Isoflurane anesthesia alters complete blood count values in lilac-crowned parrots (Amazona finschi).

Objective: To evaluate the effects of a chemical restraint with isoflurane compared to a physical restraint in hemogram values in psittacines.

Animals: 21 adult lilac-crowned parrots (Amazona finschi).

Methods: Subjects were physically restrained for blood sample collection, after this isoflurane anesthesia was administered for 30 minutes at which time a second blood sample was taken.

Enhancing microsurgical skills in neurosurgery residents of low-income countries: A comprehensive guide.

Background: The main objectives of this paper are to outline the essential tools, instruments, and equipment needed to set up a functional microsurgery laboratory that is affordable for low-income hospitals and to identify cost-effective alternatives for acquiring microsurgical equipment, such as refurbished or donated instruments, collaborating with medical device manufacturers for discounted rates, or exploring local suppliers.

Methods: Step-by-step instructions were provided on setting up the microsurgery laboratory, including recommendations for the layout, ergonomic considerations, lighting, and sterilization processes while ensuring cost-effectiveness, as well as comprehensive training protocols and a curriculum specifically tailored to enhance microsurgical skills in neurosurgery residents.

Results: We explored cost-effective options for obtaining microsurgery simulators and utilizing open-source or low-cost virtual training platforms.

A novel approach using nonlinear surfaces for dynamic aperture optimization in MBA synchrotron light sources.

MBA cell-based synchrotron light sources have enabled an unprecedented increase in beam coherence and brightness, greatly benefiting the scientific disciplines that rely on X-ray techniques. However, controlling the electron dynamics is a theoretical and technological challenge, due to the large number of parameters to adjust and constraints to satisfy when designing modern synchrotrons. Having versatile tools for the description and manipulation of electron dynamics could favor the design of these accelerators and lead to progress on several fronts in the understanding of matter.

Recurrent Neurobrucellosis in a Feral Swine Hunter.

Brucellosis is a zoonotic infectious disease caused by the bacterial genus and is most commonly transmitted to humans globally via the consumption of contaminated unpasteurized products. In a significant minority of cases, has been found to be transmitted by contact with infected swine bodily fluids such as blood. Only a small proportion of all cases of brucellosis affects the central nervous system, and of the four species of that are known to infect humans, is unusual.

Impulsive Neural Control to Schedule Antivirals and Immunomodulators for COVID-19.

New SARS-CoV-2 variants escaping the effect of vaccines are an eminent threat. The use of antivirals to inhibit the viral replication cycle or immunomodulators to regulate host immune responses can help to tackle the viral infection at the host level. To evaluate the potential use of these therapies, we propose the application of an inverse optimal neural controller to a mathematical model that represents SARS-CoV-2 dynamics in the host.

Increasing beam stability zone in synchrotron light sources using polynomial quasi-invariants.

The objective of this article is to propose a scheme to increase the stability zone of a charged particles beam in synchrotrons using a suitable objective function that, when optimized, inhibits the resonances onset in phase space and the dynamic aperture of electrons in storage rings can be improved. The proposed technique is implemented by constructing a quasi-invariant in a neighborhood of the origin of the phase space, then, by using symbolic computation software, sets of coupled differential equations for functions involved in nonlinear dynamics are obtained and solved numerically with periodic boundary conditions. The objective function is constructed by proposing that the innermost momentum solution branch of the polynomial quasi-invariant approaches to the corresponding ellipse of the linear dynamics.

Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction.

Diabetes Mellitus is a serious metabolic condition for global health associations. Recently, the number of adults, adolescents and children who have developed Type 1 Diabetes Mellitus (T1DM) has increased as well as the mortality statistics related to this disease. For this reason, the scientific community has directed research in developing technologies to reduce T1DM complications.

Phycocyanin content and nutritional profile of Arthrospira platensis from Mexico: efficient extraction process and stability evaluation of phycocyanin.

Phycocyanin is a blue natural food colorant with multiple health benefits. Here we propose an efficient phycocyanin extraction method from Arthrospira platensis from Mexico. Three extraction methods were applied to optimize the extraction process, using water and buffer as solvents, with three pH values at two agitation times.

Pinning Control for the p53-Mdm2 Network Dynamics Regulated by p14ARF.

p53 regulates the cellular response to genotoxic damage and prevents carcinogenic events. Theoretical and experimental studies state that the p53-Mdm2 network constitutes the core module of regulatory interactions activated by cellular stress induced by a variety of signaling pathways. In this paper, a strategy to control the p53-Mdm2 network regulated by p14ARF is developed, based on the pinning control technique, which consists into applying local feedback controllers to a small number of nodes (pinned ones) in the network.

Real-time Neural Inverse Optimal Control for Low-Voltage Rid-Through enhancement of Double Fed Induction Generator based Wind Turbines.

The Low-Voltage Ride-Through (LVRT) capacity of the Doubly Fed Induction Generator (DFIG) is one of the important requirements to ensure power systems stability, incorporating wind energy. While traditional control schemes present inappropriate performances under disturbances, this paper introduces a novel Neural Inverse Optimal Control (N-IOC) scheme for LVRT capacity enhancing. The developed controller is synthesized using recurrent high order neural network, which is utilized to build-up the DFIG and the DC-link dynamics.

Trajectory Tracking on Uncertain Complex Networks via NN-Based Inverse Optimal Pinning Control.

A new approach for trajectory tracking on uncertain complex networks is proposed. To achieve this goal, a neural controller is applied to a small fraction of nodes (pinned ones). Such controller is composed of an on-line identifier based on a recurrent high-order neural network, and an inverse optimal controller to track the desired trajectory; a complete stability analysis is also included.

Diagnosis, Treatment, and Prevention of Lyme Disease, Human Granulocytic Anaplasmosis, and Babesiosis: A Review.

Importance: Lyme disease, human granulocytic anaplasmosis (HGA), and babesiosis are emerging tick-borne infections.

Objective: To provide an update on diagnosis, treatment, and prevention of tick-borne infections.

Evidence Review: Search of PubMed and Scopus for articles on diagnosis, treatment, and prevention of tick-borne infections published in English from January 2005 through December 2015.

Viral Infection in Adults with Severe Acute Respiratory Infection in Colombia.

Objectives: To identify the viral aetiology in adult patients with severe acute respiratory infection (SARI) admitted to sentinel surveillance institutions in Bogotá in 2012.

Design: A cross-sectional study was conducted in which microarray molecular techniques for viral identification were used on nasopharyngeal samples of adult patients submitted to the surveillance system, and further descriptions of clinical features and relevant clinical outcomes, such as mortality, need for critical care, use of mechanical ventilation and hospital stay, were obtained.

Setting: Respiratory infections requiring hospital admission in surveillance centres in Bogotá, Colombia.

Dissolved oxygen regulation by logarithmic/antilogarithmic control to improve a wastewater treatment process.

This paper presents the automation of a real activated sludge wastewater treatment plant, which is located at San Antonio Ajijic in Jalisco, Mexico. The main objective is to create an on-line automatic supervision system, and to regulate the dissolved oxygen concentration in order to improve the performances of the process treating municipal wastewater. An approximate mathematical model is determined in order to evaluate via simulations different control strategies: proportional integral (PI), fuzzy PI and PI Logarithm/Antilogarithm (PI L/A).

Tailoring chimeric ligands for studying and biasing ErbB receptor family interactions.

Described is the development and application of a versatile semisynthetic strategy, based on a combination of sortase-mediated coupling and tetrazine ligation chemistry, which can be exploited for the efficient incorporation of tunable functionality into chimeric recombinant proteins. To demonstrate the scope of the method, the assembly of a set of bivalent ligands, which integrate members of the epidermal growth factor (EGF) ligand family, is described. By using a series of bivalent EGFs with variable intraligand spacing, the differences in structure were correlated with the ability to bias signaling in the ErbB receptor family in a cell motility assay.

Particle swarm optimization for discrete-time inverse optimal control of a doubly fed induction generator.

In this paper, the authors propose a particle swarm optimization (PSO) for a discrete-time inverse optimal control scheme of a doubly fed induction generator (DFIG). For the inverse optimal scheme, a control Lyapunov function (CLF) is proposed to obtain an inverse optimal control law in order to achieve trajectory tracking. A posteriori, it is established that this control law minimizes a meaningful cost function.

Discrete-time neural inverse optimal control for nonlinear systems via passivation.

This paper presents a discrete-time inverse optimal neural controller, which is constituted by combination of two techniques: 1) inverse optimal control to avoid solving the Hamilton-Jacobi-Bellman equation associated with nonlinear system optimal control and 2) on-line neural identification, using a recurrent neural network trained with an extended Kalman filter, in order to build a model of the assumed unknown nonlinear system. The inverse optimal controller is based on passivity theory. The applicability of the proposed approach is illustrated via simulations for an unstable nonlinear system and a planar robot.

Blood glucose level neural model for type 1 diabetes mellitus patients.

This paper deals with the blood glucose level modeling for Type 1 Diabetes Mellitus (T1DM) patients. The model is developed using a recurrent neural network trained with an extended Kalman filter based algorithm in order to develop an affine model, which captures the nonlinear behavior of the blood glucose metabolism. The goal is to derive a dynamical mathematical model for the T1DM as the response of a patient to meal and subcutaneous insulin infusion.

Engineered bivalent ligands to bias ErbB receptor-mediated signaling and phenotypes.

The ErbB receptor family is dysregulated in many cancers, and its therapeutic manipulation by targeted antibodies and kinase inhibitors has resulted in effective chemotherapies. However, many malignancies remain refractory to current interventions. We describe a new approach that directs ErbB receptor interactions, resulting in biased signaling and phenotypes.

Real-time recurrent neural state estimation.

A nonlinear discrete-time neural observer for discrete-time unknown nonlinear systems in presence of external disturbances and parameter uncertainties is presented. It is based on a discrete-time recurrent high-order neural network trained with an extended Kalman-filter based algorithm. This brief includes the stability proof based on the Lyapunov approach.

Multiphoton imaging of actin filament formation and mitochondrial energetics of human ACBT gliomas.

We studied the three-dimensional (3D) distribution of actin filaments and mitochondria in relation to ACBT glioblastoma cells migration. We embedded the cells in the spheroid form within collagen hydrogels and imaged them by in situ multiphoton microscopy (MPM). The static 3D overlay of the distribution of actin filaments and mitochondria provided a greater understanding of cell-to-cell and cell-to-substrate interactions and morphology.

Discrete-time reduced order neural observers for uncertain nonlinear systems.

This paper focusses on a novel discrete-time reduced order neural observer for nonlinear systems, which model is assumed to be unknown. This neural observer is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm, using a parallel configuration.

Discrete-time adaptive backstepping nonlinear control via high-order neural networks.

This paper deals with adaptive tracking for discrete-time multiple-input-multiple-output (MIMO) nonlinear systems in presence of bounded disturbances. In this paper, a high-order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). This paper also includes the respective stability analysis, on the basis of the Lyapunov approach, for the whole controlled system, including the extended Kalman filter (EKF)-based NN learning algorithm.

Reproducing chaos by variable structure recurrent neural networks.

In this paper, we present a new approach for chaos reproduction using variable structure recurrent neural networks (VSRNN). A neural network identifier is designed, with a variable structure that will change according to its output performance as compared to the given orbits of an unknown chaotic systems. A tradeoff between identification errors and computational complexity is discussed.