Miracle-dz/UniFAR-FTUs huggingface dataset:
CD4 þ T lymphocytes mediate acute pulmonary ischemia–reperfusion injury

Objective: Postischemic reperfusion of the lung triggers proinﬂammatory responses that stimulate injurious neutrophil chemotaxis. We hypothesized that T lymphocytes are recruited and activated during reperfusion and mediate subsequent neutrophil-induced lung ischemia–reperfusion injury. Methods: An in vivo mouse model of lung ischemia–reperfusion injury was used. C57BL/6 mice were assigned to either the sham group (left thoracotomy) or 7 study groups that underwent 1-hour left hilar occlusion followed by 1 to 24 hours of reperfusion. After in vivo reperfusion, the lungs were perfused ex vivo with buffer whereby pulmonary function was assessed. Lung vascular permeability, edema, neutrophil accumulation, and cytokine/ chemokine production (tumor necrosis factor a , interleukin 17, CCL3, and CXCL1) were assessed based on Evans blue dye leak, wet/dry weight ratio, myeloperoxidase level, and enzyme-linked immunosorbent assay, respectively. Results: A preliminary study showed that 2 hours of reperfusion resulted in greater pulmonary dysfunction than 1 or 24 hours of reperfusion. The 2-hour reperfusion period was thus used for the remaining experiments. Comparable and signiﬁcant protection from ischemia–reperfusion injury–induced lung dysfunction and injury occurred after antibody depletion of neutrophils or CD4 þ T cells but not CD8 þ T cells ( P < .05 vs immunoglobulin G control). Lung ischemia–reperfusion injury was proportional to the inﬁltration of neutrophils but not T cells. Moreover, pulmonary neutrophil inﬁltration and the production of CXCL1 (KC) were signiﬁcantly diminished by CD4 þ T-cell depletion but not vice versa
Edinburgh Research Explorer The 4th International Workshop on Software Engineering for HPC in Computational Science and Engineering

Despite the increasing demand for utilizing HPC for CSE applications, software development for HPC historically attracted little attention from the SE community. Paradoxically, the HPC CSE community has increasingly been adopting SE techniques and tools. Indeed, the development of CSE software for HPC differs significantly from the development of more traditional business inforåmation systems, from which many SE best practices and tools have been drawn. This workshop, the fourth in the series to be co-located with the Supercomputing conference series, examined two main topics: testing and trade-offs. Through presentations of work in this area and structured group discussions, the participants highlighted some of the key issues, as well as indicating the direction the community needed to go. In particular, there is a need for more high-quality research in this area that we can use as an evidence base to help developers of CSE applications to change practice and benefit from advances in software engineering.
Public Perception of Android Robots Indications from an Analysis of YouTube Comments

 Abstract — The public perception of android robots is a field of growing applied relevance. Currently, most androids are confined within controlled environments rendering interactions between potential end-users, and robots challenging. Even more challenging is for researchers to investigate end-users’ perception of androids. We exploit pre-existing YouTube comments as artifacts for quantitative content analysis to gain an indication of social perception on androids. We perform a content analysis of 10301 YouTube comments from four different videos, and reflect on the textual reactions to video stimuli of four extremely human-like android robots. We use text mining and machine learning techniques to process and analyze our corpus. Our findings reveal three equally important topics that should be considered for paving the way towards a robotic society: human-robot relationships, technical specifications, and the science fiction valley. Considering people’s attitudes, fears and wishes towards androids, researchers can increase citizen awareness, and engagement.
Analysis and Modeling of Polygonality in Retinal Tissue Based on Voronoi Diagram and Delaunay Tessellations

Several important properties of biological systems are directly related and even determined by the spatial distribution of their constituent elements. Those elements interact with each other and tend to use space in an optimal way, regarding their specific function and environmental constraints. A detailed methodology, based on Voronoi polygons and Delaunay triangles method employed to extract information on the spatial distribution of cells, is presented. On the other hand, diabetic retinopathy (DR) is defined as microvascular pathology. However, some data have suggested that the retinal photoreceptor (RPs) might be important in the pathogenesis of this ocular disease. In this study, the organization of the PRs in control and diabetic-induced rats was compared, using multiphoton microscopy. The PR mosaic was imaged at different locations in non-stained retinas. Thus, this work investigated the pathological changes in the cellular structures of the retina in the early stages of diabetes in laboratory animals. Of the different proposed tools that are highly reliable to be tested with human retinas, the metrics mean averaged distance and the mean square deviation of the angles are found (P < 0.05).
On the Effect of the Leading-Edge Separation Bubble on the Aerodynamics of Spinnakers

The spinnaker is the most powerful and one of the most used sails both in racing and cruising - yet its complex aerodynamics governed by flow separation is still not fully understood. While the flow around a spinnaker is unsteady and highly tridimensional, locally the governing fluid mechanics may be represented by the quasi-steady bidimensional flow around a cambered circular arc with a sharp leading edge. The spinnaker is typically trimmed such that the stagnation point is at the leading edge with the sail streamline separating on the suction side and reattaching within the first 10% of the chord length, forming a leading-edge separation bubble (LESB). This flow feature sets the beginning of the boundary layer, whose separation further downstream is paramount for the global aerodynamic forces on the sail. This study investigates the effect of the LESB on the boundary layer regime and downstream flow separation through particle image velocimetry on a circular arc. The existence of the combination of a critical Reynolds number and a critical angle of attack to trigger turbulent separation is demonstrated. A turbulent LESB followed by a laminar boundary layer is observed in sub-critical regime. Conversely, in a post-critical condition, a turbulent LESB ensued by a turbulent boundary layer is detected, the latter continuing all the way to trailing-edge separation. This behaviour ultimately yields a sharp lift increase and drag reduction. These findings reveal the critical effect of the leading-edge vortical structures on the global flow field and forces experienced by cambered wings with leading-edge separation, including high performance spinnakers. It is envisaged that these results will contribute to improve the design and performance of downwind yacht sails.
The analysis of hysteria : understanding conversion and dissociation

Part 1 History and concepts: some traditional concepts early neurological analyses the turn of the century the contribution of Freud combat hysteria Ernst Kretschmer post-traumatic stress disorder. Part 2 Motives and extremes: malingering, self-damage and anorexia nervosa amnesia, pseudodementia and the Ganser syndrome - denial of illness compensation issues. Part 3 Varied causes and symptoms: organic brain disease pain somatization disorder and somatization hypochondrias the diversity of hysterical complaints epidemic or communicable hysteria chronic fatigue syndromes children suggestion and hypnotic phenomena "multiple personality disorder" dissociation, repression and false memories. Part 4 Individual dynamics and clinical sub-groups: personality traits psychoses psychoanalytic concepts. Part 5 A perspective: the survival of hysteria.
A morphologic and molecular reappraisal of myoepithelial tumors of soft tissue, bone, and viscera with EWSR1 and FUS gene rearrangements

Myoepithelial tumors (MET) represent a clinicopathologically heterogeneous group of tumors, ranging from benign to highly aggressive lesions. Although MET arising in soft tissue, bone, or viscera share morphologic and immunophenotypic overlap with their salivary gland and cutaneous counterparts, there is still controversy regarding their genetic relationship. Half of MET of soft tissue and bone harbor EWSR1 or FUS related fusions, while MET arising in the salivary gland and skin often show PLAG1 and HMGA2 gene rearrangements. Regardless of the site of origin, the gold standard in diagnosing a MET relies on demonstrating its “ myoepithelial immunophenotype ” of positivity for EMA/CK and S100 protein or GFAP. However, the morphologic spectrum of MET in soft tissue and bone is quite broad and the above immunoprofile is nonspecific, being shared by other pathogenetically unrelated neoplasms. Moreover, rare MET lack a diagnostic immunoprofile but shows instead the characteristic gene fusions. In this study, we analyzed a large cohort of 66 MET with EWSR1 and FUS gene rearrangements spanning various clinical presentations, to better define their morphologic spectrum and establish relevant pathologic-molecular correlations. Genetic analysis was carried out by FISH for EWSR1/FUS rearrangements and potential partners, and/or by targeted RNA sequencing. Then, 82% showed EWSR1 rearrangement, while 18% had FUS abnormalities. EWSR1-POU5F1 occurred with predilection in malignant MET in children and young adults and these tumors had nested epithelioid morphology and clear cytoplasm. In contrast, EWSR1/FUS-PBX1/3 fusions were associated with benign and sclerotic spindle cell morphology. Tumors with EWSR1-KLF17 showed chordoma-like morphology. Our results demonstrate striking morphologic-molecular correlations in MET of bone, soft tissue and viscera, which might have implications in their clinical behavior.
Using Reinforcement Learning to Simplify Mealtime Insulin Dosing for People with Type 1 Diabetes: In-Silico Experiments

People with type 1 diabetes (T1D) struggle to calculate the optimal insulin dose at mealtime, especially when under multiple daily injections (MDI) therapy. Effectively, they will not always perform rigorous and precise calculations, but occasionally, they might rely on intuition and previous experience. Reinforcement learning (RL) has shown outstanding results in outperforming humans on tasks requiring intuition and learning from experience. In this work, we propose an RL agent that recommends the optimal meal-accompanying insulin dose corresponding to a qualitative meal (QM) strategy that does not require precise carbohydrate counting (CC) (e.g., a usual meal at noon.). The agent is trained using the soft actor-critic approach and comprises long short-term memory (LSTM) neurons. For training, eighty virtual subjects (VS) of the FDA-accepted UVA/Padova T1D adult population were simulated using MDI therapy and QM strategy. For validation, the remaining twenty VS were examined in 26-week scenarios, including intra- and inter-day variabilities in glucose. \textit{In-silico} results showed that the proposed RL approach outperforms a baseline run-to-run approach and can replace the standard CC approach. Specifically, after 26 weeks, the time-in-range ($70-180$mg/dL) and time-in-hypoglycemia ($<70$mg/dL) were $73.1\pm11.6$% and $ 2.0\pm 1.8$% using the RL-optimized QM strategy compared to $70.6\pm14.8$% and $ 1.5\pm 1.5$% using CC. Such an approach can simplify diabetes treatment, resulting in improved quality of life and glycemic outcomes.
Thermocapillary migration and interaction dynamics of droplets in a constricted domain

Migration of confined droplets in a stationary fluid medium due to thermocapillary forces is considered. An in-house solver with isosurface based interface reconstruction developed in OpenFOAM has been employed to carry out numerical simulations. Thermocapillary migration of a single droplet in a constricted domain with constriction comparable to the droplet size shows that the migration velocity has non-monotonic dependence on the droplet radius. In the case of two droplets migrating in a constricted domain, the relative slowdown of a larger droplet when a smaller droplet is trailing behind reveals the possibility of interaction which is not observed in larger domains. The effects of the constricted domain size, the initial distance of separation, the radius of the trailing droplet, and the Marangoni number are analysed for this configuration. It is observed that the constriction size and Marangoni number have more influence on the interaction and dictate whether the droplets may coalesce or move with a constant separation distance. The final steady state separation distance between the droplets does not depend on the initial separation distance, but it varies with the radius of the trailing droplet. The results from the present study reveal the physical mechanisms influencing the thermocapillary migration of droplets in constricted domains and interactions between the migrating droplets.Migration of confined droplets in a stationary fluid medium due to thermocapillary forces is considered. An in-house solver with isosurface based interface reconstruction developed in OpenFOAM has been employed to carry out numerical simulations. Thermocapillary migration of a single droplet in a constricted domain with constriction comparable to the droplet size shows that the migration velocity has non-monotonic dependence on the droplet radius. In the case of two droplets migrating in a constricted domain, the relative slowdown of a larger droplet when a smaller droplet is trailing behind reveals the possibility of interaction which is not observed in larger domains. The effects of the constricted domain size, the initial distance of separation, the radius of the trailing droplet, and the Marangoni number are analysed for this configuration. It is observed that the constriction size and Marangoni number have more influence on the interaction and dictate whether the droplets may coalesce or move with a .
Highly-tunable and strong nonreciprocity in coupled nonlinear cavity magnonics

Nonreciprocity, which violates Lorentz reciprocity, plays a pivotal role in quantum information processing and networks. Nevertheless, achieving a desired and highly-tunable level of nonreciprocity has proven to be a formidable challenge. Here, we propose a coupled nonlinear cavity magnonic system, consisting of two cavities, a second-order nonlinear element, and a yttrium-iron-garnet sphere for supporting Kerr magnons, to realize this sought-after highly-tunable nonreciprocity. We first derive the critical condition for switching between reciprocity and nonreciprocity with undriven magnons, then we numerically demonstrate that a strong magnonic nonreciprocity can be obtained by breaking the critical condition. When magnons are driven, we show that a strong magnonic nonreciprocity can also be attained within the critical condition. Compared to previous study, the introduced nonlinear element not only relaxes the critical condition to both the weak and strong regimes, but also offers an alternative path to adjust the magnonic nonreciprocity. Our work provides a promising avenue to realize highly-tunable nonreciprocal devices with Kerr magnons.
Exploring orbital-charge conversion mediated by interfaces with 
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 through spin-orbital pumping

We investigated how different materials affect the orbital-charge conversion in heterostructures with the naturally oxidized cooper capping layer. When we added a thin layer of $CuOx(3nm)$ onto yttrium iron garnet $(YIG)/W$ stacks, we observed a significant reduction in the charge current signal measured by means the spin pumping effect technique. This finding contrasts with the results of a prior study conducted on YIG/Pt/CuOx, which reported the opposite effect. On the other hand, when we added the same $CuOx(3nm)$ layer to $YIG/Ti(4nm)$ structures, there was not much change in the spin pumping signal. This occurred because Ti does not generate much orbital current at the $Ti/CuOx$ interface, unlike Pt, due to its weaker spin-orbit coupling. Interestingly, when we added the $CuOx(3nm)$ layer to $SiO_{2}/Py(5nm)/Pt(4nm)$ structures, the spin pumping signal increased. However, in $SiO_{2}/CuOx(3nm)/Pt(4nm)/Py(5nm)$ structures, the signal decreased. Finally, we delve into a theoretical analysis of the spin (orbital) Hall effect in YIG/Heavy-metal systems. These findings have the potential to advance research in the innovative field of orbitronics and contribute to the development of new technologies based on spin-orbital conversion.
UGC: Unified GAN Compression for Efficient Image-to-Image Translation

Recent years have witnessed the prevailing progress of Generative Adversarial Networks (GANs) in image-to-image translation. However, the success of these GAN models hinges on ponderous computational costs and labor-expensive training data. Current efficient GAN learning techniques often fall into two orthogonal aspects: i) model slimming via reduced calculation costs; ii) data/label-efficient learning with fewer training data/labels. To combine the best of both worlds, we propose a new learning paradigm, Unified GAN Compression (UGC), with a unified optimization objective to seamlessly prompt the synergy of model-efficient and label-efficient learning. UGC sets up semi-supervised-driven network architecture search and adaptive online semi-supervised distillation stages sequentially, which formulates a heterogeneous mutual learning scheme to obtain an architecture-flexible, label-efficient, and performance-excellent model. Extensive experiments demonstrate that UGC obtains state-of-the-art lightweight models even with less than 50% labels. UGC that compresses 40× MACs can achieve 21.43 FID on edges→shoes with 25% labels, which even outperforms the original model with 100% labels by 2.75 FID.
Generalized Finsler Geometry and the Anisotropic Tearing of Skin

A continuum mechanical theory with foundations in generalized Finsler geometry describes the complex anisotropic behavior of skin. A fiber bundle approach, encompassing total spaces with assigned linear and nonlinear connections, geometrically characterizes evolving configurations of a deformable body with the microstructure. An internal state vector is introduced on each configuration, describing subscale physics. A generalized Finsler metric depends on the position and the state vector, where the latter dependence allows for both the direction (i.e., as in Finsler geometry) and magnitude. Equilibrium equations are derived using a variational method, extending concepts of finite-strain hyperelasticity coupled to phase-field mechanics to generalized Finsler space. For application to skin tearing, state vector components represent microscopic damage processes (e.g., fiber rearrangements and ruptures) in different directions with respect to intrinsic orientations (e.g., parallel or perpendicular to Langer’s lines). Nonlinear potentials, motivated from soft-tissue mechanics and phase-field fracture theories, are assigned with orthotropic material symmetry pertinent to properties of skin. Governing equations are derived for one- and two-dimensional base manifolds. Analytical solutions capture experimental force-stretch data, toughness, and observations on evolving microstructure, in a more geometrically and physically descriptive way than prior phenomenological models.
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation

The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation tasks. However, SAM's performance significantly declines when applied to medical images, primarily due to the substantial disparity between natural and medical image domains. To effectively adapt SAM to medical images, it is important to incorporate critical third-dimensional information, i.e., volumetric or temporal knowledge, during fine-tuning. Simultaneously, we aim to harness SAM's pre-trained weights within its original 2D backbone to the fullest extent. In this paper, we introduce a modality-agnostic SAM adaptation framework, named as MA-SAM, that is applicable to various volumetric and video medical data. Our method roots in the parameter-efficient fine-tuning strategy to update only a small portion of weight increments while preserving the majority of SAM's pre-trained weights. By injecting a series of 3D adapters into the transformer blocks of the image encoder, our method enables the pre-trained 2D backbone to extract third-dimensional information from input data. The effectiveness of our method has been comprehensively evaluated on four medical image segmentation tasks, by using 10 public datasets across CT, MRI, and surgical video data. Remarkably, without using any prompt, our method consistently outperforms various state-of-the-art 3D approaches, surpassing nnU-Net by 0.9%, 2.6%, and 9.9% in Dice for CT multi-organ segmentation, MRI prostate segmentation, and surgical scene segmentation respectively. Our model also demonstrates strong generalization, and excels in challenging tumor segmentation when prompts are used. Our code is available at: https://github.com/cchen-cc/MA-SAM.
SLIDE: Reference-free Evaluation for Machine Translation using a Sliding Document Window

Reference-based metrics that operate at the sentence level typically outperform quality estimation metrics, which have access only to the source and system output. This is unsurprising, since references resolve ambiguities that may be present in the source. We investigate whether additional source context can effectively substitute for a reference. We present a metric, SLIDE (SLiding Document Evaluator), which operates on blocks of sentences using a window that slides over each document in the test set, feeding each chunk into an unmodified, off-the-shelf quality estimation model. We find that SLIDE obtains significantly higher pairwise system accuracy than its sentence-level baseline, in some cases even eliminating the gap with reference-base metrics. This suggests that source context may provide the same information as a human reference.