Miracle-dz/UniFAR-FTUs huggingface dataset:
Experience and response to a randomised controlled trial of extended-release injectable buprenorphine versus sublingual tablet buprenorphine and oral liquid methadone for opioid use disorder: protocol for a mixed-methods evaluation

Introduction Opioid use disorder (OUD) is a debilitating and persistent disorder. The standard-of-care treatment is daily maintenance dosing of sublingual buprenorphine (BUP-SL) or oral methadone (MET). Monthly, extended-release, subcutaneous injectable buprenorphine (BUP-XR) has been developed to enhance treatment effectiveness. This study aims to investigate the experiences of participants who have been offered BUP-XR (evaluation 1), health-related quality-of-life among participants who have opted to receive BUP-XR longer term (evaluation 2) and the experiences of participants allocated to receive BUP-XR or BUP-SL or MET with the offer of adjunctive personalised psychosocial intervention (evaluation 3). Methods and analysis Three qualitative–quantitative (mixed-methods) evaluations embedded in a five-centre, head-to-head, randomised controlled trial of BUP-XR versus BUP-SL and MET in the UK. Evaluation 1 is a four-centre interview anchored on an OUD-related topic guide and conducted after the 24-week trial endpoint. Evaluation 2 is a two-centre interview anchored on medications for opioid use disorder-specific quality-of-life topic guide conducted among participants after 12–24 months. Evaluation 3: single-centre interview after the 24-week trial endpoint. All evaluations include selected trial clinical measures, with evaluation 2 incorporating additional questionnaires. Target participant recruitment for evaluations 1 and 2 is 15 participants per centre (n=60 and n=30, respectively). Recruitment for evaluation 3 is 15 participants per treatment arm (n=30). Each evaluation will be underpinned by theory, drawing on constructs from the behavioural model for health service use or the health-related quality-of-life model. Qualitative data analysis will be by iterative categorisation. Ethics and dissemination Study protocol, consent materials and questionnaires were approved by the London-Brighton and Sussex research ethics committee (reference: 19/LO/0483) and the Health Research Authority (IRAS project number 255522). Participants will be provided with information sheets and informed written consent will be obtained for each evaluation. Study findings will be disseminated through peer-reviewed scientific journals. Trial registration number 2018-004460-63.
Triglyceride glucose index is independently associated with aortic intima-media thickness in patients without known atherosclerotic cardiovascular disease or diabetes

Background: The triglyceride-glucose (TyG) index is a reliable marker of insulin resistance. We aimed to investigate the relationship between the TyG index and aortic intima-media thickness (IMT) in individuals without diabetes mellitus and atherosclerotic cardiovascular disease undergoing transoesophageal echocardiography due to suspected structural heart disease. Methods: One hundred twenty-two patients enrolled in the study between January 2021 and June 2021. Patients were divided into two groups according to their median IMT; patients with an IMT equal to or less than 1.42 mm were low IMT group, and higher than 1.42 mm were high IMT group. Triglyceride-glucose index were higher in the high IMT group (8.69 ± 0.59 vs. 8.37 ± 0.53, p = .003). Results: Triglyceride-glucose index was correlated with IMT (r = 0.259, p = .005) and in binary logistic regression analysis age and TyG was independently associated with having high IMT (β = 0.076, exp(B)=1.079 95%CI = 1.041–1.118, p < . 001 and β = 0.847, exp(B)= 2.334 95%CI= 1.048–5.199, p = . 038, respectively). Conclusion: We found an independent relationship between TyG and IMT of the thoracic aorta, an early marker of subclinical atherosclerosis in patients without known atherosclerotic cardiovascular disease or diabetes. Therefore, the TyG index can identify high-risk subjects in the general population.
A BERT Based Method for Continuous Estimation of Cross-Subject Hand Kinematics From Surface Electromyographic Signals

Estimation of hand kinematics from surface electromyographic (sEMG) signals provides a non-invasive human-machine interface. This approach is usually subject-specific, so that the training on one individual does not generalise to different subjects. In this paper, we propose a method based on Bidirectional Encoder Representation from Transformers (BERT) structure to predict the movement of hands from the root mean square (RMS) feature of the sEMG signal following $\mu $ -law normalization. The method was tested for within-subject and cross-subject conditions. We trained the model with two hard sample mining methods, Gradient Harmonizing Mechanism (GHM) and Online Hard Sample Mining (OHEM). The proposed method was compared with classic approaches, including long short-term memory (LSTM) and Temporal Convolutional Network (TCN) as well as a recent method called Long Exposure Convolutional Memory Network (LE-ConvMN). Correlation coefficient (CC), normalized root mean square error (NRMSE) and time costs were used as performance metrics. Our method (sBERT-OHEM) achieved state-of-the-art performance in cross-subject evaluation as well as high performance in subject-specific tests on the Ninapro dataset. The above tests are based on the same randomly selected 10 subjects. Generally, in the cross-subject situation, with the increasing of the subjects’ number, it unavoidably leads to the decline of the performance. While the performance of our method on 38 subjects was significantly higher than the other methods on 10 subjects in cross-subject conditions, which further verified the advantage of our methods.
Molecular interplay of ADAMTS13-MDTCS and von willebrand Factor-A2: deepened insights from extensive atomistic simulations

Abstract Thrombotic thrombocytopenic purpura (TTP) is a rare and life-threatening disease. One hallmark is severe ADAMTS13 deficiency, causing ultra-large von Willebrand factor (VWF) multimers to accumulate, leading to microthrombi and lastly to microangiopathic hemolytic anemia and severe thrombocytopenia. Despite great success in recent decades, the molecular picture of the interaction between VWF and ADAMTS13 remains vague. Here, we utilized modern replica-exchange molecular dynamics simulations with the TIGER2h method to sample a vast configurational space of the isolated ADAMTS13-MDTCS domains and the exposure to its substrate and activating cofactor – the unraveled VWF-A2 domain. The sampling of binding sites and conformations was guided and filtered in agreement with available experimental evidence. We provide comprehensive information on exosites for each domain and direct pairs of interacting amino acids, for the first time. The major binding cluster for the active site of the MP domain contrasts the previous mapping of VWF-A2 residues and reciprocal binding pockets. Two major binding modes are revealed and provide access to conformational changes of an extended gatekeeper tetrad upon overcoming local latency during substrate binding and to a dedicated recruitment mechanism. Our work adds the first molecular interaction model that places previous experimental results in perspective to better understand disease-related mutations towards improved therapies. Numerous empirical targets are proposed to verify the given binding modes, to refine the overall picture of MP binding pockets, the role of Dis binding in MP activation and the passage of the Cys-rich domain through VWF-A2, thus deepening the understanding of a highly dynamic interplay. Communicated by Ramaswamy H. Sarma
Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement

Abstract This ESGE Position Statement defines the expected value of artificial intelligence (AI) for the diagnosis and management of gastrointestinal neoplasia within the framework of the performance measures already defined by ESGE. This is based on the clinical relevance of the expected task and the preliminary evidence regarding artificial intelligence in artificial or clinical settings. Main recommendations: (1) For acceptance of AI in assessment of completeness of upper GI endoscopy, the adequate level of mucosal inspection with AI should be comparable to that assessed by experienced endoscopists. (2) For acceptance of AI in assessment of completeness of upper GI endoscopy, automated recognition and photodocumentation of relevant anatomical landmarks should be obtained in ≥90% of the procedures. (3) For acceptance of AI in the detection of Barrett’s high grade intraepithelial neoplasia or cancer, the AI-assisted detection rate for suspicious lesions for targeted biopsies should be comparable to that of experienced endoscopists with or without advanced imaging techniques. (4) For acceptance of AI in the management of Barrett’s neoplasia, AI-assisted selection of lesions amenable to endoscopic resection should be comparable to that of experienced endoscopists. (5) For acceptance of AI in the diagnosis of gastric precancerous conditions, AI-assisted diagnosis of atrophy and intestinal metaplasia should be comparable to that provided by the established biopsy protocol, including the estimation of extent, and consequent allocation to the correct endoscopic surveillance interval. (6) For acceptance of artificial intelligence for automated lesion detection in small-bowel capsule endoscopy (SBCE), the performance of AI-assisted reading should be comparable to that of experienced endoscopists for lesion detection, without increasing but possibly reducing the reading time of the operator. (7) For acceptance of AI in the detection of colorectal polyps, the AI-assisted adenoma detection rate should be comparable to that of experienced endoscopists. (8) For acceptance of AI optical diagnosis (computer-aided diagnosis [CADx]) of diminutive polyps (≤5 mm), AI-assisted characterization should match performance standards for implementing resect-and-discard and diagnose-and-leave strategies. (9) For acceptance of AI in the management of polyps ≥ 6 mm, AI-assisted characterization should be comparable to that of experienced endoscopists in selecting lesions amenable to endoscopic resection.
InterPhases : phase-theoretic investigations of linguistic interfaces

As the title suggests, this book is a collection of 13 papers investigating linguistic interfaces in terms of phases. The notion of phase, originally introduced by Chomsky (2000), has been one of the central topics in the study of syntax. Since a phase defines a domain of Transfer/Spell-Out, more and more attention has been paid to interface issues in recent years. The present volume offers good examples of such investigations, showing the promising and productive aspects of the phase-theoretic study of the interfaces between syntax and other linguistic components such as phonology and semantics.
Coronary perforation incidence, outcomes and temporal trends (COPIT): a systematic review and meta-analysis

Coronary perforation is a potentially life-threatening complication of percutaneous coronary intervention (PCI). We studied incidence, outcomes and temporal trends following PCI-related coronary artery perforation (CAP). Methods Prospective systematic review and meta-analysis including meta-regression using MEDLINE and EMBASE to November 2020. We included ‘all-comer’ PCI cohorts including large PCI registries and randomised controlled trials and excluding registries or trials limited to PCI in high-risk populations such as chronic total occlusion PCI or cohorts treated only with atheroablative devices. Regression analysis and corresponding correlation coefficients were performed comparing perforation incidence, mortality rate, tamponade rate and the rate of Ellis III perforations against the midpoint (year) of data collection to determine if a significant temporal relationship was present. Results 3997 studies were screened for inclusion. 67 studies met eligibility criteria with a total of 5 568 191 PCIs included over a 38-year period (1982–2020). The overall pooled incidence of perforation was 0.39% (95% CI 0.34% to 0.45%) and remained similar throughout the study period. Around 1 in 5 coronary perforations led to tamponade (21.1%). Ellis III perforations are increasing in frequency and account for 43% of all perforations. Perforation mortality has trended lower over the years (7.5%; 95% CI 6.7% to 8.4%). Perforation risk factors derived using meta-regression were female sex, hypertension, chronic kidney disease and previous coronary bypass grafting. Coronary perforation was most frequently caused by distal wire exit (37%) followed by balloon dilation catheters (28%). Covered stents were used to treat 25% of perforations, with emergency cardiac surgery needed in 17%. Conclusion Coronary perforation complicates approximately 1 in 250 PCIs. Ellis III perforations are increasing in incidence although it is unclear whether this is due to reporting bias. Despite this, the overall perforation mortality rate (7.5%) has trended lower in recent years. Limitations of our findings include bias that may be introduced through analysis of multidesign studies and registries without pre-specified standardised perforation reporting CMore research into coronary perforation management including the optimal use of covered stents seems warranted. PROSPERO registration number CRD42020207881.
Forced harmonic response analysis of nonlinear structures using describing functions

A semianalytical quasilinear method based on the describing function formulation is proposed for the harmonic response analysis of structures with symmetrical nonlinearities. The equations of motion are converted to a set of nonlinear algebraic equations. The linear and nonlinear parts of the structure are dealt with separately, the former being represented by the constant linear receptance matrix, and the latter by the generalized quasilinear matrix which is updated at each iteration. Several examples dealing with cubic stiffness, piecewise linear stiffness, and coulomb friction type of nonlinearities are presented
Subject adaptation convolutional neural network for EEG-based motor imagery classification

Objective. Deep transfer learning has been widely used to address the nonstationarity of electroencephalogram (EEG) data during motor imagery (MI) classification. However, previous deep learning approaches suffer from limited classification accuracy because the temporal and spatial features cannot be effectively extracted. Approach. Here, we propose a novel end-to-end deep subject adaptation convolutional neural network (SACNN) to handle the problem of EEG-based MI classification. Our proposed model jointly optimizes three modules, i.e. a feature extractor, a classifier, and a subject adapter. Specifically, the feature extractor simultaneously extracts the temporal and spatial features from the raw EEG data using a parallel multiscale convolution network. In addition, we design a subject adapter to reduce the feature distribution shift between the source and target subjects by using the maximum mean discrepancy. By minimizing the classification loss and the distribution discrepancy, the model is able to extract the temporal-spatial features to the prediction of a new subject. Main results. Extensive experiments are carried out on three EEG-based MI datasets, i.e. brain–computer interface (BCI) competition IV dataset IIb, BCI competition III dataset IVa, and BCI competition IV dataset I, and the average accuracy reaches to 86.42%, 81.71% and 79.35% on the three datasets respectively. Furthermore, the statistical analysis also indicates the significant performance improvement of SACNN. Significance. This paper reveals the importance of the temporal-spatial features on EEG-based MI classification task. Our proposed SACNN model can make fully use of the temporal-spatial information to achieve the purpose.
Bounded Littlewood identities

We describe a method, based on the theory of Macdonald-Koornwinder polynomials, for proving bounded Littlewood identities. Our approach provides an alternative to Macdonald's partial fraction technique and results in the first examples of bounded Littlewood identities for Macdonald polynomials. These identities, which take the form of decomposition formulas for Macdonald polynomials of type (R,S) in terms Macdonald polynomials of type A, are q,t-analogues of known branching formulas for characters of the symplectic, orthogonal and special orthogonal groups, important in the theory of plane partitions. As applications of our results we obtain combinatorial formulas for characters of affine Lie algebras, Rogers-Ramanujan identities for such algebras complementing recent results of Griffin et al., and transformation formulas for Kaneko-Macdonald-type hypergeometric series.
Exclusive electroproduction and off-diagonal parton distributions

Off-diagonal parton distributions occur in several hard exclusive reactions. They extend the study of hadron structure beyond what can be learned from ordinary distributions and have a particularly rich spin structure. The hard scattering subprocesses in electroproduction of mesons and of real photons satisfy helicity selection rules, which provide powerful tools to test leading-twist dominance at a given value of the hard scale.
Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey

Reinforcement learning takes sequential decision-making approaches by learning the policy through trial and error based on interaction with the environment. Combining deep learning and reinforcement learning can empower the agent to learn the interactions and the distribution of rewards from state-action pairs to achieve effective and efficient solutions in more complex and dynamic environments. Deep reinforcement learning (DRL) has demonstrated astonishing performance in surpassing the human-level performance in the game domain and many other simulated environments. This paper introduces the basics of reinforcement learning and reviews various categories of DRL algorithms and DRL models developed for medical image analysis and radiation treatment planning optimization. We will also discuss the current challenges of DRL and approaches proposed to make DRL more generalizable and robust in a real-world environment. DRL algorithms, by fostering the designs of the reward function, agents interactions and environment models, can resolve the challenges from scarce and heterogeneous annotated medical image data, which has been a major obstacle to implementing deep learning models in the clinic. DRL is an active research area with enormous potential to improve deep learning applications in medical imaging and radiation therapy planning.
A Protocol for Coupling Volumetrically Dynamic In-Vitro Experiments to Numerical Physiology Simulation for a Hybrid Cardiovascular Model

Objective: The Physiology Simulation Coupled Experiment (PSCOPE) is a hybrid modeling framework that enables a physical fluid experiment to operate in the context of a closed-loop computational simulation of cardiovascular physiology. Previous PSCOPE methods coupled rigid experiments to a lumped parameter network (LPN) of physiology but are incompatible with volumetrically dynamic experiments where fluid volume varies periodically. We address this limitation by introducing a method capable of coupling multi-branch and volumetrically dynamic in-vitro experiments to an LPN. Methods: Our proposed method utilizes an iterative weighted-averaging algorithm to identify the unique solution waveforms for a given PSCOPE model. We confirm the accuracy of these PSCOPE solutions by integrating mathematical surrogates of in-vitro experiments directly into the LPN to derive reference solutions, which serve as the gold standard to validate the solutions obtained from using our proposed method to couple the same mathematical surrogates to the LPN. Finally, we illustrate a practical application of our PSCOPE method by coupling an in-vitro renal circulation experiment to the LPN. Results: Compared to the reference solution, the normalized root mean square error of the flow and pressure waveforms were 0.001%∼0.55%, demonstrating the accuracy of the coupling method. Conclusion: We successfully coupled the in-vitro experiment to the LPN, demonstrating the real-world performance within the constraints of sensor and actuation limitations in the physical experiment. Significance: This study introduces a PSCOPE method that can be used to investigate medical devices and anatomies that exhibit periodic volume changes, expanding the utility of the hybrid framework.
Synthesis of Axinyssa digitata Extract Directed Hybrid Nanoflower and Investigation of Its Antimicrobial Activity

First time in this study, the antibacterial effects of Axinyssa digitata sponge extracts and Axinyssa digitata-based cupper hybrid nanoflowers (Cu hNFs) were evaluated. Herein, hybrid nanoflowers (Cu hNFs) were produced by combining Axinyssa digitata sponge extract with Cu2+ ions in Phosphate-buffered saline (PBS) (at pH 7.4) at room temperature for three days using green synthesis method. The shape and size of hNFs were evaluated using scanning electron microscope (SEM) images. Energy dispersive X-ray spectroscopy (EDX) mapping was used to determine the presence of Cu metals and other components. X-ray diffraction (XRD) is a non-destructive analysis method that was used to determine of the crystallographic properties of materials and the phases they contain. Fourier-transform infrared spectroscopy (FT-IR) peaks were used to discuss the presence of functional groups that played a key role in the synthesis. The Cu-hNFs had antimicrobial activity against selected microorganisms. This research is expected to provide knowledge on hNFs synthesis and antimicrobial activity application investigations using Axinyssa digitata rather than biomolecules obtained through costly and time-consuming methods.
Optimizing and designing a leg shape to increase robustness of a running robot on rough terrain

The superior ability of dynamic legged locomotion in traversing rough terrain relative to wheeled or tracked mechanisms comes with the cost of fragile stability. Simple control methods that use only a few basic detection sensors and apply a single controller help robots keep their balance when traversing unforeseen rough terrain. Exploiting multiple controllers simultaneously, such as the free leg length and stiffness in our hopping monopod, can further improve robustness but is often mechanically hard to implement. This work demonstrates that a curved leg shape can improve the robustness of a robot to perturbations in both terrain levels and initial horizontal velocity without complicating the control scheme. Our work develops spring loaded inverted pendulum (SLIP) based models that manifest the coupling of the leg’s parameters and capture the rolling motion. We use these models to find an optimal combination of parameters that maximizes a measure for long-term stability−reaching a desired relative height above terrain. We demonstrate that when traversing unknown rough terrain, such optimal coupling can increase robustness to perturbations in the initial horizontal velocity by 93% relative to the optimal conventional SLIP model. We further demonstrate our results in experiments.