Miracle-dz/UniFAR-FTUs huggingface dataset:
Vitamin D3 upregulated protein 1 deficiency promotes N-methyl-N-nitrosourea and Helicobacter pylori-induced gastric carcinogenesis in mice

Objective Vitamin D3 upregulated protein 1 (VDUP1) is a potent tumour suppressor whose expression is dramatically reduced in various types of human cancers, including gastric cancer. However, the precise mechanisms underlying tumour development remain unclear. In the present study, the authors examined the effect of VDUP1 on Helicobacter pylori-induced gastric carcinogenesis in mice. Design Gastric cancer was generated in VDUP1 knockout (KO) and wild-type mice using a combination of N-methyl-N-nitrosourea treatment and H pylori infection. Fifty weeks after treatment, gastric tissues from both types of mice were examined by histopathology, immunohistochemistry and immunoblotting. In vitro tests on the human gastric cancer cell line, AGS, were also performed to identify the underlying mechanisms of cancer development. Results The overall incidence of gastric cancer was significantly higher in VDUP1 KO mice than in wild-type mice. Similarly, VDUP1 KO mice showed more severe chronic gastritis, glandular atrophy, foveolar hyperplasia, metaplasia and dysplasia. Although no differences in the apoptotic index were apparent, lack of VDUP1 increased the rate of gastric epithelial cell proliferation in non-cancerous stomachs, with corresponding increases in tumour necrosis factor alpha (TNFα) level, nuclear transcription factor kappa B (NF-κB) activation and cyclooxygenase-2 (COX-2) expression. An in vitro study showed that H pylori-associated cell proliferation and induction of TNFα, NF-κB and COX-2 were inhibited in cells transfected with VDUP1. In addition, overexpression of VDUP1 in AGS cells suppressed TNFα-induced NF-κB activation and COX-2 expression. Conclusion Our data show that VDUP1 negatively regulates H pylori-associated gastric carcinogenesis, in part by disrupting cell growth and inhibiting the induction of TNFα, NF-κB and COX-2. These findings provide important insights into the role of VDUP1 in H pylori-associated tumourigenesis.
Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse

Driven by the great advances in metaverse and edge computing technologies, vehicular edge metaverses are expected to disrupt the current paradigm of intelligent transportation systems. As highly computerized avatars of Vehicular Metaverse Users (VMUs), the Vehicle Twins (VTs) deployed in edge servers can provide valuable metaverse services to improve driving safety and on-board satisfaction for their VMUs throughout journeys. To maintain uninterrupted metaverse experiences, VTs must be migrated among edge servers following the movements of vehicles. This can raise concerns about privacy breaches during the dynamic communications among vehicular edge metaverses. To address these concerns and safeguard location privacy, pseudonyms as temporary identifiers can be leveraged by both VMUs and VTs to realize anonymous communications in the physical space and virtual spaces. However, existing pseudonym management methods fall short in meeting the extensive pseudonym demands in vehicular edge metaverses, thus dramatically diminishing the performance of privacy preservation. To this end, we present a cross-metaverse empowered dual pseudonym management framework. We utilize cross-chain technology to enhance management efficiency and data security for pseudonyms. Furthermore, we propose a metric to assess the privacy level and employ a Multi-Agent Deep Reinforcement Learning (MADRL) approach to obtain an optimal pseudonym generating strategy. Numerical results demonstrate that our proposed schemes are high-efficiency and cost-effective, showcasing their promising applications in vehicular edge metaverses.
Optimal Exploration Strategy for Regret Minimization in Unconstrained Scalar Optimization Problems

We study the problem of determining the optimal exploration strategy in an unconstrained scalar optimization problem depending on an unknown parameter to be learned from online collected noisy data. An optimal trade-off between exploration and exploitation is crucial for effective optimization under uncertainties, and to achieve this we consider a cumulative regret minimization approach over a finite horizon, with each time instant in the horizon characterized by a stochastic exploration signal, whose variance has to be designed. In this setting, under an idealized assumption on an appropriately defined information function associated with the excitation, we are able to show that the optimal exploration strategy is either to use no exploration at all (called lazy exploration) or adding an exploration excitation only at the first time instant of the horizon (called immediate exploration). A quadratic numerical example is used to illustrate the results.
Use of diploid male frequency data as an indicator of pollinator decline

Pollination deficits in agricultural and natural systems are suggestive of large reductions in pollinator populations. However, actual declines are difficult to demonstrate using census data. Here, we show census data to be misleading because many abundant pollinators exhibit high levels of production of sterile diploid males usually found only in small inbred hymenopteran populations; Euglossa imperialis exhibits high levels of diploid male production induced by low effective population sizes (Ne ≈ 15), despite being the most abundant orchid bee in lowland tropical forests in Panama. We caution that although some pollinators appear abundant on the basis of census data, their long–term persistence may be highly tenuous based on genetic evidence. We propose the use of diploid male frequency data as a metric for assessing the sustainability of bee populations.
The axonal repellent, Slit2, inhibits directional migration of circulating neutrophils

In inflammatory diseases, circulating neutrophils are recruited to sites of injury. Attractant signals are provided by many different chemotactic molecules, such that blockade of one may not prevent neutrophil recruitment effectively. The Slit family of secreted proteins and their transmembrane receptor, Robo, repel axonal migration during CNS development. Emerging evidence shows that by inhibiting the activation of Rho‐family GTPases, Slit2/Robo also inhibit migration of other cell types toward a variety of chemotactic factors in vitro and in vivo. The role of Slit2 in inflammation, however, has been largely unexplored. We isolated primary neutrophils from human peripheral blood and mouse bone marrow and detected Robo‐1 expression. Using video‐microscopic live cell tracking, we found that Slit2 selectively impaired directional migration but not random movement of neutrophils toward fMLP. Slit2 also inhibited neutrophil migration toward other chemoattractants, namely C5a and IL‐8. Slit2 inhibited neutrophil chemotaxis by preventing chemoattractant‐induced actin barbed end formation and cell polarization. Slit2 mediated these effects by suppressing inducible activation of Cdc42 and Rac2 but did not impair activation of other major kinase pathways involved in neutrophil migration. We further tested the effects of Slit2 in vivo using mouse models of peritoneal inflammation induced by sodium periodate, C5a, and MIP‐2. In all instances, Slit2 reduced neutrophil recruitment effectively (P<0.01). Collectively, these data demonstrate that Slit2 potently inhibits chemotaxis but not random motion of circulating neutrophils and point to Slit2 as a potential new therapeutic for preventing localized inflammation.
Rock Strength and Texture Evolution During Deformation in the Earth's Ductile Lithosphere: A Two‐Phase Thermodynamics Model

Building upon the two‐phase and grain damage theory, we propose a new formulation allowing to track the evolution of phase mixing/segregation during ductile deformation of a two‐phased aggregate. Our model is based on a set of variables characterizing a rock texture: the mean grain sizes and the phase proportion. During ductile deformation, activation of different micromechanical processes impacts the aggregate texture. Dislocation and diffusion creep are the two main deformation processes considered. We only account for the effect of Zener pinning in slowing down grain growth and allow for active grain‐size reduction mechanisms in the diffusion creep domain. For this purpose, an equation is proposed to track the phase mixing evolution during ductile deformation. Numerical application using anorthite rheology shows that any grain reduction mechanisms that could be active in the diffusion creep regime requires a very high partition fraction in order to reach the grain size predicted by the feldspar piezometer. Application of this model to gabbroic composition, relevant for the ductile crust, demonstrates that the strong coupling between phases grain sizes and interface evolution results in steady‐state grain sizes far below the field boundary. This effect is coeval with an important increase of mixing between the two phases. In addition, accounting for the phase mixing results in a drop of the global aggregate stress during deformation. This model allows for further comparison of mylonitized textures evolution with natural shear zones at the local and regional scales.
Transition Graph Properties of Target Class Classification

Target class classification is a mixed classification and transition model whose integrated goal is to assign objects to a certain, so called target or normal class. The classification process is iterative, and in each step an object in a certain class undergoes an action attached to that class, initiating the transition of the object to one of the classes. The sequence of transitions, which we call class transitions, must be designed to provide the final assignment of objects to the target class. The transition process can be described in the form of a directed graph, and the success of the final classification is mainly due to the properties of this graph. In our previous research we showed that the desirable structure of the transition graph is an oriented rooted tree with orientation towards the root vertex, which corresponds to the normal class. It is clear that the transition graph of an arbitrary algorithm (policy) may not have this property. In this paper we study the structure of realistic transition graphs, which makes it possible to find classification inconsistencies, helping to transfer it into the desired form. The medical interpretation of dynamic treatment regime considered in the article further clarifies the investigated framework.
Baroreflex sensitivity is higher during acute psychological stress in healthy subjects under β-adrenergic blockade

Acute psychological stress challenges the cardiovascular system with an increase in BP (blood pressure), HR (heart rate) and reduced BRS (baroreflex sensitivity). β-adrenergic blockade enhances BRS during rest, but its effect on BRS during acute psychological stress is unknown. This study tested the hypothesis that BRS is higher during acute psychological stress in healthy subjects under β-adrenergic blockade. Twenty healthy novice male bungee jumpers were randomized and studied with (PROP, n=10) or without (CTRL, n=10) propranolol. BP and HR responses and BRS [cross-correlation time-domain (BRSTD) and cross-spectral frequency-domain (BRSFD) analysis] were evaluated from 30 min prior up to 2 h after the jump. HR, cardiac output and pulse pressure were lower in the PROP group throughout the study. Prior to the bungee jump, BRS was higher in the PROP group compared with the CTRL group [BRSTD: 28 (24–42) compared with 17 (16–28) ms·mmHg−1, P<0.05; BRSFD: 27 (20–34) compared with 14 (9–19) ms·mmHg−1, P<0.05; values are medians (interquartile range)]. BP declined after the jump in both groups, and post-jump BRS did not differ between the groups. In conclusion, during acute psychological stress, BRS is higher in healthy subjects treated with non-selective β-adrenergic blockade with significantly lower HR but comparable BP.
Shadow Generation for Composite Image Using Diffusion model

In the realm of image composition, generating realistic shadow for the inserted foreground remains a formidable challenge. Previous works have developed image-to-image translation models which are trained on paired training data. However, they are struggling to generate shadows with accurate shapes and intensities, hindered by data scarcity and inherent task complexity. In this paper, we resort to foundation model with rich prior knowledge of natural shadow images. Specifically, we first adapt ControlNet to our task and then propose intensity modulation modules to improve the shadow intensity. Moreover, we extend the small-scale DESOBA dataset to DESOBAv2 using a novel data acquisition pipeline. Experimental results on both DESOBA and DESOBAv2 datasets as well as real composite images demonstrate the superior capability of our model for shadow generation task. The dataset, code, and model are released at https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBAv2.
Latent Diffusion Models for Attribute-Preserving Image Anonymization

Generative techniques for image anonymization have great potential to generate datasets that protect the privacy of those depicted in the images, while achieving high data fidelity and utility. Existing methods have focused extensively on preserving facial attributes, but failed to embrace a more comprehensive perspective that considers the scene and background into the anonymization process. This paper presents, to the best of our knowledge, the first approach to image anonymization based on Latent Diffusion Models (LDMs). Every element of a scene is maintained to convey the same meaning, yet manipulated in a way that makes re-identification difficult. We propose two LDMs for this purpose: CAMOUFLaGE-Base exploits a combination of pre-trained ControlNets, and a new controlling mechanism designed to increase the distance between the real and anonymized images. CAMOFULaGE-Light is based on the Adapter technique, coupled with an encoding designed to efficiently represent the attributes of different persons in a scene. The former solution achieves superior performance on most metrics and benchmarks, while the latter cuts the inference time in half at the cost of fine-tuning a lightweight module. We show through extensive experimental comparison that the proposed method is competitive with the state-of-the-art concerning identity obfuscation whilst better preserving the original content of the image and tackling unresolved challenges that current solutions fail to address.
Stability of conforming space-time isogeometric methods for the wave equation

We consider a family of conforming space-time finite element discretizations for the wave equation based on splines of maximal regularity in time. Traditional techniques may require a CFL condition to guarantee stability. Recent works by O. Steinbach and M. Zank (2018), and S. Fraschini, G. Loli, A. Moiola, and G. Sangalli (2023), have introduced unconditionally stable schemes by adding non-consistent penalty terms to the underlying bilinear form. Stability and error analysis have been carried out for lowest order discrete spaces. While higher order methods have shown promising properties through numerical testing, their rigorous analysis was still missing. In this paper, we address this stability analysis by studying the properties of the condition number of a family of matrices associated with the time discretization. For each spline order, we derive explicit estimates of both the CFL condition required in the unstabilized case and the penalty term that minimises the consistency error in the stabilized case. Numerical tests confirm the sharpness of our results.
Incorporating Graph Attention Mechanism into Geometric Problem Solving Based on Deep Reinforcement Learning

In the context of online education, designing an automatic solver for geometric problems has been considered a crucial step towards general math Artificial Intelligence (AI), empowered by natural language understanding and traditional logical inference. In most instances, problems are addressed by adding auxiliary components such as lines or points. However, adding auxiliary components automatically is challenging due to the complexity in selecting suitable auxiliary components especially when pivotal decisions have to be made. The state-of-the-art performance has been achieved by exhausting all possible strategies from the category library to identify the one with the maximum likelihood. However, an extensive strategy search have to be applied to trade accuracy for ef-ficiency. To add auxiliary components automatically and efficiently, we present deep reinforcement learning framework based on the language model, such as BERT. We firstly apply the graph attention mechanism to reduce the strategy searching space, called AttnStrategy, which only focus on the conclusion-related components. Meanwhile, a novel algorithm, named Automatically Adding Auxiliary Components using Reinforcement Learning framework (A3C-RL), is proposed by forcing an agent to select top strategies, which incorporates the AttnStrategy and BERT as the memory components. Results from extensive experiments show that the proposed A3C-RL algorithm can substantially enhance the average precision by 32.7% compared to the traditional MCTS. In addition, the A3C-RL algorithm outperforms humans on the geometric questions from the annual University Entrance Mathematical Examination of China.
Defying Imbalanced Forgetting in Class Incremental Learning

We observe a high level of imbalance in the accuracy of different learned classes in the same old task for the first time. This intriguing phenomenon, discovered in replay-based Class Incremental Learning (CIL), highlights the imbalanced forgetting of learned classes, as their accuracy is similar before the occurrence of catastrophic forgetting. This discovery remains previously unidentified due to the reliance on average incremental accuracy as the measurement for CIL, which assumes that the accuracy of classes within the same task is similar. However, this assumption is invalid in the face of catastrophic forgetting. Further empirical studies indicate that this imbalanced forgetting is caused by conflicts in representation between semantically similar old and new classes. These conflicts are rooted in the data imbalance present in replay-based CIL methods. Building on these insights, we propose CLass-Aware Disentanglement (CLAD) as a means to predict the old classes that are more likely to be forgotten and enhance their accuracy. Importantly, CLAD can be seamlessly integrated into existing CIL methods. Extensive experiments demonstrate that CLAD consistently improves current replay-based methods, resulting in performance gains of up to 2.56%.
Controlled Training Data Generation with Diffusion Models

In this work, we present a method to control a text-to-image generative model to produce training data specifically"useful"for supervised learning. Unlike previous works that employ an open-loop approach and pre-define prompts to generate new data using either a language model or human expertise, we develop an automated closed-loop system which involves two feedback mechanisms. The first mechanism uses feedback from a given supervised model and finds adversarial prompts that result in image generations that maximize the model loss. While these adversarial prompts result in diverse data informed by the model, they are not informed of the target distribution, which can be inefficient. Therefore, we introduce the second feedback mechanism that guides the generation process towards a certain target distribution. We call the method combining these two mechanisms Guided Adversarial Prompts. We perform our evaluations on different tasks, datasets and architectures, with different types of distribution shifts (spuriously correlated data, unseen domains) and demonstrate the efficiency of the proposed feedback mechanisms compared to open-loop approaches.
LeGO: Leveraging a Surface Deformation Network for Animatable Stylized Face Generation with One Example

Recent advances in 3D face stylization have made significant strides in few to zero-shot settings. However, the degree of stylization achieved by existing methods is often not sufficient for practical applications because they are mostly based on statistical 3D Morphable Models (3DMM) with limited variations. To this end, we propose a method that can produce a highly stylized 3D face model with desired topology. Our methods train a surface deformation network with 3DMM and translate its domain to the target style using a paired exemplar. The network achieves stylization of the 3D face mesh by mimicking the style of the target using a differentiable renderer and directional CLIP losses. Additionally, during the inference process, we utilize a Mesh Agnostic Encoder (MAGE) that takes deformation target, a mesh of diverse topologies as input to the stylization process and encodes its shape into our latent space. The resulting stylized face model can be animated by commonly used 3DMM blend shapes. A set of quantitative and qualitative evaluations demonstrate that our method can produce highly stylized face meshes according to a given style and output them in a desired topology. We also demonstrate example applications of our method including image-based stylized avatar generation, linear interpolation of geometric styles, and facial animation of stylized avatars.
InternVideo2: Scaling Video Foundation Models for Multimodal Video Understanding

We introduce InternVideo2, a new video foundation model (ViFM) that achieves the state-of-the-art performance in action recognition, video-text tasks, and video-centric dialogue. Our approach employs a progressive training paradigm that unifies the different self- or weakly-supervised learning frameworks of masked video token reconstruction, cross-modal contrastive learning, and next token prediction. Different training stages would guide our model to capture different levels of structure and semantic information through different pretext tasks. At the data level, we prioritize the spatiotemporal consistency by semantically segmenting videos and generating video-audio-speech captions. This improves the alignment between video and text. We scale both data and model size for our InternVideo2. Through extensive experiments, we validate our designs and demonstrate the state-of-the-art performance on over 60 video and audio tasks. Notably, our model outperforms others on various video-related captioning, dialogue, and long video understanding benchmarks, highlighting its ability to reason and comprehend long temporal contexts. Code and models are available at https://github.com/OpenGVLab/InternVideo2/.
Effector function of leucocytes from susceptible and resistant mice against distinct isolates of Candida albicans

Neutrophils and macrophages were generated in vitro from mice that display either high or low tissue susceptibilities to Candida albicans infection and their ability to phagocytose and kill three isolates of the yeast with different virulence characteristics was evaluated. In the absence of opsonization, phagocytosis by BALB/c and CBA/CaH neutrophils was comparable, but the killing was very poor. Opsonization with normal serum slightly decreased phagocytosis, but it had markedly different effects on killing, either enhancing or inhibiting candidacidal activity, depending on the combination of yeast isolate and mouse strain. In contrast, BALB/c macrophages showed high levels of phagocytosis and killing of both unopsonized yeasts and opsonized yeasts; whereas killing of unopsonized yeasts by CBA/CaH macrophages was poor, it was markedly enhanced by opsonization.