[Interpretation of specification for service of cancer screening for workers]

As the backbone force of China's social and economic construction, the health status of workers is closely related to the nation's productivity and social development. Currently, cancers have become one of the major diseases threatening the health of workers. However, there are still many shortcomings in the cancer screening services for the workers. To standardize cancer screening services for workers, ensure the quality of screening services, and improve the overall screening effectiveness, 19 institutions, including Peking Union Medical College Hospital of the Chinese Academy of Medical Sciences, have jointly formulated the Group Standard "Specification for service of cancer screening for workers (T/CHAA 023-2023)". This standard follows the principles of "legality, scientific rigor, advancement, and feasibility" and combines the frontier scientific advances in cancer screening. It clarifies the relevant requirements for service principles, service design, service delivery, service management, service evaluation, and improving worker cancer screening. Implementing this group standard will help connect the common screening needs of workers, employers, and cancer screening service providers, standardize the screening process, improve screening quality, and ultimately increase the early diagnosis rate and survival rate of cancer patients. Consequently, this group standard will help safeguard workers' health rights and interests, ensure the labor force resources, promote the comprehensive coordinated and sustainable development of society, and contribute to realizing the "Healthy China 2030" strategic policy.

Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic Environments

Evolution strategies (ESs), as a family of black-box optimization algorithms, recently emerge as a scalable alternative to reinforcement learning (RL) approaches such as Q-learning or policy gradient and are much faster when many central processing units (CPUs) are available due to better parallelization. In this article, we propose a systematic incremental learning method for ES in dynamic environments. The goal is to adjust previously learned policy to a new one incrementally whenever the environment changes. We incorporate an instance weighting mechanism with ES to facilitate its learning adaptation while retaining scalability of ES. During parameter updating, higher weights are assigned to instances that contain more new knowledge, thus encouraging the search distribution to move toward new promising areas of parameter space. We propose two easy-to-implement metrics to calculate the weights: instance novelty and instance quality. Instance novelty measures an instance's difference from the previous optimum in the original environment, while instance quality corresponds to how well an instance performs in the new environment. The resulting algorithm, instance weighted incremental evolution strategies (IW-IESs), is verified to achieve significantly improved performance on challenging RL tasks ranging from robot navigation to locomotion. This article thus introduces a family of scalable ES algorithms for RL domains that enables rapid learning adaptation to dynamic environments.

A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning

While reinforcement learning (RL) algorithms are achieving state-of-the-art performance in various challenging tasks, they can easily encounter catastrophic forgetting or interference when faced with lifelong streaming information. In this article, we propose a scalable lifelong RL method that dynamically expands the network capacity to accommodate new knowledge while preventing past memories from being perturbed. We use a Dirichlet process mixture to model the nonstationary task distribution, which captures task relatedness by estimating the likelihood of task-to-cluster assignments and clusters the task models in a latent space. We formulate the prior distribution of the mixture as a Chinese restaurant process (CRP) that instantiates new mixture components as needed. The update and expansion of the mixture are governed by the Bayesian nonparametric framework with an expectation maximization (EM) procedure, which dynamically adapts the model complexity without explicit task boundaries or heuristics. Moreover, we use the domain randomization technique to train robust prior parameters for the initialization of each task model in the mixture; thus, the resulting model can better generalize and adapt to unseen tasks. With extensive experiments conducted on robot navigation and locomotion domains, we show that our method successfully facilitates scalable lifelong RL and outperforms relevant existing methods.

Curriculum-Based Deep Reinforcement Learning for Quantum Control

Deep reinforcement learning (DRL) has been recognized as an efficient technique to design optimal strategies for different complex systems without prior knowledge of the control landscape. To achieve a fast and precise control for quantum systems, we propose a novel DRL approach by constructing a curriculum consisting of a set of intermediate tasks defined by fidelity thresholds, where the tasks among a curriculum can be statically determined before the learning process or dynamically generated during the learning process. By transferring knowledge between two successive tasks and sequencing tasks according to their difficulties, the proposed curriculum-based DRL (CDRL) method enables the agent to focus on easy tasks in the early stage, then move onto difficult tasks, and eventually approaches the final task. Numerical comparison with the traditional methods [gradient method (GD), genetic algorithm (GA), and several other DRL methods] demonstrates that CDRL exhibits improved control performance for quantum systems and also provides an efficient way to identify optimal strategies with few control pulses.

4D-MRI Guided Stereotactic Body Radiation Therapy for Unresectable Colorectal Liver Metastases

PURPOSE/OBJECTIVE(S)

This study evaluated the feasibilities and outcomes following four-dimensional magnetic resonance imaging (4D-MRI) guided stereotactic body radiation therapy (SBRT) for unresectable colorectal liver metastases (CRLM).

MATERIALS/METHODS

From March 2018 to January 2022, we identified 76 unresectable CRLM patients with 123 lesions who received 4D-MRI guided SBRT in our institution. 4D-MRI simulation with or without abdominal compression was conducted for all patients. The prescription dose was 50-65 Gy in 5-12 fractions. The image quality of computed tomography (CT) and MRI were compared using the Clarity Score. Clinical outcomes and toxicity profiles were evaluated.

RESULTS

The 4D-MRI significantly improved the image quality compared with CT images (mean Clarity Score: 1.67 vs 2.88, P < 0.001). The abdominal compression significantly reduced motions in cranial-caudal direction (P = 0.03) with 2 phase T2 weighted images assessing tumor motion. The median follow-up time was 12.5 months. For 98 lesions assessed for best response, the complete response, partial response and stable disease rate were 57.1 %, 30.6 % and 12.2 %, respectively. The local control (LC) rate at 2 year was 97.3%. 46.1% of patients experienced grade 1-2 toxicities and only 2.6% patients experienced grade 3 hematologic toxicities.

CONCLUSION

The 4D-MRI technique allowed precise target delineation and motion tracking in unresectable CRLM patients. High LC rate and mild toxicities were achieved. This study provided evidence for using 4D-MRI guided SBRT as an alternative treatment in unresectable CRLM.

Efficient Bayesian Policy Reuse With a Scalable Observation Model in Deep Reinforcement Learning

Bayesian policy reuse (BPR) is a general policy transfer framework for selecting a source policy from an offline library by inferring the task belief based on some observation signals and a trained observation model. In this article, we propose an improved BPR method to achieve more efficient policy transfer in deep reinforcement learning (DRL). First, most BPR algorithms use the episodic return as the observation signal that contains limited information and cannot be obtained until the end of an episode. Instead, we employ the state transition sample, which is informative and instantaneous, as the observation signal for faster and more accurate task inference. Second, BPR algorithms usually require numerous samples to estimate the probability distribution of the tabular-based observation model, which may be expensive and even infeasible to learn and maintain, especially when using the state transition sample as the signal. Hence, we propose a scalable observation model based on fitting state transition functions of source tasks from only a small number of samples, which can generalize to any signals observed in the target task. Moreover, we extend the offline-mode BPR to the continual learning setting by expanding the scalable observation model in a plug-and-play fashion, which can avoid negative transfer when faced with new unknown tasks. Experimental results show that our method can consistently facilitate faster and more efficient policy transfer.

Quantum Language Model With Entanglement Embedding for Question Answering

Quantum language models (QLMs) in which words are modeled as a quantum superposition of sememes have demonstrated a high level of model transparency and good post-hoc interpretability. Nevertheless, in the current literature, word sequences are basically modeled as a classical mixture of word states, which cannot fully exploit the potential of a quantum probabilistic description. A quantum-inspired neural network (NN) module is yet to be developed to explicitly capture the nonclassical correlations within the word sequences. We propose a NN model with a novel entanglement embedding (EE) module, whose function is to transform the word sequence into an entangled pure state representation. Strong quantum entanglement, which is the central concept of quantum information and an indication of parallelized correlations among the words, is observed within the word sequences. The proposed QLM with EE (QLM-EE) is proposed to implement on classical computing devices with a quantum-inspired NN structure, and numerical experiments show that QLM-EE achieves superior performance compared with the classical deep NN models and other QLMs on question answering (QA) datasets. In addition, the post-hoc interpretability of the model can be improved by quantifying the degree of entanglement among the word states.

[Impact of sinonasal anatomic changes after endoscopic anterior skull base surgery on nasal airflow and air conditioning: a computational fluid dynamics study]

Objective: To analyze the impact of the sinonasal anatomic changes after endonasal endoscopic anterior skull base surgery on the nasal airflow and heating and humidification by computational fluid dynamics (CFD), and to explore the correlation between the postoperative CFD parameters and the subjective symptoms of the patients. Methods: The clinical data in the Rhinology Department of the First Affiliated Hospital of Zhengzhou University from 2016 to 2021 were retrospectively analyzed. The patients received the endoscopic resection of the anterior skull base tumor were selected as the case group, and the adults whose CT scans had no sinonasal abnormalities were chosen as the control group. The CFD simulation was performed on the sinonasal models after reconstructed from the patients' sinus CT images during the post-surgical follow-up. All the patients were asked to complete the Empty Nose Syndrome 6-Item Questionnaire (ENS6Q) to assess the subjective symptoms. The comparison between two independent groups and the correlation analysis were carried out by using the Mann-Whitney U test and the Spearman correlation test in the SPSS 26.0 software. Results: Nineteen patients (including 8 males and 11 females, from 22 to 67 years old) in the case group and 2 patients (a male of 38 years old and a female of 45 years old) in the control group were enrolled in this study. After the anterior skull base surgery, the high-speed airflow moved to the upper part of the nasal cavity, and the lowest temperature shifted upwards on the choana. Comparing with the control group, the ratio of nasal mucosal surface area to nasal ventilation volume in the case group decreased [0.41 (0.40, 0.41) mm^-1 vs 0.32 (0.30, 0.38) mm^-1; Z=-2.04, P=0.041], the air flow in the upper and middle part of the nasal cavity increased [61.14 (59.78, 62.51)% vs 78.07 (76.22, 94.43)%; Z=-2.28, P=0.023], the nasal resistance decreased [0.024 (0.022, 0.026) Pa·s/ml vs 0.016 (0.009, 0.018) Pa·s/ml; Z=-2.29, P=0.022], the lowest temperature in the middle of the nasal cavity decreased [28.29 (27.23, 29.35)℃ vs 25.06 (24.07, 25.50)℃; Z=-2.28, P=0.023], the nasal heating efficiency decreased [98.74 (97.95, 99.52)% vs 82.16 (80.24, 86.91)%; Z=-2.28, P=0.023], the lowest relative humidity decreased [(79.62 (76.55, 82.69)% vs 73.28 (71.27, 75.05)%; Z=-2.28, P=0.023], and the nasal humidification efficiency decreased [99.50 (97.69, 101.30)% vs 86.09 (79.33, 87.16)%; Z=-2.28, P=0.023]. The ENS6Q total scores of all patients in the case group were less than 11 points. There was a moderate negative correlation between the proportion of the inferior airflow in the post-surgical nasal cavity negatively and the ENS6Q total scores (r_s=-0.50, P=0.029). Conclusions: The sinonasal anatomic changes after the endoscopic anterior skull base surgery alter the nasal airflow patterns, reducing the efficiency of nasal heating and humidification. However, the post-surgical occurrence tendency of the empty nose syndrome is weak.

Hamiltonian Identification via Quantum Ensemble Classification

Identifying the Hamiltonian of an unknown quantum system is a critical task in the area of quantum information. In this article, we propose a systematic Hamiltonian identification approach via quantum ensemble multiclass classification (HI-QEMC). This approach is implemented by a three-step iterative refining process, i.e., parameter interval guess, verification, and judgment. In the parameter interval guess step, the parameter interval is divided into several sub-intervals and the true Hamiltonian parameter is guessed in one of them. In the parameter interval verification step, cross verification is applied to verify the accuracy of the guess. In the parameter interval judgment step, an adaptive interval judgment (AIJ) algorithm is designed to determine the sub-interval containing the true Hamiltonian parameter. Numerical results on two typical quantum systems, i.e., two-level quantum systems and three-level quantum systems, demonstrate the effectiveness and superior performance of the proposed approach for quantum Hamiltonian identification.

Quantum Coherent Control of a Single Molecular-Polariton Rotation

We present a combined analytical and numerical study for coherent terahertz control of a single molecular polariton, formed by strongly coupling two rotational states of a molecule with a single-mode cavity. Compared to the bare molecules driven by a single terahertz pulse, the presence of a cavity strongly modifies the postpulse orientation of the polariton, making it difficult to obtain its maximal degree of orientation. To solve this challenging problem toward achieving complete quantum coherent control, we derive an analytical solution of a pulse-driven quantum Jaynes-Cummings model by expanding the wave function into entangled states and constructing an effective Hamiltonian. We utilize it to design a composite terahertz pulse and obtain the maximum degree of orientation of the polariton by exploiting photon blockade effects. This Letter offers a new strategy to study rotational dynamics in the strong-coupling regime and provides a method for complete quantum coherent control of a single molecular polariton. It, therefore, has direct applications in polariton chemistry and molecular polaritonics for exploring novel quantum optical phenomena.

Depthwise Convolution for Multi-Agent Communication With Enhanced Mean-Field Approximation

Multi-Agent settings remain a fundamental challenge in the reinforcement learning (RL) domain due to the partial observability and the lack of accurate real-time interactions across agents. In this article, we propose a new method based on local communication learning to tackle the multi-agent RL (MARL) challenge within a large number of agents coexisting. First, we design a new communication protocol that exploits the ability of depthwise convolution to efficiently extract local relations and learn local communication between neighboring agents. To facilitate multi-agent coordination, we explicitly learn the effect of joint actions by taking the policies of neighboring agents as inputs. Second, we introduce the mean-field approximation into our method to reduce the scale of agent interactions. To more effectively coordinate behaviors of neighboring agents, we enhance the mean-field approximation by a supervised policy rectification network (PRN) for rectifying real-time agent interactions and by a learnable compensation term for correcting the approximation bias. The proposed method enables efficient coordination as well as outperforms several baseline approaches on the adaptive traffic signal control (ATSC) task and the StarCraft II multi-agent challenge (SMAC).

Rare lysosomal disease registries: lessons learned over three decades of real-world evidence

Lysosomal storage disorders (LSD) are rare diseases, caused by inherited deficiencies of lysosomal enzymes/transporters, that affect 1 in 7000 to 1 in 8000 newborns. Individuals with LSDs face long diagnostic journeys during which debilitating and life-threatening events can occur. Clinical trials and classical descriptions of LSDs typically focus on common manifestations, which are not representative of the vast phenotypic heterogeneity encountered in real-world experience. Additionally, recognizing that there was a limited understanding of the natural history, disease progression, and real-world clinical outcomes of rare LSDs, a collaborative partnership was pioneered 30 years ago to address these gaps. The Rare Disease Registries (RDR) (for Gaucher, Fabry, Mucopolysaccharidosis type I, and Pompe), represent the largest observational database for these LSDs. Over the past thirty years, data from the RDRs have helped to inform scientific understanding and the development of comprehensive monitoring and treatment guidelines by creating a framework for data collection and establishing a standard of care, with an overarching goal to improve the quality of life of affected patients. Here, we highlight the history, process, and impact of the RDRs, and discuss the lessons learned and future directions.

Deep Reinforcement Learning With Quantum-Inspired Experience Replay

In this article, a novel training paradigm inspired by quantum computation is proposed for deep reinforcement learning (DRL) with experience replay. In contrast to the traditional experience replay mechanism in DRL, the proposed DRL with quantum-inspired experience replay (DRL-QER) adaptively chooses experiences from the replay buffer according to the complexity and the replayed times of each experience (also called transition), to achieve a balance between exploration and exploitation. In DRL-QER, transitions are first formulated in quantum representations and then the preparation operation and depreciation operation are performed on the transitions. In this process, the preparation operation reflects the relationship between the temporal-difference errors (TD-errors) and the importance of the experiences, while the depreciation operation is taken into account to ensure the diversity of the transitions. The experimental results on Atari 2600 games show that DRL-QER outperforms state-of-the-art algorithms, such as DRL-PER and DCRL on most of these games with improved training efficiency and is also applicable to such memory-based DRL approaches as double network and dueling network.

Fast prediction of blood flow in stenosed arteries using machine learning and immersed boundary-lattice Boltzmann method

A fast prediction of blood flow in stenosed arteries with a hybrid framework of machine learning and immersed boundary-lattice Boltzmann method (IB–LBM) is presented. The integrated framework incorporates the immersed boundary method for its excellent capability in handling complex boundaries, the multi-relaxation-time LBM for its efficient modelling for unsteady flows and the deep neural network (DNN) for its high efficiency in artificial learning. Specifically, the stenosed artery is modelled by a channel for two-dimensional (2D) cases or a tube for three-dimensional (3D) cases with a stenosis approximated by a fifth-order polynomial. An IB–LBM is adopted to obtain the training data for the DNN which is constructed to generate an approximate model for the fast flow prediction. In the DNN, the inputs are the characteristic parameters of the stenosis and fluid node coordinates, and the outputs are the mean velocity and pressure at each node. To characterise complex stenosis, a convolutional neural network (CNN) is built to extract the stenosis properties by using the data generated by the aforementioned polynomial. Both 2D and 3D cases (including 3D asymmetrical case) are constructed and examined to demonstrate the effectiveness of the proposed method. Once the DNN model is trained, the prediction efficiency of blood flow in stenosed arteries is much higher compared with the direct computational fluid dynamics simulations. The proposed method has a potential for applications in clinical diagnosis and treatment where the real-time modelling results are desired.

[Comparison of application effects of colonoscopy, fecal immunochemical test and a novel risk-adapted screening approach in colorectal cancer screening in Xuzhou population]

Objective: To compare the application effect of the colonoscopy, fecal immunochemical test (FIT) and novel risk-adapted screening approach in colorectal cancer screening in Xuzhou population. Methods: From May 2018 to April 2019, 4 280 subjects aged 50-74 were recruited from Gulou district, Yunlong district and Quanshan district of Xuzhou. They were randomly assigned to the colonoscopy group (n=863), FIT group (n=1 723) and novel risk-adapted screening approach group (n=1 694) according to the ratio of 1∶2∶2. For the novel risk-adapted screening approach group, after the risk assessment, high-risk subjects were invited to undergo colonoscopy and low-risk subjects were invited to undergo FIT examination. All FIT positive subjects were invited to undergo colonoscopy. Colonoscopy participation rate [(the number of colonoscopies completed/the number of colonoscopies invited to participate)×100%], detection rate of colorectal lesions [(the number of diagnosed patients/the number of colonoscopies completed)×100%], colonoscopy resource load (the number of colonoscopies completed/the number of diagnosed advanced tumors) and FIT resource load in each group were calculated and compared. Results: The age of all subjects was (61±6) years old, including 1 816 males (42.43%). There was no statistically significant difference in the socio-demographic characteristics of the subjects in different screening groups. The colonoscopy participation rate was 22.60% (195/863) in the colonoscopy group, 57.04% (77/135) in the FIT group, and 33.94% (149/439) in the novel risk-adapted screening approach group, respectively. The colonoscopy participation rate was higher in the FIT group than in the colonoscopy group and the novel risk-adapted screening approach group (P<0.001). The colonoscopy participation rate of novel risk-adapted screening group was significantly higher than the colonoscopy group (P<0.001). The detection rates of advanced tumors were 6.67% (13/195), 9.09% (7/77) and 8.72% (13/149), respectively, and the difference was not statistically significant (P>0.05). The colonoscopy resource load (95%CI) was 15 (13-17) in the colonoscopy group, 11 (9-14) in the FIT group and 11 (10-13) in the novel risk-adapted screening approach group, respectively. Among them, the colonoscopy resource load of high-risk individuals in the novel risk-adapted screening approach group was 12 (9-15). FIT resource loads (95%CI) were 207 (196-218) and 88 (83-94) in the FIT group and the novel risk-adapted screening approach group. Conclusion: The combined application of risk-adapted screening approach and FIT may have a good application effect in colorectal cancer screening.

Lifelong Incremental Reinforcement Learning With Online Bayesian Inference

A central capability of a long-lived reinforcement learning (RL) agent is to incrementally adapt its behavior as its environment changes and to incrementally build upon previous experiences to facilitate future learning in real-world scenarios. In this article, we propose lifelong incremental reinforcement learning (LLIRL), a new incremental algorithm for efficient lifelong adaptation to dynamic environments. We develop and maintain a library that contains an infinite mixture of parameterized environment models, which is equivalent to clustering environment parameters in a latent space. The prior distribution over the mixture is formulated as a Chinese restaurant process (CRP), which incrementally instantiates new environment models without any external information to signal environmental changes in advance. During lifelong learning, we employ the expectation-maximization (EM) algorithm with online Bayesian inference to update the mixture in a fully incremental manner. In EM, the E-step involves estimating the posterior expectation of environment-to-cluster assignments, whereas the M-step updates the environment parameters for future learning. This method allows for all environment models to be adapted as necessary, with new models instantiated for environmental changes and old models retrieved when previously seen environments are encountered again. Simulation experiments demonstrate that LLIRL outperforms relevant existing methods and enables effective incremental adaptation to various dynamic environments for lifelong learning.

Hybrid Filtering for a Class of Nonlinear Quantum Systems Subject to Classical Stochastic Disturbances

A hybrid quantum-classical filtering problem, where a qubit system is disturbed by a classical stochastic process, is investigated. The strategy is to model the classical disturbance by using an optical cavity. The relations between classical disturbances and the cavity analog system are analyzed. The dynamics of the enlarged quantum network system, which includes a qubit system and a cavity system, are derived. A stochastic master equation for the qubit-cavity hybrid system is given, based on which estimates for the state of the cavity system and the classical signal are obtained. The quantum-extended Kalman filter is employed to achieve efficient computation. The numerical results are presented to illustrate the effectiveness of our methods.

Application of an individualized nomogram in first-trimester screening for trisomy 21

OBJECTIVES

To develop and validate a nomogram based on fetal nuchal translucency thickness (NT) and ultrasonographic facial markers for screening for trisomy 21 in the first trimester of pregnancy.

METHODS

This was a retrospective case-control study using stored two-dimensional midsagittal fetal profile images captured at 11 + 0 to 13 + 6 weeks' gestation in singleton pregnancies. We included images from 302 trisomy-21 pregnancies and 322 euploid pregnancies. Cases were divided into a training set (200 euploid + 200 with trisomy 21) and a validation set (122 euploid + 102 with trisomy 21) at a ratio of approximately 2:1. For each, the maternal age, gestational age, fetal NT and karyotype were noted, and 12 ultrasonographic fetal facial markers were measured. The least absolute shrinkage and selection operator (LASSO) method and multivariable analysis were used to select automatically the discriminative markers. Logistic regression was used to develop a LASSO model, based on the selected markers, to screen for trisomy 21 in the first trimester of pregnancy. Furthermore, 60 of the 624 images were selected randomly as a retest set to evaluate the model's robustness. The predictive performance of screening for trisomy 21 of a model based on fetal NT and maternal age and of the LASSO model was assessed using the area under the receiver-operating-characteristics curve (AUC). A nomogram was developed as an individualized tool to predict patient-specific probability for trisomy 21, which is a more visual presentation of the LASSO model. The performance of the nomogram was assessed using the C-index and calibration curve.

RESULTS

Into the LASSO model were incorporated eight markers, including fetal NT, prenasal-thickness-to-nasal-bone-length ratio, facial profile line, frontomaxillary facial angle, frontonasal facial angle, mandibulomaxillary facial angle, maxilla-nasion-mandible angle and d2 (distance between the anterior edge of the prefrontal skin and the mandibulomaxillary line) (all P < 0.05). The AUCs of the LASSO model for screening for trisomy 21 were 0.983 (95% CI, 0.971-0.994) in the training set and 0.979 (95% CI, 0.966-0.993) in the validation set, and these were higher than the AUCs of all eight individual ultrasonographic markers included in the model. The AUC of the LASSO model in the retest set was 0.997 (95% CI, 0.990-1.000), indicating good robustness of the LASSO model. The AUC of the LASSO model was significantly higher than that of the model based on fetal NT and maternal age in both training and validation sets (P < 0.001 for both). The nomogram of the LASSO model showed good discrimination of trisomy 21, with C-indices of 0.983 in the training set and 0.981 in the validation set.

CONCLUSIONS

We present an individualized nomogram which incorporates fetal NT and a series of ultrasonographic facial profile markers selected by the LASSO method and multivariable analysis. This nomogram can potentially be utilized as a convenient and effective tool in screening for trisomy 21 in the first trimester of pregnancy. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Design of a Discrete-Time Fault-Tolerant Quantum Filter and Fault Detector

This paper solves the problem of discrete-time fault-tolerant quantum filtering for a class of laser-atom open quantum systems subject to the stochastic faults. We show that by using the discrete-time quantum measurements, optimal estimates of both the atomic observables and the classical fault process can be simultaneously determined in terms of recursive quantum stochastic difference equations. A dispersive interaction quantum system example is used to demonstrate the proposed filtering approach.

Defining heart disease risk for death in COVID-19 infection

BACKGROUND

Cardiovascular disease (CVD) was in common in coronavirus disease 2019 (COVID-19) patients and associated with unfavorable outcomes. We aimed to compare the clinical observations and outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients with or without CVD.

METHODS

Patients with laboratory-confirmed SARS-CoV-2 infection were clinically evaluated at Wuhan Seventh People's Hospital, Wuhan, China, from 23 January to 14 March 2020. Demographic data, laboratory findings, comorbidities, treatments and outcomes were collected and analyzed in COVID-19 patients with and without CVD.

RESULTS

Among 596 patients with COVID-19, 215 (36.1%) of them with CVD. Compared with patients without CVD, these patients were significantly older (66 vs. 52 years) and had higher proportion of men (52.5% vs. 43.8%). Complications in the course of disease were more common in patients with CVD, included acute respiratory distress syndrome (22.8% vs. 8.1%), malignant arrhythmias (3.7% vs. 1.0%) including ventricular tachycardia/ventricular fibrillation, acute coagulopathy(7.9% vs. 1.8%) and acute kidney injury (11.6% vs. 3.4%). The rate of glucocorticoid therapy (36.7% vs. 25.5%), Vitamin C (23.3% vs. 11.8%), mechanical ventilation (21.9% vs. 7.6%), intensive care unit admission (12.6% vs. 3.7%) and mortality (16.7% vs. 4.7%) were higher in patients with CVD (both P < 0.05). The multivariable Cox regression models showed that older age (≥65 years old) (HR 3.165, 95% CI 1.722-5.817) and patients with CVD (HR 2.166, 95% CI 1.189-3.948) were independent risk factors for death.

CONCLUSIONS

CVD are independent risk factors for COVID-19 patients. COVID-19 patients with CVD were more severe and had higher mortality rate, early intervention and vigilance should be taken.

[The value of spectral CT-based radiomics in preoperative prediction of lymph node metastasis of advanced gastric cancer]

Objective: To investigate the spectral CT-based radiomics in predicting preoperatively the lymph node metastasis (LNM) of advanced gastric cancer. Methods: From January 2014 to October 2018, the spectral CT imaging and clinical data of 196 gastric adenocarcinoma patients confirmed by pathology in the First Affiliated Hospital of Zhengzhou University were retrospectively enrolled (training set and test set were randomly divided according to the ratio of 1∶1). These 196 patients include143 males and 53 females, aged from 28 to 81 years, with an average age of (59±11) years, and were divided into nodular metastasis group and non-metastasis group according to clinicopathological data. The spectral parameters were measured and calculated, and the CT-reported lymph node (LN) status from CT images were obtained. 273 radiomics features were extracted from the dual-phases CT images in different energy level (40, 65 and 100 keV) to build the radiomics signature respectively. Univariate analysis was used to compare the differences of spectral parameters and radiomics features between two groups, and then the significant indicators were put into multivariable logistic regression analysis to construct combined prediction model and radiomics nomogram. In addition, the performance of prediction model in training and test set were measured using the receiver operating characteristics (ROC) curves and were compared using DeLong test. Results: Both in training set and in test set, the iodine concentration (IC) of tumor in venous phase (VP) in nodular metastasis group were higher than that in non-metastasis group [training set: 22.98 (100 mg/L)>20.31 (100 mg/L), P=0.086; test set: 25.14 (100 mg/L)>21.07 (100 mg/L), P=0.009]. The CT-reported LN status showed significant differences between the two group (P<0.001, P=0.001). The radiomics signatures 40 keV-arterial phase, 65 keV-venous phase, IC-VP of tumor and CT-reported LN status were independent indicators for prediction of preoperative LNM of advanced gastric cancer in combined prediction model (P<0.05). The radiomics nomogram predicated LNM with an area under curve (AUC) and 95% confidence interval (CI) of 0.822 (0.739-0.906) in training set and 0.819(0.732-0.906) in test set, and there were no significant differences in AUC between two sets (P>0.05). Conclusions: The spectral CT-based radiomics can be used to quantitatively predict the LNM of advanced gastric cancer preoperatively.

Learning-Based Quantum Robust Control: Algorithm, Applications, and Experiments

Robust control design for quantum systems has been recognized as a key task in quantum information technology, molecular chemistry, and atomic physics. In this paper, an improved differential evolution algorithm, referred to as multiple-samples and mixed-strategy DE (msMS_DE), is proposed to search robust fields for various quantum control problems. In msMS_DE, multiple samples are used for fitness evaluation and a mixed strategy is employed for the mutation operation. In particular, the msMS_DE algorithm is applied to the control problems of: 1) open inhomogeneous quantum ensembles and 2) the consensus goal of a quantum network with uncertainties. Numerical results are presented to demonstrate the excellent performance of the improved machine learning algorithm for these two classes of quantum robust control problems. Furthermore, msMS_DE is experimentally implemented on femtosecond (fs) laser control applications to optimize two-photon absorption and control fragmentation of the molecule CH₂BrI. The experimental results demonstrate the excellent performance of msMS_DE in searching for effective fs laser pulses for various tasks.

[Population's acceptance and attitude toward a novel fecal immunochemical test for colorectal cancer screening: a multi-center survey in China]

Objective: To investigate the acceptance and attitude toward a novel fecal immunochemical test (FIT) in colorectal cancer screening among populations in China. Methods: From May 2018 to May 2019, 2 474 people aged 50-74 years were recruited from five provinces of China (Zhejiang, Anhui, Jiangsu, Hunan and Yunnan). The general demographic characteristics, acceptance of the new FIT technology and operational difficulties through the whole screening process were obtained through questionnaire survey. Multivariate logistic regression model was used to analyze the factors related to difficulties encountered in sampling stool, reading and uploading results. Results: The subjects were (60.0±6.4) years old, and female, high school of above educated, unemployed/retired/other, married and with medical insurance status of "new rural cooperative medical care (NRCMC)" accounted for 61.7% (1 526), 29.0%(718), 34.3% (849), 92.7% (2 293) and 31.3%(775), respectively. The population's acceptance of the FIT technology was 94.8%. In the process of FIT screening, the percentage of occurred difficulties in sampling stool, reading and uploading results were 33.1% (819), 46.4% (1 147) and 62.9% (1 557), respectively. The main difficulties were the uncertainty about whether the sampling operation was standard (28.0%), the inability to accurately judge the result displayed (32.5%) and the need for help without using a smartphone (44.2%). The results of multivariate logistic regression model analysis showed that people aged 65-74 years old and with medical insurance status of "NRCMC" were more likely to encounter difficulties in sampling, and those who were unemployed/retired/other and living with 3 or more family members were less likely to encounter difficulties in sampling. Those aged 65-74 years old, farmers or migrant workers, and those with "NRCMC" were more likely to encounter difficulties in readingresults, and those with 3 or more family members were less likely to encounter difficulties in reading result. Those with "NRCMC" were more likely to encounter difficulties in uploading results, and those with education level of high school or above, living with more than 3 family members were less likely to encounter difficulties in uploading results. Conclusion: The acceptance of the new FIT technology is relatively high among the subjects. Age, education level, occupation, number of family members living together and medical insurance status might be related to difficulties encountered in sampling stool, reading and uploading results, and it can be further strengthened in terms of the technology and characteristics of sub-populations.

Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study

BACKGROUND

Preoperative evaluation of the number of lymph node metastasis (LNM) is the basis of individual treatment of locally advanced gastric cancer (LAGC). However, the routinely used preoperative determination method is not accurate enough.

PATIENTS AND METHODS

We enrolled 730 LAGC patients from five centers in China and one center in Italy, and divided them into one primary cohort, three external validation cohorts, and one international validation cohort. A deep learning radiomic nomogram (DLRN) was built based on the images from multiphase computed tomography (CT) for preoperatively determining the number of LNM in LAGC. We comprehensively tested the DLRN and compared it with three state-of-the-art methods. Moreover, we investigated the value of the DLRN in survival analysis.

RESULTS

The DLRN showed good discrimination of the number of LNM on all cohorts [overall C-indexes (95% confidence interval): 0.821 (0.785-0.858) in the primary cohort, 0.797 (0.771-0.823) in the external validation cohorts, and 0.822 (0.756-0.887) in the international validation cohort]. The nomogram performed significantly better than the routinely used clinical N stages, tumor size, and clinical model (P < 0.05). Besides, DLRN was significantly associated with the overall survival of LAGC patients (n = 271).

CONCLUSION

A deep learning-based radiomic nomogram had good predictive value for LNM in LAGC. In staging-oriented treatment of gastric cancer, this preoperative nomogram could provide baseline information for individual treatment of LAGC.

Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer

Background

Occult peritoneal metastasis (PM) in advanced gastric cancer (AGC) patients is highly possible to be missed on computed tomography (CT) images. Patients with occult PMs are subject to late detection or even improper surgical treatment. We therefore aimed to develop a radiomic nomogram to preoperatively identify occult PMs in AGC patients.

Patients and methods

A total of 554 AGC patients from 4 centers were divided into 1 training, 1 internal validation, and 2 external validation cohorts. All patients’ PM status was firstly diagnosed as negative by CT, but later confirmed by laparoscopy (PM-positive n = 122, PM-negative n = 432). Radiomic signatures reflecting phenotypes of the primary tumor (RS1) and peritoneum region (RS2) were built as predictors of PM from 266 quantitative image features. Individualized nomograms of PM status incorporating RS1, RS2, or clinical factors were developed and evaluated regarding prediction ability.

Results

RS1, RS2, and Lauren type were significant predictors of occult PM (all P < 0.05). A nomogram of these three factors demonstrated better diagnostic accuracy than the model with RS1, RS2, or clinical factors alone (all net reclassification improvement P < 0.05). The area under curve yielded was 0.958 [95% confidence interval (CI) 0.923–0.993], 0.941 (95% CI 0.904–0.977), 0.928 (95% CI 0.886–0.971), and 0.920 (95% CI 0.862–0.978) for the training, internal, and two external validation cohorts, respectively. Stratification analysis showed that this nomogram had potential generalization ability.

Conclusion

CT phenotypes of both primary tumor and nearby peritoneum are significantly associated with occult PM status. A nomogram of these CT phenotypes and Lauren type has an excellent prediction ability of occult PM, and may have significant clinical implications on early detection of occult PM for AGC.

Attosecond all-optical control and visualization of quantum interference between degenerate magnetic states by circularly polarized pulses

Controlling coherence and interference of quantum states is one of the central goals in quantum science. Different from energetically discrete quantum states, however, it remains a demanding task to visualize coherent properties of degenerate states (e.g., magnetic sublevels). It becomes further inaccessible in the absence of an external perturbation (e.g., Zeeman effect). Here, we present a theoretical analysis of all-optical control of degenerate magnetic states in the molecular hydrogen ion, $ {\rm H}_2^ + $H2+, by using two time-delayed co- and counterrotating circularly polarized attosecond extreme-ultraviolet (XUV) pulses. We perform accurate simulations to examine this model by solving the three-dimensional time-dependent Schrödinger equation. A counterintuitive phenomenon of quantum interference between degenerate magnetic sublevels appears in the time-dependent electronic probability density, which is observable by using x-ray-induced transient angular and energy-resolved photoelectron spectra. This work provides an insight into quantum interference of electron dynamics inside molecules at the quantum degeneracy level.

[A study on the targeted nanoparticles of isosorbide mononitrate on reducing the levels of inflammatory factors in rabbit models of rhinosinusitis]

Objective: To investigate the effect of isosorbide mononitrate (ISMN) targeted nanoparticles on inflammatory factors of sinusitis by establishing a rabbit model of rhinosinusitis. Methods: Thirty healthy rabbits, male and female, weighing 2.5-3.5 kg, were randomly divided into 6 groups with 5 rabbits in each group. Group A was the control group. The model of rabbit sinusitis was established in group B to F, and CT was used to confirm the model was successful. After placing tubes into the maxillary sinus in the group C to F, saline, 45 mg/ml ISMN, 45 mg/ml ISMN nanoparticles and 45 mg/ml ISMN targeted nanoparticles were used to wash the maxillary sinus, respectively. Blood samples were collected from the ear vein of rabbits on day 7, 14, 21, 28, 35 and 42 after modeling respectively. Changes in the expression levels of inflammatory factors in rabbits during the modeling process and after drug washing were detected by ELISA. After the drug intervention, the maxillary sinus mucosa was taken for real-time quantitative PCR to detect the changes in the mRNA level of inflammatory factors. SPSS 22.0 software was used to process the data. Results: Rabbit model of sinusitis was successfully established. ELISA showed that after the action of ISMN targeted nanoparticles of 1 week (42th day after modeling), the levels of (interleukin, IL) 4, IL-8, IL-17A and interferon γ (IFN-γ) in the blood were lower compared with that of 35th day after modeling, the difference was statistically significant (5.57±1.20 vs 19.73±0.68, 66.41±11.87 vs 154.68±13.13, 17.96±1.87 vs 28.23±0.80, 53.56±5.66 vs 111.93±7.29, all P<0.05). Compared with the ISMN nanoparticles and ISMN, the ISMN targeted nanoparticles reduced the levels of IL-4, IL-8, IL-17A and IFN-γ more obviously, the differences were statistically significant (13.26±1.43 vs 8.81±1.33 vs 7.14±2.16, 89.47±17.80 vs 41.07±7.77 vs 15.84±3.72, 10.28±2.07 vs 3.06±1.62 vs 1.82±0.90, 62.16±6.18 vs 35.12±4.62 vs 27.89±10.18, all P<0.05). Real-Time PCR showed that after the flushing of ISMN targeted nanoparticles, the levels of IL-4, IL-8, IL-17A and IFN-γ mRNA were lowest compared with that of the model group, ISMN nanoparticles and ISMN group. Conclusion: ISMN targeted nanoparticles can reduce the level of inflammatory factors in rabbit sinusitis model.

Nanostrain sensitivity in a wire torsion experiment

The feasibility of a thin-wire torsion stress-strain experiment with nanostrain sensitivity is demonstrated. A gauge length of 50 m was made possible by using The Monument, London, thereby restoring it to its original purpose as a scientific instrument. A wire of 150 μm diameter was studied using the load-unload method, and data were recorded in the elastic regime and through the elastic-plastic transition. Analysis of this preliminary experiment suggested some necessary improvements to the equipment and methods. Progress towards definitive experiments is described together with difficulties still to be overcome.

Vanishing and Revival of Resonance Raman Scattering

The possibility to manipulate quantum coherence and interference, apart from its fundamental interest in quantum mechanics, is essential for controlling nonlinear optical processes such as high harmonic generation, multiphoton absorption, and stimulated Raman scattering. We show, analytically and numerically, how a nonlinear optical process via resonance Raman scattering (RRS) can be manipulated in a four-level double-Λ system by using pulsed laser fields. We find that two simultaneously excited RRS paths involved in the system can generate an ultimately destructive interference in the broad-bandwidth-limit regime. This, in turn, reduces the four-level system to an equivalent three-level system in a V configuration capable of naturally vanishing RRS effects. We further show that this counterintuitive phenomenon, i.e., the RRS vanishing, can be prevented by transferring a modulated phase of the laser pulse to the system at resonance frequencies. This work demonstrates a clear signature of both quantum destructive and constructive interference by actively controlling resonant multiphoton processes in multilevel quantum systems, and it therefore has potential applications in nonlinear optics, quantum control, and quantum information science.

Characterization of entangling properties of quantum measurement via two-mode quantum detector tomography using coherent state probes

Entangled measurement is a crucial tool in quantum technology. We propose a new entanglement measure of multi-mode detection, which estimates the amount of entanglement that can be created in a measurement. To illustrate the proposed measure, we perform quantum tomography of a two-mode detector that is comprised of two superconducting nanowire single photon detectors. Our method utilizes coherent states as probe states, which can be easily prepared with accuracy. Our work shows that a separable state such as a coherent state is enough to characterize a potentially entangled detector. We investigate the entangling capability of the detector in various settings. Our proposed measure verifies that the detector makes an entangled measurement under certain conditions, and reveals the nature of the entangling properties of the detector. Since the precise characterization of a detector is essential for applications in quantum information technology, the experimental reconstruction of detector properties along with the proposed measure will be key features in future quantum information processing.

Alprostadil attenuates LPS-induced cardiomyocyte injury by inhibiting the Wnt5a/JNK/NF-κB pathway

Background

Clinical research has demonstrated that alprostadil has an anti-inflammatory effect; however, to date, its molecular mechanisms remain unclear. This study aimed to examine the anti-inflammatory activity and related mechanisms of alprostadil in lipopolysaccharide (LPS)-treated H9c2 cells.

Methods

Cell morphology was observed under an inverted light microscope, while cell viability was assessed with the 3‑(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide (MTT) assay. Enzyme-linked immunosorbent assays (ELISA) were conducted to study biochemical indicators of cellular damage, such as released lactate dehydrase (LDH) and troponin, and inflammatory cytokine levels including interleukin-1β (IL-1β), IL-6, IL-17, and tumor necrosis factor-α (TNF-α). The mRNA expression levels of Wnt5a, c‑jun N‑terminal kinase (JNK), and nuclear factor kappa B (NF-κB) were further investigated by real-time quantitative polymerase chain reaction (RT-PCR). The effects of alprostadil on the Wnt5a/JNK/NF-κB pathway in H9c2 cells was examined by Western blotting.

Results

Alprostadil increased the cell viability of LPS-stimulated H9c2 cells, reduced LDH and troponin production, and attenuated IL-1β, IL-6, IL-17, and TNF-α secretion. Moreover, alprostadil reduced the mRNA expression of Wnt5a, JNK, and NF-κB and decreased the expression of Wnt5a, NF-κB, and the ratio of p‑JNK/JNK in H9c2 cells treated with LPS. The siWnt5a or JNK inhibitor SP600125 significantly augmented the inhibitory effects of alprostadil on the Wnt5a/JNK/NF-κB pathway.

Conclusion

Our results show that alprostadil has anti-inflammatory effects and could attenuate LPS-induced injury in H9c2 cardiomyocytes via the Wnt5a/JNK/NF-κB pathway.

[A novel nanoparticle in treatment of staphylococcus aureus and pseudomonas aeruginosa biofilms]

Objective:To investigate CPC-nanoparticles of low concentrations in treatment of staphylococcus aureus and pseudomonas aeruginosa biofilms in vitro. Method: We established specific biofilms of staphylococcus aureus ATCC 25923 and pseudomonas aeruginosa ATCC 15692, and prepared CPC-nanoparticles and CPC micelle solutions of low concentrations(0.010%, 0.025% and 0.050%). AlamarBlue was used to test the viability of both planktonic staphylococcus aureus and pseudomonas aeruginosa and their biofilms after treatment for 5 minutes and 2 hours respectively in the bactericidal efficacy study.The interaction between CPC-nanoparticles and staphylococcus aureus and pseudomonas aeruginosa biofilms was observed by confocal laser scanning microscope(CLSM). Result: 0.010%, 0.025% and 0.050% CPC-nanoparticles and CPC-micelle solutions had significant bactericidal effect on planktonic staphylococcus aureus and pseudomonas aeruginosa after fiveminute exposure(P<0.05), and staphylococcus aureus and pseudomonas aeruginosa biofilms after both five-minute and two-hour treatments(P<0.05). In CLSM study, the size of staphylococcus aureus biofilms decreased, while dead bacteria of pseudomonas aeruginosa biofilms increased after two-hour treatment. Conclusion: CPC-nanoparticles had significant bactericidal effects on staphylococcus aureus and pseudomonas aeruginosa biofilms, which could be used in treatment of CRS.

[Fabrication and verification of three-dimensional prototyped models of nasal cavities and paranasal sinuses]

Objective: To make three-dimensional prototyped models of nasal cavities and paranasal sinuses for endoscopic skills training and teaching and to verify and evaluate the fidelity of the models. Methods: Imaging data of a patient with nasal septum deviation was selected for prototyping the nasal cavities model, and the imaging data of a patient with chronic rhinosinusitis who underwent the endoscopic sinus surgery 4 months ago was selected for the paranasal sinuses model. Both patients came from the Department of Rhinology, the First Affiliated Hospital of Zhengzhou University. The models were printed by the desktop-level printer based on the fused deposition modeling (FDM). The evaluations of anatomical structures and prespecified tasks on the simulators were completed by 5 residents and 5 experts of Rhinology.The models were evaluated by survey ratings based on a 5-point Likert scale. The average time to complete each task was calculated.Mann-Whitney nonparametric test was used to assess the differences between experts and residents groups. The statistical significance level was set at P<0.05. Results: All the components of the two models were prototyped in 24 h. For the nasal cavities model, the scores of each anatomical structure were more than 4.0. As to the paranasal sinuses model, except for the frontal sinus (3.6), the scores of each anatomical structure were more than 4.0. All the participants who performed the prespecified tasks evaluated the models well (>4.0). With the nasal cavity simulator, the experts completed the tasks (nasal cavity and nasopharyngeal endoscopy, removal of nasal foreign body, nasal packing, pledget insertion), and the other tasks (the nasal cavity and nasopharyngeal endoscopy, identification of ostiums of paranasal sinuses, maxillary sinus debridement with 70° nasal endoscopy) were completed with paranasal sinus model. The average time to complete each task by experts group and residents group (M (P(25), P(75))) were 10.3 (10.0, 10.7) s vs 17.1 (14.6, 20.7) s, 2.1 (2.0, 2.2) s vs 3.4 (2.6, 4.7) s, 30.1 (27.8, 32.8) s vs 59.2 (52.0, 65.4) s, 54.8 (50.1, 63.2) s vs 92.2 (79.3, 106.9) s, 9.3 (8.7, 9.9) s vs 14.5 (13.3, 14.9) s, 20.1 (19.3, 21.2) s vs 41.9 (35.5, 45.1) s, 31.1 (28.8, 35.2) s vs 52.8 (47.7, 56.3) s, respectively, and the average time to complete each task was shorter for the experts group than for the residents (U value was 0, 1.000, 0, 0, 0, 0, 0, respectively, all P<0.05). Conclusion: The models of nasal cavities and paranasal sinuses made by our desktop-level FDM 3D-printer are useful for endoscopic training skills.

[Retrospective analysis of 273 benign fibro osseous lesionsin the sinus and maxillofacial region]

Objective:To compare and analyze the incidence, clinical features ,imaging features, the preoperative serum alkalinosis phosphatase (ALP) and recurrence rates of osteoma, ossifying fibroma (OF), and fibrous dysplasia (FD) in sinus and maxillofacial benign fibrosis (FOLS). Method: The 273 cases of FOLS with paranasal sinus and maxillofacial region in our hospital, among which there were 153 cases of osteoma, 44 cases of OF, and 76 cases of FD, were reviewed retrospectively and were follow-up for 6 months to 5 years. The incidence, gender, age, lesion location, preoperative serum ALP levels and recurrence rate of different lesions were analyzed and compared. Result:Most Osteoma occurred in paranasal sinus (72.5%),among which the most common part was ethmoid sinus (50.3%) and followed by the frontal sinus (22.1%),the ratio of female to male was 2:1,and there were 27 cases (17.7%) accompanied by sinusitis.None of the patients relapsed. Most OF occurred in the maxillofaCIal bone (65.9%) and most of the lesions were monostotic (75.0%).For those occurred in the paranasal sinuses,the ethmoid sinus (27.3%)were the most common part than others. The ratio of femal to male was almost 1∶1 and there were 12 cases (27.3%) accompanied by sinusitis. The recurrence rate of the OF was 13.6%.Most FD occured in maxillofacial bone (64.5%) were the monostotic form(64.5%) ,and in which the most common part was the maxilla (50.0%). While,most of those occurred in the paranasal sinuses were the polyostotic ones,and sphenoid sinus (34.2%) were the most common parts.The ratio of femal to male was almost 1∶1 and there were 10 cases (13.2%) accompanied by sinusitis. 12 cases relapsed (14.5%),and there were 9 cases (75.0%) acceptted the first surgery before the age of 20.The average age of onset among Osteoma［(40.7±14.55)y］,OF［(28.0±17.9)y］ and FD［(20.32±15.2)y］ were significant different (P<0.01).The preoperative serum ALP content among Osteoma［(68.3±24.1)U/L, OF(130.1±107.0) U/L and FD were (127.7±78.7) U/L significant different(P<0.01).Nevertheless,there were no differences of the preoperative serum ALP content between the monostotic and polyostotic in OF cases［(117.2±92.6) U/L,(168.7±140.1) U/L respectively,( P>0.01)］,as well as that between FD cases［(122.2±82.9) U/L, (137.7±70.7) U/L (P>0.01)］. Conclusion:The differences of the inCIdence,the age of onset,leision location,preoperative serum ALP content and reccurence rate among Osteoma,OF and FD are statistically significant.Surgery is the most effective treatment at present.

Self-Paced Prioritized Curriculum Learning With Coverage Penalty in Deep Reinforcement Learning

In this paper, a new training paradigm is proposed for deep reinforcement learning using self-paced prioritized curriculum learning with coverage penalty. The proposed deep curriculum reinforcement learning (DCRL) takes the most advantage of experience replay by adaptively selecting appropriate transitions from replay memory based on the complexity of each transition. The criteria of complexity in DCRL consist of self-paced priority as well as coverage penalty. The self-paced priority reflects the relationship between the temporal-difference error and the difficulty of the current curriculum for sample efficiency. The coverage penalty is taken into account for sample diversity. With comparison to deep Q network (DQN) and prioritized experience replay (PER) methods, the DCRL algorithm is evaluated on Atari 2600 games, and the experimental results show that DCRL outperforms DQN and PER on most of these games. More results further show that the proposed curriculum training paradigm of DCRL is also applicable and effective for other memory-based deep reinforcement learning approaches, such as double DQN and dueling network. All the experimental results demonstrate that DCRL can achieve improved training efficiency and robustness for deep reinforcement learning.

Optimal and robust control of quantum state transfer by shaping the spectral phase of ultrafast laser pulses

Achieving fast and efficient quantum state transfer is a fundamental task in physics, chemistry and quantum information science. However, the successful implementation of the perfect quantum state transfer also requires robustness under practically inevitable perturbative defects. Here, we demonstrate how an optimal and robust quantum state transfer can be achieved by shaping the spectral phase of an ultrafast laser pulse in the framework of frequency domain quantum optimal control theory. Our numerical simulations of the single dibenzoterrylene molecule as well as in atomic rubidium show that optimal and robust quantum state transfer via spectral phase modulated laser pulses can be achieved by incorporating a filtering function of the frequency into the optimization algorithm, which in turn has potential applications for ultrafast robust control of photochemical reactions.