Similar Measurements Research Articles

Tobacco retail availability and smoking-A systematic review and meta-analysis.

Health policy makers worldwide have adopted evidence-based legislation, largely directed at consumers, to reduce tobacco-related harm. It is suggested that limiting supply by decreasing retail availability can also reduce cigarette smoking. To inform policy makers this systematic literature review assesses whether reducing availability is associated with smoking behaviours. Systematic literature searches of five databases were carried out up to January 2023. Included studies had at least one exposure (tobacco retail density, proximity or mixed measures thereof) and outcomes of smoking behaviour. Meta-analysis of effect estimates were undertaken if there were at least three studies with similar population, exposure and outcome measures. Sixty-two studies were included, and positive associations were found between tobacco outlet density and cigarette smoking in pregnancy, youth, adults and cessation. Meta-analyses were undertaken for retail density and ever smoking (odds ratio [OR] 1.20; 95% confidence interval [CI] 1.04, 1.37; I2 = 87.3%), and current youth smoking (OR 1.23; 95% CI 1.08, 1.40; I2 = 0.0%), adult smoking (OR 1.11; 95% CI 1.01, 1.22; I2 = 74.8%); and mixed measures of retail availability near schools and current youth smoking (OR 1.03; 95% CI 1.01, 1.05; I2 = 0.0%). There is evidence higher tobacco retail density is consistently associated with cigarette smoking in pregnant women, young people and adults when ecological studies are included in meta-analysis. Meanwhile, evidence synthesis reveals restrictive tobacco retail laws based on proximity or mixed measures near home or very near school may not reduce smoking except in men who smoke heavily. Mechanisms to reduce retailer density should be thus considered part of tobacco control strategies.

A method for miRNA diffusion association prediction using machine learning decoding of multi-level heterogeneous graph Transformer encoded representations

MicroRNAs (miRNAs) are a key class of endogenous non-coding RNAs that play a pivotal role in regulating diseases. Accurately predicting the intricate relationships between miRNAs and diseases carries profound implications for disease diagnosis, treatment, and prevention. However, these prediction tasks are highly challenging due to the complexity of the underlying relationships. While numerous effective prediction models exist for validating these associations, they often encounter information distortion due to limitations in efficiently retaining information during the encoding-decoding process. Inspired by Multi-layer Heterogeneous Graph Transformer and Machine Learning XGboost classifier algorithm, this study introduces a novel computational approach based on multi-layer heterogeneous encoder—machine learning decoder structure for miRNA-disease association prediction (MHXGMDA). First, we employ the multi-view similarity matrices as the input coding for MHXGMDA. Subsequently, we utilize the multi-layer heterogeneous encoder to capture the embeddings of miRNAs and diseases, aiming to capture the maximum amount of relevant features. Finally, the information from all layers is concatenated to serve as input to the machine learning classifier, ensuring maximal preservation of encoding details. We conducted a comprehensive comparison of seven different classifier models and ultimately selected the XGBoost algorithm as the decoder. This algorithm leverages miRNA embedding features and disease embedding features to decode and predict the association scores between miRNAs and diseases. We applied MHXGMDA to predict human miRNA-disease associations on two benchmark datasets. Experimental findings demonstrate that our approach surpasses several leading methods in terms of both the area under the receiver operating characteristic curve and the area under the precision-recall curve.

RDGAN: Prediction of circRNA-Disease Associations via Resistance Distance and Graph Attention Network.

As a series of single-stranded RNAs, circRNAs have been implicated in numerous diseases and can serve as valuable biomarkers for disease therapy and prevention. However, traditional biological experiments demand significant time and effort. Therefore, various computational methods have been proposed to address this limitation, but how to extract features more comprehensively remains a challenge that needs further attention in the future. In this study, we propose a unique approach to predict circRNA-disease associations based on resistance distance and graph attention network (RDGAN). First, the associations of circRNA and disease are obtained by fusing multiple databases, and resistance distance as a similarity matrix is used to further deal with the sparse of the similarity matrices. Then the circRNA-disease heterogeneous network is constructed based on the similiarity of circRNA-circRNA, disease-disease and the known circRNA-disease adjacency matric. Second, leveraging the three neural network modules-ResGatedGraphConv, GAT and MFConv-we gather node feature embeddings collected from the heterogeneous network. Subsequently, all the characteristics are supplied to the self-attention mechanism to predict new potential connections. Finally, our model obtains a remarkable AUC value of 0.9630 through five-fold cross-validation, surpassing the predictive performance of the other eight state-of-the-art models.

Correlation of Patient-Reported Social Determinants of Health With Census Tract Measures of Socioeconomic Disadvantage in Patients With GI Cancers in Eastern North Carolina.

Investigating the impact of social determinants of health (SDOHs) on cancer care in large populations relies on census estimates. Routine clinic SDOH screening provides timely patient-level information which could inform best practices. This study evaluated the correlation between patient-reported SDOH needs and population-level census tract measures. This was a retrospective cross-sectional study of a cohort of adult patients with GI malignancy screened for SDOHs such as financial insecurity, transportation, and food insecurity during initial outpatient evaluation at East Carolina University (formerly Vidant) Health Medical Center in Greenville, NC (November 2020-July 2021). Primary outcomes included number and severity of identified SDOH needs and area deprivation index (ADI) and census tract measures for each patient. Spearman rank correlations were calculated among patient-level needs and between patient-level needs and similar census tract measures. Of 112 patients screened, 58.9% self-identified as White (n = 66) and 41.1% as Black (n = 46). A total of 50.5% (n = 54) resided in a rural county. The collective median state ADI rank was 7 (IQR, 5-9). The median household income was $38,125 in US dollars (USD) (IQR, $31,436-$48,934 [USD]). Only 12.5% (n = 14) reported a moderate or severe financial need. Among reported needs, financial need moderately correlated with food insecurity (coefficient, 0.46; P < .001) and transportation (coefficient, 0.45; P < .001). Overall, census tract measures and reported needs poorly correlated. Lack of transportation correlated with percentage of households without a vehicle (coefficient, 0.18; P = .03) and limited access to healthy foods (coefficient, 0.18; P = .04). Given the poor correlation between reported and census needs, population-level measures may not accurately predict patient-reported needs. These findings highlight the importance of SDOH screening in the clinical setting to reduce health disparities and identify opportunities to improve care delivery.

A Topological Distance Between Multi-Fields Based on Multi-Dimensional Persistence Diagrams.

The problem of computing topological distance between two scalar fields based on Reeb graphs or contour trees has been studied and applied successfully to various problems in topological shape matching, data analysis, and visualization. However, generalizing such results for computing distance measures between two multi-fields based on their Reeb spaces is still in its infancy. Towards this, in the current article we propose a technique to compute an effective distance measure between two multi-fields by computing a novel multi-dimensional persistence diagram (MDPD) corresponding to each of the (quantized) Reeb spaces. First, we construct a multi-dimensional Reeb graph (MDRG), which is a hierarchical decomposition of the Reeb space into a collection of Reeb graphs. The MDPD corresponding to each MDRG is then computed based on the persistence diagrams of the component Reeb graphs of the MDRG. Our distance measure extends the Wasserstein distance between two persistence diagrams of Reeb graphs to MDPDs of MDRGs. We prove that the proposed measure is a pseudo-metric and satisfies a stability property. Effectiveness of the proposed distance measure has been demonstrated in (i) shape retrieval contest data - SHREC 2010 and (ii) Pt-CO bond detection data from computational chemistry. Experimental results show that the proposed distance measure based on the Reeb spaces has more discriminating power in clustering the shapes and detecting the formation of a stable Pt-CO bond as compared to the similar measures between Reeb graphs.

Multi-Domain Based Dynamic Graph Representation Learning for EEG Emotion Recognition.

Graph neural networks (GNNs) have demonstrated efficient processing of graph-structured data, making them a promising method for electroencephalogram (EEG) emotion recognition. However, due to dynamic functional connectivity and nonlinear relationships between brain regions, representing EEG as graph data remains a great challenge. To solve this problem, we proposed a multi-domain based graph representation learning (MD 2GRL) framework to model EEG signals as graph data. Specifically, MD 2GRL leverages gated recurrent units (GRU) and power spectral density (PSD) to construct node features of two subgraphs. Subsequently, the self-attention mechanism is adopted to learn the similarity matrix between nodes and fuse it with the intrinsic spatial matrix of EEG to compute the corresponding adjacency matrix. In addition, we introduced a learnable soft thresholding operator to sparsify the adjacency matrix to reduce noise in the graph structure. In the downstream task, we designed a dual-branch GNN and incorporated spatial asymmetry for graph coarsening. We conducted experiments using the publicly available datasets SEED and DEAP, separately for subject-dependent and subject-independent, to evaluate the performance of our model in emotion classification. Experimental results demonstrated that our method achieved state-of-the-art (SOTA) classification performance in both subject-dependent and subject-independent experiments. Furthermore, the visualization analysis of the learned graph structure reveals EEG channel connections that are significantly related to emotion and suppress irrelevant noise. These findings are consistent with established neuroscience research and demonstrate the potential of our approach in comprehending the neural underpinnings of emotion.

Spatial–Temporal Similarity Fusion Graph Adversarial Convolutional Networks for traffic flow forecasting

Traffic flow forecasting is integral to the advancement of intelligent transportation systems and the development of smart cities. This paper introduces a novel model, the Spatial–Temporal Similarity Fusion Graphs Adversarial Convolutional Networks (STSF-GACN), which leverages advanced data preprocessing techniques to enhance the predictive accuracy and efficiency of traffic flow forecasting.The innovation of our approach lies in the meticulous construction of the spatial–temporal similarity matrix through the precise calculation of temporal and spatial similarities. This matrix forms the backbone of our model, serving as the generator in the integrated Generative Adversarial Network (GAN) architecture. The Spatial–Temporal Similarity Fusion Adaptive Graph Convolutional Network, developed as part of our GAN’s generator, utilizes cutting-edge techniques such as the Wasserstein distance and Dynamic Time Warping to optimize the adaptive adjacency matrix, enabling the model to capture latent spatial–temporal correlations with unprecedented depth and precision.The discriminator of the GAN further refines the model by evaluating the accuracy of the traffic predictions, ensuring that the generative model produces results that are not only accurate but also robust against varying traffic conditions. This cohesive integration of GAN into the model architecture allows for a significant improvement in prediction accuracy and convergence speed, moving beyond traditional forecasting methods.

LKLPDA: A Low-Rank Fast Kernel Learning Approach for Predicting piRNA-Disease Associations.

Piwi-interacting RNAs (piRNAs) are increasingly recognized as potential biomarkers for various diseases. Investig-ating the complex relationship between piRNAs and diseases through computational methods can reduce the costs and risks associated with biological experiments. Fast kernel learning (FKL) is a classical method for multi-source data fusion that is widely employed in association prediction research. However, biological networks are noisy due to the limitations of measurement technology and inherent natural variation, which can hamper the effectiveness of the network-based ideal kernel. The conventional FKL method does not address this issue. In this study, we propose a low-rank fast kernel learning (LRFKL) algorithm, which consists of low-rank representation (LRR) and the FKL algorithm. The LRFKL algorithm is designed to mitigate the effects of noise on the network-based ideal kernel. Using LRFKL, we propose a novel approach for predicting piRNA-disease associations called LKLPDA. Specifically, we first compute the similarity matrices for piRNAs and diseases. Then we use the LRFKL to fuse the similarity matrices for piRNAs and diseases separately. Finally, the LKLPDA employs AutoGluon-Tabular for predictive analysis. Computational results show that LKLPDA effectively predicts piRNA-disease associations with higher accuracy compared to previous methods. In addition, case studies confirm the reliability of the model in predicting piRNA-disease associations. Availability and implementation: The LKLPDA software and data are freely available at https://github.com/Shiqzz/LKLPDA-master.git.

Unsupervised selective labeling for semi-supervised industrial defect detection

In industrial detection scenarios, achieving high accuracy typically relies on extensive labeled datasets, which are costly and time-consuming. This has motivated a shift towards semi-supervised learning (SSL), which leverages labeled and unlabeled data to improve learning efficiency and reduce annotation costs. This work proposes the unsupervised spectral clustering labeling (USCL) method to optimize SSL for industrial challenges like defect variability, rarity, and complex distributions. Integral to USCL, we employ the multi-task fusion self-supervised learning (MTSL) method to extract robust feature representations through multiple self-supervised tasks. Additionally, we introduce the Enhanced Spectral Clustering (ESC) method and a dynamic selecting function (DSF). ESC effectively integrates both local and global similarity matrices, improving clustering accuracy. The DSF maximally selects the most valuable instances for labeling, significantly enhancing the representativeness and diversity of the labeled data. USCL consistently improves various SSL methods compared to traditional instance selection methods. For example, it boosts Efficient Teacher by 5%, 6.6%, and 7.8% in mean Average Precision(mAP) on the Automotive Sealing Rings Defect Dataset, the Metallic Surface Defect Dataset, and the Printed Circuit Boards (PCB) Defect Dataset with 10% labeled data. Our work sets a new benchmark for SSL in industrial settings.

An adaptive spatiotemporal filter for ultrasound localization microscopy based on density canopy clustering

Ultrasound Localization Microscopy (ULM) facilitates structural and hemodynamic imaging of microvessels with a resolution of tens of micrometers. In ULM, the extraction of effective microbubble signals is crucial for image quality. Singular Value Decomposition (SVD) is currently the most prevalent method for microbubble signal extraction in ULM. Most existing ULM studies employ a fixed SVD filter threshold using empirical values which will lead to imaging quality degradation due to the insufficient separation of blood signals. In this study, we propose an adaptive and non-threshold SVD filter based on canopy-density clustering, termed DCC-SVD. This filter automatically classifies the components of the SVD based on the density of their spatiotemporal features, eliminating the need for parameter selection. In in vitro tube phantom, DCC-SVD demonstrated its ability to adaptive separation of blood and bubble signal at varying microbubble concentrations and flow rates. We compared the proposed DCC-SVD method with the Block-match 3D (BM3D) filter and a classical adaptive method called spatial similarity matrix (SSM), using concentration-variable in vivo rat brain data, as well as open-source rat kidney and mouse tumor datasets. The proposed DCC-SVD improved the global spatial resolution by approximately 4 μm from 30.39 μm to 26.02 μm. It also captured vessel structure absent in images obtained by other methods and yielded a smoother vessel intensity profile, making it a promising spatiotemporal filter for ULM imaging.

Towards Efficient Value-Based Healthcare Measurement Across Hospital Care Pathways - A Scoping Review.

Hospitals widely employ value-based healthcare (VBHC) to effectively manage healthcare quality. VBHC aims to maximize patient outcomes while minimizing costs by using quality measurements. The Dutch Erasmus Medical Centre experiences challenges with the time-consuming efforts to collect, evaluate, and present value-based quality measurements. Using similar VBHC measurement indicators across multiple care pathways could reduce these efforts. This study aims to identify such generic indicators for evaluating and monitoring VBHC across care pathways. A scoping review resulted in 33 articles from which indicators for VBHC measurement were extracted, aggregated and categorized using Donabedian's Structure-Process-Outcome model. The results of this study can inform researchers and VBHC practitioners on generic quality measurement indicators for VBHC management and guide future system development to facilitate the inclusion of standardized quality indicators in healthcare information systems.

Unsupervised online poor contact detection in the secondary circuit of voltage transformers

Voltage transformers (VTs) are fundamental and crucial instrument devices for accurate voltage measurement. Generally, measurement errors can be caused by both the VT and the secondary circuit. Most research focused on the VT but ignored the secondary circuit between it and the measurement device. Poor contact in the secondary circuit is more challenging to capture than those caused by faulty VTs. In this study, we present an unsupervised online poor contact detection method. It first exploits a time series similarity matrix derived from the measured voltage data. The physical constraints deriving from the electrical topology are utilized to eliminate the non-fault fluctuation. To ensure the versatility of the proposed method, an adaptive threshold determination strategy is designed based on knee point location. Numerical experiments compared with some state-of-the-art methods have validated the effectiveness of the proposed method. The versatility of the proposed method is further validated in three real-world cases.

The process model of self‐control: Developing a measure of self‐control strategies

AbstractThe purpose of this research was to develop and examine a measure of self‐control strategies, proposed in the process model of self‐control. Across three studies (N = 1045), we developed and refined items, examined scale dimensionality and relationships with orbiting constructs, and assessed criterion‐related validity. Findings provide initial support for a 34‐item measure of six self‐control strategies: five strategies that were theoretically proposed in the process model and one that emerged empirically from our data. The proposed Self‐Control Strategies Scale was significantly correlated with other similar measures and demonstrated expected relationships with orbiting constructs and criterion‐related validity. This measure may be useful in expanding our understanding of different strategies beneficial for long‐term goal pursuit.

Reframing climate security: The “planetary” as policy context

Much of the discussion under the label of “climate security” focuses on potential conflicts and disruptions in peripheral locations in the global south putatively triggered by climate change. If, however the analysis starts with climate, and the earth system as the point of departure for analysis, then things look very different. The speed and scale of climate disruptions is accelerating. Earth system science suggests that urgent action is needed to deal with climate change; waiting too long may make the issue impossible to address. Framing matters in terms of a planetary condition and focusing on climate rather than national security as the starting point for analysis suggests very different policy priorities. Reframing climate security to grapple with the planetary condition requires policies that first, facilitate adaptation, second work to make sustainable habitats for humanity and third, work to drastically constrain the use of fossil fuels urgently. Here, proposals for fossil fuel non-proliferation treaties and similar measures analogous with earlier arms control agreements. This provides the security sector with a much-needed direct engagement with the causes of climate change and its resultant disruptions while simultaneously reframing climate as a matter of planetary rather than national security. Tackling climate change is a matter of urgency, and failure to so effectively in the short run my derail needed efforts later, simply because the resources to do so are no longer available.

Noninvasive Monitoring of Changes in Cerebral Hemodynamics During Prolonged Field Care for Hemorrhagic Shock and Hypoxia-Induced Injuries With Portable Diffuse Optical Sensors.

Achieving simultaneous cerebral blood flow (CBF) and oxygenation measures, specifically for point-of-care injury monitoring in prolonged field care, requires the implementation of appropriate methodologies and advanced medical device design, development, and evaluation. The near-infrared spectroscopy (NIRS) method measures the absorbance of light whose attenuation is related to cerebral blood volume and oxygenation. By contrast, diffuse correlation spectroscopy (DCS) allows continuous noninvasive monitoring of microvascular blood flow by directly measuring the degree of light scattering because of red blood cell (RBC) movement in tissue capillaries. Hence, this study utilizes these two optical approaches (DCS-NIRS) to obtain a more complete hemodynamic monitoring by providing cerebral microvascular blood flow, hemoglobin oxygenation and deoxygenation in hemorrhage, and hypoxia-induced injuries. Piglet models of hemorrhage and hypoxia-induced brain injury were used with DCS and NIRS sensors placed over the preorbital to temporal skull regions. To induce hemorrhagic shock, up to 70% of the animal's total blood volume was withdrawn through graded hemorrhage serially via a syringe from a femoral artery cannula in 10 mL/kg aliquots over 1 minute every 10 minutes. A second group of animals was subjected to hypoxia for ∼1 hour through graded hypoxia by serial titration from normoxic fraction inspired oxygen of 21% to hypoxic fraction inspired oxygen of 6%. A subset of animals served as sham-controls undergoing anesthesia, instrumentation, and ventilation as the injury groups, yet experiencing no blood loss or hypoxia. We first investigated the relationship between hemorrhagic shock and no shock by using measured biomarkers, including blood flow index from DCS associated with CBF and oxygenated (HbO) and de-oxygenated hemoglobin from NIRS. The statistical analysis revealed a significant difference between no shock and hemorrhagic shock (P < .01). The HbO decreased with each blood loss as expected, yet the de-oxygenated hemoglobin was slightly changed. During hypoxia-induced global hypoxic-ischemic injury tests, the CBF results from graded hypoxia were consistent with the response previously measured during hemorrhagic shock. Moreover, HbO decreased when the animal was hypoxic, as expected. A statistical analysis was also conducted to compare the results with those of the sham controls. There is a consistency in blood flow measures in both injury mechanisms (hemorrhagic shock and hypoxia), which is significant as the new prototype system provides similar measures and trends for each brain injury type, suggesting that the optical system can be used in response to different injury mechanisms. Notably, the results support the idea that this optical system can probe the hemodynamic status of local cerebral cortical tissue and provide insight into the underlying changes of cerebral tissue perfusion at the microvascular level. These measurement capabilities can improve shock identification and monitoring of medical management of injuries, particularly hemorrhagic shock, in prolonged field care.

Feature extractor optimization for discriminative representations in Generalized Category Discovery

Generalized Category Discovery (GCD) task involves transferring knowledge from labeled known categories to recognize both known and novel categories within an unlabeled dataset. A significant challenge arises from the lack of prior information for novel categories. To address this, we develop a feature extractor that can learn discriminative features for both known and novel categories. Our approach leverages the observation that similar samples often belong to the same class. We construct a similarity matrix and employ similarity contrastive loss to increase the similarity between similar samples in the feature space. Additionally, we incorporate cluster labels to further refine the feature extractor, utilizing K-means clustering to assign these labels to unlabeled data, providing valuable supervision. Our feature extractor is optimized through the utilization of instance-level contrastive learning and class-level contrastive learning constraints. These constraints promote similarity maximization in both the instance space and the label space for instances sharing the same pseudo-labels. These three components complement each other, facilitating the learning of discriminative representations for both known and novel categories. Through comprehensive evaluations of generic image recognition datasets and challenging fine-grained datasets, we demonstrate that our proposed method achieves state-of-the-art performance.

Unsupervised feature selection based on bipartite graph and low-redundant regularization

Unsupervised feature selection (UFS) has attracted increasing attention because of the difficulty and high cost of obtaining data labels. Since the ignorance of redundancy between features and the use of a fixed similarity matrix, current UFS algorithms do not perform well. Moreover, because of the high computational complexity, existing UFS algorithms usually cannot handle large-scale data. A novel UFS method is proposed to solve these problems through bipartite graph learning with low-redundant regularization (BGLR). Firstly, BGLR uses a variance-based de-correlation anchor selection method to ensure that selected anchors can be evenly distributed across classes. Secondly, BGLR builds an adaptive bipartite graph by using selected anchors and original data in the projection space. L2,0-norm constraint is applied to the projection matrix. Thus, the feature subset can be obtained directly. Finally, a regularization term is applied to control the redundancy of selected features. Moreover, BGLR is solved by an iterative method, which can converge quickly. Experiments are performed on public datasets in comparison with some relevant methods. The experimental results demonstrate the BGLR’s effectiveness.

Identification of generator coherency in power systems with wind farm

In the context of the "dual carbon" goal, the penetration rate of new energy represented by wind power is gradually increasing. The large-scale grid connection of wind power makes the power system more complex and uncertain. The output of wind farms changes the system flow, indirectly affecting the power angle characteristics of synchronous generators, and thereby changing the coherence between generators. Affects synchronous generator homology identification. This paper proposes a generator homology identification method for power systems containing wind farms, in response to the problem that the existing methods for identifying unit homology have not taken into account the adverse effects of wind farms on identification results. Translate the influence of wind farms on the homology of synchronous generators into the contraction admittance matrix as a static electrical distance indicator; Select dynamic data reflecting the homology of synchronous generators after disturbance, and form four dynamic indicators to measure the power angle increment curve between each generator: Euclidean distance, Chebyshev distance, grey correlation, and correlation coefficient; By using the combination weighting method to determine the weights of each indicator, a comprehensive similarity matrix is formed, and the optimal clustering results are determined using fuzzy system clustering and F-statistical values. Finally, the effectiveness of the proposed method was verified using EPRI-9 node system, EPRI-36 node system, and IEEE68 node system as examples.© 2017 Elsevier Inc. All rights reserved.

Shielding Public Health: Indonesian National Police (INP)’s Measure Against COVID-19 Vaccine Certificate Forgery

The purpose of this study is to discuss the benefits of implementing several strategies carried out during the COVID-19 period. COVID-19 vaccine certificate forgery has posed a threat to public health. The Indonesian National Police (INP) has implemented various strategies to overcome this crime. The intervention strategies involve early detection and prevention, such as cyber patrol, border control, public involvement, and strict law enforcement. Other countries have also carried out similar measures; however, there are several additional strategies, such as the formation of a special force as well as utilizing blockchain technology for a more secure digital vaccine certificate system. These strategies could be a benchmark for the INP in handling cases of letter forgery.

Standardized statistical framework for comparison of biomarkers: Techniques from ADNI.

Well-chosen biomarkers have the potential to increase the efficiency of clinical trials and drug discovery and should show good precision as well as clinical validity. We suggest measures that operationalize these criteria and describe a general approach that can be used for inference-based comparisons of biomarker performance. The methods are applied to measures obtained from structural magnetic resonance imaging (MRI) from individuals with mild dementia (n=70) or mild cognitive impairment (MCI; n=303) enrolled in the Alzheimer's Disease Neuroimaging Initiative. Ventricular volume and hippocampal volume showed the best precision in detecting change over time in both individuals with MCI and with dementia. Differences in clinical validity varied by group. The methodology presented provides a standardized framework for comparison of biomarkers across modalities and across different methods used to generate similar measures and will help in the search for the most promising biomarkers. A framework for comparison of biomarkers on pre-defined criteria is presented. Criteria for comparison include precision in capturing change and clinical validity. Ventricular volume has high precision in change for both dementia and mild cognitive impairment (MCI) trials. Imaging measures' performance in clinical validity varies more for dementia than for MCI.