Showing papers by "University of North Carolina at Charlotte published in 2018"

Global, regional, and national burden of Parkinson's disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

Abstract: Summary Background Neurological disorders are now the leading source of disability globally, and ageing is increasing the burden of neurodegenerative disorders, including Parkinson's disease. We aimed to determine the global burden of Parkinson's disease between 1990 and 2016 to identify trends and to enable appropriate public health, medical, and scientific responses. Methods Through a systematic analysis of epidemiological studies, we estimated global, regional, and country-specific prevalence and years of life lived with disability for Parkinson's disease from 1990 to 2016. We estimated the proportion of mild, moderate, and severe Parkinson's disease on the basis of studies that used the Hoehn and Yahr scale and assigned disability weights to each level. We jointly modelled prevalence and excess mortality risk in a natural history model to derive estimates of deaths due to Parkinson's disease. Death counts were multiplied by values from the Global Burden of Disease study's standard life expectancy to compute years of life lost. Disability-adjusted life-years (DALYs) were computed as the sum of years lived with disability and years of life lost. We also analysed results based on the Socio-demographic Index, a compound measure of income per capita, education, and fertility. Findings In 2016, 6·1 million (95% uncertainty interval [UI] 5·0–7·3) individuals had Parkinson's disease globally, compared with 2·5 million (2·0–3·0) in 1990. This increase was not solely due to increasing numbers of older people, because age-standardised prevalence rates increased by 21·7% (95% UI 18·1–25·3) over the same period (compared with an increase of 74·3%, 95% UI 69·2–79·6, for crude prevalence rates). Parkinson's disease caused 3·2 million (95% UI 2·6–4·0) DALYs and 211 296 deaths (95% UI 167 771–265 160) in 2016. The male-to-female ratios of age-standardised prevalence rates were similar in 2016 (1·40, 95% UI 1·36–1·43) and 1990 (1·37, 1·34–1·40). From 1990 to 2016, age-standardised prevalence, DALY rates, and death rates increased for all global burden of disease regions except for southern Latin America, eastern Europe, and Oceania. In addition, age-standardised DALY rates generally increased across the Socio-demographic Index. Interpretation Over the past generation, the global burden of Parkinson's disease has more than doubled as a result of increasing numbers of older people, with potential contributions from longer disease duration and environmental factors. Demographic and potentially other factors are poised to increase the future burden of Parkinson's disease substantially. Funding Bill & Melinda Gates Foundation.

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Abstract: The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue To date, substantial works have been conducted on smart meter data analytics To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics Following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management We also review the techniques and methodologies adopted or developed to address each application In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security

Vibrio spp. infections.

Abstract: Vibrio is a genus of ubiquitous bacteria found in a wide variety of aquatic and marine habitats; of the >100 described Vibrio spp., ~12 cause infections in humans. Vibrio cholerae can cause cholera, a severe diarrhoeal disease that can be quickly fatal if untreated and is typically transmitted via contaminated water and person-to-person contact. Non-cholera Vibrio spp. (for example, Vibrio parahaemolyticus, Vibrio alginolyticus and Vibrio vulnificus) cause vibriosis - infections normally acquired through exposure to sea water or through consumption of raw or undercooked contaminated seafood. Non-cholera bacteria can lead to several clinical manifestations, most commonly mild, self-limiting gastroenteritis, with the exception of V. vulnificus, an opportunistic pathogen with a high mortality that causes wound infections that can rapidly lead to septicaemia. Treatment for Vibrio spp. infection largely depends on the causative pathogen: for example, rehydration therapy for V. cholerae infection and debridement of infected tissues for V. vulnificus-associated wound infections, with antibiotic therapy for severe cholera and systemic infections. Although cholera is preventable and effective oral cholera vaccines are available, outbreaks can be triggered by natural or man-made events that contaminate drinking water or compromise access to safe water and sanitation. The incidence of vibriosis is rising, perhaps owing in part to the spread of Vibrio spp. favoured by climate change and rising sea water temperature.

Beyond Bilinear: Generalized Multimodal Factorized High-Order Pooling for Visual Question Answering

Abstract: Visual question answering (VQA) is challenging, because it requires a simultaneous understanding of both visual content of images and textual content of questions. To support the VQA task, we need to find good solutions for the following three issues: 1) fine-grained feature representations for both the image and the question; 2) multimodal feature fusion that is able to capture the complex interactions between multimodal features; and 3) automatic answer prediction that is able to consider the complex correlations between multiple diverse answers for the same question. For fine-grained image and question representations, a “coattention” mechanism is developed using a deep neural network (DNN) architecture to jointly learn the attentions for both the image and the question, which can allow us to reduce the irrelevant features effectively and obtain more discriminative features for image and question representations. For multimodal feature fusion, a generalized multimodal factorized high-order pooling approach (MFH) is developed to achieve more effective fusion of multimodal features by exploiting their correlations sufficiently, which can further result in superior VQA performance as compared with the state-of-the-art approaches. For answer prediction, the Kullback–Leibler divergence is used as the loss function to achieve precise characterization of the complex correlations between multiple diverse answers with the same or similar meaning, which can allow us to achieve faster convergence rate and obtain slightly better accuracy on answer prediction. A DNN architecture is designed to integrate all these aforementioned modules into a unified model for achieving superior VQA performance. With an ensemble of our MFH models, we achieve the state-of-the-art performance on the large-scale VQA data sets and win the runner-up in VQA Challenge 2017.

A 3D human triculture system modeling neurodegeneration and neuroinflammation in Alzheimer's disease.

Abstract: Alzheimer's disease (AD) is characterized by beta-amyloid accumulation, phosphorylated tau formation, hyperactivation of glial cells, and neuronal loss. The mechanisms of AD pathogenesis, however, remain poorly understood, partially due to the lack of relevant models that can comprehensively recapitulate multistage intercellular interactions in human AD brains. Here we present a new three-dimensional (3D) human AD triculture model using neurons, astrocytes, and microglia in a 3D microfluidic platform. Our model provided key representative AD features: beta-amyloid aggregation, phosphorylated tau accumulation, and neuroinflammatory activity. In particular, the model mirrored microglial recruitment, neurotoxic activities such as axonal cleavage, and NO release damaging AD neurons and astrocytes. Our model will serve to facilitate the development of more precise human brain models for basic mechanistic studies in neural-glial interactions and drug discovery.

All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey

Abstract: With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.

Race and gender differences in how sense of belonging influences decisions to major in STEM

Abstract: Women and students of color are widely underrepresented in most STEM fields. In order to investigate this underrepresentation, we interviewed 201 college seniors, primarily women and people of color, who either majored in STEM or started but dropped a STEM major. Here we discuss one section of the longer interview that focused on students’ sense of belonging, which has been found to be related to retention. In our analysis, we examine the intersections of race and gender with students’ sense of belonging, a topic largely absent from the current literature. We found that white men were most likely to report a sense of belonging whereas women of color were the least likely. Further, we found that representation within one’s STEM sub-discipline, namely biology versus the physical sciences, impacts sense of belonging for women. Four key factors were found to contribute to sense of belonging for all students interviewed: interpersonal relationships, perceived competence, personal interest, and science identity. Our findings indicate that students who remain in STEM majors report a greater sense of belonging than those who leave STEM. Additionally, we found that students from underrepresented groups are less likely to feel they belong. These findings highlight structural and cultural features of universities, as well as STEM curricula and pedagogy, that continue to privilege white males.

Posttraumatic Growth: Theory, Research, and Applications

Abstract: Posttraumatic Growth reworks and overhauls the seminal 2006 Handbook of Posttraumatic Growth. It provides a wide range of answers to questions concerning knowledge of posttraumatic growth (PTG) theory, its synthesis and contrast with other theories and models, and its applications in diverse settings. The book starts with an overview of the history, components, and outcomes of PTG. Next, chapters review quantitative, qualitative, and cross-cultural research on PTG, including in relation to cognitive function, identity formation, cross-national and gender differences, and similarities and differences between adults and children. The final section shows readers how to facilitate optimal outcomes with PTG at the level of the individual, the group, the community, and society.

Strategies to Combat Antibiotic Resistance in the Wastewater Treatment Plants.

Abstract: The main goal of this manuscript is to review different treatment strategies and mechanisms for combating the antibiotic resistant bacteria (ARB) and antibiotic resistant genes (ARGs) in the wastewater environment. The high amount of antibiotics is released into the wastewater that may promote selection of ARB and ARGs which find their way into natural environments. Emerging microbial pathogens and increasing antibiotic resistance among them is a global public health issue. The propagation and spread of ARB and ARGs in the environment may result in an increase of antibiotic resistant microbial pathogens which is a worldwide environmental and public health concern. A proper treatment of wastewater is essential before its discharge into rivers, lake, or sewage system to prevent the spread of ARB and ARGs into the environment. This review discusses various treatment options applied for combating the spread of ARB and ARGs in wastewater treatment plants (WWTPs). It was reported that low-energy anaerobic-aerobic treatment reactors, constructed wetlands, and disinfection processes have shown good removal efficiencies. Nanomaterials and biochar combined with other treatment methods and coagulation process are very recent strategies regarding ARB and ARGs removal and need more investigation and research. Based on current studies a wide-ranging removal efficiency of ARGs can be achieved depending on the type of genes present and treatment processes used, still, there are gaps that need to be further investigated. In order to find solutions to control dissemination of antibiotic resistance in the environment, it is important to (1) study innovative strategies in large scale and over a long time to reach an actual evaluation, (2) develop risk assessment studies to precisely understand occurrence and abundance of ARB/ARGs so that their potential risks to human health can be determined, and (3) consider operating and environmental factors that affect the efficiency of each treatment mechanism.

Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities

Abstract: Over the last two decades (since ca. 2000), Geographic Object-Based Image Analysis (GEOBIA) has emerged as a new paradigm to analyzing high-spatial resolution remote-sensing imagery. During this ti...

Inflammation-Nature's Way to Efficiently Respond to All Types of Challenges: Implications for Understanding and Managing "the Epidemic" of Chronic Diseases.

Abstract: Siloed or singular system approach to disease management is common practice, developing out of traditional medical school education. Textbooks of medicine describe a huge number of discrete diseases, usually in a systematic fashion following headings like etiology, pathology, investigations, differential diagnoses, and management. This approach suggests that the body has a multitude of ways to respond to harmful incidences. However, physiology and systems biology provide evidence that there is a simple mechanism behind this phenotypical variability. Regardless if an injury or change was caused by trauma, infection, non-communicable disease, autoimmune disorders, or stress, the typical physiological response is: an increase in blood supply to the area, an increase in white cells into the affected tissue, an increase in phagocytic activity to remove the offending agent, followed by a down-regulation of these mechanisms resulting in healing. The cascade of inflammation is the body's unique mechanism to maintain its integrity in response to macroscopic as well as microscopic injuries. We hypothesize that chronic disease development and progression are linked to uncontrolled or dysfunctional inflammation to injuries regardless of their nature, physical, environmental, or psychological. Thus, we aim to reframe the prevailing approach of management of individual diseases into a more integrated systemic approach of treating the "person as a whole," enhancing the patient experience, ability to a make necessary changes, and maximize overall health and well-being. The first part of the paper reviews the local immune cascades of pro- and anti-inflammatory regulation and the interconnected feedback loops with neural and psychological pathways. The second part emphasizes one of nature's principles at work-system design and efficiency. Continually overwhelming this finely tuned system will result in systemic inflammation allowing chronic diseases to emerge; the pathways of several common conditions are described in detail. The final part of the paper considers the implications of these understandings for clinical care and explore how this lens could shape the physician-patient encounter and health system redesign. We conclude that healthcare professionals must advocate for an anti-inflammatory lifestyle at the patient level as well as at the local and national levels to enhance population health and well-being.

Student perception of helpfulness of facilitation strategies that enhance instructor presence, connectedness, engagement and learning in online courses

Abstract: Instructors use various strategies to facilitate learning and actively engage students in online courses. In this study, we examine student perception on the helpfulness of the twelve different facilitation strategies used by instructors on establishing instructor presence, instructor connection, engagement and learning. One hundred and eighty eight graduate students taking online courses in Fall 2016 semester in US higher education institutions responded to the survey. Among the 12 facilitation strategies, instructors' timely response to questions and instructors' timely feedback on assignments/projects were rated the highest in all four constructs (instructor presence, instructor connection, engagement and learning). Interactive visual syllabi of the course was rated the lowest, and video based introduction and instructors' use of synchronous sessions to interact were rated lowest among two of the four constructs. Descriptive statistics for each of the construct (instructor presence, instructor connection, engagement and learning) by gender, status, and major of study are presented. Confirmative factor analysis of the data provided aspects of construct validity of the survey. Analysis of variance failed to detect differences between gender and discipline (education major versus non-education major) on all four constructs measured. However, undergraduate students rated significantly lower on engagement and learning in comparison to post-doctoral and other post graduate students.

Leveraging Content Sensitiveness and User Trustworthiness to Recommend Fine-Grained Privacy Settings for Social Image Sharing

Abstract: To configure successful privacy settings for social image sharing, two issues are inseparable: 1) content sensitiveness of the images being shared; and 2) trustworthiness of the users being granted to see the images. This paper aims to consider these two inseparable issues simultaneously to recommend fine-grained privacy settings for social image sharing. For achieving more compact representation of image content sensitiveness (privacy), two approaches are developed: 1) a deep network is adapted to extract 1024-D discriminative deep features; and 2) a deep multiple instance learning algorithm is adopted to identify 280 privacy-sensitive object classes and events. Second, users on the social network are clustered into a set of representative social groups to generate a discriminative dictionary for user trustworthiness characterization. Finally, both the image content sensitiveness and the user trustworthiness are integrated to train a tree classifier to recommend fine-grained privacy settings for social image sharing. Our experimental studies have demonstrated both the efficiency and the effectiveness of our proposed algorithms.

Data availability, reusability, and analytic reproducibility: evaluating the impact of a mandatory open data policy at the journal Cognition

Abstract: Access to data is a critical feature of an efficient, progressive and ultimately self-correcting scientific ecosystem. But the extent to which in-principle benefits of data sharing are realized in practice is unclear. Crucially, it is largely unknown whether published findings can be reproduced by repeating reported analyses upon shared data ('analytic reproducibility'). To investigate this, we conducted an observational evaluation of a mandatory open data policy introduced at the journal Cognition. Interrupted time-series analyses indicated a substantial post-policy increase in data available statements (104/417, 25% pre-policy to 136/174, 78% post-policy), although not all data appeared reusable (23/104, 22% pre-policy to 85/136, 62%, post-policy). For 35 of the articles determined to have reusable data, we attempted to reproduce 1324 target values. Ultimately, 64 values could not be reproduced within a 10% margin of error. For 22 articles all target values were reproduced, but 11 of these required author assistance. For 13 articles at least one value could not be reproduced despite author assistance. Importantly, there were no clear indications that original conclusions were seriously impacted. Mandatory open data policies can increase the frequency and quality of data sharing. However, suboptimal data curation, unclear analysis specification and reporting errors can impede analytic reproducibility, undermining the utility of data sharing and the credibility of scientific findings.

Gabor Convolutional Networks

Abstract: In steerable filters, a filter of arbitrary orientation can be generated by a linear combination of a set of “basis filters.” Steerable properties dominate the design of the traditional filters, e.g., Gabor filters and endow features the capability of handling spatial transformations. However, such properties have not yet been well explored in the deep convolutional neural networks (DCNNs). In this paper, we develop a new deep model, namely, Gabor convolutional networks (GCNs or Gabor CNNs), with Gabor filters incorporated into DCNNs such that the robustness of learned features against the orientation and scale changes can be reinforced. By manipulating the basic element of DCNNs, i.e., the convolution operator, based on Gabor filters, GCNs can be easily implemented and are readily compatible with any popular deep learning architecture. We carry out extensive experiments to demonstrate the promising performance of our GCNs framework, and the results show its superiority in recognizing objects, especially when the scale and rotation changes take place frequently. Moreover, the proposed GCNs have much fewer network parameters to be learned and can effectively reduce the training complexity of the network, leading to a more compact deep learning model while still maintaining a high feature representation capacity. The source code can be found at https://github.com/bczhangbczhang .

Critical impact of vegetation physiology on the continental hydrologic cycle in response to increasing CO2.

Abstract: Predicting how increasing atmospheric CO2 will affect the hydrologic cycle is of utmost importance for a range of applications ranging from ecological services to human life and activities. A typical perspective is that hydrologic change is driven by precipitation and radiation changes due to climate change, and that the land surface will adjust. Using Earth system models with decoupled surface (vegetation physiology) and atmospheric (radiative) CO2 responses, we here show that the CO2 physiological response has a dominant role in evapotranspiration and evaporative fraction changes and has a major effect on long-term runoff compared with radiative or precipitation changes due to increased atmospheric CO2 This major effect is true for most hydrological stress variables over the largest fraction of the globe, except for soil moisture, which exhibits a more nonlinear response. This highlights the key role of vegetation in controlling future terrestrial hydrologic response and emphasizes that the carbon and water cycles are intimately coupled over land.

An overview of mammographic density and its association with breast cancer.

Abstract: In 2017, breast cancer became the most commonly diagnosed cancer among women in the US. After lung cancer, breast cancer is the leading cause of cancer-related mortality in women. The breast consists of several components, including milk storage glands, milk ducts made of epithelial cells, adipose tissue, and stromal tissue. Mammographic density (MD) is based on the proportion of stromal, epithelial, and adipose tissue. Women with high MD have more stromal and epithelial cells and less fatty adipose tissue, and are more likely to develop breast cancer in their lifetime compared to women with low MD. Because of this correlation, high MD is an independent risk factor for breast cancer. Further, mammographic screening is less effective in detecting suspicious lesions in dense breast tissue, which can lead to late-stage diagnosis. Molecular differences between dense and non-dense breast tissues explain the underlying biological reasons for why women with dense breasts are at a higher risk for developing breast cancer. The goal of this review is to highlight the current molecular understanding of MD, its association with breast cancer risk, the demographics pertaining to MD, and the environmental factors that modulate MD. Finally, we will review the current legislation regarding the disclosure of MD on a traditional screening mammogram and the supplemental screening options available to women with dense breast tissue.

SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery

Abstract: As an unsupervised dimensionality reduction method, the principal component analysis (PCA) has been widely considered as an efficient and effective preprocessing step for hyperspectral image (HSI) processing and analysis tasks. It takes each band as a whole and globally extracts the most representative bands. However, different homogeneous regions correspond to different objects, whose spectral features are diverse. Therefore, it is inappropriate to carry out dimensionality reduction through a unified projection for an entire HSI. In this paper, a simple but very effective superpixelwise PCA (SuperPCA) approach is proposed to learn the intrinsic low-dimensional features of HSIs. In contrast to classical PCA models, the SuperPCA has four main properties: 1) unlike the traditional PCA method based on a whole image, the SuperPCA takes into account the diversity in different homogeneous regions, that is, different regions should have different projections; 2) most of the conventional feature extraction models cannot directly use the spatial information of HSIs, while the SuperPCA is able to incorporate the spatial context information into the unsupervised dimensionality reduction by superpixel segmentation; 3) since the regions obtained by superpixel segmentation have homogeneity, the SuperPCA can extract potential low-dimensional features even under noise; and 4) although the SuperPCA is an unsupervised method, it can achieve a competitive performance when compared with supervised approaches. The resulting features are discriminative, compact, and noise-resistant, leading to an improved HSI classification performance. Experiments on three public data sets demonstrate that the SuperPCA model significantly outperforms the conventional PCA-based dimensionality reduction baselines for HSI classification, and some state-of-the-art feature extraction approaches. The MATLAB source code is available at https://github.com/junjun-jiang/SuperPCA .

Recently Detected Drinking Water Contaminants: GenX and Other Per- and Polyfluoroalkyl Ether Acids

Abstract: For several decades, a common processing aid in the production of fluoropolymers was the ammonium salt of perfluorooctanoic acid (PFOA). Because PFOA is persistent, bioaccumulative, and toxic, its production and use are being phased out in the United States. In 2009, the US Environmental Protection Agency stipulated conditions for the manufacture and commercial use of GenX, a PFOA replacement. While GenX is produced for commercial purposes, the acid form of GenX is also generated as a byproduct during the production of fluoromonomers. The discovery of high concentrations of GenX and related perfluoroalkyl ether acids (PFEAs) in the Cape Fear River and in finished drinking water of more than 200,000 North Carolina residents required quick action by researchers, regulators, public health officials, commercial laboratories, drinking water providers, and consulting engineers. Information about sources and toxicity of GenX as well as an analytical method for the detection of GenX and eight related PFEAs is presented. GenX/PFEA occurrence in water and GenX/PFEA removal by different drinking water treatment processes are also discussed.

An Edge Network Orchestrator for Mobile Augmented Reality

Abstract: Mobile augmented reality (MAR) involves high complexity computation which cannot be performed efficiently on resource limited mobile devices. The performance of MAR would be significantly improved by offloading the computation tasks to servers deployed with the close proximity to the users. In this paper, we design an edge network orchestrator to enable fast and accurate object analytics at the network edge for MAR. The measurement-based analytical models are built to characterize the tradeoff between the service latency and analytics accuracy in edge-based MAR systems. As a key component of the edge network orchestrator, a server assignment and frame resolution selection algorithm named FACT is proposed to mitigate the latency-accuracy tradeoff. Through network simulations, we evaluate the performance of the FACT algorithm and show the insights on optimizing the performance of edge-based MAR systems. We have implemented the edge network orchestrator and developed the corresponding communication protocol. Our experiments validate the performance of the proposed edge network orchestrator.

Relationship between the Viable but Nonculturable State and Antibiotic Persister Cells.

Abstract: Bacteria have evolved numerous means of survival in adverse environments with dormancy, as represented by "persistence" and the "viable but nonculturable" (VBNC) state, now recognized to be common modes for such survival. VBNC cells have been defined as cells which, induced by some stress, become nonculturable on media that would normally support their growth but which can be demonstrated by various methods to be alive and capable of returning to a metabolically active and culturable state. Persister cells have been described as a population of cells which, while not being antibiotic resistant, are antibiotic tolerant. This drug-tolerant phenotype is thought to be a result of stress-induced and stochastic physiological changes as opposed to mutational events leading to true resistance. In this review, we describe these two dormancy strategies, characterize the molecular underpinnings of each state, and highlight the similarities and differences between them. We believe these survival modes represent a continuum between actively growing and dead cells, with VBNC cells being in a deeper state of dormancy than persister cells.

LinkageMapView—rendering high-resolution linkage and QTL maps

Abstract: MOTIVATION Linkage and quantitative trait loci (QTL) maps are critical tools for the study of the genetic basis of complex traits. With the advances in sequencing technology over the past decade, linkage map densities have been increasing dramatically, while the visualization tools have not kept pace. LinkageMapView is a free add-on package written in R that produces high resolution, publication-ready visualizations of linkage and QTL maps. While there is software available to generate linkage map graphics, none are freely available, produce publication quality figures, are open source and can run on all platforms. LinkageMapView can be integrated into map building pipelines as it seamlessly incorporates output from R/qtl and also accepts simple text or comma delimited files. There are numerous options within the package to build highly customizable maps, allow for linkage group comparisons, and annotate QTL regions. AVAILABILITY AND IMPLEMENTATION https://cran.r-project.org/web/packages/LinkageMapView/.

The Interleukin-10 Family of Cytokines and Their Role in the CNS.

Abstract: Resident cells of the central nervous system (CNS) play an important role in detecting insults and initiating protective or sometimes detrimental host immunity. At peripheral sites, immune responses follow a biphasic course with the rapid, but transient, production of inflammatory mediators giving way to the delayed release of factors that promote resolution and repair. Within the CNS, it is well known that glial cells contribute to the onset and progression of neuroinflammation, but it is only now becoming apparent that microglia and astrocytes also play an important role in producing and responding to immunosuppressive factors that serve to limit the detrimental effects of such responses. Interleukin-10 (IL-10) is generally considered to be the quintessential immunosuppressive cytokine, and its ability to resolve inflammation and promote wound repair at peripheral sites is well documented. In the present review article, we discuss the evidence for the production of IL-10 by glia, and describe the ability of CNS cells, including microglia and astrocytes, to respond to this suppressive factor. Furthermore, we review the literature for the expression of other members of the IL-10 cytokine family, IL-19, IL-20, IL-22 and IL-24, within the brain, and discuss the evidence of a role for these poorly understood cytokines in the regulation of infectious and sterile neuroinflammation. In concert, the available data indicate that glia can produce IL-10 and the related cytokines IL-19 and IL-24 in a delayed manner, and these cytokines can limit glial inflammatory responses and/or provide protection against CNS insult. However, the roles of other IL-10 family members within the CNS remain unclear, with IL-20 appearing to act as a pro-inflammatory factor, while IL-22 may play a protective role in some instances and a detrimental role in others, perhaps reflecting the pleiotropic nature of this cytokine family. What is clear is that our current understanding of the role of IL-10 and related cytokines within the CNS is limited at best, and further research is required to define the actions of this understudied family in inflammatory brain disorders.

Intestinal microbiota enhances pancreatic carcinogenesis in preclinical models.

Abstract: Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the United States yet data are scant regarding host factors influencing pancreatic carcinogenesis. Increasing evidence support the role of the host microbiota in carcinogenesis but its role in PDAC is not well established. Herein, we report that antibiotic-mediated microbial depletion of KrasG12D/PTENlox/+ mice showed a decreased proportion of poorly differentiated tumors compared to microbiota-intact KrasG12D/PTENlox/+ mice. Subsequent 16S rRNA PCR showed that ~50% of KrasG12D/PTENlox/+ mice with PDAC harbored intrapancreatic bacteria. To determine if a similar observation in humans correlates with presence of PDAC, benign and malignant human pancreatic surgical specimens demonstrated a microbiota by 16S bacterial sequencing and culture confirmation. However, the microbial composition did not differentiate PDAC from non-PDAC tissue. Furthermore, murine pancreas did not naturally acquire a pancreatic microbiota, as germ-free mice transferred to specific pathogen-free housing failed to acquire intrapancreatic bacteria over time, which was not augmented by a murine model of colitis. Finally, antibiotic-mediated microbial depletion of Nod-SCID mice, compared to microbiota-intact, showed increased time to PDAC xenograft formation, smaller tumors, and attenuated growth. Interestingly, both xenograft cohorts were devoid of intratumoral bacteria by 16S rRNA PCR, suggesting that intrapancreatic/intratumoral microbiota is not the sole driver of PDAC acceleration. Xenografts from microbiota-intact mice demonstrated innate immune suppression by immunohistochemistry and differential regulation of oncogenic pathways as determined by RNA sequencing. Our work supports a long-distance role of the intestinal microbiota on PDAC progression and opens new research avenues regarding pancreatic carcinogenesis.

Evolutionary determinants of genome-wide nucleotide composition.

Abstract: One of the long-standing mysteries of evolutionary genomics is the source of the wide phylogenetic diversity in genome nucleotide composition (G + C versus A + T), which must be a consequence of interspecific differences in mutation bias, the efficiency of selection for different nucleotides or a combination of the two. We demonstrate that although genomic G + C composition is strongly driven by mutation bias, it is also substantially modified by direct selection and/or as a by-product of biased gene conversion. Moreover, G + C composition at fourfold redundant sites is consistently elevated above the neutral expectation—more so than for any other class of sites. Genome-wide nucleotide composition varies greatly among species. Here, the authors show that genomic G + C composition is driven by mutation bias but is also modified by natural selection or biased gene conversion.