Sensitivity Of Metrics Research Articles

Differentiating neural sensitivity and bias during face-emotion processing in youth: a computational approach.

The ability to interpret face-emotion displays is critical for the development of adaptive social interactions. Using a novel variant of a computational model and fMRI data, we examined behavioral and neural associations between two metrics of face-emotion labeling (sensitivity and bias) and age in youth. Youth and adults (n = 44, M age = 20.02, s.d. = 7.44, range = 8-36) completed an explicit face-emotion labeling fMRI task including happy to angry morphed face emotions. A drift-diffusion model was applied to choice and reaction time distributions to examine sensitivity and bias in interpreting face emotions. Model fit and reliability of parameters were assessed on adult data (n = 42). Linear and quadratic slopes modeled brain activity associated with dimensions of face-emotion valence and ambiguity during interpretation. Behaviorally, age was associated with sensitivity. The bilateral anterior insula exhibited a more pronounced neural response to ambiguity with older age. Associations between sensitivity and bias metrics and activation patterns indicated that systems encoding face-emotion valence and ambiguity both contribute to the ability to discriminate face emotions. The current study provides evidence for age-related improvement in perceptual sensitivity to facial affect across adolescence and young adulthood.

Proposal for variability-induced effective radius of elliptical gate-all-around junctionless transistors and its applicability in hydrogen gas sensors

Nano-ribbons with gate-all-around (GAA) architecture have been predicted to serve as next-generation on-chip transistors, among which, the junctionless transistor (JLT) has emerged as a promising candidate, mitigating few limitations of conventional inversion-mode metal–oxide–semiconductor field-effect transistors (MOSFETs). Due to imperfect fabrication process, the GAA architecture is often subject to cross-sectional variabilities. This work presents an analytical model for the effective radius of elliptical cross-section of the channel region having a non-circular oxide surrounding it, resulting in various structural configurations having non-uniform oxide thickness. To demonstrate the impact of cross-sectional variability of GAA-JLTs, few performance parameters, such as, drain current, threshold voltage, and so on, have been evaluated using our proposed formulation for the effective radius and the results were found to agree well with TCAD simulations. As a case study, we have emphasized on the applicability of our proposed model in variability-induced GAA-JLT-based hydrogen sensors using few sensitivity metrics. Our computations show substantial changes in the sensing device as the cross-section deviates from the ideal circular one. Our computations reveal that incorporating the effects of radius variability in the device model along with proper tuning of operating conditions can enhance the performance and accuracy of such sensors.

FKeras: A Sensitivity Analysis Tool for Edge Neural Networks

Edge computation often requires robustness to faults, e.g., to reduce the effects of transient errors and to function correctly in high radiation environments. In these cases, the edge device must be designed with fault tolerance as a primary objective. FKeras is a tool that helps design fault-tolerant edge neural networks that run entirely on chip to meet strict latency and resource requirements. FKeras provides metrics that give a bit-level ranking of neural network weights with respect to their sensitivity to faults. FKeras includes these sensitivity metrics to guide efficient fault injection campaigns to help evaluate the robustness of a neural network architecture. We show how to use FKeras in the co-design of edge NNs trained on the high-granularity endcap calorimeter dataset, which represents high energy physics data, as well as the CIFAR-10 dataset. We use FKeras to analyze a NN’s fault tolerance to consider alongside its accuracy, performance, and resource consumption. The results show that the different NN architectures have vastly differing resilience to faults. FKeras can also determine how to protect neural network weights best, e.g., by selectively using triple modular redundancy on only the most sensitive weights, which reduces area without affecting accuracy.

Accuracy of artificial intelligence-assisted endoscopy in the diagnosis of gastric intestinal metaplasia: A systematic review and meta-analysis.

Gastric intestinal metaplasia is a precancerous disease, and a timely diagnosis is essential to delay or halt cancer progression. Artificial intelligence (AI) has found widespread application in the field of disease diagnosis. This study aimed to conduct a comprehensive evaluation of AI's diagnostic accuracy in detecting gastric intestinal metaplasia in endoscopy, compare it to endoscopists' ability, and explore the main factors affecting AI's performance. The study followed the PRISMA-DTA guidelines, and the PubMed, Embase, Web of Science, Cochrane, and IEEE Xplore databases were searched to include relevant studies published by October 2023. We extracted the key features and experimental data of each study and combined the sensitivity and specificity metrics by meta-analysis. We then compared the diagnostic ability of the AI versus the endoscopists using the same test data. Twelve studies with 11,173 patients were included, demonstrating AI models' efficacy in diagnosing gastric intestinal metaplasia. The meta-analysis yielded a pooled sensitivity of 94% (95% confidence interval: 0.92-0.96) and specificity of 93% (95% confidence interval: 0.89-0.95). The combined area under the receiver operating characteristics curve was 0.97. The results of meta-regression and subgroup analysis showed that factors such as study design, endoscopy type, number of training images, and algorithm had a significant effect on the diagnostic performance of AI. The AI exhibited a higher diagnostic capacity than endoscopists (sensitivity: 95% vs. 79%). AI-aided diagnosis of gastric intestinal metaplasia using endoscopy showed high performance and clinical diagnostic value. However, further prospective studies are required to validate these findings.

Comparison of different 2D muscle indexes measured at the level of the 3rd lumbar vertebra in survival prediction in patients with renal cell carcinoma.

Low computed tomography (CT)-determined muscle mass, commonly determined with height-adjusted muscle indexes (MIs), predicts worse survival in several cancers and has been suggested as a prognostic assessment tool. Although several MIs measured at the level of the 3rd lumbar vertebra (L3) are commonly used, it remains unestablished how different L3-determined MIs perform in survival prognostication compared to each other. The objective of this study was to investigate the performance of different MIs for survival prognostication in renal cell carcinoma (RCC). We retrospectively enrolled 214 consecutive patients with RCC. We determined three L3-MIs (psoas muscle index (PMI), psoas muscle index and erector spinae index (PMI+ESI), and whole skeletal muscle index (SMI)) from preoperative CT scans. Categorization of those with low and normal muscle mass was based on the Youden Index sex-specific MI cut-offs. We determined sensitivity, specificity, and accuracy metrics for predicting 1-year, 5-year, and overall survival (OS) using Cox regression models. Low PMI, PMI+ESI, and SMI significantly predicted decreased 1-year, 5-year, and OS in uni- and multivariate models. PMI+ESI and SMI were more accurate than PMI in males, and PMI and PMI+ESI were more accurate than SMI in females in the prediction of 1-year survival. However, there were no differences in accuracies between MIs in 5-year and OS prediction. PMI+ESI performed well overall in short-term prognostication, but there were no differences between the MIs in long-term prognostication. We recommend the use of PMI+ESI for muscle evaluation, particularly when SMI cannot be evaluated.

Reduced inhibition control ability in children with ADHD due to coexisting learning disorders: an fNIRS study.

Inhibition control, as the core component of executive function, might play a crucial role in the understanding of attention deficit/hyperactivity disorder (ADHD) and specific learning disorders (SLD). Inhibition control deficits have been observed in children with ADHD or SLD. This study sought to test in a multi-modal fashion (i.e., behavior and plus brain imaging) whether inhibition control abilities would be further deteriorated in the ADHD children due to the comorbidity of SLD. A total number of 90 children (aged 6-12 years) were recruited, including 30 ADHD, 30 ADHD+SLD (children with the comorbidity of ADHD and SLD), and 30 typically developing (TD) children. For each participant, a 44-channel functional near infrared spectroscopy (fNIRS) equipment was first adopted to capture behavioral and cortical hemodynamic responses during a two-choice Oddball task (a relatively new inhibition control paradigm). Then, 50 metrics were extracted, including 6 behavioral metrics (i.e., OddballACC, baselineACC, totalACC, OddballRT, baselineRT, and totalRT) and 44 beta values in 44 channels based on general linear model. Finally, differences in those 50 metrics among the TD, ADHD, and ADHD+SLD children were analyzed. Findings showed that: (1) OddballACC (i.e., the response accuracy in deviant stimuli) is the most sensitive metric in identifying the differences between the ADHD and ADHD+SLD children; and (2) The ADHD+SLD children exhibited decreased behavioral response accuracy and brain activation level in some channels (e.g., channel CH35) than both the ADHD and TD children. Findings seem to support that inhibition control abilities would be further decreased in the ADHD children due to the comorbidity of SLD.

The curvature quantification of waveI in auditory brainstem responses detects cochlear synaptopathy in human beings.

Patients with age-related hearing loss complain often about reduced speech perception in adverse listening environment. Studies on animals have suggested that cochlear synaptopathy may be one of the primary mechanisms responsible for this phenomenon. A decreased waveI amplitude in supra-threshold auditory brainstem response (ABR) can diagnose this pathology non-invasively. However, the interpretation of the waveI amplitude in humans remains controversial. Recent studies in mice have established a robust and reliable mathematic algorithm, i.e., curve curvature quantification, for detecting cochlear synaptopathy. This study aimed to determine whether the curve curvature has sufficient test-retest reliability to detect cochlear synaptopathy in aging humans. Healthy participants were recruited into this prospective study. All subjects underwent an audiogram examination with standard and extended high frequencies ranging from 0.125 to 16kHz and an ABR with a stimulus of 80dBnHL click. The peak amplitude, peak latency, curvature at the peak, and the area under the curve of waveI were calculated and analyzed. A total of 80 individuals with normal hearing, aged 18 to 61 years, participated in this study, with a mean age of 26.4 years.Pearson correlation analysis showed a significant negative correlation between curvature and age, as well as between curvature and extended high frequency (EHF) threshold (10-16kHz). Additionally, the same correlation was observed between age and area as well as age and EHF threshold. The model comparison demonstrated that the curvature at the peak of waveI is thebest metric to correlate with EHF threshold. The curvature at the peak of wave I is the most sensitive metric for detecting cochlear synaptopathy in humans and may be applied in routine diagnostics to detectearly degenerations of the auditory nerve.

Associations of quantitative contrast sensitivity with vascular metrics on widefield swept-source OCT angiography across stages of diabetic retinopathy

PurposeTo investigate structure–function associations between contrast sensitivity (CS) and widefield swept-source optical coherence tomography angiography (WF SS-OCTA) vascular metrics across stages of non-proliferative (NPDR) and proliferative diabetic retinopathy (PDR), without...

Apriori prediction of chemotherapy response in locally advanced breast cancer patients using CT imaging and deep learning: transformer versus transfer learning.

Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response to NAC for patients with Locally Advanced Breast Cancer (LABC) before treatment initiation could be beneficial to optimize therapy, ensuring the administration of effective treatments. The objective of the work here was to develop a predictive model to predict tumor response to NAC for LABC using deep learning networks and computed tomography (CT). Several deep learning approaches were investigated including ViT transformer and VGG16, VGG19, ResNet-50, Res-Net-101, Res-Net-152, InceptionV3 and Xception transfer learning networks. These deep learning networks were applied on CT images to assess the response to NAC. Performance was evaluated based on balanced_accuracy, accuracy, sensitivity and specificity classification metrics. A ViT transformer was applied to utilize the attention mechanism in order to increase the weight of important part image which leads to better discrimination between classes. Amongst the 117 LABC patients studied, 82 (70%) had clinical-pathological response and 35 (30%) had no response to NAC. The ViT transformer obtained the best performance range (accuracy = 71 ± 3% to accuracy = 77 ± 4%, specificity = 86 ± 6% to specificity = 76 ± 3%, sensitivity = 56 ± 4% to sensitivity = 52 ± 4%, and balanced_accuracy=69 ± 3% to balanced_accuracy=69 ± 3%) depending on the split ratio of train-data and test-data. Xception network obtained the second best results (accuracy = 72 ± 4% to accuracy = 65 ± 4, specificity = 81 ± 6% to specificity = 73 ± 3%, sensitivity = 55 ± 4% to sensitivity = 52 ± 5%, and balanced_accuracy = 66 ± 5% to balanced_accuracy = 60 ± 4%). The worst results were obtained using VGG-16 transfer learning network. Deep learning networks in conjunction with CT imaging are able to predict the tumor response to NAC for patients with LABC prior to start. A ViT transformer could obtain the best performance, which demonstrated the importance of attention mechanism.

Integration of single-cell transcriptomic and chromatin accessibility on heterogenicity of human peripheral blood mononuclear cells utilizing microwell-based single-cell partitioning technology

Abstract Single-cell RNA sequencing (scRNA-Seq) deepens our understanding of cellular development and heterogeneity. However, limitations exist in unraveling cell states and gene regulatory programs. Chromatin state profiles assess gene expression potential and offer insights into transcriptional regulation. Integrated with gene expression data, chromatin accessibility region (CAR) profiles establish fundamental gene regulatory logic for cell fate. ATAC-seq (Assay for Transposase-Accessible Chromatin using Sequencing) is a highly potent approach for profiling genome-wide CARs. To investigate the power of the multiomics assay in identifying differentiated gene regulations, we conducted multiomic snATAC-seq+ snRNA-seq on PBMCs from two different donors, by utilizing the gentle and robust microwell-based single-cell partitioning technology. The assay showed high sensitivity and specificity metrics (>10,000 median unique fragments/cell, >0.7 fragments in peak score). Integrative analysis across donors revealed enriched transcription factor motifs and fragment coverage tracks in distinct cell types, correlating significantly with gene expression data. These findings highlight mRNA's intricate connections with CARs in immune cell development. Our study underscores the power of multiomic analysis in analyzing heterogeneity of PBMC cell populations and offers a toolkit to identify gene regulation specific to diverse cell types, enhancing our comprehension of epigenetic diversity.

Machine Learning Based Classification for Spam Detection

Electronic Electronic messages, i.e. e-mails, are a communication tool frequently used by individuals or organizations. While e-mail is extremely practical to use, it is necessary to consider its vulnerabilities. Spam e-mails are unsolicited messages created to promote a product or service, often sent frequently. It is very important to classify incoming e-mails in order to protect against malware that can be transmitted via e-mail and to reduce possible unwanted consequences. Spam email classification is the process of identifying and distinguishing spam emails from legitimate emails. This classification can be done through various methods such as keyword filtering, machine learning algorithms and image recognition. The goal of spam email classification is to prevent unwanted and potentially harmful emails from reaching the user's inbox. In this study, Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms are used to classify spam emails and the results are compared. Algorithms with different approaches were used to determine the best solution for the problem. 5558 spam and non-spam e-mails were analyzed and the performance of the algorithms was reported in terms of accuracy, precision, sensitivity and F1-Score metrics. The most successful result was obtained with the RF algorithm with an accuracy of 98.83%. In this study, high success was achieved by classifying spam emails with machine learning algorithms. In addition, it has been proved by experimental studies that better results are obtained than similar studies in the literature.

Machine Learning-driven Lung Cancer Detection

Machine Learning-driven Lung Cancer Detection

P150 Diagnostic utility of anti-cN1A autoantibody testing in sporadic inclusion body myositis

Abstract Background/Aims Sporadic inclusion body myositis (sIBM) is the most common acquired myopathy in patients aged over 50 years. Diagnostic criteria encompass clinical, biochemical, and myopathological features. Certain myopathological features possess high specificity but low sensitivity; reliance on such findings may lead to delayed or misdiagnoses. The discovery of autoantibodies against cytosolic 5'-Nucleotidase 1A (cN1A) in sIBM patients suggests an autoimmune component in pathogenesis, but the inclusion of this autoantibody in diagnostic criteria remains uncertain. Additionally, optimal testing methodology has not been established, with varying techniques (e.g., ELISA/line-blot) in use. We assessed antibody profiles in an sIBM cohort and reviewed diagnoses in anti-cN1A positive patients. Methods This study was based at the Northern Care Alliance NHS Foundation Trust which hosts a tertiary neuromuscular referral service for the North-West of England. Data sources included myositis clinic and immunology results databases. All patients tested using a validated myositis line-blot immunoassay (EUROLINE 16 Antigen et cN1A [EUROIMMUN]) November 2021-April 2023 were included. Diagnoses of all tested patients were identified through case-note review. Sensitivity and specificity metrics were calculated based on diagnosis and anti-cN1A antibody status. Results The total number of patients tested for anti-cN1A was 495. Of these, 10 patients had a diagnosis of sIBM. The true-positive and false-negative rates were both 50% (n = 5 each). Of the 485 patients who did not have sIBM, true-negativity was 97.52% (n = 473) and false-positivity 2.47% (n = 12). Overall, anti-cN1A had a sensitivity of 50%, specificity of 97.52%, positive predictive value of 29.41% and negative predictive value of 98.95% for sIBM (Table 1). Patients with a false-positive anti-cN1A antibody had diagnoses of: primary Raynaud’s (n = 5); undifferentiated CTD (n = 3); systemic sclerosis (n = 2); non-sIBM myositis (n = 2). Conclusion Anti-cN1A detected using the EUROLINE 16 Antigen assay has high specificity but lacks sensitivity for sIBM, consistent with previous literature. These findings suggest that this assay performs similarly to other cN1A detection methodologies and has utility in confirming the diagnosis of sIBM where there is clinical suspicion. Further real-world analysis of antibody data in larger cohorts is required to clarify the role of serological testing in sIBM diagnostic criteria, to improve diagnostic delays and misclassifications. Disclosure M. Al-Attar: None. T. Khoo: None. J.B. Lilleker: None. H. Chinoy: None.

The Detection of Pulp Stones with Automatic Deep Learning in Panoramic Radiographies: An AI Pilot Study.

This study aims to evaluate the effectiveness of employing a deep learning approach for the automated detection of pulp stones in panoramic imaging. A comprehensive dataset comprising 2409 panoramic radiography images (7564 labels) underwent labeling using the CranioCatch labeling program, developed in Eskişehir, Turkey. The dataset was stratified into three distinct subsets: training (n = 1929, 80% of the total), validation (n = 240, 10% of the total), and test (n = 240, 10% of the total) sets. To optimize the visual clarity of labeled regions, a 3 × 3 clash operation was applied to the images. The YOLOv5 architecture was employed for artificial intelligence modeling, yielding F1, sensitivity, and precision metrics of 0.7892, 0.8026, and 0.7762, respectively, during the evaluation of the test dataset. Among deep learning-based artificial intelligence algorithms applied to panoramic radiographs, the use of numerical identification for the detection of pulp stones has achieved remarkable success. It is expected that the success rates of training models will increase by using datasets consisting of a larger number of images. The use of artificial intelligence-supported clinical decision support system software has the potential to increase the efficiency and effectiveness of dentists.

Detection and classification of mandibular fractures in panoramic radiography using artificial intelligence.

This study aimed to assess the performance of a deep learning algorithm (YOLOv5) in detecting different mandibular fracture types in panoramic images. This study utilized a dataset of panoramic radiographic images with mandibular fractures. The dataset was divided into training, validation, and testing sets, with 60%, 20%, and 20% of the images, respectively. An equal number of control panoramic radiographs, which did not contain any fractures, were also randomly distributed among the three sets. The YOLOv5 deep learning model was trained to detect six fracture types in the mandible based on the anatomical location including symphysis, body, angle, ramus, condylar neck, and condylar head. Performance metrics of accuracy, precision, sensitivity (recall), specificity, dice coefficient (F1 score), and area under the curve (AUC) were calculated for each class. A total of 498 panoramic images containing 673 fractures were collected. The accuracy was highest in detecting body (96.21%) and symphysis (95.87%), and was lowest in angle (90.51%) fractures. The highest and lowest precision values were observed in detecting symphysis (95.45%) and condylar head (63.16%) fractures, respectively. The sensitivity was highest in the body (96.67%) fractures and was lowest in the condylar head (80.00%) and condylar neck (81.25%) fractures. The highest specificity was noted in symphysis (98.96%), body (96.08%), and ramus (96.04%) fractures, respectively. The dice coefficient and AUC were highest in detecting body fractures (0.921 and 0.942, respectively), and were lowest in detecting condylar head fractures (0.706 and .812, respectively). The trained algorithm achieved promising performance metrics for the automated detection of most fracture types, with the highest performance observed in detecting body and symphysis fractures. Machine learning can provide a potential tool for assisting clinicians in mandibular fracture diagnosis.

Dynamic Object Detection in Surveillance Videos using Temporal Convolutional Networks and Federated Learning in Edge Computing Environments

This research addresses the importance of advancing dynamic object detection in surveillance videos by introducing a novel framework that integrates Temporal Convolutional Networks (TCNs) and Federated Learning (FL) within edge computing environments. This research is motivated by the critical need for real-time threat response, enhanced security measures, and privacy preservation in dynamic surveillance scenarios. Leveraging TCNs, the system captures temporal dependencies, providing a comprehensive understanding of object movements. FL ensures decentralized model training, mitigating privacy concerns associated with centralized approaches. Current challenges in real-time processing, privacy preservation, and adaptability to dynamic environments are addressed through innovative solutions. Model optimization techniques optimize TCN efficiency, ensuring real-time processing. Advanced privacy-preserving mechanisms secure FL, addressing privacy concerns. Transfer learning and data augmentation enhance adaptability to dynamic scenarios. The proposed system not only addresses existing challenges but also contributes to the evolution of surveillance technology. Comprehensive metrics, including accuracy, sensitivity, specificity, and real-time processing metrics, provide a thorough evaluation of the system's performance. This research introduces an approach to dynamic object detection, combining TCN and FL in edge computing environments. Results show accuracy exceeding 97%, sensitivity and specificity at 97% and 98%, and F1 score reaching 96%.

Application of deep learning methods for automated analysis of retinal structures in ophthalmology

This article examines a current area of research in the field of ophthalmology the use of deep learning methods for automated analysis of retinal structures. This work explores the use of deep learning methods such as EfficientNet and DenseNet for the automated analysis of retinal structures in ophthalmology. EfficientNet, originally proposed to balance between accuracy and computational efficiency, and DenseNet, based on dense connections between layers, are considered as tools for identifying and classifying retina features. Automated analysis includes identifying pathologies, assessing the degree of their development and, possibly, diagnosing various eye diseases. Experiments are performed on a dataset containing a variety of images of retinal structures. Results are evaluated using metrics of accuracy, sensitivity, and specificity. It is expected that the proposed deep learning methods can significantly improve the automated analysis of retinal images, which is important for the diagnosis and monitoring of eye diseases. As a result, the article highlights the significance and promise of using deep learning methods in ophthalmology for automated analysis of retinal structures. These methods help improve the early diagnosis, treatment and monitoring of eye diseases, which can ultimately lead to improved healthcare quality and improved patient lives.

Survivability Prediction of Breast Cancer Patients Using Three Data Mining Methods: A Comparative Study

Background and aims: Breast cancer (BC) is the leading cause of mortality among women. Early diagnosis is crucial for effective treatment. This study applied suitable data mining methods that provide rules and present influential prognostic factors on the survival time of BC patients. Methods: The dataset consisted of 1574 women diagnosed between January 2002 and December 2012 at the Cancer Registry Center of Nemazi hospital in Fars Province, Iran. Patients were classified based on prognostic factors using three popular data mining methods, including decision tree (J48), Naïve Bayes (NB), and nominal logistic regression (NLR). The Weka software was considered to compare these methods using sensitivity, specificity, and accuracy metrics. The outcome of the study was the median survival time, which was categorized into three classes. Results: In total, 212 women (13.5%) died of BC, whose mean age was 49.74 years old. Overall survival rates at 2, 3, 5, and 10 years were 0.98, 0.94, 0.87, and 0.76, respectively. The mean and median survival times were 4.81 and 4.27 years. Sensitivity, specificity, and accuracy for J48 and NB were 0.480, 0.570, and 0.572, as well as 0.483, 0.610, and 0.584, respectively. In addition, the corresponding values were 0.488, 0.584, and 0.579 for NLR, respectively. Further, J48 showed that the Nottingham Prognostic Index (NPI) was the most influential prognostic factor. Conclusion: This paper sought to improve the accuracy of BC classification using data mining methods. Comparing multiple prediction models gave us an insight into the relative prediction abilities of different data mining methods. The results suggested NB as the best classifier due to its higher accuracy and specificity. Finally, J48 identified the NPI as the most effective prognostic factor.

Empirical stream thermal sensitivity cluster on the landscape according to geology and climate

Abstract. Climate change is modifying river temperature regimes across the world. To apply management interventions in an effective and efficient fashion, it is critical to both understand the underlying processes causing stream warming and identify the streams most and least sensitive to environmental change. Empirical stream thermal sensitivity, defined as the change in water temperature with a single degree change in air temperature, is a useful tool to characterize historical stream temperature conditions and to predict how streams might respond to future climate warming. We measured air and stream temperature across the Snoqualmie and Wenatchee basins, Washington, during the hydrologic years 2015–2021. We used ordinary least squares regression to calculate seasonal summary metrics of thermal sensitivity and time-varying coefficient models to derive continuous estimates of thermal sensitivity for each site. We then applied classification approaches to determine unique thermal sensitivity regimes and, further, to establish a link between environmental covariates and thermal sensitivity regimes. We found a diversity of thermal sensitivity responses across our basins that differed in both timing and magnitude of sensitivity. We also found that covariates describing underlying geology and snowmelt were the most important in differentiating clusters. Our findings and our approach can be used to inform strategies for river basin restoration and conservation in the context of climate change, such as identifying climate-insensitive areas of the basin that should be preserved and protected.

Quantitative evaluation of morphological and functional changes in meibomian glands and lipid layer thickness in patients with and without keratoconus.

To quantitatively evaluate the morphological parameters of meibomian glands (MGs) and lipid layer thickness (LLT) in patients with keratoconus (KC). In this prospective, cross-sectional study, 164 eyes of 164 keratoconus patients and 64 eyes of 64 age-matched control subjects were included. An advanced automatic MG analyzer was used to quantitatively measure the morphological and functional parameters of MGs. Morphological and functional parameters of MGs, LLT, and other ocular surface parameters were compared between the control and KC groups. The mean meibomian gland diameter, length, square, and gland area ratio (GA) were all significantly decreased in the KC group (all P < 0.05), while no significant difference was observed in the gland tortuosity index (TI) and gland signal index (SI) between the KC and control groups (all P > 0.05). There was no significant difference in the number of total and incomplete blinking among patients with different stages of keratoconus (all P > 0.05). The gland diameter, square, and TI were all negatively associated with KC severity (all P < 0.05), while no significant difference was observed among all stages of KC in gland length, GA, and SI (all P > 0.05). Moreover, the LLTs were positively correlated with the gland diameter, square, GA, and TI and negatively correlated with anterior corneal curvature or KC severity (all P < 0.05). Atrophic morphological changes in the meibomian glands were closely correlated with the severity of keratoconus. Gland diameter may be a sensitive functional morphology metric of meibomian glands in patients with keratoconus.