Functional PCA for Remotely Sensed Lake Surface Water Temperature Data

Functional principal component analysis is used to investigate a high-dimensional surface water temperature data set of Lake Victoria, which has been produced in the ARC-Lake project. Two different perspectives are adopted in the analysis: modelling temperature curves (univariate functions) and temperature surfaces (bivariate functions). The latter proves to be a better approach in the sense of both dimension reduction and pattern detection. Computational details and some results from an application to Lake Victoria data are presented

Within Lake Clustering of High Resolution Satellite Retrievals: A Functional Data and Clustering Approach

No abstract available

Effects of Psilocybin on Context-Based Fear Learning, Extinction, and Reinstatement

Fear related mental disorders, including PTSD, stem from various environmental factors; part of this includes the context in which a trauma is experienced. Many treatments for PTSD exist, but they remain largely ineffective, especially over the long-term, so new treatment routes are constantly being explored. One of these new treatment options is the psychedelic agent psilocybin, which can increase positive mood, feelings of introspection, and learning via agonizing the serotonergic 5-HT(2A) receptor. The learning benefits from psilocybin could potentially be used in exposure therapy procedures for PTSD patients, where new, safe information is learned to replace the traumatic memories, lessening the mental and physiological symptoms of the condition. This study explored the efficacy of psilocybin on enhancing fear extinction in rodents that were conditioned to fear a certain context, similar to how an individual may learn to fear places similar to their trauma location. Despite some limitations, this study did show an early-trial enhancement of fear extinction after psilocybin injection, though the effect was short-lived, and did not impact later testing trials. More research will stem from these results, to potentially explore dose- or sex-dependent effects that may impact the efficacy of the psilocybin treatment

Pursuing Inclusion and Justice While Affirming the Mental Health of Marginalized Students

This article provides best practices that instructors can use to affirm and support marginalized students’ mental health with a specific focus on students of color. Recently, campuses have witnessed renewed calls for diversity and inclusion in the wake of anti-Black violence. Advocates have called for needed structural changes. To build upon these calls for change, this article provides instructors with tools they can use in the interim to navigate questions of diversity, inclusion, and justice in the classroom. The essay centers the mental health needs of students from marginalized populations to hedge against the possibility that efforts to foster inclusion, including advocating for structural reform, contribute additional trauma to these students

The utility of MAS5 expression summary and detection call algorithms

<p>Abstract</p> <p>Background</p> <p>Used alone, the MAS5.0 algorithm for generating expression summaries has been criticized for high False Positive rates resulting from exaggerated variance at low intensities.</p> <p>Results</p> <p>Here we show, with replicated cell line data, that, when used alongside detection calls, MAS5 can be both selective and sensitive. A set of differentially expressed transcripts were identified that were found to be changing by MAS5, but unchanging by RMA and GCRMA. Subsequent analysis by real time PCR confirmed these changes. In addition, with the Latin square datasets often used to assess expression summary algorithms, filtered MAS5.0 was found to have performance approaching that of its peers.</p> <p>Conclusion</p> <p>When used alongside detection calls, MAS5 is a sensitive and selective algorithm for identifying differentially expressed genes.</p

Concentration of Heavy Metals in Three Distinct Algae Families from Humboldt County, California

Anthropogenic impacts on marine environments can impact metal fluxes and concentrations available to marine species. Monitoring these impacts is necessary to better understand the interactions between the biotic and abiotic components of these ecosystems and mitigate the risk posed by harmful toxins introduced by human activities. Biomoniters, like macroscopic algae, are useful indicators that illuminate the bioaccumulation of toxins commonly introduced from anthropogenic activity. With this in mind, the concentrations of heavy metals zinc (Zn), nickel (Ni), and copper (Cu) were analyzed via the assessment of algae (Representatives from Ulva, Mastocarpus, Fucus) in two sites in Humboldt County: Samoa (urbanized) and Petrolia (rural). Flame atomic absorption spectroscopy (FAAS) was used to quantify the concentration of metals in both algae and sedimental substrate, providing both algal metal content and a biota-sediment accumulation factor (BSAF). It was determined that the order of metal concentration followed Zn \u3e Ni ≥ Cu within algae at both locations for all three algae families. This data is consistent with previous studies of algae species as bioindicators of heavy metal contamination (Kangas et al. 1984)

Data Fusion of Remote-sensing and In-lake chlorophyll a Data Using Statistical Downscaling

Chlorophyll a is a green pigment, used as an indirect measure of lake water quality. Its strong absorption of blue and red light allows for quantification through satellite images, providing better spatial coverage than traditional in-lake samples. However, grid-cell scale imagery must be calibrated spatially using in-lake point samples, presenting a change-of-support problem. This paper presents a method of statistical downscaling, namely a Bayesian spatially-varying coefficient regression, which assimilates remotely-sensed and in-lake data, resulting in a fully calibrated spatial map of chlorophyll a with associated uncertainty measures. The model is applied to a case study dataset from Lake Balaton, Hungary