Time-Controlled Adaptive Ventilation (TCAV): a personalized strategy for lung protection.

Acute respiratory distress syndrome (ARDS) alters the dynamics of lung inflation during mechanical ventilation. Repetitive alveolar collapse and expansion (RACE) predisposes the lung to ventilator-induced lung injury (VILI). Two broad approaches are currently used to minimize VILI: (1) low tidal volume (LVT) with low-moderate positive end-expiratory pressure (PEEP); and (2) open lung approach (OLA). The LVT approach attempts to protect already open lung tissue from overdistension, while simultaneously resting collapsed tissue by excluding it from the cycle of mechanical ventilation. By contrast, the OLA attempts to reinflate potentially recruitable lung, usually over a period of seconds to minutes using higher PEEP used to prevent progressive loss of end-expiratory lung volume (EELV) and RACE. However, even with these protective strategies, clinical studies have shown that ARDS-related mortality remains unacceptably high with a scarcity of effective interventions over the last two decades. One of the main limitations these varied interventions demonstrate to benefit is the observed clinical and pathologic heterogeneity in ARDS. We have developed an alternative ventilation strategy known as the Time Controlled Adaptive Ventilation (TCAV) method of applying the Airway Pressure Release Ventilation (APRV) mode, which takes advantage of the heterogeneous time- and pressure-dependent collapse and reopening of lung units. The TCAV method is a closed-loop system where the expiratory duration personalizes VT and EELV. Personalization of TCAV is informed and tuned with changes in respiratory system compliance (CRS) measured by the slope of the expiratory flow curve during passive exhalation. Two potentially beneficial features of TCAV are: (i) the expiratory duration is personalized to a given patient's lung physiology, which promotes alveolar stabilization by halting the progressive collapse of alveoli, thereby minimizing the time for the reopened lung to collapse again in the next expiration, and (ii) an extended inspiratory phase at a fixed inflation pressure after alveolar stabilization gradually reopens a small amount of tissue with each breath. Subsequently, densely collapsed regions are slowly ratcheted open over a period of hours, or even days. Thus, TCAV has the potential to minimize VILI, reducing ARDS-related morbidity and mortality.

Identification of neuropeptides and their G protein-coupled receptors in the predatory stink bug, Arma custos (Hemiptera: Pentatomidae).

The predatory stink bug Arma custos has been selected as an effective biological control agent and has been successfully massly bred and released into fields for the control of a diverse insect pests. As a zoophytophagous generalist, A. custos relies on a complex neuropeptide signaling system to prey on distinct food and adapt to different environments. However, information about neuropeptide signaling genes in A. custos has not been reported to date. In the present study, a total of 57 neuropeptide precursor transcripts and 41 potential neuropeptide G protein-coupled receptor (GPCR) transcripts were found mainly using our sequenced transcriptome data. Furthermore, a number of neuropeptides and their GPCR receptors that were enriched in guts and salivary glands of A. custos were identified, which might play critical roles in feeding and digestion. Our study provides basic information for an in-depth understanding of biological and ecological characteristics of the predatory bug and would aid in the development of better pest management strategies based on the effective utilization and protection of beneficial natural enemies.

A Mixed-Effects Model in Which the Parameters of the Autocorrelated Error Structure Can Differ between Individuals.

Research in psychology has seen a rapid increase in the usage of experience sampling methods and daily diary methods. The data that result from using these methods are typically analyzed with a mixed-effects or a multilevel model because it allows testing hypotheses about the time course of the longitudinally assessed variable or the influence of time-varying predictors in a simple way. Here, we describe an extension of this model that does not only allow to include random effects for the mean structure but also for the residual variance, for the parameter of an autoregressive process of order 1 and/or the parameter of a moving average process of order 1. After we have introduced this extension, we show how to estimate the parameters with maximum likelihood. Because the likelihood function contains complex integrals, we suggest using adaptive Gauss-Hermite quadrature and Quasi-Monte Carlo integration to approximate it. We illustrate the models using a real data example and also report the results of a small simulation study in which the two integral approximation methods are compared.

Various arrangements of mobile genetic elements among CC147 subpopulations of Klebsiella pneumoniae harboring blaNDM-1: a comparative genomic analysis of carbapenem resistant strains.

BACKGROUND: Certain clonal complexes (CCs) of Klebsiella pneumoniae such as CC147 (ST147 and ST392) are major drivers of blaNDM dissemination across the world. ST147 has repeatedly reported from our geographical region, but its population dynamics and evolutionary trajectories need to be further studied. METHODS: Comparative genomic analysis of 51 carbapenem-nonsusceptible strains as well as three hypervirulent K. pneumoniae (hvKp) recovered during 16-months of surveillance was performed using various bioinformatics tools. We investigated the genetic proximity of our ST147 strains with publicly available corresponding genomes deposited globally and from neighbor countries in our geographic region. RESULTS: While IncL/M plasmid harboring blaOXA-48 was distributed among divergent clones, blaNDM-1 was circulated by twenty of the 25 CC147 dominant clone and were mostly recovered from the ICU. The NDM-1 core structure was bracketed by a single isoform of mobile genetic elements (MGEs) [ΔISKpn26-NDM-TnAs3-ΔIS3000-Tn5403] and was located on Col440I plasmid in 68.7% of ST392. However, various arrangements of MGEs including MITESen1/MITESen1 composite transposon or combination of MITESen1/ISSen4/IS903B/IS5/ISEhe3 on IncFIb (pB171) were identified in ST147. It seems that ST392 circulated blaNDM-1 in 2018 before being gradually replaced by ST147 from the middle to the end of sample collection in 2019. ST147 strains possessed the highest number of resistance markers and showed high genetic similarity with four public genomes that harbored blaNDM-1 on the same replicon type. Mainly, there was a convergence between clusters and isolated neighboring countries in the minimum-spanning tree. A conserved arrangement of resistance markers/MGEs was linked to methyltransferase armA which was embedded in class 1 integron in 8 isolates of ST147/ST48 high-risk clones. CONCLUSION: Our findings highlight the dynamic nature of blaNDM-1 transmission among K. pneumoniae in Iran that occurs both clonally and horizontally via various combinations of MGEs. This is the first analysis of Iranian ST147/NDM + clone in the global context.

In vitro activities of omadacycline, eravacycline, cefiderocol, apramycin, and comparator antibiotics against Acinetobacter baumannii causing bloodstream infections in Greece, 2020-2021: a multicenter study.

Resistance of Acinetobacter baumannii to multiple clinically important antimicrobials has increased to very high rates in Greece, rendering most of them obsolete. The aim of this study was to determine the molecular epidemiology and susceptibilities of A. baumannii isolates collected from different hospitals across Greece. Single-patient A. baumannii strains isolated from blood cultures (n = 271), from 19 hospitals, in a 6-month period (November 2020-April 2021) were subjected to minimum inhibitory concentration determination and molecular testing for carbapenemase, 16S rRNA methyltransferase and mcr gene detection and epidemiological evaluation. 98.9% of all isolates produced carbapenemase OXA-23. The vast majority (91.8%) of OXA-23 producers harbored the armA and were assigned mainly (94.3%) to sequence group G1, corresponding to IC II. Apramycin (EBL-1003) was the most active agent inhibiting 100% of the isolates at ≤16 mg/L, followed by cefiderocol which was active against at least 86% of them. Minocycline, colistin and ampicillin-sulbactam exhibited only sparse activity (S <19%), while eravacycline was 8- and 2-fold more active than minocycline and tigecycline respectively, by comparison of their MIC50/90 values. OXA-23-ArmA producing A. baumannii of international clone II appears to be the prevailing epidemiological type of this organism in Greece. Cefiderocol could provide a useful alternative for difficult to treat Gram-negative infections, while apramycin (EBL-1003), the structurally unique aminoglycoside currently in clinical development, may represent a highly promising agent against multi-drug resistant A. baumanni infections, due to its high susceptibility rates and low toxicity.

Trend patterns of HBsAg kinetics in chronic hepatitis B patients during nucleos(t)ide analogue therapy based on ARMA models.

BACKGROUND: Trend pattern analysis are lacking for hepatitis B surface antigen (HBsAg) kinetics in chronic hepatitis B (CHB) patients during nucleos(t)ide analogue (Nuc) therapy. We evaluated the trend patterns of HBsAg kinetics by time series analysis and forecasting times to HBsAg seroclearance accordingly. METHODS: A total of 116 CHB patients with documented three-month HBsAg levels during the previous more than five years of Nuc therapy were included. The piecewise linear trends of the autoregressive-moving average (ARMA) model were used for time series analysis of HBsAg kinetics trends. Best fitted models were created for each patient using HBsAg datasets with backtracking capability. Predicted time to HBsAg seroclearance was calculated accordingly. RESULTS: Four trend patterns of HBsAg kinetics were found: no trend (n = 22, 19.0%), single trend (n = 16, 13.8%), biphasic trend with rapid-slow decline (n = 56, 48.2%) and biphasic trend with rise-decline (n = 22, 19.0%). Except for no-trend patients, the trend became slow reduction as HBsAg declined. Only 6.1% of patients continued rapid decline when the initial HBsAg of the last trend reached <100 IU/mL. Last trend slopes < -10 and rise-decline patterns indicate greater chances of achieving HBsAg seroclearance within two years. CONCLUSION: Best fitted ARMA models of HBsAg kinetics can be created individually for patients during Nuc therapy. About 67.2% patients have biphasic trend patterns, suggesting the dynamic nature of HBsAg kinetics over time. Trend patterns and last trend slopes predict individual times to HBsAg seroclearance.

Pedestrian Flow Identification and Occupancy Prediction for Indoor Areas.

Indoor localization is used to locate objects and people within buildings where outdoor tracking tools and technologies cannot provide precise results. This paper aims to improve analytics research, focusing on data collected through indoor localization methods. Smart devices recurrently broadcast automatic connectivity requests. These packets are known as Wi-Fi probe requests and can encapsulate various types of spatiotemporal information from the device carrier. In addition, in this paper, we perform a comparison between the Prophet model and our implementation of the autoregressive moving average (ARMA) model. The Prophet model is an additive model that requires no manual effort and can easily detect and handle outliers or missing data. In contrast, the ARMA model may require more effort and deep statistical analysis but allows the user to tune it and reach a more personalized result. Second, we attempted to understand human behaviour. We used historical data from a live store in Dubai to forecast the use of two different models, which we conclude by comparing. Subsequently, we mapped each probe request to the section of our place of interest where it was captured. Finally, we performed pedestrian flow analysis by identifying the most common paths followed inside our place of interest.

Wind Speed Prediction Based on Error Compensation.

Wind speed prediction is very important in the field of wind power generation technology. It is helpful for increasing the quantity and quality of generated wind power from wind farms. By using univariate wind speed time series, this paper proposes a hybrid wind speed prediction model based on Autoregressive Moving Average-Support Vector Regression (ARMA-SVR) and error compensation. First, to explore the balance between the computation cost and the sufficiency of the input features, the characteristics of ARMA are employed to determine the number of historical wind speeds for the prediction model. According to the selected number of input features, the original data are divided into multiple groups that can be used to train the SVR-based wind speed prediction model. Furthermore, in order to compensate for the time lag introduced by the frequent and sharp fluctuations in natural wind speed, a novel Extreme Learning Machine (ELM)-based error correction technique is developed to decrease the deviations between the predicted wind speed and its real values. By this means, more accurate wind speed prediction results can be obtained. Finally, verification studies are conducted by using real data collected from actual wind farms. Comparison results demonstrate that the proposed method can achieve better prediction results than traditional approaches.

Detecting Structural Change Point in ARMA Models via Neural Network Regression and LSCUSUM Methods.

This study considers the change point testing problem in autoregressive moving average (ARMA) (p,q) models through the location and scale-based cumulative sum (LSCUSUM) method combined with neural network regression (NNR). We estimated the model parameters via the NNR method based on the training sample, where a long AR model was fitted to obtain the residuals. Then, we selected the optimal model orders p and q of the ARMA models using the Akaike information criterion based on a validation set. Finally, we used the forecasting errors obtained from the selected model to construct the LSCUSUM test. Extensive simulations and their application to three real datasets show that the proposed NNR-based LSCUSUM test performs well.

Inferred Rate of Default as a Credit Risk Indicator in the Bulgarian Bank System.

The inferred rate of default (IRD) was first introduced as an indicator of default risk computable from information publicly reported by the Bulgarian National Bank. We have provided a more detailed justification for the suggested methodology for forecasting the IRD on the bank-group- and bank-system-level based on macroeconomic factors. Furthermore, we supply additional empirical evidence in the time-series analysis. Additionally, we demonstrate that IRD provides a new perspective for comparing credit risk across bank groups. The estimation methods and model assumptions agree with current Bulgarian regulations and the IFRS 9 accounting standard. The suggested models could be used by practitioners in monthly forecasting the point-in-time probability of default in the context of accounting reporting and in monitoring and managing credit risk.

Effect of regulatory interventions on agomelatine use in Spain: A multiple intervention time-series analysis in a nationwide electronic healthcare record database.

BACKGROUND: Liver injury is an important identified risk for agomelatine and several measures were put in place to prevent and minimize such risk. The study aims to assess the impact of four interventions on the incidence of agomelatine use, particularly among patients aged ≥75 in Spain between 2011 and 2018. METHODS: Quasi-experimental interrupted time-series analysis to examine data from a nationwide electronic healthcare record database (BIFAP). Quarterly cumulative incidence of agomelatine use per 100 000 patients was calculated and the impact of four regulatory interventions was quantified. RESULTS: The incidence of agomelatine use decreased by 85% and 87% from first quarter 2011 to last quarter 2018 in patients below and above 75 years old, respectively. Regulatory actions taken were not associated with an immediate and significant falling level of use or slope. The incidence was less than expected 6 months after the first and third intervention for patients below and above 75 years old, and more than expected after the second and fourth intervention for both populations, though these analyses were underpowered to observe significant results. The downward trend became less pronounced, reaching a residual level of use, which remained stable in the last segment of the study period. CONCLUSION: New users of agomelatine decreased throughout the study period, starting before interventions took place. The effect of specific interventions might be masked by the progressive decrease tendency, constant over the study period. The effects of external factors that might overlap, unintended consequences, and issues concerning statistical modeling in situations where rates are already falling, should be considered when interpreting the results.

Population Growth Performance of Arma custos (Faricius) (Hemiptera: Pentatomidae) at Different Temperatures.

Arma custos (Fabricius) (Hemiptera: Pentatomidae) is a natural predator that can control various agricultural and forestry pests. This study aimed to clarify the effects of temperature on the growth, reproduction, and population of the predator and to simulate its population growth. Using the age-stage, two-sex life table method, 18°C, 22°C, 26°C, 30°C, and 34°C were selected as the temperature conditions. A. custos can complete its life cycle at 18°C-30°C, and the developmental duration of each A. custos stage, adult pre-oviposition period, total pre-oviposition period, and the mean generation time (T) were shortened with the increase in temperature. The pre-adult mortality was significantly reduced at 26°C and 30°C. In addition, the fecundity of a single female and the gross reproductive rate were the highest at 30°C. Significant differences were observed in the intrinsic rate of increase (r) and the finite rate of increase (λ) under different temperature conditions, and both reached the maximum at 30°C. Results showed that adult A. custos raised at 26°C had a longer lifespan and the fecundity was higher at 30°C in comparison with the other temperatures. This study is the first to report the life cycle of A. custos at different temperatures, and the results can provide a scientific theoretical basis for the indoor artificial reproduction, outdoor release, and colonization of A. custos.

Impulse Response Functions for Nonlinear, Nonstationary, and Heterogeneous Systems, Estimated by Deconvolution and Demixing of Noisy Time Series.

Impulse response functions (IRFs) are useful for characterizing systems' dynamic behavior and gaining insight into their underlying processes, based on sensor data streams of their inputs and outputs. However, current IRF estimation methods typically require restrictive assumptions that are rarely met in practice, including that the underlying system is homogeneous, linear, and stationary, and that any noise is well behaved. Here, I present data-driven, model-independent, nonparametric IRF estimation methods that relax these assumptions, and thus expand the applicability of IRFs in real-world systems. These methods can accurately and efficiently deconvolve IRFs from signals that are substantially contaminated by autoregressive moving average (ARMA) noise or nonstationary ARIMA noise. They can also simultaneously deconvolve and demix the impulse responses of individual components of heterogeneous systems, based on their combined output (without needing to know the outputs of the individual components). This deconvolution-demixing approach can be extended to characterize nonstationary coupling between inputs and outputs, even if the system's impulse response changes so rapidly that different impulse responses overlap one another. These techniques can also be extended to estimate IRFs for nonlinear systems in which different input intensities yield impulse responses with different shapes and amplitudes, which are then overprinted on one another in the output. I further show how one can efficiently quantify multiscale impulse responses using piecewise linear IRFs defined at unevenly spaced lags. All of these methods are implemented in an R script that can efficiently estimate IRFs over hundreds of lags, from noisy time series of thousands or even millions of time steps.

EP-ADTA: Edge Prediction-Based Adaptive Data Transfer Algorithm for Underwater Wireless Sensor Networks (UWSNs).

The underwater wireless sensor network is an important component of the underwater three-dimensional monitoring system. Due to the high bit error rate, high delay, low bandwidth, limited energy, and high dynamic of underwater networks, it is very difficult to realize efficient and reliable data transmission. Therefore, this paper posits that it is not enough to design the routing algorithm only from the perspective of the transmission environment; the comprehensive design of the data transmission algorithm should also be combined with the application. An edge prediction-based adaptive data transmission algorithm (EP-ADTA) is proposed that can dynamically adapt to the needs of underwater monitoring applications and the changes in the transmission environment. EP-ADTA uses the end-edge-cloud architecture to define the underwater wireless sensor networks. The algorithm uses communication nodes as the agents, realizes the monitoring data prediction and compression according to the edge prediction, dynamically selects the transmission route, and controls the data transmission accuracy based on reinforcement learning. The simulation results show that EP-ADTA can meet the accuracy requirements of underwater monitoring applications, dynamically adapt to the changes in the transmission environment, and ensure efficient and reliable data transmission in underwater wireless sensor networks.

Evaluating the accuracy of ARMA and multi-index methods for predicting winter wheat maturity date.

BACKGROUND: Accurate and timely prediction of regional winter wheat maturity date can provide essential information to improve the management of agriculture and avoid declines in the yield and quality of crops. In this paper, we propose the use of an autoregressive moving-average model to predict vegetation indices on 1, 9, and 17 May each year, and applied them to the methods of evaluating crop maturity based on vegetation indices. Growing degree days and a widely applied local empirical method were selected to explore and compare the feasibility of several methods. We analyzed winter wheat harvested from the Guanzhong Plain during 2003-2013 and used leave-one-out cross-validation to compare and verify the performance of the maturity prediction methods. RESULTS: The results demonstrated that (i) the vegetation index methods and growing degree days methods predicted maturity with higher accuracy than did the widely applied local empirical method, and (ii) the two-step filtering method based on future meteorological data from The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble exhibited the highest prediction accuracy on 1 May and had the lowest error fluctuation range on 17 May. CONCLUSION: These results provide new insights for predicting regional crop maturity, deploying agricultural harvesting equipment in various regions, and avoiding decreases in crop yields caused by adverse weather. © 2021 Society of Chemical Industry.

Stock Index Prediction Based on Time Series Decomposition and Hybrid Model.

The stock index is an important indicator to measure stock market fluctuation, with a guiding role for investors' decision-making, thus being the object of much research. However, the stock market is affected by uncertainty and volatility, making accurate prediction a challenging task. We propose a new stock index forecasting model based on time series decomposition and a hybrid model. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the stock index into a series of Intrinsic Mode Functions (IMFs) with different feature scales and trend term. The Augmented Dickey Fuller (ADF) method judges the stability of each IMFs and trend term. The Autoregressive Moving Average (ARMA) model is used on stationary time series, and a Long Short-Term Memory (LSTM) model extracts abstract features of unstable time series. The predicted results of each time sequence are reconstructed to obtain the final predicted value. Experiments are conducted on four stock index time series, and the results show that the prediction of the proposed model is closer to the real value than that of seven reference models, and has a good quantitative investment reference value.

Low-Complexity Alternatives to the Optimal Linear Coding Scheme for Transmitting ARMA Sources.

In the era of the Internet of Things, there are many applications where numerous devices are deployed to acquire information and send it to analyse the data and make informed decisions. In these applications, the power consumption and price of the devices are often an issue. In this work, analog coding schemes are considered, so that an ADC is not needed, allowing the size and power consumption of the devices to be reduced. In addition, linear and DFT-based transmission schemes are proposed, so that the complexity of the operations involved is lowered, thus reducing the requirements in terms of processing capacity and the price of the hardware. The proposed schemes are proved to be asymptotically optimal among the linear ones for WSS, MA, AR and ARMA sources.

COVID-19 impact on commodity futures volatilities.

COVID-19 pandemic has affected almost all aspects of the global economy, especially commodity futures markets, due to the disruption risk of global supply chains from the pandemic lockdown. This paper extends ARMA-GARCH models to investigate the pandemic impact on both long-run and short-term volatilities of four major commodity futures. Model-fitting results reveal that the pandemic event has enhanced long-run volatilities for all futures returns, while the daily COVID-19 infection speed has mixed effects on short-term (instantaneous) volatilities. Our extended models and research findings are useful in global supply chain risk management, commodity options trading and regulators' supervision of inflation risk.

Extensively drug-resistant Acinetobacter baumannii carrying blaOXA-23-like and armA in a hospital after an intervention in the intensive care unit which ended a long-standing endemicity.

The aim of the study was to evaluate for a long time the effectiveness of an intervention designed to reduce carbapenem-resistant Acinetobacter baumannii (CRAB) and its impact on colistin usage in the ICU of a tertiary hospital in Spain. The rate of carbapenem resistance declined drastically during the period of study (2015 to 2018), from 93.57 to 74.65%, especially in the ICU. A significant decrease in colistin usage, from 1.16 to 0.39 DOTs, was observed. Forty-nine CRAB isolates recovered nearly 1 year after starting the intervention were characterized. Most of them were recovered from patients admitted in wards other than ICU and were extensively drug-resistant, carried blaOXA-23-like and armA, and belonged to ST218. Implementation of control measures is crucial to CRAB control in ICUs but must be extended to all wards in order to eradicate CRAB from hospitals.

Investigating psychometric properties of the arm activity measure - Thai version (ArmA-TH) sub-scales using the Rasch model.

BACKGROUND: This study investigated the ArmA-TH sub-scale measurement properties based on item response theory using the Rasch model. METHODS: Patients with upper limb hemiplegia resulting from cerebrovascular and other brain disorders were asked to complete the ArmA-TH questionnaire. Rasch analysis was performed to test how well the ArmA-TH passive and active function sub-scales fit the Rasch model by investigating unidimensionality, response category functioning, reliability of person and item, and differential item functioning (DIF) for age, sex, and education. RESULTS: Participants had stroke or other acquired brain injury (n = 185), and the majority were men (126, 68.1 %), with a mean age of 55 (SD 22). Most patients (91, 49.2 %) had graduated from elementary/primary school. For the ArmA-TH passive function scale, all items had acceptable fit statistics. The scale's unidimensionality and local independence were supported. The reliability was acceptable. A disordered threshold was found for five items, and none indicated DIF. For the ArmA-TH active function scale, one item was misfit and three were locally dependent. The reliability was good. No items showed DIF. All items had disordered thresholds, and the data fitted the Rasch model better after rescoring. CONCLUSIONS: Both sub-scales of ArmA-TH fitted the Rasch model and were valid and reliable. The disordered thresholds should be further investigated.