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In an environment with multiple heterogeneous multiservers across multiple clouds and data centers, optimal workload management and server speed setting makes strong impact on the aggregated performance, cost, and profit of a cloud service provider. Such a situation provides us a more challenging opportunity to address and discuss profit maximization of a service provider in a wider scale and to generate more significant influence. In this paper, we investigate profit maximization by optimal workload management and server speed setting for multiple heterogeneous multiservers in a federated cloud or a geo-distributed data center. The heterogeneous multiservers have different sizes, speeds, power consumption models, workload, performance, costs, and profit. To conduct rigorous study, each multiserver system is modeled by an M/M/m queueing system, such that the profit of a multiserver system can be characterized analytically. We address two problems, i.e., workload management without server speed setting and workload management with server speed setting. Both problems are formulated as multi-variable optimization problems. We develop numerical algorithms to solve these problems. We also provide numerical data for the purpose of illustration. This is the first paper which analytically discusses profit maximization for multiple heterogeneous multiservers in a federated cloud or a geo-distributed data center.

Integrating user ends (UEs), edge servers (ESs), and the cloud into end-edge-cloud computing (EECC) can enhance the utilization of resources and improve quality of experience (QoE). However, the performance of EECC is significantly affected by its architecture. In this article, we classify EECC into two computing architectures types according to the visibility and accessibility of the cloud to UEs, i.e., hierarchical end-edge-cloud computing (Hi-EECC) and horizontal end-edge-cloud computing (Ho-EECC). In Hi-EECC, UEs can offload their tasks only to ESs. When the resources of ESs are exhausted, the ESs request the cloud to provide resources to UEs. In Ho-EECC, UEs can offload their tasks directly to ESs and the cloud. In this article, we construct a potential game for the EECC environment, in which each UE selfishly minimizes its payoff, study the computation offloading strategy optimization problems, and develop two potential game-based algorithms in Hi-EECC and Ho-EECC. Extensive experiments with real-world data are conducted to demonstrate the performance of the proposed algorithms. Moreover, the scalability and applicability of the two computing architectures are comprehensively analyzed. The conclusions of our work can provide useful suggestions for choosing specific computing architectures under different application environments to improve the performance of EECC and QoE.

With the widespread application of smart devices, spatial crowdsourcing (SC) has been extensively integrated into daily life. Task assignment is a crucial issue in SC and has attracted much attention. Most prior studies on task assignment ignore the importance of dependency among tasks, resulting in some ineffective matching pairs and wasting workers’ time. To this end, we formulate a new problem in SC, abbreviated as multi-stage complex task assignment (MSCTA), which aims to assign workers to multi-stage complex tasks to maximize the total profit. Compared with existing studies, MSCTA can obtain more effective assignments by considering the dependency constraints among tasks. We prove that the MSCTA problem is NP-hard and propose a greedy algorithm and a game algorithm. Specifically, both algorithms iteratively utilize a filtering module to obtain a set of executable tasks (ET) for assignment. The greedy algorithm can quickly assign the most profitable workers to the subtasks in each round of ET, and obtain a provable approximate result. The game algorithm is proved to be convergent and can win a Nash equilibrium when processing the subtasks in each round of ET. Extensive experimental results demonstrate the efficiency of our algorithm.

BackgroundTumor-associated macrophages (TAMs) are closely related to unfavorable prognosis of patients with clear cell renal cell carcinoma (ccRCC). However, the important molecules in the interaction between ccRCC and TAMs are unclear.MethodsTCGA-KIRC gene expression data of tumor tissues and normal tissues adjacent to tumor were compared to identify differentially expressed genes in ccRCC. TAMs related genes were discovered by analyzing the correlation between these differentially expressed genes and common macrophage biomarkers. Gene set enrichment analysis was performed to predict functions of TAMs related gene. The findings were further validated using RNA sequencing data obtained from the CheckMate 025 study and immunohistochemical analysis of samples from 350 patients with ccRCC. Kaplan–Meier survival curve, Cox regression analysis and Harrell’s concordance index analysis were used to determine the prognostic significance.ResultsIn this study, we applied bioinformatic analysis to explore TAMs related differentially expressed genes in ccRCC and identified 5 genes strongly correlated with all selected macrophage biomarkers:STAC3,LGALS9,TREM2,FCER1G, andPILRA. Among them,FCER1Gwas abundantly expressed in tumor tissues and showed prognostic importance in patients with ccRCC who received treatment with Nivolumab; however, it did not exhibit prognostic value in those treated with Everolimus. We also discovered that high expression levels ofFCER1Gare related to T cell suppression. Moreover, combination ofFCER1Gand macrophage biomarkerCD68can improve the prognostic stratification of patients with ccRCC from TCGA-KIRC. Based on the immunohistochemical analysis of samples from patients with ccRCC, we further validated that FCER1G and CD68 are both highly expressed in tumor tissue and correlate with each other. Higher expression of CD68 or FCER1G in ccRCC tissue indicates shorter overall survival and progression-free survival; patients with high expression of both CD68 and FCER1G have the worst outcome. Combining CD68 and FCER1G facilitates the screening of patients with a worse prognosis from the same TNM stage group.ConclusionsHigh expression ofFCER1Gin ccRCC is closely related to TAMs infiltration and suppression of T cell activation and proliferation. Combining the expression levels of FCER1G and macrophage biomarker CD68 may be a promising postoperative prognostic index for patients with ccRCC.

Owing to the ease of synthesis and the unique luminescence characteristics for the applications of white light emitting diodes (WLEDs), the red-emitting fluoride phosphors activated by Mn4+ ions are critical to achieving WLEDs with ideal color rendering index (CRI). In this work, a new type of Mn4+ doped phosphor KRb3Ge2F12:Mn4+ (KRGF:Mn) has been synthesized by a simple ion-exchange method in atmosphere. A strategy of surface modification by using a weak reducing agent oxalic acid (OA) is proposed, which can essentially improve the moisture stability of phosphor KRGF:Mn. Moreover, the modified phosphors can still maintain bright red light after being completely immersed in water for more than 100 days. A group of LEDs combined with the as-synthesized phosphor KRGF:Mn as a light source can be well applied to artificial lighting for the growth of tomatoes and Chinese cabbage plants. Additionally, the luminescence efficiency of KRGF:Mn has been optimized with high thermal stability. This work will provide high-quality red phosphor for LED lighting sources that being required for indoor plant growth.

As a fundamental problem in graph analysis, core decomposition aims to compute the core numbers of vertices in a given graph. It is a powerful tool for mining important graph structures. For dynamic graphs with real-time updates of vertices/edges, core maintenance has been utilized to update the core numbers of vertices. The previous approaches to core maintenance face challenges in terms of storage and efficiency. In this article, we investigate distributed approaches to core maintenance on a pregel-like system, which is a famous graph computing system. We first design a core decomposition algorithm to obtain core numbers of vertices in a given graph. Based on it, a distributed batch-stream combined algorithm (DBCA) is devised to efficiently maintain the core numbers when vertex/edge updates happen. In particular, we introduce a new task assignment strategy to DBCA based on diversity of the edge-cores of updated edges. To ensure that DBCA can accurately process core maintenance, we develop a message interaction protocol to resolve the problem of crosstalk among different tasks. Comprehensive experiments have been conducted on real/synthetic graphs, more specifically, in two typical distributed environments built on Supercomputing Center and Alibaba Cloud. The experiment results demonstrate that our proposed algorithms are efficient and scalable.

In a newly emerged fog computing environment, various user equipments (UE) enhance their computing power and extend their battery lifetime by computation offloading to mobile edge cloud (MEC) servers. Such an environment is distributed and competitive in nature. In this paper, we take a game theoretical approach to computation offloading optimization in a fog computing environment. Such an approach captures and characterizes the nature of a competitive environment. The main contributions of the paper can be summarized as follows. First, we formulate a non-cooperative game with both UEs and MECs as players. Each UE attempts to minimize the execution time of its tasks with an energy constraint. Each MEC attempts to minimize the product of its power consumption for computation and execution time for allocated tasks. Second, we develop a heuristic algorithm for a UE to determine its “heuristically” best response to the current situation, an algorithm for an MEC to determine its best response to the current situation, and an iterative algorithm to find the Nash equilibrium. Third, we prove that our iterative algorithm converges to a Nash equilibrium. We demonstrate numerical examples of our non-cooperative games with and without MECs' participation. We observe that our iterative algorithm always quickly converges to a Nash equilibrium. The uniqueness of our non-cooperative games is that the strategy set of a player can be discrete and the payoff function of a player can be obtained by a heuristic algorithm for combinatorial optimization. To the best of the author's knowledge, there has been no such investigation of non-cooperative games based on combinatorial optimization for computation offloading optimization in a fog computing environment.

Remaining useful life (RUL) prediction is of fundamental importance in reliability analysis and health diagnosis of complex industrial systems. Aiming at improving the prediction accuracy, this article proposes a normalizing flow embedded sequence-to-sequence (seq2seq) learning method to predict the RUL of an asset or a system. This method introduces a block of normalizing flow into the middle area of the familiar encoder–decoder structure of the seq2seq model. This normalizing flow enjoys the remarkable representation ability for the nonlinearity between input sequential data and outputs and enables the original seq2seq model to be more suitable for vibration signals of engines. The encoder and the decoder, which fall before and after the normalizing flow, are both built by gated recurrent units. Besides, a one-hot coding of clustering is concatenated with measurement data to indicate the frequently shifting vibration state, and a sensor selection method is designed to drop some weakly related and ineffective variables. Our method is tested and further analyzed by 2008 IEEE PHM challenge data (PHM08), of which many practical preprocessing methods are conducted. Numerous tests verify that our method outperforms other related deep learning methods for RUL estimation.

To evaluate the quality of life and surgical outcomes in patients with congenital cervicovaginal malformation after cervicovaginal reconstruction.Thirty-eight patients diagnosed with congenital cervicovaginal aplasia and underwent cervicovaginal reconstruction using acellular porcine small intestinal submucosa (SIS) grafts were included in the study from January 2012 to December 2019. Of these, twenty-one patients underwent conventional laparoscopy, nine underwent robotic surgery, and eight underwent laparoendoscopic single-site (LESS) surgery. Clinical characteristics, perioperative data, condition of the neovagina and neocevix, post-operation complications, body image, resumption of menstruation, sexual function, and quality of life were assessed.The average age of the patients was 16.4 ± 5.78 years. The operative procedure lasted 182.29 ± 70.85 min, with a hemoglobin decrease of 12.53 ± 7.55 g/dl. All surgery was completed successfully without complications. The total cost was highest in the robotic surgery group (P 0.001). The cosmetic scores were significantly higher in the LESS group (P 0.001). At a median follow-up of 49.79 ± 31.02 months, all patients resumed menstruation, except one patient who underwent hysterectomy due to vaginal obstruction. The average length of neovagina was 8.11 ± 0.75 cm, and the length of the cervix was 1.73 ± 1.00 cm. There were one patient with vaginal stenosis, two patients with cervical occlusion, two patients with cervical stenosis, and six patients with intrauterine device loss. Fourteen patients experienced sexual activity, with the total female sexual function index scores of 26.83 ± 3.49. Six patients had a desire of pregnancy, and one patient had pregnant via assisted-reproduction techniques. No differences in the mean physical component score (PCS) and mental component score (MCS) were identified among three different groups at baseline and all post-surgery time points (P 0.05), but with the extension of follow-up, both PCS and MCS increased significantly in all groups (P 0.001).Cervicovaginal reconstruction using an SIS graft is safe and efficient to the management of congenital cervicovaginal atresia whatever by the conventional laparoscopy, robotic surgery or LESS, with good surgical outcomes and high of quality of life.

Cloud computing and big data technologies have gained great popularity in recent years. MapReduce is still one of the most efficient and well-adopted computing paradigms for providing big data services. MapReduce applications need to be executed on cloud platform where failures are inevitable. Hadoop is the de facto implementation of MapReduce, but it deploys a coarse grained and unsatisfactory fault tolerant services. The failed tasks are rescheduled from scratch to re-execute from the very beginning, which apparently brings amount of overload for failure recovery, and the whole job would be heavily delayed as failures happen. In this paper, we propose a novel multi-trigger checkpointing approach for fast recovery of MapReduce tasks, named McTAR (a Multi-trigger Checkpointing Tactic for fAst TAsk Recovery). As a finer-grained and better fault tolerance tactic, our McTAR employs multi-trigger checkpoint generation, push-pull combined intermediate data distribution and optimized failure task prediction techniques together to make the recovery task attempt be able to start at a specific progress according to the valid checkpoint for intermediate data. In this way, McTAR could effectively speed up the recovery process of MapReduce jobs and highly reduce the task recovery delay.

As the number of cloud services (CSs) offering similar functionality is growing, more attention has been payed on the quality of service (QoS) of CSs. However, in a dynamic cloud environment, the explicit and inherent variation of QoS causes the single CS selection via collaborative filtering techniques (CSS-CFT) to be challengeable. A variation-aware approach via collaborative QoS prediction is proposed to select an optimal CS according to users' non-functional requirements. Based on time series QoS data, this approach utilizes a set of specific cloud models to quantify the variation characteristics of QoS from the four aspects including central tendency, variation range, frequency of variation and period. To exactly identify the neighboring users for a current user, this paper employs the double Mahalanobis distances to measure the similarity of QoS cloud models. The variation-aware CSS-CFT is formulated as a multi-criteria decision-making problem, and an improved TOPSIS method is exploited to solve it, by considering both the objective QoS variation and subjective user preferences during different time periods. The experiments based on a real-world dataset demonstrate that the proposed approach can enhance the accuracy of CSS-CFT in a high-variance environment without noticeable increase of selection time, in comparison to the existing approaches.

We explore miR‐150‐5p in atherosclerosis (AS). The AS model was constructed using Apo E−/− mice with an injection of the miR-150-5p mimic or an inhibitor. Pathological characteristics were assessed using Oil red O staining and Masson staining. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and Western blot were used to analyze the expressions of microRNA-150-5p (miR-150-5p), STAT1, α-SMA (α-smooth muscle actin) and proliferating cell nuclear antigen (PCNA). Targetscan and dual-luciferase reporter assay were used to analyze the interaction between miR-150-5p and STAT1. The viability, migration, cell cycle and α-SMA and PCNA expressions in oxidized low-density lipoprotein (ox-LDL)-stimulated primary human aortic smooth muscle cells (ASMCs) were assessed using molecular experiments. miR-150-5p was reduced in both AS mice and ox-LDL-stimulated human aortic smooth muscle cells but STAT1 had the opposite effect. The miR‐150‐5p inhibitor alleviated the increase of lipid plaque and reduced collagen accumulation in the aortas during AS. Upregulation of α-SMA and PCNA was reversed by miR-150-5p upregulation. STAT1 was targeted by miR‐150‐5p, and overexpressed miR-150-5p weakened the ox-LDL-induced increase of viability and migration abilities and blocked cell cycle in ASMCs, but overexpressed STAT1 blocked the effect of the miR‐150‐5p mimic. This paper demonstrates that miR-150-5p has potential as a therapeutic target in AS, with plaque stabilization by regulating ASMC proliferation and migration via STAT1.

Background The objective of this study was to evaluate the overall outcomes and complications of transvaginal mesh (TVM) placement for the management of pelvic organ prolapse (POP) with different meshes with a greater than 10-years of follow-up. Methods We performed a retrospective review of patients with POP who underwent prolapse repair surgery with placement of transvaginal mesh (Prolift kit or self-cut Gynemesh) between January 2005 and December 2010. Baseline of patient characteristics were collected from the patients’ medical records. During follow-up, the anatomical outcomes were evaluated using the POP Quantification system, and the Patient Global Impression of Improvement (PGI-I) was used to assess the response of a condition to therapy. Overall postoperative satisfaction was assessed by the following question: “What is your overall postoperative satisfaction, on a scale from 0 to 10?”. Relapse-free survival was analyzed using Kaplan–Meier curves. Results In total, 134 patients were included. With a median 12-year (range 10–15) follow-up, 52 patients (38.8%) underwent TVM surgery with Prolift, and Gynemesh was used 82 (61.2%). 91% patients felt that POP symptom improved based on the PGI-I scores, and most satisfied after operation. The recurrence rates of anterior, apical and posterior compartment prolapse were 5.2%, 5.2%, and 2.2%, respectively. No significant differences in POP recurrence, mesh-associated complications and urinary incontinence were noted between TVM surgery with Prolift versus Gynemesh. Conclusions Treatment of POP by TVM surgery exhibited long-term effectiveness with acceptable morbidity. The outcomes of the mesh kit were the same as those for self-cutmesh.

Loop closure detection (LCD) is an important portion of Simultaneous Localization and Mapping (SLAM) because of its ability to reduce accumulated position errors. In this letter, we propose a novel loop closure detection algorithm named ESA-VLAD. The crucial part of ESA-VLAD is a redesigned network with EfficientNetB0 as backbone for extracting global features, which integrates a second-order attention module in order to effectively learn the correlations between features within the feature map. A trainable Vector of Local Aggregated Descriptors (NetVLAD) is integrated in the last layer of the network to generate a compact and fixed-length global feature. Knowledge distillation strategy is adopted in training of the proposed network to accelerate the training process. For the global features, Hierarchical Navigable Small World (HNSW) is employed to retrieve the loop closure candidate images. In addition, an efficient geometrical consistency check based on local difference binary (LDB) descriptors is designed to verify loop closure matches. Experiments on several public datasets demonstrate that ESA-VLAD can obtain higher recall rates under 100% precision and less processing time per frame compared to other typical and state-of-the-art methods.

Endocervical adenocarcinoma (EAC) is an aggressive type of endocervical cancer. At present, molecular research on EAC mainly focuses on the genome and mRNA transcriptome, the investigation of small RNAs in EAC has not been fully described. Here, we systematically explored small RNAs in 14 EAC patients with different subtypes using small RNA sequencing. MiRNAs and tRNA-derived RNAs (tDRs) accounted for the majority of mapped reads and the total number of miRNAs and tDRs maintained a relative balance. To explore the correlations between small RNAs expression and EAC with different clinical characteristics, we performed the weighted gene co-expression network analysis (WGCNA) and screened for hub small RNAs. From the key modules, we identified 9 small RNAs that were significantly related to clinical characteristics in EAC patients. Gene ontology and pathway analyses revealed that these molecules were involved in the pathogenesis of EAC. Our work provided new insights into EAC pathogenesis and successfully identified several small RNAs as candidate biomarkers for diagnosis and prognosis of EAC.

Predicting the popularity of web contents in online social networks is essential for many applications. However, existing works are usually under non-incremental settings. In other words, they have to rebuild models from scratch when new data occurs, which are inefficient in big data environments. It leads to an urgent need for incremental prediction, which can update previous results with new data and conduct prediction incrementally. Moreover, the promising direction of group-level popularity prediction has not been well treated, which explores fine-grained information while keeping a low cost. To this end, we identify the problem of incremental group-level popularity prediction, and propose a novel model IGPP to address it. We first predict the group-level popularity incrementally by exploiting the incremental CANDECOMP/PARAFCAC (CP) tensor decomposition algorithm. Then, to reduce the cumulative error by incremental prediction, we propose three strategies to restart the CP decomposition. To the best of our knowledge, this is the first work that identifies and solves the problem of incremental group-level popularity prediction. Extensive experimental results show significant improvements of the IGPP method over other works both in the prediction accuracy and the efficiency.

Large-scale data centers have been widely used for cloud services, and the stability of various cloud services has received additional attention from users. Although service disruptions are not as catastrophic as they once were, their impact might be more extensive than before. These outages may trigger the migration of virtual machines (VMs) located in the failure node. However, the access time of each VM is random, unlike the accident time, which can be predicted. This means that traditional migration caused by service interruptions may result in a large number of unwanted migrations, regardless of the user’s downtime experience. Migration is an expensive process in terms of the resources needed as well as the degradation of application performance during migration. A balance between the recovery time of the service (to minimize the migration resulting from a given placement) and the downtime experience of the users (to minimize the impact of access interruptions) is needed. In this paper, we propose HMGOWM, a hybrid decision-making mechanism for automating the migration of VMs. Our proposed mechanism extends the original VM migration performance cost model, greatly reducing the downtime experience of the users. To achieve high performance and a good load balance, a multi-objective monitoring system for both VMs and physical machine nodes and an adaptive VM migration-scheduling scheme for the OpenStack cloud platform are proposed. Extensive experiment results indicate that the downtime experienced by users can be efficiently reduced and that the implementation of HMGOWM outperforms the original scheduling of the OpenStack cloud platform.

Aim: To discuss miRNA-489-3p in sepsis-induced lung injury. Materials and methods: Using NR-8383 as research cell in our study, and using LPS stimulation to sepsis induced lung injury vitro model. Measuring cell proliferation by MTS assay; using Elisa assay to measure IL-1β, IL-6 and TNF-α concentration; apoptosis cell number and apoptosis rate were evaluated using TUNEL and flow cytometry; gene and protein were measured by RT-PCR and WB assay; measuring p-NF-κB(p65) nuclear volume by Immunofluorescence (IF); using Luciferase reporter assay to analysis miRNA-489-3p and TLR4 correlation. Results: Cell proliferation rate significantly down-regulated (P < 0.001), apoptosis cell number and apoptosis rate significantly increased (P < 0.001); IL-1β, IL-6 and TNF-α concentrations significantly up-regulated (P < 0.001); TLR4 and MyD88 gene and protein significantly increased (P < 0.001), NF-κB(p65) mRNA and p-NF-κB(p65) protein and nuclear volume significantly increased (P < 0.001). However, with miRNA-489-3p supplement, the cell proliferation rate, apoptosis cell number and apoptosis rate were significantly improved (P < 0.001, respectively) via TLR4, MyD88 and NF-κB(p65) mRNA and protein significantly depressing (P < 0.001, respectively). By LUC assay, miRNA-489-3p could target to TLR4 in NR-8383 cell. Conclusion: miRNA-489-3p overexpression had effect to improve sepsis induced lung injury via regulation TLR4/MyD88/NF-κB(p65).

With the increasing popularity of wearable, implantable, and Internet of Things devices, energy-harvesting nonvolatile processors (NVPs) have become promising alternative platforms due to their durability when running on an intermittent power supply. To address the problem of an intermittent power supply, backing up of volatile data into a nonvolatile cache has been proposed to avoid the frequent need to restart the program from the beginning. However, the penalties incurred by frequent backup and recovery operations significantly degrade the system performance and waste considerable energy resources. Moreover, the increasing amounts of data to be processed pose critical challenges in energy-harvesting NVP platforms with tight energy and latency budgets. To further improve the performance of NVPs, this article adopts a retention state that can enable a system to retain data in a volatile cache to wait for power recovery instead of backing up data immediately. Based on the retention time, we propose a performance-aware cache management scheme and a pre-backup method to improve the system performance and energy utilization while guaranteeing successful backup. The pre-backup method is also optimized by retaining data in a volatile cache when receiving a high voltage warning. In particular, the nonvolatile memory (NVM) compression technique is introduced to achieve the goal of minimizing power failures and maximizing system performance. Moreover, the security problems in the sleep state are discussed with regard to the NVM compression technique to guarantee the NVP’s security. We evaluate the performance and energy consumption of our proposed algorithms in comparison with the dual-threshold scheme. The experimental results show that compared with the dual-threshold scheme, the proposed algorithms together can achieve a 52.6% energy reduction and a 13.72% performance improvement on average.

The pricing mechanism of cloud-computing resources is an essential issue for both cloud customers and service providers, especially from the point of multi-provider competition. Although various mechanisms for resource provision are proposed, few studies have focused on multi-attribute resource provision with the objective of improving benefits of both cloud customers and service providers. To address the issue, we propose a price bidding mechanism for multi-attribute cloud-computing resource provision from the perspective of a non-cooperative game, in which the information of each player (customers and providers) is incomplete to others and each player wishes to maximize his/her own benefit. More specifically, considering the fairness pricing competition, we propose a novel and incentive resource provision model referring to the Quality-of-Service (QoS) and the bidding price. Then, combining with the resource provision model, the problem of price bidding is formulated as a game to find a proper price for each cloud provider. We demonstrate the existence of Nash equilibrium solution set for the formulated game model by assuming that the quantity function of provided resources from every provider is continuous. To find a Nash equilibrium solution, we propose an Equilibrium Solution Iterative (ESI) algorithm, which is proved to converge to a Nash equilibrium. Finally, a Near-equalization Price Bidding (NPB) algorithm is proposed to modify the obtained Nash equilibrium solution. Extensive simulated experiments results and the comparison experiments with the state-of-the-art and benchmark solutions validate and show the feasibility of the proposed method.

Background Colposcopy currently plays a vital role in the diagnosis and treatment of lower genital diseases. Exposure and biopsy are two key steps in colposcopy. When the whole transformation zone or all lesions cannot be observed, we judge colposcopy as unsatisfactory. Unsatisfactory colposcopic examination may lead to the misdiagnosis of more severe diseases. The combination of colposcopy and vaginoscopy may contribute to accurate diagnosis and clinical decisions. Case summary Here, we introduce a case of posthysterectomy deep vaginal apex not fully exposed by colposcopy, and the biopsy result was a vaginal precancerous lesion. We adopted vaginoscopy to extend the observed area and expose the lesion thoroughly, and the biopsy result was vaginal squamous cancer. Conclusion The patient received a precise diagnosis and early surgery due to the combination of colposcopy and vaginoscopy.

To compare the efficacy between a bio-type femoral stem and a cemented femoral stem in hip arthroplasty for severe unstable osteoporotic intertrochanteric femur fractures (UOIFF) in elderly patients. The clinical data of 100 elderly patients with UOIFF who underwent hip arthroplasty were retrospectively analyzed. The patients were divided into a biological group (n = 48, receiving a biological-type femoral stem) and a cemented group (n = 52, receiving a cemented femoral stem). The excellent postoperative hip function recovery, perioperative related parameters, and postoperative complication rates were compared between the two groups. The changes in the preoperative and postoperative visual analogue score (VAS), Harris hip function score, functional recovery scale score (FRS), ability to perform activities of daily living (ADL), thromboelastography parameters, bone density, and health survey short form (SF-36) scores were also observed. The excellent and good rate of joint function recovery in the cemented group (90.38%) was higher than that in the biological group (75.00%) (P

Aims/Introduction Emerging evidence suggests that expression quantitative trait loci (eQTLs) are more likely to associate with complex diseases. Transient receptor potential cation channel subfamily M member 5 (TRPM5) is a ubiquitously expressed voltage‐gated cation channel that acts indispensably to trigger insulin secretion in pancreatic β‐cells. The present study evaluated the association between TRPM5 eQTL single‐nucleotide polymorphisms and the risk of gestational diabetes mellitus (GDM) in a Chinese population. Materials and Methods A total of 380 unrelated Chinese pregnant women including 241 GDM patients and 139 controls were included in this study. The eQTL single‐nucleotide polymorphisms of TRPM5 were obtained from the GTEx eQTL Browser, and were subsequently genotyped using the Agena MassARRAY iPLEX platform. Results Logistic regression analysis and linear regression analysis showed that rs35197079 and rs74848824 were significantly associated with reduced GDM risk and lower fasting plasma glucose levels after adjusting confounder factors in dominant genetic models. Stratification analysis based on pre‐pregnancy body mass index validated a strong association between rs35197079 and GDM susceptibility in underweight and normal weight individuals. Luciferase and electrophoretic mobility shift assays carried out in rat pancreatic β‐cells showed that rs35197079 was functional. Conclusions The TRPM5 eQTL single‐nucleotide polymorphism rs35197079 was associated with decreased GDM susceptibility in a Chinese population, especially in underweight and normal weight pregnant women, and it was functional in modulating gene transcription., In the present study, for the first time, we systematically examined the relationship between transient receptor potential cation channel subfamily M member 5 expression quantitative trait loci single‐nucleotide polymorphisms and gestational diabetes mellitus susceptibility. We identified that rs35197079 was significantly associated with decreased gestational diabetes mellitus risk in a Chinese population, and it could function to modulate gene transcription.

The paradigm of cloud computing has heralded a new avenue of computing, offering benefits of increased data accessibility with low cost. Continuous Writing Applications (CWA) (e.g., augmented online services for Health Care) have specific requirements on data storage, computation and bandwidth, thus are cost-sensitive with limited budgets and time. Herein, we propose an architecture of multi-cloud service provider (CSP) or “Multi-Cloud” to provide services to CWA, and design a novel resource scheduling algorithm to minimize the system cost. The system models of classic CWAs to tackle the resource requirements of users on MCP are exploited. The study can help to understand the characteristics of different resources and conclude Multi-Cloud being the most attractive to many CWA implementations. Interconnections of multiple CSPs and their load paths (i.e., data passing through possible interconnections) are introduced. We then formulate the problem and present optimal user scheduling based on Minimum First Derivative Length (MFDL) of system load paths. Theoretical analysis demonstrated that the solutions with minimized costs can be achieved by the proposed algorithm, termed “Optimal user Scheduling” for Multi-Cloud (OSMC). Through rigorous simulations regarding different influencing factors, the proposed strategy has proven to be scalable, flexible, and efficient in many practical scenarios.

This study analyzed the potential diagnosis and therapeutic challenges of retroperitoneal schwannoma (RSs) in a specialized gynecology hospital.A retrospective review was performed in our hospital from 2000 to 2018. A literature search of RSs was conducted using PubMed database.45 patients were identified (22 from our hospital and 23 from the literature review). The majority of patients presented asymptomatic (22/45). Among them, 25 cases were misdiagnosed as adnexal cysts, 13 uterine fibroids, 1 ovarian malignancy and 6 pelvic masses. Intraoperative exploration revealed that the masses were located in the retroperitoneal space. The median diameter was 6.2 cm (range 3.0-9.8 cm) in our hospital compared with 9.3 cm (6-15 cm) in literature review. Complete resection was performed in 37 patients and subtotal resection in 8 patients. The pathological results confirmed the diagnosis of benign schwannoma and no recurrence was found in the follow-up data.The preoperative diagnosis of RSs is difficult to make because of its nonspecific characteristics. In a specialized gynecology hospital, it is more important to differentiate the benign and malignant of mass before surgery. Surgical complete resection of tumor is recommended and recurrence is unusual after complete resection.

Background To describe the short-term outcomes of transvaginal natural orifice transluminal endoscopic surgery (vNOTES) for uterosacral ligament suspension (USLS) in patients with severe prolapse. Methods This was a retrospective study of patients with severe prolapse (≥ stage 3) who underwent vNOTES for USLS between May 2019 and July 2020. The Pelvic Organ Prolapse Quantification (POP-Q) score, Pelvic Organ Prolapse/Urinary Incontinence Sexual Questionnaire short form (PISQ-12) and Pelvic Floor Inventory-20 (PFDI-20) were used to evaluate physical prolapse and quality of life before and after vNOTES for USLS. Results A total of 35 patients were included. The mean operative duration was 111.7 ± 39.4 min. The mean blood loss was 67.9 ± 35.8 ml. Statistically significant differences were observed between before and after vNOTES USLS in Aa (+ 0.6 ± 1.7 versus − 2.9 ± 0.2), Ba (+ 1.9 ± 2.2 versus − 2.9 ± 0.3), C (+ 1.5 ± 2.2 versus − 6.9 ± 0.9), Ap (− 1.4 ± 1.0 versus − 3.0 ± 0.1) and Bp (− 1.1 ± 1.4 versus − 2.9 ± 0.1) (P Conclusions vNOTES for USLS may be a feasible technique to manage severe prolapse, with promising short-term efficacy and safety. Larger studies with more patients and longer follow-up periods should be performed to evaluate the long-term efficacy and safety profile of vNOTES for USLS.

Recent years have witnessed the proliferation of mobile computing and Internet-of-Things (IoT), where billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend and the development of wireless communication, edge computing, an emerging computing paradigm, has received tremendous amount of interest. By pushing data storage, computing, and control closer to the network edge, edge computing has been widely recognized as a promising solution to meet the requirements of low latency, high scalability, prompt response, and energy efficiency. In the meanwhile, with the development of neural networks, Artificial Intelligence (AI) has been applied to a variety of disciplines and proved highly successful in a vast class of intelligent applications cross many domains. Edge intelligence, aiming to facilitate the deployment of neural networks on edge computing, has received significant attention. However, there are many challenges existing for a novel design of edge computing architecture to AI applications, and their co-optimization. For instance, conventional neural networks techniques usually entail powerful computing facilities (e.g., cloud computing platforms), while the entities at the edge may have only limited resources for computations and communications. This suggests that AI algorithms should be revisited for edge computing to AI models into the edge device for efficient processing. On the other hand, the adapted deployments of neural networks at the edge empower the efficient learning systems that can provide the “smartification” across different layers, e.g., from network communications to applications, and also involve collaborations across edge to cloud. Finally, designing algorithms for small-scale edge devices in a learning ambience is all the challenging as there are several conflicting issues to account for. These include, memory management, power management, and compute capability of a node, etc.

OBJECTIVE To evaluate the prognostic performance of the revised 2018 FIGO staging system for cervical cancer. METHODS This retrospective multicenter study enrolled cervical cancer patients with 2009 FIGO Stage IA1-IIA2 who underwent surgeries between January 2006 and December 2017 in four tertiary hospitals. Patients were restaged according to the 2018 FIGO staging system by reviewing their medical data. RESULTS Of 3238 cervical cancer patients included, 1841 (56.9%) patients were restaged: 641 (34.9%) due to tumor size, 544 (29.5%) due to lymph node metastasis, 614 (33.4%) due to the inconsistency between pre- and postoperative assessments, and 42 due to the cancellation of invasion width in Stage IA. After restaging, a clear tendency of decreased recurrence-free survival (RFS) and overall survival (OS) with increasing stage was observed. Multivariate Cox analysis showed that 2018 FIGO stage, parametrial involvement, and histology were independent prognostic factors for both OS and RFS (P

The problem of optimizing hardware cost under functional safety requirement is a desirable work for a safety-critical embedded application. The state-of-the-art algorithms called enhanced explorative hardware cost optimization (EEHCO) and simplified EEHCO (SEEHCO) have been used to study this problem for a distributed embedded application by iteratively removing some processors from opened processors (i.e., open-to-close). However, EEHCO has powerful cost optimization capability but inferior time efficiency, and vice versa for SEEHCO in large-scale heterogeneous distributed embedded systems. This study presents a price performance-driven hardware cost optimization (PPHCO) method, which is the combination of PPHCO1 and PPHCO2, to achieve powerful cost optimization capability and superior time efficiency simultaneously. PPHCO1 iteratively selects the processor with the maximum price performance to open and overcomes the inferior time efficiency (i.e., close-to-open). PPHCO2 iteratively selects the processor with the minimum price performance to close and further optimizes the hardware cost on the basis of PPHCO1 without losing time efficiency (i.e., open-to-close). Through significantly reducing the iteration count, PPHCO overcomes the inferior time efficiency of the open-to-close method. Through adopting union fast functional safety verification (UFFSV), PPHCO achieves powerful cost optimization capability. Experiments confirm that PPHCO not only achieves stronger cost optimization capability but also has better time efficiency than state-of-the-art EEHCO and SEEHCO algorithms.

The main contributions of the paper are summarized as follows. We take an analytical approach in the sense that the quality of service and the price of service as well as the revenue, cost, and profit of a cloud service provider (CSP) can all be quantitatively available based on well established analytical models. We argue that the satisfaction of a customer includes two aspects, i.e., satisfaction on the price of service and satisfaction on the quality of service. We are able to derive a closed-form expression of the expected customer satisfaction of a CSP. We develop a non-cooperative game formulation for a competitive cloud computing market with competing CSPs. We discuss the market stability mechanism which creates interaction among the CSPs, give the best response of a CSP based on the other CSPs' strategies, mention the existence of the Nash equilibrium, and develop an algorithm to find the Nash equilibrium.

A novel coronavirus (SARS-CoV-2) has spread worldwide and led to high disease burden around the world. This study aimed to explore the key parameters of SARS-CoV-2 infection and to assess the effectiveness of interventions to control the coronavirus disease 2019 (COVID-19). A susceptible—exposed—infectious—asymptomatic—recovered (SEIAR) model was developed for the assessment. The information of each confirmed case and asymptomatic infection was collected from Ningbo Center for Disease Control and Prevention (CDC) to calculate the key parameters of the model in Ningbo City, China. A total of 157 confirmed COVID-19 cases (including 51 imported cases and 106 secondary cases) and 30 asymptomatic infections were reported in Ningbo City. The proportion of asymptomatic infections had an increasing trend. The proportion of elder people in the asymptomatic infections was lower than younger people, and the difference was statistically significant (Fisher’s Exact Test, P = 0.034). There were 22 clusters associated with 167 SARS-CoV-2 infections, among which 29 cases were asymptomatic infections, accounting for 17.37%. We found that the secondary attack rate (SAR) of asymptomatic infections was almost the same as that of symptomatic cases, and no statistical significance was observed (χ2 = 0.052, P = 0.819) by Kruskal–Wallis test. The effective reproduction number (Reff) was 1.43, which revealed that the transmissibility of SARS-CoV-2 was moderate. If the interventions had not been strengthened, the duration of the outbreak would have lasted about 16 months with a simulated attack rate of 44.15%. The total attack rate (TAR) and duration of the outbreak would increase along with the increasing delay of intervention. SARS-CoV-2 had moderate transmissibility in Ningbo City, China. The proportion of asymptomatic infections had an increase trend. Asymptomatic infections had the same transmissibility as symptomatic infections. The integrated interventions were implemented at different stages during the outbreak, which turned out to be exceedingly effective in China.

CANDECOMP/PARAFAC (CP) decomposition is widely used in various online social network (OSN) applications. However, it is inefficient when dealing with massive and incremental data. Some incremental CP decomposition (ICP) methods have been proposed to improve the efficiency and process evolving data, by updating decomposition results according to the newly added data. The ICP methods are efficient, but inaccurate because of serious error accumulation caused by approximation in the incremental updating. To promote the wide use of ICP, we strive to reduce its cumulative errors while keeping high efficiency. We first differentiate all possible errors in ICP into two types: the cumulative reconstruction error and the prediction error. Next, we formulate two optimization problems for reducing the two errors. Then, we propose several restarting strategies to address the two problems. Finally, we test the effectiveness in three typical dynamic OSN applications. To the best of our knowledge, this is the first work on reducing the cumulative errors of the ICP methods in dynamic OSNs.

Many recent works utilize scheduling to minimize the Flow Completion Time (FCT) in Data Center Networks (DCN), like PIAS using Shortest Job First (SJF) scheduling and pFabric using Shortest Remaining Size First (SRSF) scheduling. However, they only consider the flow size information, without consideration of available bandwidth of the network, leading to inferior performance when the network is congested. Besides, information on flow size is hard to obtain in practice. Moreover, although a centralized scheduler may have optimal scheduling decisions, it suffers from high system overhead. Therefore, a new DCN scheme is expected which is deployment-friendly and implements SRTF scheduling in a distributed manner. In this paper, we propose D-SRTF , a light-weight yet effective DCN scheme to implement SRTF scheduling. D-SRTF determines the remaining time of each flow according to the estimated remaining flow size and the available bandwidth, in order to determine the priority of each flow. Switches perform Strict Priority (SP) scheduling according to the priority of each flow, in order to realize SRTF scheduling. Experiments show that D-SRTF performs better than the currently best implementable scheme, PIAS, and could perform better than pFabric if information on flow size is available.

To attract more customers, a cloud provider tends to give some discounts to a customer if he/she rents a plenty of resources. Under this situation, a group of customers who need homogeneous cloud instances with various deadlines are prone to purchasing resources in a collaborative manner, i.e., using a coalition game, to reduce purchase costs. It is essential to design a mechanism that enables all customers to voluntarily and happily collaborate while ensuring that each customer pays at the lowest cost possible. To address this issue, we propose a mechanism to show collaborative interactions between customers and determine the number of service programs purchased from each provider to charge each cloud customer a minimum cost. We establish a coalition game based on multi-customer resource procurement and prove that there exists a unique optimal solution in the coalition game, while satisfying individual stability and group stability. In addition, the optimal solution is a solution in which the selected service program of each coalition optimizes the cost per customer and maximizes resource utilization. We propose a heuristic Deadline-constrained Resource Coalition Allocation (DRCA) algorithm to calculate the near-optimal solution. A backtracking algorithm is proposed to calculate the pseudo maximum resource utilization of the provided programs by improving the rectangular packing. Extensive experiments are performed to verify the feasibility and effectiveness of the proposed algorithm.

With the development of lightweight container virtualization technology, it becomes possible to quickly and efficiently deploy software in edge computing environments. When allocating resources to containers in edge nodes, end-to-end latency is an important performance indicator for evaluating strategies. However, it is often difficult to develop a delay model of the container cluster to obtain the real-time end-to-end delay of the service request flow. The state information is always extremely complicated in edge computing environment. Different container service combinations reach edge computing nodes at different time periods. The arrival rate of each container service flow also changes over time. In this paper, we first develop a dynamic M/D/1 queuing model to analyze the end-to-end delay of the data packets of the container service flow and use the average packet delay as the optimization goal of the Edge container resource allocation problem. Then a delay-sensitive resource allocation algorithm based on A3C (Asynchronous Advantage Actor–Critic) is proposed to solve this problem. Finally, we utilize the ESN (Echo state network) to improve the traditional A3C algorithm. Simulation shows that the ESN-based critic A3C(EC-A3C) algorithm has better performance by at least 10.9% than other algorithms in terms of latency and throughput and greatly improves the convergence speed of the network.

Objective: Cervical cancer (CC) ranks fourth most diagnosed cancer and cancer mortality in women. Long non-coding RNAs (lncRNAs) take important roles in CC development. This study aimed to identify more and novel competing endogenous RNA (ceRNA) mechanisms of lncRNAs in CC. Materials and methods: The miRNA expression dataset GSE20592 and lncRNA/mRNA expression dataset GSE63514 were downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs), differentially expressed lncRNAs (DElncRNAs), and differentially expressed miRNAs (DEmiRNAs) between CC tumor and normal samples were identified with the criteria of adj.P.Value 2. Functional enrichment analysis was performed for DEGs. The interaction pairs among lncRNAs, miRNAs and mRNAs were predicted and the ceRNA network was then constructed. Survival analysis was performed based on the TCGA dataset. Results: Totally, 42 DEmiRNAs, 25 DElncRNAs, and 518 DEGs were identified in CC tumor samples versus normal tissues. The DEGs were associated with ‘GO:0006260: DNA replication’, ‘GO:0051301: cell division’, and ‘hsa01100:Metabolic pathways’. The ceRNA network consisted of 878 lncRNA-miRNA-mRNA pairs. Of the miRNAs, lncRNAs, and genes with the top 10 interaction degrees in the ceRNA network, the upregulated cyclin dependent kinase inhibitor 2A gene (CDKN2A) was targeted by the downregulated DEmiRNAs including hsa-miR-125b-5p and hsa-miR-125a-5p, which were targeted by the upregulated DElncRNA BBOX1-AS1. The high expression level of CDKN2A contributed to the poor overall survival of patients with CC. Conclusions: The BBOX1-AS1-hsa-miR-125b-5p/hsa-miR-125a-5p-CDKN2A ceRNA network is of great value in CC development.

This study aims to investigate the application value of O-arm navigation system in sacroiliac screw placement for the treatment of unstable pelvic ring injury. A total of 40 patients (mean age = 30.75 ± 14.99 years, 25 males, 15 females) were included. From January 2016 to July 2018, 40 patients with posterior pelvic ring injury treated in our hospital were included. Of them, 19 patients underwent O-arm navigation for screw placement (O-arm group) while the other 21 received C-arm fluoroscopy guidance (C-arm group) for sacroiliac screw placement. Intraoperative outcomes and the outcome of screw placement were compared between groups. The quality of radiological images was assessed by Matta’s radiological outcome grade. The outcome of complex pelvic fracture treatment was evaluated by Majeed Functional score. All demographic and clinical characteristics were comparable between the two groups. Compared with the C-arm groups, the O-arm group had a shorter surgery time (33.19 ± 3.14 vs. 48.35 ± 4.38 min, P

Traditional data centers are shifted toward the cloud computing paradigm. These data centers support the increasing demand for computational and data storage that consumes a massive amount of energy at a huge cost to the cloud service provider and the environment. Considerable energy is wasted to constantly operate idle virtual machines (VMs) on hosts during periods of low load. Dynamic consolidation of VMs from overloaded or underloaded hosts is an effective strategy for improving energy consumption and resource utilization in cloud data centers. The dynamic consolidation of VM from an overloaded host directly influences the service level agreements (SLAs), utilization of resources, and quality of service (QoS) delivered by the system. We proposed an algorithm, namely, GradCent, based on the Stochastic Gradient Descent technique. This algorithm is used to develop an upper CPU utilization threshold for detecting overloaded hosts by using a real CPU workload. Moreover, we proposed a dynamic VM selection algorithm called Minimum Size Utilization (MSU) for selecting the VMs from an overloaded host for VM consolidation. GradCent and MSU maintain the trade-off between energy consumption minimization and QoS maximization under specified SLA goal. We used the CloudSim simulations with real-world workload traces from more than a thousand PlanetLab VMs. The proposed algorithms minimized energy consumption and SLA violation by 23% and 27.5% on average, compared with baseline schemes, respectively.

Menglei Zhang,1,2,* Fangyue Zhou,1,* Yuan He,3 Xiang Tao,4 Keqin Hua,1,2 Jingxin Ding1,2 1Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, 200011, People’s Republic of China; 2Shanghai Key Laboratory of Female Reproductive Endocrine Related Diseases, Shanghai, 200011, People’s Republic of China; 3Public Health School of Fudan University, Shanghai, 200032, People’s Republic of China; 4Department of Pathology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, 200011, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jingxin DingDepartment of Gynecology, The Obstetrics and Gynecology Hospital of Fudan University, 128 Shen-Yang Road, Shanghai, 200011, People’s Republic of ChinaTel +86-13918206746Fax +86-21-63455090Email djxdd@sina.comPurpose: This study aimed to establish a predictive model for lymph node involvement (LNI) in patients with borderline ovarian tumor (BOT) using clinicopathological factors.Patients and Methods: We collected clinical data from consecutive patients who underwent lymphadenectomy for BOT between 2001 and 2018 and analyzed their clinicopathological features. Multivariate logistic regression was used to identify all independent risk factors associated with LNI; these were then incorporated into the prediction model.Results: In total, we included 248 patients with BOT who were undergoing lymphadenectomy. These were divided into a training cohort (n=174) and a validation cohort (n=74). When considering histopathological data, 16 and 5 patients were identified to have LNI in the training and validation cohorts, respectively. Overall, 13.5% (21/156) patients with serous BOT had LNI while 0% (0/92) patients with non-serous BOT had LNI. We identified several predictors of LNI: the largest tumor being ≥ 12.2cm in diameter, the presence of lesions on the ovarian surface, and the presence of pelvic or abdominal lesions. We created a prediction model and nomogram that incorporated these three risk factors for serous BOT. The model achieved good discriminatory abilities of 0.951 and 0.848 when predicting LNI in the training and validation cohorts, respectively. The LNI-predicting nomogram had an area under curve (AUC) of 0.951 and generated well-fitted calibration curves.Conclusion: Non-serous BOT may not require lymphadenectomy as part of surgical staging. The individual risk of LNI in patients with serous BOT can be accurately estimated using our prediction model and nomogram. The use of LNI criteria provides a practical way to support the clinician in making an optimal decision relating to surgical scope for patients with BOT.Keywords: lymph node involvement, borderline ovarian tumor, lymphadenectomy, prediction model, nomogram

Aim Optimizing perfusate for static cold storage is one of the key ways of reducing organ dysfunction and rejection in organ transplantation. Here, we tested the effectiveness of the three different solutions for hypothermic uterus preservation. Methods Twenty rats were divided into four groups, five in each group. Uterine grafts were retrieved and perfused in situ. The uteri were preserved at 4°C in normal saline as control group (group NS), hypertonic citrate adenine (group HCA), histidine-tryptophan-ketoglutarate (group HTK), or university of Wisconsin solutions (group UW) for 0, 12, 24, and 48 h, respectively. HE, electron microscopy, TUNEL staining, and Cleaved Caspase3 immunohistochemical staining were assessed at each time point. Results There was no significant difference in the uterine retrieval time, perfusion time, and the amount of perfusion solution in NS, HCA, HTK, and UW groups (p > 0.05). HCA and HTK can well preserve the pathological morphology of rat uterine tissues for up to 24 h, and the apoptosis rates of the two groups are 7.2% and 7.1%, respectively, with no statistical difference (p > 0.05). Still, the protective effect of HTK on the ultrastructure of cells was much better than HCA. There was a significant difference in the apoptosis rate of UW (6.5%), HTK (8.8%), and HCA (9.4%) at 48 h, with mitochondrial and endoplasmic reticulum structure well preserved only in UW. Conclusion At 4°C, normal saline is not suitable to preserve rat uterus for more than 12 h. The morphologic results would favor the use of HTK rather than HCA for short-term hypothermic uterus preservation (≤24 h). UW is better than HTK and HCA for 48 h hypothermic uterus preservation.

Secure search techniques over encrypted cloud data allow an authorized user to query data files of interest by submitting encrypted query keywords to the cloud server in a privacy-preserving manner. However, in practice, the returned query results may be incorrect or incomplete in the dishonest cloud environment. For example, the cloud server may intentionally omit some qualified results to save computational resources and communication overhead. Thus, a well-functioning secure query system should provide a query results verification mechanism that allows the data user to verify results. In this paper, we design a secure, easily integrated, and fine-grained query results verification mechanism, by which, given an encrypted query results set, the query user not only can verify the correctness of each data file in the set but also can further check how many or which qualified data files are not returned if the set is incomplete before decryption. The verification scheme is loose-coupling to concrete secure search techniques and can be very easily integrated into any secure query scheme. We achieve the goal by constructing secure verification object for encrypted cloud data. Furthermore, a short signature technique with extremely small storage cost is proposed to guarantee the authenticity of verification object and a verification object request technique is presented to allow the query user to securely obtain the desired verification object . Performance evaluation shows that the proposed schemes are practical and efficient.

In recent years, erasure codes have become the de facto standard for data protection in large scale distributed cloud storage systems at the cost of an affordable storage overhead. However, traditional erasure coding schemes, such as Reed-Solomon codes, suffer from high reconstruction cost and I/Os. The recent past has seen a plethora of efforts to optimize the tradeoff between the reconstruction cost, I/Os and storage overhead. Quiet different from all prior studies, in this paper, our erasure coding technique makes the first attempt to take advantage of the unequal failure rates across the disks/nodes to optimize the system reliability and reconstruction performance. Specifically, our proposed technique, the Unequal Failure Protection based Local Reconstruction Code (UFP-LRC) divides the data blocks into several unequal-sized groups with local parities, assigning the data blocks stored on more failure-prone disks/nodes into the smaller-sized group, so as to provide unequal failure protection for each group. In this way, by exploiting the nonuniform local parity degrees, the proposed UFP-LRC enables the data blocks that are stored on more failure-prone disks/nodes to tolerate a greater number of failures while suffering from less repair cost than others, leading to a substantial improvement of the overall reliability and repair performance for cloud storage systems. We perform numerical analysis and build a prototype storage system to verify our approach. The analytical results show that the UFP-LRC technique gradually outperforms LRC along the increase of failure rate ratio. Also, extensive experiments show that, when compared to LRC, UFP-LRC is able to achieve a 10 to 15 percent improvement in throughput, and an 8 to 12 percent reduction in decoding latency, while retaining a comparable overall reliability.

In this paper, we try to design a service mechanism for profit optimizations of both a cloud provider and its multiple users. We consider the problem from a game theoretic perspective and characterize the relationship between the cloud provider and its multiple users as a Stackelberg game, in which the strategies of all users are subject to that of the cloud provider. The cloud provider tries to select and provision appropriate servers and configure a proper request allocation strategy to reduce energy cost while satisfying its cloud users at the same time. We approximate its servers selection space by adding a controlling parameter and configure an optimal request allocation strategy. For each user, we design a utility function which combines the net profit with time efficiency and try to maximize its value under the strategy of the cloud provider. We formulate the competitions among all users as a generalized Nash equilibrium problem (GNEP). We solve the problem by employing variational inequality (VI) theory and prove that there exists a generalized Nash equilibrium solution set for the formulated GNEP. Finally, we propose an iterative algorithm (IA), which characterizes the whole process of our proposed service mechanism. We conduct some numerical calculations to verify our theoretical analyses. The experimental results show that our IA algorithm can benefit both of a cloud provider and its multiple users by configuring proper strategies.

Background: Homologous recombination deficiency (HRD) is a common molecular characteristic of genomic instability, and has been proven to be a biomarker for target therapy. However, until now, no research has explored the changes in the transcriptomics landscape of HRD tumors. Methods: The HRD score was established from SNP array data of breast cancer patients from the cancer genome atlas (TCGA) database. The transcriptome data of patients with different HRD scores were analyzed to identify biomarkers associated with HRD. The candidate biomarkers were validated in the gene expression omnibus (GEO) database and immunotherapy cohorts. Results: Based on data from the gene expression profile and clinical characteristics from 1310 breast cancer patients, including TCGA database and GEO database, we found that downstream targets of the cGAS-STING pathway, such as CXCL10, were upregulated in HRD tumors and could be used as a predictor of survival outcome in triple-negative breast cancer (TNBC) patients. Further comprehensive analysis of the tumor immune microenvironment (TIME) revealed that the expression of CXCL10 was positively correlated with neoantigen load and infiltrating immune cells. Finally, in vivo experimental data and clinical trial data confirmed that the expression of CXCL10 could be used as a biomarker for anti-PD-1/PD-L1 therapy. Conclusions: Together, our study not only revealed that CXCL10 is associated with HRD but also introduced a potential new perspective for identifying prognostic biomarkers of immunotherapy.

Objectives This study aimed to investigate the predictive value of laparoscopy for the prediction of optimal cytoreduction and prognosis of epithelial ovarian cancer (EOC) in a Chinese population. Study design This study enrolled 162 EOC patients in Obstetrics and Gynecology Hospital of Fudan University from January 2015 to December 2016. All patients underwent preoperative CT scans and laparoscopic assessments. Each patient was scored according to the CT-based predictive model by Bristow and laparoscopy-based predictive model by Fagotti. The specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC) of each model were calculated. The predictive scores and clinicopathologic factors were all analyzed using the Kaplan-Meier method and multivariate Cox analysis. A prognostic predictive nomogram was formulated in R software. Results The AUCs of the laparoscopy-based predictive model and CT-based predictive model was 0.955 and 0.755 respectively. At a laparoscopic score ≥ 10, the possibility of optimal cytoreduction was 0, and the risk of unnecessary explorative attempts was 6%. Additionally, laparoscopic score, independent of residual tumor size and FIGO stage, was an independent prognostic factor for both overall survival (OS) and recurrence-free survival (RFS) in EOC. Notably, the predictive nomogram that we established further confirmed the prognostic value of laparoscopy for prognostic predictions in EOC. Conclusions Laparoscopy has a better discriminating performance than CT in the prediction of optimal cytoreduction in EOC. Moreover, the laparoscopic score is directly correlated with the survival of EOC patients. The laparoscopic score-based nomogram we established showed good potential to predict the prognosis of EOC patients.

Optical motion capture (MOCAP) is a commonly used technology to record the motion of non-rigid objects with high accuracy in 3D space. However, the MOCAP data has to be processed further before it can be used. The scattered reconstructed motion data must constitute a human configuration by labelling process according to the predefined template, and the missing markers have to be reconstructed to produce a stable motion trajectory. In this work, we propose a novel labelling method for motion sequences. First, a novel graph matching method is employed to determine the connection relationship of the scattered motion data for a single frame. Then, Kalman filtering is used for tracking in the motion sequence. As for the challenge coming from missing markers, we propose a new motion data preprocessing method considering the bone length constraint, which represents the information of variation in the relative position of adjacent markers. The processed motion data is input into a Long-Short Term Memory (LSTM) model to recover the missing markers and de-noise the motion data. The experiment conducted on our own dataset proves that our labelling method achieves a similar effect to Cortex, which is a commonly used commercial motion data analysis software. The experiment on CMU dataset demonstrates that our missing marker reconstruction method can achieve an art-of-state result. The labelling code will be pulished on https://github.com/Lijianfang6930/Graph-Matching-for-Marker-Labelling