High-Speed Quantum Radio-Frequency-Over-Light Communication.

Quantum dense coding (QDC) means to transmit two classical bits by only transferring one quantum bit, which has enabled high-capacity information transmission and strengthened system security. Continuous-variable QDC offers a promising solution to increase communication rates while achieving seamless integration with classical communication systems. Here, we propose and experimentally demonstrate a high-speed quantum radio-frequency-over-light (RFOL) communication scheme based on QDC with an entangled state, and achieve a practical rate of 20 Mbps through digital modulation and RFOL communication. This scheme bridges the gap between quantum technology and real-world communication systems, which bring QDC closer to practical applications and offer prospects for further enhancement of metropolitan communication networks.

Improving Fast Adversarial Training With Prior-Guided Knowledge.

Fast adversarial training (FAT) is an efficient method to improve robustness in white-box attack scenarios. However, the original FAT suffers from catastrophic overfitting, which dramatically and suddenly reduces robustness after a few training epochs. Although various FAT variants have been proposed to prevent overfitting, they require high training time. In this paper, we investigate the relationship between adversarial example quality and catastrophic overfitting by comparing the training processes of standard adversarial training and FAT. We find that catastrophic overfitting occurs when the attack success rate of adversarial examples becomes worse. Based on this observation, we propose a positive prior-guided adversarial initialization to prevent overfitting by improving adversarial example quality without extra training time. This initialization is generated by using high-quality adversarial perturbations from the historical training process. We provide theoretical analysis for the proposed initialization and propose a prior-guided regularization method that boosts the smoothness of the loss function. Additionally, we design a prior-guided ensemble FAT method that averages the different model weights of historical models using different decay rates. Our proposed method, called FGSM-PGK, assembles the prior-guided knowledge, i.e., the prior-guided initialization and model weights, acquired during the historical training process. The proposed method can effectively improve the model's adversarial robustness in white-box attack scenarios. Evaluations of four datasets demonstrate the superiority of the proposed method.

HSG-MGAF Net: Heterogeneous subgraph-guided multiscale graph attention fusion network for interpretable prediction of whole-slide image.

BACKGROUND AND OBJECTIVE: Pathological whole slide image (WSI) prediction and region of interest (ROI) localization are important issues in computer-aided diagnosis and postoperative analysis in clinical applications. Existing computer-aided methods for predicting WSI are mainly based on multiple instance learning (MIL) and its variants. However, most of the methods are based on instance independence and identical distribution assumption and performed at a single scale, which not fully exploit the hierarchical multiscale heterogeneous information contained in WSI. METHODS: Heterogeneous Subgraph-Guided Multiscale Graph Attention Fusion Network (HSG-MGAF Net) is proposed to build the topology of critical image patches at two scales for adaptive WSI prediction and lesion localization. The HSG-MGAF Net simulates the hierarchical heterogeneous information of WSI through graph and hypergraph at two scales, respectively. This framework not only fully exploits the low-order and potential high-order correlations of image patches at each scale, but also leverages the heterogeneous information of the two scales for adaptive WSI prediction. RESULTS: We validate the superiority of the proposed method on the CAMELYON16 and the TCGA- NSCLC, and the results show that HSG-MGAF Net outperforms the state-of-the-art method on both datasets. The average ACC, AUC and F1 score of HSG-MGAF Net can reach 92.7 %/0.951/0.892 and 92.2 %/0.957/0.919, respectively. The obtained heatmaps can also localize the positive regions more accurately, which have great consistency with the pixel-level labels. CONCLUSIONS: The results demonstrate that HSG-MGAF Net outperforms existing weakly supervised learning methods by introducing critical heterogeneous information between the two scales. This approach paves the way for further research on light weighted heterogeneous graph-based WSI prediction and ROI localization.

Evaluating the impact of China's MSW sorting pilot policy on urban sustainable development: Empirical evidence from 95 cities.

The escalating challenge of municipal solid waste (MSW) critically tests the sustainable development capacities of urban centers. In response, China initiated pilot policies in 2017 aimed at bolstering MSW management. The effectiveness of these initiatives, however, necessitates empirical scrutiny. This study leverages panel data spanning 95 cities at the prefectural level or higher, covering the period from 2006 to 2020, to assess the impact of the MSW sorting pilot policy on urban sustainable development using a difference-in-differences approach. The research found that the MSW sorting pilot policy has significantly increased the processing volume of MSW, thereby enhancing the sustainable development capabilities of cities. Further, the study identifies augmented fixed asset investments as a key mechanism through which pilot cities have enhanced their MSW management capabilities. Notably, the policy's stimulative effects are more pronounced in less densely populated and economically lagging regions. These findings provide critical insights for developing nations in shaping MSW sorting strategies and advancing urban sustainability.

Interpretable Inference and Classification of Tissue Types in Histological Colorectal Cancer Slides Based on Ensembles Adaptive Boosting Prototype Tree.

Digital pathology images are treated as the "gold standard" for the diagnosis of colorectal lesions, especially colon cancer. Real-time, objective and accurate inspection results will assist clinicians to choose symptomatic treatment in a timely manner, which is of great significance in clinical medicine. However, Manual methods suffers from long inspection cycle and serious reliance on subjective interpretation. It is also a challenging task for existing computer-aided diagnosis methods to obtain models that are both accurate and interpretable. Models that exhibit high accuracy are always more complex and opaque, while interpretable models may lack the necessary accuracy. Therefore, the framework of ensemble adaptive boosting prototype tree is proposed to predict the colorectal pathology images and provide interpretable inference by visualizing the decision-making process in each base learner. The results showed that the proposed method could effectively address the "accuracy-interpretability trade-off" issue by ensemble of m adaptive boosting neural prototype trees. The superior performance of the framework provides a novel paradigm for interpretable inference and high-precision prediction of pathology image patches in computational pathology.

Teleportation goes to Hertz rate.

Quantum teleportation has been developed to simultaneously realize the Hertz rate and the 64-km distance through fiber channels, which is essential to real-world application of quantum network.

Maternal exposure to fine particulate matter and brain-derived neurotrophic factor (BDNF) in the fetus: A prospective cohort study.

Maternal exposure to ambient fine particulate matter (PM2.5) during pregnancy has been associated with impaired neurobehavioral development in children. However, the specific mechanism remains unclear. Brain derived neurotrophic factor (BDNF) is an important growth factor in the nervous system. We evaluated the associations of maternal PM2.5 exposures with fetal BDNF in the umbilical cord blood in a prospective cohort study. A total of 711 eligible mother-infant pairs from the Shanghai Birth Cohort were included in the current study. Daily maternal exposures to ambient PM2.5 were assessed with a gap-filling approach at 1 * 1 km2 resolution based on self-reported home addresses. The concentrations of BDNF in the cord blood were measured by ELISA. A linear regression model was applied to evaluate the association of maternal ambient PM2.5 exposure with fetal BDNF level at birth. The median concentration of BDNF was 13,403 pg/ml. Vaginal deliveries and female infants had higher BDNF levels than cesarean deliveries and male infants. One natural log (ln) unit increase in maternal PM2.5 exposure during the second trimester was significantly associated with - 0.20 (95% CI: -0.36, -0.05) ln-unit decrease in BDNF level in all births. These effects were stronger and more significant in vaginal deliveries and in male infants. Our study suggests that BDNF in the cord blood may serve as a potential biomarker in assessing the neurodevelopmental effects of maternal PM2.5 exposure.

Deterministic manipulation of steering between distant quantum network nodes.

Multipartite Einstein-Podolsky-Rosen (EPR) steering is a key resource in a quantum network. Although EPR steering between spatially separated regions of ultracold atomic systems has been observed, deterministic manipulation of steering between distant quantum network nodes is required for a secure quantum communication network. Here, we propose a feasible scheme to deterministically generate, store, and manipulate one-way EPR steering between distant atomic cells by a cavity-enhanced quantum memory approach. While optical cavities effectively suppress the unavoidable noises in electromagnetically induced transparency, three atomic cells are in a strong Greenberger-Horne-Zeilinger state by faithfully storing three spatially separated entangled optical modes. In this way, the strong quantum correlation of atomic cells guarantees one-to-two node EPR steering is achieved, and can perserve the stored EPR steering in these quantum nodes. Furthermore, the steerability can be actively manipulated by the temperature of the atomic cell. This scheme provides the direct reference for experimental implementation for one-way multipartite steerable states, which enables an asymmetric quantum network protocol.

Integrated Transcriptome and Metabolome Dissecting Interaction between Vitis vinifera L. and Grapevine Fabavirus.

Grapevine fabavirus (GFabV) is a novel member of the Fabavirus genus associated with chlorotic mottling and deformation symptoms in grapevines. To gain insights into the interaction between GFabV and grapevines, V. vinifera cv. 'Summer Black' infected with GFabV was investigated under field conditions through physiological, agronomic, and multi-omics approaches. GFabV induced significant symptoms on 'Summer Black', and caused a moderate decrease in physiological efficiency. In GFabV-infected plants, alterations in carbohydrate- and photosynthesis-related genes might trigger some defense responses. In addition, secondary metabolism involved in plant defense was progressively induced by GFabV. Jasmonic acid and ethylene signaling were down-regulated in GFabV-infected leaves and berries along with the expression of proteins related to LRR and protein kinases, suggesting that GFabV can block the defense in healthy leaves and berries. Furthermore, this study provided biomarkers for early monitoring of GFabV infection in grapevines, and contributed to a better understanding of the complex grapevine-virus interaction.

Effect of pharmacist intervention on antibiotic prophylaxis in orthopedic internal fixation: A retrospective study.

BACKGROUND: Despite the availability of guidelines and official policies, antibiotic prophylaxis in clean surgery remains suboptimal. OBJECTIVE: The aim of this study was to evaluate the clinical effects and cost-effectiveness of pharmacist-led intervention in the perioperative anti-infection prophylaxis of patients undergoing orthopedic internal fixation. METHODS: We performed a retrospective analysis based on the medical records of internal fixation surgery in a tertiary hospital from July 2019 to June 2020. Data were divided into two groups based on whether a full-time pharmacist participated in the treatment. The research parameters included use of antibiotics, rationality of medication, postoperative complications, and related cost. To deal with selection bias, propensity score matching method was employed at a ratio of 1:1. Meanwhile, a cost-effectiveness analysis was used to evaluate the impact of pharmacist intervention on antibiotic prevention in internal fixation surgery. RESULTS: A total of 537 participants were included in this study. After matching, 236 patients were comparable in each group. During the pharmacist intervention period, less pharmacologic prophylaxis (96.6% vs 100.0%, p = 0.007) and shorter prophylaxis duration (1.60 vs 2.28 days, p < 0.001) were observed. The reasonable rate increased dramatically in usage and dosage (96.6% vs 83.9%, p < 0.001), timing of administration (94.5% vs 78.4%, p < 0.001) and medication duration (64.4% vs 37.7%, p < 0.001). In addition, pharmacist intervention yielded net economic benefits. A remarkable reduction was observed in average length of stay (10.43 vs 11.14 days, p = 0.012), drug cost ($610.57 vs $706.60, p = 0.001) and defined daily doses (2.31 vs 3.27, p < 0.001). The cost-effectiveness ratios, divided drug cost savings by cost of pharmacist time, were 28:1 for drug and 2:1 for antibiotics, respectively. CONCLUSION: Pharmacist-driven antibiotic stewardship for orthopedic internal fixation patients improved compliance with peri-procedure antibiotic prophylaxis, and reduced the cost and utilization of antibiotics. This helped to bring significant clinical and economic benefits.

Stabilization improvement of the squeezed optical fields using a high signal-to-noise ratio bootstrap photodetector.

High-precision cavity locking is crucial for squeezing optical fields. Here, a bootstrapped low-noise photodetector is utilized in the generation process of the squeezed state of light. This process is based on a combination of a modified trans-impedance amplifier (TIA) circuit and a two-stage bootstrap amplifier circuit. This not only achieves high-precision and long-term stable locking of the optical cavity, but it also improves the degree to which the light field is squeezed. The experiment results show that the detector has a high signal-to-noise ratio (SNR) of 26.7 dB at the analysis frequency of 3 MHz when measuring the shot noise with an injection optical power of 800 µW, and the equivalent optical power noise level is lower than 2.4 pW/Hz in the frequency range of 1-30 MHz. Moreover, the squeezing degree of the quadrature amplitude squeezed state light field can be improved by more than 34.9% when the detector is used for optical cavity locking. The photodetector is useful in continuous variable (CV) quantum information research.

Bridged combined fixation system versus locking plate in the treatment of patients with implant periprosthetic refracture following proximal femoral fracture surgery: A retrospective observational study.

Locking plate (LP) re-fixation is mainly used to treat postoperative implant periprosthetic refractures; however, the extensive trauma and the fixation form of LP make the operation difficult. The bridge combined fixation system (BCFS) is a new clip-rod internal fixation system, and its clinical application is in its infancy. To compare the clinical effect of BCFS and LP in the treatment of geriatric postoperative implant periprosthetic refracture following proximal femoral fracture surgery. Thirty-two patients (14 with BCFS and 18 with LP) with postoperative implant periprosthetic refracture following proximal femoral fracture surgery, who underwent surgery in our hospital, were analyzed retrospectively. The incision length, operation time, intraoperative bleeding volume, postoperative drainage volume, postoperative hospital stay, fracture healing time and complications of each patient were recorded. Regular radiographs were taken after the operation to evaluate the fracture reduction and fixation. All the patients were followed for 12 months to evaluate their limb function by Johner-Wruhs scoring criteria. The patients were followed for an average of 24.1 months, and all achieved bony union, with no complications such as infection, nonunion, and internal fixation instrument falling off and loosening after the operation. Delayed healing occurred in two cases in the LP group. The average value of surgical incision length, operation time, postoperative hospitalization time and fracture healing time in the BCFS group were significantly smaller than those in the LP group, accompanied by a decrease in intraoperative bleeding and postoperative drainage volumes (P < .05). The rate of limb function in the BCFS group (85.7%) was higher than that in the LP group (83.3%), with no significance (P > .05). The BCFS in the refracture around the implant of the proximal femoral fracture exhibited many advantages such as simple operation, strong plasticity, effective reduction of surgical trauma, promotion of fracture healing and early functional rehabilitation, etc, making it an advantageous clinical application.

Dynamic Prediction of Internet Financial Market Based on Deep Learning.

P2P lending is an important part of Internet finance, which is popular among users because of its efficiency, low cost, wide range, and ease of operation. The problem of predicting loan defaults is affected by many factors, such as the linear and nonlinear nature of the data itself and time dependence and multiple external factors, which have not been well captured in the previous work. In this paper, we propose a multiattention mechanism to capture the different effects of various time slices and various external factors on the results, introduce ARIMA and LSTM to capture the linear and nonlinear characteristics of the lending data respectively, and establish a Time Series Multiattention Prediction Model (MAT-ALSTM) based on LSTM and ARIMA. This paper uses the Lending Club dataset from the United States to prove that our model is superior to ANN, SVM, LSTM, GRU, and ARIMA models in the prediction effect of MAE, RMSE, and DA.

Boosting Fast Adversarial Training With Learnable Adversarial Initialization.

Adversarial training (AT) has been demonstrated to be effective in improving model robustness by leveraging adversarial examples for training. However, most AT methods are in face of expensive time and computational cost for calculating gradients at multiple steps in generating adversarial examples. To boost training efficiency, fast gradient sign method (FGSM) is adopted in fast AT methods by calculating gradient only once. Unfortunately, the robustness is far from satisfactory. One reason may arise from the initialization fashion. Existing fast AT generally uses a random sample-agnostic initialization, which facilitates the efficiency yet hinders a further robustness improvement. Up to now, the initialization in fast AT is still not extensively explored. In this paper, focusing on image classification, we boost fast AT with a sample-dependent adversarial initialization, i.e., an output from a generative network conditioned on a benign image and its gradient information from the target network. As the generative network and the target network are optimized jointly in the training phase, the former can adaptively generate an effective initialization with respect to the latter, which motivates gradually improved robustness. Experimental evaluations on four benchmark databases demonstrate the superiority of our proposed method over state-of-the-art fast AT methods, as well as comparable robustness to advanced multi-step AT methods. The code is released at https://github.com//jiaxiaojunQAQ//FGSM-SDI.

High-performance cavity-enhanced quantum memory with warm atomic cell.

High-performance quantum memory for quantized states of light is a prerequisite building block of quantum information technology. Despite great progresses of optical quantum memories based on interactions of light and atoms, physical features of these memories still cannot satisfy requirements for applications in practical quantum information systems, since all of them suffer from trade-off between memory efficiency and excess noise. Here, we report a high-performance cavity-enhanced electromagnetically-induced-transparency memory with warm atomic cell in which a scheme of optimizing the spatial and temporal modes based on the time-reversal approach is applied. The memory efficiency up to 67 ± 1% is directly measured and a noise level close to quantum noise limit is simultaneously reached. It has been experimentally demonstrated that the average fidelities for a set of input coherent states with different phases and amplitudes within a Gaussian distribution have exceeded the classical benchmark fidelities. Thus the realized quantum memory platform has been capable of preserving quantized optical states, and is ready to be applied in quantum information systems, such as distributed quantum logic gates and quantum-enhanced atomic magnetometry.

Deterministic distribution of multipartite entanglement in a quantum network by continuous-variable polarization states.

Quantum network plays a vitally important role in the practical application of quantum information, which requires the deterministic entanglement distribution among multiple remote users. Here, we propose a feasible scheme to deterministically distribute quadripartite entanglement by continuous-variable (CV) polarization states. The quantum server prepares the quadripartite CV polarization entanglement and distributes them to four remote users via optical fiber. In this way, the measurement of CV polarization entanglement is local oscillation free, which makes the long distance entanglement distribution in commercial optical fiber communication networks possible. Furthermore, both the Greenberger-Horne-Zeilinger-like (GHZ-like) and cluster-like polarization entangled states can be distributed among four users by controlling the beam splitter network in quantum server, which are confirmed by the extended criteria for polarization entanglement of multipartite optical modes. The protocol provides the direct reference for experimental implementation and can be directly extended to quantum network with more users, which is essential for a metropolitan quantum network.

Impact of antibiotic prophylaxis courses on postoperative complications following total joint arthroplasty: Finding from Chinese population.

WHAT IS KNOWN AND OBJECTIVE: Prolonged antibiotic prophylaxis after total joint arthroplasty (TJA) may not assist in minimizing postoperative complications, however, data based on the Chinese population have been limited. The purpose of this study is to investigate the effect of antibiotic prophylaxis on postoperative complications after TJA in Chinese patients. METHODS: We retrospectively reviewed 990 patients undergoing elective primary TJA surgery from January 2016 to June 2019. Patients who received a short course (≤3 days) of antibiotic prophylaxis were compared with those who received a longer course (>3 days). Logistic regression analysis and subgroup analysis were performed to control for potential confounders. Beyond that, survival analysis was used to determine the cumulative incidence of postoperative complications. RESULTS AND DISCUSSION: Follow-up to 12 months after surgery, the prevalence of system complications in the longer course group and the short course group were 5.1% and 3.9%, respectively (p = 0.451). Similarly, no statistical differences in incisional complications (1.5% vs. 1.8%, p > 0.999) and periprosthetic joint infection (PJI) (1.0% vs. 1.0%, p > 0.999) were observed between the two groups. After performing logistic regression analysis and survival analysis, no potential association was found between the course of antibiotic prophylaxis and postoperative complications. In addition, prolonged antibiotic prophylaxis conferred no benefit for high-risk obese patients. WHAT IS NEW AND CONCLUSION: Extended antibiotic prophylaxis did not result in a statistically significant and clinically meaningful reduction in postoperative complications. Therefore, we recommended that the duration of antibiotic prophylaxis in TJA should be shortened to 3 days or less in the Chinese population.

RNA-seq Analysis Reveals Potential Molecular Mechanisms of ZNF580/ZFP580 Promoting Neuronal Survival and Inhibiting Apoptosis after Hypoxic-ischemic Brain damage.

Neonatal hypoxic-ischemic brain damage (HIBD) is one of the main causes of neonatal acute death and chronic nervous system impairment, but still lacks effective treatments. ZNF580/ZFP580, reported in our previous studies, may be a newly identified member of the Krüppel-like factor (KLF) family, and has anti-apoptotic effects during ischemic myocardial injury. In the present study, we showed that the expression levels of both ZFP580/ZNF580 mRNA and protein increased significantly in neonatal HIBD rats and oxygen-glucose deprivation (OGD) SH-SY5Y cell models. ZNF580 overexpression promoted neuron survival and suppressed neuron apoptosis after OGD in neuron-like SH-SY5Y cells, while interference with ZNF580 resulted in the opposite results. RNA-seq analysis identified 248 differentially-expressed genes (DEGs) between ZNF580 overexpression SH-SY5Y cells and interference-expressed SH-SY5Y cells. Gene Ontology functional enrichment analysis showed that these DEGs played significant roles in the growth, development, and regeneration of axons, DNA biosynthetic processes, DNA replication, and apoptosis. Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that these DEGs were found in some pathways, including ferroptosis, glutamatergic synapses, protein processing in the endoplasmic reticulum, estrogen signaling pathways, the TGF-beta signaling pathway, and the longevity regulating pathway. The qRT-PCR validation results were consistent with RNA-seq results, which showed that HSPA5, IGFBP3, NTN4, and KLF9 increased in ZNF580-overexpressed SH-SY5Y cells and decreased in interference-expressed SH-SY5Y cells, when compared with normal cells. Together, the results suggested that ZNF580 targeted these genes to inhibit neuronal apoptosis.

Large-tuning-range frequency stabilization of an ultraviolet laser by an open-loop piezoelectric ceramic controlled Fabry-Pérot cavity.

We demonstrate a laser frequency stabilization method with large tuning range to stabilize a UV laser by installing piezoelectric ceramic actuators into a Fabry-Pérot cavity with an ultra-low expansion spacer. To suppress piezoelectric drift, a two-layer symmetrical structure is adopted for the piezoelectric actuator, and a 14.7 GHz tuning range is achieved. The short-term drift of the piezoelectric ceramics caused by temperature and creep is eliminated, and the long-term drift is 0.268 MHz/h when the Fabry-Pérot cavity is sealed in a chamber without a vacuum environment. The long-term frequency drift is mainly caused by stress release and is eliminated by compensating the cavity voltage with an open loop. Without the need for an external reference or a vacuum environment, the laser frequency stabilization system is greatly simplified, and it can be extended to wavelengths ranging from ultraviolet to infrared. Owing to its simplicity, stability, and large tuning range, it is applicable in cold atom and trapped ion experiments.