Introduction

MEXT-Supported Program for the Strategic Research Foundation at Private Universities (2014-2018)Forming a Social Well-being Research Consortium in Asi

The optimal input-independent baseline for binary classification: The Dutch Draw

Before any binary classification model is taken into practice, it is important to validate its performance on a proper test set. Without a frame of reference given by a baseline method, it is impossible to determine if a score is “good” or “bad.” The goal of this paper is to examine all baseline methods that are independent of feature values and determine which model is the “best” and why. By identifying which baseline models are optimal, a crucial selection decision in the evaluation process is simplified. We prove that the recently proposed Dutch Draw baseline is the best input-independent classifier (independent of feature values) for all order-invariant measures (independent of sequence order) assuming that the samples are randomly shuffled. This means that the Dutch Draw baseline is the optimal baseline under these intuitive requirements and should therefore be used in practice

Detecting novel application layer cybervariants using supervised learning

Cyberdefense mechanisms such as Network Intrusion Detection Systems predominantly use signature-based approaches to effectively detect known malicious activities in network traffic. Unfortunately, constructing a database with signatures is very time-consuming and this approach can only find previously seen variants. Machine learning algorithms are known to be effective software tools in detecting known or unrelated novel intrusions, but if they are also able to detect unseen variants has not been studied. In this research, we study to what extent binary classification models are accurately able to detect novel variants of application layer targeted cyberattacks. To be more precise, we focus on detecting two types of intrusion variants, namely (Distributed) Denial-of-Service and Web attacks, targeting the Hypertext Transfer Protocol of a web server. We mathematically describe how two selected datasets are adjusted in three different experimental setups and the results of the classification models deployed in these setups are benchmarked using the Dutch Draw baseline method. The contributions of this research are as follows: we provide a procedure to create intrusion detection datasets combining information from the transport, network, and application layer to be directly used for machine learning purposes. We show that specific variants are successfully detected by these classification models trained to distinguish benign interactions from those of another variant. Despite this result, we demonstrate that the performances of the selected classifiers are not symmetric: the test score of a classifier trained on A and tested on B is not necessarily similar to the score of a classifier trained on B and tested on A. At last, we show that increasing the number of different variants in the training set does not necessarily lead to a higher detection rate of unseen variants. Selecting the right combination of a machine learning model with a (small) set of known intrusions included in the training data can result in a higher novel intrusion detection rate

Detecting Novel Variants of Application Layer (D)DoS Attacks using Supervised Learning

Denial of Service (DoS) attacks and their distributed variant (DDoS) are major digital threats in today’s cyberspace. Defense mechanisms such as Intrusion Detection Systems aim at finding these and other malicious activities in network traffic. They predominantly use signature-based approaches to effectively detect intrusions. Unfortunately, constructing a database with signatures is very time-consuming and this approach can only find previously seen variants. Machine learning algorithms are known to be effective tools in detecting intrusions, but it has not been studied if they are also able to detect unseen variants. In this research, we study to what extent supervised learning algorithms are able to detect novel variants of application layer (D)DoS attacks. To be more precise, we focus on detecting HTTP attacks targeting a web server. The contributions of this research are as follows: we provide a procedure to create intrusion detection datasets combining information from the transport, network, and application layer to be directly used for machine learning purposes. We show that specific (D)DoS variants are successfully detected by binary classifiers learned to distinguish benign entries from another (D)DoS attack. Despite this result, we demonstrate that the performance of a classifier trained on detecting variant A and tested on finding variant B is not necessarily similar to its performance when trained on B and tested on A. At last, we show that using more types of (D)DoS attacks in the training set does not necessarily lead to a higher detection rate of unseen variants. Thus, selecting the right combination of a machine learning model with a (small) set of intrusions included in the training data can result in a higher novel intrusion detection rate

Parotid Gland Stem Cell Sparing Radiation Therapy for Patients With Head and Neck Cancer:A Double-Blind Randomized Controlled Trial

BACKGROUND: Radiotherapy for head and neck cancer (HNC) frequently leads to salivary gland damage and subsequent xerostomia. The radiation response of parotid glands of rats, mice, and patients critically depends on dose to its stem cells, mainly located in the gland's main ducts (stem cell rich (SCR) region). Therefore, this double-blind randomized controlled trial aimed to test the hypothesis that parotid gland stem cell sparing radiotherapy preserves parotid gland function better than currently-used whole parotid gland sparing radiotherapy. METHODS: HNC patients (n=102) treated with definitive radiotherapy were randomized between standard parotid sparing and stem cell sparing (SCS) techniques. The primary endpoint was >75% reduction in parotid gland saliva production compared to pretreatment production (FLOW12M). Secondary endpoints were several aspects of xerostomia 12 months after treatment. RESULTS: Fifty-four patients were assigned to the standard arm and 48 to the SCS arm. Only dose to the SCR regions (contralateral 16 and 11 Gy (p=0.004) and ipsilateral 26 and 16 Gy (p=0.001), standard and SCS arm respectively) and pretreatment patient-rated daytime xerostomia (35% and 13% (p=0.01), standard and SCS arm respectively) differed significantly between the arms. In the SCS arm, 1 patient (2.8%) experienced FLOW12M compared to 2 (4.9%) in the standard arm (p=1.00). However, a trend towards better relative parotid gland salivary function in favor of SCS radiotherapy was shown. Moreover, multivariable analysis showed that mean contralateral SCR region dose was the strongest dosimetric predictor for moderate-to-severe patient-rated daytime xerostomia and grade ≥2 physician-rated xerostomia, the latter including complaints of alteration in diet. CONCLUSIONS: No significant better parotid function was observed in SCS radiotherapy. However, additional multivariable analysis showed that dose to the SCR region was more predictive for development of parotid gland function related xerostomia endpoints, than dose to the entire parotid gland

Myocardial Work, an Echocardiographic Measure of Post Myocardial Infarct Scar on Contrast-Enhanced Cardiac Magnetic Resonance

This study investigates the relation of non-invasive myocardial work and myocardial viability following ST-segment elevation myocardial infarction (STEMI) assessed on late gadolinium contrast enhanced cardiac magnetic resonance (LGE CMR) and characterizes the remote zone using non-invasive myocardial work parameters. STEMI patients who underwent primary percutaneous coronary intervention (PCI) were included. Several non-invasive myocardial work parameters were derived from speckle tracking strain echocardiography and sphygmomanometric blood pressure, e.g.: myocardial work index (MWI), constructive work (CW), wasted work (WW) and myocardial work efficiency (MWE). LGE was quantified to determine infarct transmurality and scar burden. The core zone was defined as the segment with the largest extent of transmural LGE and the remote zone as the diametrically opposed segment without LGE. A total of 53 patients (89% male, mean age 58 ± 9 years) and 689 segments were analyzed. The mean scar burden was 14 ± 7% of the total LV mass, and 76 segments (11%) demonstrated transmural hyperenhancement, 280 (41%) non-transmural hyperenhancement and 333 (48%) no LGE. An inverse relation was observed between segmental MWI, CW and MWE and infarct transmurality (p < 0.05). MWI, CW and MWE were significantly lower in the core zone compared to the remote zone (p<0.05). In conclusion, non-invasive myocardial work parameters may serve as potential markers of segmental myocardial viability in post-STEMI patients who underwent primary PCI. Non-invasive myocardial work can also be utilized to characterize the remote zone, which is an emerging prognostic marker as well as a therapeutic target

Gender and age differences in the recurrence of sickness absence due to common mental disorders: a longitudinal study

Background: Common mental disorders (CMDs) are an important cause of sickness absence and long-term work disability. Although CMDs are known to have high recurrence rates, little is known about the recurrence of sickness absence due to CMDs. The aim of this study was to investigate the recurrence of sickness absence due to CMDs, including distress, adjustment disorders, depressive disorders and anxiety disorders, according to age, in male and female employees in the Netherlands. Methods: Data on sickness absence episodes due to CMDs were obtained for 137,172 employees working in the Dutch Post and Telecommunication companies between 2001 and 2007. The incidence density (ID) and recurrence density (RD) of sickness absence due to CMDs was calculated per 1000 person-years in men and women in the age-groups of < 35 years, 35-44 years, 45-54 years, and >= 55 years. Results: The ID of one episode of CMDs sickness absence was 25.0 per 1000 person-years, and the RD was 76.7 per 1000 person-years. Sickness absence due to psychiatric disorders (anxiety and depression) does not have a higher recurrence density of sickness absence due to any CMDs as compared to stress-related disorders (distress and adjustment disorders): 81.6 versus 76.0 per 1000 person-years. The ID of sickness absence due to CMDs was higher in women than in men, but the RD was similar. Recurrences were more frequent in women < 35 years and in women between 35 and 44 years of age. We observed no differences between age groups in men. Recurrences among employees with recurrent episodes occurred within 3 years in 90% of cases and the median time-to-onset of recurrence was 11 (10-13) months in men and 10 (9-12) months in women. Conclusions: Employees who have been absent from work due to CMDs are at increased risk of recurrent sickness absence due to CMDs and should be monitored after they return to work. The RD was similar in men and in women. In women < 45 years the RD was higher than in women >= 45 years. In men no age differences were observed

The epidemiology of major depressive episodes: results from the International Consortium of Psychiatric Epidemiology (ICPE) surveys

Absence of a common diagnostic interview has hampered cross-national syntheses of epidemiological evidence on major depressive episodes (MDE). Community epidemiological surveys using the World Health Organization Composite International Diagnostic Interview administered face-to-face were carried out in 10 countries in North America (Canada and the US), Latin America (Brazil, Chile, and Mexico), Europe (Czech Republic, Germany, the Netherlands, and Turkey), and Asia (Japan). The total sample size was more than 37,000. Lifetime prevalence estimates of hierarchy-free DSM-III-R/DSM-IV MDE varied widely, from 3% in Japan to 16.9% in the US, with the majority in the range of 8% to 12%. The 12-month/lifetime prevalence ratio was in the range 40% to 55%, the 30-day/12-month prevalence ratio in the range 45% to 65%, and median age of onset in the range 20 to 25 in most countries. Consistent socio-demographic correlates included being female and unmarried. Respondents in recent cohorts reported higher lifetime prevalence, but lower persistence than those in earlier cohorts. Major depressive episodes were found to be strongly co-morbid with, and temporally secondary to, anxiety disorders in all countries, with primary panic and generalized anxiety disorders the most powerful predictors of the first onset of secondary MDE. Major depressive episodes are a commonly occurring disorder that usually has a chronic-intermittent course. Effectiveness trials are needed to evaluate the impact of early detection and treatment on the course of MDE as well as to evaluate whether timely treatment of primary anxiety disorders would reduce the subsequent onset, persistence, and severity of secondary MDE. Copyright © 2003 Whurr Publishers Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/34221/1/138_ftp.pd

Prevalence and age of onset for drug use in seven international sites: Results from the international consortium of psychiatric epidemiology

This study compares lifetime prevalence and age of first use (onset) for alcohol, cannabis, and other drugs in six international sites. Data from seven epidemiologic field surveys that used compatible instruments and study designs were compiled for cross-site analyses by the International Consortium of Psychiatric Epidemiology (ICPE). The world health organization’s composite international diagnostic instrument (WHO-CIDI) and additional items were used to ascertain drug use in each site. Lifetime use rates were estimated for alcohol, cannabis, and other illicit drugs. Survival analyses were used to estimate age of onset. Study settings and main results: use of alcohol twelve or more times ranged in descending order from the Netherlands (86.3%), United States (71.7%), Ontario, Canada (71.6%); São Paulo, Brazil (66.1%), Munich, Germany (64.9%), Fresno, California (USA) (51.9%), to Mexico City (43.2%). Use of cannabis five or more times in a lifetime ranged from 28.8 in the United States to 1.7% in Mexico City, and other drugs ranged from United States (19.4%) to Mexico City (1.7%). Age of first use was similar across study sites. This study demonstrates the fundamental uniformity of onset patterns by age as contrasted with wide variations in lifetime prevalences across sites. Study findings suggest that drug use patterns may change among emigrating populations from low consumption nations as a consequence of international resettlement in nations with higher rates. Methodological limitations of the study along with recommendations for future international comparative research are discussed