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Radio resource slicing is critical to customize service provisioning in fifth-generation (5G) uplink radio access networks (RANs). Using drone-small-cells (DSCs) as aerial support for terrestrial base stations can enhance the flexibility for resource provisioning in response to traffic distribution variations. In this paper, we study a multi-DSC-assisted radio resource slicing problem for 5G uplink RANs, with the objective of minimizing the total uplink resource consumption under differentiated quality-of-service (QoS) constraints for both human-type and machine-type communication services. We begin with an interference-aware graph model to formulate the joint DSC three-dimension (3D) placement and device-DSC association problem for uplink radio resource slicing and prove that the proposed problem is NP-hard. A complexity-adjustable problem approximation is presented via screening candidate DSC deployment positions, which incorporates flight height adaptation to balance the uplink communication coverage and resource utilization. A lightweight approximation using a fixed DSC flight altitude is also provided with reduced complexity. For mathematical traceability, the DSC placement and device-DSC associations in each approximation are transformed as a special weight clique problem. An upgraded clique algorithm is then developed to determine how to deploy DSCs for a given number of DSCs. Simulation results demonstrate the proposed scheme's effectiveness in terms of resource utilization, network coverage, and drone dispatching cost.

Non-orthogonal multiple access (NOMA) is an efficient approach that can improve spectrum utilization and support massive connectivity for next-generation wireless networks. However, over a wireless channel, the superimposed NOMA signals are highly susceptible to eavesdropping, potentially leading to severe leakage of confidential information. In this article, we unleash the potential of network interference and exploit it constructively to enhance physical-layer security in NOMA networks. Particularly, three different types of network interference, including artificial noise, specifically-designed jamming signals, and inter-user interference, are well engineered to intentionally reduce information leakage while mitigating the effect on signal reception quality of legitimate users, thereby significantly enhancing the transmission security of NOMA. Furthermore, we propose interference engineering strategies for more advanced full-duplex NOMA, intelligent reflecting surface NOMA, cognitive radio NOMA, and multi-cell NOMA networks, and discuss several open research problems and challenges, which could inspire innovative interference engineering designs for secure NOMA communications., Accepted by IEEE Network as an open call article

In this article, a learning-based proactive resource sharing scheme is proposed for the next-generation core communication networks, where the available forwarding resources at a switch are proactively allocated to the traffic flows in order to maximize the efficiency of resource utilization with delay satisfaction. The resource sharing scheme consists of two joint modules, estimation of resource demands and allocation of available resources. For service provisioning, resource demand of each traffic flow is estimated based on the predicted packet arrival rate. Considering the distinct features of each traffic flow, a linear regression algorithm is developed for resource demand estimation, utilizing the mapping relation between traffic flow status and required resources, upon which a network switch makes decision on allocating available resources for delay satisfaction and efficient resource utilization. To learn the implicit relation between the allocated resources and delay, a multi-armed bandit learning-based resource allocation scheme is proposed, which enables fast resource allocation adjustment to traffic arrival dynamics. The proposed algorithm is proved to be asymptotically approaching the optimal strategy, with polynomial time complexity. Extensive simulation results are presented to demonstrate the effectiveness of the proposed resource sharing scheme in terms of delay satisfaction, traffic adaptiveness, and resource allocation gain.

A Gram-stain-negative, aerobic, rod-shaped bacteria strain, named YIM B01951

The success of an interactive method of portfolio optimization depends on not only the acquisition of a satisfactory solution for a decision maker (DM) but also the amount of cognitive effort the DM affords during interactions. For the purposes of implementing a successful interactive method, however, it is often unrealistic to assume a participating DM with a high cognitive capability. Confronted with the challenge of how a cognition-limited DM is able to remove uncertainties in his/her preference feedback and make more confident judgments during interactions so that a stable, satisfactory solution is found as early as possible, we conduct innovative investigations with respect to DM preference articulation. First, in addition to the objective functions of an optimization model, which are used in the present interactive methods as the only references for DM preference articulation, we aim to define the candidate auxiliary factors involved in the portfolio optimization model and recognize those that are closely correlated with the objective functions (called primary factors) and that can help a DM differentiate solutions if he/she is uncertain in making a judgment according to the primary factors. Next, in order to define an artificial DM to simulate the DM’s decision behaviors being assisted by auxiliary factors in the interactive process, based on a deliberately formulated value function representing the DM’s true preferences on portfolios, we introduce the decision strength (DS) to measure the DM’s capability of making a selection between two solutions. Thus, the DM’s decision behaviors are reflected by his/her DS changing with an increasing number of auxiliary factors incorporated into the primary factors. To verify the proposed interactive method for portfolio optimization, we conduct extensive comparative experiments under the participation of three types of artificial DMs with different levels of cognitive capabilities, comparative experimental results with and without auxiliary factors with respect to four performance measures are presented.

A picture is worth a thousand words. User-generated photos (UGPs) are increasingly accompanying online reviews of hotels. This article draws on media richness theory to estimate the effects of UGPs on hotel reviews’ helpfulness. Based on a sample of 1,159,590 valid reviews with 464,316 photos, we utilized an integrated analytical model incorporating both econometric analyses and image-processing techniques. The results show that reviews accompanied by UGPs are generally rated as more helpful than those with textual content only. Furthermore, photos showing guestroom objects were rated as more helpful than those showing food & beverages. Finally, the positive effects of UGPs on review helpfulness were especially prominent for hotels with lower prices and negative reviews. This study adds new insights to the online review literature and advances the methodological approach in analyzing unstructured user-generated content. This study provides important implications for hotel managers and online booking platforms regarding UGP management.

To develop and validate a radiomics signature based on magnetic resonance imaging (MRI) from multicenter datasets for preoperative prediction of pathologic response to neoadjuvant chemotherapy (NAC) in patients with osteosarcoma. We retrospectively enrolled 102 patients with histologically confirmed osteosarcoma who received chemotherapy before treatment from 4 hospitals (68 in the primary cohort and 34 in the external validation cohort). Quantitative imaging features were extracted from contrast-enhanced fat-suppressed T1-weighted images (CE FS T1WI). Four classification methods, i.e., the least absolute shrinkage and selection operator logistic regression (LASSO-LR), support vector machine (SVM), Gaussian process (GP), and Naive Bayes (NB) algorithm, were compared for feature selection and radiomics signature construction. The predictive performance of the radiomics signatures was assessed with the area under receiver operating characteristics curve (AUC), calibration curve, and decision curve analysis (DCA). Thirteen radiomics features selected based on the LASSO-LR classifier were adopted to construct the radiomics signature, which was significantly associated with the pathologic response. The prediction model achieved the best performance between good and poor responders with an AUC of 0.882 (95% CI, 0.837−0.918) in the primary cohort. Calibration curves showed good agreement. Similarly, findings were validated in the external validation cohort with good performance (AUC, 0.842 [95% CI, 0.793−0.883]) and good calibration. DCA analysis confirmed the clinical utility of the selected radiomics signature. The constructed CE FS T1WI-radiomics signature with excellent performance could provide a potential tool to predict pathologic response to NAC in patients with osteosarcoma. • The radiomics signature based on multicenter contrast-enhanced MRI was useful to predict response to NAC. • The prediction model obtained with the LASSO-LR classifier achieved the best performance. • The baseline clinical characteristics were not associated with response to NAC.

Metformin is a first-line drug for the clinical treatment of type 2 diabetes; however, it always leads to gastrointestinal tolerance, low bioavailability, short half-life, etc. Liposome acts as an excellent delivery system that could reduce drug side effects and promote bioavailability. Hyodeoxycholic acid, a cholesterol-like structure, can regulate glucose homeostasis and reduce the blood glucose levels. As an anti-diabetic active ingredient, hyodeoxycholic acid modifies liposomes to make it overcome the disadvantages of metformin as well as enhance the hypoglycemic effect. By adapting the thin-film dispersion method, three types of liposomes with different proportions of hyodeoxycholic acid and metformin were prepared (HDCA:ME-(0.5:1)-Lips, HDCA:ME-(1:1)-Lips, and HDCA:ME-(2:1)-Lips). Further, the liposomes were characterized, and the anti-type 2 diabetes activity of liposomes was evaluated. The results from this study indicated that three types of liposomes exhibited different characteristics—Excessive hyodeoxycholic acid decreased encapsulation efficiency and drug loading. In the in vivo experiments, liposomes could reduce the fasting blood glucose levels, improve glucose tolerance, regulate oxidative stress markers and protect liver tissue in type 2 diabetic mice. These results indicated that HDCA:ME-(1:1)-Lips was the most effective among the three types of liposomes prepared and showed better effects than metformin. Hyodeoxycholic acid can enhance the hypoglycemic effect of metformin and play a suitable role as an excipient in the liposome.

Meningococcal meningitis caused by Neisseria meningitidis is a reportable infectious disease in China, due to the high incidence of meningitis in the era before the availability of vaccines. The disease incidence was markedly reduced after meningococcal vaccination was introduced in the 1980s. Currently, there are polysaccharide, conjugate, and combined vaccine formulations against meningococcal meningitis in the Chinese market, almost all of which are produced by domestic manufacturers. It is necessary to further enhance national meningococcal surveillance to improve the level of prevention and control of meningococcus. However, the immune efficacy and persistence of immunity of vaccines should be monitored. More importantly, additional investments should be made to develop serogroup B meningococcal vaccines.

The past decade has seen increased research interest in studying bicontinuous structures formed via colloidal self-assembly due to their many useful applications. A new type of colloidal gel, solvent segregation-driven gel (SeedGel), has been recently demonstrated as an effective approach to arrest bicontinuous structures with unique and intriguing properties, such as thermoreversibility, structural reproducibility, and sensitive temperature response. Here, using a model system with silica particles in the 2,6-lutidine/water binary solvent, we investigate the factors controlling the domain size of a SeedGel system by varying the particle concentration, solvent ratio, and quenching protocol. A phase diagram is identified to produce SeedGels for this model system. Our results indicate that by adjusting the sample composition, it is possible to realize bicontinuous domains with well-controlled repeating distances (periodicities). In addition, the effect of quenching rate on the domain size is systematically investigated, showing that it is a very sensitive parameter to control domain sizes. By further heating SeedGel up into the spinodal region, the structure evolution under high temperatures is also investigated and discussed. These results provide important insights into how to control bicontinuous structures in SeedGel systems.

PurposeMobile health (mHealth) services are considered an important means of relieving the problems of the aging population. The efficiency of mHealth services can be enhanced by engaging more elderly users and guaranteeing their continued use. However, limited attention has been directed toward investigating elderly users' continuance intention regarding mHealth services. The purpose of this paper is to explain elderly users' continuance intention by investigating the contingent role of technology anxiety and health anxiety on affective trust and cognitive trust.Design/methodology/approachSurvey data were collected from 232 elderly users to verify the research model and hypotheses based on structural equation modeling (SEM).FindingsThis study revealed that both affective and cognitive trust enhance elderly users' continuance intention regarding their use of mHealth services. Health anxiety strengthens the effect of cognitive trust but weakens the effect of affective trust with regard to continuance intention. Furthermore, technology anxiety strengthens the effect of affective trust but not the effect of cognitive trust with regard to continuance intention.Originality/valueThis study is one of the first to examine elderly users' continuance intention regarding mHealth services use from the perspective of affective and cognitive trust, thus enriching the extant literature on the use of mHealth services. Additionally, this study sheds light on the contingent effects of technology anxiety and health anxiety on affective and cognitive trust, which have been neglected by previous research.

Outlier detection is a key data analysis technique that aims to find unusual data objects in a data set. It has been widely used in varied areas, including communication networks, finance, medicine, environmental studies, etc. Many applications in these areas involve categorical data. For example, the data set used in the application of intrusion detection normally includes a group of captured packets, which tend to have categorical attributes such as “protocol”. Although there are many outlier detection algorithms for applications involving numerical data, only a few existing schemes can handle categorical data. And the schemes designed for categorical data seriously suffer from two problems: low detection precision and high time complexity. In this paper, we present two novel outlier detection algorithms for categorical data sets. First of all, we describe a simple scheme based on entropy, Outlier Detection Tree (ODT). With ODT, a classification tree is constructed to classify the data set into two classes: a normal class and an abnormal class. Thereafter, each data object is identified as an outlier or a normal one using the if-then rules in the tree. Furthermore, we propose an advanced outlier detection algorithm, FAST-ODT, which achieves both high detection accuracy and low time complexity. Our experimental results indicate that FAST-ODT outperforms the existing algorithms in terms of outlier detection precision and computational complexity.

Yanlei Zhang,1 Zusen Ye,1 Yun Fu,2 Zheng Zhang,1 Qiang Ye,1 Feichi Chen,1 Jianhua Cheng1 1Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province 325000, People’s Republic of China; 2Department of Cardiac Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province 325000, People’s Republic of ChinaCorrespondence: Jianhua ChengDepartment of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Nanbaixiang, Ouhai District, Wenzhou 325000, Zhejiang Province, People’s Republic of ChinaTel +86 577 55579365Fax +86 577 55578999Email chengjianhua@wmu.edu.cnBackground: Cardiac myxoma is the most common primary cardiac tumor. Even though it rarely causes a stroke, it is an important risk factor. Here, we compared our clinical experience in managing myxoma patients who developed stroke complications with those who did not present with this condition at the First Affiliated Hospital of Wenzhou Medical University.Patients and Methods: The medical records were reviewed of 160 cardiac myxoma patients who were treated in our facility from January 2006 to December 2019. They were separated into either a stroke group or non-stroke group.Results: Cardiac obstructive symptoms, embolic events and constitutional symptoms were observed in 92 (57.7%), 25 (15.6%) and 18 (11.2%) patients, respectively. Among 23 cardiac myxoma ischemic stroke patients, hypoesthesia (60.9%), hemiparesis (56.5%) and facial paresis (47.8%) were the three most common neurological symptoms. The middle cerebral artery (82.6%) was the most commonly affected vessel, whereas 73.9% of the ischemic patients had multiple stroke lesions. Binary logistic regression analysis showed that coronary heart disease and tumor sizes were independently associated in the stroke group (p < 0.05). The 10 years cumulative survival rate was 87.9% for all patients after surgical intervention. There was no significant difference in the 10 years cumulative survival rate between the two groups (80.0% vs 88.9%, p =0.274 > 0.05).Conclusion: The three most common neurological symptoms (hypoesthesia, hemiparesis and facial paresis), the middle cerebral artery and multiple lesions involvements were the definitive markers of patients afflicted with cardiac myxoma stroke. Small tumor sizes were independently associated with these patients. Surgical resection is a relatively safe procedure for treating both the stroke and non-stroke patients.Keywords: cardiac myxoma, embolic complication, ischemic stroke, surgery

Multisomes are multicompartmental structures formed by a lipid-stabilized network of aqueous droplets, which are contained by an outer oil phase. These biomimetic structures are emerging as a versatile platform for soft matter and synthetic biology applications. While several methods for producing multisomes have been described, including microfluidic techniques, approaches for generating biocompatible, monodisperse multisomes in a reproducible manner remain challenging to implement due to low throughput and complex device fabrication. Here, we report on a robust method for the dynamically controlled generation of multisomes with controllable sizes and high monodispersity from lipid-based double emulsions. The described microfluidic approach entails the use of three different phases forming a water/oil/water (W/O/W) double emulsion stabilized by lipid layers. We employ a gradient of glycerol concentration between the inner core and outer phase to drive the directed osmosis, allowing the swelling of lamellar lipid layers resulting in the formation of small aqueous daughter droplets at the interface of the inner aqueous core. By adding increasing concentrations of glycerol to the outer aqueous phase and subsequently varying the osmotic gradient, we show that key structural parameters, including the size of the internal droplets, can be specifically controlled. Finally, we show that this approach can be used to generate multisomes encapsulating small-molecule cargo, with potential applications in synthetic biology, drug delivery, and as carriers for active materials in the food and cosmetics industries.

Background Aberrant DNA methylation is significantly associated with breast cancer. Methods In this study, we aimed to determine novel methylation biomarkers using a bioinformatics analysis approach that could have clinical value for breast cancer diagnosis and prognosis. Firstly, differentially methylated DNA patterns were detected in breast cancer samples by comparing publicly available datasets (GSE72245 and GSE88883). Methylation levels in 7 selected methylation biomarkers were also estimated using the online tool UALCAN. Next, we evaluated the diagnostic value of these selected biomarkers in two independent cohorts, as well as in two mixed cohorts, through ROC curve analysis. Finally, prognostic value of the selected methylation biomarkers was evaluated breast cancer by the Kaplan-Meier plot analysis. Results In this study, a total of 23 significant differentially methylated sites, corresponding to 9 different genes, were identified in breast cancer datasets. Among the 9 identified genes, ADCY4, CPXM1, DNM3, GNG4, MAST1, mir129-2, PRDM14, and ZNF177 were hypermethylated. Importantly, individual value of each selected methylation gene was greater than 0.9, whereas predictive value for all genes combined was 0.9998. We also found the AUC for the combined signature of 7 genes (ADCY4, CPXM1, DNM3, GNG4, MAST1, PRDM14, ZNF177) was 0.9998 [95% CI 0.9994–1], and the AUC for the combined signature of 3 genes (MAST1, PRDM14, and ZNF177) was 0.9991 [95% CI 0.9976–1]. Results from additional validation analyses showed that MAST1, PRDM14, and ZNF177 had high sensitivity, specificity, and accuracy for breast cancer diagnosis. Lastly, patient survival analysis revealed that high expression of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 were significantly associated with better overall survival. Conclusions Methylation pattern of MAST1, PRDM14, and ZNF177 may represent new diagnostic biomarkers for breast cancer, while methylation of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 may hold prognostic potential for breast cancer.

This paper proposes an improved cuckoo search (CS) algorithm combining nonlinear inertial weight and differential evolution algorithm (WCSDE) to overcome the shortcomings of the CS algorithm, such as low convergence accuracy, lack of information exchange within the population, and inadequate local search capabilities. Compared with other CS variants, two strategies are proposed in this paper to improve the properties of the WCSDE. On the one hand, a non-linearly decreasing inertia weight with the number of evolutionary iterations is employed in the WCSDE to improve the update method of the bird’s nest position, enhance the balance between the exploration and development capabilities, and strengthen the local optimization capability. On the other hand, the mutation and cross-selection mechanisms of the differential evolution (DE) algorithm are introduced to make up for the lack of the mutual relationship between the populations, avoid the loss of practical information, and increase the convergence accuracy. In the experiment part, 13 classic benchmark functions are selected to execute the function optimization tasks among the standard CS, the WCSDE, and other four CS variants to verify the effectiveness of the proposed algorithm from two aspects. The results and corresponding statistical analysis reveal that the proposed algorithm has better global search ability and strengthener robustness.

Competitive endogenous RNA (ceRNA) plays a vital role in cancer stem cells. In the present study, we aimed to investigate the role of the circular RNA (circRNA) antisense to the cerebellar degenera...

Network slicing is a key technology to allow resource sharing among heterogeneous operators/ services, which achieves QoS isolation for service provisioning in future communication networks. In this article, a comprehensive hierarchical soft-slicing framework is proposed to enable software-defined radio access networks supporting differentiated services with diverse QoS requirements. The proposed framework consists of network-level slicing and gNodeB-level slicing. In the network level, radio RBs are pre-allocated to each gNodeB in a large time scale, while in the gNodeB level, the pre-allocated RBs are dynamically scheduled to the services in response to the small time scale (mini-slot-level) RB request variations. The proposed framework allows the accommodation of time-varying traffic loads of differentiated services over multiple gNodeBs, while enabling dynamic inter-gNodeB RB sharing to increase the resource multiplexing gain. A case study is presented to demonstrate the effectiveness of the proposed framework, followed by a discussion on open research issues.

Background Microfibril-associated protein 2 (MFAP2) is a protein coding gene that exerts important phenotypic effects on cell motility, and increasing research has indicated that MFAP2 was correlated with many cancers. However, the functional and potential clinical role of MFAP2 in papillary thyroid cancer (PTC) has not yet been verified. Materials and methods We performed whole transcriptome sequencing on 78 paired PTC tissues and corresponding adjacent normal tissues and found that MFAP2 was highly expressed in PTC tissues. Then, we analyzed the expression of MFAP2 and its relation with the clinicopathological features of PTC in The Cancer Genome Atlas (TCGA) PTC genomic dataset. We detected MFAP2 expression in 40 paired PTC tissues and corresponding adjacent normal tissues through RT-qPCR (real time-quantitative polymerase chain reaction) to validate the sequencing data and TCGA cohort. Cell functional assays were performed to elucidate the function of MFAP2 in PTC cells, Western blot assay was performed to explore the correlation between MFAP2 and EMT (epithelial-mesenchymal transition)-related proteins. Results Statistical analysis showed that MFAP2 was obviously upregulated in PTC tissues compared to matched normal tissues, and the expression levels of MFAP2 in PTC tissues were strongly related with lymph node metastasis (p=0.016). The results of RT-qPCR of our own tissue specimens showed the same conclusions as that in TCGA dataset. The results of functional assays in PTC cell lines showed that MFAP2 could promote proliferation, colony formation, migration and invasion abilities and decrease the apoptotic rate in PTC cells. Western Blot assay showed that MFAP2 could regulate the expression of EMT-related proteins. Conclusion MFAP2 increases the proliferation, motility and decreases the apoptosis of PTC cells, and might be a potential therapeutic target for papillary thyroid cancer.

In this paper, we propose two semi-quantum dialogue (SQD) protocols by using single photons as the quantum carriers, where one requires the classical party to possess the measurement capability and the other does not have this requirement. The security towards active attacks from an outside Eve in the first SQD protocol is guaranteed by the complete robustness of present semi-quantum key distribution (SQKD) protocols, the classical one-time pad encryption, the classical party's randomization operation and the decoy photon technology. The information leakage problem of the first SQD protocol is overcome by the classical party' classical basis measurements on the single photons carrying messages which makes him share their initial states with the quantum party. The security towards active attacks from Eve in the second SQD protocol is guaranteed by the classical party's randomization operation, the complete robustness of present SQKD protocol and the classical one-time pad encryption. The information leakage problem of the second SQD protocol is overcome by the quantum party' classical basis measurements on each two adjacent single photons carrying messages which makes her share their initial states with the classical party. Compared with the traditional information leakage resistant QD protocols, the advantage of the proposed SQD protocols lies in that they only require one party to have quantum capabilities. Compared with the existing SQD protocol, the advantage of the proposed SQD protocols lies in that they only employ single photons rather than two-photon entangled states as the quantum carriers. The proposed SQD protocols can be implemented with present quantum technologies., 11 pages, 2 figures, 2 tables

Crowdfunding is an innovative strategy for financing a new business venture from the general public instead of seeking funds in traditional ways, such as issuing bonds or bank lending. This study aims to identify the determinants affecting the success of a crowdfunding campaign and how different measurements for crowdfunding success, different crowdfunding models, and the selection of subdivided determinants influence the determinants’ impacts on crowdfunding success. We set the disciplines in the search strategy to select studies related to crowdfunding success. Ultimately, 94 empirical papers are selected to reveal the different findings for the determinants of crowdfunding success; based on this information, we construct an integrated framework for future research. There has been much research on project- and creator-related factors; however, many of these factors have inconsistent relationships with crowdfunding success due to varying measurements of success. In particular, different measurements used within the same study for determinants or crowdfunding success may also produce inconsistent results. In addition, different crowdfunding models of a project have been found to induce additional findings. Our review of the determinants of crowdfunding success and the definitions of the determinants, as well as the proposed integrated framework, can help focus future work on relatively new or unique determinants rarely addressed in the existing literature. This work provides practical implications for both theory and practice, and directions for future research.

Endoscopic retrograde cholangiopancreatography (ERCP) is a major option for common bile duct (CBD) stones. Endoscopic sphincterotomy (EST), endoscopic papillary balloon dilatation (EPBD), and endoscopic sphincterotomy plus balloon dilatation (ESBD) are procedures for opening the bile duct orifice to extract CBD stones during ERCP. The optimal method for extracting small CBD stones (≤ 10 mm) has not yet been proposed. We aimed to compare the efficacy and safety of these three techniques in extracting small CBD stones.ERCP for small stones was performed between January 2009 and November 2020 at three tertiary care centers. The incidence of post-ERCP pancreatitis (PEP) was compared among EST, EPBD, and ESBD groups. First and overall success rates of stone extraction, utilization rate of mechanical lithotripsy, and other ERCP complications such as bleeding, perforation, infection, and hyperamylasemia were compared.A total of 2181 patients were enrolled between January 2009 and November 2020. The proportion of young patients (≤ 45 years) in EPBD group was more than those in EST and ESBD group. Stone size in ESBD group was much larger than EST and EPBD group. After propensity score matching, the success rates of first and overall stone extraction in the three groups were high, and the rates of mechanical lithotripsy were low, with no significant difference. The PEP incidences showed no differences among the three groups. The incidence of bleeding complication in EST group was higher than that in EPBD group. No significant differences were observed in other complications between EPBD group and ESBD group. ESBD group had higher incidence of overall, infection, and hyperamylasemia complications than EST group.EPBD is equivalent to ESBD in stone removal efficiency and complication rate, but brings a lower bleeding risk than EST. Therefore, we recommend EPBD as the first choice for small CBD stones.

Generative adversarial network (GAN) has become one of the most important neural network models for classical unsupervised machine learning. A variety of discriminator loss functions have been developed to train GAN's discriminators and they all have a common structure: a sum of real and fake losses that only depends on the actual and generated data respectively. One challenge associated with an equally weighted sum of two losses is that the training may benefit one loss but harm the other, which we show causes instability and mode collapse. In this paper, we introduce a new family of discriminator loss functions that adopts a weighted sum of real and fake parts, which we call adaptive weighted loss functions or aw-loss functions. Using the gradients of the real and fake parts of the loss, we can adaptively choose weights to train a discriminator in the direction that benefits the GAN's stability. Our method can be potentially applied to any discriminator model with a loss that is a sum of the real and fake parts. Experiments validated the effectiveness of our loss functions on an unconditional image generation task, improving the baseline results by a significant margin on CIFAR-10, STL-10, and CIFAR-100 datasets in Inception Scores and FID., Comment: 16 pages total, 7 figures, 6 tables and 2 algorithms

Fresh mulberry leaf vegetable is nutritive and becoming popular. However, available preservation technologies are deficient. In present work, the effects of two kinds of modified atmosphere packaging on postharvest quality of fresh mulberry leaf vegetable stored at 4 °C were evaluated. The respiration rate of samples in the modified polyethylene packages (MP20) was 12.88–22.65% lower than that in normal polyethylene packaging (CK). The content of total soluble solids, soluble protein, and total polyphenol in MP20 was less changed than that in CK, and the vitamin C retention was higher as well. Moreover, the lignin content in MP20 was lower than that in CK during storage (19.79% vs 13.38% at day 8), and that was significantly positively related to the polyphenol oxidase and peroxidase activities inhibition. Taken together, a packaging with moderate gas permeability (MP20) is suitable for nutrition maintenance and lignification inhibition of fresh mulberry leaf vegetable during cold storage.

Wireless Sensor Networks (WSNs) are often deployed to monitor a region of interest. With sweep coverage, mobile sensor nodes are scheduled to move along a planned route (i.e. sweep route) in order to collect the data from a series of Point of Interests (POIs) sequentially. In this paper, we generalizes the sweep coverage problem by proposing a new coverage paradigm, group sweep coverage. With group sweep coverage, the POIs are divided into several groups. A group is said to be covered when one of the POIs in the group is covered. The goal in group sweep coverage is to construct a sweep route that mobile sensor nodes should follow in order to cover all groups during each predefined period. In our research, we devised two algorithms for group sweep coverage: AGSC and DSRM. AGSC is a centralized scheme whose approximation ratio is 5Δ. Namely, the length of the sweep route generated by AGSC is at most 5Δ times that of the optimal sweep route. DSRM is a distributed scheme for large-scale networks with dynamic POIs. Compared with AGSC, DSRM leads to the same approximation ratio and better scalability. Our experimental results indicate that both AGSC and DSRM outperform the state-of-the-art schemes in terms of average and maximal sweep route length.

Many large networks, such as social networks and web graphs, have been found to exhibit a variety of different interesting features. The dynamic properties of large networks, such as the propagation and robustness feature, have been actively investigated over the past years. In our research, we attempted to understand the propagation and robustness feature of large networks quantitatively. Specifically, we employed a critical parameter based on network topology, Edge Instability (EI), to study the propagation feature. Our experimental results indicate that, compared with the existing propagation strategies, the EI-based strategy has a more serious impact on information propagation in large networks. Furthermore, we investigated the robustness performance of large networks by evaluating their connectivity in the scenario that some edges are removed gradually. The experimental results show that, among the existing edge removing strategies, removing edges with high EI hurts network robustness most. Finally, in order to understand the influence of the average EI of a network, we devised an evolution model to generate large networks with varied average EI. We found that the average EI has a significant impact on information propagation. However, it has an unnoticeable influence on network robustness when EI-based strategies are adopted.