Freedom & Movement in the Holocaust

The overall focus of our media project will be on people who were victims and targets of the Holocaust and their stories of desperate escape and efforts to avoid the Nazi regime, as well as perspectives from the side of the Nazi party. We focus on movement as a means of escape, movement of ideas, and movement through immigration in search of a new home.https://digitalcommons.butler.edu/freedom-movement-fall-2017/1012/thumbnail.jp

Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery

We introduce a novel deep learning method for detection of individual trees in urban environments using high-resolution multispectral aerial imagery. We use a convolutional neural network to regress a confidence map indicating the locations of individual trees, which are localized using a peak finding algorithm. Our method provides complete spatial coverage by detecting trees in both public and private spaces, and can scale to very large areas. We performed a thorough evaluation of our method, supported by a new dataset of over 1,500 images and almost 100,000 tree annotations, covering eight cities, six climate zones, and three image capture years. We trained our model on data from Southern California, and achieved a precision of 73.6% and recall of 73.3% using test data from this region. We generally observed similar precision and slightly lower recall when extrapolating to other California climate zones and image capture dates. We used our method to produce a map of trees in the entire urban forest of California, and estimated the total number of urban trees in California to be about 43.5 million. Our study indicates the potential for deep learning methods to support future urban forestry studies at unprecedented scales

Structure of the Pre-mRNA Leakage 39-kDa Protein Reveals a Single Domain of Integrated zf-C3HC and Rsm1 Modules

In Saccharomyces cerevisiae, the pre-mRNA leakage 39-kDa protein (ScPml39) was reported to retain unspliced pre-mRNA prior to export through nuclear pore complexes (NPCs). Pml39 homologs outside the Saccharomycetaceae family are currently unknown, and mechanistic insight into Pml39 function is lacking. Here we determined the crystal structure of ScPml39 at 2.5 Å resolution to facilitate the discovery of orthologs beyond Saccharomycetaceae, e.g. in Schizosaccharomyces pombe or human. The crystal structure revealed integrated zf-C3HC and Rsm1 modules, which are tightly associated through a hydrophobic interface to form a single domain. Both zf-C3HC and Rsm1 modules belong to the Zn-containing BIR (Baculovirus IAP repeat)-like super family, with key residues of the canonical BIR domain being conserved. Features unique to the Pml39 modules refer to the spacing between the Zn-coordinating residues, giving rise to a substantially tilted helix αC in the zf-C3HC and Rsm1 modules, and an extra helix αAB\u27 in the Rsm1 module. Conservation of key residues responsible for its distinct features identifies S. pombe Rsm1 and Homo sapiens NIPA/ZC3HC1 as structural orthologs of ScPml39. Based on the recent functional characterization of NIPA/ZC3HC1 as a scaffold protein that stabilizes the nuclear basket of the NPC, our data suggest an analogous function of ScPml39 in S. cerevisiae

Adverse Childhood Experiences Are Linked to Age of Onset and Reading Recognition in Multiple Sclerosis

BackgroundAdverse childhood experiences (ACEs) exert a psychological and physiological toll that increases risk of chronic conditions, poorer social functioning, and cognitive impairment in adulthood.ObjectiveTo investigate the relationship between childhood adversity and clinical disease features in multiple sclerosis (MS).MethodsSixty-seven participants with MS completed the ACE assessment and neuropsychological assessments as part of a larger clinical trial of cognitive remediation.ResultsAdverse childhood experience scores, a measure of exposure to adverse events in childhood, significantly predicted age of MS onset (r = –0.30, p = 0.04). ACEs were also linked to reading recognition (a proxy for premorbid IQ) (r = –0.25, p = 0.04). ACE scores were not related to age, current disability, or current level of cognitive impairment measured by the Symbol Digit Modalities Test (SDMT).ConclusionChildhood adversity may increase the likelihood of earlier age of onset and poorer estimated premorbid IQ in MS

Kynurenine–3–monooxygenase inhibition prevents multiple organ failure in rodent models of acute pancreatitis

Acute pancreatitis (AP) is a common and devastating inflammatory condition of the pancreas that is considered to be a paradigm of sterile inflammation leading to systemic multiple organ dysfunction syndrome (MODS) and death1,2 Acute mortality from AP-MODS exceeds 20%3 and for those who survive the initial episode, their lifespan is typically shorter than the general population4. There are no specific therapies available that protect individuals against AP-MODS. Here, we show that kynurenine-3-monooxygenase (KMO), a key enzyme of tryptophan metabolism5, is central to the pathogenesis of AP-MODS. We created a mouse strain deficient for Kmo with a robust biochemical phenotype that protected against extrapancreatic tissue injury to lung, kidney and liver in experimental AP-MODS. A medicinal chemistry strategy based on modifications of the kynurenine substrate led to the discovery of GSK180 as a potent and specific inhibitor of KMO. The binding mode of the inhibitor in the active site was confirmed by X-ray co-crystallography at 3.2 Å resolution. Treatment with GSK180 resulted in rapid changes in levels of kynurenine pathway metabolites in vivo and afforded therapeutic protection against AP-MODS in a rat model of AP. Our findings establish KMO inhibition as a novel therapeutic strategy in the treatment of AP-MODS and open up a new area for drug discovery in critical illness

Generalizing remotely supervised transcranial direct current stimulation (tDCS): feasibility and benefit in Parkinson’s disease

Abstract Background Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that has been shown to improve common symptoms of neurological disorders like depressed mood, fatigue, motor deficits and cognitive dysfunction. tDCS requires daily treatment sessions in order to be effective. We developed a remotely supervised tDCS (RS-tDCS) protocol for participants with multiple sclerosis (MS) to increase accessibility of tDCS, reducing clinician, patient, and caregiver burden. The goal of this protocol is to facilitate home use for larger trials with extended treatment periods. In this study we determine the generalizability of RS-tDCS paired with cognitive training (CT) by testing its feasibility in participants with Parkinson’s disease (PD). Methods Following the methods in our MS protocol development, we enrolled sixteen participants (n = 12 male, n = 4 female; mean age 66 years) with PD to complete ten open-label sessions of RS-tDCS paired with CT (2.0 mA × 20 min) at home under the remote supervision of a trained study technician. Tolerability data were collected before, during, and after each individual session. Baseline and follow-up measures included symptom inventories (fatigue and sleep) and cognitive assessments. Results RS-tDCS was feasible and tolerable for patients with PD, with at-home access leading to high protocol compliance. Side effects were mostly limited to mild sensations of transient itching and burning under the electrode sites. Similar to prior finding sin MS, we found preliminary efficacy for improvement of fatigue and cognitive processing speed in PD. Conclusions RS-tDCS paired with CT is feasible for participants with PD to receive at home treatment. Signals of benefit for reduced fatigue and improved cognitive processing speed are consistent across the PD and MS samples. RS-tDCS can be generalized to provide tDCS to a range of patients with neurologic disorders for at-home rehabilitation. Trial registration ClinicalTrials.gov Identifier: NCT02746705. Registered April 21st 2016

Academic global surgical competencies: A modified Delphi consensus study

Academic global surgery is a rapidly growing field that aims to improve access to safe surgical care worldwide. However, no universally accepted competencies exist to inform this developing field. A consensus-based approach, with input from a diverse group of experts, is needed to identify essential competencies that will lead to standardization in this field. A task force was set up using snowball sampling to recruit a broad group of content and context experts in global surgical and perioperative care. A draft set of competencies was revised through the modified Delphi process with two rounds of anonymous input. A threshold of 80% consensus was used to determine whether a competency or sub-competency learning objective was relevant to the skillset needed within academic global surgery and perioperative care. A diverse task force recruited experts from 22 countries to participate in both rounds of the Delphi process. Of the n = 59 respondents completing both rounds of iterative polling, 63% were from low- or middle-income countries. After two rounds of anonymous feedback, participants reached consensus on nine core competencies and 31 sub-competency objectives. The greatest consensus pertained to competency in ethics and professionalism in global surgery (100%) with emphasis on justice, equity, and decolonization across multiple competencies. This Delphi process, with input from experts worldwide, identified nine competencies which can be used to develop standardized academic global surgery and perioperative care curricula worldwide. Further work needs to be done to validate these competencies and establish assessments to ensure that they are taught effectivel

Individual tree detection in large-scale urban environments using high-resolution multispectral imagery

Systematic maps of urban forests are useful for regional planners and ecologists to understand the spatial distribution of trees in cities. However, manually-created urban forest inventories are expensive and time-consuming to create and typically do not provide coverage of private land. Toward the goal of automating urban forest inventory through machine learning techniques, we performed a comparative study of methods for automatically detecting and localizing trees in multispectral aerial imagery of urban environments, and introduce a novel method based on convolutional neural network regression. Our evaluation is supported by a new dataset of over 1,500 images and almost 100,000 tree annotations, covering eight cities, six climate zones, and three image capture years. Our method outperforms previous methods, achieving 73.6% precision and 73.3% recall when trained and tested in Southern California, and 76.5% precision 72.0% recall when trained and tested across the entire state. To demonstrate the scalability of the technique, we produced the first map of trees across the entire urban forest of California. The map we produced provides important data for the planning and management of California’s urban forest, and establishes a proven methodology for potentially producing similar maps nationally and globally in the future