Educating Lawyers: Preparation for the Profession of Law

Examines the unique aspects and limitations of legal education, as part of a series of reports from the foundation's Preparation for the Professions Program

Methods for Population Pharmacokinetics and Pharmacodynamics

Current applications of cross validation have been unsuccessful at identifying covariate effects in the population Pharmacokinetic/Pharmacodynamic (PK/PD) setting when other methods find a covariate effect may exist. Software that does population PK/PD modeling has a nice feature of being able to do a post hoc step without any major iterations to obtain Bayesian parameter estimates and hence predictions for subjects that were not in the dataset that was used to fit the model. This work proposes cross validation methods for longitudinal mixed effects models that are effective at identifying covariate effects when they exist.Doctor of Public Healt

Exome sequencing of Finnish isolates enhances rare-variant association power.

Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power

Allegory and animals in Olive Schreiner’s Undine : A Queer Little Child (1929)

Written and abandoned in the 1870s, and published posthumously in 1929, Undine: A Queer Little Child has remained on the margins of Olive Schreiner (1855-1920) studies, repeatedly dismissed as a juvenile and poor antecedent to The Story of An African Farm (1883), or deemed valuable primarily for its autobiographical content. This article redresses these schematic readings by analysing how Schreiner draws on allegorical forms in order to explore aspects of her burgeoning radicalism. Focusing on one of the main allegorical thrusts of the novel, provided by the zoomorphic and anthropomorphic animal characters that descend from mythical, fairytale, and Ancient Greek philosophical origins, it investigates how the protagonist’s metaphorically significant associations with animals relate to freethinking, feminist, and anti-imperialist ideas introduced by the novel. Undine thus undermines dominant nineteenth-century models of the “primitive” human or animal as less evolutionarily developed and without political platform, which can be seen to be a liberating move when the novel is read in dialogue with Jacques Derrida’s lectures on animals, and with other recent work in postcolonial ecocriticism

Enantioselective nickel-catalyzed reductive coupling reactions of alkynes and aldehydes. Synthesis of amphidinolides T1 and T4 via catalytic, stereoselective macrocyclizations

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemistry, 2005.MIT Science Library copy: 2 v. set, in leaves.Also issued in leaves, 2 v. set. Vita.Includes bibliographical references.I. Enantioselective Nickel-Catalyzed Reductive Couplings of Alkynes and Aldehydes Allylic alcohol synthesis via a nickel-catalyzed reductive coupling reaction of alkylsubstituted alkynes and aldehydes was studied for ligand effects with respect to the regioselectivity and enantioselectivity of the coupling process. A class of P-chiral, ferrocenyl phosphines was designed, synthesized, and evaluated for efficacy. Ultimately, these phosphines were found to be the most effective chiral ligands for coupling reactions of this class of alkynes, providing (E)-allylic alcohols in up to 67% ee and 85:15 regioselectivity. ... II. Total Syntheses of Amphidinolides T and T4 via Catalytic, Stereoselective Reductive Macrocyclizations Total syntheses of amphidinolides T and T4 were achieved using two nickelcatalyzed reductive coupling reactions of alkynes, with an epoxide in one case (intermolecular) and with an aldehyde in another (intramolecular). The latter was used to effect a macrocyclization, form a C-C bond and install a stereogenic center with >10:1 selectivity in both natural product syntheses. Alternative approaches in which intermolecular alkyne-aldehyde reductive coupling reactions would serve to join key fragments were investigated and are also discussed; it was found that macrocyclization was superior in several respects (diastereoselectivity, yield, and length of syntheses). Alkyneepoxide couplings were instrumental in the construction of key fragments corresponding to approximately half of the molecule of both natural products. In one case (T4 series), the alkyne-epoxide coupling exhibited very high site selectivity in a coupling of a diyne.(cont.) A model for the stereoselectivity observed in the macrocyclizations is also proposed. ... amphidinolide T1 amphidinolide T4 * site of catalytic, stereoselective macrocyclizationby Elizabeth A. Colby Davie.Ph.D

A Textbook Equity Student Advisory Board

Engaging college students in advocacy related to open educational resources (OER) is an important way to raise awareness and encourage OER adoption on campuses in higher education. The student perspective is vital to encouraging instructors and administrators to be responsive to student needs, concerns, and preferences related to course materials access, and to consider affordability and usage rights. This case study describes the library-facilitated Textbook Equity Student Advisory Board at the University of Northern Iowa, a medium-sized regional comprehensive university. The board serves as a two-way communication outlet between textbook equity campus leaders and students, including student government. This article covers how and why the group was formed and the benefits and challenges of forming and running such a group. The group\u27s scope, like the campus-wide program it informs, includes OER and related topics such as automatic textbook billing, library-licensed resources as course materials, and more. Recommendations for implementing similar groups are provided

Synergistic Signals: Exploiting Co-Engagement and Semantic Links via Graph Neural Networks

Given a set of candidate entities (e.g. movie titles), the ability to identify similar entities is a core capability of many recommender systems. Most often this is achieved by collaborative filtering approaches, i.e. if users co-engage with a pair of entities frequently enough, the embeddings should be similar. However, relying on co-engagement data alone can result in lower-quality embeddings for new and unpopular entities. We study this problem in the context recommender systems at Netflix. We observe that there is abundant semantic information such as genre, content maturity level, themes, etc. that complements co-engagement signals and provides interpretability in similarity models. To learn entity similarities from both data sources holistically, we propose a novel graph-based approach called SemanticGNN. SemanticGNN models entities, semantic concepts, collaborative edges, and semantic edges within a large-scale knowledge graph and conducts representation learning over it. Our key technical contributions are twofold: (1) we develop a novel relation-aware attention graph neural network (GNN) to handle the imbalanced distribution of relation types in our graph; (2) to handle web-scale graph data that has millions of nodes and billions of edges, we develop a novel distributed graph training paradigm. The proposed model is successfully deployed within Netflix and empirical experiments indicate it yields up to 35% improvement in performance on similarity judgment tasks