Efficient Multi-View Inverse Rendering Using a Hybrid Differentiable Rendering Method

Recovering the shape and appearance of real-world objects from natural 2D images is a long-standing and challenging inverse rendering problem. In this paper, we introduce a novel hybrid differentiable rendering method to efficiently reconstruct the 3D geometry and reflectance of a scene from multi-view images captured by conventional hand-held cameras. Our method follows an analysis-by-synthesis approach and consists of two phases. In the initialization phase, we use traditional SfM and MVS methods to reconstruct a virtual scene roughly matching the real scene. Then in the optimization phase, we adopt a hybrid approach to refine the geometry and reflectance, where the geometry is first optimized using an approximate differentiable rendering method, and the reflectance is optimized afterward using a physically-based differentiable rendering method. Our hybrid approach combines the efficiency of approximate methods with the high-quality results of physically-based methods. Extensive experiments on synthetic and real data demonstrate that our method can produce reconstructions with similar or higher quality than state-of-the-art methods while being more efficient.Comment: IJCAI202

Multiscale Latent-Guided Entropy Model for LiDAR Point Cloud Compression

The non-uniform distribution and extremely sparse nature of the LiDAR point cloud (LPC) bring significant challenges to its high-efficient compression. This paper proposes a novel end-to-end, fully-factorized deep framework that encodes the original LPC into an octree structure and hierarchically decomposes the octree entropy model in layers. The proposed framework utilizes a hierarchical latent variable as side information to encapsulate the sibling and ancestor dependence, which provides sufficient context information for the modelling of point cloud distribution while enabling the parallel encoding and decoding of octree nodes in the same layer. Besides, we propose a residual coding framework for the compression of the latent variable, which explores the spatial correlation of each layer by progressive downsampling, and model the corresponding residual with a fully-factorized entropy model. Furthermore, we propose soft addition and subtraction for residual coding to improve network flexibility. The comprehensive experiment results on the LiDAR benchmark SemanticKITTI and MPEG-specified dataset Ford demonstrates that our proposed framework achieves state-of-the-art performance among all the previous LPC frameworks. Besides, our end-to-end, fully-factorized framework is proved by experiment to be high-parallelized and time-efficient and saves more than 99.8% of decoding time compared to previous state-of-the-art methods on LPC compression

Efficient multi-view inverse rendering using a hybrid differentiable rendering method

Recovering the shape and appearance of real-world objects from natural 2D images is a long-standing and challenging inverse rendering problem. In this paper, we introduce a novel hybrid differentiable rendering method to efficiently reconstruct the 3D geometry and reflectance of a scene from multi-view images captured by conventional hand-held cameras. Our method follows an analysis-by-synthesis approach and consists of two phases. In the initialization phase, we use traditional SfM and MVS methods to reconstruct a virtual scene roughly matching the real scene. Then in the optimization phase, we adopt a hybrid approach to refine the geometry and reflectance, where the geometry is first optimized using an approximate differentiable rendering method, and the reflectance is optimized afterward using a physically-based differentiable rendering method. Our hybrid approach combines the efficiency of approximate methods with the high-quality results of physically-based methods. Extensive experiments on synthetic and real data demonstrate that our method can produce reconstructions with similar or higher quality than state-of-the-art methods while being more efficient

New insights into a microvascular invasion prediction model in hepatocellular carcinoma: A retrospective study from the SEER database and China

Background and AimsThe prognosis of liver cancer is strongly influenced by microvascular infiltration (MVI). Accurate preoperative MVI prediction can aid clinicians in the selection of suitable treatment options. In this study, we constructed a novel, reliable, and adaptable nomogram for predicting MVI.MethodsUsing the Surveillance, Epidemiology, and End Results (SEER) database, we extracted the clinical data of 1,063 patients diagnosed with hepatocellular carcinoma (HCC) and divided it into either a training (n = 739) or an internal validation cohort (n = 326). Based on multivariate analysis, the training cohort data were analyzed and a nomogram was generated for MVI prediction. This was further verified using an internal validation cohort and an external validation cohort involving 293 Chinese patients. Furthermore, to evaluate the efficacy, accuracy, and clinical use of the nomogram, we used concordance index (C-index), calibration curve, and decision curve analysis (DCA) techniques.ResultsIn accordance with the multivariate analysis, tumor size, tumor number, alpha-fetoprotein (AFP), and histological grade were independently associated with MVI. The established model exhibited satisfactory performance in predicting MVI. The C-indices were 0.719, 0.704, and 0.718 in the training, internal validation, and external validation cohorts, respectively. The calibration curves showed an excellent consistency between the predictions and actual observations. Finally, DCA demonstrated that the newly developed nomogram had favorable clinical utility.ConclusionsWe established and verified a novel preoperative MVI prediction model in HCC patients. This model can be a beneficial tool for clinicians in selecting an optimal treatment plan for HCC patients

Tumor microenvironment responsive metal nanoparticles in cancer immunotherapy

Malignant tumors have a unique tumor microenvironment (TME), which includes mild acidity, hypoxia, overexpressed reactive oxygen species (ROS), and high glutathione (GSH) levels, among others. Recently, TME regulation approaches have attracted widespread attention in cancer immunotherapy. Nanoparticles as drug delivery systems have ability to modulate the hydrophilicity of drugs to affect drug uptake and efflux in tumor. Especially, the metal nanoparticles have been extensive applied for tumor immunotherapy due to their unique physical properties and elaborate design. However, the potential deficiencies of metal nanoparticles due to their low biodegradability, toxicity and treatment side effects restrict their clinical application. In this review, we briefly introduce the feature characteristics of the TME and the recent advances in tumor microenvironment responsive metal nanoparticles for tumor immunotherapy. In addition, nanoparticles could be combined with other treatments, such as chemotherapy, radiotherapy and photodynamic therapy also is presented. Finally, the challenges and outlook for improving the antitumor immunotherapy efficiency, side effect and potential risks of metal nanoparticles has been discussed

Terlipressin May Decrease In-Hospital Mortality of Cirrhotic Patients with Acute Gastrointestinal Bleeding and Renal Dysfunction: A Retrospective Multicenter Observational Study

Acute gastrointestinal bleeding (GIB) rapidly reduces effective blood volume, thereby precipitating acute kidney injury (AKI). Terlipressin, which can induce splanchnic vasoconstriction and increase renal perfusion, has been recommended for acute GIB and hepatorenal syndrome in liver cirrhosis. Thus, we hypothesized that terlipressin might be beneficial for cirrhotic patients with acute GIB and renal impairment. In this Chinese multi-center study, 1644 cirrhotic patients with acute GIB were retrospectively enrolled. AKI was defined according to the International Club of Ascites (ICA) criteria. Renal dysfunction was defined as serum creatinine (sCr) > 133 μmol/L at admission and/or any time point during hospitalization. Incidence of renal impairment and in-hospital mortality were the primary end-points. The incidence of any stage ICA-AKI, ICA-AKI stages 1B, 2, and 3, and renal dysfunction in cirrhotic patients with acute GIB was 7.1%, 1.8%, and 5.0%, respectively. The in-hospital mortality was significantly increased by renal dysfunction (14.5% vs. 2.2%, P < 0.001) and ICA-AKI stages 1B, 2, and 3 (11.1% vs. 2.8%, P = 0.011), but not any stage ICA-AKI (5.7% vs. 2.7%, P = 0.083). The in-hospital mortality was significantly decreased by terlipressin in patients with renal dysfunction (3.6% vs. 20.0%, P = 0.044), but not in those with any stage ICA-AKI (4.5% vs. 6.0%, P = 0.799) or ICA-AKI stages 1B, 2, and 3 (0.0% vs. 14.3%, P = 0.326). Renal dysfunction increased the in-hospital mortality of cirrhotic patients with acute GIB. Terlipressin might decrease the in-hospital mortality of cirrhotic patients with acute GIB and renal dysfunction. NCT03846180 ( https://clinicaltrials.gov )

Guideline adherence of β-blocker initiating dose and its consequence in hospitalized patients with heart failure with reduced ejection fraction

Background: We aim to investigate the guideline adherence of β-blocker (BB) initiating dose in Chinese hospitalized patients with heart failure with reduced ejection fraction (HFrEF) and whether the adherence affected the in-hospital outcomes.Methods: This was a retrospective study of patients hospitalized with HFrEF who had initiated BBs during their hospitalization. We defined adherence to clinical practice guidelines as initiating BB with standard dose and non-adherence to guidelines if otherwise, and examined the association between adherence to guidelines and in-hospital BB-related adverse events. Subgroup analyses based on sex, age, coronary heart disease, and hypertension were performed.Results: Among 1,104 patients with HFrEF initiating BBs during hospitalization (median length of hospitalization, 12 days), 304 (27.5%) patients received BB with non-adherent initiating dose. This non-adherence was related to a higher risk (hazard ratio [95% confidence interval]) of BB dose reduction or withdrawal (1.78 [1.42 to 2.22], P &lt; 0.001), but not significantly associated with risks of profound bradycardia, hypotension, cardiogenic shock requiring intravenous inotropes, and severe bronchospasm requiring intravenous steroid during hospitalization.Conclusion: This study identified that over a fourth of patients had received BBs with an initiating dose that was not adherent to guidelines in Chinese hospitalized patients with HFrEF, and this non-adherence was associated with BB dose reduction or withdrawal during hospitalization

The odontoblastic differentiation of dental mesenchymal stem cells: molecular regulation mechanism and related genetic syndromes

Dental mesenchymal stem cells (DMSCs) are multipotent progenitor cells that can differentiate into multiple lineages including odontoblasts, osteoblasts, chondrocytes, neural cells, myocytes, cardiomyocytes, adipocytes, endothelial cells, melanocytes, and hepatocytes. Odontoblastic differentiation of DMSCs is pivotal in dentinogenesis, a delicate and dynamic process regulated at the molecular level by signaling pathways, transcription factors, and posttranscriptional and epigenetic regulation. Mutations or dysregulation of related genes may contribute to genetic diseases with dentin defects caused by impaired odontoblastic differentiation, including tricho-dento-osseous (TDO) syndrome, X-linked hypophosphatemic rickets (XLH), Raine syndrome (RS), hypophosphatasia (HPP), Schimke immuno-osseous dysplasia (SIOD), and Elsahy-Waters syndrome (EWS). Herein, recent progress in the molecular regulation of the odontoblastic differentiation of DMSCs is summarized. In addition, genetic syndromes associated with disorders of odontoblastic differentiation of DMSCs are discussed. An improved understanding of the molecular regulation and related genetic syndromes may help clinicians better understand the etiology and pathogenesis of dentin lesions in systematic diseases and identify novel treatment targets