Kurdish Women Rising: Gender Construction in Ideological Discourses from the PKK to Rojava

From the Washington University Senior Honors Thesis Abstracts (WUSHTA), 2017. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Sciences; Lindsey Paunovich, Editor; Helen Human, Programs Manager and Assistant Dean in the College of Arts and Sciences Mentors: Nancy Reynolds and Timothy Parson

Multinode Acoustic Systems for High Throughput Cellular Analysis

For decades, flow cytometry is used as the gold standard for cellular analyses as it measures multiple properties of single cells. Traditional flow cytometry uses the hydrodynamic focusing technique where the sheath fluid focuses cells in the sample into a narrow stream. Although such precise focusing provides accurate optical measurements, high sheath fluid pressure and high linear velocities limit analysis rate to 50,000 particles/s. Such rates are too low for detecting rare events where one cell may have to be detected in a population of about a billion cells. Therefore, it is necessary to eliminate the sheath fluid and improve throughput by several orders of magnitude by using a suitable alternate focusing mechanism that allows focusing cells in high volume samples into multiple focused streams. Toward this aim, this dissertation presents the multinode acoustic technique that uses high frequency ultrasonic waves to focus particles and cells into highly parallel focused streams. One challenge, however, is that acoustic attenuation is significant at high frequencies. Therefore, to optimize acoustic energy density within multinode acoustic flow cells, a simple model is derived based on exhaustive literature review, suggesting that attenuation may be minimized by proper choice of fabrication material. Using such acoustically transparent materials, multinode acoustic flow cells were fabricated by three approaches, which include using machined aluminum frame and glass slides, disposable rectangular glass capillaries and etched silicon flow cells fabricated using standard photolithography and deep reactive ion etching techniques. Among these, flexibility in design using microfabrication approach has allowed fabricating etched flow cells having multiple parallel channels. Such parallelization improves acoustic energy density within each channel and precisely focuses particles at volumetric throughput of few tens of mL/min. Finally, this dissertation presents a proof-of-concept flow cytometry instrumentation using laser line generation optics and microscopy image sensors for imaging parallel focused streams in multinode acoustic flow cells. High throughput and precise focusing suggest that multinode focusing is a suitable alternative to conventional hydrodynamics, and multinode acoustic flow cells integrated with such optical imaging systems incorporating real-time signal processing circuitry will provide throughput matching that required for the detection of circulating tumor cells

Convergent Approaches for Defining Functional Imaging Endophenotypes in Schizophrenia

In complex genetic disorders such as schizophrenia, endophenotypes have potential utility both in identifying risk genes and in illuminating pathophysiology. This is due to their presumed status as closer in the etiopathological pathway to the causative genes than is the currently defining clinical phenomenology of the illness and thus their simpler genetic architecture than that of the full syndrome. There, many genes conferring slight individual risk are additive or epistatic (interactive) with regard to cumulative schizophrenia risk. In addition the use of endophenotypes has encouraged a conceptual shift away from the exclusive study of categorical diagnoses in manifestly ill patients, towards the study of quantitative traits in patients, unaffected relatives and healthy controls. A more recently employed strategy is thus to study unaffected first-degree relatives of schizophrenia patients, who share some of the genetic diathesis without illness-related confounds that may themselves impact fMRI task performance. Consistent with the multiple biological abnormalities associated with the disorder, many candidate endophenotypes have been advanced for schizophrenia, including measures derived from structural brain imaging, EEG, sensorimotor integration, eye movements and cognitive performance (Allen et al., 2009), but recent data derived from quantitative functional brain imaging measures present additional attractive putative endophenotypes. We will review two major, conceptually different approaches that use fMRI in this context. One, the dominant paradigm, employs defined cognitive tasks on which schizophrenia patients perform poorly as “cognitive stress tests”. The second uses very simple probes or “task-free” approaches where performance in patients and controls is equal. We explore the potential advantages and disadvantages of each method, the associated data analytic approaches and recent studies exploring their interface with the genetic risk architecture of schizophrenia

Panel 7 Paradoxes in Alternative Work Arrangements

This panel will present and debate various paradoxes surrounding the implementation of Alternative Work Arrangements (AWA). AWA includes such topics as telecommuting, remote work, telecenters, and other conceptions of the virtual office. This is an especially relevant topic to the ICIS theme, Networking and Electronic Communities, because the nature of alternative work arrangements requires organizations to rethink the fundamental ways individuals in their communities work, both alone and in groups. The AWA perspective assumes that technologies will not eliminate jobs, but will facilitate the transformation of traditional work arrangements by allowing flexibility in “when” and “where” work is done. The impact on the nature of work, workers, work groups, businesses and home life are all relevant to a debate about AWA

What Technical and Professional Skills are Needed for Cybersecurity Roles?

The Cybersecurity Skills Survey was designed to respond to the high-demand for cybersecurity professionals, noted by the findings of SIM (Society for Information Management) IT Trends and Issues Study (2017, 2018, 2019, 2020, 2021). The findings of the IT Trends and Issues Study are based upon input from over 1,000 IT leaders representing 37 SIM Chapters. The goals of the cybersecurity skills survey were to identify: (1) What technical skills are needed for entry-level professionals in cybersecurity jobs? (2) What professional skills are needed for entry level professionals in cybersecurity jobs? (3) What technical skills are needed for early-career professionals in cybersecurity jobs? and (4) What professional skills are needed for early-career professionals in cybersecurity jobs? The survey findings provide key insights into in-demand skills and “difficult-to-find” competencies. This paper reports on 99 responses captured from IT leaders representing the SIM Chapters in St. Louis, Austin, Milwaukee, and Phoenix

A techno-economic and environmental assessment of hydroprocessed renewable distillate fuels

Thesis (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 99-106).This thesis presents a model to quantify the economic costs and environmental impacts of producing fuels from hydroprocessed renewable oils (HRO) process. Aspen Plus was used to model bio-refinery operations and supporting utilities. Material and energy balances for electricity, carbon dioxide, and water requirements as well as economic costs were obtained from these models. A discounted-cash-flow-rate-of-return (DCFROR) economic model was used to evaluate minimum product values for diesel and jet fuels under various economic conditions. The baseline gate cost for distillate fuel production were found to range between $3.80 and$4.38 per gallon depending on the size of the facility. The additional cost for maximizing jet fuel production ranged between $0.25 and$0.30 per gallon. While the cost of feedstock is the most significant portion of fuel cost, facility size, financing, and capacity utilization were found to be sensitive parameters of the gate cost. The total water use of the system was found to be 0.9 pounds of water per pound of vegetable oil processed. Lifecycle greenhouse gas emissions (GHGs) for the processing step were found to range between 10.1 and 13.0 gCO 2e per MJ of distillate fuel using an energy allocation method consistent with methods in the literature. Finally, the policy landscape for producing jet and diesel fuels from renewable oils was reviewed from the perspective of a fuel producer. It was found that the potential of HRO fuels penetrating the market is dependent on the availability of feedstocks and access to capital.by Matthew Noah Pearlson.S.M.in Technology and Polic

Structural Angle and Power Images Reveal Interrelated Gray and White Matter Abnormalities in Schizophrenia

We present a feature extraction method to emphasize the interrelationship between gray and white matter and identify tissue distribution abnormalities in schizophrenia. This approach utilizes novel features called structural phase and magnitude images. The phase image indicates the relative contribution of gray and white matter, and the magnitude image reflects the overall tissue concentration. Three different analyses are applied to the phase and magnitude images obtained from 120 healthy controls and 120 schizophrenia patients. First, a single-subject subtraction analysis is computed for an initial evaluation. Second, we analyze the extracted features using voxel based morphometry (VBM) to detect voxelwise group differences. Third, source based morphometry (SBM) analysis was used to determine abnormalities in structural networks that co-vary in a similar way. Six networks were identified showing significantly lower white-to-gray matter in schizophrenia, including thalamus, right precentral-postcentral, left pre/post-central, parietal, right cuneus-frontal, and left cuneus-frontal sources. Interestingly, some networks look similar to functional patterns, such as sensory-motor and vision. Our findings demonstrate that structural phase and magnitude images can naturally and efficiently summarize the associated relationship between gray and white matter. Our approach has wide applicability for studying tissue distribution differences in the healthy and diseased brain

Characterization of groups using composite kernels and multi-source fMRI analysis data: application to schizophrenia

Pattern classification of brain imaging data can enable the automatic detection of differences in cognitive processes of specific groups of interest. Furthermore, it can also give neuroanatomical information related to the regions of the brain that are most relevant to detect these differences by means of feature selection procedures, which are also well-suited to deal with the high dimensionality of brain imaging data. This work proposes the application of recursive feature elimination using a machine learning algorithm based on composite kernels to the classification of healthy controls and patients with schizophrenia. This framework, which evaluates nonlinear relationships between voxels, analyzes whole-brain fMRI data from an auditory task experiment that is segmented into anatomical regions and recursively eliminates the uninformative ones based on their relevance estimates, thus yielding the set of most discriminative brain areas for group classification. The collected data was processed using two analysis methods: the general linear model (GLM) and independent component analysis (ICA). GLM spatial maps as well as ICA temporal lobe and default mode component maps were then input to the classifier. A mean classification accuracy of up to 95% estimated with a leave-two-out cross-validation procedure was achieved by doing multi-source data classification. In addition, it is shown that the classification accuracy rate obtained by using multi-source data surpasses that reached by using single-source data, hence showing that this algorithm takes advantage of the complimentary nature of GLM and ICAPublicad

Altered Small-World Brain Networks in Temporal Lobe in Patients with Schizophrenia Performing an Auditory Oddball Task

The functional architecture of the human brain has been extensively described in terms of complex networks characterized by efficient small-world features. Recent functional magnetic resonance imaging (fMRI) studies have found altered small-world topological properties of brain functional networks in patients with schizophrenia (SZ) during the resting state. However, little is known about the small-world properties of brain networks in the context of a task. In this study, we investigated the topological properties of human brain functional networks derived from fMRI during an auditory oddball (AOD) task. Data were obtained from 20 healthy controls and 20 SZ; A left and a right task-related network which consisted of the top activated voxels in temporal lobe of each hemisphere were analyzed separately. All voxels were detected by group independent component analysis. Connectivity of the left and right task-related networks were estimated by partial correlation analysis and thresholded to construct a set of undirected graphs. The small-worldness values were decreased in both hemispheres in SZ. In addition, SZ showed longer shortest path length and lower global efficiency only in the left task-related networks. These results suggested small-world attributes are altered during the AOD task-related networks in SZ which provided further evidences for brain dysfunction of connectivity in SZ

A Selective Review of Multimodal Fusion Methods in Schizophrenia

Schizophrenia (SZ) is one of the most cryptic and costly mental disorders in terms of human suffering and societal expenditure (van Os and Kapur, 2009). Though strong evidence for functional, structural, and genetic abnormalities associated with this disease exists, there is yet no replicable finding which has proven accurate enough to be useful in clinical decision making (Fornito et al., 2009), and its diagnosis relies primarily upon symptom assessment (Williams et al., 2010a). It is likely in part that the lack of consistent neuroimaging findings is because most models favor only one data type or do not combine data from different imaging modalities effectively, thus missing potentially important differences which are only partially detected by each modality (Calhoun et al., 2006a). It is becoming increasingly clear that multimodal fusion, a technique which takes advantage of the fact that each modality provides a limited view of the brain/gene and may uncover hidden relationships, is an important tool to help unravel the black box of schizophrenia. In this review paper, we survey a number of multimodal fusion applications which enable us to study the schizophrenia macro-connectome, including brain functional, structural, and genetic aspects and may help us understand the disorder in a more comprehensive and integrated manner. We also provide a table that characterizes these applications by the methods used and compare these methods in detail, especially for multivariate models, which may serve as a valuable reference that helps readers select an appropriate method based on a given research question