Mean Estimates Research Articles

Cardiorespiratory abnormalities in ICU survivors of COVID-19 with postacute sequelae of SARS-CoV-2 infection are unrelated to invasive mechanical ventilation.

Postacute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (PASC) often leads to exertional intolerance and reduced exercise capacity, particularly in individuals previously admitted to an intensive care unit (ICU). However, the impact of invasive mechanical ventilation (IMV) on PASC-associated cardiorespiratory abnormalities during exercise remains poorly understood. This single-center, cross-sectional study aimed to gather knowledge on this topic. Fifty-two patients with PASC recruited ∼6 mo after ICU discharge were clustered based on their need for IMV (PASC + IMV, n = 27) or noninvasive support therapy (PASC + NIS, n = 25). Patients underwent pulmonary function and cardiopulmonary exercise testing (CPX) and were compared with a reference group (CONTROL, n = 19) comprising individuals of both sexes with similar age, comorbidities, and physical activity levels but without a history of COVID-19 illness. Individuals with PASC, irrespective of support therapy, presented with higher rates of cardiorespiratory abnormalities than CONTROL, especially dysfunctional breathing patterns, dynamic hyperinflation, reduced oxygen uptake and oxygen pulse, and blunted heart rate recovery (all P < 0.05). Only the rate of abnormal oxygen pulse was greater among PASC + IMV group than PASC + NIS group (P = 0.05). Mean estimates for all CPX variables were comparable between PASC + IMV and PASC + NIS groups (all P > 0.05). These findings indicate significant involvement of both central and peripheral factors, leading to exertional intolerance in individuals with PASC previously admitted to the ICU, regardless of their need for IMV.NEW & NOTEWORTHY We found cardiorespiratory abnormalities in ICU survivors of severe-to-critical COVID-19 with PASC to be independent of IMV need. Overall, both group of patients experienced dysfunctional breathing patterns, dynamic hyperinflation, lower oxygen uptake and oxygen pulse, and blunted heart rate responses to CPX. PASC seems to impact exertional tolerance and exercise capacity due to ventilatory inefficiency, impaired aerobic metabolism, and potential systolic and autonomic dysfunction, all of these irrespective of support therapy during ICU stay.

Likelihood-free posterior estimation and uncertainty quantification for diffusion MRI models

Abstract Diffusion magnetic resonance imaging (dMRI) allows to estimate brain tissue microstructure as well as the connectivity of the white matter (known as tractography). Accurate estimation of the model parameters (by solving the inverse problem) is thus very important to infer the underlying biophysical tissue properties and fiber orientations. Although there has been extensive research on this topic with a myriad of dMRI models, most models use standard nonlinear optimization techniques and only provide an estimate of the model parameters without any information (quantification) about uncertainty in their estimation. Further, the effect of this uncertainty on the estimation of the derived dMRI microstructural measures downstream (e.g., fractional anisotropy) is often unknown and is rarely estimated. To address this issue, we first design a new deep-learning algorithm to identify the number of crossing fibers in each voxel. Then, at each voxel, we propose a robust likelihood-free deep learning method to estimate not only the mean estimate of the parameters of a multi-fiber dMRI model (e.g., the biexponential model), but also its full posterior distribution. The posterior distribution is then used to estimate the uncertainty in the model parameters as well as the derived measures. We perform several synthetic and in-vivo quantitative experiments to demonstrate the robustness of our approach for different noise levels and out-of-distribution test samples. Besides, our approach is computationally fast and requires an order of magnitude less time than standard nonlinear fitting techniques. The proposed method demonstrates much lower error (compared to existing methods) in estimating several metrics, including number of fibers in a voxel, fiber orientation, and tensor eigenvalues. The proposed methodology is quite general and can be used for the estimation of the parameters from any other dMRI model.

Additional Effect of Extracorporeal Shockwave Therapy with Lidocaine Injection on Clinical and MRI Findings in Frozen Shoulder: A Prospective, Randomized, Double-Blinded, Placebo-Controlled Trial.

Frozen shoulder is a very common musculoskeletal condition and the evidence related to the additional effects of extracorporeal shockwave therapy (ESWT) with intra-articular (IA) lidocaine injection in individuals with frozen shoulder is rare. Therefore, this study aims to compare and investigate the additional effects of extracorporeal shockwave therapy (ESWT) with intra-articular (IA) lidocaine injection in a frozen shoulder. Sixty eligible participants with frozen shoulder were included and the active group (n = 30, age 52.12 ± 5.2years) received a lidocaine injection (1% lidocaine (Xylocaine) and 2cc (80 mg) methylprednisolone acetate) with active ESWT (3.5 bar air pressure and 2000 pulses with an energy flux density (EFD) ¼ 0.16 mJ/mm2) three sessions a week for 4 weeks. The placebo group (n = 30, age 53.56 ± 5.5years) received lidocaine injection with placebo treatment (a special head that blocked the shock waves) three sessions a week for 4 weeks. Both groups received progressive resistance exercises (PRE) to the shoulder muscles. The primary outcome was pain intensity, measured with the visual analogue scale. The other outcome measures were the thickness of the coracohumeral ligament (CHL) measured by magnetic resonance imaging (MRI), abduction, and lateral rotation range of motion (ROM), functional disability, kinesiophobia, depression status, and quality of life. Participants were assessed at baseline, after 4 weeks, 8 weeks, and at 6-month follow-up. The post-intervention at 4 weeks showed an improvement of 2.0 (CI 95% 1.71-2.28) in the active group compared to the placebo group. Similar effects were noted after 8 weeks (2.2) (CI 95% 1.91-2.48) and at the 6-month (1.9) (CI 95% 1.61-2.18) follow-up. Similar improvements were also found in the thickness of the CHL ligament (0.6) (CI 95% 0.46-0.73), abduction and lateral rotation (ROM) (-23.6) (CI 95% -27.47 to -19.72), (-18.10) (CI 95% -19.72 to -16.47), functional disability (16.2) (CI 95% 14.85-17.54), kinesiophobia (11.0 (CI 95% 10.21-11.98), depression status (4.4) (CI 95% 4.03-4.76) and quality of life (0.9) (CI 95% 0.79-1.00) (p = 0.001) at the 6-month follow-up period, where mean estimates and their confidence intervals all included worthwhile effects. There were no adverse reactions or side effects noted in either the active or placebo groups during and after the treatment. The study concluded that the addition of extracorporeal shockwave therapy after intra-articular lidocaine injection improves pain, functional disability, range of motion, kinesiophobia, depression status, and quality of life in people with frozen shoulder. https://ctri.nic.in , identifier; CTRI/2020/04/024834 prospectively registered on 24/04/2020.

Change in Cognitive Function over the Course of Major Depressive Disorder: A Systematic Review and Meta-analysis.

Major depressive disorder (MDD) is associated with significant cognitive deficits during the acute and remitted stages. The aim of this systematic review and meta-analysis was to examine the course of cognitive function whilst considering demographic, treatment, or clinical features of MDD that could moderate the extent of cognitive change. Databases were searched to identify studies that reported on cognitive function in MDD with a ≥12-week test-retest interval. Relevant studies were pooled using random effects modelling to generate an inverse-variance, weighted, mean effect size estimate (Hedges' g) of cognitive change for each cognitive variable and for an overall composite cognitive domain. Of 6898 records, 99 eligible studies were identified from which 69 were meta-analysed, consisting of 4639 MDD patients (agemean = 40.25years, female% = 64.62%) across 44 cognitive variables. In over 95% of cognitive variables, improvements were either of non-significant, negligible, or of a small magnitude, and when compared to matched healthy controls, the possibility of practice effects could not be precluded. Depressive symptom improvement and the number of previous depressive episodes moderated the extent of cognitive change, demonstrating state- and scar-like features for one-quarter of the cognitive domains. Further longitudinal studies are required to elucidate the MDD cognitive trajectory from initial onset. Findings nonetheless suggest that following pharmacological and non-pharmacological treatment, cognitive change in MDD is typically small, but the capacity for change may be less with episode recurrence. Targeting cognition early in the course of illness may facilitate better prognosis and support a more complete functional recovery.

Assessing the Policy Implications of Different Definitions for Added Sugars: An Analysis Across the Australian Packaged Food and Beverage Supply

BackgroundIn recent years, various definitions of "added sugars" have emerged across jurisdictions. Although it is clear how these definitions differ, there is limited understanding of the policy implications associated with these variations. ObjectiveTo test the potential policy implications of different definitions of “added sugars” on the Australian packaged food supply, we developed a method to estimate the content of “added sugars” in packaged foods and applied this to 3 different definitions of “added sugars”: (i) United States Food and Drug Administration (US FDA) added sugar definition, (ii) the World Health Organization (WHO) free sugar definition, and (iii) a comprehensive definition that was developed from a review of the evidence on “added sugars.” MethodsUsing a representative sample of 25,323 Australian packaged foods, the “added sugar” content and proportion of products that contain “added sugar” under the 3 definitions were estimated. In addition, a comparative analysis exploring the impact of the US FDA definition (least comprehensive) vs. the comprehensive definition was conducted to understand potential implications of adopting different regulatory definitions in Australia. ResultsThe US FDA definition identified the lowest number and proportion of products with any “added sugars” at 14,380 products (representing 56.8% of all products), followed by the WHO free sugar definition at 15,168 products (59.9%) and the comprehensive definition at 16,260 products (64.2%). The mean estimates for “added sugars” were 8.5 g/100 g, 8.7 g/100 g, and 9.6 g/100 g for the US FDA, WHO, and comprehensive definitions, respectively. Compared with the US FDA definition, the comprehensive definition captured an additional 7.4% of products, largely driven by nonalcoholic beverages, special foods and fruit, vegetables, nuts, and legumes. ConclusionsDespite small variations in different “added sugars” definitions, their application has some significant policy implications. Findings highlight the importance of applying a comprehensive regulatory definition that adequately captures all sugars that have been linked to poor health.

Single-blind test of nine methane-sensing satellite systems from three continents

Abstract. Satellite-based remote sensing enables detection and mitigation of large point sources of climate-warming methane. These satellites will have the greatest impact if stakeholders have a clear-eyed assessment of their capabilities. We performed a single-blind test of nine methane-sensing satellites from three continents and five countries, including both commercial and government satellites. Over 2 months, we conducted 82 controlled methane releases during satellite overpasses. Six teams analyzed the resulting data, producing 134 estimates of methane emissions. Of these, 80 (58 %) were correctly identified, with 46 true positive detections (34 %) and 34 true negative non-detections (25 %). There were 41 false negatives, in which teams missed a true emission, and 0 false positives, in which teams incorrectly claimed methane was present. All eight satellites that were given a nonzero emission detected methane at least once, including the first single-blind evaluation of the EnMAP, Gaofen 5, and Ziyuan 1 systems. In percent terms, quantification error across all satellites and teams is similar to aircraft-based methane remote sensing systems, with 55 % of mean estimates falling within ±50 % of the metered value. Although teams correctly detected emissions as low as 0.03 metric tons of methane per hour, it is unclear whether detection performance in this test is representative of real-world field performance. Full retrieval fields submitted by all teams suggest that in some cases it may be difficult to distinguish true emissions from background artifacts without a known source location. Cloud interference is significant and appears to vary across teams and satellites. This work confirms the basic efficacy of the tested satellite systems in detecting and quantifying methane, providing additional insight into detection limits and informing experimental design for future satellite-focused controlled methane release testing campaigns.

Asymptotic uncertainty of false discovery proportion.

Multiple testing has been a prominent topic in statistical research. Despite extensive work in this area, controlling false discoveries remains a challenging task, especially when the test statistics exhibit dependence. Various methods have been proposed to estimate the false discovery proportion (FDP) under arbitrary dependencies among the test statistics. One key approach is to transform arbitrary dependence into weak dependence and subsequently establish the strong consistency of FDP and false discovery rate under weak dependence. As a result, FDPs converge to the same asymptotic limit within the framework of weak dependence. However, we have observed that the asymptotic variance of FDP can be significantly influenced by the dependence structure of the test statistics, even when they exhibit only weak dependence. Quantifying this variability is of great practical importance, as it serves as an indicator of the quality of FDP estimation from the data. To the best of our knowledge, there is limited research on this aspect in the literature. In this paper, we aim to fill in this gap by quantifying the variation of FDP, assuming that the test statistics exhibit weak dependence and follow normal distributions. We begin by deriving the asymptotic expansion of the FDP and subsequently investigate how the asymptotic variance of the FDP is influenced by different dependence structures. Based on the insights gained from this study, we recommend that in multiple testing procedures utilizing FDP, reporting both the mean and variance estimates of FDP can provide a more comprehensive assessment of the study's outcomes.

-Inflated beta regression model for censored recurrent events.

This research introduces a multivariate -inflated beta regression ( -IBR) modeling approach for the analysis of censored recurrent event data that is particularly useful when there is a mixture of (a) individuals who are generally less susceptible to recurrent events and (b) heterogeneity in duration of event-free periods amongst those who experience events. The modeling approach is applied to a restructured version of the recurrent event data that consists of censored longitudinal times-to-first-event in length follow-up windows that potentially overlap. Multiple imputation (MI) and expectation-solution (ES) approaches appropriate for censored data are developed as part of the model fitting process. A suite of useful analysis outputs are provided from the -IBR model that include parameter estimates to help interpret the (a) and (b) mixture of event times in the data, estimates of mean -restricted event-free duration in a -length follow-up window based on a patient's covariate profile, and heat maps of raw -restricted event-free durations observed in the data with censored observations augmented via averages across MI datasets. Simulations indicate good statistical performance of the proposed -IBR approach to modeling censored recurrent event data. An example is given based on the Azithromycin for Prevention of COPD Exacerbations Trial.

Assessing electrofishing effort required to estimate species richness of fishes in nonwadeable rivers of Alabama, USA

AbstractObjectiveMuch of the fish biodiversity in the southeastern USA is found in lotic systems, but assessment of fish community structure in large nonwadeable rivers has lagged compared to streams. Sampling designs that adequately estimate species richness are a prerequisite to creating bioassessment metrics but thus far has not been attempted in highly biodiverse rivers in the southeastern USA east of the Mississippi River.MethodsFish communities in four nonwadeable rivers across Alabama spanning catchments of 2046–57,415 km2 were sampled using electrofishing in summer and fall of 2015 and 2016. All rivers were sampled during day; two rivers were also sampled at night to assess diel changes in species accumulation.ResultIn total, 55,239 fishes were collected across rivers, representing 106 species in 24 families. Species collected continued to increase as sampling increased in all river‐season combinations, rarely displaying an asymptote. Species generally accrued more quickly and accumulation curves showed more tendency to asymptote in fall than summer in all but the largest river. Species accumulated faster at night than during the day in both rivers during summer but was similar between diel periods in fall. On average, 95% of the species observed were collected in eight subreaches in fall but not until nine during summer; however, observed number of species was generally one to six species lower than mean estimates of true species richness. Sampling efforts collected approximately similar species assemblages across transects, years, season, and diel periods. Extrapolation of species accumulation curves suggested that 6–17 km of sampling distance would be required to collect all species present.ConclusionResults of this study demonstrate that assessing fish communities in species nonwadeable rivers requires an inordinate amount of sampling effort, which may be unattainable for many agencies. Further study is warranted to develop innovative solutions to overcome these logistical challenges.

Correcting for Sampling Error in between-Cluster Effects: An Empirical Bayes Cluster-Mean Approach with Finite Population Corrections

With clustered data, such as where students are nested within schools or employees are nested within organizations, it is often of interest to estimate and compare associations among variables separately for each level. While researchers routinely estimate between-cluster effects using the sample cluster means of a predictor, previous research has shown that such practice leads to biased estimates of coefficients at the between level, and recent research has recommended the use of latent cluster means with the multilevel structural equation modeling framework. However, the latent cluster mean approach may not always be the best choice as it (a) relies on the assumption that the population cluster sizes are close to infinite, (b) requires a relatively large number of clusters, and (c) is currently only implemented in specialized software such as Mplus. In this paper, we show how using empirical Bayes estimates of the cluster means can also lead to consistent estimates of between-level coefficients, and illustrate how the empirical Bayes estimate can incorporate finite population corrections when information on population cluster sizes is available. Through a series of Monte Carlo simulation studies, we show that the empirical Bayes cluster-mean approach performs similarly to the latent cluster mean approach for estimating the between-cluster coefficients in most conditions when the infinite-population assumption holds, and applying the finite population correction provides reasonable point and interval estimates when the population is finite. The performance of EBM can be further improved with restricted maximum likelihood estimation and likelihood-based confidence intervals. We also provide an R function that implements the empirical Bayes cluster-mean approach, and illustrate it using data from the classic High School and Beyond Study.

Integral mean estimates for univalent and locally univalent harmonic mappings

Abstract We verify a long-standing conjecture on the membership of univalent harmonic mappings in the Hardy space, whenever the functions have a “nice” analytic part. We also produce a coefficient estimate for these functions, which is in a sense best possible. The problem is then explored in a new direction, without the additional hypothesis. Interestingly, our ideas extend to certain classes of locally univalent harmonic mappings. Finally, we prove a Baernstein-type extremal result for the function $\log (h'+cg')$ , when $f=h+\overline {g}$ is a close-to-convex harmonic function, and c is a constant. This leads to a sharp coefficient inequality for these functions.

Enhanced Variance Estimation Techniques for Addressing Non-Response and Measurement Error Situations

The use of relevant information from auxiliary variables at the estimation stage and design stage to obtain reliable and efficient estimates is a common practice in a sample survey. Several Estimators of population variance have been suggested. However, these estimators do not consider the situation of non-response due to non-availability of respondents, refusal to respond, presence of hard- core respondents or due to non-understanding of the question thereby, reducing the efficiency of the estimators and the parameters of the auxiliary variable X̄ , S2x used are sensitive to outliers or extreme values which can either lead to underestimation or overestimation. To address the aforementioned observations, classes of variance estimators under the simultaneous influence of non-response and measurement errors using outlier-free parameters as well as calibration approaches were proposed. The properties (Bias and MSE) of the modified estimators were derived up to the first order of approximation using the Taylor series approach. The efficiency conditions of the proposed estimators over the existing estimators considered in the study were established. The empirical studies were conducted using simulation and the results revealed that the proposed class of estimators have minimum MSEs and higher PREs among all the competing estimators. These imply that the proposed estimators are more efficient and can produce a better estimate of the population mean compared to other existing estimators considered in the study.

Immunogenicity of Intradermal Versus Intramuscular BNT162b2 COVID-19 Booster Vaccine in Patients with Immune-Mediated Dermatologic Diseases: A Non-Inferiority Randomized Controlled Trial.

The intradermal route has emerged as a dose-sparing alternative during the coronavirus disease 2019 (COVID-19) pandemic. Despite its efficacy in healthy populations, its immunogenicity has not been tested in immune-mediated dermatologic disease (IMDD) patients. This assessor-blinded, randomized-controlled, non-inferiority trial recruited patients with two representative IMDDs (i.e., psoriasis and autoimmune bullous diseases) to vaccinate with fractionated-dose intradermal (fID) or standard intramuscular (sIM) BNT162b2 vaccines as a fourth booster dose under block randomization stratified by age, sex, and their skin diseases. Post-vaccination SARS-CoV-2-specific IgG and interferon-γ responses measured 4 and 12 weeks post-intervention were serological surrogates used for demonstrating treatment effects. Mean differences in log-normalized outcome estimates were calculated with multivariable linear regression adjusting for their baseline values, systemic immunosuppressants used, and prior COVID-19 vaccination history. The non-inferiority margin was set for fID to retain >80% immunogenicity of sIM. With 109 participants included, 53 received fID (all entered an intention-to-treat analysis). The fID demonstrated non-inferiority to sIM in humoral (mean outcome estimates of sIM: 3.3, ΔfID-sIM [mean, 95%CI]: -0.1, -0.3 to 0.0) and cellular (mean outcome estimates of sIM: 3.2, ΔfID-sIM [mean, 95%CI]: 0.1, -0.2 to 0.3) immunogenicity outcomes. Two psoriasis patients from the fID arm (3.8%) developed injection-site Koebner's phenomenon. Fewer fID recipients experienced post-vaccination fever (fID vs. sIM: 1.9% vs. 12.5%, p = 0.027). The overall incidence of disease flare-ups was low without a statistically significant difference between groups. The intradermal BNT162b2 vaccine is a viable booster option for IMDD patients troubled by post-vaccination fever; its role in mitigating the risk of flare-ups remains unclear.

Air pollution as a potential risk factor for hepatocellular carcinoma in Taiwanese patients after adjusting for chronic viral hepatitis.

Air pollution is a risk factor for hepatocellular carcinoma (HCC). However, the effect of air pollution on HCC risk in patients with hepatitis remains unclear. This cross-sectional study recruited 348 patients with chronic hepatitis who were tested for serum hepatitis B surface antigen (HBsAg) and for antibodies against hepatitis B core antigen (HBcIgG) and hepatitis C virus (anti-HCV) in 2022. The diagnosis of HCC was based on the International Classification of Diseases, 10th revision (ICD-10). Daily estimates of air pollutants were aggregated into mean estimates for the previous year based on the date of recruitment or HCC diagnosis. Out of 348 patients, 12 had HCC (3.4%). Patients with HCC were older (71.7 vs 50.9 years; p = 0.004), had higher proportion of HBsAg seropositivity (41.7% vs 5.1%; p < 0.001), and substantially higher levels of particulate matter 2.5 (PM 2.5 ) (21.5 vs 18.2 μg/m 3 ; p = 0.05). Logistic regression analysis revealed that the factors associated with HCC were age (odds ratio [OR]: 1.10; CI, 1.03-1.17; p = 0.01), PM 2.5 level (OR: 1.51; CI, 1.02-2.23; p = 0.04), and HBsAg seropositivity (OR: 6.60; CI, 1.51-28.85; p = 0.01) ( Table 3 ). There was a combined effect of PM 2.5 and HBsAg seropositivity on the risk of HCC development (OR: 22.17; CI, 3.33-147.45; p = 0.001). In this study, we demonstrated that PM 2.5 and HBsAg seropositivity were associated with HCC occurrence and had synergistic effects after adjusting for confounding factors.

Is There a Trade-off Between Economic Growth, Foreign Direct Investment, International Trade and Environmental Degradation? A Comparison Between Asian Developed and Developing Countries

Against the backdrop of impressive economic growth, the attractive foreign direct investment (FDI) and international trade of Asian countries, this study attempts to examine whether trade-offs between three aspects of economic growth, FDI, international trade and environmental degradation. Panel Autoregressive Distributed Lag (P-ARDL) model with pooled mean group estimates has been applied on panel data from 1996 to 2019. Panel Vector Error Correction model (VECM) Granger causality test is also used to identify multidimensional causal relationships between variables. The research results confirm the existence of a long-term equilibrium relationship between environmental degradation (CO2 emission) and the combination of economic growth, FDI and trade openness. In the short run, income positively affects CO2 emission while negative impacts and lower income elasticity have been found in the long run, implying that CO2 emission decreases and reverses its direction when income rises. Contrasting results collected from two groups of developed versus developing countries, the author reveals a linear relationship with a positive direction between economic growth and environmental degradation in developing countries while opposite results have been shown in developed nations, which supports the environmental Kuznets curve hypothesis in Asian countries. This study also provides sound proof for the ‘pollution haven hypothesis’ and the scale effects on FDI attraction and international trade. By applying relatively new estimators in the field of econometrics to compare the differences between Asian developed and developing countries, the research highlights the appropriate policies contributing to sustainable economic growth, attractive FDI and growing green trade with minimum adverse effects on the environment. JEL Codes: C33, F18, F21, F43, O44, Q40, Q51, Q53

Temporal Error Correlations in a Terrestrial Carbon Cycle Model Derived by Comparison to Carbon Dioxide Eddy Covariance Flux Tower Measurements

AbstractAtmospheric CO2 flux inversions require as input an estimate of spatial and temporal correlations of errors in their estimate of the prior mean. Some previous studies have used the differences in CO2 daily average flux estimates produced by terrestrial carbon cycle models and eddy covariance measurements to constrain the flux error correlations. Since inversions are starting to resolve the daily cycle, we set out to examine the correlations at sub‐daily time scales, as well as the correlations across years. To this end, we examine the autocorrelations in the difference between net ecosystem‐atmosphere exchange measurements from 75 AmeriFlux towers and temporally downscaled high‐spatial‐resolution flux estimates from the Carnegie‐Ames‐Stanford Approach (CASA) terrestrial carbon cycle model. We find that the daily cycle is prominent in these hourly autocorrelations and that these autocorrelations persist across years. We propose a family of functions to model these temporal correlations in atmospheric inversions, and use cross validation to determine which of the correlation functions best fits autocorrelation data from towers not in the training set. Correlation functions with a component that attempts to model the daily cycle in the differences match correlations from other towers better than those without. Those models that reproduce the same correlation structures at 1‐year intervals while modulating the amplitudes of the correlations between those intervals improve the fit still further.

Kriging-based Model Predictive Control for Lower-limb Rehabilitation Robots

Model predictive control (MPC) has emerged as a predominant method in the realm of control systems; yet, it faces distinct challenges. First, MPC often hinges on the availability of a precise and accurate system model, where even minor deviations can drastically affect the control performance. Second, it entails a high computational load due to the need to solve complex optimization problems in real time. This study introduces an innovative method that harnesses the probabilistic nature of Gaussian processes (GPs), offering a solution that is robust, adaptive, and computationally efficient for optimal control. Our methodology commences with the collection of data to learn optimal control policies. We then proceed with offline training of GPs on these data, which enables these processes to accurately grasp system dynamics, establish input–output relationships, and, crucially, identify uncertainties, thereby informing the MPC framework. Utilizing the mean and uncertainty estimates derived from GPs, we have crafted a controller that is capable of adapting to system deviations and maintaining consistent performance, even in the face of unforeseen disturbances or model inaccuracies. The convergence of the closed-loop system is assured through the application of the Lyapunov stability theorem. In our numerical experiments, the exemplary performance of our approach is demonstrated, notably in its capacity to adeptly handle the complexities of dynamic systems, even with limited training data, underlining a significant leap forward in MPC strategies.

Modelling Working Memory Capacity: Is the Magical Number Four, Seven, or Does it Depend on What You Are Counting?

Limited attentional capacity is essential to working memory. How its limit should be assessed is a debated issue. Five experiments compare Cowan's 4-units and Pascual-Leone's 7-units models of limited working memory capacity, with presentation time and attention to operative schemes as potential explanations of this discrepancy. Experiments 1a-1c used the Compound Stimuli Visual Information (CSVI) task, with long versus brief presentation. Capacity was estimated with the Bose-Einstein model, assuming a different number of attending acts in each condition. Participants' k estimates in both conditions were highly correlated and the means were not different, indicating that the same capacity is assessed in both conditions. Experiments 2 and 3 used the 5000-msec CSVI, and the Visual Array Task (VAT) in two conditions (5000- vs. 120-msec presentation). Capacity in the VAT was estimated with Morey's Bayesian method. Participants' k estimates in both VAT conditions were correlated, but the mean was higher with long presentation, suggesting that the long condition benefits from recoding or chunking. The k estimate in the CSVI correlated with the short VAT and (to a lesser degree in Exp.2) with the long VAT. The mean estimate of k in the CSVI was one unit more than in the short VAT. We conclude that the CSVI and the short VAT tap the same capacity, one unit of which in the short VAT is allocated to an operative scheme; we discuss how Cowan's and Pascual-Leone's views on limited capacity can be reconciled.

Effect of the Xanthine Oxidase Inhibitor Febuxostat on the Cardio-Ankle Vascular Index in Asymptomatic Patients with Hyperuricemia and Liver Dysfunction: A Sub-Analysis of the PRIZE Study.

The effect of uric acid (UA)-lowering therapy with xanthine oxidoreductase (XOR) inhibitors on the development of cardiovascular disease requires further investigation. This study aimed to evaluate the long-term effects of febuxostat on arterial stiffness, focusing on liver function. The PRIZE study involved random assignment of patients with asymptomatic hyperuricemia to receive either add-on febuxostat treatment (febuxostat group) or non-pharmacological treatment (control group). Of the 514 participants, 23 and 14 patients in the febuxostat and control groups, respectively, underwent assessment of arterial stiffness using the cardio-ankle vascular index (CAVI). The participants in each group were further grouped on the basis of their baseline alanine aminotransferase (ALT) or aspartate aminotransferase (AST) levels (above or below the media value or 30 U/L). The primary endpoint was the change in the CAVI from baseline to 12 and 24 months. Overall, no significant differences were found between the control and febuxostat groups in the least-squares mean estimates of changes in CAVI at 24 months (mean between-group difference, -0.41 [95% CI, -1.05 to 0.23]; p=0.204). However, there were significant differences in participants with higher baseline ALT or AST levels above 30 U/L at 24 months (mean between-group difference, -1.12 [95% CI, -2.23 to -0.01]; p=0.048 for ALT ≥ 30 U/L and -1.08 [95% CI, -2.13 to -0.03]; p=0.044 for AST ≥ 30 U/L). Two-year treatment with febuxostat demonstrated a beneficial effect on CAVI in patients with hyperuricemia and liver dysfunction.

Quantitative Assessment of Ailia coila (Hamilton, 1822) Fish Population in Kaptai Lake: A Length‐Based Approach

ABSTRACTThe Ailia coila, a small indigenous freshwater fish species in Kaptai Lake, plays a crucial role in providing employment, food, and financial security for the local people. With a high market demand, the average production of this species over the last 5 years reached about 112.27 metric tons. However, there has been a notable decline in Ailia coila fish production in Kaptai Lake in recent years. Therefore, to understand the current stock status of this species, this study employed a length‐based stock assessment method the “length‐based spawning potential ratio (LB‐SPR)”. Between August 2022 and March 2023, a total of 1219 individuals were collected through fortnightly sampling from two fish landing centers (BFDC fishery ghat and Kaptai boat ghat) and two local markets (Banarupa Bazar and Reserve Bazar). The maximum length of Ailia coila was recorded at 15.7 cm in total length, with an average length of 11.1 (±4) cm. The length‐weight relationship was estimated as W = 0.01084 L2.599 (R2 = 0.75). The value of shape parameter ‘b’ is smaller than three which indicate a negative allometric growth pattern. The estimated asymptotic length (L∞) and length at first maturity (L50%) were found to be 16.84 and 10.54 cm, respectively. The LB‐SPR revealed that the fishery is currently experiencing overfishing, with the spawning biomass (SPR = 32%) below the target reference point. The mean estimate of fishing mortality relative to natural mortality (F/M) was 3.07, more than three times the threshold F/M = 1, indicating extreme fishing mortality in this fishery. The estimated length at first capture (SL50%) was significantly higher than the length at first maturity (L50%), suggesting that the fishing is predominantly targeting mature fishes. To enhance the spawning biomass and ensure the sustainability of the fishery, it is imperative to reduce the current fishing pressure and implement appropriate management measures.