Power to the People: A Comprehensive Look at Crowdsourcing Initiatives in Cultural Institutions

Today, crowdsourcing has become an integrative approach to completing projects using the help of the general populous. These projects aid museum staff by processing large quantities of data, which otherwise could not be completed due to time and/or staff restraints. Through crowdsourcing, cultural institutions have the ability to outsource these tasks to volunteers, who can complete them at much faster rates. Although staff members are needed to validate and supervise these projects, crowdsourcing remains a useful tool in increasing public interactions and project efficiency. This thesis presents a thorough outline of what crowdsourcing is, how it is being utilized, and how volunteers can be motivated to participate. Case studies are presented, providing a comprehensive look into each of the six types of crowdsourcing. These studies include the Brooklyn Museum, September 11th Memorial and Museum, South Eastern Regional Network of Expertise and Collections, British Library, Peoria Historical Society, and Smithsonian Institution. Utilizing these critical examples, this paper presents several motivational theories of volunteer participation and outlines how this knowledge can be implemented to create a more successfully crowdsourced project

Senior Recital: Jared Hutson Leach, jazz guitar

This recital is presented in partial fulfillment of requirements for the degree Bachelor of Music in Performance. Mr. Leach studies jazz guitar with Trey Wright.https://digitalcommons.kennesaw.edu/musicprograms/1261/thumbnail.jp

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

A Locally Adaptive Regularization Based on Anisotropic Diffusion for Deformable Image Registration of Sliding Organs

We propose a deformable image registration algorithm that uses anisotropic smoothing for regularization to find correspondences between images of sliding organs. In particular, we apply the method for respiratory motion estimation in longitudinal thoracic and abdominal computed tomography scans. The algorithm uses locally adaptive diffusion tensors to determine the direction and magnitude with which to smooth the components of the displacement field that are normal and tangential to an expected sliding boundary. Validation was performed using synthetic, phantom, and 14 clinical datasets, including the publicly available DIR-Lab dataset. We show that motion discontinuities caused by sliding can be effectively recovered, unlike conventional regularizations that enforce globally smooth motion. In the clinical datasets, target registration error showed improved accuracy for lung landmarks compared to the diffusive regularization. We also present a generalization of our algorithm to other sliding geometries, including sliding tubes (e.g., needles sliding through tissue, or contrast agent flowing through a vessel). Potential clinical applications of this method include longitudinal change detection and radiotherapy for lung or abdominal tumours, especially those near the chest or abdominal wall

Qualidade de vida das pessoas com diabetes mellitus

Objective: To analyze the quality of life of people with type 2 diabetes mellitus in the three levels of the healthcare system. Method: A quantitative, cross-sectional and descriptive study carried out in primary, secondary and tertiary healthcare units with individuals in outpatient care. The validated Diabetes-39 instrument was used to evaluate quality of life. Results: The sample consisted of 53 people. There was a decreasing tendency in the quality of life impairment from the primary to the tertiary care levels. In the total sample, there were differences between domains of quality of life with the variables gender, insulin use and occupation, greater perception of quality of life impairment and disease severity in people with higher rates of glycated hemoglobin. Conclusion: Quality of life tends to worse as the disease worsens. The results suggest that quality of life is related to sociodemographic and clinical variables, therefore, these should be considered in the care

Interactive Whole-Heart Segmentation in Congenital Heart Disease

We present an interactive algorithm to segment the heart chambers and epicardial surfaces, including the great vessel walls, in pediatric cardiac MRI of congenital heart disease. Accurate whole-heart segmentation is necessary to create patient-specific 3D heart models for surgical planning in the presence of complex heart defects. Anatomical variability due to congenital defects precludes fully automatic atlas-based segmentation. Our interactive segmentation method exploits expert segmentations of a small set of short-axis slice regions to automatically delineate the remaining volume using patch-based segmentation. We also investigate the potential of active learning to automatically solicit user input in areas where segmentation error is likely to be high. Validation is performed on four subjects with double outlet right ventricle, a severe congenital heart defect. We show that strategies asking the user to manually segment regions of interest within short-axis slices yield higher accuracy with less user input than those querying entire short-axis sliceNatural Sciences and Engineering Research Council of Canada (Alexander Graham Bell Canada Graduate Scholarships-Doctoral Program (CGS D))Wistron CorporationNational Institute for Biomedical Imaging and Bioengineering (U.S.) (NAMIC U54-EB005149)Boston Children's Hospital (Translational Research Program Fellowship)Boston Children's Hospital. Office of Faculty DevelopmentHarvard Catalys

Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs

An important factor for the development of spinal degeneration, pain and the outcome of spinal surgery is known to be the balance of the spine. It must be analyzed in an upright, standing position to ensure physiological loading conditions and visualize load-dependent deformations. Despite the complex 3D shape of the spine, this analysis is currently performed using 2D radiographs, as all frequently used 3D imaging techniques require the patient to be scanned in a prone position. To overcome this limitation, we propose a deep neural network to reconstruct the 3D spinal pose in an upright standing position, loaded naturally. Specifically, we propose a novel neural network architecture, which takes orthogonal 2D radiographs and infers the spine’s 3D posture using vertebral shape priors. In this work, we define vertebral shape priors using an atlas and a spine shape prior, incorporating both into our proposed network architecture. We validate our architecture on digitally reconstructed radiographs, achieving a 3D reconstruction Dice of 0.95, indicating an almost perfect 2D-to-3D domain translation. Validating the reconstruction accuracy of a 3D standing spine on real data is infeasible due to the lack of a valid ground truth. Hence, we design a novel experiment for this purpose, using an orientation invariant distance metric, to evaluate our model’s ability to synthesize full-3D, upright, and patient-specific spine models. We compare the synthesized spine shapes from clinical upright standing radiographs to the same patient’s 3D spinal posture in the prone position from CT

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction☆

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

Miscellany

Art Literature Roy F. Powell Creditshttps://digitalcommons.georgiasouthern.edu/miscell/1001/thumbnail.jp

To See or Not to See:How Does Seeing Spellings Support Vocabulary Learning?

Purpose The aim of this study was to determine when, why, and how the presence of a word's written form during instruction aids vocabulary learning (a process known as orthographic facilitation). Method A systematic review of the research on orthographic facilitation was carried out. PsycInfo, Web of Science, ProQuest, and OpenGrey databases were searched. The search returned 3,529 results, and 23 of these met inclusion criteria. Studies were included in the review if they were written in English, published in a peer-reviewed journal, and compared vocabulary learning outcomes when words were taught with and without their written forms. Conclusions There is strong evidence that the presence of a word's written form leads to improved learning of its spelling and spoken form. There is also some evidence that it may lead to better learning of a word's meaning. A small number of studies have also shown that the presence of a word's written form benefits vocabulary learning in children with developmental language disorder, autism, Down syndrome, and reading difficulties. However, further research into the effects of orthographic facilitation in special populations is needed. In particular, ecologically valid experiments in clinical and educational settings are required in order to better understand how exposure to a word's written form can aid naturalistic vocabulary learning