Impact of diabetes mellitus on vestibular function: A scoping review

Diabetes mellitus (DM) encompasses a group of metabolic diseases that result in high blood sugar (i.e., hyperglycemia). By 2030, it is anticipated that 578 million adults worldwide will have DM, with this number growing at a faster rate in developed areas of the world.[27] If left uncontrolled, DM can cause considerable damage to several areas of the body, including the heart, kidneys, nerves, and ears. When focusing exclusively on the ears, there has been markedly less research on the vestibular system when compared to the auditory system, even though DM is a known risk factor for falling. The purpose of this study was to understand the current state of knowledge regarding DM and vestibular function and to identify gaps in knowledge that need to be explored. A scoping review of the literature was performed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) standards.[51] Search terms included medical subject headings (MeSH terms) and keywords related to DM and vestibular function. In total, 326 papers were retrieved and 43 articles met inclusion/exclusion criteria for extensive review. Findings show that studies performed on the vestibular system tend to have smaller sample sizes, inconsistent test batteries, and variable results. There is some evidence to suggest Benign Paroxysmal Positional Vertigo (BPPV) may be more prevalent in individuals with DM, but the exact percentage of those impacted is unknown. The duration and severity of DM was also found to have a significant impact on vestibular test results. As DM becomes more prevalent in our society, it is essential a standardized test battery be developed to more efficiently evaluate and diagnose vestibular disorders in this population. Findings from this study may help develop a narrower research question which could be used to conduct a systematic review. Findings from this study may also assist in the development of a randomized control trial (RCT) involving individuals with DM

Bootstrapping for text learning tasks

Journal ArticleWhen applying text learning algorithms to complex tasks, it is tedious and expensive to hand-label the large amounts of training data necessary for good performance. This paper presents bootstrapping as an alternative approach to learning from large sets of labeled data. Instead of a large quantity of labeled data, this paper advocates using a small amount of seed information and a large collection of easily-obtained unlabeled data. Bootstrapping initializes a learner with the seed information; it then iterates, applying the learner to calculate labels for the unlabeled data, and incorporating some of these labels into the training input for the learner. Two case studies of this approach are presented. Bootstrapping for information extraction provides 76% precision for a 250-word dictionary for extracting locations from web pages, when starting with just a few seed locations. Bootstrapping a text classifier from a few keywords per class and a class hierarchy provides accuracy of 66%, a level close to human agreement, when placing computer science research papers into a topic hierarchy. The success of these two examples argues for the strength of the general boot¬ strapping approach for text learning tasks

Learning dictionaries for information extraction by multi-level bootstrapping

Journal ArticleInformation extraction systems usually require two dictionaries: a semantic lexicon and a dictionary of extraction patterns for the domain. We present a multilevel bootstrapping algorithm that generates both the semantic lexicon and extraction patterns simultaneously. As input, our technique requires only unannotated training texts and a handful of seed words for a category. We use a mutual bootstrapping technique to alternately select the best extraction pattern for the category and bootstrap its extractions into the semantic lexicon, which is the basis for selecting the next extraction pattern. To make this approach more robust, we add a second level of bootstrapping (metabootstrapping) that retains only the most reliable lexicon entries produced by mutual bootstrapping and then restarts the process. We evaluated this multilevel bootstrapping technique on a collection of corporate web pages and a corpus of terrorism news articles. The algorithm produced high-quality dictionaries for several semantic categories

A matter of trust: : Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student’s demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution’s responsibility to its students

A leaf spot and blight of greenhouse tomato seedlings incited by a Herbaspirillum sp.

A leaf spot and blighting were observed on leaves of tomato transplants from a producer in Florida in 2001 and 2002. A nonfluorescent bacterium was isolated consistently from affected tissue. The typical bacterium was a gram negative, strictly aerobic, slightly curved rod with one or two flagella. Sequence analysis of the 16S rRNA indicated that two representative strains, F1 and SE1, had greater than 99% nucleotide sequence identity with Herbaspirillum huttiense and H. rubrisubalbicans. The cellular fatty acid composition of the total of 16 tomato strains was very similar to H. huttiense and H. rubrisubalbicans. Based on carbon utilization, six of nine strains tested with the Biolog system were identified as Herbaspirillum spp. The tomato strains were oxidase positive and grew at 40 degrees C, but were negative for levan production, pectate hydrolysis, and arginine dihydrolase activity. Based upon this polyphasic analysis, we concluded that the strains were most closely related to H. huttiense, although placement in this species would require further analyses. However, the tomato strains and H. rubrisubalbicans, but not H. huttiense, caused confluent necrosis when infiltrated at high concentrations into tomato leaves and were able to produce leaf spot symptoms on inoculated tomato seedlings in the greenhouse. Using pulsed-field gel electrophoresis, we determined that there was considerable variability between the strains collected in 2001 and 2002

A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student's demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution's responsibility to its students

Central Obesity and the Metabolic Syndrome: Implications for Primary Care Providers

Purpose: To describe screening measures that will determine which clients are at risk For the metabolic syndrome, common manifestations of the syndrome, preventive diagnostic considerations, and management and treatment options that primary care providers can implement. Data Sources: Review of the clinical and research literature, supplemented with specific diagnostic criteria. Conclusions: Central obesity is the cornerstone of the metabolic syndrome, which may lead to type 2 diabetes and cardiovascular disease. Generalized obesity is defined as body weight that is considerably greater than the ideal weight and that is distributed on all parts of the body. Generalized obesity has long been considered a significant risk factor for developing type 2 diabetes and cardiovascular disease. Those clients of ideal body weight have been considered at less risk For developing these conditions. However, this perception may not always be accurate. Weight distribution plays a major role in acquiring the metabolic syndrome. Because waist circumference is as important as overall body weight, central obesity is key to determining the risk. Implications for Practice: The metabolic syndrome has now been given a CPT code (277.7). It is more likely that clients at risk for or with the metabolic syndrome may first be seen by a primary care provider. Primary care providers need to be able to diagnose, treat, and provide preventive interventions for the metabolic syndrome. Clients at risk will likely be identified during routine health screening. Early detection of and interventions focused on the metabolic syndrome may reduce the occurrence of type 2 diabetes and cardiovascular disease. Use of a tape measure to determine waist circumference may help the provider to identify at-risk clients who are of normal weight, and thus not previously believed to be at risk, as well as those more obviously at risk. It is necessary to determine not only patients' overall body weight but also their waist circumference. A measuring tape may be the key tool for establishing a patient's early risk for the metabolic syndrome and, ultimately, for prevention of type 2 diabetes and cardiovascular disease. Conflict of Interest Statement: No relationship that might represent a conflict of interest exists between any of the authors and any commercial entity or product mentioned in this manuscript. No inducements have been made by any commercial entity to submit this article for publication

Technological Innovation or Educational Evolution? A Multi-disciplinary Qualitative Inquiry into Active Learning Classrooms

In recent years, many institutions have transformed traditional classrooms (TCs) into technology-rich active learning classrooms (ALCs) to accommodate the pedagogical concept of “active learning”. In order to investigate the impact of ALCs on teaching and teaching, we observed an instructor teaching in an ALC for an entire academic year, audio/video-recorded every class and took field notes. A focus group discussion was conducted with faculty from six allied health disciplines who taught weekly classes in the ALC and an online survey was distributed to students who took those classes. Data was then analysed using a qualitative constant comparative method (CCM). Findings indicated that the ALC generated greater teaching and learning enjoyment, deepened engagement, amplified interaction, enhanced group activity efficiency and fostered the development of creative ideas.  All these features were interrelated and created a synergistic effect on student learning

A unique bacteriohopanetetrol stereoisomer of marine anammox

Anaerobic ammonium oxidation (anammox) is a major process of bioavailable nitrogen removal from marine systems. Previously, a bacteriohopanetetrol (BHT) isomer, with unknown stereochemistry, eluting later than BHT using high performance liquid chromatography (HPLC), was detected in ‘Ca. Scalindua profunda’ and proposed as a biomarker for anammox in marine paleo-environments. However, the utility of this BHT isomer as an anammox biomarker is hindered by the fact that four other, non-anammox bacteria are also known to produce a late-eluting BHT stereoisomer. The stereochemistry in Acetobacter pasteurianus, Komagataeibacter xylinus and Frankia sp. was known to be 17β, 21β(H), 22R, 32R, 33R, 34R (BHT-34R). The stereochemistry of the late-eluting BHT in Methylocella palustris was unknown. To determine if marine anammox bacteria produce a unique BHT isomer, we studied the BHT distributions and stereochemistry of known BHT isomer producers and of previously unscreened marine (‘Ca. Scalindua brodeae’) and freshwater (‘Ca. Brocadia sp.’) anammox bacteria using HPLC and gas chromatographic (GC) analysis of acetylated BHTs and ultra high performance liquid chromatography (UHPLC)-high resolution mass spectrometry (HRMS) analysis of non-acetylated BHTs. The 34R stereochemistry was confirmed for the BHT isomers in Ca. Brocadia sp. and Methylocella palustris. However, ‘Ca. Scalindua sp.’ synthesise a stereochemically distinct BHT isomer, with still unconfirmed stereochemistry (BHT-x). Only GC analysis of acetylated BHT and UHPLC analysis of non-acetylated BHT distinguished between late-eluting BHT isomers. Acetylated BHT-x and BHT-34R co-elute by HPLC. As BHT-x is currently only known to be produced by ‘Ca. Scalindua spp.’, it may be a biomarker for marine anammox