Functional Cramer-Rao bounds and Stein estimators in Sobolev spaces, for Brownian motion and Cox processes

We investigate the problems of drift estimation for a shifted Brownian motion and intensity estimation for a Cox process on a finite interval $[0,T]$, when the risk is given by the energy functional associated to some fractional Sobolev space $H^1_0\subset W^{\alpha,2}\subset L^2$. In both situations, Cramer-Rao lower bounds are obtained, entailing in particular that no unbiased estimators with finite risk in $H^1_0$ exist. By Malliavin calculus techniques, we also study super-efficient Stein type estimators (in the Gaussian case)

Elevated cystatin-C concentration is associated with progression to prediabetes: the Western New York Study

OBJECTIVE – We conducted a nested case-control investigation to examine if elevated baseline concentrations of cystatin-C predicted progression from normoglycaemia to prediabetes over 6 years of follow-up from the Western New York Health Study. RESEARCH DESIGN AND METHODS – 1,455 participants from the Western New York Health Study, free of type 2 diabetes and known cardiovascular disease at baseline (1996-2001), were reexamined in 2002-2004. An incident case of prediabetes was defined as one with fasting glucose below 100 mg/dl at the baseline examination and ≥ 100 mg/dl and ≤ 125 mg/dl at the follow-up examination. All cases (n=91) were matched 1:3 to control participants based upon sex, race/ethnicity and year of study enrollment. All controls had fasting glucose levels < 100 mg/dl at both baseline and follow-up examinations. Cystatin-C concentrations and the urinary albumin to creatinine ratio were measured from frozen (-196 Cº) baseline blood and urine samples. Serum creatinine concentrations were available from the baseline examination. RESULTS –Multivariate conditional logistic regression analyses adjusted for age, baseline glucose level, HOMA-IR, body mass index, hypertension, eGFR, cigarette smoking, and alcohol use revealed a significantly increased risk of progression to prediabetes among those with elevated baseline concentrations of cystatin-C (Odds Ratio, 95% CI: 3.04, 1.34, 6.89) (upper quintile vs. the remainder). Results of secondary analyses that considered hs-CRP, IL-6, E-selectin, or sICAM did not alter these results. CONCLUSIONS - These results suggest that early renal impairment indexed with cystatin-C imparted a three-fold excess risk of progression to prediabetes in this study population. Recent evidence from randomized clinical trials (1,2) among people with prediabetes have provided convincing evidence that early intervention can significantly delay or prevent the progression to type 2 diabetes. The identification of those with prediabetes is assuming greater importance (3) especially in light of the fact that approximately 35 million adults aged 40-74 years old in the United States have prediabetes defined as impaired fasting glucose (4). Microalbuminuria occurs frequently in nondiabetic subjects and places them at increased risk for cardiovascular disease (5-7). The mechanisms behind this observation are poorly understood, however. Albuminuria may reflect underlying vascular damage (8), hypertension (9, 10) endothelial dysfunction (11, 12) and/or low-grade inflammation (13). A large percentage of type 2 individuals pass through a period of prediabetes (14) and may experience early renal dysfunction e.g., a glomerular filtration rate (GFR) above 60 ml/minute per 1.73m2. Currently used estimating equations are poor at identifying early renal impairment and better indices are of great interest (15, 16). Recently, several studies have suggested that cystatin-C levels may be a more sensitive marker of early renal impairment than either albuminuria or serum creatinine concentration (17-20). Therefore, a better understanding of a putative role for cystatin-C in the etiology of prediabetes could shed light on the renal/heart disease connection (21). Given the reported superiority of cystatin C over conventional measures of renal function, we hypothesized that cystatin-C would predict progression to prediabetes independent of serum creatinine or estimated GFR. We also investigated the role of intervening mechanisms including hypertension, insulin resistance, endothelial dysfunction and inflammation

Automatic detection of DNS manipulations

The DNS is a fundamental service that has been repeatedly attacked and abused. DNS manipulation is a prominent case: Recursive DNS resolvers are deployed to explicitly return manipulated answers to users' queries. While DNS manipulation is used for legitimate reasons too (e.g., parental control), rogue DNS resolvers support malicious activities, such as malware and viruses, exposing users to phishing and content injection. We introduce REMeDy, a system that assists operators to identify the use of rogue DNS resolvers in their networks. REMeDy is a completely automatic and parameter-free system that evaluates the consistency of responses across the resolvers active in the network. It operates by passively analyzing DNS traffic and, as such, requires no active probing of third-party servers. REMeDy is able to detect resolvers that manipulate answers, including resolvers that affect unpopular domains. We validate REMeDy using large-scale DNS traces collected in ISP networks where more than 100 resolvers are regularly used by customers. REMeDy automatically identifies regular resolvers, and pinpoint manipulated responses. Among those, we identify both legitimate services that offer additional protection to clients, and resolvers under the control of malwares that steer traffic with likely malicious goals

Genome-wide linkage analysis of age at onset of alcohol dependence: a comparison between microsatellites and single-nucleotide polymorphisms

BACKGROUND: Using the dataset provided for Genetic Analysis Workshop 14 by the Collaborative Study on the Genetics of Alcoholism, we performed genome-wide linkage analysis of age at onset of alcoholism to compare the utility of microsatellites and single-nucleotide polymorphisms (SNPs) in genetic linkage study. METHODS: A multipoint nonparametric variance component linkage analysis method was applied to the survival distribution function obtained from semiparametric proportional hazards model of the age at onset phenotype of alcoholism. Three separate linkage analyses were carried out using 315 microsatellites, 2,467 and 9,467 SNPs, spanning the 22 autosomal chromosomes. RESULTS: Heritability of age at onset was estimated to be approximately 12% (p < 0.001). We observed weak correlation, both in trend and strength, of genome-wide linkage signals between microsatellites and SNPs. Results from SNPs revealed more and stronger linkage signals across the genome compared with those from microsatellites. The only suggestive evidence of linkage from microsatellites was on chromosome 1 (LOD of 1.43). Differences in map densities between the two sets of SNPs used in this study did not appear to confer an advantage in terms of strength of linkage signals. CONCLUSION: Our study provided support for better performance of dense SNP maps compared with the sparse mirosatellite maps currently available for linkage analysis of quantitative traits. This better performance could be attributable to precise definition and high map resolutions achievable with dense SNP maps, thus resulting in increased power to detect possible loci affecting given trait or disease

Depression and Body Mass Index, Differences by Education: Evidence from a Population-based Study of Adult Women in the U.S. Buffalo-Niagara Region

The relationship between obesity and depression is well described. However, the evidence linking depression and body mass index (BMI) across the broad range of body size is less consistent. We examined the association between depressive symptoms and BMI in a sample of adult women in the Buffalo-Niagara region between 1997 and 2001. Using logistic regression, we investigated whether increased weight status beyond normal-weight was associated with a higher prevalence of depressive symptoms, and if educational attainment modified the association between obesity and depression. There was a trend for increased weight status to be associated with higher depressive symptoms (obese II/III, OR 1.57, 95% CI 1.03–2.41), whereas higher education was associated with lower odds of depressive symptoms, in an adjusted model including BMI (more than 12 but less than 16 years, OR 0.70, 95% CI 0.49–0.98; 16 or more years of education, OR 0.61, 95% CI 0.40–0.93). The association of being obese I with depressive symptoms was different for more educated (OR 2.15, 95% CI 1.27–3.62) compared to less educated women (OR 0.90, 95% CI 0.50–1.62); the sample was larger for the more educated women and reached statistical significance. There were no differences in the association for obese II/III women in strata of education. There was evidence of risk-difference heterogeneity (0.88, 95% CI 0.84–0.93). In this population-based sample of women in western New York state, increased weight was negligibly associated with depressive symptoms. The association of being obese I with depressive symptoms was different for more compared to less educated women

What's my App?: ML-based classification of RTC applications

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89

Acrodermatitis chronica atrophicans of the face: a case report and a brief review of the literature.

Acrodermatitis chronica atrophicans is a rare late manifestation of tick-borne Borrelia burgdorferi infection, manifesting as inflammatory and atrophic lesions on acral skin.We describe the case of  a 73-year-old woman with skin changes progressed to marked atrophy on her left hand and an edematous inflammatory involvement of the face. The diagnosis of acrodermatitis chronica atrophicans was made on the basis of clinical appearance, serological and histopathological findings, and the lesional detection of B. burgdorferi-specific gene segments by polymerase chain reaction.This unusual case illustrates that acrodermatitis chronica atrophicans affects not only the extremities but also the face. The clinical and histological finding of the lesions occurring on acral skin showed a prominent atrophic appearance, while the ones occurring on the face showed a prominent inflammatory appearance..</p

Online Classification of RTC Traffic

Real-time communication (RTC) platforms have become increasingly popular in the last decade, together with the spread of broadband Internet access. They are nowadays a fundamental means for connecting people and supporting the economy, which relies more and more on forms of remote working. In this context, it is particularly important to act at the network level to ensure adequate Quality of Experience (QoE) to users, where proper traffic management policies are essential to prioritize RTC traffic. This, in turn, requires in-network devices to identify RTC streams and the type of content they carry. In this paper, we propose a machine learning-based application to classify, in real-time, the media streams generated by RTC applications encapsulated in Secure Real Time Protocol (SRTP) flows. Using carefully tuned features extracted from packet characteristics, we train a model to classify streams into an ample set of classes, including media type (audio/video), video quality and redundant streams. To validate our approach, we use traffic from more than 88 hours of multi-party meeting calls made using the Cisco Webex Teams application. We reach an overall accuracy of 97% with a light-weight decision tree model, which makes decisions using only 1 second of traffic

Use of supplementary phenotype to identify additional rheumatoid arthritis loci in a linkage analysis of 342 UK affected sibling pair families

<p>Abstract</p> <p>Background</p> <p>Although rheumatoid arthritis has been shown to have moderately strong genetic component, both linked loci identified in linkage analyses and susceptibility variants from association studies are short of adequately accounting for a comprehensive catalogue of the molecular factors underlying this complex disease. The objective of this study was to use supplementary phenotype based on cumulative hazard of rheumatoid arthritis to identify linkage evidence for new and additional rheumatoid arthritis loci in a genome-wide linkage analysis of 342 affected sibling pair families from the United Kingdom.</p> <p>Methods</p> <p>Using proportional hazards model, we estimated cumulative hazard of rheumatoid arthritis and then used it as a quantitative trait in a non-parametric multipoint variance component linkage analysis with 353 microsatellite markers distributed across the 22 autosomal chromosomes.</p> <p>Results</p> <p>We identified 3 new loci with genome-wide suggestive linkage evidence for rheumatoid arthritis on 9q21.13, 15p11.1 and 20q13.33. Our results also confirmed previously reported linkage evidence in the HLA-DRB1 region on chromosome 6 and on locus 1q32.1.</p> <p>Conclusion</p> <p>This study demonstrates the potential for information gain through the use of supplementary phenotypes in genetic study of complex diseases to identify new and additional potential linked loci that are not detected by linkage analysis of traditional phenotypes; and our results provide further evidence of the involvement of multiple loci in the genetic aetiology of rheumatoid arthritis.</p

The Mediterranean Diet in the Prevention of Degenerative Chronic Diseases

Degenerative chronic diseases are a problem related to the aging phenomenon of industrialized countries due to the increase of risk factors and related comorbidity such as overweight, obesity, metabolic syndrome, diabetes, hypertension and hyperlipidemia with a consequent increased risk of cardiovascular disease (CVD) and cancer. Moreover, the significant reduction of physical activity in daily life and the huge growth in food availability have considerably increased the risk of such diseases. Particular attention should be paid to primary prevention by means of health strategies based on improvement in lifestyle intervention such as implementation of Mediterranean diet and promotion of physical activity programs. In this chapter, the protective effect of Mediterranean diet and the role of certain foods and/or their constituents are analyzed; the possible mechanisms by which Mediterranean diet is effective in the prevention of cardiovascular and other chronic diseases are presented, in particular the effects exerted by antioxidants, polyphenols, fibers, unsaturated fatty acids, and alcohol. The genetic revolution in the past decades has produced new fields of study where the interaction between foods, nutrients, and our genetic makeup is investigated. The relationship between nutrigenetics and nutrigenomics and the Mediterranean diet are the future area that research should discover