Autonomous Healthcare

) is an interferon (IFN)-stimulated gene that shows broad antiviral activities against a wide range of enveloped viruses. Here, using an IFN-stimulated gene screen against vesicular stomatitis virus (VSV)-SARS-CoV and VSV-SARS-CoV-2 chimeric viruses, we identified CH25H and its enzymatic product 25-hydroxycholesterol (25HC) as potent inhibitors of SARS-CoV-2 replication. Internalized 25HC accumulates in the late endosomes and potentially restricts SARS-CoV-2 spike protein catalyzed membrane fusion via blockade of cholesterol export. Our results highlight one of the possible antiviral mechanisms of 25HC and provide the molecular basis for its therapeutic development.

The recent technological advances in Micro Aerial Vehicles (MAVs) have triggered great interest in the robotics community, as their deployability in missions of surveillance and reconnaissance has now become a realistic prospect. The state of the art, however, still lacks solutions that can work for a long duration in large, unknown, and GPS‐denied environments. Here, we present our visual pipeline and MAV state‐estimation framework, which uses feeds from a monocular camera and an Inertial Measurement Unit (IMU) to achieve real‐time and onboard autonomous flight in general and realistic scenarios. The challenge lies in dealing with the power and weight restrictions onboard a MAV while providing the robustness necessary in real and long‐term missions. This article provides a concise summary of our work on achieving the first onboard vision‐based power‐on‐and‐go system for autonomous MAV flights. We discuss our insights on the lessons learned throughout the different stages of this research, from the conception of the idea to the thorough theoretical analysis of the proposed framework and, finally, the real‐world implementation and deployment. Looking into the onboard estimation of monocular visual odometry, the sensor fusion strategy, the state estimation and self‐calibration of the system, and finally some implementation issues, the reader is guided through the different modules comprising our framework. The validity and power of this framework are illustrated via a comprehensive set of experiments in a large outdoor mission, demonstrating successful operation over flights of more than 360 m trajectory and 70 m altitude change.

This paper presents an omnidirectional aerial manipulation platform for robust and responsive interaction with unstructured environments, toward the goal of contact-based inspection. The fully actuated tilt-rotor aerial system is equipped with a rigidly mounted end-effector, and is able to exert a 6 degree of freedom force and torque, decoupling the system's translational and rotational dynamics, and enabling precise interaction with the environment while maintaining stability. An impedance controller with selective apparent inertia is formulated to permit compliance in certain degrees of freedom while achieving precise trajectory tracking and disturbance rejection in others. Experiments demonstrate disturbance rejection, pushand-slide interaction, and on-board state estimation with depth servoing to interact with local surfaces. The system is also validated as a tool for contact-based non-destructive testing of concrete infrastructure.

This paper introduces a state estimation framework for legged robots that allows estimating the full pose of the robot without making any assumptions about the geometrical structure of its environment. This is achieved by means of an Observability Constrained Extended Kalman Filter that fuses kinematic encoder data with on-board IMU measurements. By including the absolute position of all footholds into the filter state, simple model equations can be formulated which accurately capture the uncertainties associated with the intermittent ground contacts. The resulting filter simultaneously estimates the position of all footholds and the pose of the main body. In the algorithmic formulation, special attention is paid to the consistency of the linearized filter: it maintains the same observability properties as the nonlinear system, which is a prerequisite for accurate state estimation. The presented approach is implemented in simulation and validated experimentally on an actual quadrupedal robot.

PURPOSE: To demonstrate the effects of the use of sunflower seed oil on the treatment of skin wounds. METHODS: Eighteen male Saint Inês lambs were divided in 3 groups according to the pos-operative (7, 14 and 21 days). After antisepsis and local anestesia, two 4cm² wounds on each side of the thoracic region, close to the scapule were surgically produced. The experimental wounds were treated with sunflower seed oil, with high concentration of linoleic acid (LA), and the control ones with sterilized Vaseline. Biopsies of the pos-operative wounds tissue were performed on the 7th, 14th, 21st days and histologically evaluated. RESULTS: Topic application of sunflower seed oil accelerated healing process at the 7th and 21st days, reducing wound area and increasing wound contraction. Granulation tissue increased faster on treated wounds. The epidermis of the treated wounds was completely recovered when compared to control wounds. CONCLUSION: The topic use of sunflower seed oil accelerated the healing process, and it can be used as an alternative therapy on second intention wound healing.

Dinoflagellates of the genus Symbiodinium are commonly recognized as invertebrate endosymbionts that are of central importance for the functioning of coral reef ecosystems. However, the endosymbiotic phase within Symbiodinium life history is inherently tied to a more cryptic free-living (ex hospite) phase that remains largely unexplored. Here we show that free-living Symbiodinium spp. in culture commonly form calcifying bacterial-algal communities that produce aragonitic spherulites and encase the dinoflagellates as endolithic cells. This process is driven by Symbiodinium photosynthesis but occurs only in partnership with bacteria. Our findings not only place dinoflagellates on the map of microbial-algal organomineralization processes but also point toward an endolithic phase in the Symbiodinium life history, a phenomenon that may provide new perspectives on the biology and ecology of Symbiodinium spp. and the evolutionary history of the coral-dinoflagellate symbiosis.

Memory loss is one of the most tragic symptoms of Alzheimer's disease. Our laboratory has recently demonstrated that 'i-Extract' of Ashwagandha (Withania somnifera) restores memory loss in scopolamine (SC)-induced mice. The prime target of i-Extract is obscure. We hypothesize that i-Extract may primarily target muscarinic subtype acetylcholine receptors that regulate memory processes. The present study elucidates key target(s) of i-Extract via cellular, biochemical, and molecular techniques in a relevant amnesia mouse model and primary hippocampal neuronal cultures. Wild type Swiss albino mice were fed i-Extract, and hippocampal cells from naïve mice were treated with i-Extract, followed by muscarinic antagonist (dicyclomine) and agonist (pilocarpine) treatments. We measured dendritic formation and growth by immunocytochemistry, kallikrein 8 (KLK8) mRNA by reverse transcription polymerase chain reaction (RT-PCR), and levels of KLK8 and microtubule-associated protein 2, c isoform (MAP2c) proteins by western blotting. We performed muscarinic receptor radioligand binding. i-Extract stimulated an increase in dendrite growth markers, KLK8 and MAP2. Scopolamine-mediated reduction was significantly reversed by i-Extract in mouse cerebral cortex and hippocampus. Our study identified muscarinic receptor as a key target of i-Extract, providing mechanistic evidence for its clinical application in neurodegenerative cognitive disorders.

In this paper, the problem of learning grasp stability in robotic object grasping based on tactile measurements is studied. Although grasp stability modeling and estimation has been studied for a long time, there are few robots today able of demonstrating extensive grasping skills. The main contribution of the work presented here is an investigation of probabilistic modeling for inferring grasp stability based on learning from examples. The main objective is classification of a grasp as stable or unstable before applying further actions on it, e.g. lifting. The problem cannot be solved by visual sensing which is typically used to execute an initial robot hand positioning with respect to the object. The output of the classification system can trigger a regrasping step if an unstable grasp is identified. An off-line learning process is implemented and used for reasoning about grasp stability for a three-fingered robotic hand using Hidden Markov models. To evaluate the proposed method, experiments are performed both in simulation and on a real robot system.

Cloud computing provides a new opportunity for Video Service Providers (VSP) to running compute-intensive video applications in a cost effective manner. Under this paradigm, a VSP may rent virtual machines (VMs) from multiple geo-distributed datacenters that are close to video requestors to run their services. As user demands are difficult to predict and the prices of the VMs vary in different time and region, optimizing the number of VMs of each type rented from datacenters located in different regions in a given time frame becomes essential to achieve cost effectiveness for VSPs. Meanwhile, it is equally important to guarantee users' Quality of Experience (QoE) with rented VMs. In this paper, we give a systematic method called Dynamical Request Redirection and Resource Provisioning (DYRECEIVE) to address this problem. We formulate the problem as a stochastic optimization problem and design a Lyapunov optimization framework based online algorithm to solve it. Our method is able to minimize the long-term time average cost of renting cloud resources while maintaining the user QoE. Theoretical analysis shows that our online algorithm can produce a solution within an upper bound to the optimal solution achieved through offline computing. Extensive experiments shows that our method is adaptive to request pattern changes along time and outperforms existing algorithms.

The interest of this study is to establish a mathematical model and rheological aspects of chemically reacting Casson-type nanofluid flow considering ethylene glycol-based nanoparticles with thermophoretic diffusion and Brownian motion. The basic flow equations are transmuted into non-dimensional form, these dimensionless leading equations are determined to the most excellent potential systematic using some influential similarity transformations with the employment of the Chebyshev Spectral Collocation method. The outcomes for flow rate, temperature, concentration and engineering quantities distribution are shown in terms of graphical presentation. Findings revealed that larger values of Casson fluid parameter lead to suppress velocity as well as fluid temperature. The nanofluid temperature reduces for Brownian motion parameter and enchances for thermophoresis parameter. An increase in Lewis number means a decrease in mass diffusivity resulted in to suppression in fluid concentration. The model results confirmation is tabulated, and the data assessment of the present study with formerly communicated is found excellently accurate.

Robust and accurate visual localization is a fundamental capability for numerous applications, such as autonomous driving, mobile robotics, or augmented reality. It remains, however, a challenging task, particularly for large-scale environments and in presence of significant appearance changes. State-of-the-art methods not only struggle with such scenarios, but are often too resource intensive for certain real-time applications. In this paper we propose HF-Net, a hierarchical localization approach based on a monolithic CNN that simultaneously predicts local features and global descriptors for accurate 6-DoF localization. We exploit the coarse-to-fine localization paradigm: we first perform a global retrieval to obtain location hypotheses and only later match local features within those candidate places. This hierarchical approach incurs significant runtime savings and makes our system suitable for real-time operation. By leveraging learned descriptors, our method achieves remarkable localization robustness across large variations of appearance and sets a new state-of-the-art on two challenging benchmarks for large-scale localization.

Non-human primates are routinely studied and managed in zoos, conservation breeding centers, and research centers, but there is currently limited information regarding diseases that can affect these animals. Dilated cardiomyopathy is one of the most common cardiovascular diseases in small animal clinical practice. However, there are few reports of this condition occurring in non-human primates. Here, in a spider monkey (Ateles chamek) housed in the Rio de Janeiro Zoo, we report the occurrence of dilated cardiomyopathy, its etiology, pathophysiology, clinical presentation, diagnosis through clinical examination, and the use of complementary exams (radiographic, electrocardiographic, and echocardiographic), as well as the protocol, used in the treatment. In this case, it is assumed that the occurrence of the disease was related to the peripartum period due to the hormonal and metabolic changes that occurred, and the physiological interactions of gestation and puerperium

Gait impairment is a prevalent and important difficulty for patients with multiple sclerosis (MS), a common neurological disorder. An easy to use tool to objectively evaluate gait in MS patients in a clinical setting can assist clinicians to perform an objective assessment. The overall objective of this study is to develop a framework to quantify gait abnormalities in MS patients using the Microsoft Kinect for the Windows sensor; an inexpensive, easy to use, portable camera. Specifically, we aim to evaluate its feasibility for utilization in a clinical setting, assess its reliability, evaluate the validity of gait indices obtained, and evaluate a novel set of gait indices based on the concept of dynamic time warping. In this study, ten ambulatory MS patients, and ten age and sex-matched normal controls were studied at one session in a clinical setting with gait assessment using a Kinect camera. The expanded disability status scale (EDSS) clinical ambulation score was calculated for the MS subjects, and patients completed the Multiple Sclerosis walking scale (MSWS). Based on this study, we established the potential feasibility of using a Microsoft Kinect camera in a clinical setting. Seven out of the eight gait indices obtained using the proposed method were reliable with intraclass correlation coefficients ranging from 0.61 to 0.99. All eight MS gait indices were significantly different from those of the controls (p-values less than 0.05). Finally, seven out of the eight MS gait indices were correlated with the objective and subjective gait measures (Pearson's correlation coefficients greater than 0.40). This study shows that the Kinect camera is an easy to use tool to assess gait in MS patients in a clinical setting.

The limit of detection (LOD), speed, and cost of crucial COVID-19 diagnostic tools, including lateral flow assays (LFA), enzyme-linked immunosorbent assays (ELISA), and polymerase chain reactions (PCR), have all improved because of the financial and governmental support for the epidemic. The most notable improvement in overall efficiency among them has been seen with PCR. Its significance for human health increased during the COVID-19 pandemic, when it emerged as the commonly used approach for identifying the virus. However, because of problems with speed, complexity, and expense, PCR deployment in point-of-care settings continues to be difficult. Microfluidic platforms offer a promising solution by enabling the development of smaller, more affordable, and faster PCR systems. In this review, we delve into the engineering challenges associated with the advancement of high-speed microfluidic PCR equipment. We introduce criteria that facilitate the evaluation and comparison of factors such as speed, LOD, cycling efficiency, and multiplexing capacity, considering sample volume, fluidics, PCR reactor geometry and materials, as well as heating/cooling methods. We also provide a comprehensive list of commercially available PCR devices and conclude with projections and a discussion regarding the current obstacles that need to be addressed in order to progress further in this field.

The history of drone dates far back. Human in those days must have had a yearn for the sky. This article first gives an overview of the history of research and development of drone including fixed wing and rotary wing, followed by, in particular, the history of development of rotary wing drone with the following four periods: before 1990 when radio control is at its dawn period; 1990 to 2010 when drone is at its dawn period; 2010 to 2015 when hobby-use drone is at its spread period; and 2016 and after when industrial-use drone is at its dawn period to growth period. In addition, this article presents the Japanese government’s roadmap, followed by a five-stage class of drone in terms of flight level and autonomy. In particular, autonomy will be discussed from the points of view of guidance, navigation, and control. Then with reference to an ideal state of drone in future, the article will discuss importance of the guidance system by fault tolerant control and supervisor control with implementation of the AI technology and the like.

This study develops a camera-guided frequency-modulated continuous-wave (FMCW) radar to monitor vital signs. A red-green-blue-depth (RGB-D) camera estimates the human torso landmarks and a processing unit constantly adapts the radar beams to the direction of the subjects. To constantly optimize the regions of interest for monitoring respiratory rate (RR) and heart rate (HR), a novel method, coined “singular value-based point detection (SVPD),” is designed. Vital sign extraction is then followed as the last step. Experiments are conducted for the cases of single-subject (10 subjects, 31 scenarios, and 1550 repetitions) and dual-subject monitoring (6 subjects, 6 scenarios, and 90 repetitions). Average (RR, HR) accuracies of (97.68%, 85.88%), (90.02%, 86.05%), (96.71%, 89.50%), and (97.52%, 86.71%) are achieved for the range of distances (0.5-2.5 m), azimuth angles (0°–30°), elevation angles (−30°–+30°), and incident angles (−30°–+30°), respectively. The higher chest and upper abdomen are determined as the optimal regions for RR and HR estimation respectively, with average accuracies of 98.31% and 86.93%. Finally, the capability of dual-subject monitoring at various inter-subject distances (range of 20–70 cm) is confirmed with average accuracies of 92.26% and 73.23% for RR and HR respectively.

Solving estimation problems is a fundamental component of numerous robotics applications. Prominent examples involve pose estimation, point cloud alignment, or object tracking. Algorithms for solving these estimation problems need to cope with new challenges due to an increased use of potentially poor low-cost sensors, and an ever growing deployment of robotic algorithms in consumer products which operate in potentially unknown environments. These algorithms need to be capable of being robust against strong nonlinearities, high uncertainty levels, and numerous outliers. However, particularly in robotics, the Gaussian assumption is prevalent in solutions to multivariate parameter estimation problems without providing the desired level of robustness. The goal of this tutorial is helping to address the aforementioned challenges by providing an introduction to robust estimation with a particular focus on robotics. First, this is achieved by giving a concise overview of the theory on M-estimation. M-estimators share many of the convenient properties of least-squares estimators, and at the same time are much more robust to deviations from the Gaussian model assumption. Second, we present several example applications where M-Estimation is used to increase robustness against nonlinearities and outliers.

BACKGROUND: Ventilator-associated pneumonia (VAP) is one of the most common infections in intubated intensive care unit (ICU) patients. Oral care with chlorhexidine is a conventional method for maintaining hygiene. Recently, adjuvant methods have been introduced into routine oral care, including teeth brushing and the application of moisturizing lotion. The objective of this study was to compare the incidence of VAP in critical care patients receiving oral care with and without manual teeth brushing and the application of moisturizers to the mouth. METHODS: We conducted a prospective randomized control study comprised of 220 ICU patients between 18 and 65 years of age, and of either sex. The patients were divided into two groups of 110 each. Care for the study group (group S) consisted of chlorhexidine wash, tooth brushing, and moisturizing gel over gums, buccal mucosa, and lips. The control group (group C) was treated with chlorhexidine wash only. The oral assessment was done at 4, 6, 8, and 12 hours using the Beck Oral Assessment Scale (BOAS). Pneumonia was assessed based on abnormal chest x-rays, fever, chest auscultation, endotracheal culture report, and the incidence of VAP, and mortality was observed Results: Abnormal chest x-rays, positive auscultatory findings, fevers, and positive culture reports were significantly reduced in group S compared to these measurements in group C. The incidences of VAP and mortality were also significantly lower in group S compared with the incidences in group C. CONCLUSIONS: Oral care with chlorhexidine mouth wash and the adjuvant measures reduced VAP and, consequently mortality and hospital stays. Tooth brushing along with standard oral care provides an additional advantage in the prevention of VAP in mechanically ventilated patients. Compulsory tooth brushing, if included in regular oral care yields better results in terms of decreased incidence of VAP, length of ICU stay, and mortality.

Active learning platform for accelerated discovery of potential ABO₃-type perovskite ferroelectrics.

We used multilocus sequence typing and variable number tandem repeat analysis to determine the clonal origins of Vibrio cholerae O1 El Tor strains from an outbreak of cholera that began in 2009 in Papua New Guinea. The epidemic is ongoing, and transmission risk is elevated within the Pacific region.