Generation of heralded optical `Schroedinger cat' states by photon-addition

Optical "Schr\"odinger cat" states, the non-classical superposition of two quasi-classical coherent states, serve as a basis for gedanken experiments testing quantum physics on mesoscopic scales and are increasingly recognized as a resource for quantum information processing. Here, we report the first experimental realization of optical "Schr\"odinger cats" by adding a photon to a squeezed vacuum state, so far only photon-subtraction protocols have been realized. Photon-addition gives us the advantage of using heralded signal photons as experimental triggers, and we can generate "Schr\"odinger cats" at rates exceeding $8.5 \times 10^4$ counts per second; at least one order of magnitude higher than all previously reported realizations. Wigner distributions with pronounced negative parts are demonstrated at down to -8.89 dB squeezing, even when the initial squeezed vacuum input state has low purity. Benchmarking against such a degraded squeezed input state we report a maximum fidelity of more than 80% with a maximum cat amplitude of $|\alpha| \approx 1.66$. Our experiment uses photon-addition from pairs, one of those photons is used for monitoring, giving us enhanced control; moreover the pair production rates are high and should allow for repeated application of photon-addition via repeat-stages.Comment: 5 pages, 2 figures, 1 tabl

Deep learning predicts malignancy and metastasis of solid pulmonary nodules from CT scans

In the clinic, it is difficult to distinguish the malignancy and aggressiveness of solid pulmonary nodules (PNs). Incorrect assessments may lead to delayed diagnosis and an increased risk of complications. We developed and validated a deep learning-based model for the prediction of malignancy as well as local or distant metastasis in solid PNs based on CT images of primary lesions during initial diagnosis. In this study, we reviewed the data from multiple patients with solid PNs at our institution from 1 January 2019 to 30 April 2022. The patients were divided into three groups: benign, Ia-stage lung cancer, and T1-stage lung cancer with metastasis. Each cohort was further split into training and testing groups. The deep learning system predicted the malignancy and metastasis status of solid PNs based on CT images, and then we compared the malignancy prediction results among four different levels of clinicians. Experiments confirmed that human–computer collaboration can further enhance diagnostic accuracy. We made a held-out testing set of 134 cases, with 689 cases in total. Our convolutional neural network model reached an area under the ROC (AUC) of 80.37% for malignancy prediction and an AUC of 86.44% for metastasis prediction. In observer studies involving four clinicians, the proposed deep learning method outperformed a junior respiratory clinician and a 5-year respiratory clinician by considerable margins; it was on par with a senior respiratory clinician and was only slightly inferior to a senior radiologist. Our human–computer collaboration experiment showed that by simply adding binary human diagnosis into model prediction probabilities, model AUC scores improved to 81.80–88.70% when combined with three out of four clinicians. In summary, the deep learning method can accurately diagnose the malignancy of solid PNs, improve its performance when collaborating with human experts, predict local or distant metastasis in patients with T1-stage lung cancer, and facilitate the application of precision medicine

Prime Editing: An All-Rounder for Genome Editing

Prime editing (PE), as a “search-and-replace” genome editing technology, has shown the attractive potential of versatile genome editing ability, which is, in principle, currently superior to other well-established genome-editing technologies in the all-in-one operation scope. However, essential technological solutions of PE technology, such as the improvement of genome editing efficiency, the inhibition of potential off-targets and intended edits accounting for unexpected side-effects, and the development of effective delivery systems, are necessary to broaden its application. Since the advent of PE, many optimizations have been performed on PE systems to improve their performance, resulting in bright prospects for application in many fields. This review briefly discusses the development of PE technology, including its functional principle, noteworthy barriers restraining its application, current efforts in technical optimization, and its application directions and potential risks. This review may provide a concise and informative insight into the burgeoning field of PE, highlight the exciting prospects for this powerful tool, and provide clues for questions that may propel the field forward

Comprehensive Analysis of hsa-miR-654-5p’s Tumor-Suppressing Functions

The pivotal roles of miRNAs in carcinogenesis, metastasis, and prognosis have been demonstrated recently in various cancers. This study intended to investigate the specific roles of hsa-miR-654-5p in lung cancer, which is, in general, rarely discussed. A series of closed-loop bioinformatic functional analyses were integrated with in vitro experimental validation to explore the overall biological functions and pan-cancer regulation pattern of miR-654-5p. We found that miR-654-5p abundance was significantly elevated in LUAD tissues and correlated with patients’ survival. A total of 275 potential targets of miR-654-5p were then identified and the miR-654-5p-RNF8 regulation axis was validated in vitro as a proof of concept. Furthermore, we revealed the tumor-suppressing roles of miR-654-5p and demonstrated that miR-654-5p inhibited the lung cancer cell epithelial-mesenchymal transition (EMT) process, cell proliferation, and migration using target-based, abundance-based, and ssGSEA-based bioinformatic methods and in vitro validation. Following the construction of a protein–protein interaction network, 11 highly interconnected hub genes were identified and a five-genes risk scoring model was developed to assess their potential prognostic ability. Our study does not only provide a basic miRNA-mRNA-phenotypes reference map for understanding the function of miR-654-5p in different cancers but also reveals the tumor-suppressing roles and prognostic values of miR-654-5p

Direct Parameter Estimations from Machine Learning-Enhanced Quantum State Tomography

With the power to find the best fit to arbitrarily complicated symmetry, machine-learning (ML)-enhanced quantum state tomography (QST) has demonstrated its advantages in extracting complete information about the quantum states. Instead of using the reconstruction model in training a truncated density matrix, we develop a high-performance, lightweight, and easy-to-install supervised characteristic model by generating the target parameters directly. Such a characteristic model-based ML-QST can avoid the problem of dealing with a large Hilbert space, but cab keep feature extractions with high precision, capturing the underlying symmetry in data. With the experimentally measured data generated from the balanced homodyne detectors, we compare the degradation information about quantum noise squeezed states predicted by the reconstruction and characteristic models; both are in agreement with the empirically fitting curves obtained from the covariance method. Such a ML-QST with direct parameter estimations illustrates a crucial diagnostic toolbox for applications with squeezed states, from quantum information process, quantum metrology, advanced gravitational wave detectors, to macroscopic quantum state generation

A functional reference map of the RNF8 interactome in cancer

Abstract Background RNF8 is an E3 ligase identified as a critical DNA damage-responsive protein. Recently, multiple reports have shown that RNF8 could be used as an important therapeutic target for cancer chemo/radiotherapy. However, the understanding of RNF8 remains limited due to the lack of its interactome reference map and comprehensive analysis of RNF8 in diverse cancers, which underscores the need to map the interactome of RNF8 via high-throughput methods. Results A two-way identification method based on LC–MS was designed for the identification of the RNF8 interactome with high-specificity. By in silico analysis and in vitro validation, we identified a new reference map of the RNF8 interactome network containing many new targets, such as YBX1, DNMT1, and HDCA1, new biological functions and the gene-disease associations of RNF8. Our results revealed a close relationship between RNF8 and neurodegenerative diseases or tumor-infiltrating immune cells using bulk RNA-seq and scRNA-seq datasets. As a proof of concept of our interactome map, we validated the direct binding between RNF8 and YBX1 and showed that RNF8 catalyzed the ubiquitination of YBX1. These results demonstrated that RNF8 might be a crucial regulator of YBX1. Conclusions Our work provides a unique framework for researchers and clinicians who seek to better explore or understand RNF8-regulated biological functions in cancers. This study will hopefully facilitate the rational design and further development of anti-RNF8 therapy in cancers. Graphical abstrac

RNF8 depletion attenuates hepatocellular carcinoma progression by inhibiting epithelial-mesenchymal transition and enhancing drug sensitivity

Despite substantial advances that have been made in understanding the etiology of hepatocellular carcinoma (HCC), the early-stage diagnosis and treatment of advanced-stage HCC remain a major challenge. RNF8, an E3 ligase important for the DNA damage response, has been proven to facilitate the progression of breast and lung cancer, but its role in HCC remains unclear. In this study, we find that the expression of RNF8 is up-regulated in HCC tissues and positively correlated with poor prognosis of HCC. Furthermore, silencing RNF8 by siRNAs attenuates the migration of HCC cells and inhibits epithelial-mesenchymal transition (EMT) by regulating the expressions of proteins including N-cadherin, β-catenin, snail, and ZO-1. Moreover, Kaplan‒Meier survival analysis shows that high RNF8 expression predicts poor survival benefits from sorafenib. Finally, cell viability assay demonstrates that RNF8 depletion enhances the sensitivity of HCC cells to sorafenib and lenvatinib treatment. We hypothesize that the inhibitory role of RNF8 in EMT and its enhancing effects on anti-cancer drugs orchestrate the protective effects of RNF8 deficiency in HCC, which indicates its potential in clinical application