A Mixed Methods Investigation into Latino Fathers\u27 Roles in Their Children\u27s Educational Expectations

Latino fathers make meaningful contributions toward their children’s educational expectations. Cultural factors and structural barriers may shape unique parenting roles for Latino fathers and their influence on their children’s educational expectations. To explore the culturally nuanced roles of Latino fathers, we conducted a convergent mixed-methods study with 244 emerging adults to gain their perceptions of their fathers’ parenting roles and how those roles influenced the relation between the fathers’ and emerging adult children’s educational expectations. A content analysis of qualitative data identified positive (e.g. motivation and emotional support) and negative roles (e.g. family absence and overworking) that participants perceived their fathers had in their education. Quantitatively, Latino fathers’ educational expectations predicted emerging adults’ own educational expectations, with the strongest association for fathers coded as having a positive parental role. Findings from this study support the need for more inclusive and culturally relevant research practices with Latino fathers and families. Supporting and incorporating the roles of Latino fathers in the school system may increase students’ educational expectations

3D Mapping with an Unmanned Aerial Vehicle

Missionary aviation pilots often have to land their planes on remote airstrips that might be unsafe due to runway obstructions such as encroaching vegetation or large objects that were unknowingly placed on the runway. The Falcon Unmanned Aerial Vehicle (UAV) team is partnering with ITEC to develop an imaging system using a UAV to scan these airstrips to detect these obstructions. ITEC was founded by Steve Saint, the son of martyred missionary Nate Saint, to develop technologies to aid missionaries in their work. This video highlights the work of the Falcon UAV team and the basic terms and definitions for understanding the work of the team. The Falcon UAV team focuses primarily on the use of automated 3D mapping and photogrammetry by drones to help identify obstructions to pilots landing on remote airstrips. In this video, we will explore 3D mapping and compare different options for drones to purchase and software to use in the process of mapping information.https://mosaic.messiah.edu/engr2020/1007/thumbnail.jp

Machine learning the electronic structure of matter across temperatures

We introduce machine learning (ML) models that predict the electronic structure of materials across a wide temperature range. Our models employ neural networks and are trained on density functional theory (DFT) data. Unlike other ML models that use DFT data, our models directly predict the local density of states (LDOS) of the electronic structure. This provides several advantages, including access to multiple observables such as the electronic density and electronic total free energy. Moreover, our models account for both the electronic and ionic temperatures independently, making them ideal for applications like laser-heating of matter. We validate the efficacy of our LDOS-based models on a metallic test system. They accurately capture energetic effects induced by variations in ionic and electronic temperatures over a broad temperature range, even when trained on a subset of these temperatures. These findings open up exciting opportunities for investigating the electronic structure of materials under both ambient and extreme conditions

Heart rate variability in insomnia patients: A critical review of the literature

Heart rate variability (HRV) is an objective marker that provides insight into autonomic nervous system dynamics. There is conflicting evidence regarding the presence of HRV impairment in insomnia patients. Web-based databases were used to systematically search the literature for all studies that compared the HRV of insomnia patients to controls or reported the HRV of insomnia patients before and after an intervention. 22 relevant papers were identified. Study characteristics were summarised, HRV measures were extracted and a risk of bias assessment for each study was performed. We were limited in our ability to synthesise outcome measures and perform meta-analyses due to considerable differences in patient (and control) selection, study protocols, measurement and processing techniques and outcome reporting. Risk of bias was deemed to be high in the majority of studies. As such, we cannot confirm that HRV is reliably impaired in insomnia patients nor determine the HRV response to interventions. Whilst HRV impairment in insomnia is a widely accepted concept, it is not supported by empirical evidence. Large longitudinal studies incorporating 24-hour recordings are required to elucidate the precise nature of HRV dynamics in insomnia patients

Comparative genomics and phylogenomic investigation of the class Geoglossomycetes provide insights into ecological specialization and the systematics of Pezizomycotina

Despite their global presence and ubiquity, members of the class Geoglossomycetes (Pezizomycotina, Ascomycota) are understudied systematically and ecologically. These fungi have long been presumed saprobic due to their occurrence in or near leaf litter and soils. Additionally, they lack an apparent association with other organisms, reinforcing this perception. However, observations of sporocarps near ericaceous shrubs have given rise to an alternative hypothesis that members of Geoglossomycetes may form ericoid mycorrhizae or ectomycorrhizae. This claim, however, has yet to be confirmed via microscopy or amplicon-based studies examining root communities. As a result, our current understanding of their ecology is based on cursory observations. This study presents a comparative analysis of genomic signatures related to ecological niche to investigate the hypothesis of an ericoid mycorrhizal or ectomycorrhizal ecology in the class. We compared the carbohydrate-active enzyme (CAZyme) and secondary metabolite contents of six newly sequenced Geoglossomycetes genomes with those of fungi representing specific ecologies across Pezizomycotina. Our analysis reveals CAZyme and secondary metabolite content patterns consistent with ectomycorrhizal (EcM) members of Pezizomycotina. Specifically, we found a reduction in CAZyme-encoding genes and secondary metabolite clusters that suggests a mutualistic ecology. Our work includes the broadest taxon sampling for a phylogenomic study of Pezizomycotina to date. It represents the first functional genomic and genome-scale phylogenetic study of the class Geoglossomycetes and improves the foundational knowledge of the ecology and evolution of these understudied fungi

Serotonin 5-HT\u3csub\u3e2\u3c/sub\u3e receptor activation prevents allergic asthma in a mouse model

© 2015 the American Physiological Society. Asthma is an inflammatory disease of the lung characterized by airways hyper-responsiveness (AHR), inflammation, and mucus hyperproduction. Current main-stream therapies include bronchodilators that relieve bronchoconstriction and inhaled glucocorticoids to reduce inflammation. The small molecule hormone and neurotransmitter serotonin has long been known to be involved in inflammatory processes; however, its precise role in asthma is unknown. We have previously established that activation of serotonin 5-hydroxytryptamine (5-HT)2A receptors has potent anti-inflammatory activity in primary cultures of vascular tissues and in the whole animal in vasculature and gut tissues. The 5-HT2A receptor agonist, (R)-2,5-dimethoxy-4-iodoamphetamine [(R)-DOI] is especially potent. In this work, we have examined the effect of (R)-DOI in an established mouse model of allergic asthma. In the ovalbumin mouse model of allergic inflammation, we demonstrate that inhalation of (R)-DOI prevents the development of many key features of allergic asthma, including AHR, mucus hyperproduction, airways inflammation, and pulmonary eosinophil recruitment. Our results highlight a likely role of the 5-HT2 receptors in allergic airways disease and suggest that 5-HT2 receptor agonists may represent an effective and novel small molecule-based therapy for asthma

Intrathecal enzyme replacement for Hurler syndrome: biomarker association with neurocognitive outcomes.

PurposeAbnormalities in cerebrospinal fluid (CSF) have been reported in Hurler syndrome, a fatal neurodegenerative lysosomal disorder. While no biomarker has predicted neurocognitive response to treatment, one of these abnormalities, glycosaminoglycan nonreducing ends (NREs), holds promise to monitor therapeutic efficacy. A trial of intrathecal enzyme replacement therapy (ERT) added to standard treatment enabled tracking of CSF abnormalities, including NREs. We evaluated safety, biomarker response, and neurocognitive correlates of change.MethodsIn addition to intravenous ERT and hematopoietic cell transplantation, patients (N = 24) received intrathecal ERT at four peritransplant time points; CSF was evaluated at each point. Neurocognitive functioning was quantified at baseline, 1 year, and 2 years posttransplant. Changes in CSF biomarkers and neurocognitive function were evaluated for an association.ResultsOver treatment, there were significant decreases in CSF opening pressure, biomarkers of disease activity, and markers of inflammation. Percent decrease in NRE from pretreatment to final intrathecal dose posttransplant was positively associated with percent change in neurocognitive score from pretreatment to 2 years posttransplant.ConclusionIntrathecal ERT was safe and, in combination with standard treatment, was associated with reductions in CSF abnormalities. Critically, we report evidence of a link between a biomarker treatment response and neurocognitive outcome in Hurler syndrome

Photoreversible interconversion of a phytochrome photosensory module in the crystalline state.

A major barrier to defining the structural intermediates that arise during the reversible photointerconversion of phytochromes between their biologically inactive and active states has been the lack of crystals that faithfully undergo this transition within the crystal lattice. Here, we describe a crystalline form of the cyclic GMP phosphodiesterases/adenylyl cyclase/FhlA (GAF) domain from the cyanobacteriochrome PixJ in Thermosynechococcus elongatus assembled with phycocyanobilin that permits reversible photoconversion between the blue light-absorbing Pb and green light-absorbing Pg states, as well as thermal reversion of Pg back to Pb. The X-ray crystallographic structure of Pb matches previous models, including autocatalytic conversion of phycocyanobilin to phycoviolobilin upon binding and its tandem thioether linkage to the GAF domain. Cryocrystallography at 150 K, which compared diffraction data from a single crystal as Pb or after irradiation with blue light, detected photoconversion product(s) based on Fobs - Fobs difference maps that were consistent with rotation of the bonds connecting pyrrole rings C and D. Further spectroscopic analyses showed that phycoviolobilin is susceptible to X-ray radiation damage, especially as Pg, during single-crystal X-ray diffraction analyses, which could complicate fine mapping of the various intermediate states. Fortunately, we found that PixJ crystals are amenable to serial femtosecond crystallography (SFX) analyses using X-ray free-electron lasers (XFELs). As proof of principle, we solved by room temperature SFX the GAF domain structure of Pb to 1.55-Å resolution, which was strongly congruent with synchrotron-based models. Analysis of these crystals by SFX should now enable structural characterization of the early events that drive phytochrome photoconversion

Database, Features, and Machine Learning Model to Identify Thermally Driven Metal-Insulator Transition Compounds

Metal-insulator transition (MIT) compounds are materials that may exhibit insulating or metallic behavior, depending on the physical conditions, and are of immense fundamental interest owing to their potential applications in emerging microelectronics. There is a dearth of thermally-driven MIT materials, however, which makes delineating these compounds from those that are exclusively insulating or metallic challenging. Here we report a material database comprising temperature-controlled MITs (and metals and insulators with similar chemical composition and stoichiometries to the MIT compounds) from high quality experimental literature, built through a combination of materials-domain knowledge and natural language processing. We featurize the dataset using compositional, structural, and energetic descriptors, including two MIT relevant energy scales, an estimated Hubbard interaction and the charge transfer energy, as well as the structure-bond-stress metric referred to as the global-instability index (GII). We then perform supervised classification, constructing three electronic-state classifiers: metal vs non-metal (M), insulator vs non-insulator (I), and MIT vs non-MIT (T). We identify two important descriptors that separate metals, insulators, and MIT materials in a 2D feature space: the average deviation of the covalent radius and the range of the Mendeleev number. We further elaborate on other important features (GII and Ewald energy), and examine how they affect classification of binary vanadium and titanium oxides. We discuss the relationship of these atomic features to the physical interactions underlying MITs in the rare-earth nickelate family. Last, we implement an online version of the classifiers, enabling quick probabilistic class predictions by uploading a crystallographic structure file