The Female Athlete Triad: Role of the Coach in Prevention and Intervention

Introduction The Female Athlete Triad consists of disordered eating, amenorrhea, and osteoporosis. Eating disorders affect between 1-10% of adolescent and college age women (Haller, 1992),particularly in women\u27s\u27 sports in which leanness is considered important for performance such as cross-country running. Methods A three page 17 item questionnaire was sent out to 45 coaches of women\u27s Division III cross-country teams in the Midwest region of the US. Descriptive statistics were run on the data. Results Results showed that 8% of coaches have coached an athlete with a diagnosed eating disorder. Of the top three topics for team discussions, 9% of coaches discuss disordered eating. In addition, only 43% of coaches were fully educated on the Triad- Results show that coaches want more resources to assist them in recognizing an athlete with an eating disorder. Conclusions This research shows that coaches are in a pivotal position to prevent or intervene with an athlete at risk for developing the Triad. However, coaches need more resources available to better recognize the disorder

“I want my PRC”: engagement of undergraduates with and assessment of the peer research consultant program

Purpose This paper describes the benefits found in undergraduate students working to provide research assistance to their peers. The discussion includes how soft skills are built, along with how the position has aided in both educational and building towards their future careers. The paper is submitted for the special issue on “The future of peer-led research services.” Design/methodology/approach The authors provide a viewpoint from both a peer research supervisor and a student currently working as a PRC. The paper covers the requirements and implementations at the beginning of the program along with the changes that have occurred to better streamline the process of hiring and training. The viewpoint of the PRC was a key factor in the process. Findings Soft skills are a key component of the program. The undergraduate PRCs develop confidence, leadership and communication skills through interactions with their peers. The campus community is responding to the peer model because the PRCs are currently taking the same classes or have recently taken them, and the campus is now asking for the peer mentors that assist librarians in teaching introductory classes. Practical implications For libraries considering the development of their own programs, the benefits presented can lend to their proposals on real-world application beyond the college experience, as well as how it benefits the busy schedules of librarians. Social implications The training the PRCs are provided by librarians provides credibility and trust, which encourages their peers to utilize the services. Soft skills are also one of the most requested needs for businesses beyond college. The PRC program is providing these skills, which the peer mentors use both in career readiness and their daily interactions. Originality/value This paper views a program only a few years old on how it managed through a pandemic, as well as how the supervisor adjusted training to reflect a renovation that brought about a changing desk model. With a current undergraduate PRC as the co-author, a unique perspective is brought to the writing by showing what they personally are taking away from working in the program

Polyphosphate-accumulating Bacteria: Potential Contributors to Mineral Dissolution in the Oral cavity

University of Minnesota M.S. thesis. May 2017. Major: Earth Sciences. Advisor: Jake Bailey. 1 computer file (PDF); vi, 30 pages.The role of oral bacteria in the dissolution of dental enamel and dentin that can result in carious lesions has long been solely ascribed to metabolic acid production. However, other microbial processes may also influence tooth dissolution. Recently, bacteria that accumulate polyphosphate in marine sediments have been shown to dynamically influence the solubility of phosphatic minerals. Here we show, using microscopy and genomic databases, that dental plaque and caries lesions, all contain abundant polyphosphate-accumulating bacteria. Using a culture of the model organism, Lactobacillus rhamnosus, a known polyphosphate-accumulating bacteria that is known to inhabit advanced caries lesions, we show that polyphosphate accumulation can lead to undersaturated conditions with respect to hydroxyapatite under some, but not all, oral cavity conditions. Samples of L. rhamnosus grown in various environmental conditions, including exposure to changing oxygenation conditions, input/removal of organics and trace nutrients, were collected over a course of 24 hours and stained with 4',6-diamidino-2-phenylindole (DAPI) to confirm/deny the presence of poly-p in the cells. A comparison of changes in extracellular inorganic phosphate between cultures grown under conditions that result in polyP accumulation vs conditions that did not, was used a a means of measuring the phosphate fluctuation that was likely contributed by intracellular phosphate accumulation. We suggest, through an extrapolation from our model organism results, that polyphosphate-accumulating bacteria, which we observed to be ubiquitous in oral fluids, have a similar influence on the solubility of minerals that comprise the tooth structure. These results suggest that the generation of undersaturated conditions by polyphosphate-accumulating bacteria constitutes a new potential mechanism of tooth dissolution that may augment the effects of metabolic acid production

In situ monitoring of GaSb, GaInAsSb, and AlGaAsSb

Suitability of silicon photodiode detector arrays for monitoring the spectral reflectance during epitaxial growths of GaSb, AlGaAsSb, and GaInAsSb, which have cutoff wavelengths of 1.7, 1.2, and 2.3 {micro}m, respectively, is demonstrated. These alloys were grown lattice matched to GaSb in a vertical rotating-disk reactor, which was modified to accommodate near normal reflectance without affecting epilayer uniformity. By using a virtual interface model, the growth rate and complex refractive index at the growth temperature are extracted for these alloys over the 600 to 950 nm spectral range. Excellent agreement is obtained between the extracted growth rate and that determined by ex-situ measurement. Optical constants are compared to theoretical predictions based on an existing dielectric function model for these materials. Furthermore, quantitative analysis of the entire reflectance spectrum yields valuable information on the approximate thickness of overlayers on the pregrowth substrate

Recurrent oxidant treatment induces dysregulation in the brain transcriptome of Atlantic salmon (Salmo salar) smolts

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Multiomics Provide Insights into the Key Molecules and Pathways Involved in the Physiological Adaptation of Atlantic Salmon (Salmo salar) to Chemotherapeutic-Induced Oxidative Stress

Although chemotherapeutics are used to treat infections in farmed fish, knowledge on how they alter host physiology is limited. Here, we elucidated the physiological consequences of repeated exposure to the potent oxidative chemotherapeutic peracetic acid (PAA) in Atlantic salmon (Salmo salar) smolts. Fish were exposed to the oxidant for 15 (short exposure) or 30 (long exposure) minutes every 15 days over 45 days. Unexposed fish served as the control. Thereafter, the ability of the remaining fish to handle a secondary stressor was investigated. Periodic chemotherapeutic exposure did not affect production performance, though survival was lower in the PAA-treated groups than in the control. Increased ventilation, erratic swimming, and a loss of balance were common behavioural manifestations during the oxidant exposure. The plasma reactive oxygen species levels increased in the PAA-treated groups, particularly after the third exposure, suggesting an alteration in the systemic oxidative stress status. Plasma indicators for internal organ health were affected to a certain degree, with the changes mainly observed after the second and third exposures. Metabolomics disclosed that the oxidant altered several circulating metabolites. Inosine and guanosine were the two metabolites significantly affected by the oxidative stressor, regardless of exposure time. A microarray analysis revealed that the gills and liver were more responsive to the oxidant than the skin, with the gills being the most sensitive. Moreover, the magnitude of the transcriptomic modifications depended on the exposure duration. A functional analysis showed that genes involved in immunity and ribosomal functions were significantly affected in the gills. In contrast, genes crucial for the oxidation-reduction process were mainly targeted in the liver. Skin mucus proteomics uncovered that the changes in the mucosal proteome were dependent on exposure duration and that the oxidant interfered with ribosome-related processes. Mucosal mapping revealed gill mucous cell hypertrophy after the second and third exposures, although the skin morphological parameters remained unaltered. Lastly, repeated oxidant exposures did not impede the ability of the fish to mount a response to a secondary stressor. This study provides insights into how a chemical oxidative stressor alters salmon physiology at both the systemic and mucosal levels. This knowledge will be pivotal in developing an evidence-driven approach to the use of oxidative therapeutics in fish, with some of the molecules and pathways identified as potential biomarkers and targets for assessing the physiological cost of these treatments.publishedVersio

Regulation of the molecular repertoires of oxidative stress response in the gills and olfactory organ of Atlantic salmon following infection and treatment of the parasite Neoparameoba perurans

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AI-Based Edge Acquisition, Processing and Analytics for Industrial Food Production

This article presents a novel approach to the acquisition, processing, and analytics of industrial food production by employing state-of-the-art artificial intelligence (AI) at the edge. Intelligent Industrial Internet of Things (IIoT) devices are used to gather relevant production parameters of industrial equipment and motors, such as vibration, temperature and current using built-in and external sensors. Machine learning (ML) is applied to measurements of the key parameters of motors and equipment. It runs on edge devices that aggregate sensor data using Bluetooth, LoRaWAN, and Wi-Fi communication protocols. ML is embedded across the edge continuum, powering IIoT devices with anomaly detectors, classifiers, predictors, and neural networks. The ML workflows are automated, allowing them to be easily integrated with more complex production flows for predictive maintenance (PdM). The approach proposes a decentralized ML solution for industrial applications, reducing bandwidth consumption and latency while increasing privacy and data security. The system allows for the continuous monitoring of parameters and is designed to identify potential breakdown situations and alert users to prevent damage, reduce maintenance costs and increase productivity.publishedVersio

An Intelligent Real-Time Edge Processing Maintenance System for Industrial Manufacturing, Control, and Diagnostic

This paper presents an artificial intelligence (AI) based edge processing real-time maintenance system for the purposes of industrial manufacturing control and diagnostics. The system is evaluated in a soybean processing manufacturing facility to identify abnormalities and possible breakdown situations, prevent damage, reduce maintenance costs, and increase production productivity. The system can be used in any other manufacturing or chemical processing facility that make use of motors rotating equipment in different process phases. The system combines condition monitoring, fault detection, and diagnosis using machine learning (ML) and deep learning (DL) algorithms. These algorithms are used with data resulting from the continuous monitoring of relevant production equipment and motor parameters, such as temperature, vibration, sound/noise, and current/voltage. The condition monitoring integrates intelligent Industrial Internet of Things (IIoT) devices with multiple sensors combined with AI-based techniques and edge processing. This is done to identify the parameter modifications and distinctive patterns that occur before a failure and predict forthcoming failure modes before they arise. The data from production equipment/motors is collected wirelessly using different communication protocols - such as Bluetooth low energy (BLE), Long range wide area network (LoRaWAN), and Wi-Fi - and aggregated into an edge computing processing unit via several gateways. The AI-based algorithms are embedded in the processing unit at the edge, allowing the prediction and intelligent control of the production equipment/motor parameters. IIoT devices for environmental sensing, vibration, temperature monitoring, and sound/ultrasound detection are used with embedded signal processing that runs on an ARM Cortex-M4 microcontroller. These devices are connected through either wired or wireless protocols. The system described addresses the components necessary for implementing the predictive maintenance (PdM) strategy in soybean industrial processing manufacturing environments. Additionally, it includes new elements that broaden the possibilities for prescriptive maintenance (PsM) developments to be made. The type of ML or DL techniques and algorithms used in maintenance modeling is dictated by the application and available data. The approach presented combines multiple data sources that improve the accuracy of condition monitoring and prediction. DL methods further increase the accuracy and require interpretable and efficient methods as well as the availability of significant amounts of (labeled) data.publishedVersio