Mobile Data Offloading the Growing Need with Its Solutions and Challenges

From the last few years, the popularity of video, social media and Internet gaming across a range of new devices like smartphones and tablets has created a surge of data traffic over cellular networks. Device to device connectivity will give rise to a new universe of applications that will further create stress on network capacity [3]. In the next three years alone, it is accepted that data traffic will grow towards tenfold creating a tremendous capacity crunch for operators. While data revenues are expected to only double during this period, which will create a huge gap. As a result, different innovative solutions have emerged to man age data traffic. Some of the key technologies include Wi-Fi, LTE Small Cell and Relay, femtocells, DTN-based Network, and IP flow mobility. Therefore, telecom operators need to constantly review their implement traffic offloading mechanisms that will help them manage their network load and capacity mo re efficiently. This paper describes various data offload strategies and considers the challenges and benefits associated with each of them. This paper aims to provide a survey of mobile data offloading technologies including insights from the business per spective as well

Cutaneous squamous cell carcinoma staging may influence management in users: A survey study.

PURPOSE: This study aims to determine whether there is consensus regarding staging and management of cutaneous squamous cell carcinoma (CSCC) across the various specialties that manage this disease. MATERIALS AND METHODS: A survey regarding CSCC high-risk features, staging, and management was created and emailed to cutaneous oncology experts including dermatology, head and neck surgery/surgical oncology, radiation oncology, and medical oncology. RESULTS: One hundred fifty-six (46%) of 357 invited physicians completed the survey. Depth of invasion (92%), perineural invasion (99%), histologic differentiation (85%), and patient immunosuppression (90%) achieved consensus (\u3e80%) as high-risk features of CSCC. Dermatologists were more likely to also choose clinical tumor diameter (79% vs. 54%) and histology (99% vs. 66%) as a high-risk feature. Dermatologists were also more likely to utilize the Brigham and Women\u27s Hospital (BWH) staging system alone or in conjunction with American Joint Committee on Cancer (AJCC) (71%), whereas other cancer specialists (OCS) tend to use only AJCC (71%). Respondents considered AJCC T3 and higher (90%) and BWH T2b and higher (100%) to be high risk and when they consider radiologic imaging, sentinel lymph node biopsy, post-operative radiation therapy, and increased follow-up. Notably, a large number of respondents do not use staging systems or tumor stage to determine treatment options beyond surgery in high-risk CSCC. CONCLUSION: This survey study highlights areas of consensus and differences regarding the definition of high-risk features of CSCC, staging approaches, and management patterns between dermatologists and OCS. High-risk CSCC is defined as, but not limited to, BWH T2b and higher and AJCC T3 and higher, and these thresholds can be used to identify cases for which treatment beyond surgery may be considered. Dermatologists are more likely to utilize BWH staging, likely because BWH validation studies showing advantages over AJCC were published in dermatology journals and discussed at dermatology meetings. Additional data are necessary to develop a comprehensive risk-based management approach for CSCC

Ovarian steroid hormones: A long overlooked but critical contributor to brain aging and Alzheimer’s disease

Ovarian hormones, particularly 17β-estradiol, are involved in numerous neurophysiological and neurochemical processes, including those subserving cognitive function. Estradiol plays a key role in the neurobiology of aging, in part due to extensive interconnectivity of the neural and endocrine system. This aspect of aging is fundamental for women’s brains as all women experience a drop in circulating estradiol levels in midlife, after menopause. Given the importance of estradiol for brain function, it is not surprising that up to 80% of peri-menopausal and post-menopausal women report neurological symptoms including changes in thermoregulation (vasomotor symptoms), mood, sleep, and cognitive performance. Preclinical evidence for neuroprotective effects of 17β-estradiol also indicate associations between menopause, cognitive aging, and Alzheimer’s disease (AD), the most common cause of dementia affecting nearly twice more women than men. Brain imaging studies demonstrated that middle-aged women exhibit increased indicators of AD endophenotype as compared to men of the same age, with onset in perimenopause. Herein, we take a translational approach to illustrate the contribution of ovarian hormones in maintaining cognition in women, with evidence implicating menopause-related declines in 17β-estradiol in cognitive aging and AD risk. We will review research focused on the role of endogenous and exogenous estrogen exposure as a key underlying mechanism to neuropathological aging in women, with a focus on whether brain structure, function and neurochemistry respond to hormone treatment. While still in development, this research area offers a new sex-based perspective on brain aging and risk of AD, while also highlighting an urgent need for better integration between neurology, psychiatry, and women’s health practices

Elevated gonadotropin levels are associated with increased biomarker risk of Alzheimer's disease in midlife women

IntroductionIn preclinical studies, menopausal elevations in pituitary gonadotropins, follicle-stimulating hormone (FSH) and luteinizing hormone (LH), trigger Alzheimer's disease (AD) pathology and synaptic loss in female animals. Herein, we took a translational approach to test whether gonadotropin elevations are linked to AD pathophysiology in women.MethodsWe examined 191 women ages 40–65 years, carrying risk factors for late-onset AD, including 45 premenopausal, 67 perimenopausal, and 79 postmenopausal participants with clinical, laboratory, cognitive exams, and volumetric MRI scans. Half of the cohort completed 11C-Pittsburgh Compound B (PiB) amyloid-β (Aβ) PET scans. Associations between serum FSH, LH and biomarkers were examined using voxel-based analysis, overall and stratified by menopause status. Associations with region-of-interest (ROI) hippocampal volume, plasma estradiol levels, APOE-4 status, and cognition were assessed in sensitivity analyses.ResultsFSH levels were positively associated with Aβ load in frontal cortex (multivariable adjusted P ≤ 0.05, corrected for family wise type error, FWE), an effect that was driven by the postmenopausal group (multivariable adjusted PFWE ≤ 0.044). LH levels were also associated with Aβ load in frontal cortex, which did not survive multivariable adjustment. FSH and LH were negatively associated with gray matter volume (GMV) in frontal cortex, overall and in each menopausal group (multivariable adjusted PFWE ≤ 0.040), and FSH was marginally associated with ROI hippocampal volume (multivariable adjusted P = 0.058). Associations were independent of age, clinical confounders, menopause type, hormone therapy status, history of depression, APOE-4 status, and regional effects of estradiol. There were no significant associations with cognitive scores.DiscussionIncreasing serum gonadotropin levels, especially FSH, are associated with higher Aβ load and lower GMV in some AD-vulnerable regions of midlife women at risk for AD. These findings are consistent with preclinical work and provide exploratory hormonal targets for precision medicine strategies for AD risk reduction

Markovian Dynamics on Complex Reaction Networks

Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underling population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions, the computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples.Comment: 52 pages, 11 figures, for freely available MATLAB software, see http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.htm