Showing papers by "Florida Atlantic University published in 2018"

Combined adult neurogenesis and BDNF mimic exercise effects on cognition in an Alzheimer’s mouse model

Abstract: Adult hippocampal neurogenesis (AHN) is impaired before the onset of Alzheimer's disease (AD) pathology. We found that exercise provided cognitive benefit to 5×FAD mice, a mouse model of AD, by inducing AHN and elevating levels of brain-derived neurotrophic factor (BDNF). Neither stimulation of AHN alone, nor exercise, in the absence of increased AHN, ameliorated cognition. We successfully mimicked the beneficial effects of exercise on AD mice by genetically and pharmacologically inducing AHN in combination with elevating BDNF levels. Suppressing AHN later led to worsened cognitive performance and loss of preexisting dentate neurons. Thus, pharmacological mimetics of exercise, enhancing AHN and elevating BDNF levels, may improve cognition in AD. Furthermore, applied at early stages of AD, these mimetics may protect against subsequent neuronal cell death.

A survey on addressing high-class imbalance in big data

Abstract: In a majority–minority classification problem, class imbalance in the dataset(s) can dramatically skew the performance of classifiers, introducing a prediction bias for the majority class. Assuming the positive (minority) class is the group of interest and the given application domain dictates that a false negative is much costlier than a false positive, a negative (majority) class prediction bias could have adverse consequences. With big data, the mitigation of class imbalance poses an even greater challenge because of the varied and complex structure of the relatively much larger datasets. This paper provides a large survey of published studies within the last 8 years, focusing on high-class imbalance (i.e., a majority-to-minority class ratio between 100:1 and 10,000:1) in big data in order to assess the state-of-the-art in addressing adverse effects due to class imbalance. In this paper, two techniques are covered which include Data-Level (e.g., data sampling) and Algorithm-Level (e.g., cost-sensitive and hybrid/ensemble) Methods. Data sampling methods are popular in addressing class imbalance, with Random Over-Sampling methods generally showing better overall results. At the Algorithm-Level, there are some outstanding performers. Yet, in the published studies, there are inconsistent and conflicting results, coupled with a limited scope in evaluated techniques, indicating the need for more comprehensive, comparative studies.

Terpyridine-based metallosupramolecular constructs: tailored monomers to precise 2D-motifs and 3D-metallocages.

Abstract: This overview represents a comprehensive summary of the recent developments in the growing field of terpyridine-based, discrete metallosupramolecular architectures. The N-heteroaromatic ligand [2,2′:6′,2′′]terpyridine (tpy) presents a convergent N,N′,N′′-chelating donor set and has the ability to bind diverse metal ions to form stable pseudo-octahedral 〈tpy–M2+–tpy〉 bonds. Use of 〈tpy–M2+–tpy〉 connectivity for the edges and directed organic vertices has opened the door to diverse, dynamic, utilitarian macromolecular materials. New strategies have been employed to synthesize a range of 2D- and 3D-fractals as well as novel macrocyclic constructs by employing new designer strategies, such as: triangle-based frameworks, hexagonal fractal designs, flexible polyterpyridine linkers, and noncovalent interactions for spontaneous quantitative self-assembly. Numerous examples of heteroleptic self-assembly have been described along with the synthesis of heterometallic conjugates using step-wise protocols. Utilizing multiplanar, directed spacer units in the polyterpyridine vertices, new 3D-polyhedra were obtained facilitating the assembly of hybrid fractal-dendritic materials. These constructs are shown to undergo tunable conformational transformations by responding to specific stimuli such as concentration, temperature, and counter ions. The increasing ability to exploit hierarchical self-assembly of complex, higher order supramolecular nanomaterials is discussed.

Big Data: Deep Learning for financial sentiment analysis

Abstract: Deep Learning and Big Data analytics are two focal points of data science. Deep Learning models have achieved remarkable results in speech recognition and computer vision in recent years. Big Data is important for organizations that need to collect a huge amount of data like a social network and one of the greatest assets to use Deep Learning is analyzing a massive amount of data (Big Data). This advantage makes Deep Learning as a valuable tool for Big Data. Deep Learning can be used to extract incredible information that buried in a Big Data. The modern stock market is an example of these social networks. They are a popular place to increase wealth and generate income, but the fundamental problem of when to buy or sell shares, or which stocks to buy has not been solved. It is very common among investors to have professional financial advisors, but what is the best resource to support the decisions these people make? Investment banks such as Goldman Sachs, Lehman Brothers, and Salomon Brothers dominated the world of financial advice for more than a decade. However, via the popularity of the Internet and financial social networks such as StockTwits and SeekingAlpha, investors around the world have new opportunity to gather and share their experiences. Individual experts can predict the movement of the stock market in financial social networks with the reasonable accuracy, but what is the sentiment of a mass group of these expert authors towards various stocks? In this paper, we seek to determine if Deep Learning models can be adapted to improve the performance of sentiment analysis for StockTwits. We applied several neural network models such as long short-term memory, doc2vec, and convolutional neural networks, to stock market opinions posted in StockTwits. Our results show that Deep Learning model can be used effectively for financial sentiment analysis and a convolutional neural network is the best model to predict sentiment of authors in StockTwits dataset.

Version 3 of the national Alzheimer's coordinating center's uniform data set

Abstract: Introduction In 2015, the US Alzheimer's Disease Centers (ADC) implemented Version 3 of the Uniform Data Set (UDS). This paper describes the history of Version 3 development and the UDS data that are freely available to researchers. Methods UDS Version 3 was developed after years of coordination between the National Institute on Aging-appointed Clinical Task Force (CTF), clinicians from ∼30 ADCs, and the National Alzheimer's Coordinating Center (NACC). The CTF recognized the need for updates to align with the state of the science in dementia research, while being flexible to the diverse needs and diseases studied at the ADCs. Version 3 also developed a nonproprietary neuropsychological battery. Results This paper focuses on the substantial Version 3 changes to the UDS forms related to clinical diagnosis and characterization of clinical symptoms to match updated consensus-based diagnostic criteria. Between March 2015 and March 2018, 4820 participants were enrolled using UDS Version 3. Longitudinal data were available for 25,337 of the 37,568 total participants using all UDS versions. Discussion The results from utilization of the UDS highlight the possibility for numerous research institutions to successfully collaborate, produce, and use standardized data collection instruments for over a decade.

The role of gene flow in rapid and repeated evolution of cave-related traits in Mexican tetra, Astyanax mexicanus

Abstract: Understanding the molecular basis of repeatedly evolved phenotypes can yield key insights into the evolutionary process. Quantifying gene flow between populations is especially important in interpreting mechanisms of repeated phenotypic evolution, and genomic analyses have revealed that admixture occurs more frequently between diverging lineages than previously thought. In this study, we resequenced 47 whole genomes of the Mexican tetra from three cave populations, two surface populations and outgroup samples. We confirmed that cave populations are polyphyletic and two Astyanax mexicanus lineages are present in our data set. The two lineages likely diverged much more recently than previous mitochondrial estimates of 5-7 mya. Divergence of cave populations from their phylogenetically closest surface population likely occurred between ~161 and 191 k generations ago. The favoured demographic model for most population pairs accounts for divergence with secondary contact and heterogeneous gene flow across the genome, and we rigorously identified gene flow among all lineages sampled. Therefore, the evolution of cave-related traits occurred more rapidly than previously thought, and trogolomorphic traits are maintained despite gene flow with surface populations. The recency of these estimated divergence events suggests that selection may drive the evolution of cave-derived traits, as opposed to disuse and drift. Finally, we show that a key trogolomorphic phenotype QTL is enriched for genomic regions with low divergence between caves, suggesting that regions important for cave phenotypes may be transferred between caves via gene flow. Our study shows that gene flow must be considered in studies of independent, repeated trait evolution.

Big Data fraud detection using multiple medicare data sources

Abstract: In the United States, advances in technology and medical sciences continue to improve the general well-being of the population. With this continued progress, programs such as Medicare are needed to help manage the high costs associated with quality healthcare. Unfortunately, there are individuals who commit fraud for nefarious reasons and personal gain, limiting Medicare’s ability to effectively provide for the healthcare needs of the elderly and other qualifying people. To minimize fraudulent activities, the Centers for Medicare and Medicaid Services (CMS) released a number of “Big Data” datasets for different parts of the Medicare program. In this paper, we focus on the detection of Medicare fraud using the following CMS datasets: (1) Medicare Provider Utilization and Payment Data: Physician and Other Supplier (Part B), (2) Medicare Provider Utilization and Payment Data: Part D Prescriber (Part D), and (3) Medicare Provider Utilization and Payment Data: Referring Durable Medical Equipment, Prosthetics, Orthotics and Supplies (DMEPOS). Additionally, we create a fourth dataset which is a combination of the three primary datasets. We discuss data processing for all four datasets and the mapping of real-world provider fraud labels using the List of Excluded Individuals and Entities (LEIE) from the Office of the Inspector General. Our exploratory analysis on Medicare fraud detection involves building and assessing three learners on each dataset. Based on the Area under the Receiver Operating Characteristic (ROC) Curve performance metric, our results show that the Combined dataset with the Logistic Regression (LR) learner yielded the best overall score at 0.816, closely followed by the Part B dataset with LR at 0.805. Overall, the Combined and Part B datasets produced the best fraud detection performance with no statistical difference between these datasets, over all the learners. Therefore, based on our results and the assumption that there is no way to know within which part of Medicare a physician will commit fraud, we suggest using the Combined dataset for detecting fraudulent behavior when a physician has submitted payments through any or all Medicare parts evaluated in our study.

Cognitive impairment in Parkinson’s disease: a report from a multidisciplinary symposium on unmet needs and future directions to maintain cognitive health

Abstract: People with Parkinson’s disease (PD) and their care partners frequently report cognitive decline as one of their greatest concerns. Mild cognitive impairment affects approximately 20–50% of people with PD, and longitudinal studies reveal dementia in up to 80% of PD. Through the Parkinson’s Disease Foundation Community Choice Research Award Program, the PD community identified maintaining cognitive function as one of their major unmet needs. In response, a working group of experts across multiple disciplines was organized to evaluate the unmet needs, current challenges, and future opportunities related to cognitive impairment in PD. Specific conference goals included defining the current state in the field and gaps regarding cognitive issues in PD from patient, care partner, and healthcare professional viewpoints; discussing non-pharmacological interventions to help maintain cognitive function; forming recommendations for what people with PD can do at all disease stages to maintain cognitive health; and proposing ideas for how healthcare professionals can approach cognitive changes in PD. This paper summarizes the discussions of the conference, first by addressing what is currently known about cognitive dysfunction in PD and discussing several non-pharmacological interventions that are often suggested to people with PD. Second, based on the conference discussions, we provide considerations for people with PD for maintaining cognitive health and for healthcare professionals and care partners when working with people with PD experiencing cognitive impairment. Furthermore, we highlight key issues and knowledge gaps that need to be addressed in order to advance research in cognition in PD and improve clinical care.

Speech treatment in Parkinson's disease: Randomized controlled trial (RCT)

Abstract: Background As many as 89% of people with Parkinson's disease (PD) develop speech disorders. Objectives This randomized controlled trial evaluated two speech treatments for PD matched in intensive dosage and high-effort mode of delivery, differing in subsystem target: voice (respiratory-laryngeal) versus articulation (orofacial-articulatory). Methods PD participants were randomized to 1-month LSVT LOUD (voice), LSVT ARTIC (articulation), or UNTXPD (untreated) groups. Speech clinicians specializing in PD delivered treatment. Primary outcome was sound pressure level (SPL) in reading and spontaneous speech, and secondary outcome was participant-reported Modified Communication Effectiveness Index (CETI-M), evaluated at baseline, 1, and 7 months. Healthy controls were matched by age and sex. Results At baseline, the combined PD group (n = 64) was significantly worse than healthy controls (n = 20) for SPL (P 0.05). For CETI-M, between-group comparisons showed greater improvements for LSVT LOUD and LSVT ARTIC than UNTXPD at 1 month (P = 0.02; P = 0.02). At 7 months, CETI-M between-group differences were not significant (P = 0.08). Within-group CETI-M improvements for LSVT LOUD were maintained through 7 months (P = 0.0011). Conclusions LSVT LOUD showed greater improvements than both LSVT ARTIC and UNTXPD for SPL at 1 and 7 months. For CETI-M, both LSVT LOUD and LSVT ARTIC improved at 1 month relative to UNTXPD. Only LSVT LOUD maintained CETI-M improvements at 7 months. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Langerhans cell histiocytosis in children: History, classification, pathobiology, clinical manifestations, and prognosis.

Abstract: Langerhans cell histiocytosis (LCH) is an inflammatory neoplasia of myeloid precursor cells driven by mutations in the mitogen-activated protein kinase pathway. When disease involves the skin, LCH most commonly presents as a seborrheic dermatitis or eczematous eruption on the scalp and trunk. Evaluation for involvement of other organ systems is essential, because 9 of 10 patients presenting with cutaneous disease also have multisystem involvement. Clinical manifestations range from isolated disease with spontaneous resolution to life-threatening multisystem disease. Prognosis depends on involvement of risk organs (liver, spleen, and bone marrow) at diagnosis, particularly on presence of organ dysfunction, and response to initial therapy. Systemic treatment incorporating steroids and cytostatic drugs for at least one year has improved prognosis of multisystem LCH and represents the current standard of care.

Managing Incidental Findings on Thoracic CT: Mediastinal and Cardiovascular Findings. A White Paper of the ACR Incidental Findings Committee

Abstract: The ACR Incidental Findings Committee presents recommendations for managing incidentally detected mediastinal and cardiovascular findings found on CT. The Chest Subcommittee was composed of thoracic radiologists who developed the provided guidance. These recommendations represent a combination of current published evidence and expert opinion and were finalized by informal iterative consensus. The recommendations address the most commonly encountered mediastinal and cardiovascular incidental findings and are not intended to be a comprehensive review of all incidental findings associated with these compartments. Our goal is to improve the quality of care by providing guidance on how to manage incidentally detected thoracic findings.

Logistic regression over encrypted data from fully homomorphic encryption

Abstract: One of the tasks in the 2017 iDASH secure genome analysis competition was to enable training of logistic regression models over encrypted genomic data. More precisely, given a list of approximately 1500 patient records, each with 18 binary features containing information on specific mutations, the idea was for the data holder to encrypt the records using homomorphic encryption, and send them to an untrusted cloud for storage. The cloud could then homomorphically apply a training algorithm on the encrypted data to obtain an encrypted logistic regression model, which can be sent to the data holder for decryption. In this way, the data holder could successfully outsource the training process without revealing either her sensitive data, or the trained model, to the cloud. Our solution to this problem has several novelties: we use a multi-bit plaintext space in fully homomorphic encryption together with fixed point number encoding; we combine bootstrapping in fully homomorphic encryption with a scaling operation in fixed point arithmetic; we use a minimax polynomial approximation to the sigmoid function and the 1-bit gradient descent method to reduce the plaintext growth in the training process. Our algorithm for training over encrypted data takes 0.4–3.2 hours per iteration of gradient descent. We demonstrate the feasibility but high computational cost of training over encrypted data. On the other hand, our method can guarantee the highest level of data privacy in critical applications.

The Revised National Alzheimer's Coordinating Center's Neuropathology Form-Available Data and New Analyses.

Abstract: Neuropathologic evaluation remains the gold standard for determining the presence and severity of aging-related neurodegenerative diseases. Researchers at U.S. Alzheimer's Disease Centers (ADCs) have worked for >30 years studying human brains, with the goals of achieving new research breakthroughs. Harmonization and sharing among the 39 current and past ADCs is promoted by the National Alzheimer's Coordinating Center (NACC), which collects, audits, and disburses ADC-derived data to investigators on request. The past decades have witnessed revised disease definitions paired with dramatic expansion in the granularity and multimodality of the collected data. The NACC database now includes cognitive test scores, comorbidities, drug history, neuroimaging, and links to genomics. Relatively, recent advances in the neuropathologic diagnoses of Alzheimer's disease, frontotemporal lobar degeneration (FTLD), and vascular contributions to cognitive impairment and dementia catalyzed a 2014 update to the NACC Neuropathology Form completed by all ADCs. New focal points include cerebrovascular disease (including arteriolosclerosis, microbleeds, and microinfarcts), hippocampal sclerosis, TDP-43, and FTLD. Here, we provide summary data and analyses to illustrate the potential for both hypothesis-testing and also generating new hypotheses using the NACC Neuropathology data set, which represents one of the largest multi-center databases of carefully curated neuropathologic information that is freely available to researchers worldwide.

The origins and evolution of sleep

Abstract: Sleep is nearly ubiquitous throughout the animal kingdom, yet little is known about how ecological factors or perturbations to the environment shape the duration and timing of sleep. In diverse animal taxa, poor sleep negatively impacts development, cognitive abilities and longevity. In addition to mammals, sleep has been characterized in genetic model organisms, ranging from the nematode worm to zebrafish, and, more recently, in emergent models with simplified nervous systems such as Aplysia and jellyfish. In addition, evolutionary models ranging from fruit flies to cavefish have leveraged natural genetic variation to investigate the relationship between ecology and sleep. Here, we describe the contributions of classical and emergent genetic model systems to investigate mechanisms underlying sleep regulation. These studies highlight fundamental interactions between sleep and sensory processing, as well as a remarkable plasticity of sleep in response to environmental changes. Understanding how sleep varies throughout the animal kingdom will provide critical insight into fundamental functions and conserved genetic mechanisms underlying sleep regulation. Furthermore, identification of naturally occurring genetic variation regulating sleep may provide novel drug targets and approaches to treat sleep-related diseases.

Thrust force characterization of free-swimming soft robotic jellyfish.

Abstract: Five unique soft robotic jellyfish were manufactured with eight pneumatic network tentacle actuators extending radially from their centers These jellyfish robots were able to freely swim untethered in the ocean, to steer from side to side, and to swim through orifices more narrow than the nominal diameter of the jellyfish Each of the five jellyfish robots were manufactured with a different composition of body and tentacle actuator Shore hardness A three-factor study was performed with these five jellyfish robots to determine the impact that actuator material Shore hardness, actuation frequency, and tentacle stroke actuation amplitude had upon the measured thrust force It was found that all three of these factors significantly impacted mean thrust force generation, which peaked with a half-stroke actuation amplitude at a frequency of 08 Hz

Langerhans cell histiocytosis in children: Diagnosis, differential diagnosis, treatment, sequelae, and standardized follow-up.

Abstract: A definitive diagnosis of Langerhans cell histiocytosis (LCH) requires a combination of clinical presentation, histology, and immunohistochemistry. The inflammatory infiltrate contains various proportions of LCH cells, the disease hallmark, which are round and have characteristic "coffee-bean" cleaved nuclei and eosinophilic cytoplasm. Positive immunohistochemistry staining for CD1a and CD207 (langerin) are required for a definitive diagnosis. Isolated cutaneous disease should only be treated when symptomatic, because spontaneous resolution is common. Topical steroids are first-line treatment for localized disease of skin and bone. For multifocal single-system or multisystem disease, systemic treatment with steroids and vinblastine for 12 months is the standard first-line regimen. Current research is seeking more effective regimens because recurrence rates, which increase the risk of sequelae, are still high (30-50%) in patients with multisystem disease. An active area of research is the use of targeted therapy directed at the mitogen-activated protein kinase pathway. Adequate follow-up to monitor for disease progression, relapse, and sequelae is recommended in all patients.

Language Use Contributes to Expressive Language Growth: Evidence From Bilingual Children.

Abstract: The unique relation of language use (i.e., output) to language growth was investigated for forty-seven 30-month-old Spanish–English bilingual children (27 girls, 20 boys) whose choices of which language to speak resulted in their levels of English output differing from their levels of English input. English expressive vocabularies and receptive language skills were assessed at 30, 36, and 42 months. Longitudinal multilevel modeling indicated an effect of output on expressive vocabulary growth only. The finding that output specifically benefits the development of expressive language skill has implications for understanding effects of language use on language skill in monolingual and bilingual development, and potentially, for understanding consequences of cultural differences in how much children are expected to talk in conversation with adults.

Exploring the influence of flow and psychological ownership on security education, training and awareness effectiveness and security compliance

Abstract: The purpose of this study is to investigate the impact of flow and psychological ownership on security education, training, and awareness (SETA) effectiveness, self-efficacy, and security compliance intention. The important role of experiencing flow in SETA is presented as focal antecedents of psychological ownership, self-efficacy, SETA effectiveness, and security compliance intention. To achieve these goals, we propose a theoretical framework and analyze survey data to test the hypotheses. Flow components in SETA are extended to include challenge, feedback, autonomy, immersion, and social interaction. The results illustrate that experiencing flow in SETA shows significant relationships with SETA effectiveness and psychological ownership, which in turn positively influence security compliance intention. Appropriate theoretical contributions and managerial implications are also discussed.

Old-Aged Primary Distance Indicators

Abstract: Old-aged stellar distance indicators are present in all Galactic structures (halo, bulge, disk) and in galaxies of all Hubble types and, thus, are immensely powerful tools for understanding our Universe. Here we present a comprehensive review for three primary standard candles from Population II: (i) RR Lyrae type variables (RRL), (ii) type II Cepheid variables (T2C), and (iii) the tip of the red giant branch (TRGB). The discovery and use of these distance indicators is placed in historical context before describing their theoretical foundations and demonstrating their observational applications across multiple wavelengths. The methods used to establish the absolute scale for each standard candle is described with a discussion of the observational systematics. We conclude by looking forward to the suite of new observational facilities anticipated over the next decade; these have both a broader wavelength coverage and larger apertures than current facilities. We anticipate future advancements in our theoretical understanding and observational application of these stellar populations as they apply to the Galactic and extragalactic distance scale.