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Timely and effective diagnosis of fungal keratitis (FK) is necessary for suitable treatment and avoiding irreversible vision loss for patients. In vivo confocal microscopy (IVCM) has been widely adopted to guide the FK diagnosis. We present a deep learning framework for diagnosing fungal keratitis using IVCM images to assist ophthalmologists. Inspired by the real diagnostic process, our method employs a two-stage deep architecture for diagnostic predictions based on both image-level and sequence-level information. To the best of our knowledge, we collected the largest dataset with 96,632 IVCM images in total with expert labeling to train and evaluate our method. The specificity and sensitivity of our method in diagnosing FK on the unseen test set achieved 96.65% and 97.57%, comparable or better than experienced ophthalmologists. The network can provide image-level, sequence-level and patient-level diagnostic suggestions to physicians. The results show great promise for assisting ophthalmologists in FK diagnosis. [ABSTRACT FROM AUTHOR]

The emergence of 3D Gaussian splatting (3DGS) has greatly accelerated rendering in novel view synthesis. Unlike neural implicit representations like neural radiance fields (NeRFs) that represent a 3D scene with position and viewpoint-conditioned neural networks, 3D Gaussian splatting utilizes a set of Gaussian ellipsoids to model the scene so that efficient rendering can be accomplished by rasterizing Gaussian ellipsoids into images. Apart from fast rendering, the explicit representation of 3D Gaussian splatting also facilitates downstream tasks like dynamic reconstruction, geometry editing, and physical simulation. Considering the rapid changes and growing number of works in this field, we present a literature review of recent 3D Gaussian splatting methods, which can be roughly classified by functionality into 3D reconstruction, 3D editing, and other downstream applications. Traditional point-based rendering methods and the rendering formulation of 3D Gaussian splatting are also covered to aid understanding of this technique. This survey aims to help beginners to quickly get started in this field and to provide experienced researchers with a comprehensive overview, aiming to stimulate future development of the 3D Gaussian splatting representation. [ABSTRACT FROM AUTHOR]

Background: We aimed to develop and validate a plasma extracellular vesicle circular RNA (circRNA)-based signature that can predict overall survival (OS) in first-line abiraterone therapy for metastatic castration-resistant prostate cancer (mCRPC) patients. Methods: In total, 582 mCRPC patients undergoing first-line abiraterone therapy from four institutions were sorted by three phases. In the discovery phase, 30 plasma samples from 30 case-matched patients with or without early progression were obtained to generate circRNA expression profiles using RNA sequencing. In the training phase, differentially expressed circRNAs were examined using digital droplet PCR in a training cohort (n = 203). The circRNA signature was constructed using a least absolute shrinkage and selection operator Cox regression to predict OS. In the validation phase, the prognostic ability of this signature was prospectively validated in two external cohorts (Cohort I, n = 183; Cohort II, n = 166). Results: We developed a five-circRNA signature, based on circCEP112, circFAM13A, circBRWD1, circVPS13C and circMACROD2, which successfully stratified patients into high-risk and low-risk groups. The prognostic ability of this signature was prospectively validated in two external cohorts (P < 0.0001, P < 0.0001). Patients with high-risk scores had shorter OS than patients with low-risk scores. Conclusion: This five-circRNA signature is a reliable predictor of OS for mCRPC patients undergoing abiraterone. [ABSTRACT FROM AUTHOR]

Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing. Several methods have recently been developed, with an aim to extract intuitive and interpretable deformation components. However, these techniques suffer from fundamental limitations especially for meshes with noise or large-scale nonlinear deformations, and may not always be able to identify important deformation components. In this paper we propose a mesh-based variational autoencoder architecture that is able to cope with meshes with irregular connectivity and nonlinear deformations, assuming that the analyzed dataset contains meshes with the same vertex connectivity, which is common for deformation analysis. To help localize deformations, we introduce sparse regularization in this framework, along with spectral graph convolutional operations. Through modifying the regularization formulation and allowing dynamic change of sparsity ranges, we improve the visual quality and reconstruction ability of the extracted deformation components. Our system also provides a nonlinear approach to reconstruction of meshes using the extracted basis, which is more effective than the current linear combination approach. As an important application of localized deformation components and a novel approach on its own, we further develop a neural shape editing method, achieving shape editing and deformation component extraction in a unified framework, and ensuring plausibility of the edited shapes. Extensive experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative evaluations. We also demonstrate the effectiveness of our method for neural shape editing. [ABSTRACT FROM AUTHOR]

Aim of Study. Myocardial infarction is the number one cause of death worldwide. Existing treatment methods such as drugs and surgery cannot completely restore the structure and function of the ischemic heart. In recent years, therapeutic angiogenesis has received gradually increasing attention due to its fundamental therapeutic advantages of improving microcirculation and restoring blood supply in ischemic areas. This article mainly reviewed the mechanism and effect of angiogenesis-promoting herbs. Methods. We conducted a literature search on the bioactive components of medicinal plants and their effects on angiogenesis after MI. We searched for articles in Web of Science, MEDLINE, PubMed, Scopus, Google Scholar, and China National Knowledge Infrastructure databases before April 2021. Results. In this article, we summarized the mechanisms by which copper ions, microRNA, Akt1, inflammation, oxidative stress, mitochondria, and pericytes are involved in angiogenesis after myocardial infarction. In addition, we reviewed the angiogenic effects of natural herbal medicines such as Salvia miltiorrhiza Bunge Bunge, Carthamus tinctorius L., Pueraria lobata, Astragalus, Panax ginseng C.A. Mey., Panax notoginseng (Burkill) F.H. Chen, Cinnamomum cassia (L.) J. Presl, Rehmannia glutinosa (Gaertn.) DC., Leonurus japonicus Houtt, Scutellaria baicalensis Georgi., and Geum macrophyllum Willd. Conclusions. Some herbs have the effect of promoting angiogenesis. In the future, natural proangiogenic drugs may become candidates for the treatment of cardiovascular diseases. [ABSTRACT FROM AUTHOR]

The aim of this study was to identify a urine extracellular vesicle circular RNA (circRNA) classifier that could detect high-grade prostate cancer (PCa) of Grade Group (GG) 2 or greater. For this purpose, we used RNA sequencing to identify candidate circRNAs from urinary extracellular vesicles from 11 patients with high-grade PCa and 11 case-matched patients with benign prostatic hyperplasia. Using ddPCR in a training cohort (n = 263), we built a urine extracellular vesicle circRNA classifier (Ccirc, containing circPDLIM5, circSCAF8, circPLXDC2, circSCAMP1, and circCCNT2), which was evaluated in two independent cohorts (n = 497, n = 505). Ccirc showed higher accuracy than two standard of care risk calculators (RCs) (PCPT-RC 2.0 and ERSPC-RC) in both the training cohort and the validation cohorts. In all three cohorts, this novel urine extracellular vesicle circRNA classifier plus RCs was statistically more predictive than RCs alone for predicting ≥ GG2 PCa. This assay, which does not require precollection digital rectal examination nor special handling, is repeatable, noninvasive, and can be easily implemented as part of the basic clinical workflow. [ABSTRACT FROM AUTHOR]

In geometry processing, symmetry is a universal type of high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, alignment, matching, and completion. Thus it is an important problem to analyze various symmetry forms of 3D shapes. Planar reflective symmetry is the most fundamental one. Traditional methods based on spatial sampling can be time-consuming and may not be able to identify all the symmetry planes. In this article, we present a novel learning framework to automatically discover global planar reflective symmetry of a 3D shape. Our framework trains an unsupervised 3D convolutional neural network to extract global model features and then outputs possible global symmetry parameters, where input shapes are represented using voxels. We introduce a dedicated symmetry distance loss along with a regularization loss to avoid generating duplicated symmetry planes. Our network can also identify generalized cylinders by predicting their rotation axes. We further provide a method to remove invalid and duplicated planes and axes. We demonstrate that our method is able to produce reliable and accurate results. Our neural network based method is hundreds of times faster than the state-of-the-art methods, which are based on sampling. Our method is also robust even with noisy or incomplete input surfaces. [ABSTRACT FROM AUTHOR]

Cancer therapeutic nanovaccines are ideal tools to inhibit tumor growth and provide the body with continuous protecting immune surveillance. However, the conventional subcutaneous (SC) vaccination normally induces limited anti-tumor immune responses with low therapeutic efficacy. Herein, we devised clay-based nanovaccines and directly delivered them to the spleen via intravenous (IV) injection to induce the stronger anti-tumor immunity with higher efficacy for tumor prevention and treatment. The clay, i.e., layered double hydroxide (LDH) was prepared as nanoadjuvant with the average size from 77 to 285 nm and co-loaded with the model antigen ovalbumin (OVA) and bioadjuvant CpG to form CpG/OVA-LDH (CO-LDH) nanovaccines. We found that CO-LDH-215 (the size of LDH was 215 nm) promoted dendritic cells to present the most antigen, and moreover showed the highest spleen enrichment (~ 1.67% of CO-LDH-215 enriched in the spleen at 24 h post IV injection). The in vivo immunologic data showed that CO-LDH-215 induced the most potent anti-tumor immune responses and completely prevented the growth of E.G7-OVA tumor in the mouse model. Furthermore, IV injected CO-LDH-215 nanovaccine more effectively delayed tumor growth than that SC injected, largely due to the direct and quick delivery of more nanovaccines to the spleen. This study demonstrates that the therapeutic efficacy of nanovaccines can be greatly enhanced by targeted delivery of nanovaccines to the spleen via the proper vaccination route. [ABSTRACT FROM AUTHOR]

Example-based mesh deformation methods are powerful tools for realistic shape editing. However, existing techniques typically combine all the example deformation modes, which can lead to overfitting, i.e., using an overly complicated model to explain the user-specified deformation. This leads to implausible or unstable deformation results, including unexpected global changes outside the region of interest. To address this fundamental limitation, we propose a sparse blending method that automatically selects a smaller number of deformation modes to compactly describe the desired deformation. This along with a suitably chosen deformation basis including spatially localized deformation modes leads to significant advantages, including more meaningful, reliable, and efficient deformations because fewer and localized deformation modes are applied. To cope with large rotations, we develop a simple but effective representation based on polar decomposition of deformation gradients, which resolves the ambiguity of large global rotations using an as-consistent-as-possible global optimization. This simple representation has a closed form solution for derivatives, making it efficient for our sparse localized representation and thus ensuring interactive performance. Experimental results show that our method outperforms state-of-the-art data-driven mesh deformation methods, for both quality of results and efficiency. [ABSTRACT FROM AUTHOR]

Efficient brain drug delivery has been a challenge in the treatment of Alzheimer's disease (AD) and other brain disorders as blood‐brain barrier (BBB) impedes most drugs to reach brain. To overcome this obstacle, a novel poly(lactic‐co‐glycolic acid) (PLGA) nanoparticle conjugated with CD47 extracellular domain via reactive oxygen species (ROS)‐responsive phenylborate ester bond exhibiting "do not eat me" signal and BBB penetrating peptide CRTIGPSVC (CRT) and microglia modulation agent Nec‐1s encapsulated in it is developed. The experimental results show that the designed nanoparticle efficiently increases its half‐life in blood circulation by preventing engulfment via phagocytes, and enhances its brain distribution by synergistic effect of CD47 and CRT. The high level of ROS in mouse brain releases CD47 from the nanoparticles and the resultant particles are effectively phagocytized by resident microglia. The engulfed Nec‐1s modulates pathological microglia to a beneficial state, which reduces Aβ burden, microgliosis and astrocytosis, decreases cytokine production and oxidative stress in the brains of AD mice, and finally attenuates cognition deficits and synapse loss. The results first demonstrate that the conditionally releasable "do not eat me" CD47 signal remarkably facilitates microglia‐targeted drug delivery and warrants further study to develop therapeutic agent for AD treatment. [ABSTRACT FROM AUTHOR]

Beta-amyloid (Aβ) oligomers are strongly associated with the cascade of harmful events leading to neurodegeneration in Alzheimer's disease (AD). Elimination of Aβ oligomers or inhibition of Aβ assembly is a valuable therapeutic approach for the treatment of AD. Here, we obtained a mimotope of Aβ oligomers, AOEP2, by screening a peptide library using oligomer-specific antibodies. The antibodies induced by AOEP2 specifically recognize Aβ oligomers rather than monomers and fibrils. Interestingly, the AOEP2 peptide binds to Aβ monomers and inhibits the formation of Aβ oligomers and β-sheet structure, reduces Aβ42-induced neurotoxicity, and decreases the release of proinflammatory cytokines. Taken together, AOEP2, a novel multifunctional peptide directly or indirectly targeting Aβ, has promising therapeutic potential for AD. [ABSTRACT FROM AUTHOR]

Active immunotherapy has shown promising potential for cancer treatment. However, there still remain major challenges including induction of a potent and specific T-cell response against the endogenous antigen and retention of tumor immunity. To address these problems, we used layered double hydroxide (LDH) nanoparticles (NPs) to co-deliver tyrosinase-related protein 2 (Trp2) and indoleamine 2,3-dioxygenase siRNA (siIDO) to dendritic cells (DCs). These LDH NPs were readily taken in by DCs, and escaped from endosomes into the cytoplasm. Compared with free Trp2 peptide or siIDO, the vaccination with the LDH NPs co-delivering Trp2 and siIDO significantly inhibited tumor growth in melanoma mouse models by relieving IDO-mediated immune suppression and increasing naïve and specific T cell activation process in vivo. Thus, these LDH NPs, which have a high loading capacity for peptide or siRNA effectively protect and deliver Trp2 and siIDO, overcome the immune tolerance and strengthen T cell immunity, are potential therapeutics to enhance cancer treatment. [ABSTRACT FROM AUTHOR]

Based on the net-worked radar observation model, we analyze the relationship between the ISAR images obtained simultaneously by different radars. We also propose a rotation velocity estimation algorithm based on image rotation and matching, and realize the target palstance estimation by one-dimensional search of rotation velocity. Simulation results show that the proposed algorithm can estimate the rotation velocity robustly based on the multi-radar images and the cross-range scaling can be achieved. The proposed algorithm can deal with the radar echoes for slowly moving targets and translation compensated targets, and fits for wide-band net worked radars of different center frequency and different bandwidth. [ABSTRACT FROM AUTHOR]

Hyperlink induced topic search (HITS) is one of the most authoritative and widely used personalized ranking algorithm on graphs. The HITS algorithm ranks nodes on graphs according to power iteration, and has high complexity of computation. In order to improve computation time, this paper analyzed and modeled the HITS algorithm with the random walls approach, and proposed Monte Carlo based approximation computation algorithms for the HITS ranking. Theoretical analysis and experiments show that the proposed random walk based approach of HITS ranking is not only efficient, but also has higher accuracy, and is significantly better than related works. [ABSTRACT FROM AUTHOR]

In order to overcome the problem of low solution precision of traditional nonlinear constraint algorithms, this paper-proposed a new optimization algorithm based on particle filter. It used this algorithm to solve nonlinear constraint problem, and combined the model and mechanism of particle filters. It established the optimization algorithm based on particle filter algorithm, and gave the procedures of which as well. Then it converted the nonlinear constraint optimization problems to function optimization problems, and established mathematical models of particle filter optimization algorithm for the nonlinear constraint optimization problems. Simulation results verify the validity of the new algorithm, and show that the optimization method based on particle filter has higher efficiency and rate than the traditional optimization algorithm when solving nonlinear optimization problems. Meanwhile, the proposed algorithm has its feasibility to future research on nonlinear constraint optimization problems. [ABSTRACT FROM AUTHOR]

To study the effects of the generation 4 polyamidoamine/vascular endothelial growth factor antisense oligodeoxynucleotide (G4PAMAM/VEGFASODN) compound on the expressions of vascular endothelial growth factor (VEGF) and its mRNA of breast cancer cells and on the inhibition of vascular endothelial cells. We examined the morphology of G4PAMAM/VEGFASODN compound and its pH stability, in vitro transfection efficiency and toxicity, and the expressions of VEGF and its mRNA. Methyl thiazolyl tetrazolium assay was used to detect the inhibitory function of the compound on vascular endothelial cells. The compound was about 10 nm in diameter and was homogeneously netlike. From pH 5 to 10, it showed quite a buffered ability. The 48-h transfection rate in the charge ratio of 1:40 was 98.76%, significantly higher than that of the liposome group ( P<0.05). None of the transfection products showed obvious toxicity on the cells. The expressions of both VEGF protein and its mRNA after G4PAMAM/VEGFASODN transfection decreased markedly. With a low toxicity, high safety, and high transfection rate, G4PAMAM/VEGFASODN could be a promising gene vector. Specifically, it inhibits VEGF gene expression efficiently, laying a basis for further in vivo animal studies. [ABSTRACT FROM AUTHOR]

Shiga-toxin producing Escherichia coli (STEC) causes human illness ranging from mild diarrhea to death. The bacteriophage encoded stx genes are located in the late transcription region, downstream of the antiterminator Q. The transcription of the stx genes is directly under the control of the late promoter pR', thus the sequence diversity of the region between Q and stx, here termed the pR' region, may affect Stx toxin production. Here, we compared the gene structure of the pR' region and the stx subtypes of nineteen STECs. The sequence alignment and phylogenetic analysis suggested that the pR' region tends to be more heterogeneous than the promoter itself, even if the prophages harbor the same stx subtype. Furthermore, we established and validated transcriptional fusions of the pR' region to the DsRed reporter gene using mitomycin C (MMC) induction. Finally, these constructs were transformed into native and non-native strains and examined with flow cytometry. The results showed that induction levels changed when pR' regions were placed under different regulatory systems. Moreover, not every stx gene could be induced in its native host bacteria. In addition to the functional genes, the diversity of the pR' region plays an important role in determining the level of toxin induction. [ABSTRACT FROM AUTHOR]