Longitudinal Beta-Binomial Modeling using GEE for Over-Dispersed Binomial Data

Longitudinal binomial data are frequently generated from multiple questionnaires and assessments in various scientific settings for which the binomial data are often overdispersed. The standard generalized linear mixed effects model may result in severe underestimation of standard errors of estimated regression parameters in such cases and hence potentially bias the statistical inference. In this paper, we propose a longitudinal beta-binomial model for overdispersed binomial data and estimate the regression parameters under a probit model using the generalized estimating equation method. A hybrid algorithm of the Fisher scoring and the method of moments is implemented for computing the method. Extensive simulation studies are conducted to justify the validity of the proposed method. Finally, the proposed method is applied to analyze functional impairment in subjects who are at risk of Huntington disease from a multisite observational study of prodromal Huntington disease

TwinTex: Geometry-aware Texture Generation for Abstracted 3D Architectural Models

Coarse architectural models are often generated at scales ranging from individual buildings to scenes for downstream applications such as Digital Twin City, Metaverse, LODs, etc. Such piece-wise planar models can be abstracted as twins from 3D dense reconstructions. However, these models typically lack realistic texture relative to the real building or scene, making them unsuitable for vivid display or direct reference. In this paper, we present TwinTex, the first automatic texture mapping framework to generate a photo-realistic texture for a piece-wise planar proxy. Our method addresses most challenges occurring in such twin texture generation. Specifically, for each primitive plane, we first select a small set of photos with greedy heuristics considering photometric quality, perspective quality and facade texture completeness. Then, different levels of line features (LoLs) are extracted from the set of selected photos to generate guidance for later steps. With LoLs, we employ optimization algorithms to align texture with geometry from local to global. Finally, we fine-tune a diffusion model with a multi-mask initialization component and a new dataset to inpaint the missing region. Experimental results on many buildings, indoor scenes and man-made objects of varying complexity demonstrate the generalization ability of our algorithm. Our approach surpasses state-of-the-art texture mapping methods in terms of high-fidelity quality and reaches a human-expert production level with much less effort. Project page: https://vcc.tech/research/2023/TwinTex.Comment: Accepted to SIGGRAPH ASIA 202

Grand Canonical Adaptive Resolution Simulation for Molecules with Electrons: A Theoretical Framework based on Physical Consistency

A theoretical scheme for the treatment of an open molecular system with electrons and nuclei is proposed. The idea is based on the Grand Canonical description of a quantum region embedded in a classical reservoir of molecules. Electronic properties of the quantum region are calculated at constant electronic chemical potential equal to that of the corresponding (large) bulk system treated at full quantum level. Instead, the exchange of molecules between the quantum region and the classical environment occurs at the chemical potential of the macroscopic thermodynamic conditions. T he Grand Canonical Adaptive Resolution Scheme is proposed for the treatment of the classical environment; such an approach can treat the exchange of molecules according to first principles of statistical mechanics and thermodynamic. The overall scheme is build on the basis of physical consistency, with the corresponding definition of numerical criteria of control of the approximations implied by the coupling. Given the wide range of expertise required, this work has the intention of providing guiding principles for the construction of a well founded computational protocol for actual multiscale simulations from the electronic to the mesoscopic scale.Comment: Computer Physics Communications (2017), in pres

NIR molecule induced self-assembled nanoparticles for synergistic in vivo chemo-photothermal therapy of bladder cancer

Bladder cancer (BC) is one of the commonest malignancies in the urinary system. Bladder cancer is divided into non-muscle invasive bladder cancer (NMIBC) and muscle invasive bladder cancer (MIBC) according to the depth of invasion. Besides, the prognosis of MIBC remains poor. Surgical resection combined with radiotherapy or chemotherapy is the standard treatment for MIBC. However, the major obstacle that hinders successful chemotherapy is its lack of tumor targeting. Here, we fabricated nanoparticles that respond to near-infrared laser irradiation in order to increase the drug accumulation at the tumor sites and combine chemotherapy with photothermal therapy to overcome challenges of bladder cancer treatment. IR780 and Doxorubicin (DOX)were loaded into albumin nanoparticles (IR780-DOX@Albumin NPs). In the process of IR780-DOX@Albumin NPs synthesis, the near-infrared molecule IR780 was used as the assembly molecular bridge. Under irradiation, the nanoparticles were decomposed due to the degradation of IR780 while the release of DOX increased. Nanoparticles can be ingested by tumor cells in a short time. The IR780- DOX@Albumin NPs were sensitive to near-infrared laser irradiation. Near-infrared laser irradiation can promote the release of the drugs from the nanoparticles and induce a photothermal effect, thus destroying the tumor cells. Given the excellent tumor-targeting ability and negligible toxicity to normal tissue, IR780-DOX@Albumin NPs can greatly increase the concentration of chemotherapeutic drugs in tumor cells. This study combines photothermal therapy and chemotherapy to treat MIBC, so as to avoid chemotherapy resistance, reduce the toxicity to normal cells, and achieve the purpose of improving the treatment of MIBC

Androgen deprivation therapy plus abiraterone or docetaxel as neoadjuvant therapy for very-high-risk prostate cancer: a pooled analysis of two phase II trials

Objective: The study aimed to compare the efficacy and safety of androgen deprivation therapy (ADT) with abiraterone or docetaxel versus ADT alone as neoadjuvant therapy in patients with very-high-risk localized prostate cancer.Methods: This was a pooled analysis of two single-center, randomized, controlled, phase II clinical trials (ClinicalTrials.gov: NCT04356430 and NCT04869371) conducted from December 2018 to March 2021. Eligible participants were randomly assigned to the intervention (ADT plus abiraterone or docetaxel) and control (ADT alone) groups at a 2:1 ratio. Efficacy was evaluated by pathological complete response (pCR), minimal residual disease (MRD), and 3-year biochemical progression-free survival (bPFS). Safety was also analyzed.Results: The study included 42 participants in the ADT group, 47 in the ADT plus docetaxel group, and 48 in the ADT plus abiraterone group. A total of 132 (96.4%) participants had very-high-risk prostate cancer, and 108 (78.8%) had locally advanced disease. The ADT plus docetaxel group (28%) and ADT plus abiraterone group (31%) had higher rates of pCR or MRD (p = 0.001 and p < 0.001) compared with the ADT group (2%). The 3-year bPFS was 41.9% (95% CI: 26.6–57.2), 51.1% (95% CI: 36.8–65.4), and 61.2% (95% CI: 45.5–76.9), respectively. Significant difference was found among groups in terms of bPFS (p = 0.037).Conclusion: Compared with ADT alone, neoadjuvant therapy with ADT plus docetaxel or abiraterone could achieve better pathological outcomes (pCR or MRD) for very-high-risk localized prostate cancer. The ADT plus abiraterone group showed longer bPFS than ADT alone. The combination regimens were tolerable

Deep reinforcement learning based direct torque control strategy for distributed drive electric vehicles considering active safety and energy saving performance

Distributed drive electric vehicles are regarded as a broadly promising transportation tool owing to their convenience and maneuverability. However, reasonable and efficient allocation of torque demand to four wheels is a challenging task. In this paper, a deep reinforcement learning-based torque distribution strategy is proposed to guarantee the active safety and energy conservation. The torque distribution task is explicitly formulated as a Markov decision process, in which the vehicle dynamic characteristics can be approximated. The actor-critic networks are utilized to approximate the action value and policy functions for a better control effect. To guarantee continuous torque output and further stabilize the learning process, a twin delayed deep deterministic policy gradient algorithm is deployed. The motor efficiency is incorporated into the cumulative reward to reduce the energy consumption. The results of double lane change demonstrate that the proposed strategy results in better handling stability performance. In addition, it can improve the vehicle transient response and eliminate the static deviation in the step steering maneuver test. For typical steering maneuvers, the proposed direct torque distribution strategy significantly improves the average motor efficiency and reduces the energy loss by 5.25%–10.51%. Finally, a hardware-in-loop experiment was implemented to validate the real-time executability of the proposed torque distribution strategy. This study provides a foundation for the practical application of intelligent safety control algorithms in future vehicles

Planning integrated energy systems coupling V2G as a flexible storage

Coupling the vehicle-to-grid (V2G) with integrated energy systems (IES) offers an emerging solution for decarbonisation of both energy and transport sectors. To evaluate the feasibility of coupling V2G with IES as a flexible storage, we propose an optimisation-based system planning framework embedding V2G into IES. Within this framework, stochastic features of electric vehicles (EV) fleets are simulated. The impacts of V2G on IES design are captured by assessing both economic and environmental benefits via multi-objective optimisations utilising an improved NSGA-II algorithm. Six case studies considering three cities with different climate conditions and two functional areas of residential and commercial are performed. The results manifest that Beijing-commercial case could achieve the largest mutual benefits. The EV fleets’ charging behaviour follows the time-of-use energy tariff in transition seasons while not during winter. Sensitivity analysis indicates the electricity and gas prices have significant impact on the system design. The benefits induced by growing EV penetration would gradually decrease and stabilise when the EV number reach 300, the growth of economic and environmental benefits stabilized at 1.3% and 1.8%, respectively. Overall, this study quantifies the benefits of enabling V2G in IES, and generates valuable insights for IES planners, V2G service providers, and relevant policymakers

Second generation androgen receptor antagonist, TQB3720 abrogates prostate cancer growth via AR/GPX4 axis activated ferroptosis

Purpose: Prostate cancer (PCa) poses a great threat to humans. The study aimed to evaluate the potential of TQB3720 in promoting ferroptosis to suppress prostate cancer, providing a theoretical basis for PCa therapy.Methods: PCa cells and nude mice models were divided into TQB3720, enzalutamide (ENZ), and control groups. Sulforhodamine B assay, colony formation assessment, organoids culture system, and the CCK8 assay were used for detecting proliferation. Western blot assay was processed to detect the expression of androgen receptor (AR), ferroptosis, and apoptosis-related genes. Flow cytometry was applied to measure the intracellular ROS levels. ELISA was performed to determine the cellular oxidized glutathione (GSSG) and malondialdehyde (MDA) levels. RT-qPCR was conducted to detect the mRNA expression of genes in AR signaling. BODIPYTM™ 581/591 was processed for detection of intracellular lipid peroxidation levels. The interaction of AR with other translational factor complex proteins was explored using Co-immunoprecipitation (Co-IP), and the chromatin immunoprecipitation (ChIP) assay was performed to detect the binding of AR-involved translational complex to downstream genes promoter. Luciferase reporter assay was conducted to examine the translation activity of GPX4 promoter, and immunohistochemistry (IHC) was conducted to analyze the levels of c-MYC, Ki-67 and AR in TQB3720-treated cancer tissues.Results: Here, we found TQB3720 inhibits the growth of prostate cancer in vitro and in vivo. TQB3720 treatment induced intracellular levels of GSSG and MDA significantly, by which hints AR antagonist caused ferroptosis-related cell death. Moreover, molecular evidence shown TQB3720 regulates downstream of AR signaling by binding AR resulting in inhibition of AR entry into the nucleus. Additional, we also proved that TQB3720 abrogates the interaction between AR and SP1 and leads to decrease GPX4 transcription.Conclusion: TQB3720 promotes ferroptosis in prostate cancer cells by reducing the AR/SP1 transcriptional complex binding to GPX4 promoter. As a result, it is suggested to be a potential drug for clinic prostate cancer treatment

TGFβƒ1 Promotes Gemcitabine Resistance Through Regulating the LncRNA-LET/NF90/miR-145 Signaling Axis in Bladder Cancer

High tumor recurrence is frequently observed in patients with urinary bladder cancers (UBCs), with the need for biomarkers of prognosis and drug response. Chemoresistance and subsequent recurrence of cancers are driven by a subpopulation of tumor initiating cells, namely cancer stem-like cells (CSCs). However, the underlying molecular mechanism in chemotherapy-induced CSCs enrichment remains largely unclear. In this study, we found that during gemcitabine treatment lncRNA-Low Expression in Tumor (lncRNA-LET) was downregulated in chemoresistant UBC, accompanied with the enrichment of CSC population. Knockdown of lncRNA-LET increased UBC cell stemness, whereas forced expression of lncRNA-LET delayed gemcitabine-induced tumor recurrence. Furthermore, lncRNA-LET was directly repressed by gemcitabine treatment-induced overactivation of TGFβ/SMAD signaling through SMAD binding element (SBE) in the lncRNA-LET promoter. Consequently, reduced lncRNA-LET increased the NF90 protein stability, which in turn repressed biogenesis of miR-145 and subsequently resulted in accumulation of CSCs evidenced by the elevated levels of stemness markers HMGA2 and KLF4. Treatment of gemcitabine resistant xenografts with LY2157299, a clinically relevant specific inhibitor of TGFβRI, sensitized them to gemcitabine and significantly reduced tumorigenecity in vivo. Notably, overexpression of TGFβ1, combined with decreased levels of lncRNA-LET and miR-145 predicted poor prognosis in UBC patients. Collectively, we proved that the dysregulated lncRNA-LET/NF90/miR-145 axis by gemcitabine-induced TGFβ1 promotes UBC chemoresistance through enhancing cancer cell stemness. The combined changes in TGFβ1/lncRNA-LET/miR-145 provide novel molecular prognostic markers in UBC outcome. Therefore, targeting this axis could be a promising therapeutic approach in treating UBC patients