Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of different routes of administration and doses of Sulodexide on leukocyte-endothelium interaction and tissue perfusion on an animal model of low flow and high pressure in veins

Objectives To assess the effects of different doses and routes of Sulodexide on leukocyte-endothelium interaction and tissue perfusion in a model of venous hypertension and low blood flow. Methods Six weeks after venous hypertension induction, through external iliac vein ligature male hamsters (Mesocricetus auratus) received Sulodexide at 1, 2, or 4 mg/kg/day or saline (placebo) by subcutaneous or intramuscular routes during 2 or 4 weeks. After treatments, leukocyte rolling and adhesion, functional capillary density (FCD), and venular diameter were evaluated on the affected hindlimb. Results Subcutaneous and intramuscular treatments with Sulodexide after 2 and 4 weeks, significantly reduced leukocyte rolling and adhesion and increased FCD. Sulodexide did not affect venular diameter and intramuscular treatment was more effective in reducing leukocyte adhesion than the subcutaneous one. Conclusion This preliminary study demonstrated that Sulodexide significantly decreased leukocyte-endothelium interaction and improved tissue perfusion in hamsters subjected to venous hypertension and low blood flow.
Is loop quantization in cosmology unique

We reexamine the process of loop quantization for flat isotropic models in cosmology. In particular, we contrast different inequivalent 'loop quantizations' of these simple models through their respective successes and limitations and assess whether they can lead to any viable physical description. We propose three simple requirements which any such admissible quantum model should satisfy: (i) independence from any auxiliary structure, such as a fiducial interval/cell introduced to define the phase space when integrating over noncompact manifolds; (ii) existence of a well defined classical limit, and (iii) a sensible 'Planck scale' where quantum gravitational effects become manifest. We show that even when it may seem that one can have several possible loop quantizations, these physical requirements considerably narrow down the consistent choices. Apart from the so-called improved dynamics of loop quantum cosmology, none of the other available inequivalent loop quantizations pass the above tests, showing the limitations of lattice refinement models to approximate the homogeneous sector and loop modified quantum geometrodynamics. We conclude that amongst a large class of loop quantizations in isotropic cosmology, there is a unique consistent choice.
Interpretation of the "$f_{D_s}$ puzzle" in SM and beyond

The recent measurement on the decay constant of $D_s$ shows a discrepancy between theory and experiment. We study the leptonic and semileptonic decays of $D$ and $D_s$ simultaneously within the standard model by employing a lightfront quark model. There is space by tuning phenomenological parameters which can explain the "$f_{D_s}$ puzzle" and do not contradict other experiments on the semileptonic decays. We also investigate the leptonic decays of D and $D_{s}$ with a new physics scenario, unparticle physics. The unparticle effects induce a constructive interference with the standard model contribution. The nontrivial phase in unparticle physics could produce direct CP violation which may distinguish it from other new physics scenarios.
Women Refugee’s Perceptions, Experiences and Coping Mechanisms in Situations of Sexual and Gender-Based Violence (SGBV): A Metasynthesis

Women represent almost half of the 20 million refugees worldwide, and although they play a key role in their communities, their voices and needs are often missing from research and policies directed to this population. A qualitative systematic review, or metasynthesis, was conducted to examine the experiences, perceptions, coping mechanisms, and psychological discourses of women refugees in situations of sexual and gender-based violence (SGBV) that occur during the travel, interception, and destination migratory stages. A systematic search was conducted on five multidisciplinary databases and was complemented with a manual search, resulting on a total of 511 articles which were screened for eligibility. Only qualitative, peer-reviewed, and English-written research articles were selected, and they were required to (1) focus on women refugees above 15 years of age and (2) report on their experiences, perceptions, psychological discourses, and/or coping with SGBV during their migration across countries (international migration). Ultimately, a total of 14 qualitative research articles were selected for the review. Using the thematic synthesis approach as a guideline, the results were summarized in the following themes: experiences of SGBV, perception of SGBV, perception of the risk factors creating and perpetuating their vulnerability, coping with SGBV, barriers to help-seeking, and psychological consequences. Despite the broad search, only information about some types of SGBV experiences from Asian and African refugee communities was found. Nonetheless, the available qualitative information on this topic is effectively integrated, knowledge gaps are identified, and future research, interventions, and policies using an integrated and culturally sensitive framework are suggested.
Challenges in colonic stenting: Giving up is not an option

The placement of a self-expandable metallic stent (SEMS) is considered effective for decompressing malignant colonic obstruction, both as a palliative treatment modality and as a bridge to surgery (BTS). 1,2 Through-the-scope (TTS) placement of SEMS has become the standard method for colonic stenting, achieving a high technical success rate. 3 However, technical challenges can occur in patients with intrapelvic adhesions caused by nonmalignant peritonitis or a surgery, peritoneal carcinomatosis, a score of 0 on the ColoRectal Obstruction Scoring System, 4 expansive stric-tures, or a tumor site in the right colon. 3 Additionally, the European Society of Gastrointestinal Endoscopy (ESGE) guidelines suggest that performing colorectal stenting is relatively dif ﬁ cult in patients with peritoneal metastases or tumors close to the anal verge (<5 cm). 2 However, recently developed innovations in devices and procedures can overcome most of these problems. Therefore, all endoscopists should be aware of and implement these innovations into their practice to improve outcomes and reduce adverse events.
Cystine induced-mTORC2 activation through promoting Sin1 phosphorylation to suppress cancer cell ferroptosis.

SCOPE Mechanistic target of rapamycin (mTOR) serves as a central signaling node in the coordination of cell growth and metabolism, and it functions via two distinct complexes, namely, mTORC1 and mTORC2. mTORC1 plays a crucial role in sensing amino acids, whereas mTORC2 involves in sensing growth factors. However, it remains largely unclear whether mTORC2 can sense amino acids and the mechanism by which amino acids regulate mTORC2 has not been studied. METHODS AND RESULTS After treating cells with indicated concentration of amino acids for different time, it is found that the mTORC2 activation was significantly increased in response to amino acids stimulation, especially cystine. Particularly, knockdown SLC7A11 by siRNA showed that SLC7A11-mediated cystine uptake is responsible for activating mTORC2. Mechanistically, we found that p38 is activated in response to cystine stimulation, and co-immunoprecipitation experiments suggested that p38 regulated the assembly of components within mTORC2 by mediating the phosphorylation of the mTORC2 subunit Sin1 in a cystine-dependent manner. Finally, combined with inducers and inhibitors of ferroptosis and cell viability assay, we observed that cystine-mediated regulation of the p38-Sin1-mTOR-AKT pathway induced resistance to ferroptosis. CONCLUSION These results indicate that cystine-induced activation of the p38-Sin1-mTORC2-AKT pathway suppresses ferroptosis. This article is protected by copyright. All rights reserved.
Development of a convolutional neural network (CNN) based assessment exercise recommendation system for individuals with chronic stroke: a feasibility study

ABSTRACT Background The use of artificial intelligence (AI) is revolutionizing nearly every aspect of healthcare, but the application of AI in rehabilitation is lagging behind. Clinically, gait parameters and patterns are used to evaluate stroke-specific impairment. We hypothesized that gait kinematics of individuals with stroke provide rich information for the deep-learning to predict the clinical decisions made by physiotherapist. Objective To investigate whether the results of clinical assessments and exercise recommendations by physiotherapists can be accurately predicted using a deep-learning algorithm with gait kinematics data. Method In this cross-sectional study, 40 individuals with stroke were assessed by a physiotherapist using the lower-extremity subscale of the Fugl-Meyer Assessment (FMA-LE) and Berg Balance Scale (BBS). The physiotherapist also decided whether or not the single-leg-stance was an appropriate balance training for each participant. The participants were classified as having good mobility and a low fall risk based on the cutoff scores of the two clinical scales. A convolutional neural network (CNN) was trained using gait kinematics to predict the assessment results and exercise recommendations. Results The trained model accurately predicted the results of the clinical assessments and decisions with an average prediction accuracy of 0.84 for the FMA-LE, 0.66 for the BBS, and 0.78 for the recommendation of the single-leg-stance exercise. Conclusions This CNN deep-learning model provided time-effective and accurate prediction of clinical assessment results and exercise recommendations. This study provides preliminary evidence to support the use of biomechanical data and AI to assist treatment planning and shorten the decision-making process in rehabilitation.
“SELF-ABSORBED” GeV LIGHT CURVES OF GAMMA-RAY BURST AFTERGLOWS

We investigate the effect that the absorption of high-energy (above 100 MeV) photons produced in gamma-ray burst afterglow shocks has on the light curves and spectra of Fermi Large Area Telescope (LAT) afterglows. Afterglows produced by the interaction of a relativistic outflow with a wind-like medium peak when the blast wave deceleration sets in, and the afterglow spectrum could be hardening before that peak, as the optical thickness to pair formation is decreasing. In contrast, in afterglows produced in the interaction with a homogeneous medium, the optical thickness to pair formation should increase and yield a light curve peak when it reaches unity, followed by a fast light curve decay, accompanied by spectral softening. If energy is injected in the blast wave, then the accelerated increase of the optical thickness yields a convex afterglow light curve. Other features, such as a double-peak light curve or a broad hump, can arise from the evolution of the optical thickness to photon–photon absorption. Fast decays and convex light curves are seen in a few LAT afterglows, but the expected spectral softening is rarely seen in (and difficult to measure with) LAT observations. Furthermore, for the effects of photon–photon attenuation to shape the high-energy afterglow light curve without attenuating it too much, the ejecta initial Lorentz factor must be in a relatively narrow range (50–200), which reduces the chance of observing those effects.
COMPARISON OF FORCE-FREE CORONAL MAGNETIC FIELD MODELING USING VECTOR FIELDS FROM HINODE AND SOLAR DYNAMICS OBSERVATORY

Photospheric magnetic vector maps from two different instruments are used to model the nonlinear force-free coronal magnetic field above an active region. We use vector maps inferred from polarization measurements of the Solar Dynamics Observatory/Helioseismic and Magnetic Imager (HMI) and the Solar Optical Telescope's Spectropolarimeter (SP) on board Hinode. Besides basing our model calculations on HMI data, we use both SP data of original resolution and scaled down to the resolution of HMI. This allows us to compare the model results based on data from different instruments and to investigate how a binning of high-resolution data affects the model outcome. The resulting three-dimensional magnetic fields are compared in terms of magnetic energy content and magnetic topology. We find stronger magnetic fields in the SP data, translating into a higher total magnetic energy of the SP models. The net Lorentz forces of the HMI and SP lower boundaries verify their force-free compatibility. We find substantial differences in the absolute estimates of the magnetic field energy but similar relative estimates, e.g., the fraction of excess energy and of the flux shared by distinct areas. The location and extension of neighboring connectivity domains differ and the SP model fields tend to be higher and more vertical. Hence, conclusions about the magnetic connectivity based on force-free field models are to be drawn with caution. We find that the deviations of the model solution when based on the lower-resolution SP data are small compared to the differences of the solutions based on data from different instruments.
Learning Probabilistic Topological Representations Using Discrete Morse Theory

Accurate delineation of fine-scale structures is a very important yet challenging problem. Existing methods use topological information as an additional training loss, but are ultimately making pixel-wise predictions. In this paper, we propose the first deep learning based method to learn topological/structural representations. We use discrete Morse theory and persistent homology to construct an one-parameter family of structures as the topological/structural representation space. Furthermore, we learn a probabilistic model that can perform inference tasks in such a topological/structural representation space. Our method generates true structures rather than pixel-maps, leading to better topological integrity in automatic segmentation tasks. It also facilitates semi-automatic interactive annotation/proofreading via the sampling of structures and structure-aware uncertainty.
Deep Safe Multi-Task Learning

—In recent years, Multi-Task Learning (MTL) has attracted much attention due to its good performance in many applications. However, many existing MTL models cannot guarantee that their performance is no worse than their single-task counterparts on each task. Though some works have empirically observed this phenomenon, little work aims to handle the resulting problem. In this paper, we formally deﬁne this phenomenon as negative sharing and deﬁne safe multi-task learning where no negative sharing occurs. To achieve safe multi-task learning , we propose a Deep Safe Multi-Task Learning (DSMTL) model with two learning strategies: individual learning and joint learning. We theoretically study the safeness of both learning strategies in the DSMTL model to show that the proposed methods can achieve some versions of safe multi-task learning . Moreover, to improve the scalability of the DSMTL model, we propose an extension, which automatically learns a compact architecture and empirically achieves safe multi-task learning . Extensive experiments on benchmark datasets verify the safeness of the proposed methods.
DRACo-SLAM: Distributed Robust Acoustic Communication-efficient SLAM for Imaging Sonar Equipped Underwater Robot Teams

An essential task for a multi-robot system is generating a common understanding of the environment and relative poses between robots. Cooperative tasks can be executed only when a vehicle has knowledge of its own state and the states of the team members. However, this has primarily been achieved with direct rendezvous between underwater robots, via inter-robot ranging. We propose a novel distributed multi-robot simultaneous localization and mapping (SLAM) framework for underwater robots using imaging sonar-based perception. By passing only scene descriptors between robots, we do not need to pass raw sensor data unless there is a likelihood of inter-robot loop closure. We utilize pairwise consistent measurement set maximization (PCM), making our system robust to erroneous loop closures. The functionality of our system is demonstrated using two real-world datasets, one with three robots and another with two robots. We show that our system effectively estimates the trajectories of the multi-robot system and keeps the bandwidth requirements of inter-robot communication low. To our knowledge, this paper describes the first instance of multi-robot SLAM using real imaging sonar data (which we implement offline, using simulated communication). Code link: https://github.com/jake3991/DRACo-SLAM.
Semi-autonomous Prosthesis Control Using Minimal Depth Information and Vibrotactile Feedback

A semi-autonomous prosthesis control based on computer vision can be used to improve performance while decreasing the cognitive burden, especially when using advanced systems with multiple functions. However, a drawback of this approach is that it relies on the complex processing of a significant amount of data (e.g., a point cloud provided by a depth sensor), which can be a challenge when deploying such a system onto an embedded prosthesis controller. In the present study, therefore, we propose a novel method to reconstruct the shape of the target object using minimal data. Specifically, four concurrent laser scanner lines provide partial contours of the object cross-section. Simple geometry is then used to reconstruct the dimensions and orientation of spherical, cylindrical and cuboid objects. The prototype system was implemented using depth sensor to simulate the scan lines and vibrotactile feedback to aid the user during aiming of the laser towards the target object. The prototype was tested on ten able-bodied volunteers who used the semi-autonomous prosthesis to grasp a set of ten objects of different shape, size and orientation. The novel prototype was compared against the benchmark system, which used the full depth data. The results showed that novel system could be used to successfully handle all the objects, and that the performance improved with training, although it was still somewhat worse compared to the benchmark. The present study is therefore an important step towards building a compact system for embedded depth sensing specialized for prosthesis grasping.
Assuring safety of vision-based swarm formation control

Vision-based formation control systems are attractive because they can use inexpensive sensors and can work in GPS-denied environments. The safety assurance for such systems is challenging: the vision component's accuracy depends on the environment in complicated ways, these errors propagate through the system and lead to incorrect control actions, and there exists no formal specification for end-to-end reasoning. We address this problem and propose a technique for safety assurance of vision-based formation control: First, we propose a scheme for constructing quantizers that are consistent with vision-based perception. Next, we show how the convergence analysis of a standard quantized consensus algorithm can be adapted for the constructed quantizers. We use the recently defined notion of perception contracts to create error bounds on the actual vision-based perception pipeline using sampled data from different ground truth states, environments, and weather conditions. Specifically, we use a quantizer in logarithmic polar coordinates, and we show that this quantizer is suitable for the constructed perception contracts for the vision-based position estimation, where the error worsens with respect to the absolute distance between agents. We build our formation control algorithm with this nonuniform quantizer, and we prove its convergence employing an existing result for quantized consensus.
Quantum quenches in the sinh-Gordon and Lieb–Liniger models

The non-relativistic limit of integrable field theories at equilibrium has been intensively studied in the previous years; the simplest non-trivial case relates the sinh-Gordon model to the Lieb–Liniger model (LLM). Here we study this non-relativistic limit out of equilibrium, namely in the time evolution after a quantum quench. The obtained results agree with the known ones for the LLM, thus showing that the non-relativistic limit is applicable in this out-of-equilibrium setting.
ON A SPACE DISCRETIZATION SCHEME FOR THE FRACTIONAL STOCHASTIC HEAT EQUATIONS

1 Abstract. In this work, we introduce a new discretization to the frac- tional Laplacian and use it to elaborate an approximation scheme for fractional heat equations perturbed by a multiplicative cylindrical white noise. In particular, we estimate the rate of convergence. Keywords: fractional Laplacian, cylindrical Wiener process, Sobolev spaces, heat equation, approximation scheme, finite difference scheme. Subjclass MSC(2000): 60H15, 35R11, 35A35, 26A33.
Point source detection performance of Hard X-ray Modulation Telescope imaging observation

The Hard X-ray Modulation Telescope (HXMT) will perform an all-sky survey in the hard X-ray band as well as deep imaging of a series of small sky regions. We expect various compact objects to be detected in these imaging observations. Point source detection performance of HXMT imaging observation depends not only on the instrument but also on the data analysis method that is applied since images are reconstructed from HXMT observed data with numerical methods. The denoising technique used plays an important part in the HXMT imaging data analysis pipeline along with demodulation and source detection. In this paper we have implemented several methods for denoising HXMT data and evaluated the point source detection performances in terms of sensitivities and location accuracies. The results show that direct demodulation with 1-fold cross-correlation should be the default reconstruction and regularization method, although both sensitivity and location accuracy could be further improved by selecting and tuning numerical methods in data analysis used for HXMT imaging observations.
Exploiting Selection Bias on Underspecified Tasks in Large Language Models

In this paper we motivate the causal mechanisms behind sample selection induced collider bias (selection collider bias) that can cause Large Language Models (LLMs) to learn unconditional dependence between entities that are unconditionally independent in the real world. We show that selection collider bias can become ampliﬁed in underspeciﬁed learning tasks, and although difﬁcult to overcome, we describe a method to exploit the resulting spurious correlations for determination of when a model may be uncertain about its prediction. We demonstrate an uncertainty metric that matches human uncertainty in tasks with gender pronoun underspeciﬁcation on an extended version of the Winogender Schemas evaluation set, and we provide online demos where users can evaluate spurious correlations and apply our uncertainty metric to their own texts and models. Fi-nally, we generalize our approach to address a wider range of prediction tasks.