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Atypical squamous cells of undetermined significance (ASC-US) is the most frequent but ambiguous abnormal Papanicolaou (Pap) interpretation and is generally triaged by high-risk human papillomavirus (hrHPV) testing before colposcopy. This study aimed to evaluate the performance of an artificial intelligence (AI)-based triage system to predict ASC-US cytology for cervical intraepithelial neoplasia 2+ lesions (CIN2+).More than 60,000 images were used to train this proposed deep learning-based ASC-US triage system, where both cell-level and slide-level information were extracted. In total, 1967 consecutive ASC-US Paps from 2017 to 2019 were included in this study. Histological follow-ups were retrieved to compare the triage performance between the AI system and hrHPV in 622 patients with simultaneous hrHPV testing.In the triage of women with ASC-US cytology for CIN2+, our system attained equivalent sensitivity (92.9%; 95% confidence interval [CI], 75.0%-98.8%) and higher specificity (49.7%; 95% CI, 45.6%-53.8%) than hrHPV testing (sensitivity: 89.3%; 95% CI, 70.6%-97.2%; specificity: 34.3%; 95% CI, 30.6%-38.3%) without requiring additional patient examination or testing. Additionally, the independence of this system from hrHPV testing (κ = 0.138) indicated that these 2 different methods could be used to triage ASC-US as an alternative way.This de novo deep learning-based system can triage ASC-US cytology for CIN2+ with a performance superior to hrHPV testing and without incurring additional expenses.

This study addresses the trajectory tracking problem of a stratospheric airship subject to actuator faults, full-state constraints, input saturation, and unknown external disturbances. Improved airship state-space equations are obtained by simultaneously considering actuator faults, input saturation, and external disturbances. An adaptive fault-tolerant controller with a neural network (AFTCNN) was proposed with backstepping as the main framework. A tan-type barrier Lyapunov function (TBLF) and auxiliary systems are combined to address the full-state constraints and input saturation. Adaptive fault-tolerant and radial basis function neural network (RBFNN) control laws are proposed to estimate the actuator faults, and to approximate the disturbances, respectively. Stability analysis indicates that the tracking error converges to a neighborhood of zero; the error estimation of actuator faults and disturbances is bounded, and the state constraints are never violated. The standard backstepping method with and without RBFNN, and an adaptive fault-tolerant controller with and without RBFNN are compared in the numerical simulation. The results verify that the proposed method is superior to other methods in terms of trajectory-tracking performance and estimation of actuator faults and external disturbances, with smaller forces and torques to track the trajectory.

The rapid development of unmanned aerial vehicles (UAV) puts forward a higher requirement for autonomous obstacle avoidance. Due to the limited payload and power supply, small UAVs such as quadrotors usually carry simple sensors and computation units, which makes traditional methods more challenging to implement. In this paper, a novel framework is demonstrated to control a quadrotor flying through crowded environments autonomously with monocular vision. The framework adopts a two-stage architecture, consisting of a sensing module and a decision module. The sensing module is based on an unsupervised deep learning method. And the decision module uses dueling double deep recurrent Q-learning to eliminate the adverse effects of limited observation capacity of an on-board monocular camera. The framework enables the quadrotor to realize autonomous obstacle avoidance without any prior environment information or labeled datasets for training. The trained model shows a high success rate in the simulation and a good generalization ability for transformed scenarios., 23 pages, 10 figures

This paper investigates the finite-time trajectory tracking problem of a stratospheric airship subject to full-state constraint, input saturation, and disturbance. First, a disturbance observer is designed such that the estimation of disturbances can be accomplished within fixed time. Second, a Lyapunov barrier function-based finite-time controller is constructed to address the time-varying constraints of tracking errors, while a smooth filter is used to restrict the virtual signals and to generate their derivatives. Furthermore, novel auxiliary systems are proposed to compensate the possible saturation effect and to maintain the finite-time property. Comparative simulations are carried out to evaluate the effectiveness of the proposed controller.

This paper presents an underwater lower-extremity soft exoskeleton which could provide assistance to swimming motions. With this motivation, we designed an exoskeleton called Powered Swimsuit to assist the wearer in breaststroke with fins. The actuation system was embedded inside of a portable waterproof cabin fabricated on the back of the swimsuit. The assistive force was applied to the bottom of the fins via soft cables. During the propelling period of the stroke cycle, the cables pulled the ankle joints to provide assistance to plantar flexion. Three competitive swimmers participated the experiments to evaluate the proposed soft exoskeleton. Several kinematic parameters including joint angles and muscle activities of the lower extremities were recorded simultaneously. Compared with breaststroke without assistance, the peak of surface electromyography in the sweep phase with the exoskeleton assistance decreased by 49.13% (gastrocnemius) and 74.51% (soleus) on an average. The results showed the feasibility of the proposed Powered Swimsuit in underwater motion assistance.

SUMMARY OBJECTIVE To investigate the clinical efficacy of cefazolin sodium pentahydrate combined with vacuum sealing drainage (VSD) in the treatment of open fracture complicated with soft tissue injury. METHODS Sixty-three patients with open fracture complicated with soft tissue injury were divided into observation (n = 33) and control (n = 30) groups. After surgical reduction, fixation, and repair of the fractures, the control group was treated with VSD for 10 days, and the observation group was treated with cefazolin sodium pentahydrate based on VSD for 10 days. The infection control time was recorded. After treatment, the pain of patients was evaluated. Before and after treatment, the serum levels of C-reactive protein (CRP), interleukin (IL)-6, IL-8, tumor necrosis factor α (TNF-α), cortisol, epinephrine, norepinephrine, and glucose were detected. After 6 months of treatment, the total effective rate of the treatment was evaluated. RESULTS The infection control time and Visual Analogue Scale score after treatment in the observation group were significantly lower than in the control group, respectively (P < 0.05). After the treatment, the serum levels of CRP, IL-6, IL-8, TNF-α, cortisol, epinephrine, norepinephrine, and glucose in each group were significantly lower than before the treatment (P < 0.05), and each index in observation was significantly lower than in the control group (P < 0.05). CONCLUSIONS In the treatment of open fractures complicated with soft tissue injury, cefazolin sodium pentahydrate combined with VSD can effectively reduce inflammation and stress, thus improving the treatment efficacy.

This paper presents two sliding mode controllers to address the trajectory tracking problem of unmanned airships in the presence of unknown wind disturbance. The sliding mode controller proposed first is designed by a fast power rate reaching law(FPRRL). The disturbance is compensated by a radial basis function neural network (RBFNN). To avoid the aggressive adaptation, the controller is augmented by a command filter. The controller provides good robustness and tracking performance with no chattering under the hypothesis of ideal wind field. However, serious chattering occurs when simulation is performed under discontinuous wind field. To simulate the wind in practice, the wind field employed in the simulation is generated by the combination of a constant field and white noise. The controller is improved subsequently with an extended model to suppress the chattering induced by the white noise. The enhanced controller manipulates the derivation of system input, thus attenuating the chattering. Stability analysis shows that both controllers drive the tracking error into a controllable small region near zero. Simulations are provided to validate the performance of the proposed controllers under different wind hypothesis.

This paper addresses the spatial trajectory tracking problem of a stratospheric airship in the presence of state constraint. A constrained Laguerre-based model predictive control (MPC) method integrated with sliding mode control (SMC) is proposed as the main control framework. First, by approximating the predicted control signal with Laguerre orthonormal basis functions, a linear MPC controller which is capable of dealing with the constraints explicitly is presented to track the predetermined trajectory based on the kinematic model of airship. Second, based on the dynamic model of airship, an SMC controller with a fast power rate reaching law (FPRRL) is designed to track the desired velocity calculated by the MPC controller. Furthermore, a command filter is employed to estimate the derivative of desired velocity. Stability analysis implies that the tracking error will converge to a small neighborhood of zero. Simulation validates the effectiveness of the proposed control scheme.

In this paper, to address the spatial trajectory tracking problem of unmanned airships, a robust controller based on active disturbance rejection control is presented. By transforming the airship model to a standardized form, a straightforward design approach is adopted for the design of the controller. Active disturbance rejection control is composed of a tracking differentiator, an extended state observer, and a nonlinear state error feedback. The proposed controller replaces the conventional tracking differentiator with a third-order differentiator. The new tracking differentiator provides higher tracking precision and smoother transient process. The external disturbances and model uncertainties are observed by the extended state observer and compensated in the controller design, subsequently. Comparisons with technologies frequently used in the trajectory tracking are made through numerical simulation. The comparisons validate that the proposed controller provides satisfying performance and robustness in the presence of model uncertainty and external disturbance.

Continuous-time multi-state models are commonly used to study diseases with multiple stages. Potential risk factors associated with the disease are added to the transition intensities of the model as covariates, but missing covariate measurements arise frequently in practice. We propose a likelihood-based method that deals efficiently with a missing covariate in these models. Our simulation study showed that the method performs well for both 'missing completely at random' and 'missing at random' mechanisms. We also applied our method to a real dataset, the Einstein Aging Study.

This paper proposes a reinforcement learning (RL) based path following strategy for underactuated airships with magnitude and rate saturation. The Markov decision process (MDP) model for the control problem is established. Then an error bounded line-of-sight (LOS) guidance law is investigated to restrain the state space. Subsequently, a proximal policy optimization (PPO) algorithm is employed to approximate the optimal action policy through trial and error. Since the optimal action policy is generated from the action space, the magnitude and rate saturation can be avoided. The simulation results, involving circular, general, broken-line, and anti-wind path following tasks, demonstrate that the proposed control scheme can transfer to new tasks without adaptation, and possesses satisfying real-time performance and robustness.

This paper proposes a new slide mode controller to address unmanned airships' trajectory tracking problem in the presence of unknown wind disturbance. Firstly, the nonlinear model of an airship is introduced. Secondly, a slide mode controller is designed to track the commanded trajectory, using a fast power rate reaching law(FPRRL). A newly designed command filter is adopted to avoid the aggressive adaptation. Furthermore, a radial basis function neural network(RBFNN) is employed to approximate the wind field, decreasing the tracking error caused by the disturbance. Stability analysis shows that the presented controller drives the tracking error to converge to a quite small region. Simulation results are provided to validate the satisfying performance of the proposed controller.

Multi-state models have been widely used to analyse longitudinal event history data obtained in medical and epidemiological studies. The tools and methods developed recently in this area require completely observed data. However, missing data within variables of interest are very common in practice, and they have been an issue in applications. We propose a type of expectation–maximization (EM) algorithm, which handles missingness within multiple binary covariates efficiently, for multi-state model applications. Simulation studies show that the EM algorithm performs well for both missing completely at random and missing at random covariate data. We apply the method to a longitudinal aging and cognition study data-set, the Klamath Exceptional Aging Project, whose data were collected at Oregon Health & Science University and integrated into the Statistical Models of Aging and Risk of Transition database at the University of Kentucky.

Time-homogeneous Markov models are widely used tools for analyzing longitudinal data about the progression of a chronic disease over time. There are advantages to modeling the true disease progression as a discrete time stationary Markov chain. However, one limitation of this method is its inability to handle uneven follow-up assessments or skipped visits. A continuous time version of a homogeneous Markov process multi-state model could be an alternative approach. In this article, we conduct comparisons of these two methods for unevenly spaced observations. Simulations compare the performance of the two methods and two applications illustrate the results.

Longitudinal cognitive trajectories and other factors associated with mixed neuropathologies (such as Alzheimer’s disease with co-occurring cerebrovascular disease) remain incompletely understood, despite being the rule and not the exception in older populations. The Statistical Modeling of Aging and Risk of Transition study (SMART) is a consortium of 11 different high-quality longitudinal studies of aging and cognition (N=11,541 participants) established for the purpose of characterizing risk and protective factors associated with subtypes of age-associated mixed neuropathologies (N=3,001 autopsies). While brain donation was not required for participation in all SMART cohorts, most achieved substantial autopsy rates (i.e., > 50%). Moreover, the studies comprising SMART have large numbers of participants who were followed from intact cognition and transitioned to cognitive impairment and dementia, as well as participants who remained cognitively intact until death. These data provide an exciting opportunity to apply sophisticated statistical methods, like Markov processes, that require large, well-characterized samples. Thus, SMART will serve as an important resource for the field of mixed dementia epidemiology and neuropathology.

Background: Subjective memory complaints are common in aged persons, indicating an increased, but incompletely understood, risk for dementia. Objective: To compare cognitive trajectories and autopsy results of individuals with subjective complaints after stratifying by whether a subsequent clinical dementia occurred. Design: Observational study. Setting: University of Kentucky cohort with yearly longitudinal assessments and eventual autopsies Participants: Among 516 patients who were cognitively intact and depression-free at enrollment, 296 declared a memory complaint during follow-up. Among those who came to autopsy, 118 died but never developed dementia, while 36 died following dementia diagnosis. Measurements: Cognitive domain trajectories were compared using linear mixed models adjusted for age, gender, years of education and APOE status. Neuropathological findings were compared cross-sectionally after adjustment for age at death. Results: While the groups had comparable cognitive test scores at enrollment and the time of the first declaration of a complaint, the group with subsequent dementia development had steeper slopes of decline in episodic memory and naming but not fluency or sequencing. Autopsies showed the dementia group had more severe Alzheimer pathology and a higher proportion of subjects with hippocampal sclerosis of aging and arteriolosclerosis, whereas the non-demented group had a higher proportion expressing primary age related tauopathy (PART). Conclusions: While memory complaints are common among the elderly, not all individuals progress to dementia. This study indicates that biomarkers are needed to predict whether a complaint will lead to dementia if this is used as enrollment criteria in future clinical trials.

Extending the adaptive integral LOS path following method from planar to spatial, this paper presents a novel spatial path following control method for an under-actuated stratospheric airship in the presence of unknown wind. The nonlinear dynamic model of the airship is first introduced. Then, the spatial adaptive integral LOS guidance method generates the desired attitude and estimates the wind. The backstepping and Lyapunov-based techniques are adopted to track the predefined attitude and velocity. The final design of the control system consists of three parts: guidance loop, attitude control loop and velocity control loop. Stability analysis demonstrates that the closed-loop control system is ultimately bounded. Simulation results of the typical path are proposed to illustrate the effectiveness of the proposed control method.

Beta-amyloid (Aβ) immunotherapy is a promising intervention to slow Alzheimer's disease. Aging dogs naturally accumulate Aβ and show cognitive decline. An active vaccine against fibrillar Aβ 1–42 (VAC) in aged beagles resulted in maintenance but not improvement of cognition along with reduced brain Aβ. Behavioral enrichment (ENR) led to cognitive benefits but no reduction in Aβ. We hypothesized cognitive outcomes could be improved by combining VAC with ENR in aged dogs. Aged dogs (11–12 years) were placed into 4 groups: (1) control/control (C/C); (2) control/VAC (C/V); (3) ENR/control (E/C); and (4) ENR/VAC (E/V) and treated for 20 months. VAC decreased brain Aβ, pyroglutamate Aβ, increased cerebrospinal fluid Aβ 42 and brain-derived neurotrophic factor RNA levels but also increased microhemorrhages. ENR reduced brain Aβ and prevented microhemorrhages. The combination treatment resulted in a significant maintenance of learning over time, reduced Aβ, and increased brain-derived neurotrophic factor mRNA despite increased microhemorrhages; however, there were no benefits to memory. These results suggest that the combination of immunotherapy with behavioral enrichment leads to cognitive maintenance associated with reduced neuropathology that may benefit people with Alzheimer's disease.

Background: Cerebral vascular pathology may contribute to cognitive decline experienced by some elderly near death. Given evidence for mixed neuropathologies in advanced age, preventing or reducing cerebrovascular burden in late life may be beneficial. Objective: To correlate measures of cerebral vascular pathology with cognitive trajectories. Setting: Observational study. Participants: A cohort of 2,274 individuals who came to autopsy at a mean age of 89.3 years and 82 percent of whom had at least two cognitive assessments within the last six years of life was compiled from six centers conducting longitudinal studies. Measurements: For each cognitive domain: immediate and delayed memory, language, and naming, three trajectories were examined: good, intermediate, and poor cognition. The probability of a participant belonging to each trajectory was associated with measures of cerebral vascular pathology after adjustment for demographics, APOE, and Alzheimer neuropathology. Results: A large proportion of the cohort (72-94%) experienced good or intermediate cognition in the four domains examined. The presence of arteriolosclerosis and the presence of lacunar infarcts doubled the odds of belonging to the poor cognitive trajectory for language when compared to the good trajectory. The presence of lacunar infarcts increased the odds of an intermediate or poor trajectory for immediate and delayed recall while the presence of large artery infarcts increased the odds of poor trajectories for all four cognitive domains examined. Microinfarcts and cerebral amyloid angiopathy had little effect on the trajectories. Conclusion: Indicators of cerebral vascular pathology act differently on late life cognition.

In respect to descent trajectory optimization of stratospheric airships, thermal effects were considered. Basic thermal characteristics of stratospheric airships and energy equations about the film and the internal gas were introduced. Besides, the equations of motion for the airship were described in details. To minimum the flight time, a direct collocation method was adopted to convert the trajectory optimization problem into a parameter optimization problem. In different scenarios, the optimal trajectories were developed by a numerical nonlinear solver, sparse nonlinear optimizer (SNOPT). It is shown that the solutions are greatly affected by thermal effects and solar irradiation plays an important role during descent.

Transarterial chemoembolization (TACE) is the mainstay of management for patients with unresectable hepatocellular carcinoma (HCC). The aim of this study is to evaluate the survival rates of patients with unresectable HCC following TACE performed in a single center.The authors retrospectively assessed the electronic medical records of 512 patients in whom HCC was newly diagnosed from January 2008 to December 2012 at a single tertiary medical center. Patients with decompensated hepatic function were excluded. Hepatic artery infusion chemotherapy was performed using one drug or combinations of oxaliplatin, fluorouracil and doxorubicin. The primary endpoint of the study was overall survival (OS). Survival rates were calculated using Kaplan-Meier method.A total of 512 HCC patients (425 men and 87 women; mean age, 58.9 years; age range, 38.3-86.1 years) were treated with TACE in a single center. The overall survival rates at 1, 2, and 3 years were 62%, 43%, and 37%, respectively. The overall median survival time from the start of TACE treatment was 15 months.TACE is an effective minimally invasive therapy option for palliative treatment of HCC patients.

Maintenance of cellular homeostasis is regulated by the molecular chaperones. Under pathogenic conditions, aberrant proteins are triaged by the chaperone network. These aberrant proteins, known as “clients,” have major roles in the pathogenesis of numerous neurological disorders, including tau in Alzheimer’s disease, α-synuclein and LRRK2 in Parkinson’s disease, SOD-1, TDP-43 and FUS in amyotrophic lateral sclerosis, and polyQ-expanded proteins such as huntingtin in Huntington’s disease. Recent work has demonstrated that the use of chemical compounds which inhibit the activity of molecular chaperones subsequently alter the fate of aberrant clients. Inhibition of Hsp90 and Hsc70, two major molecular chaperones, has led to a greater understanding of how chaperone triage decisions are made and how perturbing the chaperone system can promote clearance of these pathogenic clients. Described here are major pathways and components of several prominent neurological disorders. Also discussed is how treatment with chaperone inhibitors, predominately Hsp90 inhibitors which are selective for a diseased state, can relieve the burden of aberrant client signaling in these neurological disorders.

The world’s population is growing older due to improved healthcare and nutrition. As a result, Alzheimer’s disease (AD) prevalence is rapidly increasing. The focus of the current research climate is shifting from understanding AD pathology and diagnosis to primary prevention and intervention strategies. Diet represents one potential intervention strategy accessible to all. Accumulating evidence suggests diet plays a major role in risk and development of AD and AD-related chronic diseases of the periphery like cardiovascular disease (CVD) and diabetes. This paper reviews studies that have explored the relationship between “a priori” dietary patterns, AD and AD-related chronic disease risk. The dietary patterns we will review are the healthy eating index, healthy diet indicator, recommended food score, and the Mediterranean diet (MeDi). Our review of the literature suggests a generally positive association between healthy diet patterns, AD and AD-related chronic disease risk; however the magnitude of the protective effect is modest in many studies. Consequently, we can only confidently conclude that the MeDi is associated with reduced AD risk, and further studies on the remaining indices need to be carried out. It is our opinion that a combination of dietary scores could predict overall dietary quality and chronic disease risk to a greater extent than one score individually. Analysis in multi-ethnic cohorts, investigating combinations of scores must be completed before firm conclusions can be reached on the ideal combination of scores. Obtaining further insight into the association between dietary patterns, AD and AD-related chronic disease risk may help in prioritizing public health efforts and provide a stronger basis for recommendations to improve dietary patterns. Refereed/Peer-reviewed