Breast cancer is a pervasive global health concern among women. Leveraging multimodal data from enterprise patient databases-including Picture Archiving and Communication Systems (PACS) and Electronic Health Records (EHRs)-holds promise for improving prediction. This study introduces a multimodal deep-learning model leveraging mammogram datasets to evaluate breast cancer prediction. Our approach integrates frozen large-scale pretrained vision-language models, showcasing superior performance and stability compared to traditional image-tabular models across two public breast cancer datasets. The model consistently outperforms conventional full fine-tuning methods by using frozen pretrained vision-language models alongside a lightweight trainable classifier. The observed improvements are significant. In the CBIS-DDSM dataset, the Area Under the Curve (AUC) increases from 0.867 to 0.902 during validation and from 0.803 to 0.830 for the official test set. Within the EMBED dataset, AUC improves from 0.780 to 0.805 during validation. In scenarios with limited data, using Breast Imaging-Reporting and Data System category three (BI-RADS 3) cases, AUC improves from 0.91 to 0.96 on the official CBIS-DDSM test set and from 0.79 to 0.83 on a challenging validation set. This study underscores the benefits of vision-language models in jointly training diverse image-clinical datasets from multiple healthcare institutions, effectively addressing challenges related to non-aligned tabular features. Combining training data enhances breast cancer prediction on the EMBED dataset, outperforming all other experiments. In summary, our research emphasizes the efficacy of frozen large-scale pretrained vision-language models in multimodal breast cancer prediction, offering superior performance and stability over conventional methods, reinforcing their potential for breast cancer prediction.

There is a lack of robust generalizable predictive biomarkers of response to immune checkpoint blockade in multiple types of cancer. We develop hDirect-MAP, an algorithm that maps T cells into a shared high-dimensional (HD) expression space of diverse T cell functional signatures in which cells group by the common T cell phenotypes rather than dimensional reduced features or a distorted view of these features. Using projection-free single-cell modeling, hDirect-MAP first removed a large group of cells that did not contribute to response and then clearly distinguished T cells into response-specific subpopulations that were defined by critical T cell functional markers of strong differential expression patterns. We found that these grouped cells cannot be distinguished by dimensional-reduction algorithms but are blended by diluted expression patterns. Moreover, these identified response-specific T cell subpopulations enabled a generalizable prediction by their HD metrics. Tested using five single-cell RNA-seq or mass cytometry datasets from basal cell carcinoma, squamous cell carcinoma and melanoma, hDirect-MAP demonstrated common response-specific T cell phenotypes that defined a generalizable and accurate predictive biomarker.

This paper presents a novel Lasso Logistic Regression model based on feature-based time series data to determine disease severity and when to administer drugs or escalate intervention procedures in patients with coronavirus disease 2019 (COVID-19). Advanced features were extracted from highly enriched and time series vital sign data of hospitalized COVID-19 patients, including oxygen saturation readings, and with a combination of patient demographic and comorbidity information, as inputs into the dynamic feature-based classification model. Such dynamic combinations brought deep insights to guide clinical decision-making of complex COVID-19 cases, including prognosis prediction, timing of drug administration, admission to intensive care units, and application of intervention procedures like ventilation and intubation. The COVID-19 patient classification model was developed utilizing 900 hospitalized COVID-19 patients in a leading multi-hospital system in Texas, United States. By providing mortality prediction based on time-series physiologic data, demographics, and clinical records of individual COVID-19 patients, the dynamic feature-based classification model can be used to improve efficacy of the COVID-19 patient treatment, prioritize medical resources, and reduce casualties. The uniqueness of our model is that it is based on just the first 24 hours of vital sign data such that clinical interventions can be decided early and applied effectively. Such a strategy could be extended to prioritize resource allocations and drug treatment for futurepandemic events.

Deep learning holds a great promise of revolutionizing healthcare and medicine. Unfortunately, various inference attack models demonstrated that deep learning puts sensitive patient information at risk. The high capacity of deep neural networks is the main reason behind the privacy loss. In particular, patient information in the training data can be unintentionally memorized by a deep network. Adversarial parties can extract that information given the ability to access or query the network. In this paper, we propose a novel privacy-preserving mechanism for training deep neural networks. Our approach adds decaying Gaussian noise to the gradients at every training iteration. This is in contrast to the mainstream approach adopted by Google's TensorFlow Privacy, which employs the same noise scale in each step of the whole training process. Compared to existing methods, our proposed approach provides an explicit closed-form mathematical expression to approximately estimate the privacy loss. It is easy to compute and can be useful when the users would like to decide proper training time, noise scale, and sampling ratio during the planning phase. We provide extensive experimental results using one real-world medical dataset (chest radiographs from the CheXpert dataset) to validate the effectiveness of the proposed approach. The proposed differential privacy based deep learning model achieves significantly higher classification accuracy over the existing methods with the same privacy budget.

Computer-aided diagnosis (CAD) systems must constantly cope with the perpetual changes in data distribution caused by different sensing technologies, imaging protocols, and patient populations. Adapting these systems to new domains often requires significant amounts of labeled data for re-training. This process is labor-intensive and time-consuming. We propose a memory-augmented capsule network for the rapid adaptation of CAD models to new domains. It consists of a capsule network that is meant to extract feature embeddings from some high-dimensional input, and a memory-augmented task network meant to exploit its stored knowledge from the target domains. Our network is able to efficiently adapt to unseen domains using only a few annotated samples. We evaluate our method using a large-scale public lung nodule dataset (LUNA), coupled with our own collected lung nodules and incidental lung nodules datasets. When trained on the LUNA dataset, our network requires only 30 additional samples from our collected lung nodule and incidental lung nodule datasets to achieve clinically relevant performance (0.925 and 0.891 area under receiving operating characteristic curves (AUROC), respectively). This result is equivalent to using two orders of magnitude less labeled training data while achieving the same performance. We further evaluate our method by introducing heavy noise, artifacts, and adversarial attacks. Under these severe conditions, our network's AUROC remains above 0.7 while the performance of state-of-the-art approaches reduce to chance level.

Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions. An international group of passionate researchers came together with a joint mission to solve the problem with our finest models and tools. The SCOR Consortium has developed a ready-to-deploy secure infrastructure using world-class privacy and security technologies to reconcile the privacy/utility conflicts. We hope our effort will make a change and accelerate research in future pandemics with broad and diverse samples on an international scale.

MOTIVATION: Drug combinations that simultaneously suppress multiple cancer driver signaling pathways increase therapeutic options and may reduce drug resistance. We have developed a computational systems biology tool, DrugComboExplorer, to identify driver signaling pathways and predict synergistic drug combinations by integrating the knowledge embedded in vast amounts of available pharmacogenomics and omics data. RESULTS: This tool generates driver signaling networks by processing DNA sequencing, gene copy number, DNA methylation and RNA-seq data from individual cancer patients using an integrated pipeline of algorithms, including bootstrap aggregating-based Markov random field, weighted co-expression network analysis and supervised regulatory network learning. It uses a systems pharmacology approach to infer the combinatorial drug efficacies and synergy mechanisms through drug functional module-induced regulation of target expression analysis. Application of our tool on diffuse large B-cell lymphoma and prostate cancer demonstrated how synergistic drug combinations can be discovered to inhibit multiple driver signaling pathways. Compared with existing computational approaches, DrugComboExplorer had higher prediction accuracy based on in vitro experimental validation and probability concordance index. These results demonstrate that our network-based drug efficacy screening approach can reliably prioritize synergistic drug combinations for cancer and uncover potential mechanisms of drug synergy, warranting further studies in individual cancer patients to derive personalized treatment plans. AVAILABILITY AND IMPLEMENTATION: DrugComboExplorer is available at https://github.com/Roosevelt-PKU/drugcombinationprediction. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Presents an introductory editorial on the special issue for this issue of the publication which examines the fields of biomedical and health informatics.

This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

Cancer patients often show heterogeneous drug responses such that only a small subset of patients is sensitive to a given anticancer drug. With the availability of large-scale genomic profiling via next-generation sequencing, it is now economically feasible to profile the whole transcriptome and genome of individual patients in order to identify their unique genetic mutations and differentially expressed genes, which are believed to be responsible for heterogeneous drug responses. Although subtyping analysis has identified patient subgroups sharing common biomarkers, there is no effective method to predict the drug response of individual patients precisely and reliably. Herein, we propose a novel computational algorithm to predict the drug response of individual patients based on personal genomic profiles, as well as pharmacogenomic and drug sensitivity data. Specifically, more than 600 cancer cell lines (viewed as individual patients) across over 50 types of cancers and their responses to 75 drugs were obtained from the genomics of drug sensitivity in cancer database. The drug-specific sensitivity signatures were determined from the changes in genomic profiles of individual cell lines in response to a specific drug. The optimal drugs for individual cell lines were predicted by integrating the votes from other cell lines. The experimental results show that the proposed drug prediction algorithm can be used to improve greatly the reliability of finding optimal drugs for individual patients and will, thus, form a key component in the precision medicine infrastructure for oncology care.

MOTIVATION: Currently there are no curative anticancer drugs, and drug resistance is often acquired after drug treatment. One of the reasons is that cancers are complex diseases, regulated by multiple signaling pathways and cross talks among the pathways. It is expected that drug combinations can reduce drug resistance and improve patients' outcomes. In clinical practice, the ideal and feasible drug combinations are combinations of existing Food and Drug Administration-approved drugs or bioactive compounds that are already used on patients or have entered clinical trials and passed safety tests. These drug combinations could directly be used on patients with less concern of toxic effects. However, there is so far no effective computational approach to search effective drug combinations from the enormous number of possibilities. RESULTS: In this study, we propose a novel systematic computational tool DRUGCOMBORANKER: to prioritize synergistic drug combinations and uncover their mechanisms of action. We first build a drug functional network based on their genomic profiles, and partition the network into numerous drug network communities by using a Bayesian non-negative matrix factorization approach. As drugs within overlapping community share common mechanisms of action, we next uncover potential targets of drugs by applying a recommendation system on drug communities. We meanwhile build disease-specific signaling networks based on patients' genomic profiles and interactome data. We then identify drug combinations by searching drugs whose targets are enriched in the complementary signaling modules of the disease signaling network. The novel method was evaluated on lung adenocarcinoma and endocrine receptor positive breast cancer, and compared with other drug combination approaches. These case studies discovered a set of effective drug combinations top ranked in our prediction list, and mapped the drug targets on the disease signaling network to highlight the mechanisms of action of the drug combinations. AVAILABILITY AND IMPLEMENTATION: The program is available on request.

Applying engineering precepts to biological systems has spawn the field of systems biology to investigate a network of interacting components, including the coordination of internal systems of living organisms such as endocrine, nervous, and respiratory with gene and gene product expression, and behavior and environmental factors, and understand how these components together contribute to the disease initiation and progression, biological development, and health. Proceeding from systems biology, systems medicine incorporates complex and dynamic biochemical, physiological, and environmental interactions between all components of disease and health that sustain living organisms. The current special issue includes a selected number of papers presented at the 12th IEEE International Conference on BioInformatics and BioEngineering (BIBE 2012), Nov. 11-13, 2012 under a special session with the same theme, in addition to papers submitted following an open call for papers. The Special Issue presents experiences as well as technological and scientific developments stemming from some flagship projects funded by the EU under the FP7 framework programme aiming to bring together researchers working in the fields of infrastructures and technologies for integrative biomedical research, ICT for predictive and translational medicine and the VPH community at large. A total of 15 papers are included under the following scientific subdomains: 1) mHealth,Wearable Systems and Telemonitoring Services (five papers), 2) Medical Imaging (four papers), and 3) Computational Biology (six papers).

SUMMARY: Systematic studies of drug repositioning require the integration of multi-level drug data, including basic chemical information (such as SMILES), drug targets, target-related signaling pathways, clinical trial information and Food and Drug Administration (FDA)-approval information, to predict new potential indications of existing drugs. Currently available databases, however, lack query support for multi-level drug information and thus are not designed to support drug repositioning studies. DrugMap Central (DMC), an online tool, is developed to help fill the gap. DMC enables the users to integrate, query, visualize, interrogate, and download multi-level data of known drugs or compounds quickly for drug repositioning studies all within one system. AVAILABILITY: DMC is accessible at http://r2d2drug.org/DMC.aspx. CONTACT: [email protected].

BACKGROUND: Subtypes are widely found in cancer. They are characterized with different behaviors in clinical and molecular profiles, such as survival rates, gene signature and copy number aberrations (CNAs). While cancer is generally believed to have been caused by genetic aberrations, the number of such events is tremendous in the cancer tissue and only a small subset of them may be tumorigenic. On the other hand, gene expression signature of a subtype represents residuals of the subtype-specific cancer mechanisms. Using high-throughput data to link these factors to define subtype boundaries and identify subtype-specific drivers, is a promising yet largely unexplored topic. RESULTS: We report a systematic method to automate the identification of cancer subtypes and candidate drivers. Specifically, we propose an iterative algorithm that alternates between gene expression clustering and gene signature selection. We applied the method to datasets of the pediatric cerebellar tumor medulloblastoma (MB). The subtyping algorithm consistently converges on multiple datasets of medulloblastoma, and the converged signatures and copy number landscapes are also found to be highly reproducible across the datasets. Based on the identified subtypes, we developed a PCA-based approach for subtype-specific identification of cancer drivers. The top-ranked driver candidates are found to be enriched with known pathways in certain subtypes of MB. This might reveal new understandings for these subtypes. CONCLUSIONS: Our study indicates that subtype-signature defines the subtype boundaries, characterizes the subtype-specific processes and can be used to prioritize signature-related drivers.

Recent advances in automated high-resolution fluorescence microscopy and robotic handling have made the systematic and cost effective study of diverse morphological changes within a large population of cells possible under a variety of perturbations, e.g., drugs, compounds, metal catalysts, RNA interference (RNAi). Cell population-based studies deviate from conventional microscopy studies on a few cells, and could provide stronger statistical power for drawing experimental observations and conclusions. However, it is challenging to manually extract and quantify phenotypic changes from the large amounts of complex image data generated. Thus, bioimage informatics approaches are needed to rapidly and objectively quantify and analyze the image data. This paper provides an overview of the bioimage informatics challenges and approaches in image-based studies for drug and target discovery. The concepts and capabilities of image-based screening are first illustrated by a few practical examples investigating different kinds of phenotypic changes caEditorsused by drugs, compounds, or RNAi. The bioimage analysis approaches, including object detection, segmentation, and tracking, are then described. Subsequently, the quantitative features, phenotype identification, and multidimensional profile analysis for profiling the effects of drugs and targets are summarized. Moreover, a number of publicly available software packages for bioimage informatics are listed for further reference. It is expected that this review will help readers, including those without bioimage informatics expertise, understand the capabilities, approaches, and tools of bioimage informatics and apply them to advance their own studies.

Cancer is a complex disease and within a cancer, subtypes of patients with distinct behaviors often exist. The subtypes might have been caused by different hits, such as copy number aberrations (CNAs) and point mutations, on different pathways/cells-of-origin in a common tissue/organ. Identifying the subtypes with subtype-specific drivers, i.e., hits, is key to the understanding of cancer and development of novel treatments. Here, we report the development of an integrative method to identify the subtypes of cancer. Specifically, we consider CNAs and their impact on gene expressions. Based on these relations, we propose an iterative approach that alternates between kernel based gene expression clustering and gene signature selection. We applied the method to datasets of the pediatric cancer medulloblastoma (MB). The consensus number of clusters quickly converges to three; and for each of these three subtypes, the signature detection also converges to a consistent set of a few hundred highly functionally related genes. For each of the subtypes, we correlate its signature with the set of within-subtype recurrent CNA-affected genes for identifying drivers. The top-ranked driver candidates are found to be enriched with known pathways in certain subtypes of MB as well as containing novel genes that might reveal new understandings for other subtypes.

Interactions between cancer cells and factors within the tumor microenvironment (mE) are essential for understanding tumor development. The spatial relationships between blood vessel cells and cancer cells, e.g. tumor initiating cells (TICs), are an important parameter. Accurate segmentation of blood vessel is necessary for the quantization of their spatial relationships. However, this remains an open problem due to uneven intensity and low signal to noise ratio (SNR). To overcome these challenges, we propose a novel approach that integrates an oriented hidden Markov random field model (Ori-HMRF) with local clustering. The local clustering delineates boundaries of blood vessel segments with low SNR. Then blood vessel segments are viewed as random variables in the Ori-HMRF and their spatial dependence is defined based on directional information. The Ori-HMRF model suppresses noise and generates accurate blood vessel segmentation results. Experimental validations were conducted on both normal mammary and breast cancer tissues.

MOTIVATION: Prediction of synergistic effects of drug combinations has traditionally been relied on phenotypic response data. However, such methods cannot be used to identify molecular signaling mechanisms of synergistic drug combinations. In this article, we propose an enhanced Petri-Net (EPN) model to recognize the synergistic effects of drug combinations from the molecular response profiles, i.e. drug-treated microarray data. METHODS: We addressed the downstream signaling network of the targets for the two individual drugs used in the pairwise combinations and applied EPN to the identified targeted signaling network. In EPN, drugs and signaling molecules are assigned to different types of places, while drug doses and molecular expressions are denoted by color tokens. The changes of molecular expressions caused by treatments of drugs are simulated by two actions of EPN: firing and blasting. Firing is to transit the drug and molecule tokens from one node or place to another, and blasting is to reduce the number of molecule tokens by drug tokens in a molecule node. The goal of EPN is to mediate the state characterized by control condition without any treatment to that of treatment and to depict the drug effects on molecules by the drug tokens. RESULTS: We applied EPN to our generated pairwise drug combination microarray data. The synergistic predictions using EPN are consistent with those predicted using phenotypic response data. The molecules responsible for the synergistic effects with their associated feedback loops display the mechanisms of synergism. AVAILABILITY: The software implemented in Python 2.7 programming language is available from request. CONTACT: [email protected].

Peak detection is one of the most important steps in mass spectrometry (MS) analysis. However, the detection result is greatly affected by severe spectrum variations. Unfortunately, most current peak detection methods are neither flexible enough to revise false detection results nor robust enough to resist spectrum variations. To improve flexibility, we introduce peak tree to represent the peak information in MS spectra. Each tree node is a peak judgment on a range of scales, and each tree decomposition, as a set of nodes, is a candidate peak detection result. To improve robustness, we combine peak detection and common peak alignment into a closed-loop framework, which finds the optimal decomposition via both peak intensity and common peak information. The common peak information is derived and loopily refined from the density clustering of the latest peak detection result. Finally, we present an improved ant colony optimization biomarker selection method to build a whole MS analysis system. Experiment shows that our peak detection method can better resist spectrum variations and provide higher sensitivity and lower false detection rates than conventional methods. The benefits from our peak-tree-based system for MS disease analysis are also proved on real SELDI data.

Dendritic spines form postsynaptic contact sites in the central nervous system. The rapid and spontaneous morphology changes of spines have been widely observed by neurobiologists. Determining the relationship between dendritic spine morphology change and its functional properties such as memory learning is a fundamental yet challenging problem in neurobiology research. In this paper, we propose a novel algorithm to track the morphology change of multiple spines simultaneously in time-lapse neuronal images based on nonrigid registration and integer programming. We also propose a robust scheme to link disappearing-and-reappearing spines. Performance comparisons with other state-of-the-art cell and spine tracking algorithms, and the ground truth show that our approach is more accurate and robust, and it is capable of tracking a large number of neuronal spines in time-lapse confocal microscopy images.

Segmentation of neural stem cells is the preliminary step to treat and cure several brain neural diseases. There exist a number of methods to accomplish this task. However, all of these methods suffer from some problems, such as high intensity variation sensitivity, human interaction and high computational complexity. In this paper we proposed a novel edge-detection-based neural stem cell image segmentation algorithm using the local complex phase characteristics. The proposed method is an illumination and contrast invariant measurement of edge significance. Our contributions are that, local weighting summation Gaussian kernel convolution and a new model for phase deviation weighting function are introduced into the proposed model to improve the local phase measurement. In experiments, we show that the proposed method is more accurate and reliable than three existing gradient-based edge detection algorithms and Kovesi's model for neural stem cell image segmentation.

MOTIVATION: Unraveling the structure and behavior of the brain and central nervous system (CNS) has always been a major goal of neuroscience. Understanding the wiring diagrams of the neuromuscular junction connectomes (full connectivity of nervous system neuronal components) is a starting point for this, as it helps in the study of the organizational and developmental properties of the mammalian CNS. The phenomenon of synapse elimination during developmental stages of the neuronal circuitry is such an example. Due to the organizational specificity of the axons in the connectomes, it becomes important to label and extract individual axons for morphological analysis. Features such as axonal trajectories, their branching patterns, geometric information, the spatial relations of groups of axons, etc. are of great interests for neurobiologists in the study of wiring diagrams. However, due to the complexity of spatial structure of the axons, automatically tracking and reconstructing them from microscopy images in 3D is an unresolved problem. In this article, AxonTracker-3D, an interactive 3D axon tracking and labeling tool is built to obtain quantitative information by reconstruction of the axonal structures in the entire innervation field. The ease of use along with accuracy of results makes AxonTracker-3D an attractive tool to obtain valuable quantitative information from axon datasets. AVAILABILITY: The software is freely available for download at http://www.cbi-tmhs.org/AxonTracker/.

BACKGROUND: DNA copy number aberration (CNA) is very important in the pathogenesis of tumors and other diseases. For example, CNAs may result in suppression of anti-oncogenes and activation of oncogenes, which would cause certain types of cancers. High density single nucleotide polymorphism (SNP) array data is widely used for the CNA detection. However, it is nontrivial to detect the CNA automatically because the signals obtained from high density SNP arrays often have low signal-to-noise ratio (SNR), which might be caused by whole genome amplification, mixtures of normal and tumor cells, experimental noise or other technical limitations. With the reduction in SNR, many false CNA regions are often detected and the true CNA regions are missed. Thus, more sophisticated statistical models are needed to make the CNAs detection, using the low SNR signals, more robust and reliable. RESULTS: This paper presents a conditional random pattern (CRP) model for CNA detection where much contextual cues are explored to suppress the noise and improve CNA detection accuracy. Both simulated and the real data are used to evaluate the proposed model, and the validation results show that the CRP model is more robust and reliable in the presence of noise for CNA detection using high density SNP array data, compared to a number of widely used software packages. CONCLUSIONS: The proposed conditional random pattern (CRP) model could effectively detect the CNA regions in the presence of noise.

In the field of craniomaxillofacial (CMF) surgery, surgical planning can be performed on composite 3-D models that are generated by merging a computerized tomography scan with digital dental models. Digital dental models can be generated by scanning the surfaces of plaster dental models or dental impressions with a high-resolution laser scanner. During the planning process, one of the essential steps is to reestablish the dental occlusion. Unfortunately, this task is time-consuming and often inaccurate. This paper presents a new approach to automatically and efficiently reestablish dental occlusion. It includes two steps. The first step is to initially position the models based on dental curves and a point matching technique. The second step is to reposition the models to the final desired occlusion based on iterative surface-based minimum distance mapping with collision constraints. With linearization of rotation matrix, the alignment is modeled by solving quadratic programming. The simulation was completed on 12 sets of digital dental models. Two sets of dental models were partially edentulous, and another two sets have first premolar extractions for orthodontic treatment. Two validation methods were applied to the articulated models. The results show that using our method, the dental models can be successfully articulated with a small degree of deviations from the occlusion achieved with the gold-standard method.

Automated cell segmentation and tracking are critical for quantitative analysis of cell cycle behavior using time-lapse fluorescence microscopy. However, the complex, dynamic cell cycle behavior poses new challenges to the existing image segmentation and tracking methods. This paper presents a fully automated tracking method for quantitative cell cycle analysis. In the proposed tracking method, we introduce a neighboring graph to characterize the spatial distribution of neighboring nuclei, and a novel dissimilarity measure is designed based on the spatial distribution, nuclei morphological appearance, migration, and intensity information. Then, we employ the integer programming and division matching strategy, together with the novel dissimilarity measure, to track cell nuclei. We applied this new tracking method for the tracking of HeLa cancer cells over several cell cycles, and the validation results showed that the high accuracy for segmentation and tracking at 99.5% and 90.0%, respectively. The tracking method has been implemented in the cell-cycle analysis software package, DCELLIQ, which is freely available.

MOTIVATION: Loss of heterozygosity (LOH) is one of the most important mechanisms in the tumor evolution. LOH can be detected from the genotypes of the tumor samples with or without paired normal samples. In paired sample cases, LOH detection for informative single nucleotide polymorphisms (SNPs) is straightforward if there is no genotyping error. But genotyping errors are always unavoidable, and there are about 70% non-informative SNPs whose LOH status can only be inferred from the neighboring informative SNPs. RESULTS: This article presents a novel LOH inference and segmentation algorithm based on the conditional random pattern (CRP) model. The new model explicitly considers the distance between two neighboring SNPs, as well as the genotyping error rate and the heterozygous rate. This new method is tested on the simulated and real data of the Affymetrix Human Mapping 500K SNP arrays. The experimental results show that the CRP method outperforms the conventional methods based on the hidden Markov model (HMM). AVAILABILITY: Software is available upon request.

Optical microscopy is becoming an important technique in drug discovery and life science research. The approaches used to analyze optical microscopy images are generally classified into two categories: automatic and manual approaches. However, the existing automatic systems are rather limited in dealing with large volume of time-lapse microscopy images because of the complexity of cell behaviors and morphological variance. On the other hand, manual approaches are very time-consuming. In this paper, we propose an effective automated, quantitative analysis system that can be used to segment, track, and quantize cell cycle behaviors of a large population of cells nuclei effectively and efficiently. We use adaptive thresholding and watershed algorithm for cell nuclei segmentation followed by a fragment merging method that combines two scoring models based on trend and no trend features. Using the context information of time-lapse data, the phases of cell nuclei are identified accurately via a Markov model. Experimental results show that the proposed system is effective for nuclei segmentation and phase identification.

Systematic content screening of cell phenotypes in microscopic images has been shown promising in gene function understanding and drug design. However, manual annotation of cells and images in genome-wide studies is cost prohibitive. In this paper, we propose a highly efficient active annotation framework, in which a small amount of expert input is leveraged to rapidly and effectively infer the labels over the remaining unlabeled data. We formulate this as a graph based transductive learning problem and develop a novel method for label propagation. Specifically, a label regularizer method is proposed to handle the important label imbalance issue, typically seen in the cellular image screening applications. We also design a new scheme which breaks the graph into linear superposition of contributions from individual labeled samples. We take advantage of such a superposable representation to achieve fast annotation in an interactive setting. Extensive evaluations over toy data and realistic cellular images confirm the superiority of the proposed method over existing alternatives.

Mass spectrometry (MS) has shown great potential in detecting disease-related biomarkers for early diagnosis of stroke. To discover potential biomarkers from large volume of noisy MS data, peak detection must be performed first. This article proposes a novel automatic peak detection method for the stroke MS data. In this method, a mixture model is proposed to model the spectrum. Bayesian approach is used to estimate parameters of the mixture model, and Markov chain Monte Carlo method is employed to perform Bayesian inference. By introducing a reversible jump method, we can automatically estimate the number of peaks in the model. Instead of separating peak detection into substeps, the proposed peak detection method can do baseline correction, denoising and peak identification simultaneously. Therefore, it minimizes the risk of introducing irrecoverable bias and errors from each substep. In addition, this peak detection method does not require a manually selected denoising threshold. Experimental results on both simulated dataset and stroke MS dataset show that the proposed peak detection method not only has the ability to detect small signal-to-noise ratio peaks, but also greatly reduces false detection rate while maintaining the same sensitivity.

Abstract Summary: Rapid development, transparency and small size are the outstanding features of zebrafish that make it as an increasingly important vertebrate system for developmental biology, functional genomics, disease modeling and drug discovery. Zebrafish has been regarded as ideal animal specie for studying the relationship between genotype and phenotype, for pathway analysis and systems biology. However, the tremendous amount of data generated from large numbers of embryos has led to the bottleneck of data analysis and modeling. The zebrafish image quantitator (ZFIQ) software provides streamlined data processing and analysis capability for developmental biology and disease modeling using zebrafish model. Availability: ZFIQ is available for download at http://www.cbi-platform.net Contact: [email protected] Supplementary information: Additional documentation for this software package is referred to http://www.cbi-platform.net/document.htm. Application examples of this software are referred to http://www.cbi-platform.net/download.htm

MOTIVATION: Automated identification of cell cycle phases captured via fluorescent microscopy is very important for understanding cell cycle and for drug discovery. In this article, we propose a novel cell detection method that utilizes both the intensity and shape information of the cell for better segmentation quality. In contrast to conventional off-line learning algorithms, an Online Support Vector Classifier (OSVC) is thus proposed, which removes support vectors from the old model and assigns new training examples weighted according to their importance to accommodate the ever-changing experimental conditions. RESULTS: We image three cell lines using fluorescent microscopy under different experiment conditions, including one treated with taxol. Then, we segment and classify the cell types into interphase, prophase, metaphase and anaphase. Experimental results show the effectiveness of the proposed system in image segmentation and cell phase identification. AVAILABILITY: The software and test datasets are available from the authors.

BACKGROUND: The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens. RESULTS: Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using Drosophila embryos [Additional files 1, 2], dataset for cell cycle phase identification using HeLa cells [Additional files 1, 3, 4] and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%-90%. When our method is implemented in the context of a Drosophila genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms. CONCLUSION: We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens.

Abstract Neuronal dendrites and their spines affect the connectivity of neural networks, and play a significant role in many neurological conditions. Neuronal function is observed to be closely correlated with the appearance, disappearance and morphology of the spines. Automatic 3‐D reconstruction of neurons from light microscopy images, followed by the identification, classification and visualization of dendritic spines is therefore essential for studying neuronal physiology and biophysical properties. In this paper, we present a method to reconstruct dendrites using a surface representation of the dendrite. The 1‐D skeleton of the dendritic surface is then extracted by a medial geodesic function that is robust and topologically correct. This is followed by a Bayesian identification and classification of the spines. The dendrite and spines are visualized in a manner that displays the spines' types and the inherent uncertainty in identification and classification. We also describe a user study conducted to validate the accuracy of the classification and the efficacy of the visualization.

The morphological properties of axons, such as their branching patterns and oriented structures, are of great interest for biologists in the study of the synaptic connectivity of neurons. In these studies, researchers use triple immunofluorescent confocal microscopy to record morphological changes of neuronal processes. Three-dimensional (3D) microscopy image analysis is then required to extract morphological features of the neuronal structures. In this article, we propose a highly automated 3D centerline extraction tool to assist in this task. For this project, the most difficult part is that some axons are overlapping such that the boundaries distinguishing them are barely visible. Our approach combines a 3D dynamic programming (DP) technique and marker-controlled watershed algorithm to solve this problem. The approach consists of tracking and updating along the navigation directions of multiple axons simultaneously. The experimental results show that the proposed method can rapidly and accurately extract multiple axon centerlines and can handle complicated axon structures such as cross-over sections and overlapping objects.

The premise of today's drug development is that the mechanism of a disease is highly dependent upon underlying signaling and cellular pathways. Such pathways are often composed of complexes of physically interacting genes, proteins, or biochemical activities coordinated by metabolic intermediates, ions, and other small solutes and are investigated with molecular biology approaches in genomics, proteomics, and metabonomics. Nevertheless, the recent declines in the pharmaceutical industry's revenues indicate such approaches alone may not be adequate in creating successful new drugs. Our observation is that combining methods of genomics, proteomics, and metabonomics with techniques of bioimaging will systematically provide powerful means to decode or better understand molecular interactions and pathways that lead to disease and potentially generate new insights and indications for drug targets. The former methods provide the profiles of genes, proteins, and metabolites, whereas the latter techniques generate objective, quantitative phenotypes correlating to the molecular profiles and interactions. In this paper, we describe pathway reconstruction and target validation based on the proposed systems biologic approach and show selected application examples for pathway analysis and drug screening.

Early prediction of the risk of cardiovascular events in patients with chest pain is critical in order to provide appropriate medical care for those with positive diagnosis. This paper introduces a computational methodology for predicting such events in the context of robust computerized classification using mass spectrometry data of blood samples collected from patients in emergency departments. We applied the computational theories of statistical and geostatistical linear prediction models to extract effective features of the mass spectra and a simple decision logic to classify disease and control samples for the purpose of early detection. While the statistical and geostatistical techniques provide better results than those obtained from some other methods, the geostatistical approach yields superior results in terms of sensitivity and specificity in various designs of the data set for validation, training, and testing. The proposed computational strategies are very promising for predicting major adverse cardiac events within six months.

It is difficult to directly coregister the 3-D fluorescence molecular tomography (FMT) image of a small tumor in a mouse whose maximal diameter is only a few millimeters with a larger CT image of the entire animal that spans about 10 cm. This paper proposes a new method to register 2-D flat and 3-D CT image first to facilitate the registration between small 3-D FMT images and large 3-D CT images. A novel algorithm combining differential evolution and improved simplex method for the registration between the 2-D flat and 3-D CT images is introduced and validated with simulated images and real images of mice. The visualization of the alignment of the 3-D FMT and CT image through 2-D registration shows promising results.

High-throughput genome-wide RNA interference (RNAi) screening is emerging as an essential tool to assist biologists in understanding complex cellular processes. The large number of images produced in each study make manual analysis intractable; hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. In this paper, a fully automatic method for segmentation of cells from genome-wide RNAi screening images is proposed. Nuclei are first extracted from the DNA channel by using a modified watershed algorithm. Cells are then extracted by modeling the interaction between them as well as combining both gradient and region information in the Actin and Rac channels. A new energy functional is formulated based on a novel interaction model for segmenting tightly clustered cells with significant intensity variance and specific phenotypes. The energy functional is minimized by using a multiphase level set method, which leads to a highly effective cell segmentation method. Promising experimental results demonstrate that automatic segmentation of high-throughput genome-wide multichannel screening can be achieved by using the proposed method, which may also be extended to other multichannel image segmentation problems.

We propose a nonlinear diffusion method based on the gradient vector field construction to remove noises in image while preserving fine details. The blocky effect and over-smoothing, as usually seen in images processed by diffusion operators, are greatly reduced by our method. Results obtained from various images, including synthetic and magnetic resonance imaging (MRI), are used to demonstrate the performance of our new method. Comparing it with other diffusion methods, we find it obtains better performance in terms of removing noises without destroying detail features of images.

Neuron axon analysis is an important means to investigate disease mechanisms and signaling pathways in neurobiology and often requires collecting a great amount of statistical information and phenomena. Automated extraction of axons in 3D microscopic images posts a key problem in the field of neuron axon analysis. To address tortuous axons in 3D volumes, a self-adjust region growing approach referring to surface modeling and self-adjustment which takes advantage of the nature of axon (e.g., continuity), is presented. Experimental results on axon volumes show that the proposed scheme provides a reliable solution to axon retrieving and overcomes several common drawbacks from other existing methods.

As people live longer in the new millennium, it becomes a necessity to develop affordable technologies to improve the quality of life. This paper provides an overview of such techniques in drug delivery and clinical care, namely: (i) health care from organ functions to the cell behaviors; (ii) the development of implantable biosensors and instrumentation; (iii) the process and visualization systems; and (iv) nanotechnology for drug delivery. The goal is to stimulate cross-disciplinary research in the circuits and systems society aimed towards optimal clinical care and personalized therapeutic intervention.

To quantify cellular toxic responses to drug treatment or environmental stresses such as nanoparticles, a high throughput imaging modality with automated image analysis protocol is applied. Fluorescence images from human H4 neurogliomal cells exposed to different concentrations of CuO nanoparticles were collected by a high content fluorescence microscopy. A fully automated fluorescent cellular image analysis system has been developed for the consequential image analysis for cell viability. A data-driven background algorithm was used as adaptive multiple thresholding algorithm to categorize the cells into three classes: bright cells, dark cells, and background. Our image analysis approach includes: (1) the scale-space theory, namely Gaussian filtering with proper scale has been applied to the acquired images to generate local intensity maxima within each cell; (2) a novel method for defining local image intensity maxima based on the gradient vector field has been developed; and (3) a statistical model was proposed to overcome the problem of cell segmentation. Our data have shown that the automated image analysis protocol can achieve 90% success rate of cell detection compared to manual procedure. Cellular image analysis further indicated that H4 neuroglioma cells had a dose-dependent toxic response to the insult of CuO nanoparticles.

BACKGROUND: Automated identification of cell cycle phases of individual live cells in a large population captured via automated fluorescence microscopy technique is important for cancer drug discovery and cell cycle studies. Time-lapse fluorescence microscopy images provide an important method to study the cell cycle process under different conditions of perturbation. Existing methods are limited in dealing with such time-lapse data sets while manual analysis is not feasible. This paper presents statistical data analysis and statistical pattern recognition to perform this task. RESULTS: The data is generated from Hela H2B GFP cells imaged during a 2-day period with images acquired 15 minutes apart using an automated time-lapse fluorescence microscopy. The patterns are described with four kinds of features, including twelve general features, Haralick texture features, Zernike moment features, and wavelet features. To generate a new set of features with more discriminate power, the commonly used feature reduction techniques are used, which include Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Maximum Margin Criterion (MMC), Stepwise Discriminate Analysis based Feature Selection (SDAFS), and Genetic Algorithm based Feature Selection (GAFS). Then, we propose a Context Based Mixture Model (CBMM) for dealing with the time-series cell sequence information and compare it to other traditional classifiers: Support Vector Machine (SVM), Neural Network (NN), and K-Nearest Neighbor (KNN). Being a standard practice in machine learning, we systematically compare the performance of a number of common feature reduction techniques and classifiers to select an optimal combination of a feature reduction technique and a classifier. A cellular database containing 100 manually labelled subsequence is built for evaluating the performance of the classifiers. The generalization error is estimated using the cross validation technique. The experimental results show that CBMM outperforms all other classifies in identifying prophase and has the best overall performance. CONCLUSION: The application of feature reduction techniques can improve the prediction accuracy significantly. CBMM can effectively utilize the contextual information and has the best overall performance when combined with any of the previously mentioned feature reduction techniques.

Image-based, high throughput genome-wide RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Robust automated segmentation of the large volumes of output images generated from image-based screening is much needed for data analyses. In this paper, we propose a new automated segmentation technique to fill the void. The technique consists of two steps: nuclei and cytoplasm segmentation. In the former step, nuclei are extracted, labeled and used as starting points for the latter. A new force obtained from rough segmentation is introduced into the classical level set curve evolution to improve the performance for odd shapes, such as spiky or ruffly cells. A scheme of preventing curves from crossing is proposed to treat the difficulty of segmenting touching cells. We apply it to three types of drosophila cells in RNAi fluorescence images. In all cases, greater than 92% accuracy is obtained.

The lacking of automatic screen systems that can deal with large volume of time-lapse optical microscopy imaging is a bottleneck of modern bio-imaging research. In this paper, we propose an effective automated analytic system that can be used to acquire, track and analyze cell-cycle behaviors of a large population of cells. We use traditional watershed algorithm for cell nuclei segmentation and then a novel hybrid merging method is proposed for fragments merging. After a distance and size based tracking procedure, the performance of fragments merging is improved again by the sequence context information. At last, the cell nuclei can be classified into different phases accurately in a continuous hidden Markov model (HMM). Experimental results show the proposed system is very effective for cell sequence segmentation, tracking and cell phase identification.

BACKGROUND: Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities. RESULTS: We measure structural similarity between proteins by correlating the principle components of their secondary structure interaction matrix. In our approach, the Principle Component Correlation (PCC) analysis, a symmetric interaction matrix for a protein structure is constructed with relationship parameters between secondary elements that can take the form of distance, orientation, or other relevant structural invariants. When using a distance-based construction in the presence or absence of encoded N to C terminal sense, there are strong correlations between the principle components of interaction matrices of structurally or topologically similar proteins. CONCLUSION: The PCC method is extensively tested for protein structures that belong to the same topological class but are significantly different by RMSD measure. The PCC analysis can also differentiate proteins having similar shapes but different topological arrangements. Additionally, we demonstrate that when using two independently defined interaction matrices, comparison of their maximum eigenvalues can be highly effective in clustering structurally or topologically similar proteins. We believe that the PCC analysis of interaction matrix is highly flexible in adopting various structural parameters for protein structure comparison.

Advances in fluorescent probing and microscopic imaging technology provide important tools for biomedical research in studying the structures and functions of cells and molecules. Such studies require the processing and analysis of huge amounts of image data, and manual image analysis is very time consuming, thus costly, and also potentially inaccurate and poor reproducibility. In this paper, we present and combine several advanced computational, probabilistic, and fuzzy-set methods for the computerized classification of cell nuclei in different mitotic phases. We tested our proposed methods with real image sequences recorded over a period of twenty-four hours at every fifteen minutes with a time-lapse fluorescence microscopy. The experimental results have shown that the proposed methods are effective for the task of classification.

Advances in digital imaging modalities as well as other diagnosis and therapeutic techniques have generated a massive amount of diverse data for clinical research. The purpose of this study is to investigate and implement a new intuitive and space-conscious visualization framework, called DBMap, to facilitate efficient multidimensional data visualization and knowledge discovery against the large-scale data warehouses of integrated image and nonimage data. The DBMap framework is built upon the TreeMap concept. TreeMap is a space constrained graphical representation of large hierarchical data sets, mapped to a matrix of rectangles, whose size and color represent interested database fields. It allows the display of a large amount of numerical and categorical information in limited real estate of the computer screen with an intuitive user interface. DBMap has been implemented and integrated into a large brain research data warehouse to support neurologic and neuroradiologic research at the University of California, San Francisco Medical Center. For imaging specialists and clinical researchers, this novel DBMap framework facilitates another way to better explore and classify the hidden knowledge embedded in medical image data warehouses.

MOTIVATION: Microarrays are an important research tool for the advancement of basic biological sciences. However this technology has yet to be integrated with clinical decision making. We have implemented an information framework based on the Microarray Gene Expression Markup Language (MAGE-ML) specification. We are using this framework to develop a test-bed integrated database application to identify genomic and imaging markers for diagnosis of breast cancer. RESULTS: We developed extensible software architecture for retrieving data from different microarray databases using MAGE-ML and for combining microarray data with breast cancer image analysis and clinical data for correlation studies. The framework we developed will provide the necessary data integration to move microarray research from basic biological sciences to clinical applications. AVAILABILITY: Open source software will be available from SourceForge (http://sourceforge.net/projects/microsoap/).

Few information systems today offer a flexible means to define and manage the automated part of radiology processes, which provide clinical imaging services for the entire healthcare organization. Even fewer of them provide a coherent architecture that can easily cope with heterogeneity and inevitable local adaptation of applications and can integrate clinical and administrative information to aid better clinical, operational, and business decisions. We describe an innovative enterprise architecture of image information management systems to fill the needs. Such a system is based on the interplay of production workflow management, distributed object computing, Java and Web techniques, and in-depth domain knowledge in radiology operations. Our design adapts the approach of "4+1" architectural view. In this new architecture, PACS and RIS become one while the user interaction can be automated by customized workflow process. Clinical service applications are implemented as active components. They can be reasonably substituted by applications of local adaptations and can be multiplied for fault tolerance and load balancing. Furthermore, the workflow-enabled digital radiology system would provide powerful query and statistical functions for managing resources and improving productivity. This paper will potentially lead to a new direction of image information management. We illustrate the innovative design with examples taken from an implemented system.

A comprehensive data warehouse framework is needed, which encompasses imaging and non-imaging information in supporting disease management and research. The authors propose such a framework, describe general design principles and system architecture, and illustrate a multimodality neuroimaging data warehouse system implemented for clinical epilepsy research. The data warehouse system is built on top of a picture archiving and communication system (PACS) environment and applies an iterative object-oriented analysis and design (OOAD) approach and recognized data interface and design standards. The implementation is based on a Java CORBA (Common Object Request Broker Architecture) and Web-based architecture that separates the graphical user interface presentation, data warehouse business services, data staging area, and backend source systems into distinct software layers. To illustrate the practicality of the data warehouse system, the authors describe two distinct biomedical applications--namely, clinical diagnostic workup of multimodality neuroimaging cases and research data analysis and decision threshold on seizure foci lateralization. The image data warehouse framework can be modified and generalized for new application domains.

The Human Brain Program (HBP) organized by the Office of Neuroinformatics of the National Institute of Mental Health, National Institutes of Health, is a broadly based federal research initiative sponsored, in a coordinated fashion, by 15 federal organizations from four federal agencies (http://www.nimh.nih.gov/neuroinformatics/index.cfm). The scientific goals of this initiative are to enable the progress of neuroscience research through the creation of a Web- based set of distributed and federated databases, analytical and modeling tools, and simulators. Currently, neuroscientists collect complex data in ever-increasing amounts, fostering increased specialization, with resultant challenges to integrate data between and across levels of interaction, control, and function. The sheer quantity and complexity of the data are such that the field of neuroscience would benefit considerably from an information management system for its experimental data. The field should enhance its wealth of ever-increasing empirical data, accumulated from its many disciplines and experimental approaches, by developing appropriate databases and a greater capability for both theory development and simulation models. The focus section in this issue of JAMIA, on HBP research progress in neuroinformatics, collects three representative works of HBP grantees on the tools and methodology to support the bioinformatics aspects of neuroscience research. The purpose of analyzing brain ultrastructure is to understand the normal synaptic communication pathway of neurons and supporting cellular elements and the alternations of such pathways and cellular elements caused by diseases. The prevalent analytic approach is based on one or paired sections and the use of electron microscopy. Working with volumes rather than single sections, however, provides a richer and more accurate representation of brain ultrastructure. Recent advances in informatics are making volume reconstruction and three-dimensional analysis of brain ultrastructure increasingly practical and cost effective. The research reported by John Fiala and Kristen Harris1 describes such a new reconstruction system and new algorithms capable of operating on personal computers for analyzing in three dimensions the location and ultrastructure of neuronal components, such as synapses. Specifically, a volume of brain tissue from stratum radiatum of the hippocampal area CA1 is reconstructed and analyzed for synaptic density to demonstrate and compare the techniques. On the basis of the findings, these authors also propose general rules for performing synaptic density analysis on reconstructed volumes of brain ultrastructure. The amount of neuroscience information increases significantly as the HBP and related clinical research move forward. The paper by Daniel Gardner et al.2 addresses one critical issue in interoperability, i.e., facilitate neuroscience research and information exchange through a common data model (CDM), which is generalized from two prototype neurophysiology databases. These authors apply the emerging extensible markup language (XML) standard to implement data exchange between any CDM-derived data models. The authors state nine design goals that meet the essential criteria of a good data model, and adopt the data-driven design philosophy, which, they explain, is well suited to brain information. The CDM has five root classes, or “super classes”—data, site, method, model, and reference—that are a reasonable abstraction of the problem domain. The authors believe that when fully implemented using biophysical description markup language (BDML), as well as the hierarchic attribute value implementation of controlled vocabulary, the proposed CDM will have the capacity to mediate among disparate neuroscience database projects and similar resources with compatible data and data models. The experimental work of the Yale research group on neuroscience data analysis provides yet another perspective on data modeling and knowledge representation. The traditional paradigm of reductionism works reasonably well in physical sciences, but the biological domain requires a different research paradigm. A major goal for neuroscientists is to have a sound theoretic foundation that can cut across multiple biological levels, from the genetic level up through the synaptic, neuronal, network, and brain pathway levels, ultimately reaching the behavioral level. The ability to capture, relate, and analyze diverse types of data at multiple levels of abstraction is one of the central challenges of neuroinformatics. The work reported by Perry Miller et al.3 on the integrated data analysis of a particular model system of neurobiology, i.e., the olfactory system, explores new paradigms of biological research to study new relations among findings obtained at individual levels of representation. Their research paper provides an overview of SenseLab; in particular, the introduction of a flexible data model called EAV/CR (entity-attribute-value with classes and relationships) to integrate olfactory and associated data of four different databases for neuroscience research and clinical studies later on. The modeling overhead of EAV/CR, however, may not be suitable to model large amounts of very homogeneous data that have been mass-produced by high-throughput instruments, such as microarray analyzers. Although the current focus of SenseLab's activities is on basic neuroscience and neuroinformatics research, the work has a range of potential clinical and other real-world applications. We hope that the ideas and results reported in this focus section will suggest new and better ways to develop tools to support neuroinformatics research and will lead us to the next generation of bioinformatics tools and management systems. Another HBP focus section, on the progress of enabling technology for brain imaging aspects of neuroscience research, will appear in a future issue of the Journal.

In this issue of JAMIA, the focus section of papers on biomedical imaging/neuroscience is a sequel to the set of papers on bioinformatics/neuroinformatics that appeared in the January 2001 issue.1–4 Again, this issue collects three representative works by recipients of grants from the Human Brain Project*—works that focus, this time, on the tools and methodology to support the biomedical imaging aspects of neuroscience research. Brain mapping and neuroimaging have recently witnessed an exponential rise in interest, output, and productivity similar to the rise in neuroscience research. Throughout the neuroscience community, however, there is a frustration with the volume of image data that are generated and their relative inaccessibility in forms other than textual narrative. To increase the efficiency of the biomedical imaging aspect of neuroscience research, a system to provide a logical and organized system for maintaining and distributing data is much needed. The paper “A Four-dimensional Probabilistic Atlas of the Human Brain,” by John Mazziotta, Arthur Toga, Alan Evans, and colleagues5 filled such a need. It describes the development of a four-dimensional atlas and reference system that includes both macroscopic and microscopic information on the structure and function of the human brain in subjects between the ages of 18 and 90 years. By means of the International Consortium for Brain Mapping (ICBM), they include 7,000 subjects in the initial phase of the database and atlas. In addition, 5,800 subjects will contribute DNA to enable genotypephenotype-behavior correlations. This paper reports the process of developing the strategies, algorithms, data collection methods, validation approaches, database, and distribution of results and illustrates them with exemplary applications. The success of this project will provide new insights into the interrelationship between microscopic and macroscopic structure and function in the human brain and will have significant implications in basic and clinical neuroscience. Working from a different perspective, the researchers at the Stanford Psychiatry Neuroimaging Laboratory (SPNL) are addressing the issue of sharing of neuroimaging informatics tools. The paper “BrainImageJ: A Java-based Framework for Interoperability in Neuroscience, with Specific Application to Neuroimaging,”6 reports a new software framework fully implemented in Java. The design of BrainImageJ serves two goals—to stream the evolution and maintenance of neuroimaging tools in SPNL and to facilitate interoperability of tools and files through public computational data models. The framework consists of a set of programming interfaces for data modeling, file manipulation, algorithmic problem solving, and image visualization as well as an application front end. Such a framework, once it has matured, will have the potential to serve as an inter-laboratory platform for designing, disseminating, and sharing informatics tools in the neuroimaging/neuroscience community. The irregularity and variability of the cortex convolutions pose major challenges in analyzing and visualizing cortical structure, function, and development. Computational cortical cartography provides a powerful approach these problems by using surface-based visualization and analysis methods. The work reported by David Van Essen and colleagues at the Washington University7 describes an integrated software framework for carrying out surface-based analysis of cerebral cortex. The first component of this integrated system, SureFit (Surface Reconstruction by Filtering and Intensity Transformations), is used for cortical segmentation, volume visualization, and initial surface generation. The second component, Caret (Computerized Anatomical Reconstruction and Editing Tool Kit), provides a wide range of surface visualization, surface manipulation, and analysis options. The third component, SuMS (Surface Management System), provides data archive and user interface facilities for surface-related data. Most important, the Results section of this paper emphasizes information that is helpful to users interested in using this software system to analyze their own experimental data or to view surface-based atlases. Finally, we hope that the selected ideas and results represented in this second section of papers on Human Brain Program Research Progress will introduce new and better ways to develop tools to support brain imaging/neuroinformatics research and will lead us to the discoveries and realization of the next generation of biomedical imaging tools and database management systems for advancing neuroscience research.

In this paper, we address a critical issue of cooperative problem solving: the existence of conflict among distributed agents. In particular, we focus our study on cooperative knowledge-based systems. To obtain a better understanding and more balanced judgement of multiagent conflict, we provide a general scheme to study the logical structure of multiagent conflict and rational strategies of coping with it under different situations. Our research finding is that there is no grand unified theory of coping with conflict in performing complex real-world computer supported tasks. Instead, a library of alternative methods should be considered. We discuss four methods: inquiry, arbitration, persuasion, and accommodation. These methods can be combined in an order appropriate to the application domain such that if one method fails, the system will try the next. We point out merits and shortcomings of these methods and illustrate them using several high-level protocols and application examples from a prototype system, the Building Design Network.

OBJECTIVE: To investigate practical solutions that can integrate cryptographic techniques and picture archiving and communication systems (PACS) to improve the security of medical images. DESIGN: The PACS at the University of California San Francisco Medical Center consolidate images and associated data from various scanners into a centralized data archive and transmit them to remote display stations for review and consultation purposes. The purpose of this study is to investigate the model of a digital trust center that integrates cryptographic algorithms and protocols seamlessly into such a digital radiology environment to improve the security of medical images. MEASUREMENTS: The timing performance of encryption, decryption, and transmission of the cryptographic protocols over 81 volumetric PACS datasets has been measured. Lossless data compression is also applied before the encryption. The transmission performance is measured against three types of networks of different bandwidths: narrow-band Integrated Services Digital Network, Ethernet, and OC-3c Asynchronous Transfer Mode. RESULTS: The proposed digital trust center provides a cryptosystem solution to protect the confidentiality and to determine the authenticity of digital images in hospitals. The results of this study indicate that diagnostic images such as x-rays and magnetic resonance images could be routinely encrypted in PACS. However, applying encryption in teleradiology and PACS is a tradeoff between communications performance and security measures. CONCLUSION: Many people are uncertain about how to integrate cryptographic algorithms coherently into existing operations of the clinical enterprise. This paper describes a centralized cryptosystem architecture to ensure image data authenticity in a digital radiology department. The system performance has been evaluated in a hospital-integrated PACS environment.

Deductive object-oriented databases and situation theory are two important areas of research in the fields of databases and of linguistics. "AI and law" is a new field attracting both AI researchers and legal practitioners. Our research brings together the former two fields with the aim of designing knowledge applications in the latter. This is achieved through a formal model for legal reasoning, /spl Sscr//spl Mscr/ ("Situation-theoretic Model"), and a deductive object-oriented database system, /spl Qscr//spl Uscr//spl Iscr//spl Xscr//spl Oscr//spl Tscr//spl Escr/. The purpose of this paper is to introduce the key features of this formal model, based on situation theory, and to describe how this database system can implement this abstract model for complex legal reasoning applications. Concrete examples from legal precedents are used to illustrate these advanced features.

The trend in healthcare information technology is increasingly digital and multimedia oriented. The next generation of health care information systems will consist of a vast network of heterogeneous, autonomous, and distributed imaging scanners, databases, information systems, knowledge intensive applications, and large quantities of multimedia medical data. A key challenge facing system researchers and builders is to provide a new organizational framework that can integrate this varied collection of resources into what appears to be a uniform and logical conglomeration of data and knowledge store in order to increase the availability of global or previously nonaccessible information and to address demanding new information processing requirements for diverse image-assisted medical applications. The purpose of this paper is to present the authors' research toward the development of a hospital integrated framework of multimodality image base management (MIBM) for digital radiology of the future. This evolutionary framework consists of three hierarchical components: a hospital-integrated picture archiving and communication system (HI-PACS), a medical image database system (MIDS), and a set of image-based medical applications that relies on the support of MIDS and PACS. In this paper, the authors describe the system architecture, guiding principles, and design specifications of HI-PACS and MIDS and illustrate their functions and capabilities with three implemented applications, namely, patient folder workflow, distributed object management, and multimodality imaging studies. In addition, the authors conclude their findings with a summary of challenges and research directions.

We present OPUS, a framework that provides design guidelines for the systematic building of independent, and potentially heterogeneous, knowledge base programs for the domains of large physical structures. The term open-system refers to the integration of several of these programs to solve problems beyond their individual capabilities. The OPUS framework organizes data and knowledge about the domain into three layers. The kernel layer encodes primary domain knowledge into hierarchies of objects, the scenario layer contains autonomous knowledge programs for specific applications, and the utility layer supports system interface and inference methods. OPUS provides a focus for knowledge organization and rapid prototyping, supports multiple schemes of inference and representation, copes with the maintenance and reuse of knowledge, and enables different levels of abstraction and views of knowledge. An OPUS system BFC has been developed to cover wide-ranging applications in bridge evaluation. Examples are taken from this application to illustrate data constructs and reasoning schemes based on the OPUS framework.>

The purpose of this paper is to describe a computational model for legal reasoning in criminal law (i.e. trial reasoning). This logic-programming based model contains seven key components: facts of a new case, old cases, domain knowledge, meta rules, similarity matching relations, various implications, and two explicit agents, the plaintiff and the defendant, with opposing goals and reasoning strategies. The argumentation process in this model can be likened to a two-agent game. One agent puts forward an argument. The other agent recognizes the situation, generates candidates to refute the claim, and selects the best one for the next move. The game ends when any one agent can no longer make a move. Certain debate strategies of this model are illustrated in this paper with examples. In addition, the computational model presented has been used in the design and development of HELIC-II - a parallel knowledge-based system for trial reasoning.

This paper presents a set of design guidelines for the construction of complex, real-world problem-solving systems using a hybrid object-oriented deductive formalism. These guidelines address implementation issues in a multi-paradigm environment. Examples are provided in the context of a large-scale knowledge based system (KBS).>

The purpose of this paper is to present the design principles and features of a communication scheme that has been used to support cooperative problem solving within a network of knowledge-based systems. In presenting this account, we attempt to answer a set of critical yet unsettled questions of cooperative systems communication, such as when and how a knowledge-based system knows what message to send, what the conditions of success are for a message, and how individual systems cooperate with each other for different purposes. To achieve this, we first propose two key design principles: (1) the loose coupling of communication issues and knowledge representation issues, and (2) the notion of communicative acts. We then work these ideas into the communication scheme COSMO, whose key features include knowledge handlers, an operation model, organizational roles, message types, communication strategies, and protocols.>

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