Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging

The value of in vivo preclinical diffusion MRI (dMRI) is substantial. Small-animal dMRI has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. Many of the influential works in this field were first performed in small animals or ex vivo samples. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the data. This work aims to serve as a reference, presenting selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. In each section, we also highlight areas for which no guidelines exist (and why), and where future work should focus. We first describe the value that small animal imaging adds to the field of dMRI, followed by general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss how they are appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, imaging sequences and data processing, including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.Comment: 69 pages, 6 figures, 1 tabl

Application of Process Mining for Modelling Small Cell Lung Cancer Prognosis

Process mining is a relatively new method that connects data science and process modelling. In the past years a series of applications with health care production data have been presented in process discovery, conformance check and system enhancement. In this paper we apply process mining on clinical oncological data with the purpose of studying survival outcomes and chemotherapy treatment decision in a real-world cohort of small cell lung cancer patients treated at Karolinska University Hospital (Stockholm, Sweden). The results highlighted the potential role of process mining in oncology to study prognosis and survival outcomes with longitudinal models directly extracted from clinical data derived from healthcare.QC 20230628</p

A novel analytical framework for risk stratification of real‐world data using machine learning: A small cell lung cancer study

In recent studies, small cell lung cancer (SCLC) treatment guidelines based on Veterans’ Administration Lung Study Group limited/extensive disease staging and resulted in broad and inseparable prognostic subgroups. Evidence suggests that the eight versions of tumor, node, and metastasis (TNM) staging can play an important role to address this issue. The aim of the present study was to improve the detection of prognostic subgroups from a real-word data (RWD) cohort of patients and analyze their patterns using a development pipeline with thoracic oncologists and machine learning methods. The method detected subgroups of patients informing unsupervised learning (partition around medoids) including the impact of covariates on prognosis (Cox regression and random survival forest). An analysis was carried out using patients with SCLC (n = 636) with stage IIIA–IVB according to TNM classification. The analysis yielded k = 7 compacted and well-separated clusters of patients. Performance status (Eastern Cooperative Oncology Group-Performance Status), lactate dehydrogenase, spreading of metastasis, cancer stage, and CRP were the baselines that characterized the subgroups. The selected clustering method outperformed standard clustering techniques, which were not capable of detecting meaningful subgroups. From the analysis of cluster treatment decisions, we showed the potential of future RWD applications to understand disease, develop individualized therapies, and improve healthcare decision making.QC 20221115</p

Circles and Sensibilities: Music by and for Virgil Thomson

This video documents the concert titled "Circles & Sensibilities: Music by and For Virgil Thomson" at the University of Michigan Museum of Art on November 1, 2013. Exploring the artistic circle as creative milieu and engine through the compositions of early-mid 20th century American composers, students, alumni, and faculty from the School of Music, Theatre, & Dance performed works written by Virgil Thomson, Aaron Copland, Ned Rorem, David Diamond, Marc Blitzstein, Paul Bowles, and David Del Tredici. Professor Nadine Hubbs provided commentary. Performances by: John Boonenberg (piano), Jean Bernard Cerin (baritone), Jennifer Goltz (soprano), Kathryn Goodson (piano), Matthew Leslie-Santana (violin), Donald Sinta (saxophone), and Adam Tendler (piano). This concert was presented as part of the 2013 performance series, SMTD@UMMA, and was made possible with support from The Katherine Tuck Enrichment Fund, the Department of American Culture, and the Residential College. It was presented in conjunction with the UMMA exhibit Adolph Gottlieb: Sculptor. This concert was presented by The University of Michigan Museum of Art, The School of Music, Theatre & Dance, and the Lesbian-Gay-Queer Research Initiative at the Institute for Research on Women and Gender.The Katherine Tuck Enrichment FundThe Department of American CultureThe Residential Collegehttp://deepblue.lib.umich.edu/bitstream/2027.42/106998/1/Circles_and_Sensibilities-FullConcert-Final.movhttp://deepblue.lib.umich.edu/bitstream/2027.42/106998/3/ConsentForms-CirclesSensibilities.pd

Democracy and State Capacity. Exploring a J-shaped Relationship

In this article we probe the effect of democratization on the state's administrative capacity. Using time-series cross-section data, we find a curvilinear (J-shaped) relationship between the two traits. The effect of democracy on state capacity is negative at low values of democracy, nonexistent at median values, and strongly positive at high democracy levels. This is confirmed under demanding statistical tests. The curvilinear relationship is due, we argue, to the combined effect of two forms of steering and control; one exercised from above, the other from below. In strongly authoritarian states, a satisfactory measure of control from above can at times be accomplished. Control from below is best achieved when democratic institutions are fully installed and are accompanied by a broad array of societal resources. Looking at two resource measures, press circulation and electoral participation, we find that these, combined with democracy, enhance state administrative capacity