Search

Accurate gaze estimation that frees from glints and the slippage problem is challenging. Pupil-contour-based gaze estimation methods can meet this challenge, except that the gaze accuracy is low due to neglecting the pupil's corneal refrac-tion. This paper proposes a refraction-aware gaze estimation approach using the real pupil axis, which is calculated from the virtual pupil image based on the derived function between the real pupil and the refracted virtual pupil. We present a 2D gaze estimation method that regresses the real pupil normal's spherical coordinates to the gaze point. The noise and outliers of calibration data are removed by aggregation filtering and Random Sample Consensus, respectively. Moreover, we propose a 3D gaze estimation method that transforms the real pupil axis to the gaze direction. Experimental results show that the proposed gaze estimation approach has comparable accuracy to state-of-the-art pupil-center-based gaze estimation methods, which suffer from the slippage problem.

In this study, an inverse-problem method was applied to estimate the solid concentration in a solid–liquid two-phase flow. An algebraic slip mixture model was introduced to solve the forward problem of solid–liquid convective heat transfer. The time-average conservation equations of mass, momentum, energy, as well as the volume fraction equation were computed in a computational fluid dynamics (CFD) simulation. The solid concentration in the CFD model was controlled using an external program that included the inversion iteration, and an optimal estimation was performed via experimental measurements. Experiments using a fly-ash–water mixture and sand–water mixture with different solid concentrations in a horizontal pipeline were conducted to verify the accuracy of the inverse-problem method. The estimated results were rectified using a method based on the relationship between the estimated results and estimation error; consequently, the accuracy of the corrected inversion results improved significantly. After a verification through experiments, the inverse-problem method was concluded to be feasible for predicting the solid concentration, as the estimation error of the corrected results was within 7% for all experimental samples for a solid concentration of less than 50%. The inverse-problem method is expected to provide accurate predictions of the solid concentration in solid–liquid two-phase flow systems.

We study how to support elasticity, that is, the ability to dynamically adjust the parallelism (i.e., the number of GPUs), for deep neural network (DNN) training in a GPU cluster. Elasticity can benefit multi-tenant GPU cluster management in many ways, for example, achieving various scheduling objectives (e.g., job throughput, job completion time, GPU efficiency) according to cluster load variations, utilizing transient idle resources, and supporting performance profiling, job migration, and straggler mitigation. We propose EDL, which enables elastic deep learning with a simple API and can be easily integrated with existing deep learning frameworks such as TensorFlow and PyTorch. EDL also incorporates techniques that are necessary to reduce the overhead of parallelism adjustments, such as stop-free scaling and dynamic data pipeline. We demonstrate with experiments that EDL can indeed bring significant benefits to the above-listed applications in GPU cluster management.

Long-term monitoring of resting tremor is key to assess the status of patients suffering from Parkinson’s disease (PD), which is of vital importance for reasonable medication. The detection and quantification of resting tremor in reported works rely heavily on specified movements and are not appropriate for long-term monitoring in real-life condition. The purpose of this study is to develop a detection model for long-term monitoring of resting tremor and explore an effective indicator for tremor quantification. This study included long-term acceleration data from PD patients and proposed a resting tremor detection model based on machine learning classifiers and Synthetic Minority Oversampling Technique (SMOTE). Four machine learning classifiers, K-Nearest Neighbor (KNN), Random Forest (RF), Adaptive Boosting (AdaBoost), and Support Vector Machine (SVM), were compared. Furthermore, an indicator called tremor timing ratio (TTR) was defined and calculated for tremor quantification. The detection model with RF classifier achieved the highest overall accuracy of 94.81%. The sample entropy of the acceleration signal was proved most influential in the classification by exploring the feature importance. Through the Kruskal-Wallis test and the Mann-Whitney U test, the TTR had a strong correlation with the subscore of resting tremor in Unified Parkinson Disease Rating Scale (UPDRS). Such two-step evaluation process for resting tremor can detect the tremor effectively and is expected to be applied in long-term monitoring of PD patients in daily life to realize a more comprehensive assessment of PD.

Propofol is widely used for the induction and maintenance of anesthesia, which causes a rapid loss of consciousness. However, the mechanisms underlying the hypnosis effect of propofol are still not fully understood. The thalamic reticular nucleus (TRN) is crucial for regulating wakefulness, sleep rhythm generation, and sleep stability, while the role of TRN in the process of propofol-induced anesthesia is still unknown. Here, we investigated the function of the anterior TRN in propofol general anesthesia. Our results demonstrated that the neural activity of anterior TRN is suppressed during propofol anesthesia, whereas it is robustly activated from anesthesia by recording the calcium signals using fiber photometry technology. The results showed that the activation of anterior TRN neurons by chemogenetic and optogenetic methods shortens the emergency time without changing the induction time. Conversely, chemogenetic or optogenetic inhibition of the TRN neurons leads to a delay in the recovery time. Our study showed that anterior TRN is crucial for behavioral arousal without affecting the induction time of propofol anesthesia.

Endometriosis is a chronic inflammatory disease defined as the presence of endometrial tissue outside the uterus that causes pelvic pain and infertility. We used the Global Burden of Disease Study (GBD) 2017 to comprehensively analyze the burden of endometriosis between 1990 and 2017. DisMod-MR 2.1 was used to estimate the incidence and prevalence in some countries/territories with sparse or absent data. Annual percent changes were calculated to quantify endometriosis burden estimate trends. Furthermore, the sociodemographic index (SDI) was used to assess the relationship between endometriosis burden estimates and development level. Between 1990 and 2017, endometriosis age-standardized incidence and prevalence and years of life lived with disability (YLDs) decreased globally by 0.21% (95% confidence interval (CI): -0.23% to -0.20%), 0.29% (95% CI: -0.31% to -0.28%), and 0.28% (95% CI: -0.30% to -0.27%) per year, respectively. Apart from the high SDI quintiles with increasing trends of endometriosis incidence rate, prevalence rate, and YLDs, decreasing trends were observed in all SDI quintiles for all burden estimates. In conclusion, it appears that all endometriosis burden estimates have decreased globally between 1990 and 2017. However, these results are based on limited data and highlight the need for increased data collection on the incidence and prevalence of endometriosis.

Progression-free survival (PFS), defined as the time from randomization to progression of disease or death, has been indicated as an endpoint to support accelerated approval of certain cancer drugs by the U.S. FDA. The standard Kaplan–Meier (KM) estimator of PFS, however, can result in significantly biased estimates. A major source for the bias results from the substitution of censored progression times with death times. Currently, to ameliorate this bias, several sensitivity analyses based on rather arbitrary definitions of PFS censoring are usually conducted. In addition, especially in the advanced cancer setting, patients with censored progression and observed death times have the potential to experience disease progression between those two times, in which case their true PFS time is actually between those times. In this paper, we present two alternative nonparametric estimators of PFS, which statistically incorporate survival data often available for those patients who are censored with respect to progression to obtain less biased estimates. Through extensive simulations, we show that these estimators greatly reduce the bias of the standard KM estimator and can also be utilized as alternative sensitivity analyses with a solid statistical basis in lieu of the arbitrarily defined analyses currently used. An example is also given using an ECOG-ACRIN Cancer Research Group advanced breast cancer study.

An approach to design a memristor by inserting a TiOx thin layer in Pt-Ag/SiOx:Ag/TiOx/P++-Si memristor in order to exhibit analog resistive switching has been proposed. The device shows continuous resistance change under positive and negative DC sweeping bias, and the device conductance can also be modulated by consecutive potentiating and depressing pulse programming. These primitive results are beneficial to realize the learning and computing in such kind of memristor devices. High-resolution transmission electron microscopy observations demonstrate a clear interface between the thin layers of Ag nanoclusters embedded SiOx and the amorphous TiOx. The I-V analysis of Pt-Ag/SiOx:Ag/TiOx/P++-Si memristor confirms that the presence of TiOx thin layer controls the formation/rupture of Ag-filament across the Pt-Ag and P++-Si electrodes, realizing the gradual conductance modulation, which is essential to emulate the bio-synaptic characteristics.

Recently, by inserting a TiOx thin layer, we have fabricated a memristive device as Pt/SiOx:Ag/TiOx/p++-Si which features a better bi-polar gradually resistive switching under positive and negative electrical bias. Different synaptic functions including potentiation, depression, short-term potentiation and the transition from short-term memory (STM) to long-term memory (LTM) using suitably programmed voltage pulse have been implemented successfully in the memristive device. It is indicated that the Pt/SiOx:Ag/TiOx/p++-Si memristor can be used as a promising emulator for biological synapse, which could have great potential for brain-inspired neuromorphic computing.

STUDY OBJECTIVES: Shift work is commonly increasing, and some physiological changes occur as workers sleep less and their circadian rhythms are disrupted. This umbrella review not only summarizes the evidence but also evaluates the validity of the associations of shift work with different health outcomes. METHODS: We searched the MEDLINE, Web of Science, and Embase databases from their inception to April 25, 2020. For each systematic review and/or meta-analysis, we estimated the summary effect size, the 95% confidence interval, the 95% prediction interval, the between-study heterogeneity, evidence of small-study effects, and evidence of excess-significance bias. RESULTS: Eight eligible systematic reviews and meta-analyses were identified, providing data on 16 associations. We observed highly suggestive evidence for associations between shift work and myocardial infarction (having ever vs having never done shift work) and diabetes mellitus incidence (per 5-year increment in shift work). Furthermore, we observed suggestive evidence for an association between shift work and diabetes mellitus incidence (having ever vs having never done shift work). Two health outcomes, including prostate cancer incidence (having ever vs having never done shift work and rotating night shift work vs daytime work) and colorectal cancer incidence (longest vs shortest shift work time), were only supported by weak evidence. CONCLUSIONS: This umbrella review found that shift work was associated with several health outcomes with different levels of evidence. Associations for myocardial infarction and diabetes mellitus incidence were supported by highly suggestive evidence. SYSTEMATIC REVIEW REGISTRATION: Registry: PROSPERO; Identifier: CRD42020188537. CITATION: Wu Q-J, Sun H, Wen Z-Y, et al. Shift work and health outcomes: an umbrella review of systematic reviews and meta-analyses of epidemiological studies. J Clin Sleep Med. 2022;18(2):653–662.

BackgroundHomocysteine, folate, and vitamin B12involved in 1-carbon metabolism are associated with cognitive disorders. We sought to investigate the relationships between these factors and delayed neurocognitive recovery (dNCR) after non-cardiac surgery.MethodsThis was a prospective observational study of patients (n= 175) who were ≥ 60 years of age undergoing non-cardiac surgery. Patients were evaluated preoperatively and for 1 week postoperatively by using neuropsychological tests and were divided into dNCR or non-dNCR groups according to aZ-score ≤ − 1.96 on at least two of the tests. The relationship between the occurrence of dNCR and preoperative levels of homocysteine, folate, and vitamin B12was analyzed. Univariate and multivariable logistic regression analyses were conducted to identify factors associated with dNCR.ResultsDelayed neurocognitive recovery was observed in 36 of 175 patients (20.6%; 95% confidence interval [CI], 14.5–26.6%) 1 week postoperatively. Patients who developed dNCR had significantly higher median [interquartile range (IQR)] homocysteine concentrations (12.8 [10.9,14.4] μmol/L vs 10.6 [8.6,14.7] μmol/L;P= 0.02) and lower folate concentrations (5.3 [4.2,7.3] ng/mL vs 6.9 [5.3,9.5] ng/mL;P= 0.01) than those without dNCR. Compared to the lowest tertile, the highest homocysteine tertile predicted dNCR onset (odds ratio [OR], 3.9; 95% CI, 1. 3 to 11.6;P= 0.02), even after adjusting for age, sex, education, and baseline Mini Mental State Examination.ConclusionsElderly patients with high homocysteine levels who underwent general anesthesia for non-cardiac surgery have an increased risk of dNCR. This knowledge could potentially assist in the development of preventative and/or therapeutic measures.Trial registrationNCT03084393(https://www.clinicaltrials.gov)

Background: The associations of the consumption of cruciferous vegetables (CVs) and their bioactive components, isothiocyanates (ITCs), with ovarian cancer (OC) mortality have been unclear, owing to limited studies and inconsistent findings. To date, no studies have evaluated these associations among Chinese patients with OC. This study aims to provide more evidence indicating the relationships of pre-diagnosis CVs and ITC intake with OC survival.Methods: We examined the associations of pre-diagnosis CV and ITC intake with OC mortality in a hospital-based cohort (n = 853) of Chinese patients with epithelial OC between 2015 and 2020. Pre-diagnosis dietary information was evaluated with a validated food frequency questionnaire. Deaths were ascertained until March 31, 2021 via medical records and active follow-up. The associations were examined with the Cox proportional hazards model, adjusted for potential confounders, and stratified by menopausal status, residual lesions, histological type, and body mass index (BMI).Results: During a median follow-up of 37.2 months (interquartile: 24.7–50.2 months), we observed 130 deaths. The highest tertile of total CV intake was associated with better survival than the lowest tertile intake [hazard ratio (HR) = 0.57, 95% confidence interval (CI) = 0.33–0.98, p trend < 0.05]. In addition, higher intake of ITCs from CVs was associated with better survival (HRT3VS.T1 = 0.59, 95% CI = 0.36–0.99, p trend = 0.06). Significant inverse associations were also observed for subgroup analyses stratified by menopausal status, residual lesions, histological type, and BMI, although not all associations showed statistical significance.Conclusion: Increasing pre-diagnosis consumption of CVs and ITCs was strongly associated with better survival in patients with OC.

Background: Evidence of an association between dairy product and main related dairy nutrient intake, and the asthenozoospermia risk have been limited and controversial.Methods: A hospital-based case-control study including 549 men with asthenozoospermia and 581 normozoospermic controls was carried out in the infertility clinics of Shengjing Hospital of China Medical University between June, 2020 and December, 2020. Dietary intake was assessed with a validated food frequency questionnaire. According to the World Health Organization guidelines, semen parameters were collected through masturbation and were measured with WLJY9000 instrument and flow cytometry. The daily intake of dairy products and related nutrients was categorized into three groups according to control distribution, and the lowest tertile was used as the reference category. An unconditional multiple logistic regression was used to estimate the odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) for asthenozoospermia risk.Results: After adjustment for potential confounders, we found no statistically significant associations between the intake of total dairy products and asthenozoospermia risk (ORT3vs.T1 = 1.19, 95%CI = 0.85–1.67). Additionally, we generated null findings regarding the main related nutrients from dairy, including protein (ORT3vs.T1 = 1.19, 95%CI = 0.85–1.68), fat (ORT3vs.T1 = 1.28, 95%CI = 0.91–1.80), calcium (ORT3vs.T1 = 1.20, 95%CI = 0.85–1.68), saturated fatty acids (ORT3vs.T1 = 1.30, 95%CI = 0.92–1.83), and phosphorous (ORT3vs.T1 = 1.18, 95%CI = 0.84–1.67), and the asthenozoospermia risk. Of note, after stratification by body mass index (BMI), and the saturated fatty acids consumption from dairy was significantly associated with a higher asthenozoospermia risk (ORT3vs.T1 = 1.76, 95%CI = 1.01–3.09) among participants with a BMI below 25 kg/m2.Conclusion: This study provided limited evidence of an association between the intake of total dairy products and the main related dairy nutrients including protein, fat, calcium, saturated fatty acids, and phosphorus, and the asthenozoospermia risk. Further studies are warranted to confirm our findings in the future.

1AbstractSingle cell ATAC-seq (scATAC) shows great promise for studying cellular heterogeneity in epigenetic landscapes, but there remain significant challenges in the analysis of scATAC data due to the inherent high dimensionality and sparsity. Here we introduce scBasset, a sequence-based convolutional neural network method to model scATAC data. We show that by leveraging the DNA sequence information underlying accessibility peaks and the expressiveness of a neural network model, scBasset achieves state-of-the-art performance across a variety of tasks on scATAC and single cell multiome datasets, including cell type identification, scATAC profile denoising, data integration across assays, and transcription factor activity inference.

Cable-driven parallel robots (CDPRs) have the characteristic of easy deployment, which endows CDPRs with flexible workspace, freely configurable degrees-of-freedom (DOFs), and various configurations, greatly expanding their range of applications. Modular design provides excellent convenience and feasibility for deployment, which is a crucial issue of CDPR design. A highly integrated cable-driving module is designed in this paper, which includes the winding bobbin, servo motor, force sensor, external encoder, electromagnetic brake, as well as other devices. Experiments show that the maximum cable length control error is less than 0.16%, and the maximum cable tension control error is less than 8% in the back-and-forward rotation test. Furthermore, a CDPR with eight cables and six DOFs is constructed rapidly using the proposed module, whose dimension is 850 × 850 × 650 mm3. Results show that the robot’s trajectory errors are all less than 4.5 mm, and the root-mean-square-error (RMSE) is 2.1 mm. Besides, the compliance control experiments show that the robot’s tracking error in an impedance control mode is less than 2 mm, and the RMSE is 0.95 mm. Moreover, the dragging force in a teaching mode is less than 2.5 N. The proposed integrated cable-driving module could be helpful for the modular design and deployment of CDPRs.

High frequency oscillations (HFOs) in intracranial electroencephalography (iEEG) recordings are a promising clinical biomarker that can help define the epileptogenic regions in the brain. The aim of this study is to characterize the spatial and temporal distribution of HFOs in channel-wise instead of event-level as usual and to develop an automated the seizure onset zone (SOZ) identification by using a support vector machine (SVM) approach on the channel-wise features in a short-term recording. In this work, five consecutive patients with medically intractable epilepsy were enrolled. For each patient, ten-minute segments were defined from two hours of iEEG recordings during sleep state. A total of 17 channel-wise features including 6 rate-based, 6 duration-based, 3 amplitude-based, and 2 power-based features of HFOs were extracted from each 10-min segment, which including ripples (Rs, 80-250 Hz) and fast ripples (FRs, 250-500Hz) were detected automatically using validated detectors. Each channel-wise feature was ranked by using the Student's t-test method and the most distinctive features were selected to explore the characteristics of HFOs in each channel. A supervised-learning based SVM classifier with the selected channel-wise features or their combinations was developed to identify each channel within the independently clinician-defined SOZ or not. Over 3,816 chanel-10-min segments of iEEG recordings, the evaluated accuracy, sensitivity, and specificity of the proposed approach with the optimal combination of top five ranked features for SOZ identification are 86.6%, 73.0%, and 94.1%, respectively, for ten-fold cross-validation, and 86.0%, 79.2 %, and 91.8%, respectively, for the leave-1-out cross-validation. Compared with the recently reported SOZ detectors based on event-wise feature of HFOs, the channel-wise features and the combination with machine learning approach demonstrate its feasibility in SOZ identification with a relative higher performance and potentially reduce the time needed currently for long-term recording and manual inspection.

Objectives The burden of gout has been increasing globally. However, little is known about the global, regional and national distribution and time trend of this disease. We present a comprehensive analysis of the Global Burden of Disease Study 2017 on gout burden estimates for 195 countries or territories between 1990 and 2017. Methods Age-standardized prevalence and disability-adjusted life-years of gout were reported between 1990 and 2017 in 195 countries and territories, and associations between these estimates and sociodemographic index (SDI) were further explored. Total and annual percent change between 1990 and 2017 were calculated to quantify the time trends of gout burden. Results Age-standardized prevalence rates (95% uncertainty interval) per 100 000 persons were 790.90 (706.10–881.90) and 253.49 (225.69–284.02) in 2017 in males and females, respectively. The annual percent change in age-standardized prevalence (males, 0.22%; females, 0.38%) and disability-adjusted life-years (males, 0.21%; females, 0.38%) of gout increased every year from 1990 to 2017, globally. The highest increase was detected in high-SDI countries, especially in high-income North America. A non-linear association was observed between burden of gout and SDI, with the lowest estimates of gout burden when SDI value was about 0.6. High BMI was the leading risk factor for the burden of gout. Conclusion These study results suggest a globally rising trajectory of gout burden between 1990 and 2017. More effective interventions, such as detailed and intensive dietary managements and other prevention strategies for reducing obesity, should be carried out to reverse this trend, especially in females and high-SDI countries.

Space debris or uncontrolled satellites bring new and critical challenges for space robots to capture and remove them, especially when the targets are tumbling. In the face of these challenges, most existing research only focused on capturing a fixed point on the target. Distinguished from previous research, this paper attempts to investigate an autonomous tumbling target capturing method by approaching the “Dynamic Closest Point” (DCP), that is, the most suitable point on the target to be grasped and always closest to the end effector. First, an uncontrolled space object and a space robotic system are modeled, and the existence of the DCP is proved. For a practical malfunction spacecraft, the motion characteristics of the DCP and a fixed point are theoretically analyzed and compared as regards different motion types and rotation vectors. Furthermore, the desired trajectory is generated for the end effector of the space robot to approach the DCP autonomously, based on the position and velocity of the DCP estimated in real-time by hand-eye cameras. The joint velocities and angles are then resolved by combining the differential kinematics and movement constraints on the end effector. Finally, an Adams-Simulink co-simulation system is developed. Based on this, the proposed method is verified by single-arm and dual-arm capturing cases. Simulation results demonstrate that the capture time and movement ranges of the end effector are both greatly reduced with the DCP method. In contrast with traditional capturing methods, the efficiency of this proposed method is largely improved.

Summary Recently, many super-resolution technologies have been demonstrated, significantly affecting biological studies by observation of cellular structures down to nanometer precision. However, current super-resolution techniques mostly rely on wavefront engineering or wide-field imaging of signal blinking or fluctuation, and thus imaging depths are limited due to tissue scattering or aberration. Here we present a technique that is capable of imaging through an intact Drosophila brain with 20-nm lateral resolution at ∼200 μm depth. The spatial resolution is provided by molecular localization of a photoconvertible fluorescent protein Kaede, whose red form is found to exhibit blinking state. The deep-tissue observation is enabled by optical sectioning of spinning disk microscopy, as well as reduced scattering from optical clearing. Together these techniques are readily available for many biologists, providing three-dimensional resolution of densely entangled dendritic fibers in a complete Drosophila brain. The method paves the way toward whole-brain neural network studies and is applicable to other high-resolution bioimaging., Graphical Abstract, Highlights • Spatial resolution of 20 nm at ∼ 200 μm imaging depth (may be extended) • Super-resolution tracking of densely entangled neural fibers in an intact brain • All tools (FP, confocal, clearing, localization) are commercially available, Optical Imaging; Neuroscience; Techniques in Neuroscience

A cable-driven hyper-redundant manipulator receives great attention due to its slender body and flexible movement. The configuration of the driving cable is the key to manipulator design. At present, cables are arranged at equal angles on the disk, but it lacks theoretical support. The analysis about the influence of cable positions on cable tensions is relatively deficient. In this paper, the force on links is studied. The influence of cable positions on driving force is analyzed. The relationship between cable position, driving force, external force and joint angles are studied. Numerical simulation is carried out to verify the theoretical analysis. The results show the maximum of driving forces is the smallest when cables are arranged at equal angles and decreases as the cable position becomes uniform. The results explain the rationality of the equal configuration of the cables in the cable-driven hyper-redundant manipulator. It’s beneficial to guide the design of cable-driven manipulator.

Background: Evidence of associations between a pro-inflammatory diet and asthenozoospermia risk is limited. We therefore performed a case-controlled study to investigate associations between pro-inflammatory diet using dietary inflammatory index (DII) scores and asthenozoospermia risk in China.Methods: Our hospital-based case-controlled study comprised 549 incident asthenozoospermia men and 581 healthy controls. All were interviewed at the infertility clinic in Shengjing Hospital of China Medical University from June 2020 to December 2020. DII scores were calculated based on dietary intake which were assessed using a validated food frequency questionnaire. Semen parameters were analyzed according to World Health Organization guidelines. An unconditional logistic regression model was used to estimate odds ratios (ORs) and corresponding 95% confidence intervals (CIs) for asthenozoospermia risk. The lowest tertile served as the reference category for regression analyses.Results: After adjustment for age in the primary multivariable model, we failed to determine a significant negative association between DII and asthenozoospermia risk (for the highest tertile of DII scores compared to the lowest tertile) (OR = 0.77, 95% CI: 0.57–1.03). Similarly, non-significant associations were also identified in the multivariable model after adjusting for more potential confounders (OR = 0.86; 95% CI: 0.58–1.27). Additionally, in subgroup analyses stratified by age, body mass index, and smoking status, non-significant results were consistent with the main findings.Conclusions: To our knowledge, this is the first study exploring this particular topic. Our research does not support an association between DII scores and asthenozoospermia risk. Further prospective studies with more DII relevant foods and nutrients are warranted to confirm our findings.

Objective. Heterogeneous clinical responses to treatment with non-invasive brain stimulation are commonly observed, making it necessary to determine personally optimized stimulation parameters. We investigated neuroimaging markers of effective brain targets of treatment with continuous theta burst stimulation (cTBS) in mal de débarquement syndrome (MdDS), a balance disorder of persistent oscillating vertigo previously shown to exhibit abnormal intrinsic functional connectivity. Approach. Twenty-four right-handed, cTBS-naive individuals with MdDS received single administrations of cTBS over one of three stimulation targets in randomized order. The optimal target was determined based on the assessment of acute changes after the administration of cTBS over each target. Repetitive cTBS sessions were delivered on three consecutive days with the optimal target chosen by the participant. Electroencephalography (EEG) was recorded at single-administration test sessions of cTBS. Simultaneous EEG and functional MRI data were acquired at baseline and after completion of 10–12 sessions. Network connectivity changes after single and repetitive stimulations of cTBS were analyzed. Main results. Using electrophysiological source imaging and a data-driven method, we identified network-level connectivity changes in EEG that correlated with symptom responses after completion of multiple sessions of cTBS. We further determined that connectivity changes demonstrated by EEG during test sessions of single administrations of cTBS were signatures that could predict optimal targets. Significance. Our findings demonstrate the effect of cTBS on resting state brain networks and suggest an imaging-based, closed-loop stimulation paradigm that can identify optimal targets during short-term test sessions of stimulation. ClinicalTrials.gov Identifier: NCT02470377.

The authors have requested that this preprint be removed from Research Square.

Early prediction of adverse drug reaction (ADR) is crucial in clinical research. The development of electronic medical record (EMR) provides an excellent resource for retrospective studies to extract samples and establish models that can be used for prediction of clinical deterioration. However, classical statistical models like multivariate logistic regression (LR) may result in unreliable predictions when handling unbalanced datasets. To develop a trustworthy model on unbalanced ADR data, we first transformed the EMR including medical notes into numeric variables. Then we introduced support vector machine (SVM), random forest (RF), AdaBoost, XGBoost, and artificial neural network (ANN) to deal with the challenge of high dimensionality. Furthermore, we utilized the ensembling approach to tackle data imbalance. Finally, we analyzed potential model mechanisms to provide interpretability and compared methods from the perspective of procedure elapsed time. The results showed ensembling contributed considerable improvement in prediction ability of various machine intelligence models. Compared with the baseline, RF, AdaBoost and XGBoost presented superiority, and ANN without fine-tuning showed similar competence. The results of this study demonstrated the great potential of machine learning models in medical domain.

Mal de débarquement syndrome (MdDS) is a motion-induced disorder of oscillating vertigo that persists after the motion has ceased. The neuroimaging characteristics of the MdDS brain state have been investigated with studies on brain metabolism, structure, functional connectivity, and measurements of synchronicity. Baseline metabolism and resting-state functional connectivity studies indicate that a limbic focus in the left entorhinal cortex and amygdala may be important in the pathology of MdDS, as these structures are hypermetabolic in MdDS and exhibit increased functional connectivity to posterior sensory processing areas and reduced connectivity to the frontal and temporal cortices. Both structures are tunable with periodic stimulation, with neurons in the entorhinal cortex required for spatial navigation, acting as a critical efferent pathway to the hippocampus, and sending and receiving projections from much of the neocortex. Voxel-based morphometry measurements have revealed volume differences between MdDS and healthy controls in hubs of multiple resting-state networks including the default mode, salience, and executive control networks. In particular, volume in the bilateral anterior cingulate cortices decreases and volume in the bilateral inferior frontal gyri/anterior insulas increases with longer duration of illness. Paired with noninvasive neuromodulation interventions, functional neuroimaging with functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and simultaneous fMRI-EEG have shown changes in resting-state functional connectivity that correlate with symptom modulation, particularly in the posterior default mode network. Reduced parieto-occipital connectivity with the entorhinal cortex and reduced long-range fronto-parieto-occipital connectivity correlate with symptom improvement. Though there is a general theme of desynchronization correlating with reduced MdDS symptoms, the prediction of optimal stimulation parameters for noninvasive brain stimulation in individuals with MdDS remains a challenge due to the large parameter space. However, the pairing of functional neuroimaging and noninvasive brain stimulation can serve as a probe into the biological underpinnings of MdDS and iteratively lead to optimal parameter space identification.

A phase correction method that utilizes fringe color-coding in phase-measuring profilometry is proposed. Original and additional fringes are encoded into the R and B channels, respectively. The R + B channel patterns are projected onto an object, captured using a camera, where the R and B channels are extracted. Then, phase shifts obtained from the R and B channels are utilized for absolute phase calculation and correction. Simulations and experiments demonstrate that this method can correct edge order errors and avoid increasing the fringe number. Experiments performed using the R + G, G + B, and R + B channels demonstrate that R + B exhibits the least color crosstalk.

For the action localization task, pre-defined action anchors are the cornerstone of mainstream techniques. State-of-the-art models mostly rely on a dense segmenting scheme, where anchors are sampled uniformly over the temporal domain with a predefined set of scales. However, it is not sufficient because action duration varies greatly. Therefore, it is necessary for the anchors or proposals to have a variable receptive field. In this paper, we propose a method called YOLA (You Ought to Look Around) which includes three parts: 1) a robust backbone SPN-I3D for extracting spatio-temporal features. In this part, we employ a stronger backbone I3D with SPN (Segment Pyramid Network) instead of C3D to obtain multi-scale features; 2) a simple but useful feature fusion module named LFE (Local Feature Extraction). Compared with the fully connected layer and global average pooling, our LFE model is more advantageous for network to fit and fuse features. 3) a new feature segment aligning method called TPGC (Two Pathway Graph Convolution), which allows one proposal to leverage semantic features of adjacent proposals to update its content and make sure the proposals have a variable receptive field. YOLA add only a small overhead to the baseline network, and is easy to train in an end-to-end manner, running at a speed of 1097 fps. YOLA achieves a mAP of 58.3%, outperforming all existing models including both RGB-based and two stream on THUMOS'14, and achieves competitive results on ActivityNet 1.3.

We propose an absolute phase retrieval method based on three phase-shifting amount codes (3-PSA-codes) to measure the colored object with one additional pattern. 3-PSA-codes adopt the coding concept of 3-digit-codes, in which the code elements of three consecutive periods are treated as a unique code word for one period. However, to measure the colored object more effectively in the proposed method, each code element is embedded into the PSA domain and retrieved from the phase difference. Fringe patterns for the wrapped phase are artfully employed in the code element retrieval. Hence, for the first time, to the best of our knowledge, the code element related to the phase can be determined by one additional pattern. It breaks the constraint that temporal methods require multiple additional patterns to overcome the adverse effect of the surface color of objects on absolute phase retrieval. Experimental results demonstrate that the proposed 3-PSA-codes have strong robustness in the measurement of the colored object.

Objective To evaluate the impact of bundle interventions on ICU delirium prevalence, duration, and other patients' adverse outcomes. Data sources The Cochrane Library, PubMed, CINAHL, EMBASE, PsychINFO, and MEDLINE from January 2000 to July 2020. The protocol of the study was registered in International prospective register of systematic reviews (CRD42020163147). Study selection Randomized clinical trials or cohort studies that examined the following outcomes were included in the current study: ICU delirium prevalence and duration, proportion of patient-days with coma, ventilator-free days, mechanical ventilation days, ICU or hospital length of stay, and ICU or inhospital or 28-day mortality. Data extraction Using a standardized data-collection form, two authors screened the studies and extracted the data independently, and assessed the studies' quality using the Modified Jadad Score Scale for randomized clinical trials and the Newcastle-Ottawa Scale for cohort studies. Data synthesis Eleven studies with a total of 26,384 adult participants were included in the meta-analysis. Five studies (three randomized clinical trials and two cohort studies) involving 18,638 patients demonstrated that ICU delirium prevalence was not reduced (risk ratio = 0.92; 95% CI, 0.68-1.24). Meta-analysis showed that the use of bundle interventions was not associated with shortening the duration of ICU delirium (mean difference = -1.42 d; 95% CI, -3.06 to 0.22; two randomized clinical trials and one cohort study), increasing ventilator-free days (mean difference = 1.56 d; 95% CI, -1.56 to 4.68; three randomized clinical trials), decreasing mechanical ventilation days (mean difference = -0.83 d; 95% CI, -1.80 to 0.14; four randomized clinical trials and two cohort studies), ICU length of stay (mean difference = -1.08 d; 95% CI, -2.16 to 0.00; seven randomized clinical trials and two cohort studies), and inhospital mortality (risk ratio = 0.86; 95% CI, 0.70-1.06; five randomized clinical trials and four cohort studies). However, bundle interventions are effective in reducing the proportion of patient-days experiencing coma (risk ratio = 0.47; 95% CI, 0.39-0.57; two cohort studies), hospital length of stay (mean difference = -1.47 d; 95% CI, -2.80 to -0.15; four randomized clinical trials and one cohort study), and 28-day mortality by 18% (risk ratio = 0.82; 95% CI, 0.69-0.99; three randomized clinical trials). Conclusions This meta-analysis fails to support that bundle interventions are effective in reducing ICU delirium prevalence and duration, but supports that bundle interventions are effective in reducing the proportion of patient-days with coma, hospital length of stay, and 28-day mortality. Larger randomized clinical trials are needed to evaluate the impact of bundle interventions on ICU delirium and other clinical outcomes.

Due to the presence of motion artifacts (MAs), heart rate monitoring using PPG sensors in daily life and physical exercise is challenging, and there have been many studies on MA removal algorithms. However, most studies do not consider the quality evaluation of PPG signal before the MA removal. In this way, removing the MA directly regardless of whether there is motion artifact signal is not only a waste of computing resources, but also easy to introduce new noise. In this paper, the MA detection in PPG signal is performed by dividing the original signal into 6s signal segments and calculating the amplitude mean difference function (AMDF). Then the obtained AMDF is converted into a 2-D image through the Gramian Angular Field (GAF), and then classified by the Convolutional Neural Networks (CNN) classifier, so as to distinguish the contaminated signal and clean signal. In the subsequent processing, only the contaminated signal needs to remove the MAs, and the clean signal segment can be directly used for heart rate estimation. In this study, we achieve a classification accuracy of 0.966 in the local database, and a classification accuracy of 0.946 in the BIDMC PPG and Respiration Dataset published by PhysioNet. With the combination of feature extraction and SVM classifier, the proposed method has significantly improved the results.

Functional near-infrared spectroscopy (fNIRS) has the potential to become the next common noninvasive neuroimaging technique for routine clinical use. Compared to the current standard for neuroimaging, functional magnetic resonance imaging (fMRI), fNIRS boasts several advantages which increase its likelihood for clinical adoption. However, fNIRS suffers from an intrinsic interference from the superficial tissues, which the near-infrared light must penetrate before reaching the deeper cerebral cortex. Therefore, the removal of signals captured by SS channels has been proposed to attenuate the systematic interference. This study aimed to investigate the task-related systemic artefacts, in a high-density montage covering the sensorimotor cortex. We compared the association between LS and SS channels over the contralateral motor cortex which was activated by a hand clenching task, with that over the ipsilateral cortex where no task-related activation was expected. Our findings provide important guidelines regarding how to removal SS signals in a high-density whole-head montage.

Patients who underwent laparoscopic partial nephrectomy from the First Affiliated Hospital of Nanjing Medical University from May 2016 to May 2019 were randomly divided into enhanced recovery after surgery (ERAS) and control groups. The clinical indicators, preoperative and postoperative anxiety, depression, and postoperative quality of life were compared between the two groups. The recovery time, hospitalization cost, incidence of complications, and postoperative anxiety of patients in the ERAS group were lower than those of the control group. The satisfaction during hospitalization, scores of physical function, role function, emotional function, and general health status of the ERAS group were also significantly increased. Applying the ERAS to patients undergoing laparoscopic partial nephrectomy can improve their prognosis, experience of medical treatment, and life quality after surgery as well as have certain economic advantages.

Personalized recommender systems are playing an increasingly important role for online consumption platforms. Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences. Previous graph-based recommendation approaches process the observed user-item interaction graph as a ground-truth depiction of the relationships between users and items. However, especially in the implicit recommendation setting, all the unobserved user-item interactions are usually assumed to be negative samples. There are missing links that represent a user's future actions. In addition, there may be spurious or misleading positive interactions. To alleviate the above issue, in this work, we take a first step to introduce a principled way to model the uncertainty in the user-item interaction graph using the Bayesian Graph Convolutional Neural Network framework. We discuss how inference can be performed under our framework and provide a concrete formulation using the Bayesian Probabilistic Ranking training loss. We demonstrate the effectiveness of our proposed framework on four benchmark recommendation datasets. The proposed method outperforms state-of-the-art graph-based recommendation models. Furthermore, we conducted an offline evaluation on one industrial large-scale dataset. It shows that our proposed method outperforms the baselines, with the potential gain being more significant for cold-start users. This illustrates the potential practical benefit in real-world recommender systems.

At present, most shale gas exploration and development areas in China are difficult to provide sufficient and effective production data to support economic evaluation, since they are still in the initial stage of low exploration level. In addition, ecological and environmental factors are not taken into account in the evaluation process, which does not meet the needs of green energy development of China. Aiming at above problems, the dynamic economic evaluation method of shale gas resources based on calculus principle is proposed. The Arps hyperbolic decreasing curve model will be used in the evaluation of single shale gas well production, which can evaluate single well production of shale gas by fitting the existing dynamic production data to generate the production decreasing curve. Therefore, the variation regularity of the cumulative production of single well shale gas within the study area can be obtained by the model mentioned above. According to the variation regularity of the cumulative production obtained from the Arps hyperbolic decreasing curve model, the recovery period of single well cost, ultimate economic life and the ultimate economic resource can be evaluated dynamically by analyzing the variation regularity of the cumulative sales revenue and cumulative input cost of single shale gas well. Then the evaluation result can be further extend to the whole evaluation areas, in order to analyze shale gas resources ’ economic value in evaluation regions under different shale gas price conditions. The results of the above evaluation methods are not only conducive to improving the economic benefits of relative shale gas development enterprises, but also provide a basis for the national energy strategy deployment. © 2019 China Geology Editorial Office.

High-frequency oscillations (HFOs) of 80~500 Hz in the intracranial electroencephalogram (iEEG) recordings are considered as a reliable marker for epileptic location. However, a significant challenge to the clinical use of HFOs is due to the time-consuming procedure of visually identifying them. A new methodology is presented in this paper for the automated detection of HFOs based on their 2D time-frequency map employing the short-time energy (STE) estimation and the convolutional neural network (CNN) classification algorithm. The effectiveness and usefulness of the proposed method are evaluated using the clinical iEEG data acquired from five patients (28.4 ± 13.0 years) with medically intractable epilepsy. The proposed methodology presents the following significant advantages: 1) compared with the recently reported HFOs detector based on the CNN using only the 1D temporal EEG signal, the proposed method achieves a higher accuracy using the deep CNN classifier on 2D time-frequency map of HFOs, of which the evaluated sensitivity and false discovery rate (FDR) for identifying ripples are 88.16% and 12.58%, respectively, and the corresponding sensitivity and FDR are 93.37% and 8.1% for detecting fast ripples, respectively; 2) it is capable of automatically extracting the shared features of HFOs events of different patients and would be much robust, unlike other automated methodologies proposed in the literature where the characteristics of HFOs were extracted manually on the basis of researchers' knowledge, which, probably, is prone to observer bias; and 3) with the proposed STE estimation, all suspicious ripples and fast ripples could be initially found out and transformed into time-frequency map for subsequently CNN-based classification, rather than transforming and classifying the raw data, thus requiring a lower computational resource. In addition, the time occurrence of each transient event of the HFOs can be identified to be potentially useful for further seizure analysis. In conclusion, this automated detection of the HFOs combing the STE and the CNN could allow analyzing large amounts of data in a short time while assuring a relatively higher accuracy and, thus, would potentially serve to provide a clinically useful tool.

Cable-driven parallel robots (CDPRs) have the characteristics of reconfigurability, which endows CDPRs with flexible workspace, freely configurable degrees of freedom and various configurations, greatly expanding their range of applications. Modular design provides great convenience and feasibility for the realization of reconfiguration, which is a key issue of reconfiguration research. However, most existing CDPRs have problems of low modularity and low system integration, which brings inconvenience to the realization of reconfiguration. In this paper, a highly integrated and high precision cable-driving module is designed, which can accurately control the length and tension of the cable. In addition, experimental verification is performed. The single-module experiment shows that the module has good ability for cable length and cable tension control. The cable length control error is less than 0.2mm, and the cable tension control error is less than 0.8N. Furthermore, based on the proposed module, a CDPR with 8 cables and 6 degrees of freedom is constructed rapidly. The open-loop tracking error of the robot is measured by laser tracker. Results show that the tracking error is less than 4.5mm and the Root-Mean-Square-Error (RMSE) is 2.1mm. Besides, the compliance control experiment of the robot shows that the tracking error in impedance control mode is less than 2mm, and the RMSE is 0.95mm, and the drag force in teaching mode is less than 2.5N, which demonstrates good follow-up performance. The proposed compact cable-driving module with high precision could be useful for the design and rapid construction of reconfigurable CDPRs.

Cable-driven robots have great application potentials in confined spaces due to their light and slim bodies. However, their kinematic accuracy is generally much lower than that of the traditional articulated manipulators, because it is difficult to install sensors on its joints to directly measure the joint position. To solve the above problems, this paper proposed a vision-based method for cable-driven robots to simultaneously measure the manipulator configuration and the target pose. The configuration is reconstructed through the detection of Apriltag 2D code and the kinematics model of cable-driven robots. The target detection method in this paper consists of the template matching method and the natural features detection. Experiments are carried out to verify the method proposed in this article, including kinematic parameters calibration, arm-shape measurement, target pose measurement and docking test. The results show that the average position error of the robot end-effector decreases from 9.12 mm to 1.86 mm after calibration. The links’ position errors measured by the method in this paper are less than 10 mm. The position and the angle errors are respectively 2 mm and 1° at the target pose measurement, and the success rate of docking experiment is more than 98%. The experimental results show that the simultaneous measurement method of manipulator configuration and target pose proposed in this paper has a great effect on improving the motion accuracy and the working ability of the intelligent cable-driven robot.

To determine intrinsic functional connectivity (IFC) related to symptom changes induced by rTMS in mal de debarquement syndrome (MdDS), a motion perceptual disorder induced by entrainment to oscillating motion. Twenty right-handed women (mean age: 52.9 ± 12.6 years; mean duration illness: 35.2 ± 24.2 months) with MdDS received five sessions of rTMS (1 Hz right DLPFC, 10 Hz left DLPFC) over consecutive days. High-density (128-channel) resting-state EEG were recorded prior to and following treatment sessions and analyzed using a group-level independent component (IC) analysis. IFC between 19 ICs was quantified by inter-IC phase coherence (ICPC) in six frequency bands (delta, theta, low alpha, high alpha, beta, gamma). Correlational analyses between IFCs and symptoms were performed. Symptom improvement after rTMS was significantly correlated with (1) an increase in low alpha band (8–10 Hz) IFC but a decrease of IFC in all other bands, and (2) high baseline IFC in the high alpha (11–13 Hz) and beta bands (14–30 Hz). Most treatment related IFC changes occurred between frontal and parietal regions with a linear association between the degree of symptom improvement and the number of coherent IFC changes. Frequency band and region specific IFC changes correlate with and can predict symptom changes induced by rTMS over DLPFC in MdDS. MdDS symptom response correlates with high baseline IFC in most frequency bands. Treatment induced increase in long-range low alpha IFC and decreases in IFC in other bands as well as the proportion of coherent IFC changes correlate with symptom reduction.

Heat recovery of marine engine exhaust gas is an effective way of improving the onboard fuel economy and environmental compliance of fishing ships. Among such heat recovery techniques, the absorption refrigeration cycle shows potential as it can convert the exhaust thermal energy into refrigeration output and meet the onboard refrigeration requirement. However, the severe operating conditions on the shipboard poses a great challenge for its application. This paper presents an experimental investigation of an absorption refrigeration system for the heat recovery of marine engine exhaust gas. To overcome the adverse effect of the severe onboard condition on the rectification process of the absorption refrigeration system, a ternary ammonia–water–lithium bromide mixture is selected as the working fluid. A prototype of the absorption system is designed and an experimental investigation is conducted. Then, the performances of both the ternary ammonia–water–lithium bromide-based system and binary ammonia–water-based system are compared. The results show that the rectifier heat exchange area can be reduced by approximately 16% under the experimental working condition. Furthermore, the ternary system operates at a relatively lower pressure, with a refrigeration temperature of less than −15.0 °C, which is higher compared to the temperature of less than −23.6 °C associated with the binary system. Nevertheless, the ternary system achieves a remarkably higher cooling capacity. Moreover, by using the ternary ammonia–water–lithium bromide mixture, the heat loss of the prototype is reduced while the coefficient of performance and electric coefficient of performance are increased, indicating that the ternary system has a higher energy conversion efficiency.

Ethnopharmacological relevance Gathering three ancient formulas, traditional Chinese medicine Bu-Shen-Tong-Luo decoction (BSTLD) has been used to treat postmenopausal osteoporosis (PMO) at the Jiangsu Province Hospital of Chinese Medicine for decades. However, the effect of BSTLD on angiogenesis and bone resorption as well as its possible mechanism are still unknown. Aim of the study This study was aimed to evaluate the preventive effect of BSTLD on ovariectomy-induced bone loss and vasculature disorder, and to investigate the possible bone protection mechanism of BSTLD in inhibiting bone resorption by enhancing angiogenesis signaling in ovariectomy-induced osteoporosis of rats. Materials and methods The animal experiment was divided into five groups. Rats underwent either sham surgery with intact ovaries (SHAM, n = 10) or bilateral ovariectomy (OVX, n = 40). OVX rats were randomly divided into four groups and gavaged by water (vehicle, 12 mL/kg, n = 10), BSTLD (6 g/kg, n = 10), BSTLD (12 g/kg, n = 10) and 17β-estradiol (E2, 100 μg/kg, n = 10) daily for 12 weeks, respectively. The bone loss and microstructure of the distal femur were observed using micro-computed tomography (μCT). The biomechanical parameters of the femur were detected using three-point bending tests. The distribution of osteoclasts and endothelial cells were analyzed by immunohistochemistry. The mRNA and protein levels of angiogenesis-related hypoxia-inducible factor-1α (HIF-1α) and vascular endothelial growth factor (VEGF), as well as osteoclast activation-related signaling calcitonin receptor (CALCR), cathepsin K (CTSK), receptor activator of NF-κB ligand (RANKL), osteoprotegerin (OPG), and β-catenin were assayed by RT-PCR or Western blot. Results BSTLD protected trabecular bone mass density and trabecular bone microstructure from ovariectomy-induced osteoporosis in rats. BSTLD significantly reduced mRNA and protein levels of calcitonin receptor and CTSK in femoral metaphysis and inhibited bone resorption in ovariectomized rats. Furthermore, BSTLD stabilized HIF-1α activity and subsequently increased VEGF expression to enhance angiogenesis and modulated RANKL/OPG signaling in this animal model. Conclusions These results demonstrated that BSTLD reduced osteoclasts activation and bone resorption in ovariectomy-induced osteoporosis. Bone protection by BSTLD may be associated with its stimulation of HIF-1α/VEGF angiogenesis signaling and suppression of RANKL/OPG ratio. This study may provide evidence that BSTLD treats postmenopausal osteoporosis, especially with micro-circulation complication.

Cable-driven continuum manipulators are widely used in confined space applications, e.g. minimally invasive surgery. Different from conventional rigid manipulators, their end-effectors are positioned by deforming the flexible backbone. Existing workspace analysis of cable-driven continuum manipulators is based on kinematic modeling, in which the piecewise constant curvature assumption is adopted and the effect from external payload is fully neglected. In this work, we propose a method of analyzing the workspace using static analysis, which takes into account the internal cable tension, the external payload and the gravity force. Experiments are made through a continuum manipulator that is 90 mm in length and 7 mm in diameter to validate the proposed static model. Root-mean-square errors (RMSEs) are used to evaluate the differences between modeling and experiment. Results show that the average RMSEs along axial and lateral directions are 0.8 mm and 1.1 mm for the horizontal experimental layout, 0.4 mm and 0.1 mm for the vertical experimental layout. Based on the experimentally validated static model, workspace of a cable-driven continuum manipulator is analyzed. Results show that the workspace is much affected by the external axial force and lateral force, while is not affected by a pure bending moment. This indicates that the statics has to be considered in workspace analysis of continuum manipulators.

Solution processing of metal oxide semiconductors (MOS) is widely used for electronic and optoelectronic applications. However, the high-temperature annealing process that is required for device-quality films is incompatible with a flexible substrate because plastics can be thermally damaged. This study fabricates a p-type Cu2O flexible photodetector (PD) with a metal-semiconductor-metal structure on a polyethylene terephthalate (PET) substrate by irradiating a preheated precursor film with ultraviolet (UV) light. High-quality Cu2O crystals are formed on PET substrates by the charge transfer transition that is excited by the UV-irradiation at room temperature. An extremely high photo-to-dark current ratio (PDCR) of 1590 is achieved for a solution-processed Cu2O flexible PD. This flexible PD exhibits a relatively fast photoresponse (~0.2 s) and folds reproducibly after repeated bending for 500 cycles. This process does not require conventional high-temperature thermal annealing and is applicable to other solution-processed MOS, particularly those that are used for flexible electronic applications.

Background Functional near-infrared spectroscopy (fNIRS) has been increasingly employed to monitor cerebral hemodynamics in normal and diseased conditions. However, fNIRS suffers from its susceptibility to superficial activity and systemic physiological noise. The objective of the study was to establish a noise reduction method for fNIRS in a whole-head montage. New Method We have developed an automated denoising method for whole-head fNIRS. A high-density montage consisting of 109 long-separation channels and 8 short-separation channels was used for recording. Auxiliary sensors were also used to measure motion, respiration and pulse simultaneously. The method incorporates principal component analysis and general linear model to identify and remove a globally uniform superficial component. Our denoising method was evaluated in experimental data acquired from a group of healthy human subjects during a visually cued motor task and further compared with a minimal preprocessing method and three established denoising methods in the literature. Quantitative metrics including contrast-to-noise ratio, within-subject standard deviation and adjusted coefficient of determination were evaluated. Results After denoising, whole-head topography of fNIRS revealed focal activations concurrently in the primary motor and visual areas. Comparison with Existing Methods Analysis showed that our method improves upon the four established preprocessing methods in the literature. Conclusions An automatic, effective and robust preprocessing pipeline was established for removing physiological noise in whole-head fNIRS recordings. Our method can enable fNIRS as a reliable tool in monitoring large-scale, network-level brain activities for clinical uses.

Continuum robots have been attracting extensive attentions in the past few years due to their potential applications in minimally invasive surgery (MIS). Force sensing is critical in surgical applications which require palpation or deal with delicate structures. This letter addresses on the force sensing issue of surgery continuum robots. We propose a new force sensing method based on the shape reconstruction algorithm and the robot kinematic-static model. This method only requires the pose of the robot tip and the tensions of the driving cables. Experiments and simulations are carried out to validate the proposed force sensing method, and analyze the resolution and accuracy. Results show that both accuracy and resolution depend on the robot deflection angle. For 24 groups of tests, where the robot deflection angles are within 95°, the average sensing error is 3.1% with a maximum value of 7.1%, and the average resolution is ±0.19 g with a maximum value of ±0.57 g. Therefore, the proposed force sensing method is feasible to provide force information for MIS operations.

In this paper, a steam Rankine cycle is proposed for the subsea power supply. The turbine, as the key component, is connected to a power shaft to produce the power output. The pressure at the inner and outside of the turbine shaft seal can be balanced. A mathematical model is established, energy and parametric analysis are applied. The results show that this power cycle can acquire a thermal efficiency of 35% at 5000 meters depth. Besides, the electrical heating temperature and the condensing pressure are the key parameters that dominant the performance of this cycle.

In this work, a prototype of ocean current induced rotating magnetic field multi-stage rotor generator was developed. The power can be improved significantly by using multi-stage rotor structure. In order to improve the power of the prototype of the multi-stage rotor generator, theoretical and experimental investigation has been carried on. Simulation on the performance in different parameters of the rotating magnetic field electric generator was conducted, such as effects of the number of permanent magnets and the windings in axial, the turns number of windings, and the distribution of the permanent magnets, etc. The power of optimized electric generator can be raised from 1.2 to 5.7 mW. A test bed was set up to evaluate the performance of the multi-stage rotor generator under water. The simulation results fit the experimental results well. The power of the electric generator increased obviously with the increase of the flow velocity.

In the three-dimensional (3D) measurement, phase measuring profilometry (PMP) requires no less than three deformed sinusoidal patterns, limiting its applications to static scenarios only. In this paper, a stroboscopic online PMP for fast rotating object with binary dithered patterns is proposed. N frames of novel circular binary grating patterns are designed to measure a rotating object. A defocused digital light projector projects the designed patterns onto the object with stroboscopic method, the corresponding deformed patterns can be “frozen” by charge-coupled device (CCD) camera. Owing to the repeatability and periodicity of rotating movement, a synchronization control unit is used to keep the object at the fixed position in each “frozen” deformed pattern. Every projected pattern is shifted from the previous pattern by a factor of 2π/N. This method makes the images of the object appear stationary in the deformed patterns. The experimental results show the accurate 3D shape of fast rotating object can be reconstructed with the proposed method.

In this article, the combustion characteristic of coal–ammonia composite is investigated. Raw coal and preheating modified coal are treated to absorb the ammonia. Four kinds of samples are made to ...

Diffuse optical tomography (DOT), a subset of functional near-infrared spectroscopy (fNIRS), is a noninvasive functional imaging modality for studying the human brain in normal and diseased conditions. It measures changes in concentrations of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (Hb) in the blood vasculature of the brain. In contrast to functional magnetic resonance imaging (fMRI), the gold standard in human brain imaging, DOT offers the advantage of higher temporal resolution, portability, lower cost, multiple contrasts and usability for persons who cannot otherwise utilize MRI-based imaging modalities, including bedridden patients and infants, etc. The goal of the present study was to evaluate performance of a DOT method in studying dynamic patterns of brain activations involving motor control. CW-fNIRS data were acquired in four sessions from a healthy male participant when he performed a motor task in a block-design experiment. Results from experimental data showed pronounced activity in the primary motor cortex (M1), contralateral to the clenching hand. It was further observed that the M1 activity was consistent over four sessions. Furthermore, temporal dynamics of motor activity at each session further revealed well-sequenced activation patterns among M1, premotor cortex (PMC), and supplementary motor area (SMA). Timed ipsilateral motor activity suppression was also observed several seconds after the onset of contralateral M1 activity. More importantly, these temporal dynamics were similarly observed in all four sessions. These preliminary results suggest that the DOT method has the sensitivity, reliability, and spatio-temporal resolutions to study activities originated from the motor cortices. A full-scope evaluation and validation in more participants on the motor system can establish it as a promising neuroimaging tool to study, such as, infants at the risk of cerebral palsy or elders with Parkinson’s due to its portability and usability in clinical environments.