Fas (CD95) induces rapid, TLR4/IRAK4-dependent release of pro-inflammatory HMGB1 from macrophages

Although Fas (CD95) is recognized as a death receptor that induces apoptosis, recent studies indicate that the Fas/FasL system can induce pro-inflammatory cytokine production by macrophages independent of conventional caspase-mediated apoptotic signaling. The precise mechanism(s) by which Fas activates macrophage inflammation is unknown. We hypothesized that Fas stimulates rapid release of high mobility group box 1 (HMGB1) that acts in an autocrine and/or paracrine manner to stimulate pro-inflammatory cytokine production via a Toll-like receptor-4 (TLR4)/Interleukin-1 receptor associated kinase-4 (IRAK4)-dependent mechanism. Following Fas activation, HMGB1 was released within 1 hr from viable RAW267.4 cells and primary murine peritoneal macrophages. HMGB1 release was more rapid following Fas activation compared to LPS stimulation. Neutralization of HMGB1 with an inhibitory anti-HMGB1 monoclonal antibody strongly inhibited Fas-induced production of tumor necrosis factor (TNF) and macrophage inflammatory protein-2 (MIP-2). Both Fas-induced HMGB1 release and associated pro-inflammatory cytokine production were significantly decreased from Tlr4-/- and Irak4-/- macrophages, but not Tlr2-/- macrophages. These findings reveal a novel mechanism underlying Fas-mediated pro-inflammatory physiological responses in macrophages. We conclude that Fas activation induces rapid, TLR4/IRAK4-dependent release of HMGB1 that contributes to Fas-mediated pro-inflammatory cytokine production by viable macrophages

Deep quantum neural networks equipped with backpropagation on a superconducting processor

Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of quantum machine learning. In this work, we report the first experimental demonstration of training deep quantum neural networks via the backpropagation algorithm with a six-qubit programmable superconducting processor. In particular, we show that three-layer deep quantum neural networks can be trained efficiently to learn two-qubit quantum channels with a mean fidelity up to 96.0% and the ground state energy of molecular hydrogen with an accuracy up to 93.3% compared to the theoretical value. In addition, six-layer deep quantum neural networks can be trained in a similar fashion to achieve a mean fidelity up to 94.8% for learning single-qubit quantum channels. Our experimental results explicitly showcase the advantages of deep quantum neural networks, including quantum analogue of the backpropagation algorithm and less stringent coherence-time requirement for their constituting physical qubits, thus providing a valuable guide for quantum machine learning applications with both near-term and future quantum devices.Comment: 7 pages (main text) + 11 pages (Supplementary Information), 10 figure

Self-assembled albumin nanoparticles induce pyroptosis for photodynamic/photothermal/immuno synergistic therapies in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is characterized by a high degree of malignancy, early metastasis, limited treatment, and poor prognosis. Immunotherapy, as a new and most promising treatment for cancer, has limited efficacy in TNBC because of the immunosuppressive tumor microenvironment (TME). Inducing pyroptosis and activating the cyclic guanosine monophosphate-adenosine monophosphate synthase/interferon gene stimulator (cGAS/STING) signaling pathway to upregulate innate immunity have become an emerging strategy for enhancing tumor immunotherapy. In this study, albumin nanospheres were constructed with photosensitizer-IR780 encapsulated in the core and cGAS–STING agonists/H2S producer-ZnS loaded on the shell (named IR780-ZnS@HSA). In vitro, IR780-ZnS@HSA produced photothermal therapy (PTT) and photodynamic therapy (PDT) effects. In addition, it stimulated immunogenic cell death (ICD) and activated pyroptosis in tumor cells via the caspase-3–GSDME signaling pathway. IR780-ZnS@HSA also activated the cGAS–STING signaling pathway. The two pathways synergistically boost immune response. In vivo, IR780-ZnS@HSA + laser significantly inhibited tumor growth in 4T1 tumor-bearing mice and triggered an immune response, improving the efficacy of the anti-APD-L1 antibody (aPD-L1). In conclusion, IR780-ZnS@HSA, as a novel inducer of pyroptosis, can significantly inhibit tumor growth and improve the efficacy of aPD-L1

Mixed halide perovskites for spectrally stable and high-efficiency blue light-emitting diodes.

Bright and efficient blue emission is key to further development of metal halide perovskite light-emitting diodes. Although modifying bromide/chloride composition is straightforward to achieve blue emission, practical implementation of this strategy has been challenging due to poor colour stability and severe photoluminescence quenching. Both detrimental effects become increasingly prominent in perovskites with the high chloride content needed to produce blue emission. Here, we solve these critical challenges in mixed halide perovskites and demonstrate spectrally stable blue perovskite light-emitting diodes over a wide range of emission wavelengths from 490 to 451 nanometres. The emission colour is directly tuned by modifying the halide composition. Particularly, our blue and deep-blue light-emitting diodes based on three-dimensional perovskites show high EQE values of 11.0% and 5.5% with emission peaks at 477 and 467 nm, respectively. These achievements are enabled by a vapour-assisted crystallization technique, which largely mitigates local compositional heterogeneity and ion migration

NVIDIA FLARE: Federated Learning from Simulation to Real-World

Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVIDIA FLARE as an open-source software development kit (SDK) to make it easier for data scientists to use FL in their research and real-world applications. The SDK includes solutions for state-of-the-art FL algorithms and federated machine learning approaches, which facilitate building workflows for distributed learning across enterprises and enable platform developers to create a secure, privacy-preserving offering for multiparty collaboration utilizing homomorphic encryption or differential privacy. The SDK is a lightweight, flexible, and scalable Python package. It allows researchers to apply their data science workflows in any training libraries (PyTorch, TensorFlow, XGBoost, or even NumPy) in real-world FL settings. This paper introduces the key design principles of NVFlare and illustrates some use cases (e.g., COVID analysis) with customizable FL workflows that implement different privacy-preserving algorithms. Code is available at https://github.com/NVIDIA/NVFlare.Comment: Accepted at the International Workshop on Federated Learning, NeurIPS 2022, New Orleans, USA (https://federated-learning.org/fl-neurips-2022); Revised version v2: added Key Components list, system metrics for homomorphic encryption experiment; Extended v3 for journal submissio

Synthesis of carbon-supported PdSn–SnO2 nanoparticles with different degrees of interfacial contact and enhanced catalytic activities for formic acid oxidation

The conjunction of the PdSn alloy and SnO2 is of interest for improving catalytic activity in formic acid oxidation (FAO). Here, we report the synthesis of PdSn–SnO2 nanoparticles and a study of their catalytic FAO activity. Different degrees of interfacial contact between SnO2 and PdSn were obtained using two different stabilizers (sodium citrate and EDTA) during the reduction process in catalyst preparation. Compared to the PdSn alloy, PdSn–SnO2 supported on carbon black showed enhanced FAO catalytic activity due to the presence of SnO2 species. It was also found that interfacial contact between the PdSn alloy and the SnO2 phase has an impact on the activity towards CO oxidation and FAO.Web of Scienc

Beam test of a 180 nm CMOS Pixel Sensor for the CEPC vertex detector

The proposed Circular Electron Positron Collider (CEPC) imposes new challenges for the vertex detector in terms of pixel size and material budget. A Monolithic Active Pixel Sensor (MAPS) prototype called TaichuPix, based on a column drain readout architecture, has been developed to address the need for high spatial resolution. In order to evaluate the performance of the TaichuPix-3 chips, a beam test was carried out at DESY II TB21 in December 2022. Meanwhile, the Data Acquisition (DAQ) for a muti-plane configuration was tested during the beam test. This work presents the characterization of the TaichuPix-3 chips with two different processes, including cluster size, spatial resolution, and detection efficiency. The analysis results indicate the spatial resolution better than 5 $\mu m$ and the detection efficiency exceeds 99.5 % for both TaichuPix-3 chips with the two different processes

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data