l-Peptide functionalized dual-responsive nanoparticles for controlled paclitaxel release and enhanced apoptosis in breast cancer cells

Nanoparticles and macromolecular carriers have been widely used to increase the efficacy of chemotherapeutics, largely through passive accumulation provided by their enhanced permeability and retention effect. However, the therapeutic efficacy of nanoscale anticancer drug delivery systems is severely truncated by their low tumor-targetability and inefficient drug release at the target site. Here, the design and development of novel l-peptide functionalized dual-responsive nanoparticles (l-CS-g-PNIPAM-PTX) for active targeting and effective treatment of GRP78-overexpressing human breast cancer in vitro and in vivo are reported. l-CS-g-PNIPAM-PTX NPs have a relative high drug loading (13.5%) and excellent encapsulation efficiency (74.3%) and an average diameter of 275 nm. The release of PTX is slow at pH 7.4 and 25 °C but greatly accelerated at pH 5.0 and 37 °C. MTT assays and confocal experiments showed that the l-CS-g-PNIPAM-PTX NPs possessed high targetability and antitumor activity toward GRP78 overexpressing MDA-MB-231 human breast cancer cells. As expected, l-CS-g-PNIPAM-PTX NPs could effectively treat mice bearing MDA-MB-231 human breast tumor xenografts with little side effects, resulting in complete inhibition of tumor growth and a high survival rate over an experimental period of 60 days. These results indicate that l-peptide-functionalized acid - and thermally activated - PTX prodrug NPs have a great potential for targeted chemotherapy in breast cancer.</p

Cluster size convergence of the density matrix embedding theory and its dynamical cluster formulation: A study with an auxiliary-field quantum Monte Carlo solver

We investigate the cluster size convergence of the energy and observables using two forms of density matrix embedding theory (DMET): the original cluster form (CDMET) and a new formulation motivated by the dynamical cluster approximation (DCA-DMET). Both methods are applied to the half-filled one- and two-dimensional Hubbard models using a sign-problem free auxiliary-field quantum Monte Carlo impurity solver, which allows for the treatment of large impurity clusters of up to 100 sites. While CDMET is more accurate at smaller impurity cluster sizes, DCA-DMET exhibits faster asymptotic convergence towards the thermodynamic limit. We use our two formulations to produce new accurate estimates for the energy and local moment of the two-dimensional Hubbard model for U / t = 2,4,6. These results compare favorably with the best data available in the literature, and help resolve earlier uncertainties in the moment for U / t = 2

Stripe order in the underdoped region of the two-dimensional Hubbard model

Competing inhomogeneous orders are a central feature of correlated electron materials including the high-temperature superconductors. The two- dimensional Hubbard model serves as the canonical microscopic physical model for such systems. Multiple orders have been proposed in the underdoped part of the phase diagram, which corresponds to a regime of maximum numerical difficulty. By combining the latest numerical methods in exhaustive simulations, we uncover the ordering in the underdoped ground state. We find a stripe order that has a highly compressible wavelength on an energy scale of a few Kelvin, with wavelength fluctuations coupled to pairing order. The favored filled stripe order is different from that seen in real materials. Our results demonstrate the power of modern numerical methods to solve microscopic models even in challenging settings

CMDFusion: Bidirectional Fusion Network with Cross-modality Knowledge Distillation for LIDAR Semantic Segmentation

2D RGB images and 3D LIDAR point clouds provide complementary knowledge for the perception system of autonomous vehicles. Several 2D and 3D fusion methods have been explored for the LIDAR semantic segmentation task, but they suffer from different problems. 2D-to-3D fusion methods require strictly paired data during inference, which may not be available in real-world scenarios, while 3D-to-2D fusion methods cannot explicitly make full use of the 2D information. Therefore, we propose a Bidirectional Fusion Network with Cross-Modality Knowledge Distillation (CMDFusion) in this work. Our method has two contributions. First, our bidirectional fusion scheme explicitly and implicitly enhances the 3D feature via 2D-to-3D fusion and 3D-to-2D fusion, respectively, which surpasses either one of the single fusion schemes. Second, we distillate the 2D knowledge from a 2D network (Camera branch) to a 3D network (2D knowledge branch) so that the 3D network can generate 2D information even for those points not in the FOV (field of view) of the camera. In this way, RGB images are not required during inference anymore since the 2D knowledge branch provides 2D information according to the 3D LIDAR input. We show that our CMDFusion achieves the best performance among all fusion-based methods on SemanticKITTI and nuScenes datasets. The code will be released at https://github.com/Jun-CEN/CMDFusion

Epigenetic hypomethylation and upregulation of GD3s in triple negative breast cancer.

Background: Breast cancer remains a major health problem in the world. Triple-negative breast cancer (TNBC) is an aggressive subtype with very poor prognosis. Up to now, the mechanism behind TNBC\u27s activity is still unclear and no candidate drug target has been identified. Thus, it is of critical importance to elucidate the pathways in TNBC and identify the relevant biomarkers. Recent studies showed that ganglioside D3 synthase (GD3s) played a very important role in development of cancers. However, the physiological functions and associated pathways of GD3s in TNBC are still unclear. Methods: Results: Conclusions: In summary, these results suggest that GD3s may be a potential biomarker and drug target in treatment of TNBC

Parametric Modeling of a Magnetorheological Engine Mount Based on a Modified Polynomial Bingham Model

This work mainly addresses the establishment of a phenomenological mechanical model for magnetorheological (MR) engine mounts under frequency variation and magnetic variation effects. First, the mounts' reaction force is divided into three parts: a Coulomb damping force, an elastic reaction force, and a viscous damping force. Then, by using correlation analysis on these forces with the frequency and magnetic field, a modified polynomial Bingham parameterized model is proposed. This model takes external current and external loading frequency as the variables. As a result of analyzing the relationship between energy dissipation and storage caused by the external displacement excitation, an identifying method is proposed to identify the nine parameters in the model. Based on this model, an experimental scheme was designed, and the force–displacement relationship of a typical MR mount under different working conditions was tested through an experiment. By using the proposed method, the relationship of the reaction force of an MR mount with current and external loading frequency was obtained. The experimental results show that the proposed model can correctly reflect the wide-frequency dynamic characteristics of the mounts in dynamic stiffness, lagging angle, and hysteretic curve

Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform

We present Rhino, a system for accelerating tensor programs with automatic parallelization on AI platform for real production environment. It transforms a tensor program written for a single device into an equivalent distributed program that is capable of scaling up to thousands of devices with no user configuration. Rhino firstly works on a semantically independent intermediate representation of tensor programs, which facilitates its generalization to unprecedented applications. Additionally, it implements a task-oriented controller and a distributed runtime for optimal performance. Rhino explores on a complete and systematic parallelization strategy space that comprises all the paradigms commonly employed in deep learning (DL), in addition to strided partitioning and pipeline parallelism on non-linear models. Aiming to efficiently search for a near-optimal parallel execution plan, our analysis of production clusters reveals general heuristics to speed up the strategy search. On top of it, two optimization levels are designed to offer users flexible trade-offs between the search time and strategy quality. Our experiments demonstrate that Rhino can not only re-discover the expert-crafted strategies of classic, research and production DL models, but also identify novel parallelization strategies which surpass existing systems for novel models

Metagenomic next-generation sequencing for detecting lower respiratory tract infections in sputum and bronchoalveolar lavage fluid samples from children

Lower respiratory tract infections are common in children. Bronchoalveolar lavage fluid has long been established as the best biological sample for detecting respiratory tract infections; however, it is not easily collected in children. Sputum may be used as an alternative yet its diagnostic accuracy remains controversial. Therefore, this study sought to evaluate the diagnostic accuracy of sputum for detecting lower respiratory tract infections using metagenomic next-generation sequencing. Paired sputum and bronchoalveolar lavage fluid samples were obtained from 68 patients; pathogens were detected in 67 sputum samples and 64 bronchoalveolar lavage fluid samples by metagenomic next-generation sequencing, respectively. The combined pathogen-detection rates in the sputum and bronchoalveolar lavage fluid samples were 80.90% and 66.2%, respectively. For sputum, the positive predictive values (PPVs) and negative predictive values (NPVs) for detecting bacteria were 0.72 and 0.73, respectively, with poor Kappa agreement (0.30; 95% confidence interval: 0.218–0.578, P &lt; 0.001). However, viral detection in sputum had good sensitivity (0.87), fair specificity (0.57), and moderate Kappa agreement (0.46; 95% confidence interval: 0.231–0.693, P &lt; 0.001). The PPVs and NPVs for viral detection in sputum were 0.82 and 0.67, respectively. The consistency between the sputum and bronchoalveolar lavage fluid was poor for bacterial detection yet moderate for viral detection. Thus, clinicians should be cautious when interpreting the results of sputum in suspected cases of lower respiratory tract infections, particularly with regards to bacterial detection in sputum. Viral detection in sputum appears to be more reliable; however, clinicians must still use comprehensive clinical judgment