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This research focuses on the issue of adaptive fuzzy fault-tolerant position tracking for permanent magnet synchronous motors (PMSMs) subject to finite-time prescribed performance. An improved finite-time prescribed performance control strategy, in which unknown nonlinear functions can be tackled via fuzzy logic systems (FLSs), is presented via incorporating the approach of prescribed performance control with the technique of command filter. In addition, the command filtered method is utilized to conquer the 'explosion of complexity' emerged in the classic backstepping method and the error compensation mechanism is adopted to diminish the error generated by filtering process. Further, the impact of actuator failures is dealt with based on fault-tolerant control. It is proven that the designed controllers not only assure the semi-global boundedness of all the controlled system signals, but also make the output tracking error is preserved in a specified prescribed performance within a finite-time interval. Finally, simulation results are supplied to display the significance and potential of the proposed control technique. [ABSTRACT FROM AUTHOR]

In this paper, the predefined-time control problem with prescribed performance is investigated for bilateral teleoperation systems with a single master and multiple slaves. The difficulty lies in the realization of predefined time stability and synchronization control of single-master and multi-slave manipulators. A prescribed performance function is introduced and an improved error conversion mechanism for matrix transformation is designed to ensure that joint displacement tracking error is constrained. A novel coordination position error transformation is defined such that the positional synchronization of the single-master-multiple-slave manipulators is achieved. Based on graph theory and Lyapunov stability theorem, a novel adaptive neural control approach is developed with predefined time interval via the backstepping technique, such that the convergence time can be predefined by users specification, the tracking error can be limited within a prescribed bound and in the meantime can converge to zero within predefined time interval. Simulation results are provided to prove the effectiveness of the presented scheme. [ABSTRACT FROM AUTHOR]

The current thyroid ultrasound relies heavily on the experience and skills of the sonographer and the expertise of the radiologist, and the process is physically and cognitively exhausting. In this paper, we report a fully autonomous robotic ultrasound system, which is able to scan thyroid regions without human assistance and identify malignant nod- ules. In this system, human skeleton point recognition, reinforcement learning, and force feedback are used to deal with the difficulties in locating thyroid targets. The orientation of the ultrasound probe is adjusted dynamically via Bayesian optimization. Experimental results on human participants demonstrated that this system can perform high-quality ultrasound scans, close to manual scans obtained by clinicians. Additionally, it has the potential to detect thyroid nodules and provide data on nodule characteristics for American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) calculation. Current thyroid ultrasounds rely heavily on the experience and skills of the sonographer and of the radiologist, and the process is physically and cognitively exhausting. Here, the authors show a fully autonomous robotic ultrasound system, which is able to scan thyroid regions without human assistance and identify malignant nodules. [ABSTRACT FROM AUTHOR]

There is a lack of studies that concurrently differentiate the effect of the holiday season from the weekend effect on mortality risk in patients with acute myocardial infarction (AMI). We evaluated the mortality risk among patients admitted with AMI who underwent percutaneous coronary intervention, using data from the Taiwan National Health Insurance Research Database. Adult AMI patients admitted during January and February between 2013 and 2020 were enrolled and classified into the holiday season (using the Chinese New Year holiday seasons as an indicator) (n = 1729), weekend (n = 4725), and weekday (n = 14,583) groups according to the first day of admission. A multivariable logistic regression model was used to assess the risk. With the weekday group or the weekend group as the reference, the holiday season group did not have increased risks of in-hospital mortality (adjusted odds ratio [aOR] 1.15; 95% confidence intervals [CI] 0.93–1.42 or aOR 1.23; 95% CI 0.96–1.56) and 7-day mortality (aOR 1.20; 95% CI 0.90–1.58 or aOR 1.24; 95% CI 0.90–1.70). Stratified and subgroup analyses showed similar trends. We conclude that holiday season-initiated admissions were not associated with higher mortality risks in AMI admission cases than weekday or weekend admissions. [ABSTRACT FROM AUTHOR]

Recurrent neural networks (RNNs) are excellent solvers for time-variant linear matrix–vector inequality (TVLMVI). However, it is difficult for traditional RNNs to track the theoretical solution of TVLMVI under non-ideal conditions [e.g., noisy environment]. Therefore, by introducing a novel nonlinear activation function (NNAF) and time-variant-gain, a new super-predefined-time convergence and noise-tolerant RNN (SPCNT-RNN) is proposed to acquire an online solution to TVLMVI in noisy environment. The difference between SPCNT-RNN and traditional fixed-parameter RNNs (FP-RNNs) is that the error function equation of SPCNT-RNN has NNAF and time-variant-gain coefficient. Due to this difference, the SPCNT-RNN can achieve super-predefined-time convergence in both noise-free and noisy environments, which is superior to that of existing RNNs. The stability, super-predefined-time convergence, and robustness of SPCNT-RNN are theoretically demonstrated. Moreover, the simulation results between various existing RNNs and SPCNT-RNN verify the feasibility, validity, robustness and rapid convergence effect of the proposed SPCNT-RNN. [ABSTRACT FROM AUTHOR]

Sleep-disordered breathing (SDB) is a prevalent disorder characterized by recurrent episodic upper airway obstruction. Using data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), we apply principal component analysis (PCA) to seven SDB-related measures. We estimate the associations of the top two SDB PCs with serum levels of 617 metabolites, in both single-metabolite analysis, and a joint penalized regression analysis. The discovery analysis includes 3299 individuals, with validation in a separate dataset of 1522 individuals. Five metabolite associations with SDB PCs are discovered and replicated. SDB PC1, characterized by frequent respiratory events common in older and male adults, is associated with pregnanolone and progesterone-related sulfated metabolites. SDB PC2, characterized by short respiratory event length and self-reported restless sleep, enriched in young adults, is associated with sphingomyelins. Metabolite risk scores (MRSs), representing metabolite signatures associated with the two SDB PCs, are associated with 6-year incident hypertension and diabetes. These MRSs have the potential to serve as biomarkers for SDB, guiding risk stratification and treatment decisions.Sleep-disordered breathing (SDB) is a prevalent disorder linked to higher cardiovascular disease risk. Here, the authors show that summary scores reflecting SDB metabolite signatures are associated with increased risks for incident hypertension and diabetes, potentially useful in guiding risk stratification. [ABSTRACT FROM AUTHOR]

Solving matrix equation with noise interference is a challenging problem in mathematical and engineering applications. Unlike the traditional recurrent neural network, a novel varying-parameter periodic rhythm neural network (VP-PRNN) is proposed and used to solve the time-varying matrix equation in finite energy noise environment online. Particularly, VP-PRNN can enable the state solution to converge to the theoretical solution rapidly and robustly, which is also proved by theoretical analysis. Four kinds of noise are used to test the system, which proves the effectiveness of VP-PRNN. Compared with the zeroing neural network and circadian rhythms learning network with fixed parameters, VP-PRNN with variable parameters shows superior convergence performance in the disturbance of finite energy noise. [ABSTRACT FROM AUTHOR]

This study investigates an adaptive fixed-time tracking problem of nonlinear interconnected high-order systems with unknown control direction and stochastic disturbances. Under the framework of adaptive feedback, the backstepping method and fuzzy logic system are utilized to handle the stochastic disturbances and the packaged unknown nonlinearities. By utilizing the Nussbaum gain technique, an adaptive fixed-time controller is proposed to overcome the difficulties associated with unknown control directions. Distinguishing from the most existing results, a modified fixed-time control scheme is presented to deal with the positive odd integer terms from the interconnected high-order system with the help of adding a power integrator method. The designed control strategy guarantees that the tracking error converges within a fixed settling time and all signals of the closed-loop system are fixed-time stable. Simulation results validate the designed control approach. [ABSTRACT FROM AUTHOR]

Objectives: A watch and wait strategy with the goal of organ preservation is an emerging treatment paradigm for rectal cancer following neoadjuvant treatment. However, the selection of appropriate patients remains a challenge. Most previous efforts to measure the accuracy of MRI in assessing rectal cancer response used a small number of radiologists and did not report variability among them. Methods: Twelve radiologists from 8 institutions assessed baseline and restaging MRI scans of 39 patients. The participating radiologists were asked to assess MRI features and to categorize the overall response as complete or incomplete. The reference standard was pathological complete response or a sustained clinical response for > 2 years. Results: We measured the accuracy and described the interobserver variability of interpretation of rectal cancer response between radiologists at different medical centers. Overall accuracy was 64%, with a sensitivity of 65% for detecting complete response and specificity of 63% for detecting residual tumor. Interpretation of the overall response was more accurate than the interpretation of any individual feature. Variability of interpretation was dependent on the patient and imaging feature investigated. In general, variability and accuracy were inversely correlated. Conclusions: MRI-based evaluation of response at restaging is insufficiently accurate and has substantial variability of interpretation. Although some patients' response to neoadjuvant treatment on MRI may be easily recognizable, as seen by high accuracy and low variability, that is not the case for most patients. Key Points: • The overall accuracy of MRI-based response assessment is low and radiologists differed in their interpretation of key imaging features. • Some patients' scans were interpreted with high accuracy and low variability, suggesting that these patients' pattern of response is easier to interpret. • The most accurate assessments were those of the overall response, which took into consideration both T2W and DWI sequences and the assessment of both the primary tumor and the lymph nodes. [ABSTRACT FROM AUTHOR]

This paper considers the problem of leader-following consensus and fault detection for a class of multi-agent systems with Lipschitz nonlinear dynamics. To reduce the amount of redundant information and avoid checking triggering conditions continually, this paper proposes an efficient network framework with a double periodic event-triggered mechanism. Based on the proposed framework, an improved fault detection observer and a consensus controller are designed. Then, the original problem is converted into a set of stability problems with constraints. According to Lyapunov–Krasovskii theorem and the free-weighting matrix technique, sufficient conditions for solving these stability problems are derived in the form of bilinear matrix inequalities (BMIs). Further, to eliminate the nonlinear terms of BMI and obtain optimal performance, two iterative algorithms based on linear matrix inequalities (LMIs) are developed. Two simulation examples are provided to verify the practicality and validity of the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the tracking control problem of flexible joint robotic manipulator (FJRM) system subjected to system uncertainties and time-varying external disturbances (TVED) is addressed. A new disturbance observer-based neural network integral sliding mode controller with output constraints (DNISMCOC) that comprises the merits of neural networks, disturbance observer and integral sliding mode is proposed. Considering that the radial basis function neural network (RBFNN) has a fast learning convergence speed and great approximation ability, two matrices of RBFNN are utilized to estimate the parameter matrices of the dynamic model of FJRM. In view of the estimation errors of RBFNNs and TVED in the system, a disturbance observer is introduced to estimate the lump uncertainties which consist of them. Integral sliding mode is introduced for eliminating steady errors further. For ensuring security in some high-accuracy using occasions, a barrier lyapunov functions (BLF) is adopted to achieve output constraints of FJRM. To validate the effectiveness of the proposed control scheme, numerical simulations on 2-link FJRM are conducted. According to the comparisons among DNISMCOC and other state-of-the-art controllers, the superiorities of DNISMCOC in several aspects are proved. [ABSTRACT FROM AUTHOR]

Sleep serves important biological functions, and influences health and longevity through endocrine and metabolic related systems. Sleep debt, circadian misalignment and sleep disruption from obstructive sleep apnea is widespread in modern society and accumulates with life because recovery sleep is not completely restorative. Accumulated disordered sleep throughout life impacts the ageing process and the development of age-related diseases. When epidemiological and interventional studies are considered collectively, sleep loss and lower sleep duration are associated with lower morning, afternoon and 24-h testosterone; as well as higher afternoon, but not morning or 24-h cortisol. These reciprocal changes imbalances anabolic-catabolic signaling because testosterone and cortisol are respectively the main anabolic and catabolic signals in man. Fixing testosterone-cortisol balance by means of a novel dual-hormone clamp mitigates the induction of insulin resistance by sleep restriction and provided the first proof-of-concept that the metabolic harm from sleep loss can be ameliorated by approaches that do not require sleeping more. Obstructive sleep apnea is associated with lower testosterone, even after controlling for age and obesity whereas the conclusion that continuous positive airway pressure therapy has no effect on testosterone is premature because available studies are underpowered and better-quality studies suggest otherwise. High dose testosterone therapy induces OSA, but more physiological dosing may not; and this effect may be transient or may dissipate with longer term therapy. Studies investigating the origin of the diurnal testosterone rhythm, the effect of circadian misalignment on testosterone-cortisol balance, and methods to mitigate metabolic harm, are required. [ABSTRACT FROM AUTHOR]

In this article, the problem of fuzzy adaptive fixed-time control is addressed for nonstrict-feedback nonlinear systems with input saturation and dead zone. The universal approximation properties of fuzzy logic systems are employed to model the unknown nonlinear functions. A command filter-based fixed-time adaptive fuzzy control strategy is presented based on the backstepping framework and fixed-time control theory. The command filter technique is presented to address the "computational explosion" problem inherent in the backstepping scheme, and an error compensation mechanism is adopted to reduce the errors arising from command filters. Meanwhile, the non-smooth input saturation and dead zone nonlinearities are approximated using a non-affine smooth function, and they are transformed into an affine form based on the mean-value theorem. The fixed-time convergence of the tracking error and the boundedness of the closed-loop signals are proved using the fixed-time stability theory. Finally, simulation was performed to demonstrate the effectiveness of the presented method. [ABSTRACT FROM AUTHOR]

Several infections can predispose to certain malignancies in different body parts. These infections include viral, bacterial, and fungal pathogens. Imaging plays a vital role in the diagnosis, staging, and management of these neoplastic conditions. Furthermore, it can help in differentiating infection-related non-neoplastic processes that can mimic malignancies. Both radiologists and clinicians should be familiar with these conditions. This review discusses the epidemiology, pathogenesis, and imaging features of infection-related tumors. [ABSTRACT FROM AUTHOR]

This paper proposes a novel robust finite-time tracking control scheme for robotic manipulators with uncertainties and external disturbances. In order to reduce the chattering phenomenon and enhance the robustness of the system, we design a novel extended state observer with filter function for estimating the lumped term of system uncertainties and external disturbances. We adopt the backstepping mechanism to design an adaptive backstepping nonsingular fast terminal sliding mode controller, achieve finite time convergence of the tracking errors while avoiding the singularity problem. An adaptive update law is designed for the switching gain, which further weakens the chattering problem. The finite-time convergence of tracking errors and the stability of the closed-loop system are ensured by the Lyapunov criterion. In comparison with other control methods, numerical simulation on a two-link rigid robot manipulator demonstrates the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Highly accurate short-term load forecasting (STLF) is essential in the operation of power systems. However, the existing predictive methods cannot achieve an effective balance between prediction accuracy and computational cost. Furthermore, the prediction residual is rarely used to improve the predictive accuracy in STLF. This paper proposes a novel decomposition-based ensemble model for the STLF task. First, an optimized empirical wavelet transform (OEWT) is developed to rationally decompose the STLF load by combining the approximate entropy method with the empirical wavelet transform. Particularly, OEWT improves both prediction accuracy and computational cost in STLF. Second, a new hybrid machine learning method (named master learner) is proposed by rationally combining long short-term memory networks (LSTMs) with broad learning system (BLS) in STLF, effectively strengthening the predictive accuracy without significantly increasing the computational cost. Third, a residual learning model (named residual learner) is developed in the master learner to extract the effective predictive information from residual results, further improving the prediction accuracy in STLF. Fourth, an auxiliary learner is proposed by introducing another BLS to connect the input and output of the proposed model, enhancing the predictive robustness. The proposed decomposition-based ensemble model is compared with state-of-the-art and traditional models in STLF. Experimental results show that the model not only has high predictive accuracy and robustness but also low computational cost. [ABSTRACT FROM AUTHOR]

Uterine fibroids are the most common gynecologic neoplasm. Although non-degenerated fibroids are easily identifiable on imaging, degenerated fibroids, fibroid variants, and fibroids with unusual growth patterns can constitute a diagnostic dilemma. Identification of these abnormal morphologic features can alter the diagnosis of presumed uterine fibroids and hence change management plans. This article reviews the typical and atypical radiologic imaging features of uterine fibroids, with an emphasis on the pitfalls, mimics, and radiologically identifiable features that can alter clinical management plans. [ABSTRACT FROM AUTHOR]

Short-term load forecasting with high accuracy is essential to power systems. Because power loads involve high volatility and uncertainty, it is challenging to accurately perform short-term load forecasting (STLF). To solve this problem, this paper proposes a decomposition-based approximate entropy cooperation long short-term memory (DB-AEC-LSTM) model for STLF. In DB-AEC-LSTM, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is first introduced to generate the multiple electric load time series into many cooperation sub-series and decrease the reconstruction errors. Then, an Approximate Entropy Cooperation ensemble Long Short-term Memory Model is developed by using approximate entropy (ApEn) to construct an effective cooperative relationship between different time sub-series groups, greatly improving the predictive accuracy. By rationally combined the effective technologies ApEn, CEEMDAN, and AEC-LSTM, the proposed DB-AEC-LSTM can obtain competitive predictive performance in STLF. Several short-term load forecasting datasets are performed to check the predictive performance of DB-AEC-LSTM. Experimental results show that DB-AEC-LSTM has better predictive accuracy and satisfactory robustness compared with state-of-the-art and conventional predictive models. [ABSTRACT FROM AUTHOR]

In this paper, we propose a backstepping integral nonsingular fast terminal sliding mode control approach based on an extended state observer to the trajectory tracking control of robotic manipulators with LuGre friction. In order to improve convergence speed and tracking precision, an integral fast terminal sliding surface is introduced. An appropriate saturation function is designed in the control input to avoid singularity. An extended state observer is employed to estimate the uncertainties and disturbances, and they are compensated by the control laws, for which the chattering can be effectively eliminated. The backstepping method is used to design the control input of the system to ensure the globally asymptotic stability based on the Lyapunov criterion. In comparison with other control methods, simulation results show effectiveness and superiority of this proposed control scheme. [ABSTRACT FROM AUTHOR]

Images may be distorted to different degrees in the process of acquisition, transmission, and reconstruction, which is not conducive to the perception and recognition of the human eye. Therefore, it is necessary to reasonably quantify the image quality through image quality assessment. This enables people to correctly understand the image content. In practical applications, blind image quality assessment (BIQA) has attracted widespread attention in the field of image processing because it can evaluate the image itself without any prior knowledge. Support vector regression (SVR) is widely adopted in the field of BIQA, which is one important step in many BIQA methods based on a two-step framework that contains feature extraction and SVR. However, the parameters of SVR greatly affect its predictive performance and generalization ability. Therefore, grid search (GS) is usually utilized to select the appropriate parameters of SVR in the field of BIQA. However, GS may cause the omission of potential solutions when searching the best parameters of SVR, degenerating the performance of SVR. To search the promising parameters of SVR, a novel meta-heuristic algorithm named hybrid artificial bee colony and sine cosine algorithm (ABC-SCA) is proposed. Besides, a feasible scheme for SVR parameter selection is given to further improve the prediction accuracy of the BIQA algorithm. The proposed SVR parameter selection algorithm is compared with GS and several other meta-heuristic algorithms on the three image databases (LIVE, TID2013, and CSIQ) with three BIQA schemes. The experimental results show that the proposed ABC-SCA can effectively obtain the optimal parameters of SVR in the field of BIQA. [ABSTRACT FROM AUTHOR]

The positional synchronization control problem is addressed for bilateral teleoperation systems subjected to system uncertainties, exogenous forces, unknown disturbances and asymmetric communication delays. In order to guarantee fast and accurate synchronization performance, a funnel-like curve is predefined to act as the boundary for tracking errors, and the controller design process is divided into two steps based on a backstepping approach. By coordination transformation of state variables, the controller is synthesized to ensure that all transformed variables converge and the system is stabilized. System uncertainties are compensated for by using an RBF(radial basis functions) neural network. Theoretical analysis proves that the closed-loop system is stable and positional synchronization is achieved. Simulation results demonstrate that the proposed method provides satisfactory performance. [ABSTRACT FROM AUTHOR]

Splenosis is an acquired form of ectopic splenic tissue that typically arises after trauma or splenectomy. It is often an incidental image finding in an otherwise asymptomatic patient, but the spectrum of symptoms varies based on the site of implantation. Radiologists should be familiar with the imaging features of splenosis to avoid mistaking it for malignancy. Splenosis has identical imaging features to that of the native spleen on US, CT, MRI, and nuclear medicine examinations. Therefore, when the radiologic findings support the diagnosis of splenosis, the patient can be spared invasive procedures for tissue sampling. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems are becoming increasingly significant because they can convert solar energy into electricity. The conversion efficiency is related to the PV models' parameters, so it is crucial to identify the parameters of PV models. Recently, various metaheuristic methods have been proposed to identify the parameters, but they cannot provide sufficient accurate and reliable performance. To address this problem, this paper proposes a spiral-based chaos chicken swarm optimization algorithm (SCCSO) including three strategies: (1) the information-sharing strategy provides the latest information of the roosters for searching global optimal solution, beneficial to improve the exploitation ability; (2) the spiral motion strategy can enable hens and chicks to move toward their corresponding targets with a spiral trajectory, improving the exploration ability; and (3) a self-adaptive-based chaotic disturbance mechanism is introduced around the global optimal solution to generate a promising solution for the worst chick at each iteration, thereby improving the convergence speed of the chicken flock. Besides, SCCSO is used for identifying different PV models such as the single-diode, the double-diode, and PV module models. Comprehensive analysis and experimental results show that SCCSO provides better robustness and accuracy than other advanced metaheuristic methods. [ABSTRACT FROM AUTHOR]

We aimed to determine whether hospital admissions during an extended holiday period (Chinese New Year) and weekends were associated with increased mortality risk from pulmonary embolism (PE), compared to admissions on weekdays. We conducted a nationwide retrospective cohort study using Taiwan's National Health Insurance Research Database. Data of newly diagnosed PEs during the months of January and February from 2001 to 2017 were obtained from patient records and classified into three admission groups: Chinese New Year (≥ 4 consecutive holiday days), weekends, and weekdays. The adjusted odds ratios (aORs) (95% confidence intervals [CIs]) for 7-day and in-hospital mortality were calculated using multivariable logistic regression models. The 7-day and in-hospital mortality risks were higher for patients admitted during the Chinese New Year holiday (10.6% and 18.7%) compared to those admitted on weekends (8.4% and 16.1%) and weekdays (6.6% and 13.8%). These higher mortality risks for holiday admissions compared to weekday admissions were confirmed by multivariable analysis (7-day mortality: aOR = 1.68, 95% CI 1.15–2.44, P = 0.007; in-hospital mortality: aOR = 1.41, 95% CI 1.05–1.90, P = 0.022), with no subgroup effects by sex or age. Hospital admission for PE over an extended holiday period, namely Chinese New Year, was associated with an increased risk of mortality. [ABSTRACT FROM AUTHOR]

Wnt/β-catenin signaling is activated in the heart after myocardial infarction (MI). This study aims to investigate if β-catenin deletion affects post-MI ion channel gene alterations and ventricular tachycardias (VT). MI was induced by permanent ligation of left anterior descending artery in wild-type (WT) and cardiomyocyte-specific β-catenin knockout (KO) mice. KO mice showed reduced susceptibility to VT (18% vs. 77% in WT) at 8 weeks after MI, associated with reduced scar size and attenuated chamber dilation. qPCR analyses of both myocardial tissues and purified cardiomyocytes demonstrated upregulation of Wnt pathway genes in border and infarct regions after MI, including Wnt ligands (such as Wnt4) and receptors (such as Fzd1 and Fzd2). At 1 week after MI, cardiac sodium channel gene (Scn5a) transcript was reduced in WT but not in KO hearts, consistent with previous studies showing Scn5a inhibition by Wnt/β-catenin signaling. At 8 weeks after MI when Wnt genes have declined, Scn5a returned to near sham levels and K+ channel gene downregulations were not different between WT and KO mice. This study demonstrated that VT susceptibility in the chronic phase after MI is reduced in mice with cardiomyocyte-specific β-catenin deletion primarily through attenuated structural remodeling, but not ion channel gene alterations. [ABSTRACT FROM AUTHOR]

This paper focuses on the decentralized finite-time prescribed performance control problem for a class of large-scale nonlinear interconnected systems with input dead zone using an adaptive fuzzy approach. Specifically, fuzzy logic systems are utilized to approximate unknown nonlinear system functions and a finite-time prescribed performance control scheme is designed by taking advantage of both the adaptive technique and backstepping scheme. By introducing two smooth functions and utilizing the command filter backstepping design, the 'explosion of complexity' problem inherent in the conventional backstepping control is overcome, while the associated problems due to unknown interconnections are solved. The proposed control scheme guarantees that all signals within the closed-loop controlled system are bounded and the output tracking error falls within a small range predefined by the prescribed performance within a finite time. Two simulation examples are given to verify the high effectiveness of the presented control approach. [ABSTRACT FROM AUTHOR]

Purpose: Invasive pulmonary aspergillosis (IPA) is increasingly reported in patients with severe coronavirus disease 2019 (COVID-19) admitted to the intensive care unit (ICU). Diagnosis and management of COVID-19 associated pulmonary aspergillosis (CAPA) are challenging and our aim was to develop practical guidance. Methods: A group of 28 international experts reviewed current insights in the epidemiology, diagnosis and management of CAPA and developed recommendations using GRADE methodology. Results: The prevalence of CAPA varied between 0 and 33%, which may be partly due to variable case definitions, but likely represents true variation. Bronchoscopy and bronchoalveolar lavage (BAL) remain the cornerstone of CAPA diagnosis, allowing for diagnosis of invasive Aspergillus tracheobronchitis and collection of the best validated specimen for Aspergillus diagnostics. Most patients diagnosed with CAPA lack traditional host factors, but pre-existing structural lung disease and immunomodulating therapy may predispose to CAPA risk. Computed tomography seems to be of limited value to rule CAPA in or out, and serum biomarkers are negative in 85% of patients. As the mortality of CAPA is around 50%, antifungal therapy is recommended for BAL positive patients, but the decision to treat depends on the patients' clinical condition and the institutional incidence of CAPA. We recommend against routinely stopping concomitant corticosteroid or IL-6 blocking therapy in CAPA patients. Conclusion: CAPA is a complex disease involving a continuum of respiratory colonization, tissue invasion and angioinvasive disease. Knowledge gaps including true epidemiology, optimal diagnostic work-up, management strategies and role of host-directed therapy require further study. [ABSTRACT FROM AUTHOR]

An accurate and realistic force feedback is very important in determining the realism of virtual surgery. In order to improve the accuracy of force simulation in cutting procedures, we proposed a novel method for forecasting and simulating the cutting force based on a particle filtering (PF) technique. Since the probability density function (PDF) is represented by particles, it is able to estimate accurately the interaction force between the surgical tool and soft tissue during cutting processes. The root mean square error (RMSE) of the PF-based method ranges from 0.0014 to 0.0034, and the mean absolute error (MAE) is less than 0.0399. Comparison of the experiment results with other methods demonstrated that the PF-based method can achieve a higher accuracy with different cutting speeds and angles. The application of the PF-based method to a virtual liver cutting procedure confirmed the effectiveness and accuracy of this method. [ABSTRACT FROM AUTHOR]

This paper considers the finite-time control problem for a class of nonlinear stochastic systems with actuator faults/failure. A fast convergence feedback control algorithm based on backstepping finite-time command filtering is proposed. Under the framework of adaptive feedback, the fuzzy logic system is used to deal with the uncertainties of the system. Taking into account the actuator faults of both loss of effectiveness and lock-in-place, an adaptive fuzzy controller is developed. Since there is no need to calculate the derivative of the virtual control signal, the presented scheme overcomes the "explosion of complexity" problem inherent in conventional methods. A compensation mechanism is also introduced to compensate for errors caused by the filter. The proposed method ensures not only that all signals of the closed-loop system are finite-time bounded, but also that the tracking error converges to a small neighborhood around the origin. The effectiveness of the proposed method is demonstrated in the simulation results. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for the COVID-19 global pandemic has infected over 25 million people worldwide and resulted in the death of millions. The COVID-19 pandemic has also resulted in a shortage of personal protective equipment (PPE) in many regions around the world, particularly in middle- and low-income countries. The shortages of PPE, such as N95 respirators, is something that will persist until an effective vaccine is made available. Thus, devices that while being easy to operate can also be rapidly deployed in health centers, and long-term residences without the need for major structural overhaul are instrumental to sustainably use N95 respirators. In this report, we present the design and validation of a decontamination device that combines UV-C & B irradiation with mild-temperature treatment. The device can decontaminate up to 20 masks in a cycle of < 30 min. The decontamination process did not damage or reduce the filtering capacity of the masks. Further, the efficacy of the device to eliminate microbes and viruses from the masks was also evaluated. The photothermal treatment of our device was capable of eradicating > 99.9999% of the bacteria and > 99.99% of the virus tested. [ABSTRACT FROM AUTHOR]

Inositol-Requiring Enzyme 1 (IRE1) is an essential component of the Unfolded Protein Response. IRE1 spans the endoplasmic reticulum membrane, comprising a sensory lumenal domain, and tandem kinase and endoribonuclease (RNase) cytoplasmic domains. Excess unfolded proteins in the ER lumen induce dimerization and oligomerization of IRE1, triggering kinase trans-autophosphorylation and RNase activation. Known ATP-competitive small-molecule IRE1 kinase inhibitors either allosterically disrupt or stabilize the active dimeric unit, accordingly inhibiting or stimulating RNase activity. Previous allosteric RNase activators display poor selectivity and/or weak cellular activity. In this study, we describe a class of ATP-competitive RNase activators possessing high selectivity and strong cellular activity. This class of activators binds IRE1 in the kinase front pocket, leading to a distinct conformation of the activation loop. Our findings reveal exquisitely precise interdomain regulation within IRE1, advancing the mechanistic understanding of this important enzyme and its investigation as a potential small-molecule therapeutic target. The RNase activity of Inositol-Requiring Enzyme 1 (IRE1) can be allosterically regulated by ATP-competitive inhibitors of the IRE1 kinase domain. Here, the authors identify ATP-competitive IRE1 RNase activators with improved selectivity and cellular activity, and elucidate their mechanism of action. [ABSTRACT FROM AUTHOR]

Image synthesis using representations learned by deep neural networks has gained wide attention in recent years. Among the different categories of natural images, face images are very important because of their broad range of applications. However, it is very challenging to synthesize face images due to their highly complicated hierarchical structure and the uniqueness of information contained in individual face images. This paper aims at providing a comprehensive review of the recent developments and applications of face synthesis and semantic manipulations using deep learning and discusses future perspectives for improving face perception. [ABSTRACT FROM AUTHOR]

Lipopolysaccharide (LPS) resides in the outer membrane of Gram-negative bacteria where it is responsible for barrier function1,2. LPS can cause death as a result of septic shock, and its lipid A core is the target of polymyxin antibiotics3,4. Despite the clinical importance of polymyxins and the emergence of multidrug resistant strains5, our understanding of the bacterial factors that regulate LPS biogenesis is incomplete. Here we characterize the inner membrane protein PbgA and report that its depletion attenuates the virulence of Escherichia coli by reducing levels of LPS and outer membrane integrity. In contrast to previous claims that PbgA functions as a cardiolipin transporter6–9, our structural analyses and physiological studies identify a lipid A-binding motif along the periplasmic leaflet of the inner membrane. Synthetic PbgA-derived peptides selectively bind to LPS in vitro and inhibit the growth of diverse Gram-negative bacteria, including polymyxin-resistant strains. Proteomic, genetic and pharmacological experiments uncover a model in which direct periplasmic sensing of LPS by PbgA coordinates the biosynthesis of lipid A by regulating the stability of LpxC, a key cytoplasmic biosynthetic enzyme10–12. In summary, we find that PbgA has an unexpected but essential role in the regulation of LPS biogenesis, presents a new structural basis for the selective recognition of lipids, and provides opportunities for future antibiotic discovery. Structural and physiological studies show that the inner membrane protein PbgA is a crucial sensor of lipopolysaccharide (LPS) and regulates the activity of the LPS biosynthesis enzyme LpxC. [ABSTRACT FROM AUTHOR]

Cataract is the primary cause of visual impairment and can be corrected by cataract surgery. We investigated the impact of cataract on the risk of depression along with the benefits of cataract surgery. Patients newly diagnosed with cataract by ophthalmologists between 2001 and 2015 were identified from the National Health Insurance Research Database (NHIRD) in Taiwan. Non-cataract individuals were recruited by 1:1 matching for age, sex and index year. After propensity score matching, 233,258 patients in total were included in our study: 116,629 in each of the cataract and non-cataract cohorts. The primary outcome was the new diagnosis of depression by psychiatrists. In a mean follow-up period of 7.8 years, cataract was significantly associated with increased risk of developing depression (adjusted hazard ratio [aHR] = 1.78, 95% confidence interval [CI] 1.70–1.87, p < 0.001). We further divided the cataract cohort into surgery and non-surgery groups. Notably, cataract surgery group was associated with a decreased risk of depression compared with non-surgery patients (aHR = 0.75, 95% CI 0.71–0.79, p < 0.001). Our results emphasise the importance of regular screening for depression among cataract patients and the beneficial effect of cataract surgery in reducing the risk of depression. [ABSTRACT FROM AUTHOR]

Purpose: Invasive pulmonary aspergillosis is increasingly reported in patients with influenza admitted to the intensive care unit (ICU). Classification of patients with influenza-associated pulmonary aspergillosis (IAPA) using the current definitions for invasive fungal diseases has proven difficult, and our aim was to develop case definitions for IAPA that can facilitate clinical studies. Methods: A group of 29 international experts reviewed current insights into the epidemiology, diagnosis and management of IAPA and proposed a case definition of IAPA through a process of informal consensus. Results: Since IAPA may develop in a wide range of hosts, an entry criterion was proposed and not host factors. The entry criterion was defined as a patient requiring ICU admission for respiratory distress with a positive influenza test temporally related to ICU admission. In addition, proven IAPA required histological evidence of invasive septate hyphae and mycological evidence for Aspergillus. Probable IAPA required the detection of galactomannan or positive Aspergillus culture in bronchoalveolar lavage (BAL) or serum with pulmonary infiltrates or a positive culture in upper respiratory samples with bronchoscopic evidence for tracheobronchitis or cavitating pulmonary infiltrates of recent onset. The IAPA case definitions may be useful to classify patients with COVID-19-associated pulmonary aspergillosis (CAPA), while awaiting further studies that provide more insight into the interaction between Aspergillus and the SARS-CoV-2-infected lung. Conclusion: A consensus case definition of IAPA is proposed, which will facilitate research into the epidemiology, diagnosis and management of this emerging acute and severe Aspergillus disease, and may be of use to study CAPA. [ABSTRACT FROM AUTHOR]

This paper addresses the event-triggered tracking control problem of nonlinear systems with predefined performance in a fixed-time environment. Unlike the existing event-triggered schemes, the developed triggering mechanism only compares the difference between the signal to be transmitted and the holding signal. The proposed control method also considers the response of the triggering mechanism. In order to achieve prescribed control performance under the condition of unmatched uncertainties and uncertain gain functions, a new adaptive fixed-time control strategy is proposed by introducing an error transformation scheme. The proposed control method under the event-triggered framework can reduce the amount of control signals transmitted while ensuring that the system output tracking error converges to a small neighborhood close to zero within a fixed-time frame. The results of two examples, including comparative studies and a practical application, show the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

Background: Small bowel transplant (SBT) is a surgical procedure that may be used in patients with pathology resulting in severe intestinal failure resistant to conventional forms of surgical and nonsurgical treatment. Intestinal failure is defined as the failure of enterocytes to absorb sufficient macronutrients, water, and/or electrolytes to sustain homeostasis and/or promote growth. With the advancement of surgical techniques and advancements in perioperative transplant management, SBT has become an increasingly common treatment for intestinal failure, with survival rates for SBT comparable to those for other solid organ transplants. Materials and methods: This review provides background on SBT, its variations, and the associated preoperative and postoperative imaging studies with regard to surgical planning and anticipated complications. Results and conclusions: With the increasing use of SBT, radiologists will be expected to be familiar with the diagnostic studies and available endovascular interventions associated with this procedure. [ABSTRACT FROM AUTHOR]

The sarco-endoplasmic reticulum (SR/ER) plays an important role in the development and progression of many heart diseases. However, many aspects of its structural organization remain largely unknown, particularly in cells with a highly differentiated SR/ER network. Here, we report a cardiac enriched, SR/ER membrane protein, REEP5 that is centrally involved in regulating SR/ER organization and cellular stress responses in cardiac myocytes. In vitro REEP5 depletion in mouse cardiac myocytes results in SR/ER membrane destabilization and luminal vacuolization along with decreased myocyte contractility and disrupted Ca2+ cycling. Further, in vivo CRISPR/Cas9-mediated REEP5 loss-of-function zebrafish mutants show sensitized cardiac dysfunction upon short-term verapamil treatment. Additionally, in vivo adeno-associated viral (AAV9)-induced REEP5 depletion in the mouse demonstrates cardiac dysfunction. These results demonstrate the critical role of REEP5 in SR/ER organization and function as well as normal heart function and development. The sarcoplasmic (SR) and endoplasmic reticulum (ER) are involved in heart development but how this arises is unclear. Here, the authors show that loss of a SR/ER protein REEP5 causes membrane destabilization and decreased cardiac myocyte contractility, with cardiac dysfunction in mutant mouse and zebrafish models. [ABSTRACT FROM AUTHOR]

Purpose: To compare the diagnostic accuracy of extracellular gadolinium-based contrast-enhanced MRI (Gd-MRI) and gadoxetic acid-enhanced MRI (EOB-MRI) for the assessment of hepatocellular carcinoma (HCC) response to locoregional therapy (LRT) using explant correlation as the reference standard.Methods: Forty-nine subjects with cirrhosis and HCC treated with LRT who underwent liver MRI using either Gd-MRI (n = 26) or EOB-MRI (n = 23) within 90 days of liver transplantation were included. Four radiologists reviewed the MR images blinded to histology to determine the size and percentage of viable residual HCC using a per-lesion explant reference standard. Sensitivities, specificities, accuracies, and agreement with histology for the detection residual HCC were calculated.Results: Gd-MRI had greater agreement with histology (ICC: 0.98 [0.95-0.99] vs. 0.80 [0.63-0.90]) and greater sensitivity for viable HCC (76% [13/17 50-93%] vs. 58% [7/12; 28-85%]) than EOB-MRI; specificities were similar (84% [16/19; 60-97%] vs. 85% [23/27; 66-96%]). Areas under ROC curves for detecting residual viable tumor were 0.80 (0.64-0.92) for Gd-MRI and 0.72 (0.55-0.85) for EOB-MRI. Gd-MRI had greater inter-rater agreement than EOB-MRI for determining the size of residual viable HCC (ICC: 0.96 [0.92-0.98] vs. 0.85 [0.72-0.92]).Conclusion: Gd-MRI may be more accurate and precise than EOB-MRI for the assessment of viable HCC following LRT. [ABSTRACT FROM AUTHOR]

Aortic aneurysms are a significant cause of mortality, and the presence of multiple aneurysms may affect treatment plans. The purpose of this study was to determine the frequency of abdominal aortic aneurysms (AAAs) in patients with thoracic aortic aneurysms (TAAs) and to establish whether patient specific factors, such as gender and comorbidities, influenced the frequency of AAAs, thereby indicating if and when abdominal aortic evaluation is justified. Electronic medical records were reviewed from 1000 patients with a computed tomography (CT) angiogram of the chest and abdomen and a clinical diagnosis of TAA from Cardiac Surgery clinic between 2008 and 2013. 538 patients with history of aortic intervention, dissection, rupture or trauma were excluded. The frequency of AAAs among the 462 remaining patients was established, and statistical analysis was used to elucidate differences in frequency based on age, gender, comorbidities, and TAA location. Overall, 104 of 462 (22.5 %) patients with a TAA also had an AAA. There were significant differences in the frequency of AAA based on TAA location, age, and comorbidities. The following comorbidities showed positive associations with AAA using logistic regression analysis: age ≥65 (P < 0.0001; OR 30.1; CI 7.14-126.61), smoking history (P < 0.0001; OR 4.1; 2.35-7.30), and hypertension (P = 0.024; OR 2.1; CI 1.11-4.16). Aneurysms in the proximal/mid descending (P < 0.0001; OR 4.96; CI 2.32-10.61) and diaphragm level (P < 0.0001; OR 38.4; CI 14.71-100.15) of the aorta also showed a positive association with AAAs when adjusted for age and gender. AAA screening in patients with TAA is a reasonable, evidence-based option regardless of the TAA location, with the strongest support in patients >age 55, with systemic hypertension, a smoking history and/or a TAA in the descending thoracic aorta. [ABSTRACT FROM AUTHOR]

This paper addresses the stability and tracking control problem of miniature unmanned multirotor aerial vehicle (MUMAV) in the presence of bounded uncertainty. The uncertainty may appear from unmodeled dynamics, underactuated property, input disturbance and flying environment. Nonlinear robust adaptive sliding mode control algorithm is designed by using Lyapunov function. Robust adaptation laws are designed to learn and compensate the bounded parametric uncertainty. Lyapunov analysis shows that the proposed algorithms can guarantee asymptotic stability and tracking control property of the linear and angular dynamics of MUMAV system. Compared with other existing control methods, the proposed design is very simple and easy to implement as it does not require multiple design steps, augmented auxiliary signals and exact bound of the uncertainty. Experimental results on a miniature unmanned quadrotor aerial vehicle are presented to illustrate of effectiveness of the proposed design for real-time applications. [ABSTRACT FROM AUTHOR]

Rapid and accurate detection of collision between virtual objects is crucial for many virtual reality based applications. In order to ensure a high-level of accuracy and to meet the real-time requirement, a fast collision detection algorithm between soft bodies is proposed. The developed algorithm is a combination of stochastic methods and particle swarm optimization with adaptive Cauchy mutation. The hierarchical bounding box method is used for a rough detection in order to filter out obvious disjoint space, and the problem is converted to a nonlinear optimization problem based on the distance of points. Then particle swarm optimization with adaptive Cauchy mutation is used to find the optimal solution. When it is updated iteratively, keeping some particles experience value and variation of other particles avoids particles trapped in local optimum, which further accelerates the speed of collision detection. The algorithm is implemented and evaluated through experiments and the results confirm the advantages of the developed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose multiple parameter models based adaptive switching control system for robot manipulators. We first uniformly distribute the parameter set into a finite number of smaller compact subsets. Then, distributed candidate controllers are designed for each of these smaller compact subsets. Using Lyapunov inequality, a candidate controller is identified from the finite set of distributed candidate controllers that best estimates the plant at each instant of time. The design reduced the observer-controller gains by reducing modeling errors and uncertainties via identifying an appropriate control/model via choosing largest guaranteed decrease in the value of the Lyapunov function energy function. Compared with CE based CAC design, the proposed design requires smaller observer-controller gains to ensure stability and tracking performance in the presence of large-scale modeling errors and disturbance uncertainties. In contrast with existing adaptive method, multiple model based distributed hybrid design can be used to reduce the energy consumption of the industrial robotic manipulator for large scale industrial automation by reducing actuator input energy. Finally, the proposed hybrid adaptive control design is experimentally tested on a 3-DOF Phantom robot manipulator to demonstrate the theoretical development for real-time applications. [ABSTRACT FROM AUTHOR]