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Background The spread of spinal anesthesia was influenced by many factors, and the effect of body height on spinal anesthesia is still arguable. This study aimed to explore the impact of height on the spread of spinal anesthesia and the stress response in parturients. Methods A total of ninety-seven parturients were allocated into two groups according to their height: the shorter group (body height was shorter than 158 cm) and taller group (body height was taller than 165 cm). Spinal anesthesia was performed with the same amount of 12 mg plain ropivacaine in mothers of different heights. The primary outcome of the study was the success or failure of the spinal anesthesia. The secondary outcomes of the study were stress response, time to T6 sensory level, the incidence of hypotension, the satisfaction of abdominal muscle relaxation and patient VAS scores. Results The rate of successful spinal anesthesia in the shorter group was significantly higher than that in the taller group (p = 0.02). The increase of maternal cortisol level in the shorter group was lower than that in the taller group at skin closure (p = 0.001). The incidence of hypotension (p = 0.013), time to T6 sensory block (p = 0.005), the quality of abdominal muscle relaxation (p p p p = 0.022), amniotic fluid index (p = 0.022) were significantly associated with successful spinal anesthesia. Conclusions It’s difficult to use a single factor to predict the spread of spinal anesthesia. Patient’s vertebral column length, amniotic fluid index and abdominal girth were the high determinant factors for predicting the spread of spinal anesthesia. Trials registration ChiCTR-ROC-17012030 (Chictr.org.cn), registered on 18/07/2017.

Background The wide spread of plasmid-mediated colistin resistance by mobile colistin resistance (MCR) in Enterobacteriaceae severely limits the clinical application of colistin as a last-line drug against bacterial infection. The identification of colistin potentiator from natural plants or their compound preparation as antibiotic adjuncts is a new promising strategy to meet this challenge. Methods Herein, the synergistic activity, as well as the potential mechanism, of Pingwei pill plus antibiotics against MCR-positive Gram-negative pathogens was examined using checkerboard assay, time-killing curves, combined disk test, western blot assay, and microscope analysis. Additionally, the Salmonella sp. HYM2 infection models of mouse and chick were employed to examine the in vivo efficacy of Pingwei pill in combination with colistin against bacteria infection. Finally, network pharmacology and molecular docking assay were used to predicate other actions of Pingwei pill for Salmonella infection. Results Our results revealed that Pingwei Pill synergistically potentiated the antibacterial activity of colistin against MCR-1-positive bacteria by accelerating the damage and permeability of the bacterial outer membrane with an FIC (Fractional Inhibitory Concentration) index less than 0.5. The treatment of Pingwei Pill neither inhibited bacterial growth nor affected MCR production. Notably, Pingwei Pill in combination with colistin significantly prolonged the median survival in mouse and chick models of infection using the Salmonella sp. strain HYM2, decreased bacteria burden and organ index of infected animal, alleviated pathological damage of cecum, which suggest that Pingwei Pill recovered the therapeutic performance of colistin for MCR-1- positive Salmonella infection in mice and the naturally infected host chick. Pharmacological network topological analysis, molecular docking, bacterial adhesion, and invasion pathway verification assays were performed to identify the other molecular mechanisms of Pingwei Pill as a colistin potentiator against Gram-negative bacteria infection. Conclusion Taken together, NMPA (National Medical Products Administration)-approved Pingwei Pill is a promising adjuvant with colistin for MCR-positive bacterial infection with a shortened R&D (research and development) cycle and affordable R&D cost and risk.

This study examined the relationship between older adults’ subjective versus objective sleep and subjective cognitive decline (SCD), and explored the role of depression on this association. One hundred and four community-dwelling older adults underwent a week of actigraphic sleep monitoring, and completed a series of neuropsychological screeners. Older adults’SCD score was positively correlated with subjective insomnia, but not with objective sleep parameters. Further mediation modeling revealed that older adults’depression mediated the association between subjective insomnia and SCD. Subjective sleep, rather than objective sleep, may be a more sensitive indicator for older adults’ SCD, with depression symptoms appearing to account for most of the variance. These findings extend our perspective on the relationship between sleep disruption and cognitive decline, and highlight the necessity of early targeted interventions on sleep to reduce the risk of cognitive impairment in the elderly with SCD.

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-U.S. institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection.

Introduction Sleep deprivation (SD) has a negative influence on mood and emotion processing, and previous studies have elucidated the impaired coupling within the default network (DN) after SD. However, the dynamic characteristic with high temporal precision was rarely investigated in the DN after SD. Methods Here, the resting-state EEG after nocturnal sleep (NS) and SD was collected from 31 participants. The cortical electrical activities of the posterior cingulate cortex (PCC) and the anterior medial prefrontal cortex (aMPFC) were reconstructed applying the eLORETA, and the functional connectivity (FC) of PCC-aMPFC was calculated using the power envelope connectivity (PEC). Results Compared with NS, the power spectrums of the PCC and the FC of PCC-aMPFC were significantly reduced in the α band after SD. Interestingly, the impaired PCC-aMPFC integration was positively correlated with the decreased positive affect, implying that the DN plays a critical role in the subjective mood state. Our moderation analysis further revealed that the intensity of the DN posterior-anterior interaction moderated sleep loss and positive affect. Discussion Overall, the results reveal the strong relationship between the uncoupling of DN and the feeling down of mood. Our research may contribute towards a better understanding of the mood and cognition processing after sleep loss.

Background Previous neuroimaging studies revealed abnormal resting-state functional connectivity between distributed brain areas in patients with major depressive disorder. Those abnormalities were normalized after treatment. Moreover, the functional connectivity could predict clinical response to those treatments. However, there has currently been no meta-analysis to verify these findings. Methods The current study aimed to investigate how the resting-state connectivity patterns predict antidepressant response to various treatments across depressive studies by using coordinate-based meta-analysis named activation likelihood estimation. The relevant articles were obtained by searching on PubMed and Web of Science. Results Following exclusion criteria of inappropriate studies, seventeen papers with 392 individual depressive patients were included. Those articles contained repetitive transcranial magnetic stimulation (rTMS) treatment, pharmacotherapy, cognitive behavioral therapy (CBT), electroconvulsive therapy (ECT) and transcutaneous vagus nerve stimulation in patients with depression. Meta-analysis revealed that clinical response to all treatments could be predicted by baseline default mode network connectivity in patients with depression. The rTMS treatment had larger effect size compared to other treatment strategies. Furthermore, subgroup meta-analysis showed that the baseline connectivity of perigenual anterior cingulate cortex (pgACC) and ventral medial prefrontal cortex could predict symptoms improvement of rTMS treatment. Limitations More resting-state connectivity studies of CBT and ECT treatment are needed. Conclusions This study highlighted crucial role of DMN, especially the pgACC, in understanding the underlying treatment mechanism of depression.

OBJECTIVEPrimary intracranial rhabdomyosarcoma (PIRMS) is rare, and the effects of the treatment strategy on overall survival (OS) are unclear. This study aimed to evaluate risk factors pertinent to OS and to propose an optimal treatment strategy.METHODSClinical data of patients with PIRMS treated at Beijing Tiantan Hospital and from the English-language literature between 1946 and 2018 were reviewed. A literature review was performed via Ovid, MEDLINE, Embase, PubMed, Web of Science, and Cochrane databases using the terms “rhabdomyosarcoma,” “intracranial,” “cerebral,” and “brain.” Previously published data were processed and used according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.RESULTSThere were 8 males (66.7%) and 4 females with PIRMS at our institution, with a mean age of 24.3 years. Gross-total resection was achieved in 4 patients (33.3%), and adjuvant radiation and chemotherapy were administered in 5 (45.5%) and 3 (27.3%) patients, respectively. After a mean follow-up period of 13.7 months, all patients developed local-regional recurrence and died of the disease. Twenty-nine cases (14 female and 15 male) were reported in the literature with a median age of 9.0 years. After a mean follow-up duration of 18.6 months, 13 patients (44.8%) developed recurrences, 7 patients (24.1%) had extracranial metastasis, and 14 patients (48.3%) died. In the pooled cases, adjuvant radiation (hazard ratio [HR] 0.089, 95% confidence interval [CI] 0.027–0.288, p < 0.001) and age < 10 years (HR 0.227, 95% CI 0.077–0.666, p = 0.007) were independent predictors of good local-regional progression-free survival (LR-PFS). Adjuvant radiation therapy (HR 0.301, 95% CI 0.110–0.828, p = 0.020) and age < 10 years (HR 0.359, 95% CI 0.131–0.983, p = 0.046) were significant predictors for favorable OS in the multivariate model.CONCLUSIONSDue to the rarity of the disease, a poor outcome of PIRMS was demonstrated based on the pooled cohort. Use of radiation was associated with improved outcomes and should be considered to improve OS/LR-PFS. Further study is required to identify the optimal treatment regimen.Systematic review no.: CRD42019121249 (crd.york.ac.uk/PROSPERO/)

Objective Recent studies have demonstrated that sleep not only facilitates memory consolidation but also benefits more complex cognitive skills such as decision-making in young adults. Older adults use different decision strategies compared with young adults, which leaves the role of sleep in older adults' decision-making unclear. We investigated the age-by-sleep effect on decision-making. Methods We recruited 67 young adults (ages 18 to 29 years) and 66 older adults (ages 60 to 79 years) and randomly assigned them into the "sleep" or "wake" study condition. They were given a modified Iowa gambling task to perform before and after a 12-hour interval with sleep or wakefulness. Results Using the typical model-free analysis, we found that young adults' between-session performance improved greater than that of older adults regardless of the sleep/wake condition. Furthermore, older adults with longer total sleep time showed a greater improvement in the selection of one "good" deck. To further examine the sleep effect on age-related differences in cognitive processes underlying decision-making, we conducted computational modelling. This more fine-grained analysis revealed that sleep improved feedback sensitivity for both young and older adults while it increased loss aversion for older adults but not for young adults. Conclusion These findings indicate that sleep promotes learning-based decision-making performance via facilitating value representation, and such modulation is distinct in young compared to older adults.

Objective Intracranial pineoblastomas are rare neoplasms with poor prognosis. The aim of this study was to describe the independent prognostic factors and treatment strategies for overall survival in pediatric and adult patients. Methods Sixty-four patients were surgically treated between January 2012 and December 2018. Results The series included 37 (57.8%) males and 27 (42.2%) females. Gross total resection was achieved in 41 (64.1%) cases, and the 1-, 3-, and 5-year rates of overall survival were 86.3, 52.3, and 36.6%, respectively. In the pediatric group (n=42), 28 patients (66.7%) were male, with the median, and the mean age was 4 and 6.2±4.7 years, respectively. After a median follow-up of 25.0 months, twenty-six patients (61.9%) died, and the 1-, 3-, and 5-year rates of overall survival were 84.9, 46.4, and 26.7%, respectively. Postoperative radiotherapy (p=0.058) and postoperative chemotherapy (p=0.183) had a positive influence on the increased overall survival. Meanwhile, postoperative radiotherapy combined with chemotherapy following surgery had a positive impact on overall survival (p=0.174, Log rank). In the adult group, the mean overall survival was 67.3±9.3 months (range, 0.8-95.3 months), and the 1-, 3-, and 5-year rates of overall survival were 89.5, 64.4, and 64.4%, respectively. In this group, no statistical association was observed between clinical factors and outcomes. However, patients who received postoperative radiotherapy (60.7 vs 57.6 month, mean survival; p=0.510, Log rank) or chemotherapy (63.0 vs 59.9 month, mean survival; p=0.404, Log rank) had better survival rates compared with those who declined. Conclusion In the pediatric group, surgery with postoperative radiotherapy and chemotherapy was a favorable factor for overall survival. In the adult group, a positive trend in overall survival was found when patients received radiation and/or chemotherapy following surgery.

SUMMARYWe present a 3D reconstruction method using brightness and camera motion estimation for registering local colon structure in colonoscopy. The proposed method is based on reverse projection from 2D fold contours to 3D space, motion estimation from 3D reconstructed points between neighboring frames, and model registration to reconstruct the fold structure. On the synthetic colon, the average percentages of the reconstructed depth error and circumference error are about 14.2% and 15.2%, respectively. The accuracy is enough for the navigation and control in capsule robot. This work demonstrates that the proposed method is superior to the methods using single-frame-based brightness intensity.

The objective of the paper is to investigate and explore the effects of progressive muscle relaxation training (PMR) on image qualities of computed tomography (CT) coronary angiography and the relief of anxiety and pain of patients, as well as the improvement of sleep qualities of patients. The SPSS 16.0 statistics software was applied to respectively obtain the mean number, the standard deviation, and P value, etc. and analyze the variances of each group; the analysis was statistically significant at P < 0 05. The results of the study showed that CT could clearly indicate the anatomy of the thigh muscles and adjacent organs, and could better distinguish the bone density; however, its partitioning accuracy of the erector spinae was slightly worse. Therefore, it was inferred that morphological parameters such as CT values would provide persuasive data for clinical diagnosis, intervention therapy, and rehabilitation exercises, etc.

Insomnia has become the second highly prevalent mental illness, secondary only to depression. The hyperarousal model of insomnia is one of the theories dedicated to clarifying the pathophysiological mechanism of insomnia disorder. The resting-state and sleeping EEG provide substantial evidence for this model. The current review firstly summarizes the analytic methods of the resting-state and sleeping EEG, and these methods can be mainly used to analyze the EEG data during wakefulness, sleep onset period and all sleep stages. Through literature review, we found that the EEG evidence of hyperarousal in insomniacs included impaired sleep continuity and architecture, enhanced frequency of arousal, delayed daytime sleep latency, and increased β activity during wakefulness and non-rapid eye movement sleep. Some interventions, such as cognitive behavioral therapy for insomnia, non-benzodiazepine hypnotics and slow oscillating transcranial direct current stimulation during sleep, turned out to be the effective therapies that could reduce the cortical hyperarousal in patients with insomnia. Power spectral analysis might aid in differentiating some insomnia subtypes, and could be used in the diagnosis and evaluation of the treatment response in patients with insomnia, but the confounding factors such as age, gender, and EEG band subdivision, should be considered. Future studies should focus on different insomnia subtypes, adopt a unified EEG band subdivision, and consider the possible interferences of patients’ age and gender. Based on the polysomnography derived sleep scoring, some meso/micro-analyses such as EEG source imaging and EEG time-frequency analysis should also be our primary concerns.

Automated pavement distress detection benefits road maintenance and operation by providing the condition and location of various distress rapidly. Existing work generally relies on manual labor or specific algorithms trained by dedicated datasets, which hinders the efficiency and applicable scenarios of methods. Street view map provides interactive panoramas of a large scale of urban roadway network, and is updated in a recurrent manner by the provider. This paper proposed a deep learning method based on a pre-trained neural network architecture to identify and locate different distress in real-time. About 20,000 street view images were collected and labeled as the training dataset using the Baidu e-map. Eight types of distress are notated using Yolov3 deep learning architecture. The scale-invariant feature transform (SIFT) descriptors combined with GPS and bounding boxes were applied to judge the deterioration of the distress. A decision tree was designed to evaluate the change of the distress over some time. A typical road in Shanghai was selected to verify the effectiveness of the proposed model. The images of the road from 2015 to 2017 were collected from the street view map. The results showed that the mean average precision of the proposed algorithm is 88.37%, demonstrating the vast potential of applying this method to detect pavement distress. 43 distress were newly generated, and 49 previous distress were patched in the two years. The proposed method can assist the authorities to schedule the maintenance activities more effectively.

Diffuse low-grade and intermediate-grade gliomas, also known as lower-grade gliomas (LGGs), are a class of central nervous system tumors. Overall survival varies greatly between patients, highlighting the importance of evaluating exact outcomes to facilitate individualized clinical management. We aimed to identify an mRNA-based prognostic signature to predict the survival of patients with LGGs. A total of 874 LGGs from two public datasets were included. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to select the most prognostic mRNAs and build a risk score. A nomogram incorporating the risk score and clinical factors was established for individualized survival prediction. The performance of the nomogram was assessed in the training set (329 patients), internal validation set (140 patients), and external validation set (405 patients). 21 most prognostic mRNAs remained following the LASSO Cox regression. The 21-mRNA signature successfully stratified patients into high- and low-risk groups (P

Background To investigate the role of transmuscular quadratus lumborum block (TMQLB) for postoperative pain control, patient satisfaction and recovery in laparoscopic adrenalectomy. Methods Seventy-two patients aged between 18 and 70 years with an ASA I-II and scheduled for laparoscopic adrenalectomy were randomized to receive a single-shot TMQLB with 0.4 ml/kg 0.5 % ropivacaine or 0.4 ml/kg 0.9 % saline as placebo. The primary endpoint was pain on movement at 12 h after surgery evaluated by the numeric rating scale (NRS, 0–10). P-values Results NRS on movement at 12 h after surgery was lower in the TMQLB group compared with the control (median 2 vs. 3, p = 0.024). Intraoperative fentanyl consumption was lower in the TMQLB group (247.08 ± 63.54 vs. 285.44 ± 74.70, p = 0.022). The rate of using postoperative rescue tramadol was also lower in the TMQLB group (5.6 vs. 27.8 %, p = 0.027). Similar incidences of nausea and vomiting were observed (11.1 vs. 25 %, p = 0.220). Patient satisfaction of pain service was better in the TMQLB group (83.3 vs. 25 %, p p = 0.004) and flatus (18.5 vs. 23.5 h, p = 0.006). Conclusions TMQLB showed better control of postoperative pain on movement for laparoscopic adrenalectomy with improved patients’ satisfaction of anesthesia, shorter time to ambulation and flatus. Trial registration This study was registered at Clinicaltrials.gov (NCT03942237; registration date: 08/05/2019; enrollment date: 10/05/2019).

Insomnia disorder (ID) has become the second-most common mental disorder. Despite burgeoning evidence for increased high-frequency electroencephalography (EEG) activity and cortical hyperarousal in ID, the detailed spectral features of this disorder during wakefulness and different sleep stages remain unclear. Therefore, we adopted a meta-analytic approach to systematically assess existing evidence on EEG spectral features in ID. Hedges's g was calculated by 148 effect sizes from 24 studies involving 977 participants. Our results demonstrate that, throughout wakefulness and sleep, patients with ID exhibited increased beta band power, although such increases sometimes extended into neighboring frequency bands. Patients with ID also exhibited increased theta and gamma power during wakefulness, as well as increased alpha and sigma power during rapid eye movement (REM) sleep. In addition, ID was associated with decreased delta power and increased theta, alpha, and sigma power during NREM sleep. The EEG measures of absolute and relative power have similar sensitivity in detecting spectral features of ID during wakefulness and REM sleep; however, relative power appeared to be a more sensitive biomarker during NREM sleep. Our study is the first statistics-based review to quantify EEG power spectra across stages of sleep and wakefulness in patients with ID.

Large-amplitude wind-induced vibrations of hangers have been observed on the Xihoumen Bridge, and the excitation mechanism is still unknown at present. In the present study, the mechanism ...

Background The novel coronavirus (COVID-19) pandemic has affected humans worldwide and led to unprecedented stress and mortality. Detrimental effects of the pandemic on mental health, including risk of post-traumatic stress disorder (PTSD), have become an increasing concern. The identification of prospective neurobiological vulnerability markers for developing PTSD symptom during the pandemic is thus of high importance. Methods Before the COVID-19 outbreak (September 20, 2019–January 11, 2020), some healthy participants underwent resting-state functional connectivity MRI (rs-fcMRI) acquisition. We assessed the PTSD symptomology of these individuals during the peak of COVID-19 pandemic (February 21, 2020–February 28, 2020) in China. This pseudo-prospective cohort design allowed us to test whether the pre-pandemic neural connectome status could predict the risk of developing PTSD symptom during the pandemic. Results A total of 5.60% of participants (n = 42) were identified as being high-risk to develop PTSD symptom and 12.00% (n = 90) exhibited critical levels of PTSD symptoms during the COVID-19 pandemic. Pre-pandemic measures of functional connectivity (the neural connectome) prospectively classified those with heightened risk to develop PTSD symptom from matched controls (Accuracy = 76.19%, Sensitivity = 80.95%, Specificity = 71.43%). The trained classifier generalized to an independent sample. Continuous prediction models revealed that the same connectome could accurately predict the severity of PTSD symptoms within individuals (r2 = 0.31p, Graphical abstract Image 1, Highlights • COVID-19 pandemic brought out PTSD symptoms indeed. • The predated neural conncectome status can reliably predict vulnerability for developing the PTSD during COVID-19 pandemic. • The neural connectome preceding to COVID-19 pandemic can predict the severity of PTSD symptoms. • We provided a robust classifier to forewarn individuals at high risks of developing PTSD in the future.

In this paper, a high-speed train–CRTS III slab track–subgrade coupled dynamic model is established. With the model, the influence of vehicle number on the dynamic characteristics of a train–CRTS III slab track–subgrade coupled system with smooth and random track irregularity conditions for conventional and vibration-reduction CRTS III slab tracks are theoretically studied and analyzed. Some conclusions are drawn from the results: (1) the largest dynamic responses of the coupled system for all items and cases are no longer changed when the vehicle number exceeds three, and three vehicles are adequate to guarantee the simulation precision to investigate the dynamic responses of the coupled system. (2) The acceleration of the car body has almost no relation with the vehicle number, and only one vehicle is needed to study the vehicle dynamics using the train–CRTS III slab track–subgrade coupled dynamic model. (3) For the conventional CRTS III slab track on a subgrade, the vehicle number has a negligible influence on the accelerations of the rail, slab, and concrete base, the positive and negative bending moments of the rail, the compressive force of the fastener, and the positive bending stress of slab, but it has a large influence on the tension force of the fastener, and the negative bending stresses of the slab and concrete base. Only one vehicle is needed to study track dynamics without considering the tension force of the fastener, the negative bending stresses of the slab and concrete base, otherwise, two or more vehicles are required. (4) For vibration reduction of the CRTS III slab track on a subgrade, the number of vehicles has some influence on the dynamic responses of all track components, and at least two vehicles are required to investigate the track dynamics.

Detecting Zero-Day intrusions has been the goal of Cybersecurity, especially intrusion detection for a long time. Machine learning is believed to be the promising methodology to solve that problem, numerous models have been proposed but a practical solution is still yet to come, mainly due to the limitation caused by the out-of-date open datasets available. In this paper, we propose an approach for Zero-Day intrusion detection based on machine learning, using flow-based statistical data generated by CICFlowMeter as training dataset. The machine learning classification model used is selected from eight most popular classification models, based on their cross validation results, in terms of precision, recall, F1 value, area under curve (AUC) and time overhead. Finally, the proposed system is tested on the testing dataset. To evaluate the feasibility and efficiency of tested models, the testing datasets are designed to contain novel types of intrusions (intrusions have not been trained during the training process). The normal data in the datasets are generated from real life traffic flows generated from daily use. Promising results have been received with the accuracy as high as almost 100%, false positive rate as low as nearly 0%, and with a reasonable time overhead. We argue that with the proper selected flow based statistical data, certain machine learning models such as MLP classifier, Quadratic discriminant analysis, K-Neighbor classifier have satisfying performance in detecting Zero-Day attacks.

Recently, high performance energy storage devices are increasingly required in many new fields such as smartphone, pilotless automobile. Lithium-ion capacitors (LICs) have become the promising energy storage devices because of the higher power density, electrostatic capacity and long cycle life. Nevertheless, the limitation of the battery-type anode electrode and the capacitance-type cathode electrode with slow kinetics and low specific surface area leads to the LICs remaining lower energy density in high current density. In this report, a high performance LIC assembled by Mn3O4-graphene coupled with activated polyaniline-derived carbon (APDC) is firstly presented. Mn3O4-G composite material exhibits an outstanding invertible capacity of 489.8 mAh g−1 (at 1 A g−1) in a wide working window (0.01–3 V vs. Li/Li+) with an excellent coulombic efficiency in half cell, which is the highest capacitance reported for Mn3O4 so far. By utilization of Mn3O4-G composite as anode and APDC with the large surface of 1641.9 m2 g−1 as cathode, the assembled LIC of Mn3O4-G//APDC possesses an energy density of 97.2 Wh kg−1 at power density of 62.5 W kg−1, even at a relatively higher power density of 6250 W kg−1, its energy density can retain 5.0 Wh kg−1.

Prognostic significance of serum microcystin in hepatocellular carcinoma has not been well investigated. The aim of the study was to reveal the relationship between serum microcystin-LR and prognosis in these patients. There were 650 early-stage hepatitis B-induced hepatocellular carcinoma patients, who were not affected by hepatitis C, cirrhosis, heavy drinking or excessive aflatoxin exposure. All of them underwent hepatectomy and were followed up for 5 years. Tumor relapse and overall death were recorded. Blood specimens were collected on admission and at the time of relapse. Serum levels of microcystin-LR and fluorescent oxidation products (FlOP_360, FlOP_320 and FlOP_400) were measured separately using enzyme-linked immunosorbent assay and fluorescence spectrometry. Multifactorial COX regression analysis suggested that serum microcystin-LR ≥ 0.97 ng/ml was associated with the increased risk of the tumor relapse (HR: 1.53, 95% CI: 1.35–1.77) and serum microcystin-LR ≥ 1.09 ng/ml was related to the higher risk of the overall death (HR: 1.58, 95% CI: 1.35–1.84) in the follow-up period. Furthermore, there was a linear relationship between serum level of microcystin-LR and serum levels of FlOP_360, FlOP_320 and FlOP_400 (P = 0.001, P = 0.023, P = 0.047). Serum levels of these fluorescent oxidation products were also higher in the patients with tumor relapse (P

In ovarian cancer patients, tumor fibrosis and angiotensin-driven fibrogenic signaling have been shown to inversely correlate with survival. We sought to enhance drug delivery and therapeutic efficacy by remodeling the dense extracellular matrix in two orthotopic human ovarian carcinoma xenograft models. We hypothesized that targeting the angiotensin signaling axis with losartan, an approved angiotensin system inhibitor, could reduce extracellular matrix content and the associated "solid stress," leading to better anticancer therapeutic effect. We report here four translatable findings: (i) losartan treatment enhances the efficacy of paclitaxel-a drug used for ovarian cancer treatment-via normalizing the tumor microenvironment, resulting in improved vessel perfusion and drug delivery (ii) losartan depletes matrix via inducing antifibrotic miRNAs that should be tested as candidate biomarkers of response or resistance to chemotherapy (iii) although losartan therapy alone does not reduce tumor burden, it reduces both the incidence and the amount of ascites formed and (iv) our retrospective analysis revealed that patients receiving angiotensin system inhibitors concurrently with standard treatment for ovarian cancer exhibited 30 mo longer overall survival compared with patients on other antihypertensives. Our findings provide the rationale and supporting data for a clinical trial on combined losartan and chemotherapy in ovarian cancer patients. 116 6 2210 2219

As the pace of life accelerates, poor sleep quality has become a common health problem. A wealth of sleep studies based on electroencephalogram (EEG) have found that poor sleep quality has decreased alpha rhythm during night-time sleep. However, whether the EEG rhythm during an afternoon nap has a similar impairment has seldom been investigated. By recruiting 35 subjects for a 90-min nap, we explored the relationship between sleep quality and the alpha rhythm, and the contribution of naps to subjective sleep experience. We found that the power of alpha rhythm in the occipital lobe negatively correlated with sleep quality. However, there was no correlation between the sleep quality at night and the sleep duration of the nap, nor the sleep quality and the proportion of each sleep period of a nap. Our present results indicated that the quality of night-time sleep did not affect the macrostructure of the nap. Interestingly, the negative correlation between the alpha rhythm of a nap and sleep quality at night continuously existed in Wake, N1 sleep, and N2 sleep, and had the most substantial value during N2 sleep of a nap. This implies that an impaired occipital lobe alpha rhythm is a robust biomarker for poor sleep. The research is of particular significance for developing electrical neural stimulation therapy to improve sleep quality, especially for the stimuli regions for selection and the time windows for implementation.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), which is causing the coronavirus disease-2019 (COVID-19) pandemic, poses a global health threat. However, it is easy to confuse COVID-19 with seasonal influenza in preliminary clinical diagnosis. In this study, the differences between influenza and COVID-19 in epidemiological features, clinical manifestations, comorbidities and pathogen biology were comprehensively compared and analyzed. SARS-CoV-2 causes a higher proportion of pneumonia (90.67 vs. 17.07%) and acute respiratory distress syndrome (12.00 vs. 0%) than influenza A virus. The proportion of leukopenia for influenza patients was 31.71% compared with 12.00% for COVID-19 patients (P = 0.0096). The creatinine and creatine kinase were significantly elevated when there were COVID-19 patients. The basic reproductive number (R0) for SARS-CoV-2 is 2.38 compared with 1.28 for seasonal influenza A virus. The mutation rate of SARS-CoV-2 ranges from 1.12 × 10−3 to 6.25 × 10−3, while seasonal influenza virus has a lower evolutionary rate (0.60-2.00 × 10−6). Overall, this study compared the clinical features and outcomes of medically attended COVID-19 and influenza patients. In addition, the S477N and N439K mutations on spike may affect the affinity with receptor ACE2. This study will contribute to COVID-19 control and epidemic surveillance in the future.

Sleep staging is important in sleep research since it is the basis for sleep evaluation and disease diagnosis. Related works have acquired many desirable outcomes. However, most of current studies focus on time-domain or frequency-domain measures as classification features using single or very few channels, which only obtain the local features but ignore the global information exchanging between different brain regions. Meanwhile, brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. To explore the electroencephalography (EEG)-based brain mechanisms of sleep stages through functional connectivity, especially from different frequency bands, we applied phase-locked value (PLV) to build the functional connectivity network and analyze the brain interaction during sleep stages for different frequency bands. Then, we performed the feature-level, decision-level and hybrid fusion methods to discuss the performance of different frequency bands for sleep stages. The results show that (1) PLV increases in the lower frequency band (delta and alpha bands) and vice versa during different stages of non-rapid eye movement (NREM), (2) alpha band shows a better discriminative ability for sleeping stages, (3) the classification accuracy of feature-level fusion (six frequency bands) reaches 96.91% and 96.14% for intra-subject and inter-subjects respectively, which outperforms decision-level and hybrid fusion methods.

The novel coronavirus (COVID-19) pandemic has led to a surge in mental distress and fear-related disorders, including posttraumatic stress disorder (PTSD). Fear-related disorders are characterized by dysregulations in fear and the associated neural pathways. In the present study, we examined whether individual variations in the fear neural connectome can predict fear-related symptoms during the COVID-19 pandemic. Using machine learning algorithms and back-propagation artificial neural network (BP-ANN) deep learning algorithms, we demonstrated that the intrinsic neural connectome before the COVID-19 pandemic could predict who would develop high fear-related symptoms at the peak of the COVID-19 pandemic in China (Accuracy rate = 75.00%, Sensitivity rate = 65.83%, Specificity rate = 84.17%). More importantly, prediction models could accurately predict the level of fear-related symptoms during the COVID-19 pandemic by using the prepandemic connectome state, in which the functional connectivity of lvmPFC (left ventromedial prefrontal cortex)—rdlPFC (right dorsolateral), rdACC (right dorsal anterior cingulate cortex)—left insula, lAMY (left amygdala)—lHip (left hippocampus) and lAMY—lsgACC (left subgenual cingulate cortex) was contributed to the robust prediction. The current study capitalized on prepandemic data of the neural connectome of fear to predict participants who would develop high fear-related symptoms in COVID-19 pandemic, suggesting that individual variations in the intrinsic organization of the fear circuits represent a neurofunctional marker that renders subjects vulnerable to experience high levels of fear during the COVID-19 pandemic.

Objectives Transmuscular quadratus lumborum block (TQLB) may provide postoperative analgesia in patients undergoing intraperitoneal surgeries. The purpose of this study was to examine the potential efficacy of TQLB among patients undergoing retroperitoneal procedures, such as the laparoscopic partial nephrectomy (LPN). Methods This prospective, randomized, controlled study was conducted from August 2017 to November 2018 at Peking Union Medical College Hospital (Beijing, China). Patients who were scheduled for a LPN, aged 18-70 years old with an ASA physical status score of I - II were randomly assigned to receive either TQLB with 0.6 ml/kg of 0.5% ropivacaine plus general anesthesia (TQLB group) or general anesthesia alone (control group). Patient-controlled intravenous analgesia with morphine was initiated immediately upon surgery completion. The primary outcome was the cumulative consumption of morphine within 8 h after surgery. The secondary outcome included postoperative consumptions of morphine at other time points, pain score at rest and during activity, postoperative nausea and vomitting (PONV), and recovery related parameters. Results Totally 30 patients per group were recruited in the study. The 8 h consumption of morphine was lower in the TQLB group than in the control group (median, 0.023 mg/kg vs. 0.068 mg/kg, U=207.5, P

Highlights • This study examined the resting-state functional network connectivity underlying eating disorder symptoms in a large sample of healthy young adults (n = 693). • Individuals with higher levels of eating disorder symptoms displayed weaker intra-network connectivity of the executive control network and basal ganglia network, as well as weaker inter-network connectivity in the three examined networks (i.e., the executive control network, basal ganglia network, and default mode network). • The findings suggest that these neural circuits may play a key role in symptoms of disordered eating in healthy adults. They further reveal that the less efficient information exchange within and between intrinsic networks associated with self-referential thinking, inhibitory control, and reward sensitivity are strongly related to eating disorder symptoms., Previous neuroimaging research of eating disorders such as anorexia nervosa and bulimia nervosa has mainly focused on clinical patients, indicating the crucial role of intrinsic connectivity networks involved in aberrant behavioral control (i.e., executive control network), reward reactivity (i.e., basal ganglia network), and excessive self-focused and body-focused ruminations (i.e., default mode network) in the onset and maintenance of eating disturbances. However, examinations of large-scale resting-state networks that support the role of cognitive control, reward sensitivity, and self-directed thinking in disordered eating have rarely involved non-clinical samples from the general population. This study, involving a total of 693 healthy young adults (68.69% females; mean age, 18.37 years), investigated these issues by using pre-defined functional regions of interest from the executive control network, basal ganglia network, default mode network, and a seed-based region of interest-to-region of interest approach. After statistically controlling for differences in age, sex, body mass index, and head motion, we observed significant associations of higher levels of eating disorder symptoms, especially bulimia-type eating (i.e., binge eating and a combination of binge eating and compensatory behaviors, such as purging via self-induced vomiting or laxative use, and compulsive exercise), with weaker intra-network and inter-network functional synchrony. These results remained significant after excluding underweight, overweight, and obese participants. These findings suggest that these neural circuits may play a key role in the symptoms of disordered eating in healthy adults. They further reveal that the less efficient information exchange within and between intrinsic networks associated with self-referential thinking, inhibitory control, and reward sensitivity are strongly related to eating disorder symptoms.

[This corrects the article DOI: 10.1371/journal.pone.0228473.].

On-demand station-based one-way carsharing is widely adopted for battery electric vehicle sharing systems, which is regarded as a supplement of urban mobility and a promising approach to the utilization of green energy vehicles. The service model of these carsharing systems allows users to select vehicles based on their own judgment on vehicle battery endurance, while users tend to pick up vehicles with the longest endurance distances. This phenomenon makes instant-access systems lose efficiency on matching available vehicles with diverse user requests and limits carsharing systems for higher capacity. We proposed a vehicle assignment method to allocate vehicles to users that maximize the utility of battery, which requires the system to enable short-term reservation rather than instant access. The methodology is developed from an agent-based discrete event simulation framework with a first-come-first-serve logic module for instant access mode and a resource matching optimization module for short-term reservation mode. Results show that the short-term reservation mode can at most serve 20% more users and create 47% more revenue than instant access mode under the scenario of this research. This paper also points out the equilibrium between satisfying more users by efficiently allocating vehicles and distracting users by disabling instant access and suggests that the reservation time could be 15 minutes.

In view of the low robustness of the existing traditional PCB defect detection algorithms, this paper applies a PCB defect detection and recognition algorithm based on deep convolutional nerual network framework SSD(Single Shot Detector). This algorithm structure utilizes multi-scale feature maps to customise boundary boxes with different scales, and applies small convolution kernel (3*3)to predict the classification results and boundary box information. Then the detection results gracefully optimize by non-maximum suppression (NMS). Finally, in order to prove the superiority of this algorithm, this paper conducts comparative experiments. The experimental results show that the algorithm has a significant improvement in the accuracy of PCB defect detection, and the identification accuracy of PCB nodules can be as high as 94.69%. It has good applicability in the application of PCB defect detection.

BACKGROUND: Multifocal visual evoked potential (MF-VEP) assesses a wider visual field than full-field VEP (FF-VEP) and potentially offers a more precise analysis of optic nerve injury and repair following optic neuritis. MF-VEP may offer advantages over FF-VEP as an endpoint in clinical trials of remyelinating therapies.OBJECTIVE: MF-VEP testing was used to study changes in visual pathways in 48% of RENEW [phase II, opicinumab (anti-LINGO-1; BIIB033) vs. placebo after first acute unilateral optic neuritis] participants.METHODS: This exploratory MF-VEP RENEW substudy compared mean outcomes at weeks 24 and 32 among participants in the intent-to-treat (ITT; n = 39; 72% female; mean age: 32.3 years) and per-protocol (PP; n = 31; 71% female; mean age: 32.2 years) populations in affected and fellow eye latency from fellow eye baseline latency and affected and fellow eye amplitude from their own baselines. Treatment differences were evaluated using analysis of covariance (week 24) and a mixed-effect model of repeated measures (week 32). Last observation carried forward was used to impute missing data at week 24.RESULTS: A trend for improvement in affected eye MF-VEP latency with opicinumab versus placebo was seen in the ITT and PP populations at weeks 24 and 32. Both treatment groups in the ITT population experienced partial recovery of amplitude in the affected eye at week 32. Notably, the mean change in fellow eye amplitude at weeks 24 and 32 was - 17.57 and - 31.41 nanovolts (nV) in placebo but only - 0.59 and 1.93 nV in the opicinumab group [differences at weeks 24 and 32: 16.98 nV (p = 0.050) and 33.33 nV (p CONCLUSION: Results from this substudy showed advantages of MF-VEP over FF-VEP in multicenter studies of central nervous system reparative therapies and provide novel evidence that fellow eye visual pathway amplitude loss occurs after optic neuritis but can potentially be prevented by opicinumab treatment.REGISTRATION: ClinicalTrials.gov identifier NCT01721161.

Lying posture influences both neural activity and cognitive performance, and it is essential to sleep hygiene. Whereas, no neuroimaging research has investigated the effect of lying position on brain activity in waking and sleeping conditions. Therefore, we recruited 35 participants to perform a within-participant simultaneous EEG-fMRI recording with lying supine and lateral postures. Our results showed that sleep onset latency (SOL) was affected by both sleep position preference (SPP) and lying poses. SOL in supine was significantly shorter than that in lateral posture. The correlation analysis between SPP and sleep parameters indicated that individuals who prefer supine had less SOL and N2 sleep durations. However, we did not find this significant correlation in lateral-prefer individuals. Besides, different sleep positions mainly caused an alteration of the differences in brain activity patterns. In supine posture, the brain activities in the left precuneus, and anterior cingulate cortex were greater than those in lateral position. However, in the lateral posture, the status was just the opposite. Finally, we also found that the right putamen was sensitive to habitual sleep posture in the awake state. The participants who prefer to lie supine tend to have higher activity in the putamen. Our study may help with the understanding of the contribution of lying posture on brain activity and its relationship with posture preference in sleep.

In order to overcome the limitations of the current college English teaching mode in China, and to expand the service scope of 5G technology and artificial intelligence technology, this paper proposes an optimization strategy of English teaching mode in colleges and universities based on 5G technology and artificial intelligence technology. This optimization strategy breaks the traditional college English teaching mode in China, and introduces 5G technology and artificial intelligence technology, which greatly improves the intelligence of English classroom in colleges and universities. At the same time, the optimization strategy can also make 5G technology and artificial intelligence technology skillfully combine, which can improve the quality of English teaching in colleges and universities from many aspects, and comprehensively and systematically realize the teaching concept of "teach students according to their aptitude". The results show that the optimization strategy can not only overcome the limitations of English model in China in colleges and universities , but also improve the service scope of 5G technology and artificial intelligence technology, so as to improve students' English listening, speaking, reading and writing ability in multiple dimensions in colleges and universities.

Primary intracranial leiomyosarcomas (PILMSs) are extremely rare neoplasms, and their management remains unclear. The authors aimed to enunciate the radiological features and design a treatment protocol based on previously published cases combined with our series. Clinical data from all 12 cases of PILMS treated at their institute between 2008 and 2018 were reviewed. Meanwhile, they searched the Ovid MEDLINE, Embase, PubMed, Web of Science and Cochrane databases using the keywords "leiomyosarcoma" and "intracranial," "central nervous system," "cerebral," or "brain" Previously published data were processed and used according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The authors' cohort included 4 males (33.3%) and 8 females (66.7%) ranging in age from 13 to 67 years with a mean of 44.1 ± 5.0 years. Gross total resection (GTR), subtotal resection, and partial resection were achieved in 8 (66.7%), 3 (25%), and 1 (8.3%) patient, respectively, and only four patients (33.3%) received adjuvant therapy after surgery. After a mean follow-up of 30.5 ± 7.6 months, 6 patients (50%) experienced recurrences, and all of them died at the final. Twenty-seven patients (15 were male and 12 were female), in the literature cases, were identified between January 1980 and November 2019, presenting with PILMS. GTR and non-GTR were achieved in 21 (77.8%) and 6 (22.2%) patients, respectively. Postoperative radiotherapy was administrated in 17 patients (63.0%), and postoperative chemotherapy was administrated in 9 patients (33.3%), After a mean follow-up of 22.2 ± 4.1 months, recurrence and death occurred in 8 (36.4%) and 4 (14.8%) cases, respectively. In the pooled cases, the univariate analysis model revealed that only GTR was a significantly favorable factor for increased progression-free survival (hazard ratio 0.270, 95% confidence interval = 0.093-0.787, p = 0.016) and overall survival (hazard ratio 0.255, 95% confidence interval = 0.073-0.890, p = 0.032). GTR was recommended as an optimal treatment; meanwhile, postoperative radiation was also a choice to help increase the survival of patients of PILMS.

Accumulating evidence supports a bidirectional relationship between sleep disruption and Alzheimer's disease (AD) pathology. Among various sleep electroencephalography activities, the sleep spindle is one specific electroencephalographic rhythm that has potential to be a biomarker for AD. This review explores the association between sleep spindles and AD-related dementia from a neuropsychological perspective by a systematic re-examining of recent findings. In general, sleep spindles, characterized by density, amplitude, duration, and frequency, are disrupted in AD. Moreover, its functional coupling with slow oscillation also suffers in AD. While preliminary, our observations and comparisons suggest that spindle density rather than frequency and fast spindles rather than slow spindles could be more sensitive to AD-related dementia, and spindle plasticity provides possibilities for targeted interference. In conclusion, quantitative and qualitative features of sleep spindles represent potential non-invasive and cost-effective biomarkers for AD and provide both therapeutic and public health implications.

To investigate the early epidemic of COVID-19, a total of 176 confirmed COVID-19 cases in Shiyan city, Hubei province, China were surveyed. Our data indicated that the rate of emergence of early confirmed COVID-19 cases in Hubei province outside Wuhan was dependent on migration population, and the second-generation of patients were family clusters originating from Wuhan travelers. Epidemiological investigation indicated that the reproductive number (R0) under containment strategies was 1.81, and asymptomatic SARS-CoV-2 carriers were contagious with a transmission rate of 10.7%. Among the 176 patients, 53 were admitted to the Renmin Hospital of Hubei University of Medicine. The clinical characteristics of these 53 patients were collected and compared based on a positive RT-PCR test and presence of pneumonia. Clinical data showed that 47.2% (25/53) of COVID-19 patients were co-infected with Mycoplasma pneumoniae, and COVID-19 patients coinfected with M. pneumoniae had a higher percentage of monocytes (P < 0.0044) and a lower neutrophils percentage (P < 0.0264). Therefore, it is important to assess the transmissibility of infected asymptomatic individuals for SARS-CoV-2 transmission; moreover, clinicians should be alert to the high incidence of co-infection with M. pneumoniae in COVID-19 patients.

As one of the most common sleep-related respiratory disorders, obstructive sleep apnea (OSA) is characterized by excessive snoring, repetitive apnea, arousal, sleep fragmentation, and intermittent nocturnal hypoxemia. Focused on the resting-state brain imaging techniques, we reviewed the OSA-related resting-state electroencephalogram and resting-state functional magnetic resonance imaging (rsfMRI) studies. Compared with the healthy control group, patients with OSA presented increased frontal and central δ/θ powers during resting-state wakefulness, and their slow-wave activity showed a positive correlation with apnea–hypopnea index. For rsfMRI, the prefrontal cortex and insula may be the vital regions for OSA and are strongly related to the severity of the disease. Meanwhile, some large-scale brain networks, such as the default-mode network, salience network, and central executive network, play pivotal roles in the pathology of OSA. We then discussed the contribution of resting-state brain imaging as an evaluation approach for disease interventions. Finally, we briefly introduced the effects of OSA-related physiological and mental diseases and discussed some future research directions from the perspective of resting-state brain imaging.

We first described the 2019 novel coronavirus infection in 10 children occurring in areas other than Wuhan. The coronavirus diseases in children are usually mild and epidemiological exposure is a key clue to recognize pediatric case. Prolonged virus shedding is observed in respiratory tract and feces at the convalescent stage.

Sleep deprivation disrupted functional and structural brain areas which are associated with cognition and emotion in healthy participants. However, the effect of age on the structural changes after sleep restriction remains unclear. In the current study, gray matter volume was calculated in 43 young adults and 37 old adults before and after sleep restriction. Two-way mixed analysis of variance (between-subject factor: deprivation; within-subject factor: age) was then employed to investigate differences in gray matter volume changes between young and old adults. Gaussian random field theory was applied for multiple comparison correction. Results revealed that sleep restriction decreases gray matter volume in the right thalamus, left precuneus, and postcentral gyrus. More importantly, we found a significant deprivation × age interaction effect mainly in the right dorsal/ventral anterior insula where the gray matter volume increased in young adults after sleep restriction but showed no difference in old adults. These findings highlight the crucial role of the anterior insula in the neural mechanisms underlying sleep lose, especially among young adults. The current work provided structural evidence for describing emotional dysfunction and suggests the potential effect of age on functional and structural changes after sleep restriction.

Background: The outbreak of the 2019 novel coronavirus (2019-nCoV, also named SARS-CoV-2) has spread from Wuhan to outside of Hubeprovince and other countries

Renewable Portfolio Standards (RPS), as the strategic requirement effectively fits the top-level institution design of China's low-carbon energy transition, will help to promote China's energy structure optimization and power market reform. The success of its implementation depends on the scientific quasi-parameters design of the system. At present, China's tradeable green certificate (TGC) market is still in the voluntary trading stage. There are differences in natural attributes and transaction prices, resulting in the market segmentation and market distortion of the TGC market. Considering the non-neutral technology, this paper uses system dynamics to construct a long-term trading model under China's RPS, and analyzes the impact of relevant system quasi-parameters on the market trading process. The results show that:(1) The scientific quasi-parameters design of the RPS, such as technical conversion coefficient, TGC valid period, TGC benchmark price and RPS quota, will encourage and guide manufacturers to conduct TGC transactions, which will promote the development of renewable energy. (2) The introduction of the technical conversion coefficient can effectively promote TGC transactions, improve China's social welfare and optimize the power source structure. Therefore, the scientific design of the relevant quasi-parameters and strengthening of the supervision mechanism are conducive to implementing the RPS effectively in China.

Aging associates with sleep dysfunction as well as brain alterations. However, the association between age-related brain alterations and their subjective sleep changes is less understood. To address this issue, we recorded T1 weighted structural and resting-state functional magnetic resonance imaging from both young ( n = 62) and older adults ( n = 108). In addition, all participants completed a battery of psychometric tests, including the Pittsburg Sleep Quality Index. We found that the age-related atrophy of cerebral gray matter, hippocampal and thalamic volume were associated with subjective sleep decline, and the atrophy of cerebral gray matter mediated the age effect on sleep. In addition, older adults exhibited decreased functional connectivity within the medial temporal lobe subsystem than their young counterparts. Moreover, there is a significant positive association between sleep and functional connectivity in young but not in older adults. In light of our findings, we suggest a neuropathological model in which age-related brain alterations may partially explain the well-documented declines in sleep with aging.

Renewable energy is playing an increasingly important role in energy security and environmental protection. As China has a huge demand for renewable energy and also has abundant wind resources, it is vital that government, investors and operators work together to ensure the sustainable development of the wind energy industry. Even though China is already a leader in wind power generation with the largest installed wind power capacity in the world, it has continued to build new wind power facilities. However, due to industrial immaturity and the need for significant investment, wind power investors and operators are faced with uncertainty about the attendant risks. To achieve risk mitigation and sustainability, this paper proposes an investment risk evaluation and optimization process for Chinese wind power projects. The Monte Carlo method is first used to evaluate the investment risks, after which a multi-objective programming model is built for the optimization. A specific case in western China is examined to demonstrate the proposed methodology, with the evaluation results indicating that the project has high investment risk. Based on the case study, the key risk factors are identified and optimization suggestions given for China’s wind power projects. The proposed methodology and findings contribute to research on the planning, investment and sustainable operation of renewable energy power generation projects in other areas in China.

Cadmiun sulfide (CdS) is considered as a promising semiconductor photocatalyst for its outstanding light absorbance and photoinduced charge separation, but it suffers from severe photocorrosion, mainly due to the sluggish kinetics of holes transfer. In this work, we introduce the strong local Coulomb attraction, which is demonstrated via the in situ K-edge X-ray absorption near edge (XANES) analysis, to accelerate the migration of holes for long-term photocatalytic activity. As a proof-of-concept sample, the hole-attraction Fe2O3/CdS nanorods evidence an outstanding stability of at least 6 days without obvious deactivation, and feature with an excellent H2 evolution rate of 20.7 mmol g−1 h−1. Moreover, this Coulomb attraction strategy can be leveraged to enhance the photocatalytic performance of Zn0.5Cd0.5S, indicating the wide application of the strategy.