent emission limits to short GRB coherent emission models, placing constraints on key parameters including the radio emission efficiency of the nearly merged neutron stars ( $\epsilon_r\lesssim10^{-4}$ ), the fraction of magnetic energy in the GRB jet ( $\epsilon_B\lesssim2\times10^{-4}$ ), and the radio emission efficiency of the magnetar remnant ( $\epsilon_r\lesssim10^{-3}$ ). Comparing the limits derived for our full GRB sample (along with those in the literature) to the same emission models, we demonstrate that our fluence limits only place weak constraints on the prompt emission predicted from the interaction between the relativistic GRB jet and the interstellar medium for a subset of magnetar parameters. However, the 30-min flux density limits were sensitive enough to theoretically detect the persistent radio emission from magnetar remnants up to a redshift of $z\sim0.6$ . Our non-detection of this emission could imply that some GRBs in the sample were not genuinely short or did not result from a binary neutron star merger, the GRBs were at high redshifts, these mergers formed atypical magnetars, the radiation beams of the magnetar remnants were pointing away from Earth, or the majority did not form magnetars but rather collapse directly into black holes.
Generalized Fully Coherent Closed-Form Receiver Design for Joint Radar and Communication System

Conventional radars repeat the transmission of the same waveform after a predefined interval of time called pulse-repetition-interval (PRI). This technique helps estimate the range and Doppler shift of the targets and suppress clutter. However, in dual-function radar communication (DFRC), a different symbol waveform is transmitted after each PRI. Depending on the number of targets, radar receiver output yields several peaks representing different targets’ ranges. Each peak comes with its side-lobes called range-side-lobes (RSL). In DFRC, due to different symbol waveform transmission, peaks and RSLs do not remain coherent, making Doppler shift estimation and clutter suppression challenging tasks. In most of the available literature, iterative receive filters have been designed for DFRC to minimize RSLs and achieve coherent output for different waveforms. However, the proposed receive filter does not guarantee coherent output for more than two waveforms. In contrast, we proposed two novel closed-form algorithms to design receive filters for DFRC that guarantee coherent output response for several waveforms and suppress RSLs. Simulation results demonstrate that the proposed receivers achieve full coherency, and the RSLs are significantly lower than the conventional method. Furthermore, the advantage of achieving coherent output response is shown in target detection and bit-error-rate improvement.
Interactive and interpretable machine learning models for human machine collaboration

I envision a system that enables successful collaborations between humans and machine learning models by harnessing the relative strength to accomplish what neither can do alone. Machine learning techniques and humans have skills that complement each other — machine learning techniques are good at computation on data at the lowest level of granularity, whereas people are better at abstracting knowledge from their experience, and transferring the knowledge across domains. The goal of this thesis is to develop a framework for human-in-the-loop machine learning that enables people to interact effectively with machine learning models to make better decisions, without requiring in-depth knowledge about machine learning techniques. Many of us interact with machine learning systems everyday. Systems that mine data for product recommendations, for example, are ubiquitous. However these systems compute their output without end-user involvement, and there are typically no life or death consequences in the case the machine learning result is not acceptable to the user. In contrast, domains where decisions can have serious consequences (e.g., emergency response panning, medical decision-making), require the incorporation of human experts’ domain knowledge. These systems also must be transparent to earn experts’ trust and be adopted in their workflow. The challenge addressed in this thesis is that traditional machine learning systems are not designed to extract domain experts’ knowledge from natural workflow, or to provide pathways for the human domain expert to directly interact with the algorithm to interject their knowledge or to better understand the system output. For machine learning systems to make a real-world impact in these important domains, these systems must be able to communicate with highly skilled human experts to leverage their judgment and expertise, and share useful information or patterns from the data. In this thesis, I bridge this gap by building human-in-the-loop machine learning models and systems that compute and communicate machine learning results in ways that are compatible with the human decision-making process, and that can readily incorporate human experts’ domain knowledge. I start by building a machine learning model that infers human teams’ planning decisions from the structured form of natural language of team meetings. I show that the model can infer a human teams’ final plan with 86% accuracy
Effective connectivity in the default mode network after paediatric traumatic brain injury

Children who experience a traumatic brain injury (TBI) are at elevated risk for a range of negative cognitive and neuropsychological outcomes. Identifying which children are at greatest risk for negative outcomes can be difficult due to the heterogeneity of TBI. To address this barrier, the current study applied a novel method of characterizing brain connectivity networks, Bayesian multi‐subject vector autoregressive modelling (BVAR‐connect), which used white matter integrity as priors to evaluate effective connectivity—the time‐dependent relationship in functional magnetic resonance imaging (fMRI) activity between two brain regions—within the default mode network (DMN). In a prospective longitudinal study, children ages 8–15 years with mild to severe TBI underwent diffusion tensor imaging and resting state fMRI 7 weeks after injury; post‐concussion and anxiety symptoms were assessed 7 months after injury. The goals of this study were to (1) characterize differences in positive effective connectivity of resting‐state DMN circuitry between healthy controls and children with TBI, (2) determine if severity of TBI was associated with differences in DMN connectivity and (3) evaluate whether patterns of DMN effective connectivity predicted persistent post‐concussion symptoms and anxiety. Healthy controls had unique positive connectivity that mostly emerged from the inferior temporal lobes. In contrast, children with TBI had unique effective connectivity among orbitofrontal and parietal regions. These positive orbitofrontal‐parietal DMN effective connectivity patterns also differed by TBI severity and were associated with persisting behavioural outcomes. Effective connectivity may be a sensitive neuroimaging marker of TBI severity as well as a predictor of chronic post‐concussion symptoms and anxiety.
IDR: Self-Supervised Image Denoising via Iterative Data Refinement

The lack of large-scale noisy-clean image pairs restricts supervised denoising methods' deployment in actual applications. While existing unsupervised methods are able to learn image denoising without ground-truth clean images, they either show poor performance or work under impractical settings (e.g., paired noisy images). In this paper, we present a practical unsupervised image denoising method to achieve state-of-the-art denoising performance. Our method only requires single noisy images and a noise model, which is easily accessible in practical raw image denoising. It performs two steps iteratively: (1) Constructing a noisier-noisy dataset with random noise from the noise model; (2) training a model on the noisier-noisy dataset and using the trained model to refine noisy images to obtain the targets used in the next round. We further approximate our full iterative method with a fast algorithm for more efficient training while keeping its original high performance. Experiments on real-world, synthetic, and correlated noise show that our proposed unsupervised denoising approach has superior performances over existing unsupervised methods and competitive performance with supervised methods. In addition, we argue that existing denoising datasets are of low quality and contain only a small number of scenes. To evaluate raw image denoising performance in real-world applications, we build a high-quality raw image dataset SenseNoise-500 that contains 500 real-life scenes. The dataset can serve as a strong benchmark for better evaluating raw image denoising. Code and dataset will be released11https://github.com/zhangyi-3/IDR