Experimental quantification of coherence of a tunable quantum detector

Quantum coherence is a fundamental resource that quantum technologies exploit to achieve performance beyond that of classical devices. A necessary prerequisite to achieve this advantage is the ability of measurement devices to detect coherence from the measurement statistics. Based on a recently developed resource theory of quantum operations, here we quantify experimentally the ability of a typical quantum-optical detector, the weak-field homodyne detector, to detect coherence. We derive an improved algorithm for quantum detector tomography and apply it to reconstruct the positive-operator-valued measures (POVMs) of the detector in different configurations. The reconstructed POVMs are then employed to evaluate how well the detector can detect coherence using two computable measures. As the first experimental investigation of quantum measurements from a resource theoretical perspective, our work sheds new light on the rigorous evaluation of the performance of a quantum measurement apparatus

Bibliometric analysis of worldwide research trends on breast cancer about inflammation

BackgroundThe most prevalent cancer and the second-leading cause of cancer-related mortality in women is breast cancer. Growing interest has been shown in recent years in learning more about the processes behind the development of breast cancer. It has been shown that persistent inflammation may play a significant role in the advancement of breast cancer. However, a comprehensive and objective analysis on the state of inflammation in breast cancer research is still lacking. This study was aim to undertake a bibliometric analysis of breast cancer research associated with inflammation between 2013 and 2022 in order to identify the trends, dynamics, and scientific outputs in the field.MethodsFrom 2013 to 2022, original and review publications on breast cancer and inflammation-associated research were retrieved from the Web of Science Core Collection (WOSCC) database. To examine the position of yearly publications, journals, nations, institutions, and authors, we employed two bibliometric tools (CiteSpace and VOSviewer). After that, by examining keyword visualization and keyword bursts, we determined the hot research fields related to inflammation in breast cancer.Resultswe discovered 6902 publications regarding inflammation in breast cancer by using our retrieval approach. In terms of the number of publications, The United States ranked first in the global study, followed by China and Italy. In terms of institutions, the University of Texas System, UT MD Anderson Cancer Center, and University of California System are in the top 3 for the quantity of publications published. The most popular journal for this field research is “CANCERS.” Ueno NT, Woodward WA, Cristofanilli M, and others have made significant contributions to the understanding of inflammation in breast cancer. In the end, we conducted a biclustering analysis on keywords and discovered three clusters that represent research hotspots.ConclusionAccording to the global trend, the research output of inflammation in breast cancer is increasing. The information provided in this article, including the cooperation network information of authors, nations, journals, and institutions, may help researchers to better understand hotspots and developing patterns in this discipline. At present, the focus of study gradually shifts from “phenotype study” to “therapeutic research”. It is recommended to pay attention to the latest hot spots, such as targeted therapy, antimicrobial activity and nanoparticle

Water refilling along vessels at initial stage of willow cuttage revealed by move contrast CT

Cuttage is a widely used technique for plant propagation, whose success relies on the refilling for water transport recovery. However, requirements for refilling characterization studies, including large penetration depth, fast temporal resolution and high spatial resolution, cannot be reached simultaneously via conventional imaging techniques. So far, the dynamic process of water refilling along the vessels at the initial stage of cuttage, as well as its characteristics, remains unclear. Hereby, we developed a move contrast X-ray microtomography method which achieves 3D dynamic non-destructive imaging of water refilling at the initial stage of willow branch cuttage, without the aid of any contrast agent. Experimental results indicate three primary refilling modalities in vessels: 1) the osmosis type, mainly manifested by the osmosis of tissue through the vessel wall into the cavity; 2) the linear type, revealed as the tissue permeates to a certain extent where the liquid column in the vessels is completely formed; and 3) an osmosis-linear mixed type refilling as an intermediate state. Further analysis also exhibits a “temporal-spatial relay” mode of refilling between adjacent vessels. Since the vessel length is quite limited, the cavitation and the relay refilling mode of vessels can be an important way to achieve long-distance water transport

“Mn-locking” effect by anionic coordination manipulation stabilizing Mn-rich phosphate cathodes

High-voltage cathodes with high power and stable cyclability are needed for high-performance sodium-ion batteries. However, the low kinetics and inferior capacity retention from structural instability impede the development of Mn-rich phosphate cathodes. Here, we propose light-weight fluorine (F) doping strategy to decrease the energy gap to 0.22 eV from 1.52 eV and trigger a “Mn-locking” effect—to strengthen the adjacent chemical bonding around Mn as confirmed by density functional theory calculations, which ensure the optimized Mn ligand framework, suppressed Mn dissolution, improved structural stability and enhanced electronic conductivity. The combination of in situ and ex situ techniques determine that the F dopant has no influence on the Na+ storage mechanisms. As a result, an outstanding rate performance up to 40C and an improved cycling stability (1000 cycles at 20C) are achieved. This work presents an effective and widely available light-weight anion doping strategy for high-performance polyanionic cathodes

Biomechanical analysis of a new cannulated screw for unstable femoral neck fractures

BackgroundThe treatment of unstable femoral neck fractures (FNFs) remains a challenge. In this study, a new cannulated screw for unstable FNFs was designed to provide a new approach for the clinical treatment of these injuries, and its biomechanical stability was analyzed using finite element analysis and mechanical tests.MethodsAn unstable FNF model was established. An internal fixation model with parallel inverted triangular cannulated screws (CSs) and a configuration with two superior cannulated screws and one inferior new cannulated screw (NCS) were used. The biomechanical properties of the two fixation methods were compared and analyzed by using finite element analysis and mechanical tests.ResultsThe NCS model outperformed the CSs model in terms of strain and stress distribution in computer-simulated reconstruction of the inverted triangular cannulated screw fixation model for unstable FNFs. In the biomechanical test, the NCS group showed significantly smaller average femoral deformation (1.08 ± 0.15 mm vs. 1.50 ± 0.37 mm) and fracture line displacement (1.43 ± 0.30 mm vs. 2.01 ± 0.47 mm). In the NCS group, the mean stiffness was significantly higher than that in the CSs group (729.37 ± 82.20 N/mm vs. 544.83 ± 116.07 N/mm), and the mean compression distance was significantly lower than that in the CSs group (2.87 ± 0.30 mm vs. 4.04 ± 1.09 mm).ConclusionThe NCS combined with two ordinary cannulated screws in an inverted triangle structure to fix unstable FNFs can provide better biomechanical stability than CSs and exhibit a length- and angle-stable construct to prevent significant femoral neck shortening

Ubiquitin ligase RNF125 targets PD-L1 for ubiquitination and degradation

As a critical immune checkpoint molecule, PD-L1 is expressed at significantly higher levels in multiple neoplastic tissues compared to normal ones. PD-L1/PD-1 axis is a critical target for tumor immunotherapy, blocking the PD-L1/PD-1 axis is recognized and has achieved unprecedented success in clinical applications. However, the clinical efficacy of therapies targeting the PD-1/PD-L1 pathway remains limited, emphasizing the need for the mechanistic elucidation of PD-1/PD-L1 expression. In this study, we found that RNF125 interacted with PD-L1 and regulated PD-L1 protein expression. Mechanistically, RNF125 promoted K48-linked polyubiquitination of PD-L1 and mediated its degradation. Notably, MC-38 and H22 cell lines with RNF125 knockout, transplanted in C57BL/6 mice, exhibited a higher PD-L1 level and faster tumor growth than their parental cell lines. In contrast, overexpression of RNF125 in MC-38 and H22 cells had the opposite effect, resulting in lower PD-L1 levels and delayed tumor growth compared with parental cell lines. In addition, immunohistochemical analysis of MC-38 tumors with RNF125 overexpression showed significantly increased infiltration of CD4+, CD8+ T cells and macrophages. Consistent with these findings, analyses using The Cancer Genome Atlas (TCGA) public database revealed a positive correlation of RNF125 expression with CD4+, CD8+ T cell and macrophage tumor infiltration. Moreover, RNF125 expression was significantly downregulated in several human cancer tissues, and was negatively correlated with the clinical stage of these tumors, and patients with higher RNF125 expression had better clinical outcomes. Our findings identify a novel mechanism for regulating PD-L1 expression and may provide a new strategy to increase the efficacy of immunotherapy

Informative scene decomposition for crowd analysis, comparison and simulation guidance

Crowd simulation is a central topic in several fields including graphics. To achieve high-fidelity simulations, data has been increasingly relied upon for analysis and simulation guidance. However, the information in real-world data is often noisy, mixed and unstructured, making it difficult for effective analysis, therefore has not been fully utilized. With the fast-growing volume of crowd data, such a bottleneck needs to be addressed. In this paper, we propose a new framework which comprehensively tackles this problem. It centers at an unsupervised method for analysis. The method takes as input raw and noisy data with highly mixed multi-dimensional (space, time and dynamics) information, and automatically structure it by learning the correlations among these dimensions. The dimensions together with their correlations fully describe the scene semantics which consists of recurring activity patterns in a scene, manifested as space flows with temporal and dynamics profiles. The effectiveness and robustness of the analysis have been tested on datasets with great variations in volume, duration, environment and crowd dynamics. Based on the analysis, new methods for data visualization, simulation evaluation and simulation guidance are also proposed. Together, our framework establishes a highly automated pipeline from raw data to crowd analysis, comparison and simulation guidance. Extensive experiments and evaluations have been conducted to show the flexibility, versatility and intuitiveness of our framework