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Perovskite transition metal oxides are promising materials for supercapacitor electrodes due to their high theoretical capacities. However, these materials still suffer from poor conductivity, low specific capacitance, and moderate cycle stability, restraining their practical applications. In this study, LaMnO3@CC-PPy materials were prepared by two-step electrodeposition based on the inspiring design of coaxial cables. To this end, electrochemically active LaMnO3 was first grown on carbon cloth (CC) with good flexibility and conductivity and then followed by further coating with polypyrrole (PPy) layer. The best PPy load was identified by adjusting the deposition time. The resulting LaMnO3@CC-PPy electrodes showed excellent specific capacitance reaching 862 F g-1 at 1 A g-1 with retention rate of 75% at high current density of 10 A g-1, indicative of excellent rate performance. The cycle stability of the electrodes also improved after 3000 cycles at 10 A g-1 with a retention rate reaching 66%. To assemble asymmetric supercapacitor (ASC) devices, NiCo2O4@CC cathodes were prepared by electrodeposition. Ultra-high energy density of about 73 Wh kg-1 and good cycle stability were recorded with the devices. The high performance of the as-obtained materials was attributed to the existence of internal and external double electric channels, as well as the abundant internal space. These features ensured good conductivity, rapid charge transfer, and fast ion diffusion, thereby significantly improving the overall material cycle stability. In sum, these findings look promising for future preparation of high-performance perovskite supercapacitors.

Neural networks with a large number of parameters are prone to overfitting problems when trained on a relatively small training set. Introducing weight penalties of regularization is a promising technique for solving this problem. Taking inspiration from the dynamic plasticity of dendritic spines, which plays an important role in the maintenance of memory, this letter proposes a brain-inspired developmental neural network based on dendritic spine dynamics (BDNN-dsd). The dynamic structure changes of dendritic spines include appearing, enlarging, shrinking, and disappearing. Such spine plasticity depends on synaptic activity and can be modulated by experiences—in particular, long-lasting synaptic enhancement/suppression (LTP/LTD), coupled with synapse formation (or enlargement)/elimination (or shrinkage), respectively. Subsequently, spine density characterizes an approximate estimate of the total number of synapses between neurons. Motivated by this, we constrain the weight to a tunable bound that can be adaptively modulated based on synaptic activity. Dynamic weight bound could limit the relatively redundant synapses and facilitate the contributing synapses. Extensive experiments demonstrate the effectiveness of our method on classification tasks of different complexity with the MNIST, Fashion MNIST, and CIFAR-10 data sets. Furthermore, compared to dropout and L2 regularization algorithms, our method can improve the network convergence rate and classification performance even for a compact network.

Using anteroposterior (AP) hip radiograph, we measured several indexes to investigate the association with bone mineral density (BMD) before THA and found a highly effective index to predict femoral BMD. This technique is helpful for both patients and clinicians to identify potential candidates with low BMD to whom DXA examination is particularly recommended. The purpose of the study is to identify patients with low bone mineral density (BMD) prior to total hip arthroplasty with the help of AP hip radiographs. Indexes on AP hip radiographs and T-scores from DXA examination of the lumbar spine and the affected hip were acquired from patients before THA. Indexes measured on AP hip radiographs including the canal calcar ratio (CCR), canal flare index (CFI), morphological cortical index (MCI), canal bone ratio (CBR), and canal bone area ratio (CBAR). The relevance between indexes and the T-score of femora was evaluated by correlation analysis, and the diagnostic value of indexes for osteopenia was examined by receiver operating characteristic (ROC) curves. A total of 81 patients were included. The average value of CBR-7, CBR-10, and CBAR (7–10) were highly related to the T-score of femora (r = − 0.592, r = − 0.634, and r = − 0.631, respectively, p

Partially missing bands in the preset region for Hough transform might strongly affect the accuracy of indexing Kikuchi patterns and reduce the quality of electron backscatter diffraction (EBSD) maps. This paper proposes a novel local band detection method for such kind of low-quality patterns. The approach involves rotating bands to vertical direction, detecting the local line segments through calculating the largest horizontal average grey gradient with a constant interval of 400 pixels in vertical direction, and applying Hough transform as well as weighted averaging to these line clusters to unify the edges of Kikuchi band. Therefore, even if only part of a Kikuchi band is visible, the entire band can also be accurately extracted. The average interplanar angle error obtained by the proposed method is approximately 29.0% less than those obtained by Hough transform-based technique. Moreover, the comparison of mean angular deviation (MAD) is also discussed. The average MAD of this method is about 38.5% lower than that of Hough transform-based technique. Consequently, the local Kikuchi band detection method is expected to be used for post-processing and re-indexing the EBSD low-quality patterns.

Background Enhanced recovery after surgery (ERAS) program advocates implementation of perioperative goal-directed fluid therapy and reduced application of colloidal fluids. It should be used reasonably selectively in high-risk patients despite the clear efficacy of human albumin (HA). Therefore, it is vital to identify the risk factors for the use of HA. This study aims to determine the incidence and risk factors of HA administration in patients undergoing total hip or knee arthroplasty (THA, TKA). Methods We identified patients undergoing THA or TKA in multiple institutions from 2014 to 2016 and collected patient demographics and perioperative variables. The criterion of HA administration was defined as a postoperative albumin level < 32 g/L or 32 to 35 g/L for at-risk patients. We compared 14 variables between patients who received HA administration and those who did not after stratification by the preoperative albumin (pre-ALB) level. Multivariable regressions identified the independent risk factors associated with HA administration. Results In total, 958 (20.3%) of 4713 patients undergoing THA and 410 (9.7%) of 4248 patients undergoing TKA received HA administration. In addition to pre-ALB < 35 g/L, preoperative anemia (odds ratio [OR] 2.12, P = 0.001; OR 1.39, P < 0.001) and drain use (OR 3.33, P = 0.001; OR 4.25, P < 0.001) were also independent risk factors for HA administration after THA regardless pre-ALB < 35 g/L or not, and patients undergoing TKA diagnosed of rheumatoid arthritis or ankylosing spondylitis tended to receive HA administration regardless pre-ALB < 35 g/L or not (OR 3.67, P = 0.002; OR 2.06, P < 0.001). Conclusions The incidence of HA administration was high in patients undergoing THA or TKA, and several variables were risk factors for HA administration. This finding may aid surgeons in preoperatively identifying patients requiring HA administration and optimizing perioperative managements.

Triplet-triplet annihilation (TTA) upconversion has shown promising potentials in the augmentation of solar energy conversion. However, challenging issues exist in improving TTA upconversion efficiencies in solid-states, one of which is the back energy transfer from upconverted singlet annihilators to sensitizers, resulting in decreasing upconversion emission. Here we present a light-harvesting molecular wire consisting of dendrons with 9,10-diphenylanthracene derivatives (DPAEH) at the periphery and p-phenylene ethynylene oligomers (PPE) as the wire core. The peripheral DPAEH antenna funnels singlet excitonic energy to the wire on a 12 ps time scale. Incorporating the molecular wire into the TTA upconversion solid consisting of the DPAEH annihilator and the porphyrin sensitizer evidently improves the upconversion quantum yield from 1.5% to 2.7% upon 532 nm excitation by suppressing the back energy transfer from the singlet annihilator to the sensitizer. This finding offers a potential route to use a singlet energy light-harvesting architecture for enhancing TTA upconversion.

Because of the rise of deep learning and neural networks, algorithms based on deep learning have also been developed and subtly applied in daily life. This paper hoped to use neural network-based face recognition with absolute distance estimation and research done with drones to achieve the target tracking effect through the recognition ability of neural networks. The aerial images were used to identify different people or behaviors through which distances could be calculated to establish the foundation for subsequent research and development. According to our experiments, performing the face recognition model and distance estimation met expected results, and it also had a specific accuracy rate from different angles and distances. In summary, identification and distance estimate had better accuracy within the effective length, and we could expect the speed and convenience of identification to be realized on other devices.

Previous cross-sectional studies have demonstrated the high prevalence of depression and comorbidity in knee osteoarthritis (KOA), and KOA or its impact on lifestyle was seen as a potential trigger factor of depression and comorbidity. However, the exact onset and progression pattern of depression and comorbidity in KOA was still unknown.Group-based trajectory modeling (GBTM) analysis was conducted in the 2833 subjects selected from the osteoarthritis initiative (OAI) database. Eight-year trajectories were determined and described. Baseline characteristics were investigated in multi-variable regression to detect the risk factors of the unfavored trajectory.Stable trajectory (70.4%) and worsening trajectory (29.6%) were identified in comorbidity. The risk factors for the worsening trajectory membership were the obesity (OR = 1.47 CI = [1.20, 1.79], P 0.001), older age (OR = 1.74, CI = [1.41, 2.16], P 0.001), and smoke (OR = 1.30, CI = [1.08, 1.57], P 0.01) at baseline. Stable trajectory (52.0%), slow-worsening trajectory (40.5%), and fast-worsening trajectory (7.5%) were identified in depression symptoms. The risk factors for the fast-worsening trajectory membership were female (OR = 1.51 CI = [1.03, 2.20], P 0.05), lower income (OR = 1.52, CI = [1.01, 2.27], P 0.05), and smoke (OR = 1.30, CI = [1.08, 1.57], P 0.01) at baseline.A significant amount of KOA subjects tends to develop depression symptoms and comorbidity. Managing related risk factors, like weight loss or smoking cessation, might have considerable significance in preventing or delaying depression symptoms and comorbidity in KOA. Key Points • The first study investigating the trajectory of comorbidity progression in KOA. • Approximately 7.5% of KOA patients tend to develop depression symptoms quite rapidly, and 30% of KOA patients tend to develop comorbidity • Risk factors of worsening trajectories were identified: obesity, older age, smoking, female, and lower income.

Background & Aims Acute viral hepatitis (AVH) represents an important global health problem; however, the progress in understanding AVH is limited because of the priority of combating persistent HBV and HCV infections. Therefore, an improved understanding of the burden of AVH is required to help design strategies for global intervention. Methods Data on 4 major AVH types, including acute hepatitis A, B, C, and E, excluding D, were collected by the Global Burden of Disease (GBD) 2019 database. Age-standardized incidence rates and disability-adjusted life year (DALY) rates for AVH were extracted from GBD 2019 and stratified by sex, level of socio-demographic index (SDI), country, and territory. The association between the burden of AVH and socioeconomic development status, as represented by the SDI, was described. Results In 2019, there was an age-standardized incidence rate of 3,615.9 (95% CI 3,360.5–3,888.3) and an age-standardized DALY rate of 58.0 (47.3–70.0) per 100,000 person-years for the 4 major types of AVH. Among the major AVH types, acute hepatitis A caused the heaviest burden. There was a significant downward trend in age-standardized DALY rates caused by major incidences of AVH between 1990 and 2019. In 2019, regions or countries located in West and East Africa exhibited the highest age-standardized incidence rates of the 4 major AVH types. These rates were stratified by SDI: high SDI and high-middle SDI locations recorded the lowest incidence and DALY rates of AVH, whereas the low-middle SDI and low SDI locations showed the highest burden of AVH. Conclusions The socioeconomic development status and burden of AVH are associated. Therefore, the GBD 2019 data should be used by policymakers to guide cost-effective interventions for AVH. Lay summary We identified a negative association between socioeconomic development status and the burden of acute viral hepatitis. The lowest burden of acute viral hepatitis was noted for rich countries, whereas the highest burden of acute viral hepatitis was noted for poor countries.

An electron backscatter diffraction device is an important accessory for a scanning electron microscope and can provide crystal structure orientation and phase content data through analysis of electron backscatter diffraction patterns. The acquisition of these data depends on pattern indexing, including interplanar angle calculation and crystal plane indexation. The coordinates of the source point are key points for interplanar angle calculation, and they vary with the movement of the incident beam. In this study, we first combined the grey gradient calculation with screen moving method to achieve accurate positioning of source point and obtained coordinates of source point with sub-pixel precision. The errors of three coordinates were 0.07%, 0.06% and 0.04%, respectively. By using this coordinate of source point to conduct interplanar angle calculation the maximum error was 0.53°, which was a good proof of the accuracy of source point positioning. Then we established the relationship between source point coordinates variation and incident beam movement. Coordinates can be given out based on the displacement of beam directly. And to illustrate the accuracy, interplanar angle calculation was performed and the maximum error was 0.81°. This means that the relationship between variation of source point coordinates and beam movement is highly accurate.

The friction pendulum bearing (FPB) is commonly used as a base isolation system in buildings. The FPB is a passive control method and can protect the structures from a specified dynamic load, but is not suitable to other cases and types of dynamic loading. An innovative distributed tunable friction pendulum system (DTFPS) is proposed and studied. The DTFPS contains multiple bearings, most of which are conventional FPBs, whereas the rest are tunable friction pendulum bearings (TFPBs). The TFPB has a sliding surface with a high friction coefficient and an adjustable pressure system. The oil pressure in the TFPB can be tuned by the adjustable pressure system, thus the friction force in the DTFPS can be changed accordingly. Theoretical analysis and dynamic tests were carried out to study the performance of the DTFPS. Both theoretical and test results show that the friction force in the DTFPS can be tuned online. Numerical simulations were conducted for a base-isolated building equipped with the DTFPS. The H-inf method, which considers different time delays, was adopted to control the friction force of the DTFPS. With 0.02 s of time delay, the DTFPS can reduced the displacement of the isolation layer without significantly increase the floor acceleration and inter-story displacement of superstructure. The control effect of the H-inf control was improved up to 139%, compared with passive control.

Background Dietary factors, such as consumption of preserved foods, fresh vegetables, and fruits, have been linked to the risk of nasopharyngeal carcinoma (NPC). However, little is known about associations between dietary patterns and the risk of NPC in NPC-endemic areas. Objectives We aimed to evaluate whether dietary patterns are associated with NPC risk. Methods We studied 2554 newly diagnosed NPC patients aged 20–74 y living in 3 endemic regions of southern China, and 2648 population-based controls frequency-matched to case patients by age, sex, and region, between 2010 and 2014. Dietary components were derived from food frequency data in adulthood and adolescence using principal component analysis. Four dietary components were identified and highly similar in adulthood and adolescence. We used multivariable unconditional logistic regression to calculate ORs with 95% CIs for the association between dietary patterns and NPC risk. Results Compared with the lowest quartile, individuals in the highest quartile of the “plant-based factor” in adulthood had a 52% (OR: 0.48; 95% CI: 0.38, 0.59) decreased risk of NPC, and those in the highest quartile of the “animal-based factor” had a >2-fold (OR: 2.26; 95% CI: 1.85, 2.77) increased risk, with a monotonic dose-response trend (P-trend

BACKGROUND Surgery is the primary curative option in patients with hepatocellular carcinoma (HCC). However, recurrence within 2 years is observed in 30%–50% of patients, being a major cause of mortality. AIM To construct and verify a non-invasive prediction model combining contrast-enhanced ultrasound (CEUS) with serology biomarkers to predict the early recurrence of HCC. METHODS Records of 744 consecutive patients undergoing first-line curative surgery for HCC in one institution from 2016–2018 were reviewed, and 292 local patients were selected for analysis. General characteristics including gender and age, CEUS liver imaging reporting and data system (LIRADS) parameters including wash-in time, wash-in type, wash-out time, and wash-out type, and serology biomarkers including alanine aminotransferase, aspartate aminotransferase, platelets, and alpha-fetoprotein (AFP) were collected. Univariate analysis and multivariate Cox proportional hazards regression model were used to evaluate the independent prognostic factors for tumor recurrence. Then a nomogram called CEUS model was constructed. The CEUS model was then used to predict recurrence at 6 mo, 12 mo, and 24 mo, the cut-off value was calculate by X-tile, and each C-index was calculated. Then Kaplan-Meier curve was compared by log-rank test. The calibration curves of each time were depicted. RESULTS A nomogram predicting early recurrence (ER), named CEUS model, was formulated based on the results of the multivariate Cox regression analysis. This nomogram incorporated tumor diameter, preoperative AFP level, and LIRADS, and the hazard ratio was 1.123 (95% confidence interval [CI]: 1.041-1.211), 1.547 (95%CI: 1.245-1.922), and 1.428 (95%CI: 1.059-1.925), respectively. The cut-off value at 6 mo, 12 mo, and 24 mo was 100, 80, and 50, and the C-index was 0.748 (95%CI: 0.683-0.813), 0.762 (95%CI: 0.704-0.820), and 0.762 (95%CI: 0.706-0.819), respectively. The model showed satisfactory results, and the calibration at 6 mo was desirable; however, the calibration at 12 and 24 mo should be improved. CONCLUSION The CEUS model enables the well-calibrated individualized prediction of ER before surgery and may represent a novel tool for biomarker research and individual counseling.

Exercise monitoring systems for rehabilitation are usually not able to pinpoint the exact part for patients’ exercise. The research objective is to develop the projection-based motion recognition (PMR) algorithm based on depth data and wide-accepted methods to solve this matter. We regard a motion trajectory as a combination of basic posture units, and then project the basic posture units onto a 2-D space via a projection mapping. Each motion trajectory is transformed to a 2-D motion trajectory map by sequentially connecting the basic posture units involved in the motion trajectory. Finally, we employ a convolutional neural network (CNN)-based classifier to classify the trajectory maps. Accurate classification rate reaches as high as 95.21%. The originality of PMR algorithm lies in (1) it has the generalization capability to some extent since it only adopts popular methods and contains an essential and comprehensive mechanism; (2) the resultant trajectory map may reveal the information about how well a patient execute the rehabilitation assignments.

Background The therapeutic efficacy of mesenchymal stem cells (MSCs) of different tissue origins on metabolic disorders can be varied in many ways but remains poorly defined. Here we report a comprehensive comparison of human MSCs derived from umbilical cord Wharton’s jelly (UC-MSCs), dental pulp (PU-MSCs), and adipose tissue (AD-MSCs) on the treatment of glucose and lipid metabolic disorders in type II diabetic mice. Methods Fourteen-to-fifteen-week-old male C57BL/6 db/db mice were intravenously administered with human UC-MSCs, PU-MSCs, and AD-MSCs at various doses or vehicle control once every 2 weeks for 6 weeks. Metformin (MET) was given orally to animals in a separate group once a day at weeks 4 to 6 as a positive control. Body weight, blood glucose, and insulin levels were measured every week. Glucose tolerance tests (GTT) and insulin tolerance tests (ITT) were performed every 2 weeks. All the animals were sacrificed at week 6 and the blood and liver tissues were collected for biochemical and histological examinations. Results UC-MSCs showed the strongest efficacy in reducing fasting glucose levels, increasing fasting insulin levels, and improving GTT and ITT in a dose-dependent manner, whereas PU-MSCs showed an intermediate efficacy and AD-MSCs showed the least efficacy on these parameters. Moreover, UC-MSCs also reduced the serum low-density lipoprotein cholesterol (LDL-C) levels with the most prominent potency and AD-MSCs had only very weak effect on LDL-C. In contrast, AD-MSCs substantially reduced the lipid content and histological lesion of liver and accompanying biomarkers of liver injury such as serum aspartate transaminase (AST) and alanine aminotransferase (ALT) levels, whereas UC-MSCs and PU-MSCs displayed no or modest effects on these parameters, respectively. Conclusions Taken together, our results demonstrated that MSCs of different tissue origins can confer substantially different therapeutic efficacy in ameliorating glucose and lipid metabolic disorders in type II diabetes. MSCs with different therapeutic characteristics could be selected according to the purpose of the treatment in the future clinical practice.

Objectives The study aims to analyze the clinical characteristics of head and neck mucosal melanoma (MMHN) and the effects of multiple treatment modalities on distant metastasis, recurrence and survival rates to provide a reference for the individualized treatment of MMHN. Methods We retrospectively reviewed 262 patients with stage III–IVb MMHN treated from March 1986 to November 2018 at our cancer center. Results The median follow-up time was 34.0 months (range 1–262 months). The 5-year overall survival (OS), distant metastasis-free survival (DMFS) and disease-free survival (DFS) probabilities were 37.7%, 30.2%, and 20.3%, respectively. The 5-year OS rates for patients with stage III, stage IVA, and stage IVB MMHN were 67.0%, 24.1% and 8.3%, respectively (P P P P Conclusions The main reason for treatment failure in MMHN is distant metastasis. Immunologic/targeted therapy and surgery are recommended to improve the survival of MMHN. The American Joint Committee on Cancer (AJCC) 8th edition staging system for MMHN does stage this disease effectively.

Published studies have reported many inconsistent results regarding the comparison of same-day discharge total joint arthroplasty (TJA) and inpatient TJA. More notably, many recent studies comparing same-day discharge TJA with fast-track TJA presented higher rates of complications for same-day discharge TJA, which raises concerns about the safety of same-day discharge TJA. We systematically searched the MEDLINE, Embase, and Cochrane Central Register of Controlled Trials databases up to June 2020 for studies comparing mortality, readmission, and complications in same-day discharge and inpatient total hip or knee arthroplasty. Studies that used inpatient TJA as the control could be further divided into fast-track inpatient TJA (length of stay [LOS] ≤ 2 days) and traditional inpatient TJA (no restrictions on LOS). Relative risks were pooled to compare the outcomes of the same-day discharge group and the control group. According to selection criteria and quality assessment, 14 studies including 222,766 cases were identified. There was no significant difference in the risk of mortality (RR = 1.42, CI [0.67, 3.01]) or readmission (RR = 0.93, CI [0.79, 1.10]) between same-day discharge TJA and inpatient TJA. Compared with fast-track TJA, the rate of overall complications in same-day discharge TJA was significantly higher (RR = 1.67, CI [1.45, 1.93]), while the rates of overall complications were similar between same-day discharge and traditional inpatient TJA (RR = 0.83, CI [0.67, 1.03]). The overall safety of same-day discharge TJA is satisfactory; however, more complications were detected in same-day discharge TJA than that in fast-track TJA. Complications in same-day discharge TJA might be underestimated in some previous studies taking long-staying inpatient TJA as control. Being more cautious about complications is necessary in the care of same-day discharge TJA, and extensive prospective studies are needed to explore the optimized option that weighs both cost and complications.

Lower canal fill ratio was reported to correlate with aseptic loosening in many studies. However, the most widely used standard of fill ratio seemed inapplicable to Dorr type C femurs. We aimed to adapt the method of measuring the fill ratio in Dorr type C femurs and compare the outcomes among patients with different fill ratios. Twenty patients with Corail stems implanted in their Dorr type C femurs received spectrum CT to evaluate the whole-stem's fill ratio. Pearson Correlation Coefficient was calculated to assess the correlation between the fill ratio in X-ray film and spectrum CT. Then 87 THAs were involved in this study, divided into the fill ratio ≤ 80% group and the fill ratio > 80% group. Clinical and radiological outcomes were evaluated with a mean follow-up of 8.2 years. Fill ratio at 2 cm below the lesser trochanter in anterior–posterior X-ray film correlated with the whole-stem's fill ratio (r = 0.50, P = 0.02). Survival rate of stem, function scores, and radiological outcomes between the two groups showed no significant difference. In the fill ratio > 80% group, intraoperative fracture was significantly higher (19% VS 5%, P

Background Existing research indicates that tea drinking may exert beneficiary effects on mental health. However, associations between different types of tea intake and mental health such as depression have not been fully examined. The purpose of this study was to examine the associations of green tea, fermented tea, and floral tea consumption with depressive symptoms. Methods We used data from the 2018 wave of the Chinese Longitudinal Healthy Longevity Survey, a nationwide survey on older adults in mainland China. A total of 13,115 participants (mean age 83.7 years, 54.2% were women) with valid responses were included in the analysis. The type (green, fermented [black, Oolong, white, yellow, dark, and compressed teas], and floral) and the frequency of tea consumption were recorded, and depressive symptoms were assessed using 10-item of the Center for Epidemiologic Studies Depression Scale (CES-D-10). We examined the associations between the type and the frequency of tea intake and depression, controlling for a set of demographic, socioeconomic, psychosocial, behavioral, and health-related variables. Results Overall, intakes of green tea, fermented tea, and floral tea were all significantly associated with lower prevalence of depressive symptoms, independent of other risk factors. Compared with the group of no tea intake, the adjusted ORs of depressive symptoms for daily green tea, fermented tea, and floral tea intake were 0.85 (95% CI: 0.76–0.95), 0.87 (95% CI: 0.76–0.99), and 0.70 (95% CI: 0.59–0.82), respectively. Linear associations were observed between the frequencies of all three types of tea intake and depressive symptoms (P Conclusions Among Chinese older adults, regularly consumed any type of tea (green, fermented, or floral) were less likely to show depressive symptoms, the associations seemed more pronounced among floral tea and green tea drinkers.

Background Hepatocellular carcinoma (HCC) is the most common malignant disease worldwide. Although the diagnosis and treatment of HCC have greatly improved in the recent years, there is still a lack of accurate methods to predict the prognosis of patients. Evidence has shown that Hippo signaling in tissues adjacent to HCC plays a significant role in HCC development. In the present study, we aimed to construct a model based on the expression of Hippo‐related genes (HRGs) in tissues adjacent to HCC to predict the prognosis of HCC patients. Methods Gene expression data of paired normal tissues adjacent to HCC (PNTAH) and clinical information were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The HRG signature was constructed using four canonical Hippo‐related pathways. Univariate Cox regression analysis was used to screen survival‐related HRGs. LASSO and multivariate Cox regression analyses were used to construct the prognostic model. The true and false positive rates of the model were confirmed using receiver operating characteristic (ROC) analysis. Results The prognostic model was constructed based on the expression levels of five HRGs (NF2, MYC, BIRC3, CSNK1E, and MINK1) in PNTAH. The mortality rate of HCC patients increased as the risk score determined by the model increased. Furthermore, the risk score was found to be an independent risk factor for the survival of patients. ROC analysis showed that the prognostic model had a better predictive value than the other conventional clinical parameters. Moreover, the reliability of the prognostic model was confirmed in TCGA‐LIHC cohort. A nomogram was generated to predict patient survival. An exploration of the predictive value of the model in HCC tissues indicated that the model is PNTAH‐specific. Conclusions We developed and validated a prognostic model based on the expression levels of five HRGs in PNTAH, and this model should be helpful in predicting the prognosis of patients with HCC., We built a prognostic model based on the Hippo signaling for the first time, and found that the expression of the Hippo signaling in adjacent tissues can better predict the survival of liver cancer patients.

Persons living with diabetes (PLWD) with newly diagnosed tuberculosis are at greater risk of poor treatment outcomes. Identifying and prioritizing high-risk subgroups of PLWD and tuberculosis for tuberculosis programs to target has been rarely performed. We investigated risk factors for poor tuberculosis treatment outcomes among PLWD and developed a predictive risk score for tuberculosis control prioritization. Among PLWD diagnosed with tuberculosis, demographic, clinical, and tuberculosis treatment outcome data were collected. Poor treatment outcomes included treatment failure, death, default, and transfer. Multivariable logistic regression modeling was used to analyze risk factors of poor treatment outcomes. Risk scores were derived based on regression coefficients to classify participants at low-, intermediate-, and high-risk of poor treatment outcomes. Among 335 PLWD newly diagnosed with tuberculosis, 109 were cured and 172 completed treatment. Multivariable logistic regression found that risk factors of poor treatment outcomes included bacteriologically-positivity, low body mass index, no physical activity, and pulmonary cavitation. Rates of poor treatment outcomes in low- (0–2), intermediate- (3–4), and high-risk (5–8) groups were 4.2%, 10.5%, and 55.4% (Ptrend

OBJECTIVE: To exam the association of cognitive decline with APOE ε4 allele carriage and dietary protein intake and investigate whether there is a gene-diet (GxD) interaction of APOE ε4 allele carriage and dietary protein intake on cognitive decline in a nationwide cohort of older adults. METHODS: A cohort study of participants from Chinese Longitudinal Healthy Longevity Survey was conducted from 2008 t0 2014. A total of 3,029 participants (mean age of 77.0 years, SD=9.0; 49.3% were women) was enrolled. We genotyped APOE ε4 allele for each participant and calculated the diversity of dietary protein intake (DDPI) by summing up the frequency of intake of the 6 protein-rich foods (meats, fish, eggs, nuts, dairy products, and bean products). We assessed cognitive function using the Mini-Mental State Examination (MMSE). We used ordinal regression model to estimate the independent and joint effects of APOE ε4 carrier and dietary protein intake on cognitive decline, adjusting for potential confounders of age, sex, education, socio-economic status, lifestyles, BMI, and cardiometabolic conditions. RESULTS: There was significant association between carrying APOE ε4 allele and faster cognitive decline (Odds ratio: 1.19, 95% CI= 1.00–1.42), independent of potential confounders. While the associations of DDPI and the intake of 6 protein-rich foods with cognitive decline did not reach any statistical significance. We observed significant interactions of APOE ε4 with DDPI and fish intake, at multiple correction-adjusted Ps

Tuberculosis is a serious public health challenge facing mankind and one of the top ten causes of death. Diagnostic imaging plays an important role, particularly for the diagnosis and treatment planning of tuberculosis patients with negative microbiology results. This article illustrates a number of atypical computed tomography (CT) appearances of pulmonary tuberculosis (PTB), including (I) clustered micronodules (CMNs) sign; (II) reversed halo sign (RHS); (III) tuberculous pneumatocele; (IV) hematogenously disseminated PTB with predominantly diffuse ground glass opacity manifestation; (V) hematogenously disseminated PTB with randomly distributed non-miliary nodules; (VI) PTB changes occur on the background of emphysema or honeycomb changes of interstitial pneumonia; and (VII) PTB manifesting as organizing pneumonia. While the overall incidence of PTB is decreasing globally, the incidence of atypical manifestations of tuberculosis is increasing. A good understanding of the atypical CT imaging changes of active PTB shall help the diagnosis and differential diagnosis of PTB in clinical practice.

Objectives To investigate the proportion of insulin‐dependent diabetes mellitus (IDDM) patients among diabetic patients undergoing total joint arthroplasty (TJA) and whether insulin dependence is associated with postoperative complications. Methods A systematic literature search was performed in EMBASE, PubMed, Ovid, Medline, the Cochrane Library, Web of Science, the China Science and Technology Journal Database, and China National Knowledge Infrastructure from the inception dates to 10 September 2019. Observational studies reporting adverse events with IDDM following TJA were included. Primary outcomes were cardiovascular complications, pulmonary complications, kidney complications, wound complications, infection, and other complications within 30 days of surgery. Secondary outcomes were the proportion of IDDM patients among diabetic patients undergoing TJA and its time trend. Results A total of 19 studies involving 85,689 participants were included. Among patients undergoing TJA, 26% of diabetic patients had IDDM. Compared with non‐insulin‐dependent diabetes (NIDDM), the incidences of cardiac arrest (risk ratio [RR], 2.346; 95% confidence interval [CI], 1.553 to 3.546), renal failure (relative risk [RR], 2.758; 95% CI, 1.830 to 4.156), deep incisional surgical site infection (RR, 1.968; 95% CI, 1.107 to 3.533), wound dehiscence (RR, 2.209; 95% CI, 1.830 to 4.156), and death (RR, 2.292; 95% CI, 1.568 to 3.349) were all significantly increased in IDDM. A significant time trend was witnessed for the prevalence of IDDM (P = 0.014). There was no statistical significance for organ/space surgical site infection, thrombotic events (deep venous thrombosis/ pulmonary embolism), and revision rates. Conclusion Insulin‐dependent diabetes is an independent high‐risk factor for increased adverse outcomes relative to NIDDM, suggesting that hierarchical and optimal blood glucose management may contribute to reducing the adverse complications after surgery for these patients. In addition, because the risk of sepsis, deep wound infection, organ/space surgical site infection, urinary tract infection, renal insufficiency, and renal failure significantly increase after TJA in IDDM patients, more active postoperative antimicrobial prophylaxis may be needed on the premise of protecting renal function., Results of the meta‐analysis.

BackgroundWhether the cement should be used in the total knee arthroplasty (TKA) was still in controversy. This meta-analysis was performed to compare the efficacy of two kinds of fixation.MethodsRandomized controlled trials (RCTs), prospective/retrospective observational studies from PubMed (on 2019 September), EMBASE (on 2019 September), and the Cochrane Central Register of Controlled Trials (CENTRAL) and Web of Science (on 2019 September) were searched. Only studies followed more than 2 years was included for the review. The PRISMA guidelines and Cochrane Handbook were adopted to assess the quality of the results reported in included studies to ensure that the results of our meta-analysis were reliable and veritable. The continuous and dichotomous outcomes were collected in a standard form, and the data were analyzed by Review Manager 5.3 software. Finally, the results were presented in the Forest plots.ResultsTwenty-six studies involving 2369 patients in cementless TKA and 2654 patients in cemented TKA were included. The rate of revision was not significantly different in two groups (p= 0.55). More than eight reasons caused revision were found in our study, the aseptic loosing was the most common, followed by the periprosthetic joint infection (PJI), neither was significantly different (p= 0.88 and 0.45, respectively). While significantly better long-term functional recovery was found in cementless TKA in terms of Knee Society Function Score (p= 0.004) and manipulation under anesthesia (p= 0.007).ConclusionCementless fixation did not decrease the rate of revision after the total knee arthroplasty compared with the cemented fixation, while the long-term functional recovery was significantly better in the cementless group.

Large nonlinear optical (NLO) coefficient (dij), high laser damage threshold (LDT), and phase matching (PM) are crucial for the practical applications of IR NLO crystals. A great number of IR NLO m...

In this paper, we introduce a multi-model ensemble framework for medical image segmentation. We first collect a set of state-of-the-art models in this field and further improve them through a series of architecture refinement moves and a set of specific training skills. We then integrate these fine-tuned models into a more powerful ensemble framework. Preliminary experiment results show that the proposed multi-model ensemble framework performs well under the given polyp and instrument datasets.

We here report a new pentagonal network structure of the PtM2 (M = S, Se, Te) monolayers with the P21/c (no. 14) space group. The electronic structure and thermoelectric properties of the pentagonal PtM2 monolayers are calculated through the VASP and BoltzTraP codes. We verify their dynamic and thermodynamic stabilities by calculating their phonon spectra and simulating ab initio molecular dynamics. It is found that the new material belongs to the medium-wide indirect band gap semiconductors from the PBE and HSE06 methods. At 300 K, the lattice thermal conductivities (Kl) of the pentagonal PtTe2 in the x and y directions are the smallest among these three materials, being 1.77 and 5.17 W/m K, respectively. The anisotropic zT values (2.60/1.14) in the x/y direction of the pentagonal PtTe2 at 300 K are much greater than those of the pentagonal PtSe2 (1.75/0.82) and the pentagonal PtS2 (0.58/0.16) at 300 K. Importantly, the p-type pentagonal PtTe2 also has excellent thermoelectric properties at 600 K, with a zT value of 5.03 in the x direction, indicating that the p-type pentagonal PtTe2 has a good application potential in the thermoelectric field.

Breast cancer is one of the most common malignancies in women and is characterized by active immunogenicity. Immune cell infiltration plays an important role in the development of breast cancer. The degree of infiltration influences both the response to and effect of treatment. However, immune infiltration is a complex process. Differences in oxygen partial pressure, blood perfusion and nutrients in the tumor microenvironment (TME) suggest that infiltrating immune cells in different sites experience different microenvironments with corresponding changes in the metabolic mode, that is, immune cell metabolism is heterogenous in the TME. Furthermore, the present review found that lipid metabolism can support the immunosuppressive microenvironment in breast cancer based on a review of published literature. Research in this field is still ongoing; however, it is vital to understand the metabolic patterns and effects of different microenvironments for antitumor therapy. Therefore, this review discusses the metabolic responses of various immune cells to different microenvironments in breast cancer and provides potentially meaningful insights for tumor immunotherapy.

Background: This study aimed to investigate the clinical outcome and related risk factors of fetal lateral ventriculomegaly (VM). Methods: A retrospective analysis was performed on 255 cases diagnosed as fetal VM. Prenatal imaging examination was carried out. The pregnancy outcomes were investigated through follow-up. According to the prognosis of children, they were divided into case group and control group. Multivariate logistic regression was used to analyze the factors influencing the prognosis of hydrocephalus. Results: After excluding the cases with either loss of follow-up or incomplete information, 102 cases were followed up. Twelve cases with poor prognosis were set as the case group. According to the maternal age, gestational age, gender of children, and follow-up time, 3 cases were selected from the other 90 cases for each child in the case group, respectively, and selected as the control group. Paired comparative analysis was performed on 48 cases. Using prognosis as a dependent variable, multivariate logistic regression analysis of the statistically significant factors indicated that the change speed of width ratio (CSWR) and maximum lateral ventricular width (MW) were associated with fetal prognosis. Conclusions: Our results suggested that CSWR and MW may have the value of predicting fetal prognosis.

Piezoelectric and thermoelectric materials that can directly convert mechanical and thermal energy into electricity have attracted great interest because of their practical applications in overcoming the challenges of the energy crisis. In this research, a new family of two-dimensional (2D) group-VI Janus ternary compounds with α and γ phases are predicted. After the stability testing, only the α-TeSSe monolayer has dynamic and thermal stability. The band structure and the optic, piezoelectric, and thermoelectric performances of the Janus α-TeSSe monolayer are calculated via the first-principles calculations. Janus α-TeSSe is a narrow indirect bandgap semiconductor with a value of 0.953 eV at HSE06 functional considering the spin-orbit coupling (SOC), which is beneficial to its thermoelectric performance, and its excellent absorption coefficients indicate that it may be a promising optoelectronic material. The piezoelectric calculations show that Janus α-TeSSe exhibits not only appreciable in-plane piezoelectricity (d11 = 17.17 pm/V ) but also superior vertical piezoelectricity (d31 = 0.22 pm/V). Furthermore, a new TansOpt code is used to calculate the electrical transport coefficients with a constant electron-phonon coupling approximation, which is more accurate than the constant relaxation time approximation. The origin of ultralow lattice thermal conductivity is also discussed in detail. Finally, ultrahigh ZT values of 0.77 and 1.95 occur in n-type and p-type doping at 600 K, respectively, indicating it is a promising thermoelectric material. Our work demonstrates that Janus α-TeSSe monolayers have potential applications in optoelectronic, piezoelectric, and thermoelectric devices, which will greatly stimulate research-related experiments.

Periodontitis is a progressive inflammation disease, the clinical management of which remains a challenge. The traditional management may control periodontal inflammation, but failed to regenerate functional periodontium. This review summarizes the most advancing regenerative techniques regarding stem cell culture and scaffold fabrication, such as cell sheeting, spheroid culture, electrospinning and 3D printing. The applications of different techniques manifest tremendous potential of regenerating the complete and functional periodontium. Albeit promising, new technologies have met with their own drawbacks such as insufficient vascularization and precision, which necessitate deeper modification. Thus, this review also points out the potential perspectives and methods aiming at their disadvantages, illuminating the directions of future researches to successful clinical scenarios.

In most of the existing studies on the band selection using the convolutional neural networks (CNNs), there is no exact explanation of how feature learning helps to find the important bands. In this letter, a CNN-based band selection method is presented, and the process of feature tracing is explained in detail. First, a 1-D CNN model is designed and trained to reach high accuracy. Next, the derivative of the sum of partial output combinations of a layer is obtained with respect to the input layer of the CNN. Then, the derivative maps are used to obtain the sample saliency maps and class saliency maps (CSMs). Finally, the bands are selected using the CSMs. The proposed model is verified by experiments on different data sets and compared with other related methods. The results show that the proposed model can achieve better performance than the other methods.

Air pollution is a serious environmental problem that has attracted much attention. Air quality prediction can provide useful information for urban environmental governance decision-making and residents’ daily health control. However, existing research methods have suffered from a weak ability to capture the spatial correlations and fail to model the long-term temporal dependencies of air quality. To overcome these limitations, we propose a multi-scale spatiotemporal graph convolution network (MST-GCN), which consists of a multi-scale block, several spatial-temporal blocks and a fusion block. We first divide the extracted features into several groups based on their domain categories, and represent the spatial correlations across stations as two graphs. Then we combine the grouped features and the constructed graphs in pairs to form a multi-scale block that feeds into spatial-temporal blocks. Each spatial-temporal block contains a graph convolution layer and a temporal convolution layer, which can model the spatial correlations and long-term temporal dependencies. To capture the group interactions, we use a fusion block to fuse multiple groups. Extensive experiments on a real-world dataset demonstrate that our model achieves the highest performance compared with state-of-the-art and baseline models for air quality prediction.

Purpose Glucocorticoids are a mainstay to control postoperative pain, inflammation, nausea and vomiting (PONV) in total knee arthroplasty (TKA). Understanding the optimal dose and route of glucocorticoids administration in TKA is of great significance in speedy functional recovery. We aimed to summarize, evaluate and rank order the efficacy of glucocorticoids regimens in TKA. Methods Electronic databases (PubMed et al.) were systematically searched from inception up to April 30, 2020. The primary outcomes were visual analogue scale (VAS), range of motion (ROM) and knee society score (KSS). C-reactive Protein (CRP) and PONV were also evaluated. Multivariable Bayesian random effects models were used to synthesize and rank the comparative efficacy of glucocorticoids regimens. Results A total of 34 eligible randomized controlled trials with 11 different glucocorticoids regimens were assessed. Overall inconsistency and heterogeneity were acceptable. Multiple medium dose perioperative intravenous injection (IV) ranked first in the analgesia network and a single high doses of preoperative IV ranked first in the inflammation and PONV network. There was no statistically significant increase in ROM or KSS in all the glucocorticoid formulations and doses compared with controls on postoperative day 30. Conclusions Glucocorticoid multiple intravenous injection was preferable to a single intravenous injection (preoperative and postoperative), periarticular injection and intra-articular injection in analgesia. Based on the available evidence, a medium dose of hydrocortisone of 2–4 mg/kg is optimal.

This study presents a stereo vision robotic arm assistance system, in which five degrees of catching can be performed by the robot arm in a single instance. The algorithm of the control system is built for population-based optimization and specifically aimed to assist people with disabilities. The proposed stereo vision-based robot arm system enables users to manipulate objects based on the robot’s ability to aim at objects by using computer vision. The stereo vision system counts the parameters by focusing on the real-world position of the instance in the coordinate system. A trained deep fully connected network is then adopted to compensate the location measurement errors incurred by the inaccurate parameters measured from the deep learning procedure. Subsequently, the proposed Q-learning-based swarm optimization algorithm is adopted to solve the forward kinematics problem and count the angles of each servo. The performance of the robot arm is compared with several real-life experiments to test its ability to grip a target object in different positions.

Background & Aims Although abnormal liver chemistries are linked to a higher risk of coronavirus disease 2019 (COVID‐19)‐related death, liver manifestations may be diverse and even confusing. Thus, we performed a meta‐analysis of published liver manifestations and described the liver damage in patients with COVID‐19 who died or discharged alive. Approach & Results We searched PubMed, Google Scholar, medRxiv, bioRxiv, the Cochrane Library, Embase, and three Chinese electronic databases through April 22, 2020. We analyzed pooled data on liver chemistries stratified by the main clinical outcome of COVID‐19, using a fixed or random‐effects model. In our meta‐analysis of 19 studies, which included a total of 4,103 patients, the pooled mean alanine aminotransferase and aspartate aminotransferase levels were respectively 31.7 and 51.0 IU/L in the patients with COVID‐19 who died and 27.7 and 32.9 IU/L in those discharged alive (both p < 0.0001). Compared with the patients discharged alive, those who died tended to have lower albumin levels but longer prothrombin time and higher international standardized ratio. Conclusions In this meta‐analysis, according to the main clinical outcome of COVID‐19, we comprehensively described three patterns of liver impairment related to COVID‐19, hepatocellular injury, cholestasis, and hepatocellular disfunction. The patients who died from COVID‐19 tended to have different liver chemistries from those discharged alive. Special caution should be given to the patients with relatively higher index of liver chemistries.

Background Epilepsy and migraine are both considered as paroxysmal neurologic disorders. Previous studies have reported some cases with comorbidity of these two diseases. As the underlying molecular mechanism remains unclear, we performed a network-and-pathway-based method with candidate gene sets of epilepsy and migraine to explore it. Methods : Comparing the candidate genes between epilepsy and migraine, we identified 21 common genes. Functional enrichment analysis indicated that epilepsy and migraine are dysfunctional in the similar biological processes, such as glutamatergic transmissions, channel activities, and transporter activities. We also explored many shared pathways between these two diseases such as neuroactive ligand-receptor interaction. Results By combining systematical analysis and previous studies review, we finally identified six essential genes associated with the comorbidity of epilepsy and migraine. Conclusions This is the first time to address the common ground of epilepsy and migraine by a systematic biology method. The present study provides a novel way to explain the potential mechanisms of these two diseases and a new set of therapeutic targets.

Performance regressions of large-scale software systems often lead to both financial and reputational losses. In order to detect performance regressions, performance tests are typically conducted in an in-house (non-production) environment using test suites with predefined workloads. Then, performance analysis is performed to check whether a software version has a performance regression against an earlier version. However, the real workloads in the field are constantly changing, making it unrealistic to resemble the field workloads in predefined test suites. More importantly, performance testing is usually very expensive as it requires extensive resources and lasts for an extended period. In this work, we leverage black-box machine learning models to automatically detect performance regressions in the field operations of large-scale software systems. Practitioners can leverage our approaches to complement or replace resource-demanding performance tests that may not even be realistic in a fast-paced environment. Our approaches use black-box models to capture the relationship between the performance of a software system (e.g., CPU usage) under varying workloads and the runtime activities that are recorded in the readily-available logs. Then, our approaches compare the black-box models derived from the current software version with an earlier version to detect performance regressions between these two versions. We performed empirical experiments on two open-source systems and applied our approaches on a large-scale industrial system. Our results show that such black-box models can effectively and timely detect real performance regressions and injected ones under varying workloads that are unseen when training these models. Our approaches have been adopted in practice to detect performance regressions of a large-scale industry system on a daily basis.

This study proposes a design for a wearable guide device for blind or visually impaired persons on the basis of video streaming and deep learning. This work mainly aims to provide supplementary assistance to white canes used by visually impaired persons and offer them increased freedom of movement and independence using the proposed wearable device. The considerable amount of environmental information provided by the device also ensures enhanced safety for its users. Computer vision in the proposed device uses an RGB camera instead of the RGBD camera commonly used in computer vision. Deep learning is applied to convert RGB images into depth images and calculate the plane for detecting indoor objects and safe walking routes. A convolutional neural network (CNN) is adopted, and its neural network structure, which is similar to that of the human brain, simulates a neural transmission mechanism similar to that triggered in human learning. Therefore, this system can learn a large number of feature routes and then generate a model from the learning result. The proposed system can help blind or visually impaired persons identify flat and safe walking routes.

A compact dual-polarized filtering base-station antenna is presented in this letter. The entire size (include the ground plane) of the element is only 0.53λ × 0.72λ × 0.14λ (λ is the wavelength of the central working frequency). Four vertical slots etched on the coupling cross slot are for improving the high-frequency suppression of the antenna. The C-slots on the Gnd1 reduce the cross-polarization ratio at the axial direction. The antenna shows a wide working bandwidth of 25.6% and a low cross-polarization ratio about 22 dB. In order to validate the design, a 1 × 3 linear array is fabricated and tested. The antenna array works at 3.3–4.0 GHz with the average gain about 11 dBi. Besides, the array remains a good out of band rejection more than −20 dB up to 9 GHz which exhibits a good harmonic suppression performance over a wideband.

Improving robots with self-learning ability is one of the critical challenges for the researchers in the area of cognitive robotics and artificial general intelligence. This robot will decide when, where, and what to learn in a continuous visual environment by itself. Here we focus on the procedural knowledge learning, which is sequential and considered harder to understand compared with declarative knowledge in the cognitive system. Inspired by the architecture of the human brain which has integrated well different kinds of cognitive functions, a Brain-inspired Active Learning Architecture (BALA) is proposed for procedural knowledge understanding based on Baxter robot and human interaction. The BALA model contains four main parts: inspired by Primary Visual Pathway, a Convolutional Neural Network (CNN) is constructed for spatial information abstraction; inspired by the Hippocampus Pathway (especially the recurrent loops in CA3 sub-region), a Recurrent Neural Network (RNN) is built for sequential information processing related with procedural knowledge; inspired by the Prefrontal Cortex, a Knowledge Graph based on Bag Of Words (BOW) is constructed for declarative knowledge generation and association; inspired by the Basal Ganglia Pathway, we select Q matrix for Reinforcement Learning (RL). The CNN and RNN parts will be firstly pre-trained on ImageNet dataset and standard Youtube Video-Scene dataset respectively. Then, the RNN, Knowledge Graph, and Q matrix will be dynamically updated in the Baxter robot’s interactive learning procedure with human cooperators. The BALA could actively and incrementally recognize different kinds of procedural knowledge. In 22-type daily-life videos with procedure knowledge (e.g., opening the door, wiping the table, or taking the phone), the BALA model gets the best performance compared with standard CNN, RNN, RL, and other integrative methods. The BALA model is a small step on integrative intelligence interaction between the Baxter robot and human cooperator.

Aims Temporal lobe epilepsy (TLE) is the most common focal epilepsy syndrome in adults and frequently develops drug resistance. Studies have investigated the value of peripheral DNA methylation signature as molecular biomarker for diagnosis or prognosis. We aimed to explore methylation biomarkers for TLE diagnosis and pharmacoresistance prediction. Methods We initially conducted genome‐wide DNA methylation profiling in TLE patients, and then selected candidate CpGs in training cohort and validated in another independent cohort by employing machine learning algorithms. Furthermore, nomogram comprising DNA methylation and clinicopathological data was generated to predict the drug response in the entire patient cohort. Lastly, bioinformatics analysis for CpG‐associated genes was performed using Ingenuity Pathway Analysis. Results After screening and validation, eight CpGs were identified for diagnostic biomarker with an area under the curve (AUC) of 0.81 and six CpGs for drug‐resistant prediction biomarker with an AUC of 0.79. The nomogram for drug‐resistant prediction comprised methylation risk score, disease course, seizure frequency, and hippocampal sclerosis, with AUC as high as 0.96. Bioinformatics analysis indicated drug response–related CpGs corresponding genes closely related to DNA methylation. Conclusions This study demonstrates the ability to use peripheral DNA methylation signature as molecular biomarker for epilepsy diagnosis and drug‐resistant prediction.

White organic light-emitting diodes (WOLEDs) using thermally activated delayed fluorescence (TADF)-based single emissive layer (SEL) have attracted enormous attention because of their simple device structure and full exciton utilization potential for high efficiency. However, WOLEDs made of an all-TADF SEL usually exhibit serious efficiency roll-off and poor color stability due to serious exciton-annihilation and unbalanced radiative decays of different TADF emitters. Herein, a new strategy is proposed to manipulate the TADF-sensitized fluorescence process by combining dual-host systems of high triplet energy with a conventional fluorescent emitter of complementary color. The multiple energy-funneling paths are modulated and short-range Dexter energy transfer is largely suppressed due to the steric effect of peripheral tert-butyl group in the blue TADF sensitizer. The resulting all-fluorescent WOLEDs achieve an unprecedentedly high external quantum efficiency of 21.8% with balanced white emission of Commission Internationale de l'Eclairage coordinate of (0.292, 0.343), accompanied with good color stability, reduced efficiency roll-off, and prolonged operational lifetime. These findings demonstrate the validity of this strategy for precisely allocating the exciton harvesting in SEL WOLEDs.

Various types of theoretical algorithms have been proposed for 6D pose estimation, e.g., the point pair method, template matching method, Hough forest method, and deep learning method. However, they are still far from the performance of our natural biological systems, which can undertake 6D pose estimation of multi-objects efficiently, especially with severe occlusion. With the inspiration of the Müller-Lyer illusion in the biological visual system, in this paper, we propose a cognitive template-clustering improved LineMod (CT-LineMod) model. The model uses a 7D cognitive feature vector to replace standard 3D spatial points in the clustering procedure of Patch-LineMod, in which the cognitive distance of different 3D spatial points will be further influenced by the additional 4D information related with direction and magnitude of features in the Müller-Lyer illusion. The 7D vector will be dimensionally reduced into the 3D vector by the gradient-descent method, and then further clustered by K-means to aggregately match templates and automatically eliminate superfluous clusters, which makes the template matching possible on both holistic and part-based scales. The model has been verified on the standard Doumanoglou dataset and demonstrates a state-of-the-art performance, which shows the accuracy and efficiency of the proposed model on cognitive feature distance measurement and template selection on multiple pose estimation under severe occlusion. The powerful feature representation in the biological visual system also includes characteristics of the Müller-Lyer illusion, which, to some extent, will provide guidance towards a biologically plausible algorithm for efficient 6D pose estimation under severe occlusion.

Dodging emergent dangers is an innate cognitive ability for animals, which helps them to survive in the natural environment. The retina-superior colliculus (SC)-pulvinar–amygdala–periaqueductal gray pathway is responsible for the visual fear responses, and it is able to quickly detect the looming obstacles for innate dodging. Inspired by the mechanism of the visual fear responses pathway, we propose a brain-inspired emergent obstacle dodging method to model the functions of the related brain regions. This method first detects the moving direction and speed of the salient point of moving objects (retina). Then, we detect the looming obstacles (SC). Third, we modulate attention to the most dangerous area (pulvinar). Fourth, if the degree of danger exceeds the threshold (amygdala), the unmanned ariel vehicle (UAV) moves back to dodge it (periaqueductal gray). Two types of experiments are conducted to validate the effectiveness of the proposed model. In a simulated scene, we simulate the process of mice’s fear responses by putting looming dark lights shining on them. In a natural scene, we apply the proposed model to the UAV emergent obstacles dodging. Compared to the stereo vision model, the proposed model is not only more biologically realistic from the mechanisms perspective, but also more accurate and faster for computation.

Significant variability exists in the trajectories of late-life cognitive decline; however, their associated lifestyle factors remain less studied. We examined these trajectories among elderly participants from the recent five waves (at three-year intervals) of the Chinese Longitudinal Healthy Longevity Study (CLHLS) from 2002 to 2014. Participants from this cohort were included if they completed at least four waves of measurements. Mini-Mental State Examination (MMSE) scores, demographics, medical diagnoses (e.g., hypertension, diabetes, and heart disease), and lifestyle-related information (e.g., smoking, drinking alcohol, and exercise) were collected from participants (N = 2,584; mean age at baseline = 73.3) at least four times across 12 years. MMSE scores were entered into a latent class mixed model analysis. Subsequently, demographic, medical, and lifestyle predictors were entered into multinomial logistic regression models to predict the trajectories. One of the four emerged classes (no decline) was characterized by an absence of cognitive decline; the other three exhibited various degrees of cognitive decline. The inclusion of lifestyle factors significantly improved the prediction of the different trajectories, above and beyond demographics and medical variables; the 'no decline' class was significantly more likely to report exercising regularly. Changes in cognitive functioning across the late-life period are characterized by multiple trajectories. Cognitive decline is not inevitable across the late-life period; the absence of such cognitive decline is partly explained by certain lifestyle factors.

Background and Aims: The prognosis of intrahepatic cholangiocarcinoma (ICC) after radical resection is far from satisfactory, but the effect of postoperative transarterial chemoembolization (p-TACE) remains controversial. This multi-center retrospective study was to evaluate the clinical value of p-TACE and identify the selected patients who would benefit from p-TACE. Methods: Data of ICC patients who underwent radical resection with/without p-TACE therapy was obtained from 12 hepatobiliary centers in China between Jan 2014 and Jan 2017. Overall survival (OS) was set as the primary endpoint, which was analyzed by the Kaplan-Meier method before and after propensity score matching (PSM). Subgroup analysis was conducted based on the established staging system and survival risk stratification. Results: A total of 335 patients were enrolled in this study, including 39 patients in the p-TACE group and 296 patients in the non-TACE group. Median OS in the p-TACE group was longer than that in the non-TACE group (63.0 months vs. 18.0 months, P=0.041), which was confirmed after 1:1 PSM (P=0.009). According to the 8th TNM staging system, patients with stage II and stage III stage would be benefited from p-TACE (P=0.021). Subgroup analysis stratified by risk factors showed that p-TACE could only benefit patients with risk factors

Background Cognitive impairment is a major contributor to mortality among the elderly. However, the relationship between cognitive impairment evaluated by educational levels and mortality and the trend between cognitive impairment and mortality with time are unclear. We aim to evaluate the differences in associations of cognitive impairment, taking the stratification by educational levels into account, with all-cause mortality and further explore the relationship of cognitive impairment with mortality in different age and sex groups in two cohorts ascertained 6 years apart in China. Methods A total of 13,906 and 13,873 Chinese elderly aged 65 years and older were included in the 2002–2008 and 2008–2014 cohorts from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Mortality data was ascertained from interviews with family members or relatives of participants. Cognitive function, evaluated by the Mini-Mental State Examination (MMSE), were defined by different cut-offs taking educational background into account. Cox models were used to explore the relationship of cognitive impairment with mortality. Results For the 2002–2008 and 2008–2014 cohorts, 55,277 and 53,267 person-years were followed up, and the mean (SD) age were 86.5 (11.6) and 87.2 (11.3) years, respectively. Compared to normal cognition, cognitive impairment was independently associated with higher mortality risk after controlling for potential confounders, with hazard ratios (HRs) of 1.32 (95% confidence interval [CI], 1.25–1.39) in 2002–2008 cohort and 1.26 (95% CI, 1.19–1.32) in 2008–2014 cohort, stratified by educational levels. The trend of cognitive impairment with all-cause mortality risk decreased from 2002 to 2008 to 2008–2014 cohort, while no significant interaction of cognitive impairment with cohort for all-cause mortality was observed. The associations of cognitive impairment and mortality were decreased with age in the two cohorts. Conclusions Cognitive impairment evaluated by different cut-offs were associated with increased risk of mortality, especially among those aged 65–79 years in the two cohorts; this advocates that periodic screening for cognitive impairment among the elderly is warranted.