Spatiotemporal modeling of under-five mortality and associated risk factors in Ethiopia using 2000-2016 EDHS data.

BACKGROUND: The under-five mortality rate serves as a key indicator of the performance of a country's healthcare system. Despite a minor decline, Ethiopia continues to face a persistently high under-five mortality rate across different zones. Thus, this study aimed to identify the risk factors of under-five mortality and the spatiotemporal variation in Ethiopian administrative zones. METHOD: This study used the 2000-2016 Ethiopian Demographic and Health Survey (EDHS) data which were collected using a two-stage sampling method. A total of 43,029 (10,873 in 2000, 9,861 in 2005, 11,654 in 2011, and 10,641 in 2016) weighted sample under-five child mortality were used. The space-time dynamic model was employed to account for spatial and time effects in 65 administrative zones in Ethiopia. RESULTS: From the result of a general nesting spatial-temporal dynamic model, there was a significant space-time interaction effect [γ = -0.1444, 95% CI(-0.6680, -0.1355)] for under-five mortality. The increase in the percentages of mothers illiteracy [ß = 0.4501, 95% CI (0.2442, 0.6559)], not vaccinated[ß= 0.7681, 95% CI (0.5683, 0.9678)], unimproved water[ß= 0.5801, CI (0.3793, 0.7808)] were increased death rates for under five children while increased percentage of contraceptive use [ß= -0.6609, 95% CI (-0.8636, -0.4582)] and antenatal care unit visit > 4 times [ß= -0.1585, 95% CI(-0.1812, -0.1357)] were contributed to the decreased under-five mortality rate at the zone in Ethiopia. CONCLUSIONS: Even though the mortality rate for children under five has decreased over time, still there is higher in different zones of Ethiopia. There exists spatial and temporal variation in under-five mortality among zones. Therefore, it is very important to consider spatial neighborhood's and temporal context when aiming to avoid under-five mortality.

Online Collaborative Perception of Full Bridge Deck Driving Visual of Far Blind Area on Suspension Bridge during Vortex-Induced Vibration.

During a vertical vortex-induced vibration (VVIV), an undulating bridge deck will affect drivers' sightlines, causing the phenomenon of drifting and changes in the far blind area, thus presenting a potential threat to driving safety. Consequently, to ensure the safety of driving on a suspension bridge deck under VVIV, it is necessary to perceive the far blind spot caused by the occlusion of the driving sightlines under this condition, and to establish an online perception and evaluation mechanism for driving safety. With a long-span suspension bridge experiencing VVIV as the engineering background, this paper utilizes the acceleration integration algorithm and the sine function fitting method to achieve the online perception of real-time dynamic configurations of the main girder. Then, based on the configurations, the maximum height of the driver's far blind area and effective sight distance are calculated accordingly, and the impact of different driving conditions on them is discussed. The proposed technical framework for driving safety perception in far blind spots is feasible, as it can achieve real-time estimation of the maximum height and effective distance of the far blind area, thereby providing technical support for bridge-vehicle-human collaborative perception and traffic control during vortex-induced vibration.

Online Intelligent Perception of Front Blind Area of Vehicles on a Full Bridge Based on Dynamic Configuration Monitoring of Main Girders.

Establishing an online perception mechanism for a driver's front blind area on a full bridge under vertical vortex-induced vibration (VVIV) is essential for ensuring road safety and traffic control on bridge decks under specific conditions. Based on accelerations of vibration monitoring of the main girders, this paper uses a real-time acceleration integration algorithm to obtain real-time displacements of measurement points; realizes the real-time estimation of the dynamic configurations of a main girder through parametric function fitting; and then can perceive the front blind area for vehicles driving on bridges experiencing VVIV in real time. On this basis, taking a long-span suspension bridge suffering from VVIV as an engineering example, the influence of different driving conditions on the front blind area is examined. Then, the applicability of the intelligent perception technology framework of the front blind area is verified. The results indicate that, during VVIV, the driver's front blind area changes periodically and the vehicle model has the most significant impact on the front blind area; in contrast, the vehicle's speed and the times of the vehicle entering the bridge have minimal impact on it. Meanwhile, it is shown that the framework can accurately perceive front blind areas of vehicles driving on the bridge, and identify different vehicle models, speeds and times of vehicle bridge entries in real time.

[Research progress of ultrasound in diagnosis and treatment of shoulder joint diseases].

Objective: To review the research progress of ultrasound in the diagnosis and treatment of shoulder diseases, in order to provide a theoretical basis for the further development of ultrasound in shoulder surgery. Methods: The recent literature on the application of ultrasound in the shoulder joint was extensively reviewed. The application of ultrasound in the diagnosis and treatment of shoulder joint diseases, and the advantages and disadvantages of ultrasound were analysed, and the development trend of ultrasound technology in the shoulder joint area was prospected. Results: At present, the diagnosis of shoulder joint diseases mainly relies on MRI, however, with the development of ultrasound technology, ultrasound with the characteristics of convenient, reliable, and real-time dynamic evaluation is more and more recognized in the diagnosis process of shoulder joint diseases, combined with three-dimensional ultrasound, ultrasound intervention, and elastography can improve the accuracy, sensitivity, and specificity of the diagnosis, and is suitable for the diagnosis and treatment of various shoulder joint diseases, which is expected to carry out early prevention of shoulder joint diseases in the future and achieve more refined and minimally invasive treatment. Conclusion: Ultrasound technology has wide application prospect in shoulder joint diseases, but it is still in the developing stage, and the subjective dependence needs to be solved further.

Variation and stability of rhizosphere bacterial communities of Cucumis crops in association with root-knot nematodes infestation.

Introduction: Root-knot nematodes (RKN) disease is a devastating disease in Cucumis crops production. Existing studies have shown that resistant and susceptible crops are enriched with different rhizosphere microorganisms, and microorganisms enriched in resistant crops can antagonize pathogenic bacteria. However, the characteristics of rhizosphere microbial communities of Cucumis crops after RKN infestation remain largely unknown. Methods: In this study, we compared the changes in rhizosphere bacterial communities between highly RKN-resistant Cucumis metuliferus (cm3) and highly RKN-susceptible Cucumis sativus (cuc) after RKN infection through a pot experiment. Results: The results showed that the strongest response of rhizosphere bacterial communities of Cucumis crops to RKN infestation occurred during early growth, as evidenced by changes in species diversity and community composition. However, the more stable structure of the rhizosphere bacterial community in cm3 was reflected in less changes in species diversity and community composition after RKN infestation, forming a more complex and positively co-occurrence network than cuc. Moreover, we observed that both cm3 and cuc recruited bacteria after RKN infestation, but the bacteria enriched in cm3 were more abundant including beneficial bacteria Acidobacteria, Nocardioidaceae and Sphingomonadales. In addition, the cuc was enriched with beneficial bacteria Actinobacteria, Bacilli and Cyanobacteria. We also found that more antagonistic bacteria than cuc were screened in cm3 after RKN infestation and most of them were Pseudomonas (Proteobacteria, Pseudomonadaceae), and Proteobacteria were also enriched in cm3 after RKN infestation. We hypothesized that the cooperation between Pseudomonas and the beneficial bacteria in cm3 could inhibit the infestation of RKN. Discussion: Thus, our results provide valuable insights into the role of rhizosphere bacterial communities on RKN diseases of Cucumis crops, and further studies are needed to clarify the bacterial communities that suppress RKN in Cucumis crops rhizosphere.

Research on a real-time dynamic monitoring method for silent aspiration after stroke based on semisupervised deep learning: A protocol study.

Objective: This study aims to establish a real-time dynamic monitoring system for silent aspiration (SA) to provide evidence for the early diagnosis of and precise intervention for SA after stroke. Methods: Multisource signals, including sound, nasal airflow, electromyographic, pressure and acceleration signals, will be obtained by multisource sensors during swallowing events. The extracted signals will be labeled according to videofluoroscopic swallowing studies (VFSSs) and input into a special dataset. Then, a real-time dynamic monitoring model for SA will be built and trained based on semisupervised deep learning. Model optimization will be performed based on the mapping relationship between multisource signals and insula-centered cerebral cortex-brainstem functional connectivity through resting-state functional magnetic resonance imaging. Finally, a real-time dynamic monitoring system for SA will be established, of which the sensitivity and specificity will be improved by clinical application. Results: Multisource signals will be stably extracted by multisource sensors. Data from a total of 3200 swallows will be obtained from patients with SA, including 1200 labeled swallows from the nonaspiration category from VFSSs and 2000 unlabeled swallows. A significant difference in the multisource signals is expected to be found between the SA and nonaspiration groups. The features of labeled and pseudolabeled multisource signals will be extracted through semisupervised deep learning to establish a dynamic monitoring model for SA. Moreover, strong correlations are expected to be found between the Granger causality analysis (GCA) value (from the left middle frontal gyrus to the right anterior insula) and the laryngeal rise time (LRT). Finally, a dynamic monitoring system will be established based on the former model, by which SA can be identified precisely. Conclusion: The study will establish a real-time dynamic monitoring system for SA with high sensitivity, specificity, accuracy and F1 score.

Tobacco Root Microbial Community Composition Significantly Associated With Root-Knot Nematode Infections: Dynamic Changes in Microbiota and Growth Stage.

The root-knot nematode (RKN) is an important pathogen that affects the growth of many crops. Exploring the interaction of biocontrol bacteria-pathogens-host root microbes is the theoretical basis for improving colonization and controlling the effect of biocontrol bacteria in the rhizosphere. Therefore, 16S and 18S rRNA sequencing technology was used to explore the microbial composition and diversity of tobacco roots (rhizosphere and endophytic) at different growth stages in typical tobacco RKN-infected areas for 2 consecutive years. We observed that RKN infection changed the α-diversity and microbial composition of root microorganisms and drove the transformation of microorganisms from bacteria to fungi. The abundance of Sphingomonas decreased significantly from 18% to less than 3%, while the abundance of Rhizobiaceae increased from 4 to 15% at the early growth stage during the first planting year, and it promoted the proliferation of Chryseobacterium at the late growth stage in rhizosphere microorganisms with the highest abundance of 17%. The overall trend of rhizosphere microorganisms changed in the early growth stage with increasing growth time. The specific results were as follows: (1) Rhizobiaceae and Chryseobacterium increased rapidly after 75 days, became the main abundant bacteria in the rhizosphere microorganisms. (2) The dominant flora in fungi were Fusarium and Setophoma. (3) Comparing the root microbes in 2017 and 2018, RKN infection significantly promoted the proliferation of Pseudomonas and Setophoma in both the rhizosphere and endophytes during the second year of continuous tobacco planting, increasing the relative abundance of Pseudomonas from 2 to 25%. Pseudomonas was determined to play an important role in plant pest control. Finally, a total of 32 strains of growth-promoting bacteria were screened from tobacco rhizosphere bacteria infected with RKN through a combination of 16S rRNA sequencing and life-promoting tests. The results of this research are helpful for analyzing the relationship between RKNs and bacteria in plants, providing reference data for elucidating the pathogenesis of RKNs and new ideas for the biological control of RKNs. GRAPHICAL ABSTRACT.

Optimal control for multistage uncertain random dynamic systems with multiple time delays.

A complex system exhibiting uncertainty as well as randomness is an uncertain random dynamic system. A novel idea of an uncertain random matrix is introduced as an interaction between uncertainty and randomness. Thereby a time-delayed uncertain random dynamic system is formulated. And then the corresponding time-delayed optimal control problem is first presented in this paper. To tackle a problem like that, a method is proposed to translate it into an equivalent optimization problem without time delay. The relevant recursion equations are developed with the dynamic programming technique to solve the converted optimization problem. Solving the related recursion equations yields the answer to such an optimization problem. The presented approach offers a unified framework for tackling the discrete-time uncertain random optimal control problem involving time delays. In this framework, three algorithms are successfully devised in doing with time-delayed optimal control problems for dynamic systems regarding linear, quadratic, and cubic controls. Meanwhile, the optimal solutions for numerical examples are found by the raised algorithms. Finally, a problem of optimal control for a two-spool turbofan engine is investigated as an application of our results.

A dynamic surgical navigational approach for immediate implantation and transcrestal sinus augmentation.

Real-time dynamic navigation shows various advantages over static guides in the placement of dental implants. The goal of this article is to highlight a safe and alternative approach for transcrestal sinus augmentation and immediate implantation by dynamic navigation. It elaborates and defines numerous advantages of the trace and place workflow over the fiducial technique in dynamic navigation. The usage of osseodensifying burs were shown to have higher bone-implant contact, stability, and insertion torque. Their application allows drill-tip calibration that can thus be used for dynamic navigation allowing a real-time surgical evaluation for the implant placement. This article describes a novel technique for transcrestal sinus augmentation during implant placement with osseodensifying burs using dynamic navigation.

Automatic Adaptation of Model Neurons and Connections to Build Hybrid Circuits with Living Networks.

Hybrid circuits built by creating mono- or bi-directional interactions among living cells and model neurons and synapses are an effective way to study neuron, synaptic and neural network dynamics. However, hybrid circuit technology has been largely underused in the context of neuroscience studies mainly because of the inherent difficulty in implementing and tuning this type of interactions. In this paper, we present a set of algorithms for the automatic adaptation of model neurons and connections in the creation of hybrid circuits with living neural networks. The algorithms perform model time and amplitude scaling, real-time drift adaptation, goal-driven synaptic and model tuning/calibration and also automatic parameter mapping. These algorithms have been implemented in RTHybrid, an open-source library that works with hard real-time constraints. We provide validation examples by building hybrid circuits in a central pattern generator. The results of the validation experiments show that the proposed dynamic adaptation facilitates closed-loop communication among living and artificial model neurons and connections, and contributes to characterize system dynamics, achieve control, automate experimental protocols and extend the lifespan of the preparations.

Prediction and identification of discrete-time dynamic nonlinear systems based on adaptive echo state network.

In this paper, a new prediction and identification method based on adaptive echo state network (AESN) is proposed to identify a class of discrete-time dynamic nonlinear systems (DDNS). Firstly, according to the characteristics of input signals, the reservoir state update equation of AESN can be adaptively adjusted. In order to guarantee the echo state property of AESN, a sufficient condition for echo state property is given. Secondly, the reservoir parameters of AESN are optimized to improve the identification and prediction performance of AESN. Thirdly, an improved online output weights learning method based on historical reservoir state and output error is given. Finally, the effectiveness of the proposed method is verified by simulation examples.

Geometric and dosimetric accuracy and imaging dose of the real-time tumour tracking system of a gimbal mounted linac.

PURPOSE: To suggest a comprehensive testing scheme to evaluate the geometric and dosimetric accuracy and the imaging dose of the VERO dynamic tumour tracking (DTT) for its clinical implementation. METHODS: Geometric accuracy was evaluated for gantry 0° and 90° in terms of prediction (EP), mechanical (EM) and tracking (ET) errors for sinusoidal patterns with 10 and 20 mm amplitudes, 2-6 s periods and phase shift up to 1 s and for 3 patient patterns. The automatic 4D model update was investigated simulating changes in the breathing pattern during treatment. Dosimetric accuracy was evaluated with gafchromic films irradiated in static and moving phantom with and without DTT. The entrance skin dose (ESD) was assessed using a solid state detector and gafchromic films. RESULTS: The RMS of EP, EM, and ET were up to 0.8, 0.5 and 0.9 mm for all non phased-shifted motion patterns while for the phased-shifted ones, EP and ET increased to 2.2 and 2.6 mm. Up to 4 updates are necessary to restore a good correlation model, according to type of change. For 100 kVp and 1 mA s X-ray beam, the ESD per portal due to 20 s fluoroscopy was 16.6 mGy, while treatment verification at a frequency of 1 Hz contributed with 4.2 mGy/min. CONCLUSIONS: The proposed testing scheme highlighted that the VERO DTT system tracks a moving target with high accuracy. The automatic update of the 4D model is a powerful tool to guarantee the accuracy of tracking without increasing the imaging dose.

Stochastic spatio-temporal dynamic model for gene/protein interaction network in early Drosophila development.

In order to investigate the possible mechanisms for eve stripe formation of Drosophila embryo, a spatio-temporal gene/protein interaction network model is proposed to mimic dynamic behaviors of protein synthesis, protein decay, mRNA decay, protein diffusion, transcription regulations and autoregulation to analyze the interplay of genes and proteins at different compartments in early embryogenesis. In this study, we use the maximum likelihood (ML) method to identify the stochastic 3-D Embryo Space-Time (3-DEST) dynamic model for gene/protein interaction network via 3-D mRNA and protein expression data and then use the Akaike Information Criterion (AIC) to prune the gene/protein interaction network. The identified gene/protein interaction network allows us not only to analyze the dynamic interplay of genes and proteins on the border of eve stripes but also to infer that eve stripes are established and maintained by network motifs built by the cooperation between transcription regulations and diffusion mechanisms in early embryogenesis. Literature reference with the wet experiments of gene mutations provides a clue for validating the identified network. The proposed spatio-temporal dynamic model can be extended to gene/protein network construction of different biological phenotypes, which depend on compartments, e.g. postnatal stem/progenitor cell differentiation.