Free-Form B-spline Deformation Model for Groupwise Registration

In this work, we extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment. Our results indicate that increasing the complexity of the deformation model improves registration accuracy significantly, especially at cortical regions

Deconstructing Disability: A Philosophy for Inclusion

This article offers derrida's deconstruction as a philosophy and practical strategy that challenges the assumed, factual nature of "disability" as a construct explaining human differences. The appeal of deconstruction lies in the contradictory philosophy currently articulated by the inclusion movement, a philosophy that simultaneously supports the disability construct as objective reality while calling for students "with disabilities" to be placed in educational settings designed for students considered nondisabled. This article proposes deconstruction as one coherent philosophical orientation for inclusion, an approach that critiques the political and moral hierarchy of ability and disability. A deconstructionist critique of disability is explained and demonstrated. Practical suggestions for the utilization of deconstruction by special educators are outlined.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68721/2/10.1177_074193259701800605.pd

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Technological elites, the meritocracy, and postracial myths in Silicon Valley

Entre as modernas elites tecnológicas digitais, os mitos da meritocracia e da façanha intelectual são usados como marcadores de raça e gênero por uma supremacia branca masculina que consolida recursos de forma desproporcional em relação a pessoas não brancas, principalmente negros, latinos e indígenas. Os investimentos em mitos meritocráticos suprimem os questionamentos de racismo e discriminação, mesmo quando os produtos das elites digitais são infundidos com marcadores de raça, classe e gênero. As lutas históricas por inclusão social, política e econômica de negros, mulheres e outras classes desprotegidas têm implicado no reconhecimento da exclusão sistêmica, do trabalho forçado e da privação de direitos estruturais, além de compromissos com políticas públicas dos EUA, como as ações afirmativas, que foram igualmente fundamentais para reformas políticas voltadas para participação e oportunidades econômicas. A ascensão da tecnocracia digital tem sido, em muitos aspectos, antitética a esses esforços no sentido de reconhecer raça e gênero como fatores cruciais para inclusão e oportunidades tecnocráticas. Este artigo explora algumas das formas pelas quais os discursos das elites tecnocráticas do Vale do Silício reforçam os investimentos no pós racialismo como um pretexto para a re-consolidação do capital em oposição às políticas públicas que prometem acabar com práticas discriminatórias no mundo do trabalho. Por meio de uma análise cuidadosa do surgimento de empresas de tecnologias digitais e de uma discussão sobre como as elites tecnológicas trabalham para mascarar tudo, como inscrições algorítmicas e genéticas de raça incorporadas em seus produtos, mostramos como as elites digitais omitem a sua responsabilidade por suas reinscrições pós raciais de (in)visibilidades raciais. A partir do uso de análise histórica e crítica do discurso, o artigo revela como os mitos de uma meritocracia digital baseados em um “daltonismo racial” tecnocrático emergem como chave para a manutenção de exclusões de gênero e raça.Palavras-chave: Tecnologia. Raça. Gênero.Among modern digital technology elites, myths of meritocracy and intellectual prowess are used as racial and gender markers of white male supremacy that disproportionately consolidate resources away from people of color, particularly African Americans, Latino/as and Native Americans. Investments in meritocratic myths suppress interrogations of racism and discrimination even as the products of digital elites are infused with racial, class, and gender markers. Longstanding struggles for social, political, and economic inclusion for African Americans, women, and other legally protected classes have been predicated upon the recognition of systemic exclusion, forced labor, and structural disenfranchisement, and commitments to US public policies like affirmative action have, likewise, been fundamental to political reforms geared to economic opportunity and participation. The rise of the digital technocracy has, in many ways, been antithetical to these sustained efforts to recognize race and gender as salient factors structuring technocratic opportunity and inclusion. This paper explores some of the ways in which discourses of Silicon Valley technocratic elites bolster investments in post-racialism as a pretext for re-consolidations of capital, in opposition to public policy commitments to end discriminatory labor practices. Through a careful analysis of the rise of digital technology companies, and a discussion of how technology elites work to mask everything from algorithmic to genetic inscriptions of race embedded in their products, we show how digital elites elide responsibility for their post-racial re-inscriptions of racial visibilities (and invisibilities). Using historical and critical discourse analysis, the paper reveals how myths of a digital meritocracy premised on a technocratic colorblindness emerge key to perpetuating gender and racial exclusions.Keywords: Technology. Race. Gender

Multimodal Image Registration for Preoperative Planning and Image-Guided Neurosurgical Procedures

Image registration is the process of transforming images acquired at different time points, or with different imaging modalities, into the same coordinate system. It is an essential part of any neurosurgical planning and navigation system because it facilitates combining images with important complementary, structural, and functional information to improve the information based on which a surgeon makes critical decisions. Brigham and Women's Hospital (BWH) has been one of the pioneers in developing intraoperative registration methods for aligning preoperative and intraoperative images of the brain. This article presents an overview of intraoperative registration and highlights some recent developments at BWH

Unbiased Groupwise Registration of White Matter Tractography

We present what we believe to be the first investigation into unbiased multi-subject registration of whole brain diffusion tractography of the white matter. To our knowledge, this is also the first entropy-based objective function applied to fiber tract registration. To define the probability of fiber trajectories for the computation of entropy, we take advantage of a pairwise fiber distance used as the basis for a Gaussian-like kernel. By employing several values of the kernel’s scale parameter, the method is inherently multi-scale. Results of experiments using synthetic and real datasets demonstrate the potential of the method for simultaneous joint registration of tractography

Bayesian characterization of uncertainty in intra-subject non-rigid registration

In settings where high-level inferences are made based on registered image data, the registration uncertainty can contain important information. In this article, we propose a Bayesian non-rigid registration framework where conventional dissimilarity and regularization energies can be included in the likelihood and the prior distribution on deformations respectively through the use of Boltzmann’s distribution. The posterior distribution is characterized using Markov Chain Monte Carlo (MCMC) methods with the effect of the Boltzmann temperature hyper-parameters marginalized under broad uninformative hyper-prior distributions. The MCMC chain permits estimation of the most likely deformation as well as the associated uncertainty. On synthetic examples, we demonstrate the ability of the method to identify the maximum a posteriori estimate and the associated posterior uncertainty, and demonstrate that the posterior distribution can be non-Gaussian. Additionally, results from registering clinical data acquired during neurosurgery for resection of brain tumor are provided; we compare the method to single transformation results from a deterministic optimizer and introduce methods that summarize the high-dimensional uncertainty. At the site of resection, the registration uncertainty increases and the marginal distribution on deformations is shown to be multi-modal

fMRI-DTI modeling via landmark distance atlases for prediction and detection of fiber tracts

The overall goal of this research is the design of statistical atlas models that can be created from normal subjects, but may generalize to be applicable to abnormal brains. We present a new style of joint modeling of fMRI, DTI, and structural MRI. Motivated by the fact that a white matter tract and related cortical areas are likely to displace together in the presence of a mass lesion (brain tumor), in this work we propose a rotation and translation invariant model that represents the spatial relationship between fiber tracts and anatomic and functional landmarks. This landmark distance model provides a new basis for representation of fiber tracts and can be used for detection and prediction of fiber tracts based on landmarks. Our results indicate that the measured model is consistent across normal subjects, and thus suitable for atlas building. Our experiments demonstrate that the model is robust to displacement and missing data, and can be successfully applied to a small group of patients with mass lesions

A Unifying Approach to Registration, Segmentation, and Intensity Correction

We present a statistical framework that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image inhomogeneities, anatomical labelmap, and a mapping from the atlas to the image space. An example of the approach is given for a brain structure-dependent affine mapping approach. The algorithm produces high quality segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. In addition, we show that the approach performs better than similar methods which separate the registration and segmentation problems