WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminant Analysis

Deep neural networks are susceptible to generating overconfident yet erroneous predictions when presented with data beyond known concepts. This challenge underscores the importance of detecting out-of-distribution (OOD) samples in the open world. In this work, we propose a novel feature-space OOD detection score based on class-specific and class-agnostic information. Specifically, the approach utilizes Whitened Linear Discriminant Analysis to project features into two subspaces - the discriminative and residual subspaces - for which the in-distribution (ID) classes are maximally separated and closely clustered, respectively. The OOD score is then determined by combining the deviation from the input data to the ID pattern in both subspaces. The efficacy of our method, named WDiscOOD, is verified on the large-scale ImageNet-1k benchmark, with six OOD datasets that cover a variety of distribution shifts. WDiscOOD demonstrates superior performance on deep classifiers with diverse backbone architectures, including CNN and vision transformer. Furthermore, we also show that WDiscOOD more effectively detects novel concepts in representation spaces trained with contrastive objectives, including supervised contrastive loss and multi-modality contrastive loss.Comment: Accepted by ICCV 2023. Code is available at: https://github.com/ivalab/WDiscOOD.gi

KESDT: knowledge enhanced shallow and deep Transformer for detecting adverse drug reactions

Adverse drug reaction (ADR) detection is an essential task in the medical field, as ADRs have a gravely detrimental impact on patients' health and the healthcare system. Due to a large number of people sharing information on social media platforms, an increasing number of efforts focus on social media data to carry out effective ADR detection. Despite having achieved impressive performance, the existing methods of ADR detection still suffer from three main challenges. Firstly, researchers have consistently ignored the interaction between domain keywords and other words in the sentence. Secondly, social media datasets suffer from the challenges of low annotated data. Thirdly, the issue of sample imbalance is commonly observed in social media datasets. To solve these challenges, we propose the Knowledge Enhanced Shallow and Deep Transformer(KESDT) model for ADR detection. Specifically, to cope with the first issue, we incorporate the domain keywords into the Transformer model through a shallow fusion manner, which enables the model to fully exploit the interactive relationships between domain keywords and other words in the sentence. To overcome the low annotated data, we integrate the synonym sets into the Transformer model through a deep fusion manner, which expands the size of the samples. To mitigate the impact of sample imbalance, we replace the standard cross entropy loss function with the focal loss function for effective model training. We conduct extensive experiments on three public datasets including TwiMed, Twitter, and CADEC. The proposed KESDT outperforms state-of-the-art baselines on F1 values, with relative improvements of 4.87%, 47.83%, and 5.73% respectively, which demonstrates the effectiveness of our proposed KESDT

Efficient Neural Radiance Fields for Interactive Free-viewpoint Video

This paper aims to tackle the challenge of efficiently producing interactive free-viewpoint videos. Some recent works equip neural radiance fields with image encoders, enabling them to generalize across scenes. When processing dynamic scenes, they can simply treat each video frame as an individual scene and perform novel view synthesis to generate free-viewpoint videos. However, their rendering process is slow and cannot support interactive applications. A major factor is that they sample lots of points in empty space when inferring radiance fields. We propose a novel scene representation, called ENeRF, for the fast creation of interactive free-viewpoint videos. Specifically, given multi-view images at one frame, we first build the cascade cost volume to predict the coarse geometry of the scene. The coarse geometry allows us to sample few points near the scene surface, thereby significantly improving the rendering speed. This process is fully differentiable, enabling us to jointly learn the depth prediction and radiance field networks from RGB images. Experiments on multiple benchmarks show that our approach exhibits competitive performance while being at least 60 times faster than previous generalizable radiance field methods.Comment: SIGGRAPH Asia 2022; Project page: https://zju3dv.github.io/enerf

Neuroinflammation and brain–peripheral interaction in ischemic stroke: A narrative review

Excessive immune activation within the lesion site can be observed after stroke onset. Such neuroinflammation within the brain parenchyma represents the innate immune response, as well as the result of the additional interactions between peripheral and resident immune cells. Accumulative studies have illustrated that the pathological process of ischemic stroke is associated with resident and peripheral immunity. The infiltration of peripheral immune cells within the brain parenchyma implicitly contributes to secondary brain injuries. Therefore, better understanding of the roles of resident and peripheral immune reactions toward ischemic insult is necessary. In this review, we summarized the interaction between peripheral and resident immunity on systemic immunity and the clinical outcomes after stroke onset and also discussed various potential immunotherapeutic strategies

Effects on different full-coverage designs and materials of crack propagation in first mandibular molar: an extended finite element method study

Objectives: This study aims to investigate the biomechanical properties of fracture resistance in cracked teeth using five different full-coverage restorations made of three different materials.Materials and Methods: A 3D model of a mandibular first molar was created to design five different full-coverage repair models: crown, crown with composite resin filling inside, occlusal veneer, occlusal veneer with composite resin filling inside and onlay. These repair models were fabricated using three different materials, namely, zirconia, lithium disilicate (LDS), and a hybrid polymer-infiltrated ceramic network material (PIC). In total, 15 repair models were tested using the extended finite element method (XFEM), with an occlusal load of 5000 N applied slowly to the occlusal surface of the restoration. The analysis of stress distribution in the restoration and dentin crack line was conducted to measure and record the crack initial load on the restoration and dentin.Results: The results showed that restorations on the occlusal surface significantly improved crack resistance, with zirconia exhibiting superior fracture resistance among the materials tested. Restorations of crown with composite resin filling inside demonstrated the highest resistance to fracture, while occlusal veneers showed the lowest. MPS concentration was observed at the interface between the restoration and dentin and at the root bifurcation, with the highest values at the top of crack development. Dentin covered by oxidized restorations had the highest displacement, while PIC restorations exhibited the lowest. Pulp analysis revealed selective MPS concentration and strain patterns in models with zirconia restorations and onlay, with pronounced pulp displacement in zirconia restorations and onlay. Enamel analysis indicated larger MPS values and displacements in zirconia restoration models and onlay, with higher strain in onlay. Restoration played a crucial role in protecting the tooth, with crack propagation initial loads in dentin surpassing restorations in experimental groups.Conclusion: This study confirms that full-coverage restorations significantly increased the fracture resistance of cracked teeth, with zirconia restorations significantly protecting the underlying cracked tooth. Elimination of fracture lines in the restoration design can improve fracture resistance in cracked teeth. The findings have implications for dental prosthetic design and clinical practice

A tough and mechanically stable adhesive hydrogel for non-invasive wound repair

Wound healing has been a great challenge throughout human history. Improper treatment for wounds is so easy to lead to infection and a series of serious symptoms, even death. Because of the ability of absorbing fluid and keeping a moist environment, the hydrogel with 3D networks is ideal candidate for wound dressing. More important, it has good biocompatibility. However, most of the hydrogel dressings reported have weak mechanical properties and adhesion properties, which greatly limit their clinical application. Herein, a tough adhesive hydrogel with good mechanical stability for non-invasive wound repair is reported. The hydrogel is composed of polyethylene glycol dimethacrylate (PEGDA), chitosan (CS) and chitin nano-whisker (CW). PEGDA and CS form interpenetrating network hydrogel through free radical polymerization reaction under the UV light. The introduction of CW further enhances the toughness of the hydrogel. The pH-sensitive CS can form adhesion to various materials through topological adhesion. As a wound closure repair material, PEGDA/CS/CW hydrogel not only has the characteristic of effectively closing the wound, defending against invading bacteria, and keeping the wound clean, but also has good tensile and mechanical stability, which is expected to realize the closure and repair of joints and other moving parts of the wound. This adhesive hydrogel is proven a promising material for wound closure repair

Genistein Inhibition of Topoisomerase IIα Expression Participated by Sp1 and Sp3 in HeLa Cell

Genistein (4′, 5, 7-trihydroxyisoflavone) is an isoflavone compound obtained from plants that has potential applications in cancer therapy. However, the molecular mechanism of the action of genistein on cancer cell apoptosis is not well known. In this study, we investigated the effect of genistein on topoisomerase II-α (Topo IIα), an important protein involved in the processes of DNA replication and cell proliferation. The results revealed that inhibition of Topo IIα expression through the regulation of Specificity protein 1 and Specificity protein 3 may be one of the reasons for genistein’s induction of HeLa cell apoptosis

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte