Collagen fibers mediate MRI-detected water diffusion and anisotropy in breast cancers

AbstractCollagen 1 (Col1) fibers play an important role in tumor interstitial macromolecular transport and cancer cell dissemination. Our goal was to understand the influence of Col1 fibers on water diffusion, and to examine the potential of using noninvasive diffusion tensor imaging (DTI) to indirectly detect Col1 fibers in breast lesions. We previously observed, in human MDA-MB-231 breast cancer xenografts engineered to fluoresce under hypoxia, relatively low amounts of Col1 fibers in fluorescent hypoxic regions. These xenograft tumors together with human breast cancer samples were used here to investigate the relationship between Col1 fibers, water diffusion and anisotropy, and hypoxia. Hypoxic low Col1 fiber containing regions showed decreased apparent diffusion coefficient (ADC) and fractional anisotropy (FA) compared to normoxic high Col1 fiber containing regions. Necrotic high Col1 fiber containing regions showed increased ADC with decreased FA values compared to normoxic viable high Col1 fiber regions that had increased ADC with increased FA values. A good agreement of ADC and FA patterns was observed between in vivo and ex vivo images. In human breast cancer specimens, ADC and FA decreased in low Col1 containing regions. Our data suggest that a decrease in ADC and FA values observed within a lesion could predict hypoxia, and a pattern of high ADC with low FA values could predict necrosis. Collectively the data identify the role of Col1 fibers in directed water movement and support expanding the evaluation of DTI parameters as surrogates for Col1 fiber patterns associated with specific tumor microenvironments as companion diagnostics and for staging

A Stable Biologically Motivated Learning Mechanism for Visual Feature Extraction to Handle Facial Categorization

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task

Signal classification using novel pattern recognition methods and wavelet transforms

A complete pattern recognition system consists of a sensor that gathers the observations to be classified or described; a feature extraction mechanism that computes numeric or symbolic information from the observations; and a classification or description scheme that does the actual job of classifying or describing observations, relying on the extracted features. A pattern recognition example, in this dissertation, is the Ballistocardiogram (BCG). The BCG measurement, recording systems, and signal pre-processing were studied as part of the work. The thesis reviews various BCG measurement techniques and devices, noise removal from the measurements and segmentation methods of the BCG signal. Different types of wavelet transforms (WTs), as feature extraction methods, were studied and applied for the classification of BCG. A novel feature extraction method called Time-frequency moments singular value decomposition (TFMSVD) was also developed yielding results similar to the WT. The development of machine learning algorithms is essential in developing intelligent systems such as autonomous robots. Artificial neural networks (ANNs) are one of the technologies in learning systems. Usually the learning process is based on training ANNs with a representative set of real world examples and then the trained network is embedded into a system. There are, however, a number of problems with most existing ANN structures. These include time consuming training, large amounts of training data and the fact that complicated structures are difficult to implement in embedded systems and integrated circuits, in particular. The aim of the study was to address the above problems by developing novel methods for well-known pattern classification test data sets such as IRIS and Vowel data as well as for BCG. The developed learning algorithms (QuickLearn, CombilNet and its example SF-ART) performed equally well in pattern classification performance with conventional ANNs although SF-ART required less than ten training cycles. The QuickLearn algorithm classifies data almost as well as the traditional ANNs although it requires only one learning cycle

Signal classification using novel pattern recognition methods and wavelet transforms

A complete pattern recognition system consists of a sensor that gathers the observations to be classified or described; a feature extraction mechanism that computes numeric or symbolic information from the observations; and a classification or description scheme that does the actual job of classifying or describing observations, relying on the extracted features. A pattern recognition example, in this dissertation, is the Ballistocardiogram (BCG). The BCG measurement, recording systems, and signal pre-processing were studied as part of the work. The thesis reviews various BCG measurement techniques and devices, noise removal from the measurements and segmentation methods of the BCG signal. Different types of wavelet transforms (WTs), as feature extraction methods, were studied and applied for the classification of BCG. A novel feature extraction method called Time-frequency moments singular value decomposition (TFMSVD) was also developed yielding results similar to the WT. The development of machine learning algorithms is essential in developing intelligent systems such as autonomous robots. Artificial neural networks (ANNs) are one of the technologies in learning systems. Usually the learning process is based on training ANNs with a representative set of real world examples and then the trained network is embedded into a system. There are, however, a number of problems with most existing ANN structures. These include time consuming training, large amounts of training data and the fact that complicated structures are difficult to implement in embedded systems and integrated circuits, in particular. The aim of the study was to address the above problems by developing novel methods for well-known pattern classification test data sets such as IRIS and Vowel data as well as for BCG. The developed learning algorithms (QuickLearn, CombilNet and its example SF-ART) performed equally well in pattern classification performance with conventional ANNs although SF-ART required less than ten training cycles. The QuickLearn algorithm classifies data almost as well as the traditional ANNs although it requires only one learning cycle

Automated Lung Ultrasound Pulmonary Disease Quantification Using an Unsupervised Machine Learning Technique for COVID-19

COVID-19 is an ongoing global health pandemic. Although COVID-19 can be diagnosed with various tests such as PCR, these tests do not establish pulmonary disease burden. Whereas point-of-care lung ultrasound (POCUS) can directly assess the severity of characteristic pulmonary findings of COVID-19, the advantage of using US is that it is inexpensive, portable, and widely available for use in many clinical settings. For automated assessment of pulmonary findings, we have developed an unsupervised learning technique termed the calculated lung ultrasound (CLU) index. The CLU can quantify various types of lung findings, such as A or B lines, consolidations, and pleural effusions, and it uses these findings to calculate a CLU index score, which is a quantitative measure of pulmonary disease burden. This is accomplished using an unsupervised, patient-specific approach that does not require training on a large dataset. The CLU was tested on 52 lung ultrasound examinations from several institutions. CLU demonstrated excellent concordance with radiologist findings in different pulmonary disease states. Given the global nature of COVID-19, the CLU would be useful for sonographers and physicians in resource-strapped areas with limited ultrasound training and diagnostic capacities for more accurate assessment of pulmonary status

Correlation of changes in hemodynamic response as measured by cerebral optical spectrometry with subjective pain ratings in volunteers and patients: a prospective cohort study

Andreas Eisenried,1,2 Naola Austin,1 Benjamin Cobb,1 Alireza Akhbardeh,3 Brendan Carvalho,1 David C Yeomans,1 Alexander Z Tzabazis1,4 1Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; 2Department of Anesthesiology, University Hospital Erlangen, Erlangen, Germany; 3ROPAmedics LLC, San Francisco, CA, USA; 4Department of Anesthesiology and Critical Care, University Hospital Schleswig-Holstein, Lübeck, Germany Purpose: Noninvasive cerebral optical spectrometry is a promising candidate technology for the objective assessment physiological changes during pain perception. This study’s primary objective was to test if there was a significant correlation between the changes in physiological parameters as measured by a cerebral optical spectrometry-based algorithm (real-time objective pain assessment [ROPA]) and subjective pain ratings obtained from volunteers and laboring women. Secondary aims were performance assessment using linear regression and receiver operating curve (ROC) analysis.Patients and methods: Prospective cohort study performed in Human Pain Laboratory and Labor and Delivery Unit. After institutional review board approval, we evaluated ROPA in volunteers undergoing the cold pressor test and in laboring women before and after epidural or combined spinal epidural placement. Linear regression was performed to measure correlations. ROCs and corresponding areas under the ROCs (AUC), as well as Youden’s indices, as a measure of diagnostic effectiveness, were calculated.Results: Correlations between numeric rating scale or visual analog scale and ROPA were significant for both volunteers and laboring women. AUCs for both volunteers and laboring women with numeric rating scale and visual analog scale subjective pain ratings as ground truth revealed at least good (AUC: 70%–79%) to excellent (AUC >90%) distinction between clinically meaningful pain severity differentiations (no/mild–moderate–severe).Conclusion: Cerebral Optical Spectrometry-based ROPA significantly correlated with subjectively reported pain in volunteers and laboring women, and could be a useful monitor for clinical circumstances where direct assessment is not available, or to complement patient-reported pain scores. Keywords: pain, assessment, objective, subjective, quantification, cerebral optical spectrometr