Corporate social sustainability in supply chains: a thematic analysis of the literature

This paper maps out different research strands using thematic analysis on the literature pertaining to large companies’ efforts on social sustainability in their supply chains. The data corpus for this thematic analysis is a broad sample of the literature with articles from different journals and employing different research methodologies. Each of the high-level themes is identified at a level high enough to apply to research into not only social but also economic or environmental sustainability. These eight themes – stakeholder pressure; governance; contingencies; practices; partnerships; barriers and enablers; performance; and optimisation for performance improvement and trade-off – are then woven into a thematic map. We call this map the ‘4P’ model as it suggests that pressure and partnerships influence practices, which in turn impact performance. Researchers can use this thematic classification not only to position their research within the social sustainability literature but also to integrate research on economic, environmental and social sustainability

EEBoost: a general method for prediction and variable selection based on estimating equations

Abstract The modern statistical literature is replete with methods for performing variable selection and prediction in standard regression problems. However, simple models may misspecify or fail to capture important aspects of the data generating process such as missingness, correlation, and over/underdispersion. This realization has motivated the development of a large class of estimating equations which account for these data characteristics and often yield improved inference for lowdimensional parameters. In this paper we introduce EEBoost, a novel strategy for variable selection and prediction which can be applied in any problem where inference would typically be based on estimating 1 equations. The method is simple, flexible, and easily implemented using existing software. Extended abstract The modern statistical literature is replete with methods for performing variable selection and prediction in standard regression problems. However, simple models may misspecify or fail to capture important aspects of the data generating process such as missingness, correlation, and over/underdispersion. This realization has motivated the development of a large class of estimating equations which account for these data characteristics and often yield improved inference for low-dimensional parameters. In this paper we introduce EEBoost, a novel strategy for variable selection and prediction which can be applied in any problem where inference would typically be based on estimating equations. The method is simple, flexible, and easily implemented using existing software. The EEBoost algorithm is obtained as a straightforward modification of the standard boosting (or functional gradient descent) technique. We show that EEBoost is closely related to a class of L 1 constrained projected likelihood ratio minimizations, and therefore produces similar variable selection paths to penalized methods without the need to apply constrained optimization algorithms. The flexibility of EEBoost is illustrated by applying it to simulated examples with correlated outcomes (based on generalized estimating equations) and time-to-event data with missing covariates (based on inverse probability weighted estimating equations). In both cases, EEBoost outperforms standard variable selection methods which do not account for the relevant data characteristics

Harmonized neonatal brain MR image segmentation model for cross-site datasets

Accurate segmentation of white matter, gray matter and cerebrospinal fluid from neonatal brain MR images is of great importance in characterizing early brain development. Deep-learning-based methods have been successfully applied to neonatal brain MRIs with superior performance if testing subjects were acquired with the same imaging protocols/scanners as training subjects. However, for the testing subjects acquired with different imaging protocols/scanners, they cannot achieve accurate segmentation results due to large appearance/pattern differences between the testing and training subjects. Besides, imaging artifacts, like head motion, which are inevitable during the imaging acquisition process, also pose a challenge for the segmentation methods. To address these issues, in this paper, we propose a harmonized neonatal brain MR image segmentation model that harmonizes testing images acquired by different protocols/scanners into the domain of training images through a cycle-consistent generative adversarial network (CycleGAN). Meanwhile, the artifacts can be largely alleviated during the harmonization. Then, a densely-connected U-Net based segmentation model trained in the domain of training images can be applied robustly for segmenting the harmonized testing images. Comparisons with existing methods illustrate the better performance of the proposed method on neonatal brain MR images from cross-sites, a grand segmentation challenge, as well as images with artifacts

Topological correction of infant white matter surfaces using anatomically constrained convolutional neural network

Reconstruction of accurate cortical surfaces without topological errors (i.e., handles and holes) from infant brain MR images is very important in early brain development studies. However, infant brain MR images typically suffer extremely low tissue contrast and dynamic imaging appearance patterns. Thus, it is inevitable to have large amounts of topological errors in the segmented infant brain tissue images, which lead to inaccurately reconstructed cortical surfaces with topological errors. To address this issue, inspired by recent advances in deep learning, we propose an anatomically constrained network for topological correction on infant cortical surfaces. Specifically, in our method, we first locate regions of potential topological defects by leveraging a topology-preserving level set method. Then, we propose an anatomically constrained network to correct those candidate voxels in the located regions. Since infant cortical surfaces often contain large and complex handles or holes, it is difficult to completely correct all errors using one-shot correction. Therefore, we further enroll these two steps into an iterative framework to gradually correct large topological errors. To the best of our knowledge, this is the first work to introduce deep learning approach for topological correction of infant cortical surfaces. We compare our method with the state-of-the-art methods on both simulated topological errors and real topological errors in human infant brain MR images. Moreover, we also validate our method on the infant brain MR images of macaques. All experimental results show the superior performance of the proposed method

MR fingerprinting enables quantitative measures of brain tissue relaxation times and myelin water fraction in the first five years of life

Quantitative assessments of normative brain development using MRI are of critical importance to gain insights into healthy neurodevelopment. However, quantitative MR imaging poses significant technical challenges and requires prohibitively long acquisition times, making it impractical for pediatric imaging. This is particularly relevant for healthy subjects, where imaging under sedation is not clinically indicated. MR Fingerprinting (MRF), a novel MR imaging framework, provides rapid, efficient, and simultaneous quantification of multiple tissue properties. In this study, a 2D MR Fingerprinting method was developed that achieves a spatial resolution of 1x1x3mm3 with rapid and simultaneous quantification of T1, T2 and myelin water fraction (MWF). Phantom experiments demonstrated that accurate measurements of T1 and T2 relaxation times were achieved over a wide range of T1 and T2 values. MRF images were acquired cross-sectionally from 28 typically developing children, 0 to five years old, who were enrolled in the UNC/UMN Baby Connectome Project. Differences associated with age of R1 (=1/T1), R2 (=1/T2) and MWF were obtained from several predefined white matter regions. Both R1 and R2 exhibit a marked increase until ~20 months of age, followed by a slower increase for all WM regions. In contrast, the MWF remains at a negligible level until ~6 months of age for all predefined ROIs and gradually increases afterwards. Depending on the brain region, rapid increases are observed between 6 and 12 months to 6-18 months, followed by a slower pace of increase in MWF. Neither relaxivities nor MWF were significantly different between the left and right hemispheres. However, regional differences in age-related R1 and MWF measures were observed across different white matter regions. In conclusion, our results demonstrate that the MRF technique holds great potential for multi-parametric assessments of normative brain development in early childhood

ABCnet: Adversarial bias correction network for infant brain MR images

Automatic correction of intensity nonuniformity (also termed as the bias correction) is an essential step in brain MR image analysis. Existing methods are typically developed for adult brain MR images based on the assumption that the image intensities within the same brain tissue are relatively uniform. However, this assumption is not valid in infant brain MR images, due to the dynamic and regionally-heterogeneous image contrast and appearance changes, which are caused by the underlying spatiotemporally-nonuniform myelination process. Therefore, it is not appropriate to directly use existing methods to correct the infant brain MR images. In this paper, we propose an end-to-end 3D adversarial bias correction network (ABCnet), tailored for direct prediction of bias fields from the input infant brain MR images for bias correction. The "ground-truth" bias fields for training our network are carefully defined by an improved N4 method, which integrates manually-corrected tissue segmentation maps as anatomical prior knowledge. The whole network is trained alternatively by minimizing generative and adversarial losses. To handle the heterogeneous intensity changes, our generative loss includes a tissue-aware local intensity uniformity term to reduce the local intensity variation in the corrected image. Besides, it also integrates two additional terms to enhance the smoothness of the estimated bias field and to improve the robustness of the proposed method, respectively. Comprehensive experiments with different sizes of training datasets have been carried out on a total of 1492 T1w and T2w MR images from neonates, infants, and adults, respectively. Both qualitative and quantitative evaluations on simulated and real datasets consistently demonstrate the superior performance of our ABCnet in both accuracy and efficiency, compared with popularly available methods

Longitudinal development of the cerebellum in human infants during the first 800 days

Revealing early dynamic development of the normative cerebellar structures contributes to exploring cerebellum-related neurodevelopmental disorders. Here, leveraging infant-tailored cerebellar image processing techniques, we studied the dynamic volumetric developmental trajectories of cerebellum and 27 cerebellar sub-regions and their relationships with behavioral scores based on 511 high-resolution structural MRI scans during the first 800 postnatal days. The ratio of the entire cerebellum to the intracranial volume increases rapidly at first and then peaks at 13 months after birth. Both the absolute and relative volumes of most cerebellar sub-structures exhibit rapid increase at first, then the relative volumes decrease slightly after arriving at peaks (except for X lobules). Each lobule depicts larger absolute volume in males than in females. The within-subject variation of the cerebellar volumetric percentile score is generally stable. The volumetric development of several lobules (e.g., V, Crus I, and Crus II) has a significantly positive correlation with fine motor skills during the age range examined

Evaluating the evolution and inter-individual variability of infant functional module development from 0 to 5 years old

The segregation and integration of infant brain networks undergo tremendous changes due to the rapid development of brain function and organization. Traditional methods for estimating brain modularity usually rely on group-averaged functional connectivity (FC), often overlooking individual variability. To address this, we introduce a novel approach utilizing Bayesian modeling to analyze the dynamic development of functional modules in infants over time. This method retains inter-individual variability and, in comparison to conventional group averaging techniques, more effectively detects modules, taking into account the stationarity of module evolution. Furthermore, we explore gender differences in module development under awake and sleep conditions by assessing modular similarities. Our results show that female infants demonstrate more distinct modular structures between these two conditions, possibly implying relative quiet and restful sleep compared with male infants

2016 Symposium: 35th Anniversary - Legal Feminism: Looking Back, Looking Forward, Part Two

Law & Inequality: A Journal of Theory and Practice Presents its 35th Annual Symposium Honoring Catharine A. MacKinnon and her work, Toward a Feminist Theory of State, at 30 years In 1989, Catharine A. MacKinnon—the founder of Law & Inequality: A Journal of Theory and Practice—published her groundbreaking work, Toward a Feminists Theory of State. Since that date, her work has been cited countless times and her legal theories have influenced scholars and practitioners throughout the country and across the world. The Journal’s 35th Anniversary Symposium honors MacKinnon and her influence on feminist legal theory. Specifically, the Symposium will examine Toward a Feminist Theory of State nearly 30 years after its publication, exploring where the arguments in the book stand today and what has yet to transpire in terms of its vision. Legal scholars and practitioners commented on MacKinnon’s feminist legal theories as applied to subjects including consent, evolving ideas about gender, international security, poverty, and family violence. MacKinnon delivers a responsive keynote address

3rd Annual MLK Convocation | Facing North: Implicit Bias in Minnesota Courts

January 22, 2018 Sponsored by the Law School Diversity Committee, the 3rd Annual MLK Convocation—“Facing North: Implicit Bias in Minnesota Courts”—featured a discussion between The Honorable Pamela G. Alexander (‘77) of Minnesota’s 4th Judicial District and Professor Francis X. Shen, moderated by Dean Garry W. Jenkins, about how implicit bias operates, how it manifests itself in the courtroom, and how it might be mitigated in the American criminal justice system. Sponsored by the Law School Diversity Committee, the 3rd Annual MLK Convocation—“Facing North: Implicit Bias in Minnesota Courts”—featured a discussion between The Honorable Pamela G. Alexander (‘77) of Minnesota’s 4th Judicial District and Professor Francis X. Shen, moderated by Dean Garry W. Jenkins, about how implicit bias operates, how it manifests itself in the courtroom, and how it might be mitigated in the American criminal justice system