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Climate Education Course Proposal for School of Sustainability

Seeking to address the lack of eco-literacy, we developed a syllabus for a course at Loyola University Chicago with the intended purpose of instilling climate knowledge to others. Through our own experiences studying environmental sustainability, we shared the same issue of lacking inadequate communication skills about climate change for different audiences. To develop the course structure and content, we gathered knowledge from community experts in environmental education and researched what other schools have implemented in the environmental education field. Our product is a syllabus, learning outcomes, and rationale for a new course devoted to teaching Loyola undergraduates how to educate others about climate change in ways that enable people to help avert the climate crisis

Deletion of low molecular weight protein tyrosine phosphatase (Acp1) protects against stress-induced cardiomyopathy.

The low molecular weight protein tyrosine phosphatase (LMPTP), encoded by the ACP1 gene, is a ubiquitously expressed phosphatase whose in vivo function in the heart and in cardiac diseases remains unknown. To investigate the in vivo role of LMPTP in cardiac function, we generated mice with genetic inactivation of the Acp1 locus and studied their response to long-term pressure overload. Acp1(-/-) mice develop normally and ageing mice do not show pathology in major tissues under basal conditions. However, Acp1(-/-) mice are strikingly resistant to pressure overload hypertrophy and heart failure. Lmptp expression is high in the embryonic mouse heart, decreased in the postnatal stage, and increased in the adult mouse failing heart. We also show that LMPTP expression increases in end-stage heart failure in humans. Consistent with their protected phenotype, Acp1(-/-) mice subjected to pressure overload hypertrophy have attenuated fibrosis and decreased expression of fibrotic genes. Transcriptional profiling and analysis of molecular signalling show that the resistance of Acp1(-/-) mice to pathological cardiac stress correlates with marginal re-expression of fetal cardiac genes, increased insulin receptor beta phosphorylation, as well as PKA and ephrin receptor expression, and inactivation of the CaMKIIδ pathway. Our data show that ablation of Lmptp inhibits pathological cardiac remodelling and suggest that inhibition of LMPTP may be of therapeutic relevance for the treatment of human heart failure

Machine learning in Huntington’s disease:exploring the Enroll-HD dataset for prognosis and driving capability prediction

Background: In biomedicine, machine learning (ML) has proven beneficial for the prognosis and diagnosis of different diseases, including cancer and neurodegenerative disorders. For rare diseases, however, the requirement for large datasets often prevents this approach. Huntington’s disease (HD) is a rare neurodegenerative disorder caused by a CAG repeat expansion in the coding region of the huntingtin gene. The world’s largest observational study for HD, Enroll-HD, describes over 21,000 participants. As such, Enroll-HD is amenable to ML methods. In this study, we pre-processed and imputed Enroll-HD with ML methods to maximise the inclusion of participants and variables. With this dataset we developed models to improve the prediction of the age at onset (AAO) and compared it to the well-established Langbehn formula. In addition, we used recurrent neural networks (RNNs) to demonstrate the utility of ML methods for longitudinal datasets, assessing driving capabilities by learning from previous participant assessments. Results: Simple pre-processing imputed around 42% of missing values in Enroll-HD. Also, 167 variables were retained as a result of imputing with ML. We found that multiple ML models were able to outperform the Langbehn formula. The best ML model (light gradient boosting machine) improved the prognosis of AAO compared to the Langbehn formula by 9.2%, based on root mean squared error in the test set. In addition, our ML model provides more accurate prognosis for a wider CAG repeat range compared to the Langbehn formula. Driving capability was predicted with an accuracy of 85.2%. The resulting pre-processing workflow and code to train the ML models are available to be used for related HD predictions at: https://github.com/JasperO98/hdml/tree/main . Conclusions: Our pre-processing workflow made it possible to resolve the missing values and include most participants and variables in Enroll-HD. We show the added value of a ML approach, which improved AAO predictions and allowed for the development of an advisory model that can assist clinicians and participants in estimating future driving capability.</p

Using individual-based bioenergetic models to predict the aggregate effects of disturbance on populations : a case study with beaked whales and Navy sonar

Funding: This research was supported by the Office of Naval Research (https://www.onr.navy.mil/) grant N0001419WX00431 and N000142012045: “Integrating information on displacement caused by mid-frequency active sonar and measurements of prey field into a population consequences of disturbance model for beaked whales” awarded to Dave Moretti, ND, SW, JH, LT, KB-B, AdR & VH. Funding support for tagging was provided by the US Navy's Office of Naval Research and Living Marine Resources program, the Chief of Naval Operations' Energy and Environmental Readiness Division and the NOAA Fisheries Ocean Acoustics Program.Anthropogenic activities can lead to changes in animal behavior. Predicting population consequences of these behavioral changes requires integrating short-term individual responses into models that forecast population dynamics across multiple generations. This is especially challenging for long-lived animals, because of the different time scales involved. Beaked whales are a group of deep-diving odontocete whales that respond behaviorally when exposed to military mid-frequency active sonar (MFAS), but the effect of these nonlethal responses on beaked whale populations is unknown. Population consequences of aggregate exposure to MFAS was assessed for two beaked whale populations that are regularly present on U.S. Navy training ranges where MFAS is frequently used. Our approach integrates a wide range of data sources, including telemetry data, information on spatial variation in habitat quality, passive acoustic data on the temporal pattern of sonar use and its relationship to beaked whale foraging activity, into an individual-based model with a dynamic bioenergetic module that governs individual life history. The predicted effect of disturbance from MFAS on population abundance ranged between population extinction to a slight increase in population abundance. These effects were driven by the interaction between the temporal pattern of MFAS use, baseline movement patterns, the spatial distribution of prey, the nature of beaked whale behavioral response to MFAS and the top-down impact of whale foraging on prey abundance. Based on these findings, we provide recommendations for monitoring of marine mammal populations and highlight key uncertainties to help guide future directions for assessing population impacts of nonlethal disturbance for these and other long-lived animals.Publisher PDFPeer reviewe

Clinical impairment in premanifest and early Huntington's disease is associated with regionally specific atrophy.

TRACK-HD is a multicentre longitudinal observational study investigating the use of clinical assessments and 3-Tesla magnetic resonance imaging as potential biomarkers for future therapeutic trials in Huntington's disease (HD). The cross-sectional data from this large well-characterized dataset provide the opportunity to improve our knowledge of how the underlying neuropathology of HD may contribute to the clinical manifestations of the disease across the spectrum of premanifest (PreHD) and early HD. Two hundred and thirty nine gene-positive subjects (120 PreHD and 119 early HD) from the TRACK-HD study were included. Using voxel-based morphometry (VBM), grey and white matter volumes were correlated with performance in four domains: quantitative motor (tongue force, metronome tapping, and gait); oculomotor [anti-saccade error rate (ASE)]; cognition (negative emotion recognition, spot the change and the University of Pennsylvania smell identification test) and neuropsychiatric measures (apathy, affect and irritability). After adjusting for estimated disease severity, regionally specific associations between structural loss and task performance were found (familywise error corrected, P < 0.05); impairment in tongue force, metronome tapping and ASE were all associated with striatal loss. Additionally, tongue force deficits and ASE were associated with volume reduction in the occipital lobe. Impaired recognition of negative emotions was associated with volumetric reductions in the precuneus and cuneus. Our study reveals specific associations between atrophy and decline in a range of clinical modalities, demonstrating the utility of VBM correlation analysis for investigating these relationships in HD

Multispectral optoacoustic tomography for non-invasive disease phenotyping in pediatric spinal muscular atrophy patients

Proximal spinal muscular atrophy (SMA) is a rare progressive, life limiting genetic motor neuron disease. While promising causal therapies are available, meaningful prognostic biomarkers for therapeutic monitoring are missing. We demonstrate handheld Multispectral Optoacoustic Tomography (MSOT) as a novel non-invasive imaging approach to visualize and quantify muscle wasting in pediatric SMA. While MSOT signals were distributed homogeneously in muscles of healthy volunteers (HVs), SMA patients showed moth-eaten optoacoustic signal patterns. Further signal quantification revealed greatest differences between groups at the isosbestic point for hemoglobin (SWL 800 nm). The SWL 800 nm signal intensities further correlated with clinical phenotype tested by standard motor outcome measures. Therefore, handheld MSOT could enable non-invasive assessment of disease burden in SMA patients