Quantivine: A Visualization Approach for Large-Scale Quantum Circuit Representation and Analysis.

Quantum computing is a rapidly evolving field that enables exponential speed-up over classical algorithms. At the heart of this revolutionary technology are quantum circuits, which serve as vital tools for implementing, analyzing, and optimizing quantum algorithms. Recent advancements in quantum computing and the increasing capability of quantum devices have led to the development of more complex quantum circuits. However, traditional quantum circuit diagrams suffer from scalability and readability issues, which limit the efficiency of analysis and optimization processes. In this research, we propose a novel visualization approach for large-scale quantum circuits by adopting semantic analysis to facilitate the comprehension of quantum circuits. We first exploit meta-data and semantic information extracted from the underlying code of quantum circuits to create component segmentations and pattern abstractions, allowing for easier wrangling of massive circuit diagrams. We then develop Quantivine, an interactive system for exploring and understanding quantum circuits. A series of novel circuit visualizations is designed to uncover contextual details such as qubit provenance, parallelism, and entanglement. The effectiveness of Quantivine is demonstrated through two usage scenarios of quantum circuits with up to 100 qubits and a formal user evaluation with quantum experts. A free copy of this paper and all supplemental materials are available at https://osf.io/2m9yh/?view_only=0aa1618c97244f5093cd7ce15f1431f9.

Prostaglandin E2 promotes Th17 differentiation induces corneal epithelial cell apoptosis and participates in the progression of dry eye.

This study is mainly based on T helper type 17 (Th17) cells analysis of the mechanism of prostaglandin E2 (PGE2) promoting the progression of dry eye (DE). Scopolamine and dry environment were used to induce mice DE model. Celecoxib was used to inhibit PGE2. Corneal epithelial cells and CD4+ T cells were used to construct a co-culture system. The osmotic pressure was increased by adding NaCl to simulate DE in vitro. AH6809 and E7046 were used to pre-culture to inhibit EP2/4 in T cells to verify the effect of exogenous PGE2 on Th17 cell differentiation and corneal epithelial cell apoptosis. The function of Th17 cells was analyzed by detecting RORγt and interleukin-17 (IL-17). PGE2 was instilled on the ocular surface to induce DE symptoms of mice. AH6809 and E7046 were used to inhibit EP2/4. The corneal epithelial cell apoptosis was observed by TUNEL. The proportion of Th17 cells in corneal tissue and draining lymph nodes (DLNs) was detected by flow cytometry. In DE mice, the concentration of PGE2 and IL-17 increased in tears, and the proportion of Th17 increased, while inhibition of PGE2 alleviated the symptoms of DE and inhibited Th17 differentiation. Hypertonic environment induces corneal epithelial cells to secrete PGE2. PGE2 promoted the expression of EP2/4 and the differentiation of Th17 cells in vitro. The hypertonic environment promoted PGE2 level and the apoptosis of corneal epithelial cells in the co-culture system. PGE2 alone did not cause corneal epithelial cell apoptosis, while PGE2 promoted apoptosis by promoting Th17. Blocking EP2/4 reduced the induction of Th17 differentiation by PGE2 and the promoted corneal epithelial cell apoptosis. Animal experiments showed that exogenous PGE2 induced DE symptoms. Blocking EP2/4 not only inhibited the proportion of Th17, but also alleviated the apoptosis of corneal epithelial cells caused by PGE2. PGE2 induces aggravation of inflammation by promoting the level of Th17 in the ocular surface, and causes corneal epithelial cell apoptosis, thereby participating in the progression of DE.

A dual-modal graph learning framework for identifying interaction events among chemical and biotech drugs.

Drug-drug interaction (DDI) identification is essential to clinical medicine and drug discovery. The two categories of drugs (i.e. chemical drugs and biotech drugs) differ remarkably in molecular properties, action mechanisms, etc. Biotech drugs are up-to-comers but highly promising in modern medicine due to higher specificity and fewer side effects. However, existing DDI prediction methods only consider chemical drugs of small molecules, not biotech drugs of large molecules. Here, we build a large-scale dual-modal graph database named CB-DB and customize a graph-based framework named CB-TIP to reason event-aware DDIs for both chemical and biotech drugs. CB-DB comprehensively integrates various interaction events and two heterogeneous kinds of molecular structures. It imports endogenous proteins founded on the fact that most drugs take effects by interacting with endogenous proteins. In the modality of molecular structure, drugs and endogenous proteins are two heterogeneous kinds of graphs, while in the modality of interaction, they are nodes connected by events (i.e. edges of different relationships). CB-TIP employs graph representation learning methods to generate drug representations from either modality and then contrastively mixes them to predict how likely an event occurs when a drug meets another in an end-to-end manner. Experiments demonstrate CB-TIP's great superiority in DDI prediction and the promising potential of uncovering novel DDIs.

Factors associated with interstitial lung disease in patients with rheumatoid arthritis: A systematic review and meta-analysis.

OBJECTIVES: Interstitial lung disease (ILD) is frequent in patients with rheumatoid arthritis (RA) and is a potentially life-threatening complication with significant morbidity and mortality. This meta-analysis aims to systematically determine the factors associated with the development of rheumatoid arthritis-related interstitial lung disease (RA-ILD). MATERIALS AND METHODS: All primary studies which reported the factors associated with of RA-ILD were eligible for the review except case reports. The Cochrane Library, PubMed, Embase, Web of Science, Chinese Biological Medicine Database (CBM), China National Knowledge Infrastructure (CNKI), and WANFANG electronic databases were searched through to December 30, 2022, for studies investigating the factors associated with RA-ILD. The methodological quality assessment of the eligible studies was performed using the Newcastle-Ottawa Scale (NOS). 2 reviewers extracted relevant data independently. Then, weighed mean differences (WMDs) or pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were obtained for the relationships between the factors and RA-ILD. The statistical meta-analysis, subgroup and sensitivity analyses were performed using the Review Manager 5.3, and publication bias with Egger's test were performed using the Stata12.0 software. RESULTS: A total of 22 articles were screened for a meta-analysis which involved 1887 RA-ILD patients and 8066 RA without ILD patients. Some identified factors that were associated with an increased risk of RA-ILD included male sex (OR = 1.92, 95% CI: 1.54-2.39; P < 0.00001), older age (WMD = 5.77 years, 95% CI: 3.50-8.04; P < 0.00001), longer duration of RA (WMD = 0.80 years, 95% CI 0.12-1.47; P = 0.02), older age at onset of RA (WMD = 6.41 years, 95% CI: 3.17-9.64; P = 0.0001), smoking (OR = 1.69, 95% CI: 1.30-2.18; P < 0.0001). Five factors of laboratory items associated with the development of RA-ILD were evaluated in the meta-analysis. Compared with RA without ILD patients, positive rheumatoid factor (RF) (OR = 1.72, 95% CI: 1.47-2.01; P < 0.00001) and positive anti-citrullinated protein antibodies (ACPA) (OR = 1.58, 95% CI: 1.31-1.90; P < 0.00001) increased the risk of RA-ILD. Meanwhile, RF titer (WMD = 183.62 (IU/mL), 95% CI: 66.94-300.30; P = 0.002) and ACPA titer (WMD = 194.18 (IU/mL), 95% CI: 115.89-272.47; P < 0.00001) were significantly associated with increased risk of RA-ILD. Elevated erythrocyte sedimentation rate (ESR) (WMD = 7.41 (mm/h), 95% CI: 2.21-12.61; P = 0.005) and C-reactive protein (CRP) (WMD = 4.98 (mg/L), 95% CI: 0.76-9.20; P = 0.02) were also significantly associated with the development of the RA-ILD, whereas antinuclear antibody (ANA) positive status was not significantly associated with increased risk of RA-ILD (OR = 1.27, 95% CI: 1.00-1.60; P = 0.05). CONCLUSIONS: This meta-analysis showed that male gender, older age, longer duration of RA, older age at onset of RA, smoking, positive RF, positive ACPA, elevated RF titer, elevated ACPA titer, higher ESR and higher CRP were associated with RA-ILD.

Hydrodynamic Kelvin-Helmholtz instability on metallic surface.

Kelvin-Helmholtz instability on metallic surface is relevant to intense oblique impact in many physical processes such as explosive welding, Inertial Confinement Fusion and planetary impact events. Evolution of instability results in the formation of wavy morphology leading to material bonding or even mixing. However, mostly due to lack method to describe the dynamic behavior, instability mechanism controlled by elastoplastic properties of metal remains elusive. Here, we introduce a theory to reveal the evolution characteristics aroused by tangential velocity. Our simulations find that the unstable metallic surfaces exhibit amplitude growth and tangential motion by overcoming the depression of yield strength to generate wavy morphology. For diverse loading velocities, corrugated surfaces and material properties, an instability boundary distinguishes all unstable evolutions. Our analytical method with scale-independent variables reproducing numerical findings reveals plentiful characteristics of instability in strength materials. For designed loading velocities and material in oblique impact experiment in laboratory, the property of corrugated surfaces becomes an important factor to determine instability evolution.

Clinical treatment of lumbar disc herniation.

OBJECTIVE: To investigate the clinical effect of radiofrequency ozone and injection of anti-inflammatory analgesic solution into the internal orifice of nerve root combined with traditional Chinese medicine hook operation in the treatment of lumbar disc herniation. METHODS: Patients with lumbar disc herniation who were admitted to our hospital on December 20, 2017 and June 19, 2019 were selected as the main research objects, and the included patients were divided into control group, basic group and comprehensive group by random number table method. Control group was treated with radiofrequency ozone therapy, basic group was treated with injection of anti-inflammatory analgesic solution into the internal orifice of nerve root in addition to the control group, comprehensive group was treated with traditional Chinese medicine hook operation in addition to the basic group. The clinical treatment effects were observed. RESULTS: A total of 153 patients were included in this study, including 40 in the control group, 40 in the basic group, and 73 in the comprehensive group. The results showed that the NRS scores of control group were 3±0.98, 2±0.93 and 2±0.85 at 1 month, 3 months and 1 year after treatment, respectively. NRS scores in the basic group were 3±0.18, 2±0.33, and 2±0.15, respectively. NRS scores in the comprehensive group were 2±0.78, 1±0.54, and 1±0.77, respectively. Compared with the control group, there were significant differences in basic group and comprehensive group at each time point (P < 0. 05). At the same time, compared with the basic group, the NRS score of the comprehensive group was statistically different (P < 0.05). CONCLUSION: Radiofrequency ozone and injection of anti-inflammatory analgesic solution into the internal orifice of nerve root combined with hook operation can obtain good short-term and medium-term effects in the treatment of lumbar disc herniation. It is a safe and effective minimally invasive treatment method. KEY WORDS: Internal orifice of nerve root, Lumbar disc herniation, Ozone.

Alliance chain-based simulation on a new clinical research data pricing model.

Background: Multicenter clinical research faces many challenges, including how to quantitatively evaluate the data contribution of each research center. However, few data pricing model meets the requirements to the scenario. Thus, a suitable mechanism to measure the data value for clinical research is required. Methods: Extensive documents were acquired and analyzed, including a rare disease list from the National Health Commission, data structures of the electronic medical records (EMR) system, diagnosis-related groups (DRGs) regulations from the Health Commission of Zhejiang Province, and the Clinical Service Price List of Zhejiang Province. Nine senior experts were invited as consultants from hospital and enterprises with professional field of clinical research, data governance, and health economics. After brainstorming and expert evaluation, seven data attributes were identified as the main factors affecting the value of medical data. Different weights were assigned for each attribute based on its influence on data value. Each attribute was quantized to an index based on proposed algorithms. The data value models for chronic diseases and other diseases were distinguished given the different sensitivity of data timeliness. A simulation system using blockchain and federated learning techniques was constructed to verify the data pricing model in the scenario of clinical research. Results: A comprehensive clinical data pricing model is proposed and the simulation of three research centers with 50 million real clinical data entries was conducted to verify its effectiveness. It demonstrates that the proposed model can compute medical data value quantitatively. Conclusions: Quantitative evaluation of the value of medical data for multicenter clinical research based on the proposed data pricing model works well in simulation. This model will be improved by real-world applications in the near future.

Rapid On-site identification of geographical origin and storage age of tangerine peel by Near-infrared spectroscopy.

The feasibility of identifying geographical origin and storage age of tangerine peel was explored by using a handheld near-infrared (NIR) spectrometer combined with machine learning. A handheld NIR spectrometer (900-1700 nm) was used to scan the outer surface of tangerine peel and collect the corresponding NIR diffuse reflectance spectra. Principal component analysis (PCA) combined with Mahalanobis distance were used to detect outliers. The accuracies of all models in the anomaly set were much lower than that in calibration set and test set, indicating that the outliers were effectively identified. After removing the outliers, in order to initially explore the clustering characteristics of tangerine peels, PCA was performed on tangerine peels from different origins and the same origin with different storage ages. The results showed that the tangerine peels from the same origin or the same storage age had the potential to cluster, indicating that the spectral data of the same origin or the same storage age had a certain similarity, which laid the foundation for subsequent modeling and identification. However, there were quite a few samples with different origins or different storage ages overlapped and could not be distinguished from each other. In order to achieve qualitative identification of origin and storage age, Savitzky-Golay convolution smoothing with first derivative (SGFD) and standard normal variate (SNV) were used to preprocess the raw spectra. Random forest (RF), K-nearest neighbor (KNN) and linear discriminant analysis (LDA) were used to establish the discriminant model. The results showed that SGFD-LDA could accurately distinguish the origin and storage age of tangerine peel at the same time. The origin identification accuracy was 96.99%. The storage age identification accuracy was 100% for Guangdong tangerine peel and 97.15% for Sichuan tangerine peel. This indicated that the near-infrared spectroscopy (NIRS) combine with machine learning can simultaneously and rapidly identify the origin and storage age of tangerine peel on site.

Bioinspired Scene Classification by Deep Active Learning With Remote Sensing Applications.

Accurately classifying sceneries with different spatial configurations is an indispensable technique in computer vision and intelligent systems, for example, scene parsing, robot motion planning, and autonomous driving. Remarkable performance has been achieved by the deep recognition models in the past decade. As far as we know, however, these deep architectures are incapable of explicitly encoding the human visual perception, that is, the sequence of gaze movements and the subsequent cognitive processes. In this article, a biologically inspired deep model is proposed for scene classification, where the human gaze behaviors are robustly discovered and represented by a unified deep active learning (UDAL) framework. More specifically, to characterize objects' components with varied sizes, an objectness measure is employed to decompose each scenery into a set of semantically aware object patches. To represent each region at a low level, a local-global feature fusion scheme is developed which optimally integrates multimodal features by automatically calculating each feature's weight. To mimic the human visual perception of various sceneries, we develop the UDAL that hierarchically represents the human gaze behavior by recognizing semantically important regions within the scenery. Importantly, UDAL combines the semantically salient region detection and the deep gaze shifting path (GSP) representation learning into a principled framework, where only the partial semantic tags are required. Meanwhile, by incorporating the sparsity penalty, the contaminated/redundant low-level regional features can be intelligently avoided. Finally, the learned deep GSP features from the entire scene images are integrated to form an image kernel machine, which is subsequently fed into a kernel SVM to classify different sceneries. Experimental evaluations on six well-known scenery sets (including remote sensing images) have shown the competitiveness of our approach.

A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales.

As the COVID-19 pandemic continues to threaten various regions around the world, obtaining accurate and reliable COVID-19 data is crucial for governments and local communities aiming at rigorously assessing the extent and magnitude of the virus spread and deploying efficient interventions. Using data reported between January and February 2020 in China, we compared counts of COVID-19 from near-real-time spatially disaggregated data (city level) with fine-spatial scale predictions from a Bayesian downscaling regression model applied to a reference province-level data set. The results highlight discrepancies in the counts of coronavirus-infected cases at the district level and identify districts that may require further investigation.

Detection of hypomimia in patients with Parkinson's disease via smile videos.

BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disease characterized by the impairment of facial expression, known as hypomimia. Hypomimia has serious impacts on patients' ability to communicate, and it is difficult to detect at early stages of the disease. Furthermore, due to bradykinesia or other reasons, it is inconvenient for PD patients to visit the hospital. Therefore, it is appealing to develop an auxiliary diagnostic method that remotely detects hypomimia. METHODS: We proposed an automatic detection system for Parkinson's hypomimia based on facial expressions (DSPH-FE). DSPH-FE provides a convenient remote service for those who potentially suffer from hypomimia and only requires patients to input their facial videos. Specifically, patients can detect hypomimia through two aspects: geometric features and texture features. Geometric features focus on visually representing structures of facial muscles. Facial expression factors (FEFs) are used as the first metric to quantify the current activation state of the facial muscles. Facial expression change factors (FECFs) are subsequently used as the second metric to calculate the moving trajectories of the activation states in the videos. Geometric features primarily concentrate on spatial information, with little involvement of temporal information. Thus, the extended histogram of oriented gradients (HOG) algorithm is introduced. This algorithm can extract texture features within multiple continuous frames and incorporate the temporal information into the features. Finally, these features are applied to four machine learning algorithms to model the relationship between these features and hypomimia. RESULTS: The DSPH-FE detection system achieved the best performance when concatenating geometric features and texture features, resulting in a F1 score of 0.9997. The best F1 scores achieved with geometric features and texture features were 0.8286 and 0.9446, respectively. This indicated that both geometric features and texture features have an ability to predict hypomimia, and demonstrated that temporal information can boost the model performance. Thus, DSPH-FE is an effective supportive tool in the medical management of PD patients. CONCLUSIONS: Comprehensive experiments demonstrated that proposed features fit well with real-world videos and are beneficial in the clinical diagnosis of hypomimia. In particular, hypomimia had a greater impact on eyes and mouths when patients are smiling.

Linear analysis of Atwood number effects on shear instability in the elastic-plastic solids.

The evolution of shear instability between elastic-plastic solid and ideal fluid which is concerned in oblique impact is studied by developing an approximate linear theoretical model. With the velocities expressed by the velocity potentials from the incompressible and irrotational continuity equations and the pressures obtained by integrating momentum equations with arbitrary densities, the motion equations of the interface amplitude are deduced by considering the continuity of normal velocities and the force equilibrium with the perfectly elastic-plastic properties of solid at interface. The completely analytical formulas of the growth rate and the amplitude evolution are achieved by solving the motion equations. Consistent results are performed by the model and 2D Lagrange simulations. The characteristics of the amplitude development and Atwood number effects on the growth are discussed. The growth of the amplitude is suppressed by elastic-plastic properties of solids in purely elastic stage or after elastic-plastic transition, and the amplitude oscillates if the interface is stable. The system varies from stable to unstable state as Atwood number decreasing. For large Atwood number, elastic-plastic properties play a dominant role on the interface evolution which may influence the formation of the wavy morphology of the interface while metallic plates are suffering obliquely impact.

Data governance system of the National Clinical Research Center for Child Health in China.

BACKGROUND: Since the national big data strategy was unveiled at the fifth plenary session of the 18th CPC (Communist Party of China) Central Committee, the big data industry has been flourishing in China. Various successful industrial data governance systems have emerged with the rapid development of big data technologies and data management theories. City Brain and Enterprise Data Middle Platform are considered the best data governance systems in urban and corporate governance, respectively. However, in the health and medical sectors, issues of data operation occur frequently due to a lack of systematic data governance. These problems need to be urgently addressed, as health and medical data have been defined as national fundamental strategic resources. Clinical researchers have an increasing demand for data analysis. METHODS: Therefore, the Medical Data Governance System (MDGS) has been designed to improve data quality and provide simple and convenient data analysis tools for the National Clinical Research Center for Child Health. The MDGS consists of the Medical Data Platform (MDP) and Operation Management System (OMS). The MDP comprises acquisition layer, middle platform, and application layer that persistently elevates data quality and significantly shortens data analysis duration. Organization construction, management regulations, and technical standards are included in the OMS, which guarantees the sustainable operation of the MDGS. The MDGS was established to advance state-of-the-art and state-of-practice data governance for the health and medical sectors in China. RESULTS: With the first phase of the MDGS, the quantity and quality of research projects increase, research transformation speeds up, and the researchers' job satisfaction increased. CONCLUSIONS: Based on our preliminary achievements, it was necessary and feasible to establish the MDGS. It is important to have comprehensive requirement study, top-level design, refined planning, phase-by-phase implementation, and continual optimization.

Scene Categorization by Deeply Learning Gaze Behavior in a Semisupervised Context.

Accurately recognizing different categories of sceneries with sophisticated spatial configurations is a useful technique in computer vision and intelligent systems, e.g., scene understanding and autonomous driving. Competitive accuracies have been observed by the deep recognition models recently. Nevertheless, these deep architectures cannot explicitly characterize human visual perception, that is, the sequence of gaze allocation and the subsequent cognitive processes when viewing each scenery. In this paper, a novel spatially aware aggregation network is proposed for scene categorization, where the human gaze behavior is discovered in a semisupervised setting. In particular, as semantically labeling a large quantity of scene images is labor-intensive, a semisupervised and structure-preserved non-negative matrix factorization (NMF) is proposed to detect a set of visually/semantically salient regions from each scenery. Afterward, the gaze shifting path (GSP) is engineered to characterize the process of humans perceiving each scene picture. To deeply describe each GSP, a novel spatially aware CNN termed SA-Net is developed. It accepts input regions with various shapes and statistically aggregates all the salient regions along each GSP. Finally, the learned deep GSP features from the entire scene images are fused into an image kernel, which is subsequently integrated into a kernel SVM to categorize different sceneries. Comparative experiments on six scene image sets have shown the advantage of our method.

A Multi-Scale Activity Transition Network for Data Translation in EEG Signals Decoding.

Electroencephalogram (EEG) is a non-invasive collection method for brain signals. It has broad prospects in brain-computer interface (BCI) applications. Recent advances have shown the effectiveness of the widely used convolutional neural network (CNN) in EEG decoding. However, some studies reveal that a slight disturbance to the inputs, e.g., data translation, can change CNN's outputs. Such instability is dangerous for EEG-based BCI applications because signals in practice are different from training data. In this study, we propose a multi-scale activity transition network (MSATNet) to alleviate the influence of the translation problem in convolution-based models. MSATNet provides an activity state pyramid consisting of multi-scale recurrent neural networks to capture the relationship between brain activities, which is a translation-invariant feature. In the experiment, Kullback-Leibler divergence is applied to measure the degree of translation. The comprehensive results demonstrate that our method surpasses the AUC of 0.0080, 0.0254, 0.0393 in 1, 5, and 10 KL divergence compared to competitors with various convolution structures.

Weakly Supervised Complets Ranking for Deep Image Quality Modeling.

Despite the competitive prediction performance, recent deep image quality models suffer from the following limitations. First, it is deficiently effective to interpret and quantify the region-level quality, which contributes to global features during deep architecture training. Second, human visual perception is sensitive to compositional features (i.e., the sophisticated spatial configurations among regions), but explicitly incorporating them into a deep model is challenging. Third, the state-of-the-art deep quality models typically use rectangular image patches as inputs, but there is no evidence that these rectangles can reflect arbitrarily shaped objects, such as beaches and jungles. By defining the complet, which is a set of image segments collaboratively characterizing the spatial/geometric distribution of multiple visual elements, we propose a novel quality-modeling framework that involves two key modules: a complet ranking algorithm and a spatially-aware dual aggregation network (SDA-Net). Specifically, to describe the region-level quality features, we build complets to characterize the high-order spatial interactions among the arbitrarily shaped segments in each image. To obtain complets that are highly descriptive to image compositions, a weakly supervised complet ranking algorithm is designed by quantifying the quality of each complet. The algorithm seamlessly encodes three factors: the image-level quality discrimination, weakly supervised constraint, and complet geometry of each image. Based on the top-ranking complets, a novel multi-column convolutional neural network (CNN) called SDA-Net is designed, which supports input segments with arbitrary shapes. The key is a dual-aggregation mechanism that fuses the intracomplet deep features and the intercomplet deep features under a unified framework. Thorough experimental validations on a series of benchmark data sets demonstrated the superiority of our method.

Context-Aware Deep Spatiotemporal Network for Hand Pose Estimation From Depth Images.

As a fundamental and challenging problem in computer vision, hand pose estimation aims to estimate the hand joint locations from depth images. Typically, the problems are modeled as learning a mapping function from images to hand joint coordinates in a data-driven manner. In this paper, we propose a context-aware deep spatiotemporal network, a novel method to jointly model the spatiotemporal properties for hand pose estimation. Our proposed network is able to learn the representations of the spatial information and the temporal structure from the image sequences. Moreover, by adopting the adaptive fusion method, the model is capable of dynamically weighting different predictions to lay emphasis on sufficient context. Our method is examined on two common benchmarks, the experimental results demonstrate that our proposed approach achieves the best or the second-best performance with the state-of-the-art methods and runs in 60 fps.

Perioperative multidisciplinary implementation enhancing recovery after hip arthroplasty in geriatrics with preoperative chronic hypoxaemia.

We investigated risk factors for postoperative serious adverse events (SAEs) in elderly patients with preoperative chronic hypoxaemia undergone total hip arthroplasty (THA) or hemiarthroplasty and performed an implementation to modify and improve clinical outcome. A retrospective medical record review was performed to identify geriatric patients who receiving THA or hemiarthroplasty at a single university teaching hospital, Kunming, Yunnan, China between January 2009 and August 2017. Total of 450 elderly patients were included in the study. Data were collected on baseline characteristics, detailed treatments, and adverse events. Univariate and multivariate logistic regression analysis were used to identify risk factors for SAEs. In multivariate regression analysis, a higher occurrence of general anaesthesia and multiple episodes of hypotension were associated with higher risk of SAEs (general anesthesia: odds ratio [OR] 5.09, 95% confidence interval [CI] 1.96-13.24, P = 0.001; hypotension time: OR 4.29, 95% CI 1.66-11.10, P = 0.003). After the multidisciplinary implementation, the postoperative length of stay was decreased from 15 days to 10 days (P < 0.0001); incidence of SAEs was decreased from 21.1% to 7.0% (P = 0.002), and the all-cause mortality rate within 30 days decreased from 4.6% to 1.0% (P = 0.040). Our observational study demonstrated that an increasing application of general anaesthesia and longer time of hypotension were associated with an increased risk of postoperative SAEs in patients after THA or hemiarthroplasty. Additionally, optimizing stable haemodynamics under higher application of combined-spinal epidural anaesthesia was associated with improved outcome up to 30 days after THA or hemiarthroplasty.

Effects of preoperative chronic hypoxemia on geriatrics outcomes after hip arthroplasty: A hospital-based retrospective analysis study.

The partial pressure of oxygen decreases as altitude increases, the preoperative chronic hypoxemia (CH) may have a plausible clinical impact. Risk factors for postoperative serious adverse events (pSAEs) in patients living in high altitudes during primary hip arthroplasty (HA) are not clear.This is an observational study embracing patients from January 1, 2011 to December 31, 2015 at Yan'an Hospital of Kunming City, a 1338-bed municipal teaching hospital of Kunming Medical University. Univariate analysis revealed that significant differences between patients with and without preoperative CH occurred in intraoperative hypotension (77 [33%] vs 34 [47%], P = .040) and that significant differences between patients with and without pSAEs occurred in following variables: preoperative CH (32 [57%] vs 199 [80%], P < .001), intraoperative hypotension (37 [66%] vs 74 [30%], P < .001), highest noradrenaline support (.09 [.01-.21] vs .03 [.01-.05] µg/kg/min, P < .001), higher application of general anesthesia (15 [27%] vs 29 [12%], P = .004), and lower of combined-spinal epidural anesthesia (CSEA) (21 [37%] vs 165 [66%], P < .001). The general anesthesia and intraoperative hypotension remained the independent risk factors for pSAEs (P < .05), while the preoperative CH presented by decreasing its risk (P < .05).This study suggests that various intraoperative events including general anesthesia, hypotension were risk factors for the development of pSAEs. Preoperative CH, presenting with decreased incidence of intensive care unit (ICU) admission and pSAEs, may mimic hypoxic preconditioning in organic protection, for which further study is needed to uncover the underlying mechanisms.

A Recommendation System to Facilitate Business Process Modeling.

This paper presents a system that utilizes process recommendation technology to help design new business processes from scratch in an efficient and accurate way. The proposed system consists of two phases: 1) offline mining and 2) online recommendation. At the first phase, it mines relations among activity nodes from existing processes in repository, and then stores the extracted relations as patterns in a database. At the second phase, it compares the new process under construction with the premined patterns, and recommends proper activity nodes of the most matching patterns to help build a new process. Specifically, there are three different online recommendation strategies in this system. Experiments on both real and synthetic datasets are conducted to compare the proposed approaches with the other state-of-the-art ones, and the results show that the proposed approaches outperform them in terms of accuracy and efficiency.