98 research outputs found
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
BDNF-loaded chitosan-based mimetic mussel polymer conduits for repair of peripheral nerve injury
Care for patients with peripheral nerve injury is multifaceted, as traditional methods are not devoid of limitations. Although the utilization of neural conduits shows promise as a therapeutic modality for peripheral nerve injury, its efficacy as a standalone intervention is limited. Hence, there is a pressing need to investigate a composite multifunctional neural conduit as an alternative treatment for peripheral nerve injury. In this study, a BDNF-loaded chitosan-based mimetic mussel polymer conduit was prepared. Its unique adhesion characteristics allow it to be suture-free, improve the microenvironment of the injury site, and have good antibacterial properties. Researchers utilized a rat sciatic nerve injury model to evaluate the progression of nerve regeneration at the 12-week postoperative stage. The findings of this study indicate that the chitosan-based mimetic mussel polymer conduit loaded with BDNF had a substantial positive effect on myelination and axon outgrowth. The observed impact demonstrated a favorable outcome in terms of sciatic nerve regeneration and subsequent functional restoration in rats with a 15-mm gap. Hence, this approach is promising for nerve tissue regeneration during peripheral nerve injury
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 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
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