Effects of Ox-LDL on Macrophages NAD(P)H Autofluorescence Changes by Two-photon Microscopy

Ox-LDL uptakes by macrophage play a critical role in the happening of atherosclerosis. Because of its low damage on observed cells and better signal-to- background ratio, two-photon excitation fluorescence microscopy is used to observe NAD(P)H autofluorescence of macrophage under difference cultured conditions- bare cover glass, coated with fibronectin or poly-D-lysine. The results show that the optimal condition is fibronectin coated surface, on which, macrophages profile can be clearly identified on NAD(P)H autofluorescence images collected by two-photon microscopy. Moreover, different morphology and intensities of autofluorescence under different conditions were observed as well. In the future, effects of ox-LDL on macrophages will be investigated by purposed system to research etiology of atherosclerosis.Comment: Submitted on behalf of TIMA Editions (http://irevues.inist.fr/tima-editions

Photocatalytic Transfer Hydrogenation Reactions Using Water as the Proton Source

Transfer hydrogenation using liquid hydrogen carriers as the direct proton sources under mild conditions has received extensive attention in the research area of organic synthesis. The emerging photocatalytic water-donating transfer hydrogenation (PWDTH) is a promising alternative over the conventional hydrogenation technology due to the advantages of being eco-friendly. This paper focuses on the recent advances in the rising and rapidly developing field of PWDTH reactions, devoted to elucidating the mechanism of the hydrogen transfer process and rationalizing the design principles of efficient photocatalysts. Finally, the current challenges and future opportunities are described.Web of Science13117567755

Measuring the Hubble Constant Using Strongly Lensed Gravitational Wave Signals

The measurement of the Hubble constant $H_0$ plays an important role in the study of cosmology. In this letter, we propose a new method to constrain the Hubble constant using the strongly lensed gravitational wave (GW) signals. By reparameterizing the waveform, we find that the lensed waveform is sensitive to the $H_0$. Assuming the scenario that no electromagnetic counterpart of the GW source can be identified, our method can still give meaningful constraints on the $H_0$ with the information of the lens redshift. We then apply Fisher information matrix and Markov Chain Monte Carlo to evaluate the potential of this method. For the space-based GW detector, TianQin, the $H_0$ can be constrained within a relative error of $\sim$ 0.3-2\%, using a single strongly lensed GW event. Precision varies according to different levels of electromagnetic information.Comment: 8 pages, 4 figure

Experimental realization of quantum non-reciprocity based on cold atomic ensembles

In analog to counterparts widely used in electronic circuits, all optical non-reciprocal devices are basic building blocks for both classical and quantum optical information processing. Approaching the fundamental limit of such devices, where the propagation of a single photon exhibits a good non-reciprocal characteristic, requires an asymmetric strong coupling between a single photon and a matter. Unfortunately it has been not realized yet. Here, we propose and experimentally realize a quantum non-reciprocity device with low optical losses and a high isolation of larger than 14 dB based on the cold atoms. Besides, the non-reciprocal transmission of a quantum qubit and non-reciprocal quantum storage of a true single photon are also realized. All results achieved would be very promising in building up quantum non-reciprocal devices for quantum networks.Comment: 7 pages, 4 figure

Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning

Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identification of new therapeutic targets in drug discovery. However, the contemporary AI technologies, particularly deep machine learning (ML), severely suffer from non-interpretability, which might uncontrollably lead to incorrect predictions. Interpretability is particularly crucial to ML for clinical diagnosis as the consumers must gain necessary sense of security and trust from firm grounds or convincing interpretations. In this work, we propose a tensor-network (TN)-ML method to reliably predict lung cancer patients and their stages via screening Raman spectra data of Volatile organic compounds (VOCs) in exhaled breath, which are generally suitable as biomarkers and are considered to be an ideal way for non-invasive lung cancer screening. The prediction of TN-ML is based on the mutual distances of the breath samples mapped to the quantum Hilbert space. Thanks to the quantum probabilistic interpretation, the certainty of the predictions can be quantitatively characterized. The accuracy of the samples with high certainty is almost 100$\%$. The incorrectly-classified samples exhibit obviously lower certainty, and thus can be decipherably identified as anomalies, which will be handled by human experts to guarantee high reliability. Our work sheds light on shifting the ``AI for biomedical sciences'' from the conventional non-interpretable ML schemes to the interpretable human-ML interactive approaches, for the purpose of high accuracy and reliability.Comment: 10 pages, 7 figure