Accurate Measure Research Articles

A Meta-Analysis of the Moderators in the Relationship Between Morphological Awareness and Chinese Reading Accuracy

ABSTRACT Purpose This study aimed to deepen the understanding of the relationship between morphological awareness and Chinese reading accuracy by exploring the factors that influence this connection through a meta-analysis. Method We conducted a meta-analysis using data from 84 articles, comprising 104 effect sizes and a sample of 14,348 children (mean ages = 3.76 to 13.79 years). The study examined potential moderators such as script type (traditional and simplified script), age, and the type of reading accuracy measure (word and character reading), as well as the interaction between these moderators on the abovementioned relationship. Results The analysis revealed that script type was a significant moderator in the relationship. The correlation between morphological awareness and word/character reading was stronger in traditional script than in simplified script. Age also had a significant moderating effect in the relationship, with the correlation increasing significantly with age. However, neither the type of reading accuracy measure nor the interaction between measure type and script type significantly moderated the relationship between MA and Chinese reading accuracy. Conclusion The moderation by script type suggests that the differences in morpheme orthography between the two scripts can affect the relationship between morphological awareness and reading accuracy. The moderator of age indicates that the influence of morphological awareness on reading accuracy may grow stronger as children get older. The lack of a moderating effect by the type of reading accuracy measure suggests that the correlation between morphological awareness and word/character reading is consistent across these measures.

Different view of the diagnostics test accuracy measures and optimal cut-off point selection procedure under tree or umbrella ordering

ABSTRACT In the realm of medical diagnostic testing, diagnostic tests can assume either binary forms, distinguishing between diseased and non-diseased states, or ordinal forms, categorizing states from non-diseased to various stages (1 to K). Another significant classification scheme for multi-class scenarios is tree or umbrella ordering, which entails several unordered sub-classes (subtypes) within a biomarker. Within tree or umbrella ordering, the classifier assesses whether the marker measurement for one class surpasses or falls below those for the other classes. Although Receiver Operating Characteristic (ROC) curves and summary measures have been adapted to accommodate tree and umbrella ordering, these approaches often yield cut-off points that generate highly sensitive tests for certain disease subtypes while compromising specificity for others. This may not be ideal for all diseases. Hence, in this investigation, we explore diverse measures of diagnostic test accuracy and optimal cut-off point selection procedures under tree or umbrella ordering to foster more specific tests. We present numerical examples and simulation studies and demonstrate the approach using real data on lung cancer.

A more accurate measure of endometrial cancer incidence rates in New Mexico: A hysterectomy-corrected analysis

A more accurate measure of endometrial cancer incidence rates in New Mexico: A hysterectomy-corrected analysis

Assessing diagnostic accuracy: 18F-FDG PET-CT scans in low-grade infection detection among post-traumatic long bone non-unions; a literature review and clinical data.

The diagnosis of low-grade infection in post-traumatic long bone non-unions poses challenges due to the absence of clinical signs. This study aimed to review the available literature on the diagnostic accuracy of imaging techniques for low-grade infections and assess the diagnostic accuracy of 18F-FDG PET-CT scans for low-grade infection in post-traumatic long bone non-unions. A mini-review was conducted using Pubmed in March 2024. A retrospective study was conducted including adult patients with a long bone non-union, suspected of infection. All patients underwent 18F-FDG PET-CT scans as the index test before surgical intervention, with peri‑operative cultures obtained during surgery serving as the reference standard. Quantitative analyses were performed on the standardized uptake value (SUV) measurements obtained from the 18F-FDG PET-CT scans. Diagnostic accuracy measures including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Receiver operating characteristic (ROC) curve analysis was employed to evaluate the discriminatory ability of SUV measurements. Literature suggests that 18F-FDG PET-CT is the most accurate imaging technique to detect low-grade infections. The study included a total of 51 18F-FDG PET-CT scans and cultures from 50 patients with long bone non-unions. The diagnostic accuracy was found to be 0.67 (95 % CI 0.44-0.87). The PPV and NPV were calculated as 0.79 (95 % CI 0.53-1.00) and 0.52 (95 % CI 0.30-0.73), respectively. Quantitative analyses of SUV measurements demonstrated a low level of accuracy, with all area under the curve (AUC) values < 0.75 and ROC curves showing a trajectory fairly parallel to the diagonal line. The findings of this study indicate that in post-traumatic long bone non-unions, where a low-grade fracture-related infection (FRI) is suspected, the 18F-FDG PET-CT has a performance that is advantageous over other imaging techniques. A careful interpretation of the scan results is warranted, possibly including the quantitative analysis on tracer uptake as an adjunct. Nevertheless, the diagnostic accuracy in this condition is not as good as in early-onset FRI cases, and this should be taken into account when treating these challenging cases.

Is L2 pronunciation affected by increased task complexity in pronunciation-unfocused speaking tasks?

Abstract This study examines the effects of task complexity on second language (L2) pronunciation accuracy and global pronunciation measures in pronunciation-unfocused tasks and assesses the relationship between acoustic and listener-based pronunciation measures. Eighty-two Catalan/Spanish learners of English performed simple and complex versions of a problem-solving monologic speaking task, for which the oral stops /p, t, k/ and vowel contrasts /iː/-/ɪ/ and /æ/-/ᴧ/ were embedded in the lexical items used to perform the task. Pronunciation accuracy was gauged through acoustic measurements of laryngeal timing (voice onset time), vowel contrastiveness and nativelikeness (Mahalanobis distances), and native speakers’ ratings of comprehensibility and accentedness. Results revealed detrimental effects of increased task complexity on the productions of oral stops and speech comprehensibility and accentedness; however, no consistent task complexity effects were found on vowel accuracy. The analysis also revealed an association between segmental accuracy and global dimensions of L2 speech.

Hierarchical Attention and Contextual Embedding for Robust Sarcasm Detection

Objective: The study aims to develop a technique for sarcasm detection in social media text to enrich the sentiment analysis. Methods: The methodology used in the present research is as follows: emotion-embedded vectors for capturing the emotional content of the text, dynamic contextual modulation for the adaption of the model to the given context, and Hierarchical attention mechanism for segmentation of the text at the different level of abstraction. Findings: Evaluation with the test set proved that the proposed model achieved accuracy rates 89% higher than the benchmark models. Including these several complicated methods helped achieve greater accuracy and F1 measures, contributing to sarcasm detection efficiency. Novelty: This approach ensures that vital sarcastic alerts are detected, providing a better text analysis and increasing sentiment analysis's accuracy and robustness. Keywords: Sarcasm detection, Sentiment analysis, Natural language processing, Deep learning, Opinion mining

Measuring fisheries outcomes from coastal wetland restoration: a review and meta‐analysis

Coastal wetlands, including saltmarshes, mangrove forests, and seagrass meadows, can support fisheries by providing habitat and exporting food and nutrients to adjacent or distant ecosystems. However, there is insufficient understanding about the extent to which wetland restoration can benefit fishery species. Here, we report qualitative and quantitative reviews of literature to evaluate the approaches used to measure or estimate fisheries outcomes of coastal wetland restoration and test for the effects of restoration on fisheries outcomes. Most studies focused on saltmarshes and mangroves, with fewer studies focussing on seagrasses. We found that effects of wetland restoration on fish populations have mainly been assessed for species that reside in coastal wetlands for at least part of their life cycle, while few studies have tested effects on fish populations via trophic subsidies. Moreover, few studies have directly assessed the effects of restoration on fisheries production. Instead, most studies have measured abundances of individual species at restored sites and compared those to undisturbed and/or disturbed areas. Restored areas contained similar fish densities to undisturbed or disturbed areas; but few studies compared restored areas to both undisturbed and disturbed areas at the same time. Overall, we did not find consistent and robust evidence for fisheries outcomes from wetland restoration projects because (1) there were not many studies, (2) the studies reported substantial variability in outcomes, and (3) the density of fish measured at a restoration site is not an accurate measure of the total fisheries benefit as adult fishes do not always reside in the restored habitat.

Using QALYs as an Outcome for Assessing Global Prediction Accuracy in Diabetes Simulation Models.

(1) To demonstrate the use of quality-adjusted life-years (QALYs) as an outcome measure for comparing performance between simulation models and identifying the most accurate model for economic evaluation and health technology assessment. QALYs relate directly to decision making and combine mortality and diverse clinical events into a single measure using evidence-based weights that reflect population preferences. (2) To explore the usefulness of Q2, the proportional reduction in error, as a model performance metric and compare it with other metrics: mean squared error (MSE), mean absolute error, bias (mean residual), and R2. We simulated all EXSCEL trial participants (N = 14,729) using the UK Prospective Diabetes Study Outcomes Model software versions 1 (UKPDS-OM1) and 2 (UKPDS-OM2). The EXSCEL trial compared once-weekly exenatide with placebo (median 3.2-y follow-up). Default UKPDS-OM2 utilities were used to estimate undiscounted QALYs over the trial period based on the observed events and survival. These were compared with the QALYs predicted by UKPDS-OM1/2 for the same period. UKPDS-OM2 predicted patients' QALYs more accurately than UKPDS-OM1 did (MSE: 0.210 v. 0.253; Q2: 0.822 v. 0.786). UKPDS-OM2 underestimated QALYs by an average of 0.127 versus 0.150 for UKPDS-OM1. UKPDS-OM2 predictions were more accurate for mortality, myocardial infarction, and stroke, whereas UKPDS-OM1 better predicted blindness and heart disease. Q2 facilitated comparisons between subgroups and (unlike R2) was lower for biased predictors. Q2 for QALYs was useful for comparing global prediction accuracy (across all clinical events) of diabetes models. It could be used for model registries, choosing between simulation models for economic evaluation and evaluating the impact of recalibration. Similar methods could be used in other disease areas. Diabetes simulation models are currently validated by examining their ability to predict the incidence of individual events (e.g., myocardial infarction, stroke, amputation) or composite events (e.g., first major adverse cardiovascular event).We introduce Q2, the proportional reduction in error, as a measure that may be useful for evaluating and comparing the prediction accuracy of econometric or simulation models.We propose using the Q2 or mean squared error for QALYs as global measures of model prediction accuracy when comparing diabetes models' performance for health technology assessment; these can be used to select the most accurate simulation model for economic evaluation and to evaluate the impact of model recalibration in diabetes or other conditions.

Executive functions and processing speed in covert cerebral small vessel disease.

Executive dysfunction and slowed processing speed are central cognitive impairments in cerebral small vessel disease (cSVD). It is unclear whether the subcomponents of executive functions become equally affected and whether computerized tests are more sensitive in detecting early cognitive changes over traditional tests. The associations of specific executive abilities (cognitive flexibility, inhibitory control, working memory) and processing speed with white matter hyperintensities (WMHs) and Instrumental Activities of Daily Living (IADL) were examined. In the Helsinki Small Vessel Disease Study, 152 older individuals without stroke or dementia were assessed with brain magnetic resonance imaging and comprehensive neuropsychological evaluation. WMH volumes were obtained with automated segmentation. Executive functions and processing speed measures included established paper-and-pencil tests and the computer-based Flexible Attention Test (FAT), Simon task and Sustained Attention to Response Task. White matter hyperintensity volume and IADL were associated with multiple cognitive measures across subdomains independently of demographic factors. The highest effect sizes were observed for FAT numbers and number-letter tasks (tablet modifications from the Trail Making Test), FAT visuospatial span, Simon task and semantic verbal fluency. Some of the widely used tests such as Stroop inhibition, phonemic fluency and digit span were not significantly associated with either WMHs or IADL. Processing speed and executive function subcomponents are broadly related to functional abilities and WMH severity in covert cSVD, but the strength of associations within subdomains is heavily dependent on the assessment method. Digital tests providing precise measures of reaction times and response accuracy seem to outperform many of the conventional paper-and-pencil tests.

Note on the radical inflation in the estimates of error variance in measurement models

This note discusses the radical technical inflation in error variance and the related standard error of test scores from both conceptual and empirical viewpoints. This technical inflation arises as a direct consequence of the technical underestimation of item-score correlation by the product–moment coefficient of correlation (PMC), which is embedded in the traditional estimators of reliability such as coefficients alpha, theta, omega, or rho (maximal reliability). Specifically, in educational settings where compilations usually include both easy and difficult items, the estimate by PMC may substantially deviate from the true association between an item and the score. Consequently, the use of traditional estimators of reliability leads to technically inflated estimates of standard errors, as the error variance related to these traditional measurement models is significantly inflated, resulting in deflated reliability estimates. In educational testing, employing deflation-corrected standard errors, calculated using deflation-corrected reliability estimators, would provide a more accurate measure of the test score’s true precision.

The role of interoception in lifestyle factors: a systematic review

Abstract Interoception, the perception of the internal state of the body, has been shown to play an important role in various conditions. A clear overview of the relationship between interoception and lifestyle factors has to the best of our knowledge not yet been presented. This systematic review aims to provide insight into the current state of evidence about the role of interoception in lifestyle factors. A protocol for this systematic review was registered in PROSPERO (CRD42023465957). A systematic search conducted in PubMed, Embase, and Web of Science databases included terms such as interoception (accuracy, attention, awareness, sensitivity, or sensibility), as well as lifestyle factors (alcohol consumption, cognitive leisure and relaxation activities, eating behavior, exercise, sleep, or smoking). Studies written in English, using qualitative data, including adults, using at least one measure of interoception, and using at least on measure of a lifestyle factor were included. The review included 68 studies, in which 47 measures of interoceptive accuracy and 34 measures of interoceptive attention were identified. Six studies quantified interoceptive awareness (e.g., the interaction between interoceptive accuracy and attention). 12 studies focused on alcohol consumption, 22 studies on cognitive leisure and relaxation activities, 19 studies on eating behavior, 9 studies on exercise, 4 studies on sleep, and 5 studies on smoking. Generally, cognitive leisure and relaxation activities, eating behavior, and fasting showed positive relations with interoception. Conflicting evidence on the relation between exercise and some interoceptive domains, and moderate evidence on the positive relation with other interoceptive domains was found. Greater sleep difficulties were related to enhanced interoception scores and alcohol consumption was negatively related with interoception, while results on the relation between interoception and smoking behavior showed varying results. Key messages • Some lifestyle factors positively influence interoception and vice versa, while for other factors the results are varying or conflicting. • The role of interoceptive skills in lifestyle factors is important for lifestyle and health management.

Aortic integral distensibility based on the auto-segmented dynamic motion of the aortic wall

Abstract Background Aortic stiffness is important in cardiovascular diseases and predicting outcomes. Echocardiography is a widely used noninvasive imaging modality for assessing aortic stiffness, but its accuracy can be limited by the low spatial resolution of ultrasound-based images and the subjectivity of manual measurements of aortic dimensions. Automated segmentation analysis can provide a more accurate and objective measure. Objective An automated algorithm and software have been developed for segmenting the aortic wall. A new measurement technique called "Aortic Integral Distensibility (AID)" has been formulated using this framework. This method overcomes limitations of traditional method focusing solely on the two positions of Aortic Distensibility (AD). Method In this particular investigation, 112 participants were subjected to echocardiography in order to quantify the dynamics of the aortic wall. An automated algorithm was devised to delineate the aortic wall by utilizing an M-mode echocardiography scan. The statistical analysis encompassed the measurement of AD and AID before and after the age of 65 group. This analysis was conducted considering various risk factors, including age, gender, weight, BMI, smoking, and blood pressure. Additionally, clinical parameters, specifically AIx @75 (Augmentation Index) and Framingham Risk Score (FRS), were evaluated as well. Inter-rater reliability was conducted between the traditional AD and the newly implemented AID. Results The results indicate that the group under 65 had more distensibility than the group over 65, and AID measurements were slightly higher than AD measurements. SBP showed a strong correlation with AID, rather than AD. AIx @75 and FRS had a negative relationship with distensibility. In the 65 years or older group, there was a significant negative correlation observed between AIx @75 and AID (r = -0.4, p = 0.012). Furthermore, a strong correlation was also observed between FRS and AID (r = -0.53, p = 0.002). Bland-Altman analysis demonstrated that the automated methodology had better reproducibility and less variability in distensibility measurements than the manual methodology. Conclusion The study suggests that AID could be valuable for assessing aortic stiffness. AID could help clinicians diagnose and manage cardiovascular diseases more accurately. The automatic approach provides reliable measurements for aortic distensibility and strain, which has clinical implications.Schematic Chart of the Study Process

(MIS)measuring cognitive load and arousal in deception: A multitrait–multimethod analysis

AbstractPurposeCognitive load and arousal are cornerstones of many deception detection strategies and theories; in turn, their effective measurement is critical. However, fundamental criteria for establishing the quality and accuracy of measures have largely been overlooked. In this study, we examined the reliability and construct validity of common cognitive load and arousal measures.MethodWe obtained three independent secondary datasets in which participants (N = 238) had lied or told the truth about witnessing a suspicious event. Using a multitrait–multimethod analysis, we assessed three measures of their cognitive load and arousal: participants' self‐reports, trained coders' observations, and objective behaviours.ResultsAlthough all measures were reliable, they achieved differing levels of validation. Specifically, measures of cognitive load showed evidence of convergent validity, but not discriminant validity. There was no empirical support for the construct validity of arousal measures.ConclusionsThese findings suggest that inconsistencies in the diagnosticity of cues to deception and theory support may be attributable to the measures employed. Researchers may not be assessing constructs of interest, particularly in the case of arousal.

Relationships between capnogram parameters by mainstream and sidestream techniques at different breathing frequencies

Capnography, routinely used in operating rooms and intensive care units, reveals essential information on lung ventilation and ventilation–perfusion matching. Mainstream capnography directly measures CO2 in the breathing circuit for accurate analysis and is considered a reference technique. Sidestream capnography, however, analyzes gas away from the patient leading to potentially less accurate measures. While these methodological differences impact the capnogram indices in mechanically ventilated patients, such assessments during spontaneous breathing are essentially lacking. Accordingly, we aimed to compare mainstream and sidestream capnography in spontaneously breathing subjects, focusing on differences in capnogram shape and dead space indices at various respiratory rates. Simultaneous mainstream and sidestream time and volumetric capnography were performed on spontaneously breathing adults (n = 35). Measurements were performed during controlled low (10/min), medium (12/min), and high (20/min) breathing rates as a challenge. Correlation and Bland–Altman analyses were used to assess trends and agreements between time and volumetric capnography indices obtained by the mainstream and sidestream techniques, including end-tidal CO2 (ETCO2), shape factors reflecting the slopes of phases 2 and 3, and anatomical and physiological dead space fractions. ETCO2 and physiological dead space measured by mainstream and sidestream techniques showed excellent correlations (r > 0.90, p < 0.001 for all breathing rates) and agreements. While strong correlations and moderate agreements were evidenced in the parameters reflecting the late phase of expiration (phase 3 slope and exhaled CO2 volume), these relationships were weaker for indices related to the early phase of expiration (phase 2 slope, anatomical dead space). Changing breathing frequency caused significant alterations in all capnography parameters, which were detectable by both mainstream and sidestream techniques. Sidestream capnography cannot substitute the more accurate mainstream technique for measuring the absolute values of shape factors and ventilation dead space fractions. However, sidestream capnography is also able to detect and track changes in uneven alveolar emptying, ventilation–perfusion matching and ventilation dead space fraction in spontaneously breathing subjects.

Creation of an Anomaly Detection System for Blockchain-based Cloud Deployments that Consider Privacy and Delay.

Cloud-based deployments are increasingly vulnerable to many kinds of assaults; hence powerful anomaly detection systems are needed to prevent security breaches. While effective to some degree, current methods like RSSI, GTM, and APG have drawbacks in terms of scalability, precision, and accuracy. In order to improve the forecast accuracy of cloud assaults and optimize blockchain-based cloud installations, this study suggests a novel anomaly detection system that combines multimodal feature analysis, deep learning models, and QoS-aware sidechains. The suggested strategy considerably surpasses traditional approaches in terms of precision, accuracy, recall, and AUC performance by optimizing feature variance across various sample types and utilizing cutting-edge deep learning algorithms. Additionally, the framework shows increased efficiency in terms of throughput, energy usage, and block mining latency, makingcombining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), the system analyses system logs to find anomalous patterns of activity linked to various threats. The architecture guarantees the transparency and integrity of system records through the use of Blockchain technology, while Deep Learning models offer accurate and quick anomaly detection. The advantages of both Deep Learning and Blockchain technology support the choice to merge them. Blockchain technology ensures the accuracy of anomaly detection and prevents tampering with system records by providing a distributed, immutable ledger. On the other hand, deep learning models produce high precision, accuracy, recall, and AUC measures because of their remarkable pattern recognition skills and ability to adjust to shifting attack strategies. Experimental data analyses show the effectiveness of the suggested framework.

Enhancing IoT Security through an Artificial Neural Network Approach

This study aims to fortify Internet of Things (IoT) security through the strategic implementation of Artificial Neural Networks (ANNs). With the rapid expansion of IoT devices, traditional security measures have struggled to cope with the dynamic and complex nature of these environments. ANNs, known for their adaptability, are explored as a promising solution to enhance security. The central objective is to significantly improve the accuracy of IoT security measures by optimizing ANN architectures. Using a curated dataset with key environmental parameters, the study evaluates three ANN models—Backpropagation Neural Network (BPNN), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM). The evaluation metrics include accuracy, precision, recall, and F1-score across different train-test splits. Results show that LSTM consistently outperforms BPNN and MLP, demonstrating superior accuracy and the ability to capture temporal dependencies within IoT security data. Implications stress the importance of aligning model selection with specific application goals, considering factors like computational efficiency. In conclusion, this research contributes valuable insights into the practical implementation of ANNs for IoT security, guiding future optimization efforts and addressing real-world deployment challenges to safeguard sensitive data and ensure system resilience in the evolving IoT landscape.

CNN-Based Models for Emotion and Sentiment Analysis Using Speech Data

The study aims to present an in-depth Sentiment Analysis (SA) grounded by the presence of emotions in the speech signals. Nowadays, all kinds of web-based applications ranging from social media platforms and video-sharing sites to e-commerce applications provide support for Human–Computer Interfaces (HCIs). These media applications allow users to share their experiences in all forms such as text, audio, video, GIF, and so on. The most natural and fundamental form of expressing oneself is through speech. Speech-Based Sentiment Analysis (SBSA) is the task of gaining insights into speech signals. It aims to classify the statement as neutral, negative, or positive. On the other hand, Speech Emotion Recognition (SER) categorizes speech signals into the following emotions: disgust, fear, sadness, anger, happiness, and neutral. It is necessary to recognize the sentiments along with the profoundness of the emotions in the speech signals. To cater to the above idea, a methodology is proposed defining a text-oriented SA model using the combination of CNN and Bi-LSTM techniques along with an embedding layer, applied to the text obtained from speech signals; achieving an accuracy of 84.49%. Also, the proposed methodology suggests an Emotion Analysis (EA) model based on the CNN technique highlighting the type of emotion present in the speech signal with an accuracy measure of 95.12%. The presented architecture can also be applied to different other domains like product review systems, video recommendation systems, education, health, security, and so on.

Evaluation of the Effectiveness of Compression Therapy Combined with Exercises Versus Exercises Only Among Lipedema Patients Using Various Outcome Measures

The treatment of lipedema remains challenging, largely due to widespread misconceptions. Selecting the appropriate treatment method necessitates the use of accurate outcome measures. This study aims to evaluate the effectiveness of compression therapy combined with exercises versus exercises alone in lipedema patients using various outcome measures. Twenty-four women with lipedema were divided into two equal groups: one group received compression therapy plus exercises while the other group performed exercises only. The effectiveness of the treatment was assessed before and after the intervention using several measures: an SF-36 questionnaire, a symptom severity survey, circumference (via 3D scanning), and body composition analysis. Significant improvements were observed in the SF-36 Physical Functioning and SF-36 Energy/Fatigue scores among participants in the compression group. Additionally, there was a reduction in the heaviness of extremities, the disproportion between the trunk and limbs, and the level of swelling in the compression therapy. Circumferences decreased in both groups. Although more circumferences were significantly reduced in the compression group, the reduction at the point above the knee was greater in the non-compression group. Compression therapy is an effective treatment for lipedema. Various measures, such as quality-of-life questionnaires and symptom severity surveys, can be used as valuable tools for assessing the effectiveness of lipedema treatment.

Application of Bayesian Causal Inference in the Study of the Relationship Between Biodiversity and Aboveground Biomass of Subtropical Forest in Eastern China

The relationship between biodiversity and ecosystem function is crucial for understanding the structure and processes of subtropical forest ecosystems. However, the extent to which biodiversity influences subtropical forest biomass remains unclear. This study applies Bayesian causal inference to explore causal relationships between forest Aboveground Biomass (AGB) and its potential driving factors (biodiversity factors, biotic factors and abiotic factors) based on Huangshan Forest Dynamics Plots. Furthermore, hypothetical interventions are introduced to these driving factors within the causal network to estimate their potential impact on AGB. The causal relationship network reveals that species diversity and functional diversity are the most direct factors influencing AGB, whereas phylogenetic diversity exerts only an indirect effect. Biotic and abiotic factors also contribute indirect effects on AGB, potentially by influencing other mediating indexes. Intervention analysis shows that with low-level interventions on direct influencing factors, the probability of low AGB is as high as 84%. As the intervention level increases to high, the probability of low AGB decreases by 36%. Moreover, AGB demonstrates a particularly sensitive response to changes in Rao’s quadratic entropy (RaoQ) intervention levels, more so than to other factors, highlighting its critical role in maintaining forest biomass. Therefore, we contend that functional diversity, due to its direct reflection of species’ roles in ecosystem processes, is a more accurate measure of the impact of biodiversity on biomass compared to species or phylogenetic diversity and the interplay between abiotic and biotic factors and biodiversity should not be overlooked. This approach offers a powerful tool for exploring causal relationships, thereby providing a more nuanced and accurate understanding of the relationship between biodiversity and forest ecosystem function.

Changes and prognostic impact of noninvasive myocardial work indices in patients undergoing transcatheter aortic valve implantation.

The prognostic significance of noninvasive myocardial work (MW) indices in patients undergoing transcatheter aortic valve implantation (TAVI) has not been adequately examined. We retrospectively selected 88 consecutive patients (mean age 79.9 ± 6.4 years, 40% males) with severe aortic stenosis scheduled for TAVI enrolled in the EffecTAVI registry. Exclusion criteria were prior valve surgery, atrial fibrillation, and left bundle branch block (LBBB) at baseline. Global work index (GWI), global constructive work (GCW), global wasted work (GWW), and global work efficiency (GWE) were measured by echocardiography at baseline and at 30 days. Accuracy of the noninvasive measures was assessed by invasive evaluation of MW. In the overall population, a significant reduction in GWI (2406 ± 567 vs. 2063 ± 515 mmHg% before and after TAVI respectively, P < 0.001), GCW (2783 ± 616 vs. 2380 ± 495 mmHg%, P < 0.001) and GWW (238 ± 203 vs. 186 ± 135 mmHg%, P = 0.015) was observed at 30 days after TAVI. GWE improved only in patients who did not develop left ventricular dyssynchrony due to new-onset LBBB or pacemaker implantation following TAVI. In a multivariable Cox-regression analysis, GWE after TAVI (hazard ratio 0.892, 95% confidence interval 0.81-0.97; P = 0.011) was the strongest predictor of adverse events (a composite of all-cause death, worsening of dyspnea, or rehospitalization for cardiovascular events) at 1-year follow-up. TAVI results in significant changes in MW indices, including an early decrease in GWI, GCW and GWW, and an improvement in GWE in patients without left ventricle dyssynchrony. A GWE equal or less than 92% at 30 days is indicative of poor clinical outcomes at 1 year.