Chaminade our Sainted Founder

In praise of Father Chaminade, founder. File contains chorus and verses in separate pages, as well as a typewritten page of lyrics only

Hymn to Our Mother

In praise of Mar

AI deployment on GBM diagnosis: a novel approach to analyze histopathological images using image feature-based analysis

Background: Glioblastoma (GBM) is one of the most common malignant primary brain tumors, which accounts for 60–70% of all gliomas. Conventional diagnosis and the decision of post-operation treatment plan for glioblastoma is mainly based on the feature-based qualitative analysis of hematoxylin and eosin-stained (H&amp;E) histopathological slides by both an experienced medical technologist and a pathologist. The recent development of digital whole slide scanners makes AI-based histopathological image analysis feasible and helps to diagnose cancer by accurately counting cell types and/or quantitative analysis. However, the technology available for digital slide image analysis is still very limited. This study aimed to build an image feature-based computer model using histopathology whole slide images to differentiate patients with glioblastoma (GBM) from healthy control (HC). Method: Two independent cohorts of patients were used. The first cohort was composed of 262 GBM patients of the Cancer Genome Atlas Glioblastoma Multiform Collection (TCGA-GBM) dataset from the cancer imaging archive (TCIA) database. The second cohort was composed of 60 GBM patients collected from a local hospital. Also, a group of 60 participants with no known brain disease were collected. All the H&amp;E slides were collected. Thirty-three image features (22 GLCM and 11 GLRLM) were retrieved from the tumor volume delineated by medical technologist on H&amp;E slides. Five machine-learning algorithms including decision-tree (DT), extreme-boost (EB), support vector machine (SVM), random forest (RF), and linear model (LM) were used to build five models using the image features extracted from the first cohort of patients. Models built were deployed using the selected key image features for GBM diagnosis from the second cohort (local patients) as model testing, to identify and verify key image features for GBM diagnosis. Results: All five machine learning algorithms demonstrated excellent performance in GBM diagnosis and achieved an overall accuracy of 100% in the training and validation stage. A total of 12 GLCM and 3 GLRLM image features were identified and they showed a significant difference between the normal and the GBM image. However, only the SVM model maintained its excellent performance in the deployment of the models using the independent local cohort, with an accuracy of 93.5%, sensitivity of 86.95%, and specificity of 99.73%. Conclusion: In this study, we have identified 12 GLCM and 3 GLRLM image features which can aid the GBM diagnosis. Among the five models built, the SVM model proposed in this study demonstrated excellent accuracy with very good sensitivity and specificity. It could potentially be used for GBM diagnosis and future clinical application.</p

A sustainability-based model for dealing with the uncertainties of post-disaster temporary housing

In the aftermath of natural disasters, temporary housing (TH) needs to be provided for displaced people to mitigate human suffering. In the numerous cases in which a chosen TH strategy is unsuitable for the case-specific local conditions, the TH’s negative impacts tend to intensify, especially when decision-makers have to change their initial plan. The unsuitability of the initial plans and the resulting need to change them are usually due to the uncertainty of post-disaster conditions. As most TH provision strategies have weaknesses, the most suitable strategy will thus be the one that best matches the specific circumstances of each scenario. This paper presents a new model to determine the most appropriate strategy to minimize conflicts between local requirements and TH characteristics. The model was calibrated by analyzing the 2003 earthquake in Bam, Iran, and the 2004 earthquake and tsunami in Aceh, Indonesia.Peer ReviewedPostprint (author's final draft

Phase transitions in a network with range dependent connection probability

We consider a one-dimensional network in which the nodes at Euclidean distance $l$ can have long range connections with a probabilty $P(l) \sim l^{-\delta}$ in addition to nearest neighbour connections. This system has been shown to exhibit small world behaviour for $\delta < 2$ above which its behaviour is like a regular lattice. From the study of the clustering coefficients, we show that there is a transition to a random network at $\delta = 1$. The finite size scaling analysis of the clustering coefficients obtained from numerical simulations indicate that a continuous phase transition occurs at this point. Using these results, we find that the two transitions occurring in this network can be detected in any dimension by the behaviour of a single quantity, the average bond length. The phase transitions in all dimensions are non-trivial in nature.Comment: 4 pages, revtex4, submitted to Physical Review

Drug-encapsulating EGF-sensitive liposomes for EGF-overexpressing cancer therapies

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from PDF version of thesis.Includes bibliographical references (p. 68-72).'Smart' targeted drug carriers have long been sought after in the treatment of epidermal growth factor (EGF)-overexpressing cancers due to the potential advantages, relative to current clinical therapies (generally limited to surgery, radiation therapy, traditional chemotherapy, and EGF receptor inhibitors (EGFRIs)), of using such 'smart' targeted drug delivery systems. However, progress toward this goal has been challenged by the difficulty of creating a drug carrier that can autonomously detect and respond to tumor cells in the body. 'Smart' micron-size drug-encapsulating epidermal growth factor (EGF)-sensitive liposomes for EGF-overexpressing cancer therapies have been developed and studied. These drug-encapsulating liposomes remain inert until they are exposed to an abnormal concentration of EGF. As a drug delivery system, these drug-encapsulating liposomes could release pharmaceutical agents specifically in the immediate neighborhood of tumors overexpressing EGF, thereby maximizing the effective amount of drug received by the tumor while minimizing the effective systemic toxicity of the drug. Additionally, quantitative mathematical models were developed to characterize multiple critical rate processes (including drug leakage from drug-encapsulating liposomes and distribution of (drug-encapsulating) liposomes in blood vessels) associated i with (drug-encapsulating) liposomes in general.(cont.) These quantitative mathematical models provide a low-cost and rapid method for screening novel drug-encapsulating liposome compositions, configurations, and synthetic methods to identify liposome compositions, configurations, and synthetic methods that would deliver optimal performance. The results provide a stepping stone toward the development of EGF-sensitive liposomes for clinical use. More generally, they also present implications for future development of other targeted drug delivery vehicles.by Albert Wong.S.M

Low-Loss All-Optical Zeno Switch in a Microdisk Cavity Using EIT

We present theoretical results of a low-loss all-optical switch based on electromagnetically induced transparency and the classical Zeno effect in a microdisk resonator. We show that a control beam can modify the atomic absorption of the evanescent field which suppresses the cavity field buildup and alters the path of a weak signal beam. We predict more than 35 dB of switching contrast with less than 0.1 dB loss using just 2 micro-Watts of control-beam power for signal beams with less than single photon intensities inside the cavity.Comment: Updated with new references, corrected Eq 2a, and added introductory text. 7 pages, 5 figures, 3 table

Using Fine Resolution Orthoimagery and Spatial Interpolation to Rapidly Map Turf Grass in Suburban Massachusetts

This paper explores the use of spatial interpolative methods in conjunction with object based image analysis to estimate turf grass land cover quantity and allocation in Greater Boston, Massachusetts, USA. The goal is to learn how accurately turf grass can be estimated if only a limited portion of the study area is mapped. First, turf grass land cover is mapped at the 0.5 m resolution across the entire Plum Island Ecosystems (PIE) Long Term Ecological Research (LTER) site, a 1143-km2 area. Second, the turf grass map is aggregated into 120 m cells (N = 84,661). Third, a random sample of these 120 m cells are selected to generate an estimate of the unselected cells using four estimation methods - Inverse Distance Weighting, Kriging, Polygonal Interpolation, and Mean Estimation. The difference between known and estimated values is recorded using 120 m cell and census block group stratifications. This process is repeated 500 times for sample sizes of 2.5%, 5.0%, 7.5% and 10.0% of the study area, for a total of 2000 iterations. The average error statistics are reported by sample size, strata, and estimation method. Inverse distance weighting performed best in terms of total error across all sample sizes. It was found that by mapping only 2.5% of the study area, all four methods outperformed a recently published approach to estimating turf grass in terms of overall error

Out of equilibrium: understanding cosmological evolution to lower-entropy states

Despite the importance of the Second Law of Thermodynamics, it is not absolute. Statistical mechanics implies that, given sufficient time, systems near equilibrium will spontaneously fluctuate into lower-entropy states, locally reversing the thermodynamic arrow of time. We study the time development of such fluctuations, especially the very large fluctuations relevant to cosmology. Under fairly general assumptions, the most likely history of a fluctuation out of equilibrium is simply the CPT conjugate of the most likely way a system relaxes back to equilibrium. We use this idea to elucidate the spacetime structure of various fluctuations in (stable and metastable) de Sitter space and thermal anti-de Sitter space.Comment: 27 pages, 11 figure