Residual Mean Research Articles

A sex-specific comparison of valve-in-valve transcatheter aortic valve implantation

Abstract Introduction Valve-in-valve (ViV) transcatheter aortic valve implantation (TAVI) is a viable and less-invasive alternative to open heart surgery in selected patients with bioprosthetic valve dysfunction. Since patients receiving initially a new bioprosthetic aortic valve are predominantly male, data on a sex-specific comparison of ViV-TAVI is scarce. Thus, we compared patient characteristics, procedural outcomes, and 1-year outcomes of female (F) versus male (M) patients with failing bioprosthetic aortic valves undergoing ViV-TAVI. Methods A total of 375 Patients at 3 German centres were included in this analysis. The 1-year mortality was the primary outcome measure, secondary outcomes included VARC-3 defined technical and device success and early safety. Results Overall, 160 (42.6 %) women and 215 (57.4 %) men were included. Women were older (F 81 (77; 84), p < 0.001), had a greater surgical risk (STS PROM F: 9.1 (6.1; 13,9) %, p < 0.001) and a higher rate of previous coronary bypass surgery (F: 25.6 % vs M: 40 %, p = 0.004), while presenting mostly failed valve sizes < 20 mm (F: 59.4 % vs M: 33.4 %, p < 0.001) and therefore higher gradients (F: 40 (31; 50) mmHg vs M: 32 (25; 42) mmHg, p < 0.001). Aortic regurgitation was the mode of failure in 7.5 % of F vs. 14.4 % of M. The number of stentless valves was higher in men (F: 8,1 % vs M: 14 %, p = 0.08). Patients received a self-expanding TAVI prostheses in 79.4 % and 75.8 % respectively in F and M patients. The rates of technical success (F: 75.6 % vs M: 83.3 %, p =0.06), device success (F: 52.5 % vs M: 58.2 %, p = 0.27) and early safety (F: 67.5 % vs M: 67 %, p = 0.91) were comparable between genders. Residual mean gradients were higher in F (F: 15 (11;22) mmHg vs M: 15 (10; 20) mmHg, p = 0.03), while the rates of residual regurgitations were greater in M patients (F: 1 (0.6 %) vs M: 11 (5.1 %), p = 0.04). The Kaplan-Meier estimated 30-day mortality rate (F: 2.5% vs M: 3.3%, p = 0.66) and 1-year mortality rates (F: 10 % vs M: 92.6 %, p = 0.37) were comparable between genders. Conclusion: F and M patients treated by ViV-TAVI have comparable short-term outcomes according to VARC-3-defined endpoints and similar mortality rates after 30 days and 1 year. The residual gradients in F and residual regurgitations in M could be explained by structural features of their failed valves. Further large scale studies are needed to determine, if such procedural outcomes are truly sex-specific.

DNA methylation clocks struggle to distinguish inflammaging from healthy aging, but feature rectification improves coherence and enhances detection of inflammaging.

There is no apparent biological significance of the CpGs selected by first- and next-generation clocksThe range of residuals for first- and next-generation clock predications on healthy samples is very large; for all models tested, a prediction error of +/-10-20 years is within the 95% range of variation for healthy controls and does not signify age accelerationThere is no significant shift in the mean of residuals for patient populations relative to healthy populations for most studied first- and next-generation clocks. For those with significance, the effect size is very small.Hypothesis-driven feature pre-selection, coupled with modified forward step-wise selection yields age predictors on par with first and next-generation clocks. EN/ML is not needed.Disease-related shifts at different CpG probes, along with learned model weights, can be either positive or negative; their combination leads to de-coherence effect in linear models.Model coherence can be induced by rectifying features to have only positive shifts in patient samples; this provides a better resolution between health and disease in DNAm age models, and expectedly, introduces more non-linearity to the input data.

Effects of Non-Tariff Barriers on Promotion of East African Community Integration: A Case of Magasins Généraux du Rwanda

Regional integration in East Africa has proven to strengthen investments within member nations. However, differing non-tariff barrier policies among East African Community (EAC) partner states have posed significant challenges to progress. This study assessed the effects of non-tariff barriers on the promotion of EAC integration, using Magasins Généraux du Rwanda (MAGERWA) as a case study. Specifically, the study evaluated the effects of technical barriers to trade, analysed the impact of import quotas, and scrutinised the effects of foreign exchange restrictions on EAC integration. The study was anchored in three theories: Liberal Intergovernmentalism Theory, Economic Integration Theories, and the Heckscher-Ohlin Theory. It used a descriptive research design with both quantitative and qualitative approaches. The target population consisted of 2,045 individuals. A sample size of 335 was determined using the Yamane formula and selected through both simple random and purposive sampling techniques. Data were collected using a questionnaire survey and analysed using SPSS version 26. Bivariate analysis, including P-values, was used to evaluate the relationship between independent and dependent categorical variables. Descriptive statistics such as mean and standard deviation, as well as single and multiple logistic regression models, were used to assess the strength of the relationships. A p-value < 0.05 and a 95% confidence interval were used for inference. Results were presented in frequency and cross-tabulation tables. The study found that technical barriers to trade had a statistically significant relationship with the promotion of EAC integration (r = 0.953, p<0.05). Similarly, import quotas showed a significant relationship with EAC integration (r = 0.954). Foreign exchange limitations also demonstrated a significant correlation with EAC integration, with a correlation coefficient of.966. The regression analysis showed a mean square of 199.267 and a residual mean square of 0.107, with an F-value of 1864.147 and a p-value of.000b. The researcher suggested that EAC member states should implement the planned goals of the community to promote free trade in the region, thereby enhancing economic integration.

Psychometric analysis and validation of the SF-34 PREG: An adaptation of the SF-36 for assessing quality of life in pregnant women.

This study aims to perform a psychometric analysis of the 36-item short form health survey (SF-36) in a population of pregnant women and validate an adapted version of the instrument to measure health-related quality of life specific to pregnancy. A cross-sectional study design was carried out with data collection between 7 and 36 weeks of gestation, with a total sample of 547 pregnant women divided into two randomized subsamples. Data were collected between September 2021 and April 2023. An exploratory factor analysis was initially performed on one subsample, followed by a confirmatory factor analysis on the other. Internal consistency was assessed using Cronbach's alpha and McDonald's omega, and correlations between factors were analyzed. Results from the exploratory factor analysis proposed a seven-factor model explaining 56% of the variance. All proposed dimensions achieved Cronbach's alpha scores above 0.75, with a total test score of 0.92. Furthermore, all dimensions exhibited positive and statistically significant correlations. Bartlett's test of sphericity and the Kaiser-Meyer-Olkin test yielded values of 5599 (P < 0.001) and 0.871, respectively. The confirmatory factor analysis reaffirmed this model with good fit indices: χ2 792 (P < 0.001), comparative fit index (CFI) and Tucker-Lewis index (TLI) >0.90, and standardized root mean square residual (SRMR) and root mean square error of approximation (RMSEA) <0.06. This study developed and validated the "SF-34 PREG," an adapted version of the SF-36, specifically designed for pregnant women. The SF-34 PREG demonstrated high reliability and a robust factorial structure, making it a more precise and relevant tool for assessing health-related quality of life during pregnancy. Although it does not include the social functioning dimension of the original SF-36, the SF-34 PREG offers improved relevance and accuracy for this specific population. Further research and clinical use of the SF-34 PREG are recommended.

External parameter calibration of laser scanner based on the combination of plane-control-based and plane-constraints-based methods

Accurate calibration of the external parameters of a laser scanner is crucial to ensure consistency in data representation from multiple sources. Plane features, known for their simplicity and ease of extraction, are widely used in external parameter calibration. The use of control planes with known plane parameters enables the simultaneous estimation of both translation and rotation parameters, while the use of planes with unknown parameters allows for efficient self-calibration. In this paper, we introduce a novel method that combines the strengths of both types of plane calibration methods to calibrate the external parameters of a laser scanner. The approach, with the help of a Gauss-Helmert adjustment model, integrates control and constraint planes to calibrate the external parameters. The method boasts the ease and accessibility offered by the plane constraint method, utilizing fewer control features while successfully estimating the external parameters of the laser scanner. To evaluate the feasibility of this method, two sets of 12 calibration experiments were conducted on the mobile measurement system platform. Notably, the residual Root Mean Square Errors (RMSE) obtained from these experiments were comparable to those achieved using traditional plane-control-based method. Furthermore, the external parameter correlation aligns with that obtained using the plane-control-based method, while the plane parameter correlation exhibits similarity to that obtained using the plane-constraint-based method. These results confirm that our method effectively minimizes the requirement for control features and enables more convenient calibration of the external parameters of the laser scanner.

INFLUENCE OF INCLUSIVE PEDAGOGICAL PRACTICES ON STUDENTS ACADEMIC ACHIEVEMENT IN PUBLIC SECONDARY SCHOOLS IN GATSIBO DISTRICT, RWANDA

Background:The most popular strategy for meeting every childs educational needs is inclusive education. Teachers in industrialized and developing nations have different perspectives on inclusive practice. The general objective of this study is to assess the influence of inclusive pedagogical practices on students academic achievement in public Secondary schools of Gatsibo District, Rwanda. Specifically, the study focused on to examine effectiveness of inclusive lesson planning on students academic achievements, to examine the influence of inclusive lesson evaluation on students academic achievement, and to ascertain the relationship between inclusive pedagogical practices and students academic achievement in public Secondary schools of Gatsibo District, Rwanda. Materials and Methods:This study employed a quantitative with descriptive research design. Target population was with 121 teachers and head teachers including19 teachers and one head teacher from G.S. Nyarubuye, 34 teachers and one head teacher from G.S. Bihinga, 34 teachers and one head teacher from G.S. Nyamirama and 30 teachers and one head teacher of G.S Nyakayaga. The census sampling technique was applied where all entire population of 121 participants were taken as sample size of the study. The instrument for gathering data was a structured questionnaire. Using the Statistical Package for Social Science (SPSS) version 26.0, the acquired data was examined descriptively. Inferential statistics will be shown to illustrate the link between the variables. The tables frequencies, means, and standard deviations will be used to display the results. This research was quantitative approach, the results were presented relied on research objectives. The research adopted a regression equation to establish the influence of inclusive pedagogical practices on students academic achievement in Public secondary schools. Results:On the effectiveness of inclusive lesson planning on students academic achievement in Public secondary schools, the results of data analysispresented that the overall mean was 4.14 that was between agree (4) and strongly agree (5) which presented that there was a significant effectiveness of inclusive lesson planning on students academic achievement in Public secondary schools. On the influence of inclusive lesson evaluation on students academic achievements in Public secondary schools, the resultsshowed that the overall mean was 4.34 that was between agree (4) and strongly agree (5) which presented that there was a significant influence of inclusive lesson evaluation on students academic achievements in Public secondary schools. The results of data analysisshowed that the relationship between inclusive pedagogical practices and students academic achievement was .947** whichpresented that there was a statistically significant relationship betweeninclusive pedagogical practices and students academic achievement in Public secondary schools. The results presented that the variables were statistically significant with regression mean square of 102.587 and residual mean square was 0.099 while F=1035.116 and P-value =.000b that concluded that there was a significant relationship between inclusive pedagogical practices and students academic achievement in Public secondary schools of Gatsibo District, Rwanda. Conclusion:Researcher suggested that teachers should apply the inclusive pedagogy in their teaching and promote the behavior that encourage students and create the friendship between teacher and students, this will promote the students academic achievement in Public secondary schools.

AGE ESTIMATION USING DIAPHYSEAL LONG BONE LENGTHS IN A NON-ADULT SKELETAL SAMPLE FROM GONUR DEPE, TURKMENISTAN

Introduction. The aim of this study is to provide group-specific regression equations for age estimation of immature human skeletal remains younger than 12 years of age from the diaphyseal length of the six long bones. Further, to compare inverse and classical calibration models for age estimation and the suitability of previously published regression methods for archaeological and present-day populations in relation to the analyzed sample. Materials and methods. The studied sample consists of 128 non-adult individuals from Gonur Depe – the major BMAC site in Turkmenistan (2300–1500 BCE). Regression formulae were obtained for each of the six long bones, separately for entire sample (0–12 years of age) and for two subsamples below and above 2 years of age. For each equation the coefficient of determination (R2) and F-statistics were calculated. Residuals were tested for normality of distribution, autocorrelation, homoscedasticity, and the equality of mean to zero (one-sample t-test). Comparison between inverse and classical calibration was provided using paired samples t-test. To assess the applicability of other regression formulae to the studied sample both the mean residuals (MR) and mean of the absolute value of the residuals (MAR) were calculated, as an estimate of bias and accuracy respectively. Results. All regression models showed a strong statistical significance and high R2 value. The slope coefficients of the regression lines of diaphyseal length upon age are greater for the upper limb bones both in the entire sample and two subsamples separately. The lower limb bones are characterized by lower growth rates. In contrast to the inverse calibration, for the classical model the mean standard errors (MSE) were smaller for the upper limb bones rather than for the lower limb bones. For the lower limb bones the standard error of the estimate (SE) was generally smaller in inverse formulae. Comparison of both models for the femur however shows their equal performance. For the exception of proposed and classical calibration formulae all inverse models for femur diaphyseal length show consistent differences from zero in relation to the studied sample. Discussion. The inverse and classical calibration models as a technique for age estimation using diaphyseal long bone lengths are both equally applicable in the studied sample. It is recommended to use the equations for the lower limb bones in the inverse model and for the upper limb bones in the classical model. The Gonur Depe population is characterized by relatively higher growth rates of the upper limb bones and distal limb segments relative to the proximal ones. Most of the previously published inverse calibration models are not recommended for uncritically use due to the high risk of obtaining biased estimates on samples that are different chronologically and/or territorially.

Supra-Annular Versus Intra-Annular Self-Expanding Valves in Small Aortic Annulus: A Propensity Score-Matched Study

BackgroundTranscatheter aortic valve replacement (TAVR) with self-expanding valves (SEVs) may have different outcomes with supra-annular valves (SAVs) or intra-annular valves (IAVs) in patients with small aortic annuli (SAA), but this topic remains underexplored. We aimed to evaluate outcomes between different SEVs, namely SAVs (CoreValve/Evolut R/PRO/PRO+/FX) vs. IAVs (Portico/Navitor). MethodsSingle-center data with patients with SAA (maximum diameter <23 mm) who underwent TAVR from 2013 to 2023 with SEVs, followed by 1:1 propensity score matching (PSM). ResultsWe obtained 86 PSM pairs with median age of 83.0 years (SAVs) and 82.0 years (IAVs), with women representing 77.6% of the PSM cohort. After TAVR, we did not find statistically significant differences for the following outcomes: Valve Academic Research Consortium-3 periprocedural mortality, technical success, device success, clinical efficacy, and rates of paravalvular leak were not statistically significantly different, but we found higher rates of permanent pacemaker implantation in the IAV group (1.2 vs. 8.1%; p = 0.029). Despite the larger indexed effective orifice area with SAVs (median 1.0 vs. 0.8 cm2/m2, p = 0.001), we did not find statistically significant differences between the groups in terms of residual mean gradients >20 mmHg (0.0 vs. 2.3%, p = 0.155), and severe prosthesis-patient mismatch (2.3 vs. 5.8%, p = 0.390). No statistically significant difference was observed in survival (log-rank p = 0.950) and stroke (p = 0.6547) between patients who received SAVs and IAVs. For patients with SAA, TAVR with SEV devices is safe. ConclusionsIAVs and SAVs are associated with comparable device performance in terms of hemodynamic structural and nonstructural dysfunction. Randomized data are needed to validate these findings and guide informed device selection.

Design and Rationale of the V-Wave Shunt MitraClip Study

Heart failure (HF) and moderate-to-severe mitral regurgitation (MR) with residual elevations in left atrial pressure (LAP) after MitraClip may remain symptomatic and experience subsequent HF readmissions. The V-Wave interatrial shunt system is a permanent interatrial septal implant that shunts blood from the left-to-right atrium and serves to continuously unload the left atrium. Although the V-Wave shunt has previously been studied in patients with HF, the safety and feasibility of its deployment at the time of the MitraClip procedure is unknown. The V-Wave Shunt MitraClip Study (NCT04729933) is an early feasibility study that aims to demonstrate the safety and efficacy of implantation of the V-Wave shunt device at the time of MitraClip procedure. Patients with moderate-to-severe secondary MR with left ventricular ejection fraction 20% to 50% and New York Heart Association functional class III/IV symptoms despite optimal medical therapy, residual mean LAP ≥20 mm Hg after MitraClip, and mean LAP-right atrial pressure difference ≥5 mm Hg are included. The primary safety end point is a composite outcome of all-cause death, stroke, myocardial infarction device embolization, cardiac tamponade, or device-related re-intervention or surgery at 30 days. Patients will be followed up to 5 years. Enrollment is ongoing, with 30-day results expected by the end of 2024. The V-Wave Shunt Mitraclip Study aims to demonstrate the safety and efficacy of the implantation of the V-Wave interatrial shunt device at the time of index MitraClip placement which may serve as an adjunctive method by which continuous left atrial unloading may be achieved.

FORECASTING INFLATION IN INDONESIA USING THE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE METHOD

Indonesia faces significant economic challenges, particularly inflation, which affects the economic, social, and cultural sectors. High inflation can exacerbate poverty, alter consumption patterns, and contribute to social injustice, whereas low inflation can enhance national income and stimulate economic activities. Given its fluctuating nature, inflation in Indonesia requires accurate forecasting to inform policy-making and economic decisions. This study aims to forecast inflation in Indonesia for the next eight months using the Autoregressive Integrated Moving Average (ARIMA) method. Monthly inflation data from January 2020 to April 2024 obtained from Bank Indonesia were analyzed. The ARIMA model, suitable for short-term forecasting, was selected due to its ability to handle data trends, non-stationarity, and noise filtering. The Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests to ensure stationarity. Initial ADF tests showed the presence of a unit root in the original data and the first differencing data, but data became stationary after the second differencing. The KPSS test confirmed a unit root in the original data and trend stationarity after the second and third differencing. Ordinary Least Squares (OLS) regression on the original data revealed a significant time trend, indicating deterministic trends. The optimal model identified was ARIMA(0,2,1) with AIC=51.81, as it met the criteria for normality, independence, and zero mean of residuals. This model effectively forecasts inflation from May to December 2024, which showed an increase with inflation values ​​of 3.02, 3.05, 3.07, 3.10, 3.12, 3.14, 3.17, and 3.19.

Comparing the allometric model to machine learning algorithms for aboveground biomass estimation in tropical forests

Estimating aboveground biomass (AGB) pools is critical for understanding the structural characteristics of forest ecosystems, as the quantitative analysis of biomass contributes to a better understanding of carbon sequestration and in studying the productivity within forest ecosystems. A more precise estimation of AGB is necessary for the estimation of the amount of carbon stored in an ecosystem that is needed for the valuation of the ecosystem services. Though regression models have been used previously for both tree-level and stand-level biomass estimations, supervised machine learning models have rarely been put to test in field-based estimations of AGB. This study was done in the tropical forests of Northeast India (two reserve forests in Tripura) to compare the allometric performance of weighted nonlinear regression model (NLM) to random forest regression (RFR), extreme gradient boost regression (XGB), and support vector machine regression (SVR) in estimating tree-level AGB. Due to their ease of measurement in field conditions and ensuring practical use, tree diameter at breast height (DBH) and tree height (H) were used as the independent variables to train the models. 506 trees representing 21 local tree species were inventoried. Then, the four models (NLM, RFR, XGB, and SVM) were trained separately on the data and compared to find an optimal method to estimate AGB using modeling efficiency of fit (MEfit) and Root Mean Squared Error (RMSE). The models were then validated using the predicted residual error sum of squares (PRESS) statistics. The best fit model in our study was using RFR, with a high MEfit (0.95–0.99), low RMSE (63.10–132.39 kg), and outperforming all other methods in the PRESS statistics, with the highest modeling efficiency of prediction (0.94–0.99) and lowest mean of absolute PRESS residuals (34.99–78.23). The results from Northeast India presented here provide preliminary evidence that RFR could be a reliable method for determining the field-level carbon stock and understanding the biomass dynamics for evergreen and moist deciduous tropical forests. The model accuracy may be constrained by a limited number of variables chosen to suit practical use in the field, and incorporating additional tree-level or stand-level variables such as crown diameter and stand basal area could enhance the robustness and generalizability of AGB estimation.

Assessing the impacts of climate change on agriculture and water systems via coupled human-hydrological modeling

Understanding the intricate relationship between farmers’ water usage and its hydrological effects is crucial for developing adaptable water policies. However, conducting such an analysis proves challenging due to the lack of appropriate modeling tools that comprehensively integrate water policies, water utilization, and hydrological processes. To address this challenge, this study introduces an innovative socio-hydrological framework to investigate the interplay between farmer actions and water resources. This framework integrates an agent-based model, which is based on the Value-Belief-Norm (VBN) theory, linked with a distributed hydrological model (SWAT-MODFLOW) to capture farmer behaviors. The modeling framework is applied to the Mahabad River Basin to assess water use and hydrological impacts. To assess the framework's ability, Nash–Sutcliffe (NS) efficiency and the coefficient of determination (R2) are computed for simulating runoff, while Mean Absolute Residual Error (MAE) and Root Mean Square Error (RMSE) are computed for simulating groundwater head. Results demonstrate the acceptable performance of the proposed model, with NS = 0.61, R2 = 0.69, MAE = 1.16, and RMSE = 1.92. Moreover, this study integrates climate change data from the 6th IPCC report to evaluate the model's responsiveness to altering climate conditions. Findings suggest that farmers facing economic challenges tend to opt for high-profit crops to ameliorate their financial situation. So, without policy changes, climate change will reduce crop yield, farmer income, and water storage. Furthermore, the study evaluates enhancing irrigation efficiency and groundwater extraction restrictions to mitigate climate change effects. Enhancing irrigation efficiency annually conserves 38.39 MCM, boosts crop yields by 6 %, elevates farmer incomes, and encourages a shift toward low-water-consuming crops, contributing to regional groundwater sustainability. Overall, the results of this study can enhance our comprehension of the impact of human activities on hydrological cycles, offering valuable insights for water managers.

Estimation of chronological age of Risso’s dolphin (Grampus griseus) based on DNA methylation frequency

Age of cetaceans is an important data set for their conservation and management. Although the chronological age of a toothed whale has traditionally estimated from the number of annual tooth growth layer groups (GLGs), this method has potential difficulties such as errors due to the subjectivity of the age-readers and time-consuming sample processing, in addition to lethal tooth sampling in many cases. To overcome these difficulties through a non-lethal approach, considerable amount of efforts has been made in recent years to construct chronological age estimation models based on the correlation between chronological and biological age expressed as the frequency of DNA methylation at CpG (cytosine-phosphate-guanine) sites. The present study, by using next-generation sequencing on DNA extracted from the skin tissues of 30 individuals (15 males and 15 females), performed methylation analysis on multiple CpG sites in the three genes previously reported as age-linked in cetaceans (GRIA2, TET2, and CDKN2A) to construct an age estimation model for Risso’s dolphins. Key aspects of this study are: (1) the chronological ages of Risso’s dolphins of wild origins used in this study had been determined by the GLGs of teeth prior to this study, and (2) as Risso’s dolphins are said to have a lifespan of 30 years, samples were collected from individuals aged 1–29 years, covering from young to old ages. Among the 11 CpG sites in GRIA2, 6 CpG sites in TET2, and 7 CpG sites in CDKN2A analyzed, the CpG sites in GRIA2 showed a statistically significant correlation with age, whereas those in TET2 and CDKN2A did not show such a strong correlation. Using multiple regression analysis on the CpG sites in the three genes, the best fitted model was expressed as follows: y=-0.7548 + 5.6164x(GRIA2_CpG3)+2.6187x(TET2_CpG6)-1.5907x(CDKN2A_CpG3) (R²=0.7062, residual mean=2.98, standard deviation=2.3). With the mean error of 2.98 years between the estimated and known chronological ages, the accuracy of this model was found to be comparable to or higher than that of previously reported age estimation models for cetaceans. This study demonstrated for the future that the age of wild Risso’s dolphins can be estimated by using their skin samples collected with minimal invasiveness.

Impact of the Polar Vortex on Sub‐Seasonal O/N2 Variability in the Lower Thermosphere Using GOLD and WACCM‐X

AbstractWe provide observational evidence that the stability of the stratospheric Polar vortex (PV) is a significant driver of sub‐seasonal variability in the thermosphere during geomagnetically quiet times when the PV is anomalously strong or weak. We find strong positive correlations between the Northern Annular Mode (NAM) index and subseasonal (10–90 days) Global Observations of the Limb and Disk (GOLD) O/N2 perturbations at low to mid‐northern latitudes, with a largest value of +0.55 at ∼30.0°N when anomalously strong or weak (NAM &gt;2.5 or &lt; −2.1) vortex times are considered. Strong agreement for O/N2 variability and O/N2‐NAM correlations is found between GOLD observations and the Whole Atmosphere Community Climate Model with thermosphere‐ionosphere eXtension (WACCM‐X) simulations, which is then used to delineate the global distribution of O/N2‐NAM correlations. We find negative correlations between subseasonal variability in WACCM‐X O/N2 and NAM at high northern and southern latitudes (as large as −0.54 at ∼60.0°S during anomalous vortex times). These correlations suggest that PV driven upwelling at low latitudes is accompanied by corresponding downwelling at high latitudes in the lower thermosphere (∼80–120 km), which is confirmed using calculations of residual mean meridional circulation from WACCM‐X.

Cardiac MRI segmentation using shifted-window multilayer perceptron mixer networks

Objectives. In this work, we proposed a deep-learning segmentation algorithm for cardiac magnetic resonance imaging to aid in contouring of the left ventricle, right ventricle, and Myocardium (Myo). Approach. We proposed a shifted window multilayer perceptron (Swin-MLP) mixer network which is built upon a 3D U-shaped symmetric encoder-decoder structure. We evaluated our proposed network using public data from 100 individuals. The network performance was quantitatively evaluated using 3D volume similarity between the ground truth contours and the predictions using Dice score coefficient, sensitivity, and precision as well as 2D surface similarity using Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMSD). We benchmarked the performance against two other current leading edge networks known as Dynamic UNet and Swin-UNetr on the same public dataset. Results. The proposed network achieved the following volume similarity metrics when averaged over three cardiac segments: Dice = 0.952 ± 0.017, precision = 0.948 ± 0.016, sensitivity = 0.956 ± 0.022. The average surface similarities were HD = 1.521 ± 0.121 mm, MSD = 0.266 ± 0.075 mm, and RMSD = 0.668 ± 0.288 mm. The network shows statistically significant improvement in comparison to the Dynamic UNet and Swin-UNetr algorithms for most volumetric and surface metrics with p-value less than 0.05. Overall, the proposed Swin-MLP mixer network demonstrates better or comparable performance than competing methods. Significance. The proposed Swin-MLP mixer network demonstrates more accurate segmentation performance compared to current leading edge methods. This robust method demonstrates the potential to streamline clinical workflows for multiple applications.

Modeling to COVID-19 and cancer data: Using the generalized Burr-Hatke model

This paper describes an extended version of the Burr–Hatke distribution via generalizing its survival function. This model is called as the generalized Burr-Hatke distribution. The suggested model's hazard rate function can be non-increasing (decreasing) form. Related statistical characteristics of the proposed model are identified. We provide closed-form expressions for the density function, hazard rate function, moments and their related metrics. In addition, some of the proposed model's reliability attributes, such as mean residual life and mean inactivity time, are determined both empirically and visually. The characterization of GBH model based on hazard rate, truncated and conditional moments is also provided. The maximum likelihood technique is employed for estimating the unknown model parameters. To explore the behavior of the estimates, Monte Carlo Simulation can be used. Finally, the new model's performance is verified using two sets of real data.

Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer

ObjectivesAccurate segmentation of the clinical target volume (CTV) of CBCT images can observe the changes of CTV during patients' radiotherapy, and lay a foundation for the subsequent implementation of adaptive radiotherapy (ART). However, segmentation is challenging due to the poor quality of CBCT images and difficulty in obtaining target volumes. An uncertainty estimation- and attention-based semi-supervised model called residual convolutional block attention-uncertainty aware mean teacher (RCBA-UAMT) was proposed to delineate the CTV in cone-beam computed tomography (CBCT) images of breast cancer automatically.MethodsA total of 60 patients who undergone radiotherapy after breast-conserving surgery were enrolled in this study, which involved 60 planning CTs and 380 CBCTs. RCBA-UAMT was proposed by integrating residual and attention modules in the backbone network 3D UNet. The attention module can adjust channel and spatial weights of the extracted image features. The proposed design can train the model and segment CBCT images with a small amount of labeled data (5%, 10%, and 20%) and a large amount of unlabeled data. Four types of evaluation metrics, namely, dice similarity coefficient (DSC), Jaccard, average surface distance (ASD), and 95% Hausdorff distance (95HD), are used to assess the model segmentation performance quantitatively.ResultsThe proposed method achieved average DSC, Jaccard, 95HD, and ASD of 82%, 70%, 8.93, and 1.49 mm for CTV delineation on CBCT images of breast cancer, respectively. Compared with the three classical methods of mean teacher, uncertainty-aware mean-teacher and uncertainty rectified pyramid consistency, DSC and Jaccard increased by 7.89–9.33% and 14.75–16.67%, respectively, while 95HD and ASD decreased by 33.16–67.81% and 36.05–75.57%, respectively. The comparative experiment results of the labeled data with different proportions (5%, 10% and 20%) showed significant differences in the DSC, Jaccard, and 95HD evaluation indexes in the labeled data with 5% versus 10% and 5% versus 20%. Moreover, no significant differences were observed in the labeled data with 10% versus 20% among all evaluation indexes. Therefore, we can use only 10% labeled data to achieve the experimental objective.ConclusionsUsing the proposed RCBA-UAMT, the CTV of breast cancer CBCT images can be delineated reliably with a small amount of labeled data. These delineated images can be used to observe the changes in CTV and lay the foundation for the follow-up implementation of ART.

Investigating molecular markers linked to acute myocardial infarction and cuproptosis: bioinformatics analysis and validation in the AMI mice model.

Cuproptosis-related key genes play a significant role in the pathological processes of acute myocardial infarction (AMI). However, a complete understanding of the molecular mechanisms behind this participation remains elusive. This study was designed to identify genes and immune cells critical to AMI pathogenesis. Based on the GSE48060 dataset (31 AMI patients and 21 healthy persons, GPL570-55999), we identified genes associated with dysregulated cuproptosis and the activation of immune responses between normal subjects and patients with a first myocardial attack. Two molecular clusters associated with cuproptosis were defined in patients with AMI. Immune infiltration analysis showed that there was significant immunity heterogeneity among different clusters. Multiple immune responses were closely associated with Cluster2-specific differentially expressed genes (DEGs). The generalized linear model machine model presented the best discriminative performance with relatively lower residual and root mean square error, and a higher area under the curve (AUC = 0.870). A final two-gene-based generalized linear model was constructed, exhibiting satisfactory performance in two external validation datasets (AUC = 0.719, GSE66360 and AUC = 0.856, GSE123342). Column graph, calibration curve, and decision curve analyses also proved the accuracy of AMI prediction. We also constructed a mouse C57BL/6 model of AMI (3 h, 48 h, and 1 week) and used qRT-PCR and immunofluorescence to detect the expression changes of CBLB and ZNF302. In this study, we present a systematic analysis of the complex relationship between cuproptosis and a first AMI attack, and provide new insights into the diagnosis and treatment of AMI.

A deep learning framework for blockage mitigation and duration prediction in mmWave wireless networks

Millimeter-Wave (mmWave) communication can be highly affected by blockages, which can drastically decrease the signal strength at the receiver side. To overcome the impact of blockages, predicting the optimal mitigation technique and accurately estimating the duration of the blockage events are crucial for maintaining reliable and high-performance mmWave communication systems. Prior works on mitigating blockages have proposed a variety of model and protocol-based blockage mitigation solutions that concentrate on a singular technique at a time, like switching the current beam to an alternative beam at the current base station or client. In this paper, we tackle the overarching question: what blockage mitigation technique should be employed? and what is the optimal sub-selection within that technique? We also address the blockage duration estimation problem. We solve these problems by developing a Gated Recurrent Unit (GRU) model, trained on data from periodic message exchanges in mmWave systems. We tested our neural network models by utilizing a mmWave simulator that is commercially available and widely used in wireless communication to compile a large amount of dataset for this purpose. Our findings reveal that our proposed method introduces no extra communication overhead, while achieving remarkable accuracy, exceeding 91%, in predicting the optimal blockage mitigation technique. Moreover, the blockage duration estimation model achieves a very high accuracy with a residual mean error of less than 0.04 s. Finally, we demonstrate that our proposed blockage mitigation method substantially boosts the volume of data transferred in comparison to various other blockage mitigation strategies.

Quantitative Analysis of the Vertical Interactions between Dust, Zonal Wind, and Migrating Diurnal Tide on Mars and the Role of Gravity Waves

In the atmospheric system of Mars, vertical interactions are crucial, yet quantitative studies addressing this issue remain scarce. Based on simulations using the Mars PCM-LMDZ, we present the first frequency-domain quantitative analysis of the vertical interactions among Martian atmospheric dust, zonal circulation, and the migrating diurnal tide (DW1), employing Partial Directed Coherence (PDC) techniques to quantify the strength of associations between different variables. Our findings reveal a chain of influence where sub-seasonal-scale dust signals in the troposphere, through the Doppler effect of middle atmospheric zonal winds, transmit modulated energy to the DW1 in the upper mesosphere, thereby facilitating interlayer atmospheric interactions. The radiative heating from dust activities enhances the residual mean meridional circulation, which, under the influence of the Coriolis force, further accelerates the westerlies. Although gravity wave activity also contributes to the acceleration of the westerlies, its forcing generally remains below 5 m/s, which is relatively weak compared to the impact of intense dust activities in the warm scenario experiments (approximately 20 m/s). Overall, this study quantifies the interactions among atmospheric layers by means of PDC technology and analytically demonstrates how dust energy is transferred to mesospheric tides by shaping the zonal winds in between.