Frequently hypercyclic semigroups

We study frequent hypercyclicity in the context of strongly continuous semigroups of operators. More precisely, we give a criterion (sufficient condition) for a semigroup to be frequently hypercyclic, whose formulation depends on the Pettis integral. This criterion can be verified in certain cases in terms of the infinitesimal generator of semigroup. Applications are given for semigroups generated by Ornstein-Uhlenbeck operators, and especially for translation semigroups on weighted spaces of $p$-integrable functions, or continuous functions that, multiplied by the weight, vanish at infinity

Data-driven curation, learning and analysis for inferring evolving IoT botnets in the wild

The insecurity of the Internet-of-Things (IoT) paradigm continues to wreak havoc in consumer and critical infrastructure realms. Several challenges impede addressing IoT security at large, including, the lack of IoT-centric data that can be collected, analyzed and correlated, due to the highly heterogeneous nature of such devices and their widespread deployments in Internet-wide environments. To this end, this paper explores macroscopic, passive empirical data to shed light on this evolving threat phenomena. This not only aims at classifying and inferring Internet-scale compromised IoT devices by solely observing such one-way network traffic, but also endeavors to uncover, track and report on orchestrated "in the wild" IoT botnets. Initially, to prepare the effective utilization of such data, a novel probabilistic model is designed and developed to cleanse such traffic from noise samples (i.e., misconfiguration traffic). Subsequently, several shallow and deep learning models are evaluated to ultimately design and develop a multi-window convolution neural network trained on active and passive measurements to accurately identify compromised IoT devices. Consequently, to infer orchestrated and unsolicited activities that have been generated by well-coordinated IoT botnets, hierarchical agglomerative clustering is deployed by scrutinizing a set of innovative and efficient network feature sets. By analyzing 3.6 TB of recent darknet traffic, the proposed approach uncovers a momentous 440,000 compromised IoT devices and generates evidence-based artifacts related to 350 IoT botnets. While some of these detected botnets refer to previously documented campaigns such as the Hide and Seek, Hajime and Fbot, other events illustrate evolving threats such as those with cryptojacking capabilities and those that are targeting industrial control system communication and control services

A multicenter study of Clostridium difficile infection-related colectomy, 2000-2006

BACKGROUND: The incidence of Clostridium difficile infection (CDI) has been increasing. Previous studies report that the number of colectomies for CDI is also rising. Outside of a few notable outbreaks, there are few published data documenting increasing severity of CDI. The specific aims of this multiyear, multicenter study were to assess CDI-related colectomy rates and compare CDI-related colectomy rates by CDI surveillance definition. METHODS: Cases of CDI and patients who underwent colectomy were identified electronically from 5 US tertiary-care centers from July 2000 through June 2006. Chart review was performed to determine if a colectomy was for CDI. Monthly CDI-related colectomy rates were calculated as the number of CDI-related colectomies per 1,000 CDI cases. Data between observational groups were compared using χ(2) and Mann-Whitney U tests. Logistic regression was performed to evaluate risk factors for CDI-related colectomy. RESULTS: 8569 cases of CDI were identified and 75 patients had CDI-related colectomy. The overall colectomy rate was 8.7/1,000 CDI cases. The CDI-related colectomy rate ranged from 0 to 23 per 1,000 CDI episodes across hospitals. The colectomy rates for healthcare facility (HCF)-onset CDI was 4.3/1000 CDI cases and 16.5 /1000 CDI cases for community-onset CDI (p <.05). There were significantly more CDI-related colectomies at hospitals B and C (p<.05). CONCLUSIONS: The overall CDI-related colectomy rate was low, and there was no significant change in the CDI-related colectomy rate over time. Onset of disease outside of the study hospital was an independent risk factor for colectomy

Multicenter study of the impact of community-onset Clostridium difficile infection on surveillance for C. difficile infection

OBJECTIVE: To evaluate the influence of community-onset/healthcare facility-associated cases on Clostridium difficile infection (CDI) incidence and outbreak detection. DESIGN: Retrospective cohort. SETTING: Five acute-care healthcare facilities in the United States. METHODS: Positive stool C. difficile toxin assays from July 2000 through June 2006 and healthcare facility exposure information were collected. CDI cases were classified as hospital-onset (HO) if they were diagnosed > 48 hours after admission or community-onset/healthcare facility-associated if they were diagnosed ≤ 48 hours from admission and had recently been discharged from the healthcare facility. Four surveillance definitions were compared: HO cases only and HO plus community-onset/healthcare facility-associated cases diagnosed within 30 (HCFA-30), 60 (HCFA-60) and 90 (HCFA-90) days after discharge from the study hospital. Monthly CDI rates were compared. Control charts were used to identify potential CDI outbreaks. RESULTS: The HCFA-30 rate was significantly higher than the HO rate at two healthcare facilities (p<0.01). The HCFA-30 rate was not significantly different from the HCFA-60 or HCFA-90 rates at any healthcare facility. The correlations between each healthcare facility’s monthly rates of HO and HCFA-30 CDI were almost perfect (range, 0.94–0.99, p<0.001). Overall, 12 time points had a CDI rate >3 SD above the mean, including 11 by the HO definition and 9 by the HCFA-30 definition, with discordant results at 4 time points (κ = 0.794, p<0.001). CONCLUSIONS: Tracking community-onset/healthcare facility-associated cases in addition to HO cases captures significantly more CDI cases but surveillance of HO CDI alone is sufficient to detect an outbreak

Bivariate genetic modelling of the response to an oral glucose tolerance challenge: A gene x environment interaction approach

AIMS/HYPOTHESIS: Twin and family studies have shown the importance of genetic factors influencing fasting and 2 h glucose and insulin levels. However, the genetics of the physiological response to a glucose load has not been thoroughly investigated. METHODS: We studied 580 monozygotic and 1,937 dizygotic British female twins from the Twins UK Registry. The effects of genetic and environmental factors on fasting and 2 h glucose and insulin levels were estimated using univariate genetic modelling. Bivariate model fitting was used to investigate the glucose and insulin responses to a glucose load, i.e. an OGTT. RESULTS: The genetic effect on fasting and 2 h glucose and insulin levels ranged between 40% and 56% after adjustment for age and BMI. Exposure to a glucose load resulted in the emergence of novel genetic effects on 2 h glucose independent of the fasting level, accounting for about 55% of its heritability. For 2 h insulin, the effect of the same genes that already influenced fasting insulin was amplified by about 30%. CONCLUSIONS/INTERPRETATION: Exposure to a glucose challenge uncovers new genetic variance for glucose and amplifies the effects of genes that already influence the fasting insulin level. Finding the genes acting on 2 h glucose independently of fasting glucose may offer new aetiological insight into the risk of cardiovascular events and death from all causes

Implementing automated surveillance for tracking Clostridium difficile infection at multiple healthcare facilities

Automated surveillance utilizing electronically available data has been found to be accurate and save time. An automated CDI surveillance algorithm was validated at four CDC Prevention Epicenters hospitals. Electronic surveillance was highly sensitive, specific, and showed good to excellent agreement for hospital-onset; community-onset, study facility associated; indeterminate; and recurrent CDI

Serum metabolites reflecting gut microbiome alpha diversity predict type 2 diabetes

Type 2 diabetes (T2D) is associated with reduced gut microbiome diversity, although the cause is unclear. Metabolites generated by gut microbes also appear to be causative factors in T2D. We therefore searched for serum metabolites predictive of gut microbiome diversity in 1018 females from TwinsUK with concurrent metabolomic profiling and microbiome composition. We generated a Microbial Metabolites Diversity (MMD) score of six circulating metabolites that explained over 18% of the variance in microbiome alpha diversity. Moreover, the MMD score was associated with a significantly lower odds of prevalent (OR[95%CI] = 0.22[0.07;0.70], P = .01) and incident T2D (HR[95%CI] = 0.31[0.11,0.90], P = .03). We replicated our results in 1522 individuals from the ARIC study (prevalent T2D: OR[95%CI] = 0.79[0.64,0.96], P = .02, incident T2D: HR[95%CI] = 0.87[0.79,0.95], P = .003). The MMD score mediated 28%[15%,94%] of the total effect of gut microbiome on T2D after adjusting for confounders. Metabolites predicting higher microbiome diversity included 3-phenylpropionate(hydrocinnamate), indolepropionate, cinnamoylglycine and 5-alpha-pregnan-3beta,20 alpha-diol monosulfate(2) of which indolepropionate and phenylpropionate have already been linked to lower incidence of T2D. Metabolites correlating with lower microbial diversity included glutarate and imidazole propionate, of which the latter has been implicated in insulin resistance. Our results suggest that the effect of gut microbiome diversity on T2D is largely mediated by microbial metabolites, which might be modifiable by diet