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Objective To compare outcomes after surgery between patients who were not prescribed opioids and patients who were prescribed opioids. Summary of background data Postoperative opioid prescriptions carry significant risks. Understanding outcomes among patients who receive no opioids after surgery may inform efforts to reduce these risks. Methods We performed a retrospective study of adult patients who underwent surgery between January 1, 2019 and October 31, 2019. The primary outcome was the composite incidence of an emergency department visit, readmission, or reoperation within 30 days of surgery. Secondary outcomes were postoperative pain, satisfaction, quality of life, and regret collected via postoperative survey. A multilevel, mixed-effects logistic regression was performed to evaluate differences between groups. Results In a cohort of 22,345 patients, mean age (standard deviation) was 52.1 (16.5) years and 13,269 (59.4%) patients were female. About 3175 (14.2%) patients were not prescribed opioids, of whom 422 (13.3%) met the composite adverse event endpoint compared to 2255 (11.8%) of patients not prescribed opioids (P = 0.015). Patients not prescribed opioids had a similar probability of adverse events {11.7% [95% confidence interval (CI) 10.2%-13.2%] vs 11.9% (95% CI 10.6%-13.3%]}. Among 12,872 survey respondents, patients who were not prescribed an opioid had a similar rate of high satisfaction [81.7% (95% CI 77.3%-86.1%) vs 81.7% (95% CI 77.7%-85.7%)] and no regret [(93.0% (95% CI 90.8%-95.2%) vs 92.6% (95% CI 90.4%-94.7%)]. Conclusions Patients who were not prescribed opioids after surgery had similar clinical and patient-reported outcomes as patients who were prescribed opioids. This suggests that minimizing opioids as part of routine postoperative care is unlikely to adversely affect patients.

In this article, we investigate the problem of social link inference in a target location-aware social network (LSN), which aims at predicting the unobserved links between users within the network. This problem is critical for downstream applications, including network completion and friend recommendation. In addition to the network structures commonly used in general link prediction, the studies tailored for social link inference in an LSN leverage user trajectories from the spatial aspect. However, the temporal factor lying in user trajectories is largely overlooked by most of the prior studies, limiting the capabilities of capturing the temporal relevance between users. Moreover, effective user matching by fusing different views, i.e., social, spatial, and temporal factors, remains unresolved, which hinders the potential improvement of link inference. To this end, this article devises a novel multiview matching network (MVMN) by regarding each of the three factors as one view of any target user pair. MVMN enjoys the flexibility and completeness of modeling each factor by developing its suitable matching module: 1) location matching module; 2) time-series matching module; and 3) relation matching module. Each module learns a view-specific representation for matching, and MVMN fuses them for final link inference. Extensive experiments on two real-world data sets demonstrate the superiority of our approach against several competitive baselines for link prediction and sequence matching, validating the contribution of its key components.

Most existing multiview clustering methods are based on the original feature space. However, the feature redundancy and noise in the original feature space limit their clustering performance. Aiming at addressing this problem, some multiview clustering methods learn the latent data representation linearly, while performance may decline if the relation between the latent data representation and the original data is nonlinear. The other methods which nonlinearly learn the latent data representation usually conduct the latent representation learning and clustering separately, resulting in that the latent data representation might be not well adapted to clustering. Furthermore, none of them model the intercluster relation and intracluster correlation of data points, which limits the quality of the learned latent data representation and therefore influences the clustering performance. To solve these problems, this article proposes a novel multiview clustering method via proximity learning in latent representation space, named multiview latent proximity learning (MLPL). For one thing, MLPL learns the latent data representation in a nonlinear manner which takes the intercluster relation and intracluster correlation into consideration simultaneously. For another, through conducting the latent representation learning and consensus proximity learning simultaneously, MLPL learns a consensus proximity matrix with k connected components to output the clustering result directly. Extensive experiments are conducted on seven real-world datasets to demonstrate the effectiveness and superiority of the MLPL method compared with the state-of-the-art multiview clustering methods.

This study measures financial inclusion for developing countries. Given that financial inclusion is determined by three dimensions: penetration, availability, and use of financial services, this study develops a composite financial inclusion index (FI index) by adding “mobile money”–related indicators to the three dimensions to correspond to the degree of financial inclusion in these economies. To address the main research question, a measure of financial inclusion is constructed using a two-stage principal component analysis (PCA) method by assigning weights endogenously. Data were collected through secondary sources, including the World Bank (WB) and International Monetary Fund (IMF) reports for 2012–2018International Monetary Fund, 2020 International Monetary Fund (IMF) reports for 2012–2018. A new detailed index of financial inclusion measurement termed overall FI index was built based on the study. It is a useful tool for policymaking and evaluation. Our composite FI index is easy to calculate and comparable across economies over time. Particularly, this approach can address the criticism of arbitrary weight selection and comprehensively reflects the degree of financial inclusion.

Outsourcing medical data to healthcare cloud has become a popular trend. Since medical data of patients contain sensitive personal information, they should be encrypted before outsourcing. However, information retrieval methods based on plaintext cannot be directly applied to encrypted data. In this paper, we present a new cryptographic primitive named conjunctive keyword search with secure channel free and autonomous path delegation function (AP-SCF-PECKS), which can be applied in scenarios where patients want to search for and autonomous delegate their private medical information without revealing their private key. Particularly, the proposed solution allows patients to set up multi-hop delegation path with their preferences, and the delegated doctors in the path can search for and access the patient's private medical information with priority from high to low. Patients can ensure that authorized doctors are always trustworthy, and unauthorized users cannot obtain the private medical information of patients. Moreover, the scheme supports the conjunctive keyword search, secure channel free, and is secure against chosen keyword attack, chosen ciphertext attack, and keyword guessing attack. The security of proposed scheme has been formally proved in the standard model. Finally, the performance evaluations demonstrate that the overhead of proposed scheme are modest for healthcare cloud scenarios.

Data on predictors and time to relapse in patients with psoriasis who discontinue therapy in a real-world setting are scarce.To investigate predictors of relapse after withdrawal of ustekinumab in patients with psoriasis.This study screened 500 patients with psoriasis who received ustekinumab (669 treatment episodes) between 2011 and 2018. Overall, 202 patients (accounting for 304 treatment episodes) who had responded to therapy and were withdrawn from ustekinumab treatment were included.The cumulative probabilities of being relapse-free at 6, 12, 18, 24, and 36 months after withdrawal from ustekinumab treatment were 49.3%, 12.6%, 5.3%, 4.7%, and 1.6%, respectively. Multivariate regression analyses with a generalized estimating equation showed that after adjustments, biologic-naive status, maximum improvement in Psoriasis Area and Severity Index during ustekinumab treatment, time to achieve a 50% improvement in baseline Psoriasis Area and Severity Index score after initiation of ustekinumab, family history of psoriasis, chronic kidney disease, and immunosuppressant use while not taking ustekinumab were significant predictors of time to relapse following discontinuation of ustekinumab.Nonrandomized allocation of duration of treatment and follow-up.Given the high rates of relapse, withdrawal of ustekinumab from patients with well-controlled psoriasis cannot be recommended.

Realistic speech-driven 3D facial animation is a challenging problem due to the complex relationship between speech and face. In this paper, we propose a deep architecture, called Geometry-guided Dense Perspective Network (GDPnet), to achieve speaker-independent realistic 3D facial animation. The encoder is designed with dense connections to strengthen feature propagation and encourage the re-use of audio features, and the decoder is integrated with an attention mechanism to adaptively recalibrate point-wise feature responses by explicitly modeling interdependencies between different neuron units. We also introduce a non-linear face reconstruction representation as a guidance of latent space to obtain more accurate deformation, which helps solve the geometry-related deformation and is good for generalization across subjects. Huber and HSIC (Hilbert-Schmidt Independence Criterion) constraints are adopted to promote the robustness of our model and to better exploit the non-linear and high-order correlations. Experimental results on the public dataset and real scanned dataset validate the superiority of our proposed GDPnet compared with state-of-the-art model. The code is available for research purposes at http://cic.tju.edu.cn/faculty/likun/projects/GDPnet.

Mobile edge computing (MEC) allows edge servers to be placed at cellular base stations. App vendors like Uber and YouTube can rent computing resources and deploy latency-sensitive applications on edge servers for their users to access. Non-orthogonal multiple access (NOMA) is an emerging technique that facilitates the massive connectivity of 5G networks, further enhancing the capability of MEC. The edge user allocation (EUA) problem faces new challenges in 5G NOMA-based MEC systems. In this study, we investigate the EUA problem in a multi-cell multi-channel downlink power-domain NOMA-based MEC system. The main objective is to help mobile app vendors maximize their benefit by allocating maximum users to edge servers in a specific area at the lowest computing resource and transmit power costs. To this end, we introduce a decentralized game-theoretic approach to effectively select a channel and edge server for each user while fulfilling their resource and data rate requirements. We theoretically and experimentally evaluate our solution, which significantly outperforms various state-of-the-art and baseline approaches.

Aim The Enhanced Recovery After Surgery (ERAS® ) Society guidelines aim to standardise perioperative care in colorectal surgery via 25 principles. We aimed to assess the variation in uptake of these principles across an international network of colorectal units. Method An online survey was circulated amongst European Society of Coloproctology members in 2019/20. For each ERAS® principle, respondents were asked to score how frequently the principle was implemented in their hospital, from 1 ('rarely') to 4 ('always'). Respondents were also asked to recall whether practice had changed since 2017. Subgroup analyses based on hospital characteristics were conducted. Results Of hospitals approached, 58% responded to the survey (195/335), with 296 individual responses (multiple responses were received from some hospitals). The majority were European (163/195 [83.6%]). Overall, respondents indicated they 'most often' or 'always' adhered to most individual ERAS® principles (18/25 [72%]). Variability in uptake of principles was reported, with universal uptake of some principles (e.g., prophylactic antibiotics; early mobilisation) and inconsistency from 'rarely' to 'always' in others (e.g., no nasogastric intubation; no preoperative fasting and carbohydrate drinks). In alignment with 2018 ERAS® guideline updates, adherence to principles for prehabilitation, managing anaemia, and postoperative nutrition appears to have increased since 2017. Conclusions Uptake of ERAS® principles varied across hospitals, and not all 25 principles were equally adhered to. Whilst some principles exhibited a high level of acceptance, others had a wide variability in uptake indicative of controversy or barriers to uptake. Further research into specific principles is required to improve ERAS® implementation.

Hepatocellular carcinoma is one of the commonest cancer in the world. Despite curative resection, recurrence remains the largest challenge. Many risk factors were identified for predicting recurrence, including liver fibrosis and cirrhosis. Transient elastography (Fibroscan) is an accurate tool in measuring liver fibrosis. This study aimed to evaluate the use of preoperative liver stiffness measurement (LSM), with Fibroscan in predicting long-term recurrence of hepatocellular carcinoma (HCC) after curative resection.A prospective cohort study was conducted from February 2010 - June 2017 in Prince of Wales hospital. All consecutive patients with HCC undergone hepatectomy were included. Demographic factors, preoperative LSM, tumor characteristics and operative details were assessed. Primary outcome and secondary outcome were overall survival and disease free survival at 1 year, 3 year and 5 year respectively.A total of 401 cases were included. Patients with LSM ≥12kPa had significantly lower 5-year overall survival rate (75.1% vs 57.3%, p 0.001) and disease free survival rate (45.8% vs. 26.7%, p 0.001). On multivariate analysis, pre-operative creatinine and vascular invasion of tumor were significant factors in predicting early recurrence (p = 0.012 and p = 0.004). LSM ≥12kPa were the only significant factor in predicting late recurrence (p = 0.048).Pre-operative liver stiffness measurement could predict the late recurrence of hepatocellular carcinoma after curative resection.

Secondary amyloid A amyloidosis, a lethal complication, is induced when acute or chronic infection coexists with over-secretion of the serum amyloid A 1 (SAA1) protein and deposition in key internal organs. Previously, using the whole-exome sequencing method, we identified a novel deleterious mutation SAA1.2 in rheumatoid arthritis (RA) patients. However, the role of SAA1 in RA pathogenesis and its complications remains unknown. The purpose of this study was to determine the pathogenetic roles of SAA1 protein isoforms in RA progression. We modified an experimental adenovirus infection protocol to introduce SAA1.2 gene alleles into the knee joints of mice and used SAA1.3 and SAA1.5 as controls. Micro-computed tomography analysis was applied to determine changes in bone morphology and density. Immunohistochemical (IHC) analysis, flow cytometry, enzyme-linked immunosorbent assay (ELISA), and real-time polymerase chain reaction (RT-PCR) were used to investigate disease progression and cytokine alterations in the course of adenoviral SAA-induced knee joint inflammation and bone destruction. We found that the arthritis-inducing effect of SAA1.2 transcription in the knee joints and mutant SAA1 protein secretion in blood resulted in the stimulation of immune responses, leading to CD8+ T cell and pro-inflammatory cytokine elevation, such as interleukin (IL)-6, IL-22, matrix metalloproteinase (MMP)-3, MMP-9, with subsequent synovial inflammation and bone destruction. These findings indicate that SAA1 protein isoforms, particularly SAA1.2, play a significant role in the induction and progression of RA and may have potential value in the early diagnosis and severity prediction of RA.

We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions, or adherent raindrops, from a short sequence of images captured by a moving camera. Our method leverages motion differences between the background and obstructing elements to recover both layers. Specifically, we alternate between estimating dense optical flow fields of the two layers and reconstructing each layer from the flow-warped images via a deep convolutional neural network. This learning-based layer reconstruction module facilitates accommodating potential errors in the flow estimation and brittle assumptions, such as brightness consistency. We show that the proposed approach learned from synthetically generated data performs well to real images. Experimental results on numerous challenging scenarios of reflection and fence removal demonstrate the effectiveness of the proposed method., Project page: https://alex04072000.github.io/SOLD/ Code: https://github.com/alex04072000/SOLD Extension of the CVPR 2020 paper: arXiv:2004.01180

OBJECTIVE Free tissue transfer is widely used for head and neck reconstruction. In certain circumstances, vein grafting is required to elongate free flap pedicles to connect them to appropriate recipient vessels. Because of controversy regarding the use of interposition vein grafts in free tissue reconstruction, this paper reports vein graft indications, techniques, safety, and outcomes for head and neck microvascular surgery. METHODS Twenty-six patients (23 men and 3 women) who underwent interposition vein grafting concurrent with free tissue transfer were included in this study. The most common reason for head and neck reconstruction with vein graft was tumor recurrence, followed by flap salvage. The interposition vein grafts were applied in two manners as temporary arteriovenous (A-V) loop and conduit to extend the length of the free flap for venous drainage. RESULTS The most common reconstructions were anterolateral thigh flaps (15 cases), followed by vastus lateralis myocutaneous (3 cases) and radial forearm (2 cases) flaps. The common recipient vessels were superior thyroid artery, superficial temporal artery and external jugular vein. The free flap loss rate was 7.7% with vein grafts and 4.9 without vein grafts (p = 0.380). The free flap complication rate was 50.0% and 16.8% in patients with and without vein grafts, respectively (p

Metal–organic frameworks (MOFs) containing open metal sites are important materials for acetylene (C2H2) adsorption. However, it is inefficient or even impossible to search suitable MOFs by molecular simulation method in nearly infinite MOFs space. Therefore, machine learning (ML) methods are adopted in the material screening and prediction of high-performance MOFs. In this paper, architecture, chemical and structural features are used to analyze the C2H2 adsorption performance of the MOFs. Different ML algorithms are applied to perform classification and regression analysis to the factors affecting material adsorption. By decision tree (DT) algorithm, it is found that only PV, GSA, and Cu-OMS are sufficient to determine the high adsorption of the MOFs. Furthermore, the influence of topology on the performance of MOFs is obtained. Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Back Propagation Neural Network (BPNN), are introduced to analyze the quantitative structure–property relationship (QSPR) between C2H2 adsorption and the features of MOFs. The prediction of the GBDT model is found to have the highest accuracy, with R2 as 0.93 and RMSE as 11.58. In addition, the GBDT model is used for feature analysis, and the contribution of each feature to the performance is obtained, which is of great significance for the design and analysis of MOFs. The successful application of ML to MOFs screening greatly reduce the calculation time and provides important reference for the design and synthesis of new MOFs.

Cartoon is a common form of art in our daily life and automatic generation of cartoon images from photos is highly desirable. However, state-of-the-art single-style methods can only generate one style of cartoon images from photos and existing multi-style image style transfer methods still struggle to produce high-quality cartoon images due to their highly simplified and abstract nature. In this article, we propose a novel multi-style generative adversarial network (GAN) architecture, called MS-CartoonGAN, which can transform photos into multiple cartoon styles. MS-CartoonGAN uses only unpaired photos and cartoon images of multiple styles for training. To achieve this, we propose to use (1) a hierarchical semantic loss with sparse regularization to retain semantic content and recover flat shading in different abstract levels, (2) a new edge-promoting adversarial loss for producing fine edges, and (3) a style loss to enhance the difference between output cartoon styles and make training process more stable. We also develop a multi-domain architecture, where the generator consists of a shared encoder and multiple decoders for different cartoon styles, along with multiple discriminators for individual styles. By observing that cartoon images drawn by different artists have their unique styles while sharing some common characteristics, our shared network architecture exploits the common characteristics of cartoon styles, achieving better cartoonization and being more efficient than single-style cartoonization. We show that our multi-domain architecture can theoretically guarantee to output desired multiple cartoon styles. Through extensive experiments including a user study, we demonstrate the superiority of the proposed method, outperforming state-of-the-art single-style and multi-style image style transfer methods.

Background The insidious nature of BCG-osteomyelitis makes it challenging for clinicians to detect it early on. Methods This 12-year retrospective analysis was conducted at a single tertiary hospital in central Taiwan. Electronic medical records of pediatric patients treated for BCG-osteomyelitis were reviewed. Demographics, clinical features, and laboratory findings were compared with patients diagnosed with culture-proven pyogenic osteomyelitis. Results In total, eight patients fulfilled our inclusion criteria. Their median age was 16 months, and no obvious gender prevalence was found. Six of the eight patients had lesions involving the lower extremities. When compared with the pyogenic osteomyelitis group, age of disease onset was found to be significantly younger in the BCG osteomyelitis group (p=0.038). Absence of fever and pain in the BCG osteomyelitis group was found to be statistically significant when compared with the pyogenic group (p=0.002 and p=0.026 respectively). CRP and ESR were found to be significantly higher in the pyogenic osteomyelitis group (p=0.000 and p=0.004 respectively). Conclusion BCG-related osteomyelitis must be considered when evaluating an afebrile child presenting with an unexplainable swelling or limp, and especially when the lesion is located on a lower limb. Laboratory studies may reveal normal WBC and CRP, with a normal to modest elevation of ESR. Imaging studies, including plain radiographs, magnetic resonance imaging (MRI), or computed tomography (CT) should be employed to rule out BCG-related osteomyelitis. Early diagnosis help minimize inappropriate antibiotics use, and may lead to a better outcome.

Background The aim of the study was to explore the treatment outcomes and prognostic factors for patients with previously irradiated but unresectable recurrent head and neck squamous cell carcinoma (rHNSCC) treated by stereotactic body radiotherapy (SBRT) plus cetuximab at a single institute in Taiwan. Methods From February 2016 to March 2019, 74 patients with previously irradiated but unresectable rHNSCC were treated with SBRT plus cetuximab. All patients received irradiation to the gross tumor and/or nodal area with 40-50 Gy in five fractions, with each fraction interval ≥ 2 days over a 2-week period by using the CyberKnife M6 machine. An18F-fluorodeoxyglucose positron emission tomography-computed tomography (PET-CT) scan was performed before treatment for treatment target delineation (n = 74) and 2 months later for response evaluation (n = 60). The median follow-up time was 9 months (range 1-36 months). Results The treatment response rate was complete response: 25.0%, partial response: 41.7%, stable disease: 11.7%, and progressive disease: 21.7% based on the criteria of the Response Evaluation Criteria in Solid Tumors (n = 72) and complete metabolic response: 21.7%, partial metabolic response: 51.7%, stable metabolic disease: 13.3%, and progressive metabolic disease: 13.3% based on PET-CT (n = 60), respectively. The 1-/2-year overall survival (OS) and progression-free survival (PFS) rates were 42.8%/22.0% and 40.5%/19.0%, respectively. In the logistic regression model, a re-irradiation interval > 12 months was observed to be the only significant prognostic factor for a favorable treatment response. In the Cox proportional hazards model, a re-irradiation interval > 12 months and gross tumor volume (GTV) ≦ 50 ml were favorable prognostic factors of OS and PFS. Conclusion SBRT plus cetuximab provides a promising salvage strategy for those patients with previously irradiated but unresectable rHNSCC, especially those with a re-irradiation interval > 12 months or GTV ≦ 50 ml.

Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing. Several methods have recently been developed, with an aim to extract intuitive and interpretable deformation components. However, these techniques suffer from fundamental limitations especially for meshes with noise or large-scale nonlinear deformations, and may not always be able to identify important deformation components. In this paper we propose a mesh-based variational autoencoder architecture that is able to cope with meshes with irregular connectivity and nonlinear deformations, assuming that the analyzed dataset contains meshes with the same vertex connectivity, which is common for deformation analysis. To help localize deformations, we introduce sparse regularization in this framework, along with spectral graph convolutional operations. Through modifying the regularization formulation and allowing dynamic change of sparsity ranges, we improve the visual quality and reconstruction ability of the extracted deformation components. Our system also provides a nonlinear approach to reconstruction of meshes using the extracted basis, which is more effective than the current linear combination approach. As an important application of localized deformation components and a novel approach on its own, we further develop a neural shape editing method, achieving shape editing and deformation component extraction in a unified framework, and ensuring plausibility of the edited shapes. Extensive experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative evaluations. We also demonstrate the effectiveness of our method for neural shape editing.

Purpose This study aimed to evaluate the effects of thyroid-stimulating hormone (TSH) suppressive therapy on bone mineral density (BMD) and bone turnover markers (BTMs) in differentiated thyroid cancer (DTC) patients after postoperative 1-2 years in Northeast China. Methods Five male, sixteen premenopausal, and eight postmenopausal female DTC patients receiving TSH suppressive therapy after thyroidectomy were enrolled. Patients were matched with healthy controls in a ratio of 1:2. All participants completed postoperative 1-year follow-up, and postmenopausal women completed 2-year follow-up. We measured BMD of the lumbar spine (LS), femoral neck (FN), and total hip (TH) using dual-energy X-ray absorptiometry (DXA). Bone formation marker P1NP and bone resorption marker β-CTX were also evaluated. Fracture risks were assessed by FRAX. Results There was no difference in BMD and BTMs between DTC patients and controls in the male group at 1-year follow-up. In the premenopausal women, the LS-BMD, FN-BMD, and TH-BMD in DTC patients were all higher than those in controls, but only FN-BMD showed a significant difference. The change rate of P1NP showed a significant difference between DTC patients and controls, while no difference was found in the β-CTX level. In the postmenopausal women, no difference in BMD and BTMs were observed between DTC patients and controls at the 1-year and 2-year follow-up. Conclusion Our study indicated that postoperative 1-year TSH suppressive therapy did not show detrimental effects on BMD and BTMs in men, premenopausal, and postmenopausal DTC patients. The 2-year postoperative TSH suppressive therapy did not lead to additional loss of bone mass in postmenopausal DTC patients.

This study examined the joint associations of sleep patterns and physical activity (PA) with all-cause, cardiovascular disease (CVD), and cancer mortality.A total of 341,248 adults (mean age = 39.7 years; men: 48.3%) were included in the study, with a 15-year follow-up. Participants reported sleep duration and disturbances (difficulty falling asleep, easily awakened, or use of sleeping medication). PA was classified into 4 levels:7.5, 7.5-14.9, 15.0-29.9, and ≥30.0 metabolic equivalent hours per week (MET-h/week). To understand the joint associations of sleep patterns and PA with mortality, Cox proportional hazard models were conducted, with exposure variables combining sleep duration/disturbances and PA.Compared with the reference group (sleeping 6-8 h/day), individuals who slept8 h/day had higher risk for all-cause mortality (hazard ratio (HR) = 1.307, 95% confidence interval (95%CI): 1.248-1.369), CVD mortality (HR = 1.298, 95%CI: 1.165-1.445), and cancer mortality (HR = 1.128, 95%CI: 1.042-1.220). Short sleep duration was not associated with mortality risk. Increased risk of all-cause and CVD mortality was found in participants who had difficulty falling asleep (HR = 1.120, 95%CI: 1.068-1.175; HR = 1.163, 95%CI: 1.038-1.304, respectively), and used sleeping medication (HR = 1.261, 95%CI: 1.159-1.372; HR = 1.335, 95%CI: 1.102-1.618, respectively) compared with those who slept well. Long sleep duration and sleep disturbances were not associated with risk of all-cause and CVD mortality among individuals achieving a PA level of ≥15 MET-h/week, and in particular among those achieving ≥30 MET-h/week.Long sleep duration, difficulty falling asleep, and use of sleeping medication were related to a higher risk of death. Being physically active at a moderate intensity for 25-65 min/day eliminated these detrimental associations.

Objectives Emerging evidence implicates atypical plasticity in the neurophysiology of autism spectrum disorder (ASD). Specifically, autistic people demonstrated hyperplasticity in response to theta-burst stimulation (TBS). We hypothesized that autistic adults would display hyperplasticity to TBS and that repetitive transcranial magnetic stimulation (rTMS) – which potentiates brain inhibitory mechanisms – would ‘stabilize’ hyperplasticity. Methods Using a randomized, cross-over design, plasticity was assessed using TBS in the left motor cortex (M1) in 31 autistic adults and 30 sex-, intelligence quotient-, and age-matched controls. Autistic adults (n = 29) were further randomized (1:1) to receive a single session of active (n = 14) or sham (n = 15) rTMS (6000 pulses at 20 Hz) over left M1 and plasticity was reassessed on the next day following rTMS. Results Both long-term potentiation (LTP) and long-term depression (LTD) were significantly increased in the ASD group, indicating hyperplasticity. Active, but not sham rTMS, attenuated LTD in autistic adults. Conclusions We provided further evidence for the presence of brain hyperplasticity in ASD. To our knowledge, this is the first study to show preliminary evidence that an excessive LTD in ASD can be ‘stabilized’ using rTMS. Such ‘stabilizing’ effect of rTMS on LTP was not observed, likely due to small sample size or a more specific ‘attenuating’ effect of rTMS on LTD, compared to LTP. Significance These findings indicate atypical brain inhibitory mechanisms behind hyperplasticity in ASD. Utilizing a larger sample, future replication studies could investigate therapeutic opportunities of ‘mechanism-driven’ rTMS.

Broadcast encryption provides a promising technique of data access control for specified users in cloud computing. A data uploader can generate a ciphertext for a set of chosen users such that only the intended users are able to learn the data content. However, with the rapidly increasing of collaboration between users, it is desired to extend the receiver set to grant the decryption right for more users. The existing broadcast encryption systems cannot be capable for this scenario. In this paper, we first take this problem into consideration and give a solution. We take the merits of identity-based cryptosystem and propose a notion of EIBBE: a flexible data access control with receiver extendable for cloud computing based on broadcast encryption. It allows the authorized receiver to extend the receiver set S stated in the ciphertext by adding a new receiver set $S'$ without re-encryption. Both the users in S and $S'$ can obtain the data successfully. The maximum number of extended receivers is determined by the data uploader. We then give a concrete construction of EIBBE and provide a rigorous security analysis of our proposed scheme. Finally, we demonstrate the scheme's efficiency and feasibility.

Trigeminal neuralgia (TN) is a disease characterized by recurring, short-lived, electric shock-like pain experienced on one side of the face. Microvascular decompression (MVD) is one of the most effective surgical interventions for resolving TN caused by neurovascular compression. This study aimed to determine the predictive and prognostic factors of surgical outcomes.This retrospective cohort study enrolled patients diagnosed with TN who underwent MVD at our hospital during 2013-2019. The demographic information, pain character, peri-operative Barrow Neurological Institute (BNI) scale, medication, operative finding were recorded. And the outcome was Outcomes were divided into drug-free and drug-dependent group. Predisposing factors for each outcome were analyzed by one-way analysis of variance, followed by a Mann-Whitney U test or Kruskal-Wallis test.A total of 104 consecutive patients received MVD to treat TN, and 88 patients were enrolled in this study. The overall postoperative drug-free outcome was 72.7%. A significant difference in drug-free outcomes was observed for patients with typical TN (80.8%) compared with patients with atypical TN (33.33%, p = 0001). When severe venous compression was encountered during MVD, the drug-free outcome fell to 50% (10/20, p = 0.009). The Mann-Whitney U test indicated typical TN as a positive predictive factor of a drug-free outcome, whereas severe venous compression was a negative predictive factor. The patients with preoperative BNI score of 4 had better improvement than others (p = 0.045). Age, onset duration, and arterial loop had no specific difference in this study.In our study, atypical TN and severe venous compression were associated with poor outcomes. Regrouping atypical TN into precise diagnosis represents an immediate priority according to our result. The preoperative BNI score could be used as an effective predictive tool for the outcome of MVD surgery.

Background/Aims: We aimed to determine the association between blood urea level and incident cirrhosis, hepatic decompensation, and hepatocellular carcinoma in chronic liver disease (CLD) patients.Methods: The association between blood urea level and liver fibrosis/liver-related events were evaluated on continuous scale with restricted cubic spline curves based on generalized additive model or Cox proportional hazards models. Then, the above associations were evaluated by urea level within intervals.Results: Among 4,282 patients who had undergone liver stiffness measurement (LSM) by transient elastography, baseline urea level had a U-shaped association with LSM and hepatic decompensation development after a median follow-up of 5.5 years. Compared to patients with urea of 3.6–9.9 mmol/L, those with urea ≤3.5 mmol/L (adjusted hazard ratio [aHR], 4.15; 95% confidence interval [CI], 1.68–10.24) and ≥10 mmol/L (aHR, 5.22; 95% CI, 1.86–14.67) had higher risk of hepatic decompensation. Patients with urea ≤3.5 mmol/L also had higher risk of incident cirrhosis (aHR, 3.24; 95% CI, 1.50–6.98). The association between low urea level and incident cirrhosis and hepatic decompensation was consistently observed in subgroups by age, gender, albumin level, and comorbidities. The U-shaped relationship between urea level and LSM was validated in another population screening study (n=917). Likewise, urea ≤3.5 mmol/L was associated with a higher risk of incident cirrhosis in a territory-wide cohort of 12,476 patients with nonalcoholic fatty liver disease at a median follow-up of 9.9 years (aHR, 1.27; 95% CI, 1.03–1.57).Conclusions: We identified a U-shaped relationship between the urea level and liver fibrosis/incident cirrhosis/hepatic decompensation in patients with CLD.

On-detector digital electronics in high-energy physics (HEP) experiments is increasingly being implemented by means of static random access memory (SRAM)-based field programmable gate arrays (FPGAs), due to their reconfigurability, fast data processing, and transfer. Radiation-induced single-event upsets (SEUs) in the configuration hinder the correct operation, since they may alter the programmed routing paths and logic functions. In most data acquisition systems, data from clustered front-end modules are aggregated by a single board, which then transmits data to off-detector electronics for acquisition and triggering. In this work, we present a novel scrubber capable of correcting radiation-induced upsets in the configuration of SRAM-based FPGAs by majority voting across clustered modules. We designed it for the Aerogel Ring Imaging CHerenkov (ARICH) counter of the Belle II experiment at the KEK Laboratory, Tsukuba, Japan. We discuss the architecture of the system and its implementation in a Virtex-5 FPGA, in the aggregator board, for correcting the configuration of up to six Spartan-6 LX45 FPGAs, on pertaining front-end modules. We discuss results from neutron irradiation tests at the TRIGA reactor of the Jožef Stefan Institute, Ljubljana, Slovenia, which show that our solution corrects an average of 6.2 k upsets in each target FPGA before the failure of the scrubber. Moreover, with respect to the Xilinx soft error mitigation (SEM) controller, our design improved the mean time before failure of data readout by 30%.

Registering the 2D images (2D space) with the 3D model of the environment (3D space) provides a promising solution to outdoor Augmented Reality (AR) virtual-real registration. In this work, we use the position and orientation of the ground camera image to synthesize a corresponding rendered image from the outdoor large-scale 3D image-based point cloud. To achieve the virtual-real registration, we indirectly establish the spatial relationship between 2D and 3D space by matching the above two kinds (2D/3D space) of cross-domain images. However, matching cross-domain images goes beyond the capability of handcrafted descriptors and existing deep neural networks . To address this issue, we propose an end-to-end network, Y-Net, to learn Domain Robust Feature Representations (DRFRs) for the cross-domain images. Besides, we introduce a cross-domain-constrained loss function that balances the loss in image content and cross-domain consistency of the feature representations. Experimental results show that the DRFRs simultaneously preserve the representation of image content and suppress the influence of independent domains. Furthermore, Y-Net outperforms the existing algorithms on extracting feature representations and achieves state-of-the-art performance in cross-domain image retrieval . Finally, we validate the Y-Net-based registration approach on campus to demonstrate its possible applicability.

OBJECTIVE Patient-Reported Outcomes Measurement Information System® (PROMIS® ) measures can be administered via computerized adaptive testing (CAT) or fixed short forms (FSF), but the empirical benefits of CAT versus FSF are unknown in juvenile myositis (JM). This study assesses if PROMIS CAT is feasible, precise, correlated with FSF, and less prone to respondent burden and floor/ceiling effects than FSF in JM. METHODS Patients 8-17 yo (self-report and parent proxy) and parents of patients 5-7 yo (only parent proxy) completed PROMIS Fatigue, Pain Interference, Upper Extremity Function, Mobility, Anxiety and Depressive Symptoms measures. Pearson correlations, paired t-tests, and Cohen's D were calculated between PROMIS CAT and FSF. McNemar test assessed floor/ceiling effects between CAT and FSF. Precision and respondent burden were examined across the T-score range. RESULTS Data from 67 patient-parent dyads were analyzed. CAT and FSF mean scores did not significantly differ except in parent proxy Anxiety and Fatigue (effect size: 0.23 and 0.19, respectively). CAT had less pronounced floor/ceiling effects at the less symptomatic extreme in all domains except self-report Anxiety. Increased item burden and higher standard errors were seen in less symptomatic scorers for CAT. Modified stopping rules limiting CAT item administration did not decrease precision. CONCLUSION PROMIS CAT appears to be feasible and correlated with FSF. CAT had less pronounced floor/ceiling effects, allowing detection of individual differences in less symptomatic patients. Modified stopping rules for CAT may decrease respondent burden. CAT can be considered for long-term follow-up of JM patients.

Background There is an absence of clinically relevant epidemiological data in regional Australia pertaining to haematological malignancies. Aim To determine the incidence and geographical variation of haematological malignancies in North Queensland using a clinically appropriate disease classification. Methods Retrospective, observational study of individual patient data records of all adults diagnosed with a haematological malignancy between 2005-2014 and residing within The Townsville Hospital Haematology catchment region. We report descriptive summaries, incidence rates and incidence-rate ratios of haematologic malignancies by geographic regions. Results 1581 haematological malignancies (69% lymphoid, 31% myeloid) were diagnosed over the 10-year study period. Descriptive data is presented for 58 major subtypes as per the WHO diagnostic classification of tumours of haematopoietic and lymphoid tissues. The overall median age at diagnosis was 66 years with a male predominance (60%). We demonstrate a temporal increase in the incidence of haematologic malignancies over the study period. We observed geographical variations in the age-standardised incidence rates per 100,000 ranging from 0.5 to 233.5. Our data suggests an increased incidence rate ratio for haematological malignancies in some postcodes within the Mackay area compared to other regions. Conclusion This study successfully reports on the incidence of haematological malignancies in regional Queensland using a clinically meaningful diagnostic classification system and identifies potential geographic hotspots. We advocate for such contemporary, comprehensive, and clinically meaningful epidemiological data reporting of blood cancer diagnoses in wider Australia. Such an approach will have significant implications toward developing appropriate data-driven management strategies and public health responses for haematological malignancies. This article is protected by copyright. All rights reserved.

Deep neural network (DNN) shows great promise in providing more intelligence to ubiquitous end devices. However, the existing partition-offloading schemes adopt data-parallel or model-parallel collaboration between devices and the cloud, which does not make full use of the resources of end devices for deep-level parallel execution. This paper proposes eDDNN (i.e. enabling Distributed DNN), a collaborative inference scheme over heterogeneous end devices using cross-platform web technology, moving the computation close to ubiquitous end devices, improving resource utilization, and reducing the computing pressure of data centers. eDDNN implements D2D communication and collaborative inference among heterogeneous end devices with WebRTC protocol, divides the data and corresponding DNN model into pieces simultaneously, and then executes inference almost independently by establishing a layer dependency table. Besides, eDDNN provides a dynamic allocation algorithm based on deep reinforcement learning to minimize latency. We conduct experiments on various datasets and DNNs and further employ eDDNN into a mobile web AR application to illustrate the effectiveness. The results show that eDDNN can achieve the latency decrease by 2.98x, reduce mobile energy by 1.8x, and relieve the computing pressure of the edge server by 2.57x, against a typical partition-offloading approach.

Inspired by nature’s molecular machines, the scientific research on solid-state molecular rotors is of great interest yet remains largely unexplored. Herein, we report a unique example of a thermal...

Rare earth elements (lanthanide and yttrium, abbreviated as REY) provide important support for national security, energy production, environmental protection and economic growth. In recent years, many studies have shown that the content of REY in some coal or coal by-products is close to or even higher than that in traditional REY ore, which makes coal a potential source of REY. This study took the No. 17 coal of Panzhou mining area in Liupanshui coal field as the research object, and the content and occurrence modes of REY in raw coal and 7 density fraction samples were studied. The results show that the content of REY in the raw coal reaches 220.67 μg/g, which is significantly higher than the average REY in the World coal and Chinese coal as reported in the literature. Gravity separation has a certain pre-enrichment effect on REY and the fraction sample ＞1.8 g/cm3 has the highest REY of 426.27 μg/g (equal to rare earth oxide 0.1%). Study on correlation between REY and ash yields reveals that the organic affinity of the light rare earth is higher than the medium rare earth and the heavy rare earth. As shown by XRD and XRF, the major minerals in coal are kaolinite, quartz and calcite, and the content of REY in coal is significantly related to Al and Si and kaolinite, which implies that REY occur mainly in kaolinite. LA-ICP-MS was adopted to determine chemical composition of micro-zone in kaolinite in coal and the results show that there is no obvious correlation between REY and the content of Al and Si. This indicates that the REY occurred probably in the form of adsorption. The research results provide a scientific reference for the potential extraction of the associated REY in the coal.

The characteristics of the short circuit current of renewable energy sources are quite different from those of traditional generators. When applied to tie lines and collector lines of renewable energy sources, the traditional directional elements may not operate correctly. A new method of fault direction identification suitable for inverter power sources and doubly fed power sources based on the fault features of renewable energy sources is proposed. The fault type is judged by sequence voltage. The fault direction is identified by comparing the amplitude between the sequence currents and the phase angle between the sequence voltage and the sequence current, without distinguishing whether the crowbar of the DFIG is triggered. The results of digital simulations and experiments show that this method is not affected by the types of renewable energy sources connected to the power grid and can correctly judge the fault direction in different fault types, which has good engineering application value.

Objective: Anticancer treatment may be associated with damage to the reproductive organs and risk of infertility in children with cancer. The collectivist cultural norms of Asian societies may lead...

Background/purpose Esophageal variceal bleeding (EVB) is a medical emergency in patients with portal hypertension (PHT). However, studies on the long-term outcomes of prophylactic endoscopic variceal ligation (EVL) in pediatric-onset PHT are lacking. Methods Between 1999 and 2020, patients who received EVL in the Electronic Report System of the Pediatric Endoscopy Unit were included in this retrospective study. EVL was classified as primary prophylaxis when it was performed for esophageal varices (EVs) without previous bleeding. If it was implemented in acute EVB, the subsequent EVL was classified as secondary prophylaxis. Results Fifty-eight patients aged 10 months to 33 years with 31 males were included. Thirty-eight patients were classified as primary prophylaxis group, and twenty, secondary prophylaxis group. The primary prophylaxis group experienced fewer 5-year EVB events than the secondary prophylaxis group (cumulative risk: 14.4% versus 32.4%). Still, it didn't significantly affect overall survival and biliary atresia transplant-free survival. Long-term mortality was significantly associated with higher serum direct bilirubin levels (≥0.55 mg/dL) and lower albumin levels (≤2.54 mg/dL) at the first EVL. Aspartate aminotransferase-to-platelet ratio index (APRI) with a cut-off value of 1.24 helped to predict EV presence at the initial esophagogastroduodenoscopy (EGD) (AUROC = 0.762, sensitivity 75.0%, and specificity 66.7%). Conclusion Primary prophylactic EVL, despite reducing acute EVB, may not change overall survival and biliary atresia transplant-free survival. APRI > 1.24 may predict EV presence at the first EGD and help to schedule a surveillance EGD. Higher direct bilirubin and lower albumin levels at the first EVL may relate to long-term mortality.

In this paper, the Difference of Seismic Response across Different Spans (DSR) in the longitudinal distribution of High-Speed Railway Multi-spans Simply Supported Bridge (HSRSB) under longitudinal earthquake excitation is investigated, and an evaluation method which can intuitively reflect the difference of seismic response is proposed. A feasible way to strengthen the connection stiffness between adjacent girders is proposed to control DSR. The rationality of the finite element model used is verified by comparing the numerical results with the experimental ones, showing a satisfactory agreement. Comparing the seismic response of a bridge model considering the subgrade-track constraints (BCTM) and a bridge model without subgrade-track constraints (BWTM), it is found that the DSR in the longitudinal distribution causes some new disadvantages, which are neglected in BWTM. The BCTM considering DLC generates a model called BCDM. The effect of the number of span on DSR are studied based on BCDM. The analysis of this model showed that DLC suppresses the DSR and reduces the seismic response of most bridge components. It also transfers the seismic disadvantage from the bridge part to the subgrade-track structure. As it is more convenient and cost-effective to repair the base plate of the subgrade than the bridge components after earthquake seismic event, this disadvantage transfer is in favor of forming a new anti-seismic system that subgrade-track structure is used to protect the bridge part.

There have been many studies focusing on edge server deployment and service placement in mobile edge computing (MEC) respectively, but rare works took both of them into consideration. However, edge server deployment and service placement are coupling issues in practice, where the former affects the latter. Besides, the economic benefit of the MEC platform is also a consideration. Due to different service request rates and prices, appropriate service placement solutions are needed to increase the overall profit. In this paper, we propose a complete process combining edge server and service placement, where service placement explicitly takes into account the structure of current edge server placement and different service request rates and prices. We design a joint edge server deployment and service placement model with the goal of maximizing the overall profit of all edge servers under the constraints of the number of edge servers, the relationship among edge servers and base stations, the storage capacity and the computing capacity of each edge server. We propose a two-step method including the clustering algorithm and nonlinear programming to solve the formulated problem. Extensive evaluations based on the real-world dataset demonstrate that the proposed algorithm outperforms the baseline methods.

Edge Computing, extending cloud computing, has emerged as a prospective computing paradigm. It allows a SaaS (Software-as-a-Service) vendor to allocate its users to nearby edge servers to minimize network latency and energy consumption on their devices. From the SaaS vendor's perspective, a cost-effective SaaS user allocation (SUA) aims to allocate maximum SaaS users on minimum edge servers. However, the allocation of excessive SaaS users to an edge server may result in severe interference and consequently impact SaaS users data rates. In this paper, we formally model this problem and prove that finding the optimal solution to this problem is NP-hard. Thus, we propose ISUAGame, a game-theoretic approach that formulates the interference-aware SUA (ISUA) problem as a potential game. We analyze the game and show that it admits a Nash equilibrium. Then, we design a novel decentralized algorithm for finding a Nash equilibrium in the game as a solution to the ISUA problem. The performance of this algorithm is theoretically analyzed and experimentally evaluated. The results show that the ISUA problem can be solved effectively and efficiently.

Classic inventory theories typically focus on the operational trade-offs to optimize inventory decisions. However, managers of public firms who obtain stock-based incentives may alter inventory operations to influence the stock price. We develop a stylized model, which shows that, in the presence of an interest in the stock price, managers over-install inventory when it can either inflate sales or deflate the reported cost of goods sold even if the market anticipates such actions. We analyze the joint and marginal effects of the stock-based incentives and the cost of using inventory to manage earnings, which may provide useful implications for the detection of inventory distortion and the design of management incentive plans. We then conduct an empirical analysis based on the financial data of U.S. publicly listed retailers and manufacturers. We find positive (negative) correlation between firms’ abnormal excess inventory and the stock-based incentives of their top executives (the inventory manipulation cost). Moreover, the marginal effect of the stock-based incentives on the abnormal excess inventory is the strongest when the inventory manipulation cost is intermediate. Our empirical analysis also shows that this effect becomes statistically weaker after the passage of the Sarbanes–Oxley Act. This is in line with the prediction of our analytical model about the effect of the accuracy of financial reporting. This paper was accepted by Vishal Gaur, operations management.

Edge computing is a new distributed computing paradigm extending the cloud computing paradigm, offering much lower end-to-end latency, as real-time, latency-sensitive applications can now be deployed on edge servers that are much closer to end-users than distant cloud servers. In edge computing, edge user allocation (EUA) is a critical problem for any app vendors, who need to determine which edge servers will serve which users. This is to satisfy application-specific optimization objectives, e.g., maximizing users' overall quality of experience, minimizing system costs, and so on. In this paper, we focus on the cost-effectiveness of user allocation solutions with two optimization objectives. The primary one is to maximize the number of users allocated to edge servers. The secondary one is to minimize the number of required edge servers, which subsequently reduces the operating costs for app vendors. We first model this problem as a bin packing problem and introduce an approach for finding optimal solutions. However, finding optimal solutions to the NP-hard EUA problem in large-scale scenarios is intractable. Thus, we propose a heuristic to efficiently find sub-optimal solutions to large-scale EUA problems. Extensive experiments conducted on real-world data demonstrate that our heuristic can solve the EUA problem effectively and efficiently, outperforming the state-of-the-art and baseline approaches.

While input or output-perturbation-based adversarial training techniques have been exploited to enhance the generalization capability of a variety of nonfuzzy and fuzzy classifiers by means of dynamic regularization, their performance may perhaps be very sensitive to some inappropriate adversarial samples. In order to avoid this weakness and simultaneously ensure enhanced generalization capability, this work attempts to explore a novel knowledge adversarial attack model for the zero-order Tagaki-Sugeno-Kang (TSK) fuzzy classifiers. The proposed model is motivated by exploiting the existence of special knowledge adversarial attacks from the perspective of the human-like thinking process when training an interpretable zero-order TSK fuzzy classifier. Without any direct use of adversarial samples, which is different from input or output perturbation-based adversarial attacks, the proposed model considers adversarial perturbations of interpretable zero-order fuzzy rules in a knowledge-oblivion and/or knowledge-bias or their ensemble to mimic the robust use of knowledge in the human thinking process. Through dynamic regularization, the proposed model is theoretically justified for its strong generalization capability. Accordingly, a novel knowledge adversarial training method called KAT is devised to achieve promising generalization performance, interpretability, and fast training for zero-order TSK fuzzy classifiers. The effectiveness of KAT is manifested by the experimental results on 15 benchmarking UCI and KEEL datasets.

N4-methylcytosine (4mC) is a type of DNA modification which could regulate several biological progressions such as transcription regulation, replication and gene expressions. Precisely recognizing 4mC sites in genomic sequences can provide specific knowledge about their genetic roles. This study aimed to develop a deep learning-based model to predict 4mC sites in the Escherichia coli. In the model, DNA sequences were encoded by word embedding technique 'word2vec'. The obtained features were inputted into 1-D convolutional neural network (CNN) to discriminate 4mC sites from non-4mC sites in Escherichia coli genome. The examination on independent dataset showed that our model could yield the overall accuracy of 0.861, which was about 4.3% higher than the existing model. To provide convenience to scholars, we provided the data and source code of the model which can be freely download from https://github.com/linDing-groups/Deep-4mCW2V.

Most previous EEG-based emotion recognition methods studied hand-crafted EEG features extracted from different electrodes. In this paper, we study the relation among different EEG electrodes and propose a deep learning method to automatically extract the spatial features that characterize the functional relation between EEG signals at different electrodes. Our proposed deep model is called ATtention-based LSTM with Domain Discriminator (ATDD-LSTM) that can characterize nonlinear relations among EEG signals of different electrodes. To achieve state-of-the-art emotion recognition performance, the architecture of ATDD-LSTM has two distinguishing characteristics: (1) By applying the attention mechanism to the feature vectors produced by LSTM, ATDD-LSTM automatically selects suitable EEG channels for emotion recognition, which makes the learned model concentrate on the emotion related channels in response to a given emotion; (2) To minimize the significant feature distribution shift between different sessions and/or subjects, ATDD-LSTM uses a domain discriminator to modify the data representation space and generate domain-invariant features. We evaluate the proposed ATDD-LSTM model on three public EEG emotional databases (DEAP, SEED and CMEED) for emotion recognition. The experimental results demonstrate that our ATDD-LSTM model achieves superior performance on subject-dependent (for the same subject), subject-independent (for different subjects) and cross-session (for the same subject) evaluation.

An electrohydrodynamic (EHD) gas pump inside a square channel is experimentally evaluated for its effectiveness in flow delivery. The gas pump with three sets of electrode assemblies (separately wi...

Background Restless legs syndrome (RLS), a neurological disorder, often affects sleep quality in hemodialysis patients. This study aimed to evaluate acupressure's effect on the severity of RLS symptoms and sleep quality in hemodialysis patients with RLS. Material and methods This study is a cluster-randomized crossover pilot study. Patients were randomized to two sequences: acupressure for one month and observation for another month (AC); and observation for one month and acupressure for another month (CA). For the four-week acupressure intervention, patients received 36 min of acupressure three times weekly during their hemodialysis sessions. The acupoints were on the bilateral lower limbs, including Zusanli (ST36), Yanglingquan (GB34), Sanyinjiao (SP6), Xuanzhong (GB39), Chengshan (BL57), and Taichong (LR3). RLS severity and sleep quality (measured with the Pittsburgh sleep quality index, PSQI) were measured at baseline, month 1, and month 2. Results AC sequence (n = 14) was similar to the CA sequence (n = 9) in terms of gender, age, education, employment, marital status, comorbid disease, BMI, duration of dialysis, medication for RLS and insomnia, RLS severity, and PSQI. The general linear mixed model revealed no significant carryover effect on RLS severity, PSQI, and the seven subscales of PSQI. A significant treatment effect (acupressure) was only observed in RLS severity (p = 0.0013) but not in PSQI and the seven subscales. The significant period effect was observed in RLS severity (p = 0.0250) and the subscale of sleep disturbance (p = 0.0021). Conclusion In hemodialysis patients with RLS, acupressure can alleviate the severity of RLS but cannot improve sleep quality.

Background Robotic distal pancreatectomy has been accepted to be safe and effective for pancreatic tail lesion. Whether spleen preservation by preserving the splenic vessels with robot assistance is feasible and beneficial remains controversial. Here we would like to compare the operative outcomes of robotic distal pancreatectomy and splenectomy (DPS) with robotic spleen preserving distal pancreatectomy by means of splenic vessel preservation (SVP). Methods Between March 2011 and September 2019, 56 consecutive patients undergoing robotic distal pancreatectomy were identified, with 28 patients in each group. Patient demographics, histopathology findings and operative outcomes were prospectively collected and compared between the two groups. A subgroup analysis was made after excluding malignant and pancreatic lesions >6 cm in the DPS group. Results The two groups had similar conversion rate, blood loss, morbidity and pancreatic fistula rate. There was no operative mortality. The SVP group had shorter median operative time (245 vs 303.5 min, P = 0.019) and shorter median hospital stay (5 vs 6 days, P = 0.019) than the DPS group. However, all malignant lesions occurred in the DPS group and lesion size in DPS group was significantly larger. After matching, there were 28 SVP and 15 DPS. The histopathology findings and lesion size became comparable. The SVP group still had shorter operative time (245 vs 290 min, P = 0.022) and shorter hospital stay (5 vs 7 days, P = 0.014) than the DPS group. Conclusion Apart from avoiding risk of overwhelming postsplenectomy sepsis, robotic SVP had additional advantage of shorter operative time and shorter hospital stay than robotic DPS.

Objectives This study assessed the efficacy and safety of novel β-lactam/β-lactamase inhibitor combinations in adult patients with hospital-acquired pneumonia (HAP) or ventilator-associated pneumonia (VAP). Methods PubMed, Web of Science, the Cochrane Library, Ovid MEDLINE, Embase and EBSCO databases were searched for randomized controlled trials (RCTs) published before September 13, 2020. Only RCTs comparing the treatment efficacy of novel β-lactam/β-lactamase inhibitor combinations with other antibiotics for HAP/VAP in adult patients were included in this integrated analysis. Results Three RCTs were included, and no significant difference in clinical cure rate of the test of cure (TOC) was observed between the novel β-lactam/β-lactamase inhibitor combination and comparators (odds ratio [OR], 1.01; 95% CI, 0.81-1.27; I2 = 35%). The 28-day all-cause mortality was 16.2% and 17.6% for patients receiving the novel β-lactam/β-lactamase inhibitor combination and those receiving comparators, respectively, and no significant difference was noted (OR, 0.90; 95% CI, 0.69-1.16; I2 = 11%). Compared with the comparators, the novel β-lactam/β-lactamase inhibitor combination was associated with a similar microbiological response (OR, 1.06; 95% CI, 0.73-1.54; I2 = 64%) and a similar risk of AEs (treatment-emergent AEs [TEAEs]: OR, 1.04; 95% CI, 0.83-1.30; I2 = 0%; serious AEs: OR, 1.14; 95% CI, 0.79-1.63; I2 = 68%; treatment discontinuation for TEAE: OR, 0.90; 95% CI, 0.62-1.31; I2 = 11%). Conclusions The clinical and microbiological responses of novel β-lactam/β-lactamase inhibitor combinations in the treatment of HAP/VAP were similar to those of other available antibiotics. These combinations also shared a similar safety profile to that of comparators.