xPF: Packet Filtering for Low-Cost Network Monitoring

The ever-increasing complexity in network infrastructures is making critical the demand for network monitoring tools. While the majority of network operators rely on low-cost open-source tools based on commodity hardware and operating systems, the increasing link speeds and complexity of network monitoring applications have revealed inefficiencies in the existing software organization, which may prohibit the use of such tools in high-speed networks. Although several new architectures have been proposed to address these problems, they require significant effort in re-engineering the existing body of applications. We present an alternative approach that addresses the primary sources of inefficiency without significantly altering the software structure. Specifically, we enhance the computational model of the Berkeley packet filter (BPF) to move much of the processing associated with monitoring into the kernel, thereby removing the overhead associated with context switching between kernel and applications. The resulting packet filter, called xPF, allows new tools to be more efficiently implemented and existing tools to be easily optimized for high-speed networks. We present the design and implementation of xPF as well as several example applications that demonstrate the efficiency of our approach

Pattern-driven security, privacy, dependability and interoperability management of iot environments

Achieving Security, Privacy, Dependability and Interoperability (SPDI) is of paramount importance for the ubiquitous deployment and impact maximization of Internet of Things (IoT) applications. Nevertheless, said requirements are not only difficult to achieve at system initialization, but also hard to prove and maintain at run-time. This paper highlights an approach to tackling the above challenges, through the definition of pattern language and a framework that can guarantee SPDI in IoT orchestrations. By integrating pattern reasoning engines at the various layers of the IoT infrastructure, and a machine-processable representation of said pattern through Drools rules, the proposed framework can provide ways to fulfill SPDI requirements at design time, and also provide the means to guarantee those SPDI properties and manage the orchestrations accordingly. Moreover, an application example of the framework is presented in an Industrial IoT monitoring environment

The impact of comorbid impulsive/compulsive disorders on problematic internet use

Background and Aims: Problematic Internet Use (PIU) is commonplace but is not yet recognized as a formal mental disorder. Excessive Internet use could result from other conditions such as gambling disorder. The aim of the study was to assess the impact of impulsive-compulsive comorbidities on the presentation of PIU, defined using Young’s Diagnostic Questionnaire. Methods: 123 adults aged 18-29 years were recruited using media advertisements, and attended the research center for a detailed psychiatric assessment, including interviews, completion of questionnaires and neuropsychological testing. Participants were classified into three groups: PIU with no comorbid impulsive/compulsive disorders (n=18), PIU with one or more comorbid impulsive/compulsive disorders (n=37), and healthy controls (n=67). Differences between the three groups were characterized in terms of demographic, clinical, and cognitive variables. Effect sizes for overall effects of group were also reported. Results: The three groups did not differ significantly on age, gender, levels of education, nicotine consumption, or alcohol use (small effect sizes). Quality of life was significantly impaired in PIU irrespective of whether or not individuals had comorbid impulsive/compulsive disorders (large effect size). However, impaired response inhibition and decision-making were only identified in PIU with impulsive/compulsive comorbidities (medium effect sizes)

Selection in Reported Epidemiological Risks: An Empirical Assessment

BACKGROUND: Epidemiological studies may be subject to selective reporting, but empirical evidence thereof is limited. We empirically evaluated the extent of selection of significant results and large effect sizes in a large sample of recent articles. METHODS AND FINDINGS: We evaluated 389 articles of epidemiological studies that reported, in their respective abstracts, at least one relative risk for a continuous risk factor in contrasts based on median, tertile, quartile, or quintile categorizations. We examined the proportion and correlates of reporting statistically significant and nonsignificant results in the abstract and whether the magnitude of the relative risks presented (coined to be consistently ≥1.00) differs depending on the type of contrast used for the risk factor. In 342 articles (87.9%), ≥1 statistically significant relative risk was reported in the abstract, while only 169 articles (43.4%) reported ≥1 statistically nonsignificant relative risk in the abstract. Reporting of statistically significant results was more common with structured abstracts, and was less common in US-based studies and in cancer outcomes. Among 50 randomly selected articles in which the full text was examined, a median of nine (interquartile range 5–16) statistically significant and six (interquartile range 3–16) statistically nonsignificant relative risks were presented (p = 0.25). Paradoxically, the smallest presented relative risks were based on the contrasts of extreme quintiles; on average, the relative risk magnitude was 1.41-, 1.42-, and 1.36-fold larger in contrasts of extreme quartiles, extreme tertiles, and above-versus-below median values, respectively (p < 0.001). CONCLUSIONS: Published epidemiological investigations almost universally highlight significant associations between risk factors and outcomes. For continuous risk factors, investigators selectively present contrasts between more extreme groups, when relative risks are inherently lower

Local Literature Bias in Genetic Epidemiology: An Empirical Evaluation of the Chinese Literature

BACKGROUND: Postulated epidemiological associations are subject to several biases. We evaluated whether the Chinese literature on human genome epidemiology may offer insights on the operation of selective reporting and language biases. METHODS AND FINDINGS: We targeted 13 gene-disease associations, each already assessed by meta-analyses, including at least 15 non-Chinese studies. We searched the Chinese Journal Full-Text Database for additional Chinese studies on the same topics. We identified 161 Chinese studies on 12 of these gene-disease associations; only 20 were PubMed-indexed (seven English full-text). Many studies (14–35 per topic) were available for six topics, covering diseases common in China. With one exception, the first Chinese study appeared with a time lag (2–21 y) after the first non-Chinese study on the topic. Chinese studies showed significantly more prominent genetic effects than non-Chinese studies, and 48% were statistically significant per se, despite their smaller sample size (median sample size 146 versus 268, p < 0.001). The largest genetic effects were often seen in PubMed-indexed Chinese studies (65% statistically significant per se). Non-Chinese studies of Asian-descent populations (27% significant per se) also tended to show somewhat more prominent genetic effects than studies of non-Asian descent (17% significant per se). CONCLUSION: Our data provide evidence for the interplay of selective reporting and language biases in human genome epidemiology. These biases may not be limited to the Chinese literature and point to the need for a global, transparent, comprehensive outlook in molecular population genetics and epidemiologic studies in general

Investigating the value of radiomics stemming from DSC quantitative biomarkers in IDH mutation prediction in gliomas

Objective: This study aims to assess the value of biomarker based radiomics to predict IDH mutation in gliomas. The patient cohort consists of 160 patients histopathologicaly proven of primary glioma (WHO grades 2–4) from 3 different centers. Methods: To quantify the DSC perfusion signal two different mathematical modeling methods were used (Gamma fitting, leakage correction algorithms) considering the assumptions about the compartments contributing in the blood flow between the extra- and intra vascular space. Results: The Mean slope of increase (MSI) and the K1 parameter of the bidirectional exchange model exhibited the highest performance with (ACC 74.3% AUROC 74.2%) and (ACC 75% AUROC 70.5%) respectively. Conclusion: The proposed framework on DSC-MRI radiogenomics in gliomas has the potential of becoming a reliable diagnostic support tool exploiting the mathematical modeling of the DSC signal to characterize IDH mutation status through a more reproducible and standardized signal analysis scheme for facilitating clinical translation

An accessory middle scalene muscle causing thoracic outlet syndrome

The aim of our study is to present a very rare accessory middle scalene muscle, leading to thoracic outlet syndrome. In particular, a muscular bundle was discovered on a male cadaver connecting the middle portion of the middle scalene muscle with the anterior scalene muscle insertion to Lisfranc`s tubercle. This triangular accessory muscle and, especially, its sharp medial border compressed the middle and lower trunk of the brachial plexus and the subclavian artery. This anomaly is of great importance because it emphasises the fact that it is not primarily the anterior scalene muscle that produces symptoms of thoracic outlet syndrome but the anterior displacement of the middle scalene muscle or its accessory muscular bands. We also present the relative international literature and the clinical significance of our finding

Quantifying Selective Reporting and the Proteus Phenomenon for Multiple Datasets with Similar Bias

Meta-analyses play an important role in synthesizing evidence from diverse studies and datasets that address similar questions. A major obstacle for meta-analyses arises from biases in reporting. In particular, it is speculated that findings which do not achieve formal statistical significance are less likely reported than statistically significant findings. Moreover, the patterns of bias can be complex and may also depend on the timing of the research results and their relationship with previously published work. In this paper, we present an approach that is specifically designed to analyze large-scale datasets on published results. Such datasets are currently emerging in diverse research fields, particularly in molecular medicine. We use our approach to investigate a dataset on Alzheimer's disease (AD) that covers 1167 results from case-control studies on 102 genetic markers. We observe that initial studies on a genetic marker tend to be substantially more biased than subsequent replications. The chances for initial, statistically non-significant results to be published are estimated to be about 44% (95% CI, 32% to 63%) relative to statistically significant results, while statistically non-significant replications have almost the same chance to be published as statistically significant replications (84%; 95% CI, 66% to 107%). Early replications tend to be biased against initial findings, an observation previously termed Proteus phenomenon: The chances for non-significant studies going in the same direction as the initial result are estimated to be lower than the chances for non-significant studies opposing the initial result (73%; 95% CI, 55% to 96%). Such dynamic patters in bias are difficult to capture by conventional methods, where typically simple publication bias is assumed to operate. Our approach captures and corrects for complex dynamic patterns of bias, and thereby helps generating conclusions from published results that are more robust against the presence of different coexisting types of selective reporting

Accessory muscles around the superior radioulnar joint: a morphological study

During anatomical dissections on 36 human elbow joints, we observed the presence of three muscles around the superior radioulnar joint usually neglected by the classical anatomical literature, the lateral tensor muscle of the annular ligament (with an incidence of 16.6%), the medial tensor muscle of the annular ligament (11.1%) and the accessory supinator muscle (16.6%). We could not establish any association between the subject handedness and the sidedness of these aberrant muscles, neither we found a predominance in sidedness or in gender. Furthermore, we did not notice any significant predominance related to the occupation and specifically to heavy labor occupations. On the contrary we found these muscles to be more common in non-laborers, which supports the assumption that these variants constitute rather genetic than epigenetic traits. The knowledge of such anatomical variants facilitate the surgeon operating in the elbow region to better interpret supernumerary muscular bundles in the operative field