In Silico Design of Heterogeneous Microvascular Trees Using Generative Adversarial Networks and Constrained Constructive Optimization.

Objective: Designing physiologically adequate microvascular trees is of crucial relevance for bioengineering functional tissues and organs. Yet, currently available methods are poorly suited to replicate the morphological and topological heterogeneity of real microvascular trees because the parameters used to control tree generation are too simplistic to mimic results of the complex angiogenetic and structural adaptation processes in vivo.

Methods: We propose a method to overcome this limitation by integrating a conditional deep convolutional generative adversarial network (cDCGAN) with a local fractal dimension-oriented constrained constructive optimization (LFDO-CCO) strategy.

Reinforcement Learning Model for Managing Noninvasive Ventilation Switching Policy.

Noninvasive ventilation (NIV) has been recognized as a first-line treatment for respiratory failure in patients with chronic obstructive pulmonary disease (COPD) and hypercapnia respiratory failure, which can reduce mortality and burden of intubation. However, during the long-term NIV process, failure to respond to NIV may cause overtreatment or delayed intubation, which is associated with increased mortality or costs. Optimal strategies for switching regime in the course of NIV treatment remain to be explored.

Comprehensive breathing variability indices enhance the prediction of extubation failure in patients on mechanical ventilation.

Background And Objective: Despite the numerous studies on extubation readiness assessment for patients who are invasively ventilated in the intensive care unit, a 10-15% extubation failure rate persists. Although breathing variability has been proposed as a potential predictor of extubation failure, it is mainly assessed using simple statistical metrics applied to basic respiratory parameters. Therefore, the complex pattern of breathing variability conveyed by continuous ventilation waveforms may be underexplored.

Pulsatility damping in the microcirculation: Basic pattern and modulating factors.

Blood flow pulsatility is an important determinant of macro- and microvascular physiology. Pulsatility is damped largely in the microcirculation, but the characteristics of this damping and the factors that regulate it have not been fully elucidated yet. Applying computational approaches to real microvascular network geometry, we examined the pattern of pulsatility damping and the role of potential damping factors, including pulse frequency, vascular viscous resistance, vascular compliance, viscoelastic behavior of the vessel wall, and wave propagation and reflection.

Assessment of respiratory system compliance under pressure control ventilation without an inspiratory pause maneuver.

. The measurement of the static compliance of the respiratory system () during mechanical ventilation requires zero end-inspiratory flow. An inspiratory pause maneuver is needed if the zero end-inspiratory flow condition cannot be satisfied under normal ventilation.

Identifying Patient-Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning.

Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient-ventilator asynchrony (PVA) occurs when ventilatory support does not match the needs of the patient and is associated with a series of adverse clinical outcomes. Deep learning methods have shown a strong discriminative ability for PVA detection, but they require a large number of annotated data for model training, which hampers their application to this task.

An interpretable 1D convolutional neural network for detecting patient-ventilator asynchrony in mechanical ventilation.

Background And Objective: Patient-ventilator asynchrony (PVA) is the result of a mismatch between the need of patients and the assistance provided by the ventilator during mechanical ventilation. Because the poor interaction between the patient and the ventilator is associated with inferior clinical outcomes, effort should be made to identify and correct their occurrence. Deep learning has shown promising ability in PVA detection; however, lack of network interpretability hampers its application in clinic.

Complete mitochondrial genome and the phylogenetic position of a new species, (Perciformes: Sciaenidae) from Chinese waters.

In this study, the complete mitogenome of a new species, (Chao et al. 2019) was obtained. Its mitogenome is 18,451 bp in length, consisting of 37 genes with the typical gene order and direction of transcription in vertebrates.

Risk Factors for Patient-Ventilator Asynchrony and Its Impact on Clinical Outcomes: Analytics Based on Deep Learning Algorithm.

Patient-ventilator asynchronies (PVAs) are common in mechanically ventilated patients. However, the epidemiology of PVAs and its impact on clinical outcome remains controversial. The current study aims to evaluate the epidemiology and risk factors of PVAs and their impact on clinical outcomes using big data analytics.

Characteristics on the oxidation stability of infant formula powder with different ingredients during storage.

Infant formula powder is prone to oxidation reaction during storage, which leads to the decrease of milk powder quality. The whole milk powder (WMP) was formulated, and the characteristics of infant formula powder were tracked during storage. The addition of metal ions, polyunsaturated fatty acids, and vitamins could reduce the peroxide value and increase the thiobarbituric acid value in the infant formula powder during the early stage of storage.

Preprocessing Unevenly Sampled RR Interval Signals to Enhance Estimation of Heart Rate Deceleration and Acceleration Capacities in Discriminating Chronic Heart Failure Patients from Healthy Controls.

. The deceleration capacity (DC) and acceleration capacity (AC) of heart rate, which are recently proposed variants to the heart rate variability, are calculated from unevenly sampled RR interval signals using phase-rectified signal averaging. Although uneven sampling of these signals compromises heart rate variability analyses, its effect on DC and AC analyses remains to be addressed.

Detection of patient-ventilator asynchrony from mechanical ventilation waveforms using a two-layer long short-term memory neural network.

Background And Objective: Mismatch between invasive mechanical ventilation and the requirements of patients results in patient-ventilator asynchrony (PVA), which is associated with a series of adverse clinical outcomes. Although the efficiency of the available approaches for automatically detecting various types of PVA from the ventilator waveforms is unsatisfactory, the feasibility of powerful deep learning approaches in addressing this problem has not been investigated.

Methods: We propose a 2-layer long short-term memory (LSTM) network to detect two most frequently encountered types of PVA, namely, double triggering (DT) and ineffective inspiratory effort during expiration (IEE), on two datasets.

Reproductive Biology of (Perciformes: Sciaenidae) in Fujian Waters, Southern China.

is a newly described species from the Family Sciaenidae (Perciformes). The species is commonly found in shallow coastal waters along both sides of the Taiwan Strait, on the west sides of Zhejiang, Fujian, Guangdong and Hong Kong and east side of Taiwan, and has been misidentified for decades. We studied the reproductive biology of from Fujian coastal waters, southern China, using gonadosomatic index (GSI) and gonad histology analyses.

sp. nov., a marine bacterium isolated from surface sea water in the South China Sea.

A Gram-stain-negative and facultatively anaerobic bacterial strain, designated GUO, was isolated from surface water collected from the South China Sea. Cells were non-flagellate, yellow, non-spore-forming and rod-shaped. The 16S rRNA gene sequence comparisons with species in the genus showed that strain GUO shares the highest similarity of 97.

[Optimization of the parameters of microcirculatory structural adaptation model based on improved quantum-behaved particle swarm optimization algorithm].

The vessels in the microcirculation keep adjusting their structure to meet the functional requirements of the different tissues. A previously developed theoretical model can reproduce the process of vascular structural adaptation to help the study of the microcirculatory physiology. However, until now, such model lacks the appropriate methods for its parameter settings with subsequent limitation of further applications.

Pulse wave velocity in the microcirculation reflects both vascular compliance and resistance: Insights from computational approaches.

Objective: PWV is the speed of pulse wave propagation through the circulatory system. mPWV emerges as a novel indicator of hypertension, yet it remains unclear how different vascular properties affect mPWV. We aim to identify the biomechanical determinants of mPWV.

The degree of heart rate asymmetry is crucial for the validity of the deceleration and acceleration capacity indices of heart rate: A model-based study.

The deceleration capacity (DC) and acceleration capacity (AC) of heart rate are a pair of indices used for evaluating the autonomic nervous system (ANS). We assessed the role of heart rate asymmetry (HRA) in defining the relative performance of DC and AC using a mathematical model, which is able to generate a realistic RR interval (RRI) time series with controlled ANS states. The simulation produced a set of RRI series with random sympathetic and vagal activities.

Do the deceleration/acceleration capacities of heart rate reflect cardiac sympathetic or vagal activity? A model study.

Despite increased application of the deceleration capacity (DC) and acceleration capacity (AC) of heart rate indices as indicators of autonomic nervous system (ANS) function, it remains controversial as to whether they reflect cardiac sympathetic or vagal activity. We addressed this problem using a cardiovascular system model that allows analysis of DC and AC under controllable levels of sympathetic and vagal activities. Multi-scale DCs and ACs with various timescales T and wavelet scales s were computed from the simulated RR interval series under randomly fluctuating levels of ANS activity, and the correlations of the indices to ANS functions were assessed.

Simulation of microcirculatory hemodynamics: estimation of boundary condition using particle swarm optimization.

Estimation of the boundary condition is a critical problem in simulating hemodynamics in microvascular networks. This paper proposed a boundary estimation strategy based on a particle swarm optimization (PSO) algorithm, which aims to minimize the number of vessels with inverted flow direction in comparison to the experimental observation. The algorithm took boundary values as the particle swarm and updated the position of the particles iteratively to approach the optimization target.

[A new method of measuring the pulse based on facial video].

This paper proposes a new method of non-contact pulse measurement by analyzing a clip of human facial video. The method is based on photo plethysmography (PPG) and independent component analysis (ICA) model. A clip of color facial video shot under normal lighting condition is firstly discomposed into RGB channel sequences.