Elucidating a fresh perspective on the interplay between exosomes and rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by chronic synovitis and the destruction of bones and joints. Exosomes are nanoscale lipid membrane vesicles originating from multivesicular bodies and are used as a vital means of intercellular communication. Both exosomes and the microbial community are essential in RA pathogenesis. Multiple types of exosomes from different origins have been demonstrated to have effects on various immune cells through distinct mechanisms in RA, which depend on the specific cargo carried by the exosomes. Tens of thousands of microorganisms exist in the human intestinal system. Microorganisms exert various physiological and pathological effects on the host directly or through their metabolites. Gut microbe-derived exosomes are being studied in the field of liver disease; however, information on their role in the context of RA is still limited. Gut microbe-derived exosomes may enhance autoimmunity by altering intestinal permeability and transporting cargo to the extraintestinal system. Therefore, we performed a comprehensive literature review on the latest progress on exosomes in RA and provided an outlook on the potential role of microbe-derived exosomes as emerging players in clinical and translational research on RA. This review aimed to provide a theoretical basis for developing new clinical targets for RA therapy

Integrated Face Analytics Networks through Cross-Dataset Hybrid Training

Face analytics benefits many multimedia applications. It consists of a number of tasks, such as facial emotion recognition and face parsing, and most existing approaches generally treat these tasks independently, which limits their deployment in real scenarios. In this paper we propose an integrated Face Analytics Network (iFAN), which is able to perform multiple tasks jointly for face analytics with a novel carefully designed network architecture to fully facilitate the informative interaction among different tasks. The proposed integrated network explicitly models the interactions between tasks so that the correlations between tasks can be fully exploited for performance boost. In addition, to solve the bottleneck of the absence of datasets with comprehensive training data for various tasks, we propose a novel cross-dataset hybrid training strategy. It allows "plug-in and play" of multiple datasets annotated for different tasks without the requirement of a fully labeled common dataset for all the tasks. We experimentally show that the proposed iFAN achieves state-of-the-art performance on multiple face analytics tasks using a single integrated model. Specifically, iFAN achieves an overall F-score of 91.15% on the Helen dataset for face parsing, a normalized mean error of 5.81% on the MTFL dataset for facial landmark localization and an accuracy of 45.73% on the BNU dataset for emotion recognition with a single model.Comment: 10 page

Predator-prey survival pressure is sufficient to evolve swarming behaviors

The comprehension of how local interactions arise in global collective behavior is of utmost importance in both biological and physical research. Traditional agent-based models often rely on static rules that fail to capture the dynamic strategies of the biological world. Reinforcement learning has been proposed as a solution, but most previous methods adopt handcrafted reward functions that implicitly or explicitly encourage the emergence of swarming behaviors. In this study, we propose a minimal predator-prey coevolution framework based on mixed cooperative-competitive multiagent reinforcement learning, and adopt a reward function that is solely based on the fundamental survival pressure, that is, prey receive a reward of $-1$ if caught by predators while predators receive a reward of $+1$. Surprisingly, our analysis of this approach reveals an unexpectedly rich diversity of emergent behaviors for both prey and predators, including flocking and swirling behaviors for prey, as well as dispersion tactics, confusion, and marginal predation phenomena for predators. Overall, our study provides novel insights into the collective behavior of organisms and highlights the potential applications in swarm robotics

Unveiling advanced mechanisms of inhalable drug aerosol dynamics using computational fluid dynamics and discrete element method

Capsule-based dry powder inhalers (DPIs) are widely used to treat chronic obstructive pulmonary disease (COPD) by delivering active pharmaceutical ingredients (APIs) via inhalation into human respiratory systems. Previous research has shown that the actuation flow rate, aerodynamic particle size distribution (APSD), and particle shape of lactose carriers are factors that can influence the particle deposition patterns in human respiratory systems. Understanding the dynamics of APIs transport in DPIs and airways can provide significant value for the design optimization of DPIs and particle shapes to enhance the delivery of APIs to the designated lung sites, i.e., small airways. Thus, it is necessary to investigate how to modulate the above-mentioned factors to increase the delivery efficacy to small airways and enhance the therapeutic effect to treat COPD. Compared with in vitro and in vivo methods, computational fluid-particle dynamics (CFPD) models allow researchers to study the transport dynamics of inhalable therapeutic dry powders in both DPI and human respiratory systems. However, existing CFPD models neglect particle-particle interactions, and most existing airway models lack peripheral lung airway and neglect the airway deformation kinematics. Such deficiencies can lead to inaccurate predictions of particle transport and deposition. This study developed a one-way coupled computational fluid dynamics (CFD) and discrete element method (DEM) model to simulate the particle-particle and particle-device interactions, and the transport of API-carrier dry powder mixtures with different shapes of carriers in a DPI flow channel. The influence of actuation flow rate (30 to 90 L/min) and particle shape (aspect ratio equals 1, 5, and 10) on lactose carrier dynamics in a representative DPI, i.e., SpirivaTM HandihalerTM, has been investigated. Subsequently, an elastic truncated whole-lung model has also been developed to predict particle transport and deposition from mouth to alveoli, with disease-specific airway deformation kinematics. Numerical results indicate that 90 L/min actuation flow rate generates the highest delivery efficiency of Handihaler, as approximately 26% API reaches the deep lung region. The elastic truncated whole-lung modeling results show that noticeable differences of predictions between static and elastic lung models can be found, which demonstrates the necessity to model airway deformation kinematics in virtual lung models

STRUKTUR KOMUNITAS ECHINODERMATA DI PERAIRAN PANTAI GAPANG, DESA IBOIH, KECAMATAN SUKAKARYA, SABANG

Banda Ace

A Bearing-Angle Approach for Unknown Target Motion Analysis Based on Visual Measurements

Vision-based estimation of the motion of a moving target is usually formulated as a bearing-only estimation problem where the visual measurement is modeled as a bearing vector. Although the bearing-only approach has been studied for decades, a fundamental limitation of this approach is that it requires extra lateral motion of the observer to enhance the target's observability. Unfortunately, the extra lateral motion conflicts with the desired motion of the observer in many tasks. It is well-known that, once a target has been detected in an image, a bounding box that surrounds the target can be obtained. Surprisingly, this common visual measurement especially its size information has not been well explored up to now. In this paper, we propose a new bearing-angle approach to estimate the motion of a target by modeling its image bounding box as bearing-angle measurements. Both theoretical analysis and experimental results show that this approach can significantly enhance the observability without relying on additional lateral motion of the observer. The benefit of the bearing-angle approach comes with no additional cost because a bounding box is a standard output of object detection algorithms. The approach simply exploits the information that has not been fully exploited in the past. No additional sensing devices or special detection algorithms are required

HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation

Controllable human image generation (HIG) has numerous real-life applications. State-of-the-art solutions, such as ControlNet and T2I-Adapter, introduce an additional learnable branch on top of the frozen pre-trained stable diffusion (SD) model, which can enforce various conditions, including skeleton guidance of HIG. While such a plug-and-play approach is appealing, the inevitable and uncertain conflicts between the original images produced from the frozen SD branch and the given condition incur significant challenges for the learnable branch, which essentially conducts image feature editing for condition enforcement. In this work, we propose a native skeleton-guided diffusion model for controllable HIG called HumanSD. Instead of performing image editing with dual-branch diffusion, we fine-tune the original SD model using a novel heatmap-guided denoising loss. This strategy effectively and efficiently strengthens the given skeleton condition during model training while mitigating the catastrophic forgetting effects. HumanSD is fine-tuned on the assembly of three large-scale human-centric datasets with text-image-pose information, two of which are established in this work. As shown in Figure 1, HumanSD outperforms ControlNet in terms of accurate pose control and image quality, particularly when the given skeleton guidance is sophisticated

From 2D Images to 3D Model:Weakly Supervised Multi-View Face Reconstruction with Deep Fusion

We consider the problem of Multi-view 3D Face Reconstruction (MVR) with weakly supervised learning that leverages a limited number of 2D face images (e.g. 3) to generate a high-quality 3D face model with very light annotation. Despite their encouraging performance, present MVR methods simply concatenate multi-view image features and pay less attention to critical areas (e.g. eye, brow, nose and mouth). To this end, we propose a novel model called Deep Fusion MVR (DF-MVR) and design a multi-view encoding to a single decoding framework with skip connections, able to extract, integrate, and compensate deep features with attention from multi-view images. In addition, we develop a multi-view face parse network to learn, identify, and emphasize the critical common face area. Finally, though our model is trained with a few 2D images, it can reconstruct an accurate 3D model even if one single 2D image is input. We conduct extensive experiments to evaluate various multi-view 3D face reconstruction methods. Our proposed model attains superior performance, leading to 11.4% RMSE improvement over the existing best weakly supervised MVRs. Source codes are available in the supplementary materials