Activation Mapping Analysis Research Articles

A Multi-label Artificial Intelligence Approach for Improving Breast Cancer Detection With Mammographic Image Analysis.

Breast cancer remains a major global health concern. This study aimed to develop a deep-learning-based artificial intelligence (AI) model that predicts the malignancy of mammographic lesions and reduces unnecessary biopsies in patients with breast cancer. In this retrospective study, we used deep-learning-based AI to predict whether lesions in mammographic images are malignant. The AI model learned the malignancy as well as margins and shapes of mass lesions through multi-label training, similar to the diagnostic process of a radiologist. We used the Curated Breast Imaging Subset of Digital Database for Screening Mammography. This dataset includes annotations for mass lesions, and we developed an algorithm to determine the exact location of the lesions for accurate classification. A multi-label classification approach enabled the model to recognize malignancy and lesion attributes. Our multi-label classification model, trained on both lesion shape and margin, demonstrated superior performance compared with models trained solely on malignancy. Gradient-weighted class activation mapping analysis revealed that by considering the margin and shape, the model assigned higher importance to border areas and analyzed pixels more uniformly when classifying malignant lesions. This approach improved diagnostic accuracy, particularly in challenging cases, such as American College of Radiology Breast Imaging-Reporting and Data System categories 3 and 4, where the breast density exceeded 50%. This study highlights the potential of AI in improving the diagnosis of breast cancer. By integrating advanced techniques and modern neural network designs, we developed an AI model with enhanced accuracy for mammographic image analysis.

Mango leaf disease diagnosis using Total Variation Filter Based Variational Mode Decomposition

Mango leaf diseases significantly threaten mango cultivation, impacting both yield and quality. Accurate and early diagnosis is essential for effectively managing and controlling these diseases. This study introduces a novel approach for diagnosing mango leaf diseases, leveraging Total Variation Filter-based Variational Mode Decomposition. The proposed method enhances the extraction of disease-specific features from leaf images by decomposing them into intrinsic mode functions while simultaneously reducing noise and preserving important edge information. Experimental results demonstrate that the proposed method effectively isolates relevant patterns associated with various mango leaf diseases, improving diagnostic accuracy compared to traditional methods. Deep learning models, DenseNet121 and VGG-19, are used for feature extraction from sub-band images, and extracted features are concatenated and fed to Random Forest for classification. Utilizing tenfold cross-validation, our model demonstrated enhanced classification accuracy (98.85 %), specificity (99.37 %), and sensitivity (98.0 %) in detecting diseases from Mango leaf images. Feature maps and Gradient-weighted Class Activation Mapping analysis was conducted to visualize and scrutinize the essential regions crucial for accurate predictions. Statistical analysis indicates that our proposed architecture outperforms pre-trained models and existing mango leaf disease detection methods. This diagnostic approach can be a rapid disease detection tool for imaging specialists utilizing leaf images. The robustness and efficiency of the presented work in handling complex and noisy image data make it a promising tool for automated agricultural disease diagnosis systems, facilitating timely and precise interventions in mango orchards.

Uncovering tidal treasures: automated classification of faint tidal features in DECaLS data

ABSTRACT Tidal features are a key observable prediction of the hierarchical model of galaxy formation and contain a wealth of information about the properties and history of a galaxy. Modern wide-field surveys such as LSST and Euclid will revolutionize the study of tidal features. However, the volume of data will prohibit visual inspection to identify features, thereby motivating a need to develop automated detection methods. This paper presents a visual classification of ∼2000 galaxies from the DECaLS survey into different tidal feature categories: arms, streams, shells, and diffuse. We trained a convolutional neural network (CNN) to reproduce the assigned visual classifications using these labels. Evaluated on a testing set where galaxies with tidal features were outnumbered $\sim 1:10$, our network performed very well and retrieved a median $98.7\pm 0.3$, $99.1\pm 0.5$, $97.0\pm 0.8$, and $99.4^{+0.2}_{-0.6}$ per cent of the actual instances of arm, stream, shell, and diffuse features respectively for just 20 per cent contamination. A modified version that identified galaxies with any feature against those without achieved scores of $0.981^{+0.001}_{-0.003}$, $0.834^{+0.014}_{-0.026}$, $0.974^{+0.008}_{-0.004}$, and $0.900^{+0.073}_{-0.015}$ for the accuracy, precision, recall, and F1 metrics, respectively. We used a gradient-weighted class activation mapping analysis to highlight important regions on images for a given classification to verify the network was classifying the galaxies correctly. This is the first demonstration of using CNNs to classify tidal features into sub-categories, and it will pave the way for the identification of different categories of tidal features in the vast samples of galaxies that forthcoming wide-field surveys will deliver.

Estimation of the Global Equivalence Ratio of a Swirl Combustor from a Small Number of Absorption Spectra Using Machine Learning.

A new optical diagnostic method that predicts the global fuel-air equivalence ratio of a swirl combustor using absorption spectra from only three optical paths is proposed here. Under normal operation, the global equivalence ratio and total flow rate determine the temperature and concentration fields of the combustor, which subsequently determine the absorption spectra of any combustion species. Therefore, spectra, as the fingerprint for a produced combustion field, were employed to predict the global equivalence ratio, one of the key operational parameters, in this study. Specifically, absorption spectra of water vapor at wavenumbers around 7444.36, 7185.6, and 6805.6 cm-1 measured at three different downstream locations of the combustor were used to predict the global equivalence ratio. As it is difficult to find analytical relationships between the spectra and produced combustion fields, a predictive model was a data-driven acquisition. The absorption spectra as an input were first feature-extracted through stacked convolutional autoencoders and then a dense neural network was used for regression prediction between the feature scores and the global equivalence ratio. The model could predict the equivalence ratio with an absolute error of ±0.025 with a probability of 96%, and a gradient-weighted regression activation mapping analysis revealed that the model leverages not only the peak intensities but also the variations in the shape of absorption lines for its predictions.

Geographical discrimination of Asian red pepper powders using 1H NMR spectroscopy and deep learning-based convolution neural networks

This study investigated an innovative approach to discriminate the geographical origins of Asian red pepper powders by analyzing one-dimensional 1H NMR spectra through a deep learning-based convolution neural network (CNN). 1H NMR spectra were collected from 300 samples originating from China, Korea, and Vietnam and used as input data. Principal component analysis − linear discriminant analysis and support vector machine models were employed for comparison. Bayesian optimization was used for hyperparameter optimization, and cross-validation was performed to prevent overfitting. As a result, all three models discriminated the origins of the test samples with over 95 % accuracy. Specifically, the CNN models achieved a 100 % accuracy rate. Gradient-weighted class activation mapping analysis verified that the CNN models recognized the origins of the samples based on variations in metabolite distributions. This research demonstrated the potential of deep learning-based classification of 1H NMR spectra as an accurate and reliable approach for determining the geographical origins of various foods.

Deep learning from atrioventricular plane displacement in patients with Takotsubo syndrome: lighting up the black-box.

The spatiotemporal deep convolutional neural network (DCNN) helps reduce echocardiographic readers' erroneous 'judgement calls' on Takotsubo syndrome (TTS). The aim of this study was to improve the interpretability of the spatiotemporal DCNN to discover latent imaging features associated with causative TTS pathophysiology. We applied gradient-weighted class activation mapping analysis to visualize an established spatiotemporal DCNN based on the echocardiographic videos to differentiate TTS (150 patients) from anterior wall ST-segment elevation myocardial infarction (STEMI, 150 patients). Forty-eight human expert readers interpreted the same echocardiographic videos and prioritized the regions of interest on myocardium for the differentiation. Based on visualization results, we completed optical flow measurement, myocardial strain, and Doppler/tissue Doppler echocardiography studies to investigate regional myocardial temporal dynamics and diastology. While human readers' visualization predominantly focused on the apex of the heart in TTS patients, the DCNN temporal arm's saliency visualization was attentive on the base of the heart, particularly at the atrioventricular (AV) plane. Compared with STEMI patients, TTS patients consistently showed weaker peak longitudinal displacement (in pixels) in the basal inferoseptal (systolic: 2.15 ± 1.41 vs. 3.10 ± 1.66, P < 0.001; diastolic: 2.36 ± 1.71 vs. 2.97 ± 1.69, P = 0.004) and basal anterolateral (systolic: 2.70 ± 1.96 vs. 3.44 ± 2.13, P = 0.003; diastolic: 2.73 ± 1.70 vs. 3.45 ± 2.20, P = 0.002) segments, and worse longitudinal myocardial strain in the basal inferoseptal (-8.5 ± 3.8% vs. -9.9 ± 4.1%, P = 0.013) and basal anterolateral (-8.6 ± 4.2% vs. -10.4 ± 4.1%, P = 0.006) segments. Meanwhile, TTS patients showed worse diastolic mechanics than STEMI patients (E'/septal: 5.1 ± 1.2 cm/s vs. 6.3 ± 1.5 cm/s, P < 0.001; S'/septal: 5.8 ± 1.3 cm/s vs. 6.8 ± 1.4 cm/s, P < 0.001; E'/lateral: 6.0 ± 1.4 cm/s vs. 7.9 ± 1.6 cm/s, P < 0.001; S'/lateral: 6.3 ± 1.4 cm/s vs. 7.3 ± 1.5 cm/s, P < 0.001; E/E': 15.5 ± 5.6 vs. 12.5 ± 3.5, P < 0.001). The spatiotemporal DCNN saliency visualization helps identify the pattern of myocardial temporal dynamics and navigates the quantification of regional myocardial mechanics. Reduced AV plane displacement in TTS patients likely correlates with impaired diastolic mechanics.

Assessment of inspiration and technical quality in anteroposterior thoracic radiographs using machine learning

IntroductionChest radiographs are the most performed radiographic procedure, but suboptimal technical factors can impact clinical interpretation. A deep learning model was developed to assess technical and inspiratory adequacy of anteroposterior chest radiographs. MethodsAdult anteroposterior chest radiographs (n = 2375) were assessed for technical adequacy, and if otherwise technically adequate, for adequacy of inspiration. Images were labelled by an experienced radiologist with one of three ground truth labels: inadequate technique (n = 605, 25.5 %), adequate inspiration (n = 900, 37.9 %), and inadequate inspiration (n = 870, 36.6 %). A convolutional neural network was then iteratively trained to predict these labels and evaluated using recall, precision, F1 and micro-F1, and Gradient-weighted Class Activation Mapping analysis on a hold-out test set. Impact of kyphosis on model accuracy was assessed. ResultsThe model performed best for radiographs with adequate technique, and worst for images with inadequate technique. Recall was highest (89 %) for radiographs with both adequate technique and inspiration, with recall of 81 % for images with adequate technique and inadequate inspiration, and 60 % for images with inadequate technique, although precision was highest (85 %) for this category. Per-class F1 was 80 %, 81 % and 70 % for adequate inspiration, inadequate inspiration, and inadequate technique respectively. Weighted F1 and Micro F1 scores were 78 %. Presence or absence of kyphosis had no significant impact on model accuracy in images with adequate technique. ConclusionThis study explores the promising performance of a machine learning algorithm for assessment of inspiratory adequacy and overall technical adequacy for anteroposterior chest radiograph acquisition. Implications for practiceWith further refinement, machine learning can contribute to education and quality improvement in radiology departments.

Omics-CNN: A comprehensive pipeline for predictive analytics in quantitative omics using one-dimensional convolutional neural networks

Background and objectiveThe development of machine learning-based models that can be used for the prediction of severe diseases has been one of the main concerns of the scientific community. The current study seeks to expand a highly sophisticated tool, the Convolutional Neural Networks, making it applicable in multidimensional omics data classification problems and testing the newly introduced method on publicly available transcriptomics and proteomics data. MethodsIn this study, we introduce Omics-CNN, a Convolutional Neural Network-based pipeline, which couples Convolutional Neural Networks with dimensionality reduction, preprocessing, clustering, and explainability techniques to make them suitable to build highly accurate and interpretable classification models from high-throughput omics data. The developed tool has the potential to classify patients depending on the expression of genetic and clinical factors and identify features that can act as diagnostic biomarkers. Regarding dimensionality reduction, univariate and multivariate techniques were explored and compared. Gradient Weighted Class Activation Mapping analysis was performed to determine the most important features in the classification of the samples after training the model. ResultsThe newly introduced pipeline was applied to one transcriptomics and one proteomics dataset for the identification of diagnostic models and biosignatures for Ischemic Stroke (IS) and COVID-19 infection, reporting highly accurate biosignatures with accuracies of 96 % and 95.41 %, respectively. Meanwhile, classification models based solely on a small part of attributes provided lower predictive accuracy, but identified compact transcript biosignature (KRT15, VPRBP, TNFRSF4, GORASP2) for Ischemic Stroke and protein biosignature (ADGRB3, VNN2, AGER, CIAPIN1) for Covid-19 infection diagnosis, respectively. ConclusionsOmics-CNN, overcame the inherent problems of applying Convolutional Neural Networks for the training diagnostic models with quantitative omics data, outperforming previous models of machine learning developed using the same datasets for Ischemic Stroke and Covid-19 infection diagnosis, determining the most contributing biomarkers for both diseases.

Carbon Dioxide Concentration Estimation in Nonuniform Temperature Fields Based on Single-Pass Tunable Diode Laser Absorption Spectroscopy.

We propose a novel technique to accurately predict carbon dioxide (CO2) concentrations even in flow fields with temperature gradients based on a single laser path absorption spectrum measurement and machine learning. Concentration measurements in typical tunable diode laser absorption spectroscopy are based on a ratio of two integrated absorbances, each from a spectral line with different temperature dependence. However, the inferred concentrations can deviate significantly from the actual concentrations in the presence of temperature gradients. Furthermore, it is also difficult to find an analytical expression to compensate for the effect of nonuniform temperature profiles on concentration measurements. In this study, the entire absorption feature was considered since its shape and peak intensities vary with temperature and concentration. Specifically, a predictive model is obtained in a data-driven manner that can identify and compensate for the effect of a nonuniform temperature field on the spectrum. Despite a very detailed understanding of the CO2 absorption spectrum, it is nearly impossible to collect sufficient spectra for model acquisition by varying all temperature gradient conditions. Therefore, the model was obtained using only simulated data, much like the concept of a "digital twin". Finally, the predictive performance of the acquired model was verified using experimental data. In all test cases, the predictive performance of the model was superior to that of the two-line method. Additionally, a gradient-weighted regression activation mapping analysis confirmed that the model utilizes both the peak intensities as well as the change in the shape of absorption lines for prediction.

Evaluasi Pengunjung Terhadap Kualitas Pantai Palippis Sebagai Ruang Publik Tepian Air Berbasis Placemaking

Palippis Beach is an underwater coastal tourism object (snorkeling) and a rest area, but there is a conflict with managers who prioritize finance and a lack of visitors from year to year, which causes Pantai Palippis being not optimal as a waterfront public space. The aim of this research is to determine the quality of Palippis Beach as a waterfront public space and the recommendations of its development based on the placemaking approach. This research was conducted from November 2019 until February 2020 (4 months). This research used primary and secondary data which collected by field surveys, questionnaires, interviews, documentation and literature studies. The research location takes a part of the rest area which is located between Polewali Mandar and Majene Regencies. The analysis used in this research is Importance Performance Analysis (IPA) method, activity mapping analysis (behavior mapping), and infrastructure analysis. The results showed that the quality value of Palippis Beach using the IPA questionnaire method gets a value of 83.50% which declared as a high quality because it is between 76% -100%. The intensity of use from Palippis Beach scores 54.54% on weekdays and 53.2% on holidays which is considered high because it is in the range of 51% -75%. There are several tourist infrastructure that still need to be equipped, such as tourism services, security, and cleanliness so quality improvement recommendations are required based on the four quadrants resulting from the Cartesian diagram of the IPA method. These directives are recommendations for the development, maintenance, utilization and adjustment of tourist area infrastructure in accordance with applicable standards.

Lean Production Implementation on Coffee Beans Business

The purpose of this study is to analyze the application of Lean Production implementation in Kopi Pak Tani. Waste and a big map of the production process originated from Kopi Pak Tani will be used as data. While the big map of the production process is obtained by observation. The background of this research is to achieve a competitive advantage in the production process in order to compete with other competitors. This research applies the qualitative method which consists of 2 different ways for gaining data. First, data obtained by the interview process will proceed to the data reduction method, data presentation method, and conclusion determination method. Second, data from observation will through VALSAT Process Activity Mapping Analysis. The results of this study will become a guideline to implement a lean business process by looking at the mapping analysis of process activity. Furthermore, from this study, the discovered wastes are in lead time, inventory, defect, and transportation.

Deep-UV excitation fluorescence microscopy for detection of lymph node metastasis using deep neural network

Deep-UV (DUV) excitation fluorescence microscopy has potential to provide rapid diagnosis with simple technique comparing to conventional histopathology based on hematoxylin and eosin (H&E) staining. We established a fluorescent staining protocol for DUV excitation fluorescence imaging that has enabled clear discrimination of nucleoplasm, nucleolus, and cytoplasm. Fluorescence images of metastasis-positive/-negative lymph nodes of gastric cancer patients were used for patch-based training with a deep neural network (DNN) based on Inception-v3 architecture. The performance on small patches of the fluorescence images was comparable with that of H&E images. Gradient-weighted class activation mapping analysis revealed the areas where the trained model identified metastatic lesions in the images containing cancer cells. We extended the method to large-size image analysis enabling accurate detection of metastatic lesions. We discuss usefulness of DUV excitation fluorescence imaging with the aid of DNN analysis, which is promising for assisting pathologists in assessment of lymph node metastasis.

Selinexor in Combination with Induction and Consolidation Therapy in Older Adults with AML Is Highly Active

Background: Acute myeloid leukemia (AML) is an aggressive myeloid cancer of the hematopoietic system that primarily affects older adults and is characterized by therapy resistance and dismal outcomes. Novel approaches to treat AML are desperately needed. Selinexor is a small-molecule inhibitor of the nuclear export protein XPO1. Treatment of AML cells with selinexor was shown to sensitize them to chemotherapy in part by blocking the nuclear export of topoisomerase II, resulting in increased DNA strand breaks when an anthracycline is present. Furthermore, selinexor has shown encouraging results when combined with chemotherapy in the relapsed setting. This abstract reports on the initial results of a randomized phase II study of induction and consolidation with or without selinexor in newly diagnosed patients with AML 60 years of age or older. Methods: Patients 60 years of age or older with newly diagnosed de novo AML were randomized between 7+3 or 7+3+selinexor induction. Responding patients could then go on to high dose cytarabine consolidation with or without selinexor as per their initial treatment assignment. Patients in the selinexor arm who completed all planned consolidation could then move to maintenance therapy with selinexor alone. During induction cytarabine was dosed at 100 mg/m2 by continuous infusion for 7 days and daunorubicin was dosed at 60 mg/m2 on days 1-3. In consolidation cytarabine was dosed at 1.5 gm/m2 given Q12 hours on days 1, 3 and 5. Selinexor was dosed at 60 mg PO on days 1, 3, 8, 10, 15 and 17 during induction and consolidation and on days 1 and 8 every 21 days in maintenance. Optional blood samples were collected just prior to and at several time points after selinexor during induction. Mononuclear cells were isolated from these samples, lysed and lysates evaluated for protein pathway drug target activation mapping by reverse phase phosphoprotein array (RPPA). Results: Thirteen patients have been enrolled to date with 12 available for evaluation. Of the 12 evaluable patients 6 were randomized to each arm. Baseline demographics are listed in Table 1. Overall the combination was well tolerated with no patients discontinuing selinexor secondary to treatment related adverse events. In the standard arm 3 of 6 patients achieved a complete remission. Of the 3 responders 1 has relapsed, 1 has gone on to transplant and one has completed consolidation chemotherapy. In the selinexor arm 5 of 6 patients achieved a complete remission and 1 patient achieved a morphologic leukemia free state. All patients given selinexor achieved a response. Of the 5 responders 3 have gone on to transplant, 1 is on maintenance therapy (completed 14 cycles) and one is completing consolidation therapy. In the standard arm no patients died during induction and 3 of 6 patients have died from progressive disease. In the selinexor arm 1 patient died during induction and none of the remaining patients have progressed (Figure 1 and Table 2). No difference in the AE profile was noted between arms and no unexpected side effects were observed. The patient on long term maintenance selinexor is tolerating it without significant AEs to date. Blood samples were available from 3 patients from the selinexor arm. RPPA based protein pathway activation mapping analysis of PBMCs revealed a significant increase in phosphorylation of AMPK, BAD and LC3B at one-hour post treatment that returned to baseline by 2 hours suggesting a transient increase in these pathways that was subsequently suppressed (Figure 2). Conclusions: Selinexor in combination with standard induction and consolidation therapy appears highly active in older de novo AML patients. Selinexor may increase response by modulation of AMPK, autophagy and BAD phosphorylation. Enrollment is ongoing. Disclosures Pardee: Spherix Intellectual Property: Research Funding; CBM Bipharma: Membership on an entity's Board of Directors or advisory committees; Amgen: Speakers Bureau; Celgene: Speakers Bureau; Pharmacyclics/Janssen: Speakers Bureau; Karyopharm: Research Funding; Rafael Pharmaceuticals: Consultancy, Research Funding. Manuel:Novartis: Speakers Bureau; Jazz Pharmaceuticals: Speakers Bureau. Powell:Pfizer: Consultancy, Research Funding; Rafael Pharmaceuticals: Consultancy, Research Funding; Novartis: Consultancy, Speakers Bureau; Jazz Pharmaceuticals: Consultancy, Research Funding, Speakers Bureau; Janssen: Research Funding. OffLabel Disclosure: Selinexor is not approved for the treatment of AML

The role of Marshall bundle epicardial connections in atrial tachycardias after atrial fibrillation ablation

Atrial tachycardias (ATs) are often seen in the context of atrial fibrillation (AF) ablation. To evaluate the role of the Marshall bundle (MB) network in left atrial (LA) ATs using high-density 3-dimensional mapping. A total of 199 ATs were mapped in 140 patients (112 male, mean age: 61.8 years); 133 (66.8%) were macroreentrant and 66 (33.2%) were scar-related reentry circuits. MB-dependent ATs were suggested by activation mapping analysis and confirmed with entrainment along the circuit. The MB network participated in 60 (30.2%) reentrant ATs: 31 perimitral ATs (PMATs) and 29 localized reentry circuits. Of 60 MB-related ATs, 49 (81.6%) terminated with radiofrequency (RF) ablation: 44 (73.3%) at the MB-LA junction and 5 (8.3%) at the MB-coronary sinus (CS) junction, while 9 (15%) terminated after 2.5-5 cc of ethanol infusion inside the vein of Marshall (VOM). Of the 31 PMATs, 17 (54.8%) terminated at the MB-LA junction, 5 (16.1%) at the MB-CS junction, and 7 (22.6%) with ethanol infusion. Of the 29 localized reentry circuits using the MB, 27 (93.1%) terminated at the MB-LA junction, none at the MB-CS junction, and 2 (6.9%) after ethanol infusion. Recurrences were mostly observed after RF ablation (18 of 37 patients, 49%) compared to ethanol infusion (1 of 9 patients, 11%) (P = .06). MB reentrant ATs accounted for up to 30.2% of the left ATs after AF ablation. Ablation of the MB-LA or CS-MB connections or ethanol infusion inside the VOM is required to treat these arrhythmias.

Production process analysis using value stream mapping at East Java sugarcane industry

The purpose of this research is to identify the type of waste and to propose improvements for waste minimization on the processing process at the sugarcane industry. This research uses Value Stream Mapping and Value Stream Analysis Tools (VALSAT) method. The results showed that the highest waste in the process production consisted of waiting time and inappropriate processing. Recommendations for improvements that given include reduction of time at several stages of the process, and perform periodic maintenance of the machine. Process Activity Mapping analysis shows reduced lead time from 1212.07 minutes to 1176.23 minutes.

Abstract 2043: Protein pathway activation mapping analysis of metastatic breast cancer reveals potential new targets for personalized therapy.

Abstract Introduction: It is well known that the development of distant metastasis is the strongest prognostic factor associated with cancer mortality. Recent genomic and proteomic studies have demonstrated that the molecular profile of the metastatic lesions significantly differs from the primary tumor. For this reason, to improve response to therapy the identification of new drug targets needs to be based on the molecular profile of the metastatic lesions rather than the primary tumor. Since most targeted therapies work by modulating hyperactivated protein kinase signaling, the aim of this study was to utilize reverse phase protein arrays (RPPA) to quantitatively measure the drug target signaling architecture to identify new therapeutic options for Stage IV breast cancer patients based on the molecular profile of the metastatic lesion. Methods: Snap frozen material collected from 25 metastatic breast cancer patients enrolled in a prospective phase II trial (“Side Out” 1) were used for this study. Sites of metastasis were: liver (n=12), skin (n=5), lymph nodes (n=5), breast (n=2), and pelvis (n=1). All samples were subjected to Laser Capture Microdissection (LCM) and RPPA whereby the activation/phosphorylation level of 55 drug targets and linked substrates for FDA cleared/experimental therapies were measured. Results: Unsupervised hieratical clustering analysis revealed distinct patient subgroups driven largely by pathway activation more than by site of metastasis. Based on the activation level of the drug targets and the downstream effectors, three major clusters were identified. Each cluster appeared to be driven by a specific subset of drug targets: a) pan-HER family members, b) EGFR/SRC/ERK/mTOR, and c) IGFR/RAF/MEK/PLK1. Conclusion: This is the first study evaluating the activation status of FDA approved/experimental drug targets and downstream effectors in a relatively large number of breast cancer metastatic lesions. This type of approach provides a unique opportunity for identifying new drug targets for this group of patients and provides initial insights on which pathways should be considered in the delivery of targeted therapy to this group of patients. 1. A Pilot Study Utilizing Molecular Profiling by IHC, FISH, DNA Microarray, and Reverse Phase Protein Microarray (RPMA) of Patients’ Tumors to Find Potential Targets and Select Treatments for Patients with Metastatic Breast Cancer, sponsored by the Side Out Foundation Citation Format: Mariaelena Pierobon, Kimberly A. Reeder, Gayle Jameson, Nicholas Robert, Stephen P. Anthony, Jasgit Sachdev, Linda Vocila, Julia Wulfkuhle, Elisa Baldelli, Lance Liotta, Emanuel F. Petricoin. Protein pathway activation mapping analysis of metastatic breast cancer reveals potential new targets for personalized therapy. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2043. doi:10.1158/1538-7445.AM2013-2043

An application of autoregressive model to pattern discrimination of brain electrical activity mapping.

Using the autoregressive model, we developed a method of illustrating significant regional differences between two records of brain electrical activity mapping. This new approach of brain electrical activity mapping analysis through a statistical taxonomy may be clinically useful for evaluation of the normal development of EEG in children and for localizing functional brain abnormality.