Miracle-dz/UniFAR-FTUs huggingface dataset:
Surgical benefits of bidirectional knotless barbed sutures over conventional sutures for uterine repair during cesarean section-A meta-analysis of randomized controlled trials

To analyze the surgical benefits of bidirectional knotless barbed suture (BS) compared with conventional sutures for uterine closure during cesarean section. The databases were searched using the following keywords: “Cesarean Section,” “Uterine closure,” “Barbed suture” and “Conventional suture. ” Randomized control trials reporting the comparison of bidirectional knotless BS with conventional sutures for closing uterine incision were included. The outcome measures were closing time of uterine incision, the number of additional hemostatic sutures used, blood loss parameters, and the total duration of surgery. A random or fixed-effects model was used to obtain the pooled estimates using the inverse variance method. The heterogeneity was assessed using the I2 test and the GRADE approach was used to assess the quality of evidence. Out of 15 full-text assessed, three randomized controlled trials were included. We observed significantly short uterine incision closure time with BS [standardised mean difference -1.51; 95% confidence interval (CI): -1.97, -1.06; I2=64%; GRADE approach evidence: Moderate], significantly lesser need of additional hemostatic sutures (risk ratio: 0.39; 95% CI: 0.28, 0.54; I2=0%; GRADE approach evidence: High) and significantly less blood loss during uterine incision closure [-0.47 (95% CI:-0.75, -0.19); I2 =0%; GRADE approach evidence: moderate]. with no significant difference in total blood loss, the need of blood transfusion, and total duration of surgery. The use of bidirectional knotless BS for uterine closure can reduce suturing time and the additional suture requirement.
Brucellosis in pregnancy and its response to the changing immunoglobulin A: A prospective controlled study

Objective: This study aimed to define the rare Brucella infection in pregnancy and its effects on immunoglobulins (Ig). Materials and Methods: This prospective study has conducted Brucella screening using the Rose Bengal test on pregnant and non-pregnant outpatients who did not show any specific Brucella symptoms. The immunoglobulin levels were measured using the enzyme-linked immunosorbent assay. The study group consisted of pregnant women who were at 20 weeks or below gestation and applied to our hospital outpatient clinic for routine check-ups. The control group consisted of healthy patients who applied for routine controls. Results: This study included a total of 584 participants, 293 of whom were controls and 291 were the study (pregnant) participants. The study revealed a 1.5% incidence of Brucella during pregnancy. In acute and chronic Brucella infection, lower levels of IgA response were observed in pregnant cases compared to the control group. Conclusion: Brucella infection is a disease that can cause fetal problems, especially in endemic areas. The role of the altered IgA response in pathologies that are associated with Brucella infection stands out as a new target for disease pathophysiology.
Inhibition of human recombinant T‐type calcium channels by phytocannabinoids in vitro

T‐type Ca channels (ICa) regulate neuronal excitability and contribute to neurotransmitter release. The phytocannabinoids Δ9‐tetrahydrocannabinol and cannabidiol effectively modulate T‐type ICa, but effects of other biologically active phytocannabinoids on these channels are unknown. We thus investigated the modulation of T‐type ICa by low abundance phytocannabinoids.
Who is @ease? Visitors’ characteristics and working method of professionally supported peer‐to‐peer youth walk‐in centres, anonymous and free of charge

Although mental disorders often emerge early in life, only a minority of young people receive timely and appropriate mental health care. A worldwide youth mental health movement aims to prevent development and persistence of psychiatric disorders. As part of this movement, the first four @ease centres were opened in the Netherlands. @ease is a youth driven, professionally supported initiative, providing peer‐to‐peer counselling, anonymous and free of charge, for people aged 12–25.
Assessing Bone Marrow Activity with [18F]FLT PET in Patients with Essential Thrombocythemia and Prefibrotic Myelofibrosis: A Proof of Concept

Objectives: This study aims to assess the value of FLT-PET as a non-invasive tool to differentiate between patients with ET and Pre-PMF. This study is a pilot study to have a proof of concept only. Methods: This is a prospective, interventional study where a total of 12 patients were included. Each patient underwent FLT PET imaging as well as bone marrow examination (gold standard). In addition, semi-quantitative (SUVmax and SUVmean) measurements of FLT uptake in the liver, spleen, and Lspine, SUVmean, as well as the Total Lesion Glycolysis (TLG) of the Lspine were performed. Results from the two patient cohorts were compared using = Kruskal-Wallis statistical test. A P-value of <. 05 is considered to be statistically significant. Results: The differences in FLT SUVmax and SUVmean measurements in the three organs (liver, spleen, and LSpine) between the ET and Pre-PMF patients were not statistically significant (P > .05). In contrast, TLG measurements in the LSpine were statistically different (P = .013), and therefore, compared to gold standard bone marrow results, TLG can separate ET and Pre-PMF patients. Conclusion: This study is a proof of concept about the potential to discriminate between ET and pre-PMF patients in a non-invasive way. TLG of the LSpine in FLT PET images is a potential quantitative parameter to distinguish between ET and pre-PMF patients.
Reactive Whole-Body Obstacle Avoidance for Collision-Free Human-Robot Interaction with Topological Manifold Learning

—Safe collaboration between human and robots in a common unstructured environment becomes increasingly critical with the emergence of Industry 4.0. However, to accomplish safe, robust, and autonomous collaboration with humans, modern robotic systems must possess not only effective proximity perception but also reactive obstacle avoidance. Unfortunately, for most robotic systems, their shared working environment with human operators may not always be static, instead often dynamically varying and being constantly cluttered with unanticipated obstacles or hazards. In this paper, we present a novel methodology of reactive whole-body obstacle avoidance methodology that safeguards the human who enters the robot’s workspace through achieving conﬂict-free human-robot interactions even in a dynamically constrained environment. Unlike existing Jacobian-type or geometric approaches, our proposed methodology leverages both topological manifold learning and latest deep learning advances, therefore can not only be readily generalized into other unseen problem settings, but also achieve high computing efﬁciency with concrete theoretical basis. Furthermore, in sharp contrast to the industrial cobot setting, our methodology allows a robotic arm to proactively avoid obstacles of arbitrary 3D shapes without direct contacting. To solidify our study, we implement and validate our methodology with a robotic platform consisting of dual 6-DoF robotic arms with optimized proximity sensor placement, both of which are capable of working collaboratively with different levels of interference. Speciﬁcally, one arm will perform reactive whole-body obstacle avoidance while achieving its pre-determined objective, with the other arm emulating the presence of a human collaborator with independent and potentially adversary movements. We have showcased the complete video demonstrations of our robotic experiments in this link : https: //sites.google. com/view/roa-a/home .
MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design

Molecule design is a fundamental problem in molecular science and has critical applications in a variety of areas, such as drug discovery, material science, etc. However, due to the large searching space, it is impossible for human experts to enumerate and test all molecules in wet-lab experiments. Recently, with the rapid development of machine learning methods, especially generative methods, molecule design has achieved great progress by leveraging machine learning models to generate candidate molecules. In this paper, we systematically review the most relevant work in machine learning models for molecule design. We start with a brief review of the mainstream molecule featurization and representation methods (including 1D string, 2D graph, and 3D geometry) and general generative methods (deep generative and combinatorial optimization methods). Then we summarize all the existing molecule design problems into several venues according to the problem setup, including input, output types and goals. Finally, we conclude with the open challenges and point out future opportunities of machine learning models for molecule design in real-world applications.
FD-SLAM: 3-D Reconstruction Using Features and Dense Matching

It is well known that visual SLAM systems based on dense matching are locally accurate but are also susceptible to long-term drift and map corruption. In contrast, feature matching methods can achieve greater long-term consistency but can suffer from inaccurate local pose estimation when feature information is sparse. Based on these observations, we propose an RGB-D SLAM system that leverages the advantages of both approaches: using dense frame-to-model odometry to build accurate sub-maps and on-the-fly feature-based matching across sub-maps for global map optimisation. In addition, we incorporate a learning-based loop closure component based on 3-D features which further stabilises map building. We have evaluated the approach on indoor sequences from public datasets, and the results show that it performs on par or better than state-of-the-art systems in terms of map reconstruction quality and pose estimation. The approach can also scale to large scenes where other systems often fail.
Local continuity of angular momentum and noether charge for matter in general relativity

Conservation laws have many applications in numerical relativity. However, it is not straightforward to define local conservation laws for general dynamic spacetimes due the lack of coordinate translation symmetries. In flat space, the rate of change of energy-momentum within a finite spacelike volume is equivalent to the flux integrated over the surface of this volume; for general spacetimes it is necessary to include a volume integral of a source term arising from spacetime curvature. In this work a study of continuity of matter in general relativity is extended to include angular momentum of matter and Noether currents associated with gauge symmetries. Expressions for the Noether charge and flux of complex scalar fields and complex Proca fields are found using this formalism. Expressions for the angular momentum density, flux and source are also derived which are then applied to a numerical relativity collision of boson stars in 3D with non-zero impact parameter as an illustration of the methods.
Aeroelastic characterisation of a bio-inspired flapping membrane wing

Natural fliers like bats exploit the complex fluid–structure interaction between their flexible membrane wings and the air with great ease. Yet, replicating and scaling the balance between the structural and fluid-dynamical parameters of unsteady membrane wings for engineering applications remains challenging. In this study, we introduce a novel bio-inspired membrane wing design and systematically investigate the fluid–structure interactions of flapping membrane wings. The membrane wing can passively camber, and its leading and trailing edges rotate with respect to the stroke plane. We find optimal combinations of the membrane properties and flapping kinematics that out-perform their rigid counterparts both in terms of increased stroke-average lift and efficiency, but the improvements are not persistent over the entire input parameter space. The lift and efficiency optima occur at different angles of attack and effective membrane stiffnesses which we characterise with the aeroelastic number. At optimal aeroelastic numbers, the membrane has a moderate camber between 15% and 20% and its leading and trailing edges align favourably with the flow. Higher camber at lower aeroelastic numbers leads to reduced aerodynamic performance due to negative angles of attack at the leading edge and an over-rotation of the trailing edge. Most of the performance gain of the membrane wings with respect to rigid wings is achieved in the second half of the stroke when the wing is decelerating. The stroke-maximum camber is reached around mid-stroke but is sustained during most of the remainder of the stroke which leads to an increase in lift and a reduction in power. Our results show that combining the effect of variable stiffness and angle of attack variation can significantly enhance the aerodynamic performance of membrane wings and has the potential to improve the control capabilities of micro air vehicles.
Injection testing backed refactoring

Injection-based testing for refactoring is a pattern that minimizes the need for manual editing when altering the internal behaviour of a code base which does not have unit tests yet in place. Neither does it rely on a compilation or a linking process nor does it make assumptions on the structure of the code. Thus, it can be particularly useful for refactoring code that has been written in scripting languages, and specifically targets the research and engineering context. We describe the pattern and propose a set of functions for its application. The applicability of code injection for refactoring is highlighted via specific examples for deriving unit and integration tests. Finally, we comment on customizing the pattern and give practical advice for its implementation.
Manipulating Twitter Through Deletions

Research into influence campaigns on Twitter has mostly relied on identifying malicious activities from tweets obtained via public APIs. By design, these approaches ignore deleted tweets. However, bad actors can delete content strategically to manipulate the system. Here, we provide the first exhaustive, large-scale analysis of anomalous deletion patterns involving more than a billion deletions by over 11 million accounts. Estimates based on publicly available Twitter data underestimate the true deletion volume. A small fraction of accounts delete a large number of tweets daily. We uncover two abusive behaviors that exploit deletions. First, limits on tweet volume are circumvented, allowing certain accounts to flood the network with over 26 thousand daily tweets. Second, coordinated networks of accounts engage in repetitive likes and unlikes of content that is eventually deleted, which can manipulate ranking algorithms. These kinds of abuse can be exploited to amplify content and inflate popularity, while evading detection. Our study provides platforms and researchers with new methods for identifying social media abuse.
Individual and Team Trust Preferences for Robotic Swarm Behaviors

Trust between humans and multi-agent robotic swarms may be analyzed using human preferences. These preferences are expressed by an individual as a sequence of ordered comparisons between pairs of swarm behaviors. An individual’s preference graph can be formed from this sequence. In addition, swarm behaviors may be mapped to a feature vector space. We formulate a linear optimization problem to locate a trusted behavior in the feature space. Extending to human teams, we define a novel distinctiveness metric using a sparse optimization formulation to cluster similar individuals from a collection of individuals’ labeled pairwise preferences. The case of anonymized unlabeled pairwise preferences is also examined to find the average trusted behavior and minimum covariance bound, providing insights into group cohesion. A user study was conducted, with results suggesting that individuals with similar trust profiles can be clustered to facilitate human-swarm teaming.
Data-driven Stabilization of Discrete-time Control-affine Nonlinear Systems: A Koopman Operator Approach

In recent years data-driven analysis of dynamical systems has attracted a lot of attention and transfer operator techniques, namely, Perron-Frobenius and Koopman operators are being used almost ubiquitously. Since data is always obtained in discrete-time, in this paper, we propose a purely data-driven approach for the design of a stabilizing feedback control law for a general class of discrete-time control-affine non-linear systems. In particular, we use the Koopman operator to lift a control-affine system to a higher-dimensional space, where the control system's evolution is bilinear. We analyze the controllability of the lifted bilinear system and relate it to the controllability of the underlying non-linear system. We then leverage the concept of Control Lyapunov Function (CLF) to design a state feedback law that stabilizes the origin. Furthermore, we demonstrate the efficacy of the proposed method to stabilize the origin of the Van der Pol oscillator and the chaotic Henon map from the time-series data.
Vibrational Entropy of Crystalline Solids from Covariance of Atomic Displacements

The vibrational entropy of a solid at finite temperature is investigated from the perspective of information theory. Ab initio molecular dynamics (AIMD) simulations generate ensembles of atomic configurations at finite temperature from which we obtain the N-body distribution of atomic displacements, ρN. We calculate the information-theoretic entropy from the expectation value of lnρN. At a first level of approximation, treating individual atomic displacements independently, our method may be applied using Debye–Waller B-factors, allowing diffraction experiments to obtain an upper bound on the thermodynamic entropy. At the next level of approximation we correct the overestimation through inclusion of displacement covariances. We apply this approach to elemental body-centered cubic sodium and face-centered cubic aluminum, showing good agreement with experimental values above the Debye temperatures of the metals. Below the Debye temperatures, we extract an effective vibrational density of states from eigenvalues of the covariance matrix, and then evaluate the entropy quantum mechanically, again yielding good agreement with experiment down to low temperatures. Our method readily generalizes to complex solids, as we demonstrate for a high entropy alloy. Further, our method applies in cases where the quasiharmonic approximation fails, as we demonstrate by calculating the HCP/BCC transition in Ti.
Implications of Mortality Displacement for Effect Modification and Selection Bias

Mortality displacement is the concept that deaths are moved forward in time (e.g., a few days, several months, and years) by exposure from when they would occur without the exposure, which is common in environmental time-series studies. Using concepts of a frail population and loss of life expectancy, it is understood that mortality displacement may decrease rate ratio (RR). Such decreases are thought to be minimal or substantial depending on study populations. Environmental epidemiologists have interpreted RR considering mortality displacement. This theoretical paper reveals that mortality displacement can be formulated as a built-in selection bias of RR in Cox models due to unmeasured risk factors independent from exposure of interest, and mortality displacement can also be viewed as an effect modifier by integrating the concepts of rate and loss of life expectancy. Thus, depending on the framework through which we view bias, mortality displacement can be categorized as selection bias in the bias taxonomy of epidemiology, and simultaneously mortality displacement can be seen as an effect modifier. This dichotomy provides useful implications regarding policy, effect modification, exposure time-windows selection, and generalizability, specifically why research in epidemiology may produce unexpected and heterogeneous RR over different studies and sub-populations.
Classically Replaceable Operation

Quantum information science provides powerful technologies beyond the scope of classical physics. In practice, accurate control of quantum operations is a challenging task with current quantum devices. The implementation of high ﬁdelity and multi-qubit quantum operations consumes massive resources and requires complicated hardware design to ﬁght against noise. An approach to improving the performance and eﬃciency is to replace some quantum operations with tractable classical processing. Despite the common practice of this approach, rigorous criteria to determine whether a given quantum operation is replaceable “classically” are still missing. In this work, we deﬁne the classically replaceable operations in four general scenarios. In each scenario, we provide their necessary and suﬃcient criteria and point out the corresponding classical processing. For a practically favourable case of unitary classically replaceable operations, we show that the replaced classical processing is deterministic. Beyond that, we regard the irreplaceability with classical processing as a quantum resource and relate it to the performance of a channel in a non-local game, as manifested in a robustness measure.
Velocity continuation with Fourier neural operators for accelerated uncertainty quantification

Seismic imaging is an ill-posed inverse problem that is challenged by noisy data and modeling inaccuracies—due to errors in the background squared-slowness model. Uncertainty quantiﬁcation is essential for determining how variability in the background models affects seismic imaging. Due to the costs associated with the forward Born modeling operator as well as the high dimensionality of seismic images, quantiﬁcation of uncertainty is computationally expensive. As such, the main contribution of this work is a survey-speciﬁc Fourier neural operator surrogate to velocity continuation that maps seismic images associated with one background model to another virtually for free. While being trained with only 200 background and seismic image pairs, this surrogate is able to accurately predict seismic images associated with new background models, thus accelerating seismic imaging uncertainty quantiﬁcation. We support our method with a realistic data example in which we quantify seismic imaging uncertainties using a Fourier neural operator surrogate, illustrating how variations in background models affect the position of reﬂectors in a seismic image.
A Large Scale Multi-View RGBD Visual Affordance Learning Dataset

The physical and textural attributes of objects have been widely studied for recognition, detection and segmentation tasks in computer vision. A number of datasets, such as large scale ImageNet, have been proposed for feature learning using data hungry deep neural networks and for hand-crafted feature extraction. To intelligently interact with objects, robots and intelligent machines need the ability to infer beyond the traditional physical/textural attributes, and understand/learn visual cues, called visual affordances, for affordance recognition, detection and segmentation. To date there is no publicly available large dataset for visual affordance understanding and learning. In this paper, we introduce a large scale multi-view RGBD visual affordance learning dataset, a benchmark of 47210 RGBD images from 37 object categories, annotated with 15 visual affordance categories. To the best of our knowledge, this is the first ever and the largest multi-view RGBD visual affordance learning dataset. We benchmark the proposed dataset for affordance segmentation and recognition tasks using popular Vision Transformer and Convolutional Neural Networks. Several state-of-the-art deep learning networks are evaluated each for affordance recognition and segmentation tasks. Our experimental results showcase the challenging nature of the dataset and present definite prospects for new and robust affordance learning algorithms. The dataset is publicly available at https://sites.google. com/view/afaqshah/dataset.