Question: A community-based cross-sectional study on the association between electronic media usage and adolescent health and well-being.

This study comprehensively examines the impact of violent video games on various aspects of behavior in children and young adults, including aggressive behavior, cognition, affect, physiological arousal, and prosocial behavior. The findings, derived from a meta-analysis of existing scientific literature, consistently indicate that playing violent video games is associated with an increase in aggressive behavior. This conclusion is supported by both experimental and non-experimental studies conducted in diverse settings with participants of both genders. The analysis further reveals that exposure to violent video games not only heightens physiological arousal and intensifies aggression-related thoughts and feelings but also leads to a reduction in prosocial behavior, highlighting the multifaceted effects of such media on young individuals.

There is a general consensus that Internet addiction (IA) is mainly related to social aspects of the Web, especially among adolescents. The empirical link between poor social skills and IA is well documented; however, theoretical explanations for this relationship are less developed. One possibility is that people with poor social skills are especially prone to develop a preference for online social interaction (POSI), which, in turn, predicts problematic usage. This hypothesis has been tested for loneliness and social anxiety, but not for self-esteem (SE; one of the main antecedents of IA). Furthermore, the mediating role of the perceived relevance of some Internet features (e.g., anonymity) in the relationship between SE and POSI has never been investigated. A cross-sectional study was conducted with 257 adolescents. Using mediation analyses, we found evidence among females for the mediating role of (a) POSI in the relationship between SE and IA, and (b) the subjective relevance of some Internet features in the association between SE and POSI. No significant effects were found for males.

In recentyears, electronicgames have assumed an importantplace in the lives of children and adolescents. Children acquire digital literacy informally, through plaj and neither schools nor other educational institutions take suficient account of this important aspect. We consider that multimedia design for training and education should combine the mostpowerfulfedtures of interactive multimedia design with the most effective principles of technologically-mediated learning. An examination of the evolution of the design of videogames is a good way to analyze the main contributions and characteristics ofgames-ba~edlearnin~ environments. At the same time, we will discuss the main obstacles and challenges to the use ofgames for learning. (

Using social media during work hours for non-work-related reasons is becoming commonplace. Organizations are therefore challenged with identifying and overcoming the consequences of such use. Social media-induced technostress has been identified as an important unintended consequence of using social media at work, as it could negatively impact job performance. This study draws on Person-Environment Fit to investigate the relationship between social media-induced technostress and job performance in IT professionals, and the moderating effect of job characteristics on this relationship. The results indicate that social media-induced technostress is negatively related to job performance and the negative impact of social media-induced technostress is intensified when the job characteristics are low. This work extends the literature on job-stress, social media, technostress, and job characteristics.

This article reviews recently published research about consumers in digital and social media marketing settings. Five themes are identified: (i) consumer digital culture, (ii) responses to digital advertising, (iii) effects of digital environments on consumer behavior, (iv) mobile environments, and (v) online word of mouth (WOM). Collectively these articles shed light from many different angles on how consumers experience, influence, and are influenced by the digital environments in which they are situated as part of their daily lives. Much is still to be understood, and existing knowledge tends to be disproportionately focused on WOM, which is only part of the digital consumer experience. Several directions for future research are advanced to encourage researchers to consider a broader range of phenomena.

BACKGROUND
The practice of joint physical custody, where children spend equal time in each parent's home after they separate, is increasing in many countries. It is particularly common in Sweden, where this custody arrangement applies to 30 per cent of children with separated parents. The aim of this study was to examine children's health-related quality of life after parental separation, by comparing children living with both parents in nuclear families to those living in joint physical custody and other forms of domestic arrangements.

METHODS
Data from a national Swedish classroom study of 164,580 children aged 12 and 15-years-old were analysed by two-level linear regression modelling. Z-scores were used to equalise scales for ten dimensions of wellbeing from the KIDSCREEN-52 and the KIDSCREEN-10 Index and analysed for children in joint physical custody in comparison with children living in nuclear families and mostly or only with one parent.

RESULTS
Living in a nuclear family was positively associated with almost all aspects of wellbeing in comparison to children with separated parents. Children in joint physical custody experienced more positive outcomes, in terms of subjective wellbeing, family life and peer relations, than children living mostly or only with one parent. For the 12-year-olds, beta coefficients for moods and emotions ranged from -0.20 to -0.33 and peer relations from -0.11 to -0.20 for children in joint physical custody and living mostly or only with one parent. The corresponding estimates for the 15-year-olds varied from -0.08 to -0.28 and from -0.03 to -0.13 on these subscales. The 15-year-olds in joint physical custody were more likely than the 12-year-olds to report similar wellbeing levels on most outcomes to the children in nuclear families.

CONCLUSIONS
Children who spent equal time living with both parents after a separation reported better wellbeing than children in predominantly single parent care. This was particularly true for the 15-year-olds, while the reported wellbeing of 12-years-olds was less satisfactory. There is a need for further studies that can account for the pre and post separation context of individual families and the wellbeing of younger age groups in joint physical custody.

(1) To compare the prevalence of bullying and victimization among boys and girls and by age in 40 countries. (2) In 6 countries, to compare rates of direct physical, direct verbal, and indirect bullying by gender, age, and country. Cross-sectional self-report surveys including items on bullying and being bullied were obtained from nationally representative samples of 11, 13 and 15 year old school children in 40 countries, N = 202,056. Six countries (N = 29,127 students) included questions about specific types of bullying (e. g., direct physical, direct verbal, indirect). Exposure to bullying varied across countries, with estimates ranging from 8.6% to 45.2% among boys, and from 4.8% to 35.8% among girls. Adolescents in Baltic countries reported higher rates of bullying and victimization, whereas northern European countries reported the lowest prevalence. Boys reported higher rates of bullying in all countries. Rates of victimization were higher for girls in 29 of 40 countries. Rates of victimization decreased by age in 30 of 40 (boys) and 25 of 39 (girls) countries. There are lessons to be learned from the current research conducted in countries where the prevalence is low that could be adapted for use in countries with higher prevalence.

BACKGROUND
The Internet is a primary source of health information for many. Since the widespread adoption of social media, user-generated health-related content has proliferated, particularly around long-term health issues such as diabetes and common mental health disorders (CMHDs).

OBJECTIVE
To explore perceptions and experiences of engaging with health information online in a sample of young adults familiar with social media environments and variously engaged in consuming user-generated content.

METHODS
Forty semi-structured interviews were conducted with young adults, aged 18-30, with experience of diabetes or CMHDs. Data were analysed following a thematic networks approach to explore key themes around online information-seeking and content consumption practices.

RESULTS
Although participants primarily discussed well-rehearsed approaches to health information-seeking online, particularly reliance on search engines, their accounts also reflected active engagement with health-related content on social media sites. Navigating between professionally produced websites and user-generated content, many of the young adults seemed to appreciate different forms of health knowledge emanating from varied sources. Participants described negotiating health content based on social media practices and features and assessing content heuristically. Some also discussed habitual consumption of content related to their condition as integrated into their everyday social media use.

CONCLUSION
Technologies such as Facebook, Twitter and YouTube offer opportunities to consume and assess content which users deem relevant and useful. As users and organizations continue to colonize social media platforms, opportunities are increasing for health communication and intervention. However, how such innovations are adopted is dependent on their alignment with users' expectations and consumption practices.

Psychological predictors of young adults': use of social networking sites. Acknowledgements: The authors would like to thank Shari Walsh for assistance in the design of the study and Eric Livingston for assistance in data collection. Abstract Young people are increasingly using social networking sites (SNSs), like Myspace and Facebook, to engage with others. The use of SNSs can have both positive and negative effects on the individual; however, few research studies identify the types of people who frequent these Internet sites. This study sought to predict young adults' use of SNSs and addictive tendency towards the use of SNSs from their personality characteristics and levels of self-esteem. University students (N = 201), aged 17 to 24 years, reported their use of SNSs and addictive tendencies for SNSs use, and completed the NEO Five-Factor Personality Inventory 1 and the Coopersmith Self-Esteem Inventory. 2 Multiple regression analyses revealed that, as a group, the personality and self-esteem factors significantly predicted both level of SNS use and addictive tendency but did not explain a large amount of variance in either outcome measure. The findings indicated that extraverted and unconscientious individuals reported higher levels of both SNS use and addictive tendencies. Future research should attempt to identify which other psychosocial characteristics explain young people's level of use and propensity for addictive tendencies for these popular Internet sites. Social networking site use 3 The proliferation of social networking sites (SNSs) has created a phenomenon that engages millions of Internet users around the world, especially young people. Given the popularity of these sites and their importance in young people's lives to facilitate communication and relationships, it is important to understand the factors influencing SNS use, especially at higher levels, and to identify those who may be prone to developing addictive tendencies towards new communication technologies. 5 As with other communication technologies, 6,7 a useful starting point may be to examine the role of personality traits and self-esteem on young people's SNS use. Researchers have confirmed repeatedly that the five-factor model of personality adequately accounts for and explains personality by taking the approach that personality consists of five traits; openness to experience (pursuing and appreciating all types of experience), conscientiousness (control, regulation and direction of goals and impulses), extraversion (amount and intensity of interpersonal interactions), agreeableness (the type of interactions a person prefers to have with others), and neuroticism (degree of emotional adjustment and instability). 8 Self esteem …

OBJECTIVE
To investigate the factors associated with exposure to in-real-life (IRL) bullying, cyberbullying, and both IRL and cyberbullying and to explore the relationship between these types of bullying and mental health among 13-16-year-old Swedish boys and girls.

METHODS
Data was derived from a cross-sectional web-based study of 13-16-year-old students in northern Sweden (n=1214, response rate 81.9%).

RESULTS
The combination of IRL- and cyberbullying was the most common type of bullying. A non-supportive school environment and poor body image were related to exposure to bullying for both genders but the relationship was more distinct in girls. All types of bullying were associated with depressive symptoms in both boys and girls and all forms of bullying increased the likelihood of psychosomatic problems in girls.

CONCLUSIONS
Cyberbullying can be seen as an extension of IRL bullying. A combination of IRL- and cyberbullying seems to be particularly negative for mental health. Interventions should focus on improved school environment and body image as well as anti-violence programmes. Gender aspects of bullying need to be acknowledged.

It has been noted that adolescents may be more susceptible to pathological gambling. Not only is it usually illegal, but it appears to be related to high levels of problem gambling and other delinquent activities such as illicit drug taking and alcohol abuse. This paper examines risk factors not only in adolescent gambling but also in videogame playing (which shares many similarities with gambling). There appear to be three main forms of adolescent gambling that have been widely researched. Adolescent gambling activities and general risk factors in adolescent gambling are provided. As well, the influence of technology on adolescents in the form of both videogames and the Internet are examined. It is argued that technologically advanced forms of gambling may be highly appealing to adolescents.


Question: A community-based cross-sectional study on the impact of electronic media usage on adolescent health and well-being.

Creation of a Youth Physical Activity Compendium for Health and Well-being Assessment
BACKGROUND
This paper introduces a comprehensive Compendium of Energy Expenditures tailored specifically for youth, aimed at facilitating the scoring of physical activity questionnaires and the estimation of energy expenditure levels in young individuals.

METHOD/RESULTS
Inspired by the adult Compendium of Physical Activities, this youth-focused compendium features over 200 activities typically engaged in by young people, along with their corresponding MET intensity values. A thorough review of existing literature on energy costs associated with youth activities was conducted and integrated into the compendium. Approximately 35% of the MET levels were directly derived from empirical data collected from young participants, while the remaining estimates were extrapolated from the adult compendium.

CONCLUSION
The Youth Physical Activity Compendium serves as a valuable resource for researchers and professionals seeking to quantify physical activity and energy expenditure in children and adolescents across various settings, enhancing our understanding of their health and well-being.

The past decades have witnessed a dramatic increase in the number of youths using the Internet, especially for communicating with peers. Online activity can widen and strengthen the social networks of adolescents and emerging adults (Subrahmanyam & Smahel, 2011), but it also increases the risk of Internet addiction. Using a framework derived from Griffiths (2000a), this study examined associations between online friendship and Internet addiction in a representative sample (n = 394) of Czech youths ages 12-26 years (M = 18.58). Three different approaches to friendship were identified: exclusively offline, face-to-face oriented, Internet oriented, on the basis of the relative percentages of online and offline associates in participants' friendship networks. The rate of Internet addiction did not differ by age or gender but was associated with communication styles, hours spent online, and friendship approaches. The study revealed that effects between Internet addiction and approaches to friendship may be reciprocal: Being oriented toward having more online friends, preferring online communication, and spending more time online were related to increased risk of Internet addiction; on the other hand, there is an alternative causal explanation that Internet addiction and preference for online communication conditions young people's tendency to seek friendship from people met online.

Over 500 million people interact daily with Facebook. Yet, whether Facebook use influences subjective well-being over time is unknown. We addressed this issue using experience-sampling, the most reliable method for measuring in-vivo behavior and psychological experience. We text-messaged people five times per day for two-weeks to examine how Facebook use influences the two components of subjective well-being: how people feel moment-to-moment and how satisfied they are with their lives. Our results indicate that Facebook use predicts negative shifts on both of these variables over time. The more people used Facebook at one time point, the worse they felt the next time we text-messaged them; the more they used Facebook over two-weeks, the more their life satisfaction levels declined over time. Interacting with other people "directly" did not predict these negative outcomes. They were also not moderated by the size of people's Facebook networks, their perceived supportiveness, motivation for using Facebook, gender, loneliness, self-esteem, or depression. On the surface, Facebook provides an invaluable resource for fulfilling the basic human need for social connection. Rather than enhancing well-being, however, these findings suggest that Facebook may undermine it.

The impact of exposure to violence in the media on the long-term development and short-term expression of aggressive behavior has been well documented. However, gaps in this literature remain, and in particular the role of violent media exposure in shaping violent and other serious antisocial behavior has not been investigated. Further, studies of violent media effects typically have not sampled from populations with confirmed histories of violent and/or nonviolent antisocial behavior. In this study, we analyzed data on 820 youth, including 390 juvenile delinquents and 430 high school students, to examine the relation of violent media use to involvement in violence and general aggression. Using criterion scores developed through cross-informant modeling of data from self, parent/guardian, and teacher/staff reports, we observed that childhood and adolescent violent media preferences contributed significantly to the prediction of violence and general aggression from cumulative risk totals. Findings represent a new and important direction for research on the role of violent media use in the broader matrix of risk factors for youth violence.

This paper argues that the recent concerns about video game “addiction” have been based less on scientific facts and more upon media hysteria. By examining the literature, it will be demonstrated that the current criteria used for identifying this concept are both inappropriate and misleading. Furthermore, by presenting four case studies as examples it will be demonstrated how such claims of video game addiction can be inaccurately applied. It is concluded that the most likely reasons that people play video games excessively are due to either ineffective time management skills, or as a symptomatic response to other underlying problems that they are escaping from, rather than any inherent addictive properties of the actual games.

This study of mobile persuasion system explores the use of a mobile phone, when attached to an everyday object used by an everyday behavior, becomes a tool to sense and influence that behavior. This mobile persuasion system, called Playful Bottle system, makes use of a mobile phone attached to an everyday drinking mug and motivates office workers to drink healthy quantities of water. A camera and accelerometer sensors in the phone are used to build a vision/motion-based water intake tracker to detect the amount and regularity of water consumed by the user. Additionally, the phone includes hydration games in which natural drinking actions are used as game input. Two hydration games are developed: a single-user TreeGame with automated computer reminders and a multi-user ForestGame with computer-mediated social reminders from members of the group playing the game. Results from 7-week user study with 16 test subjects suggest that both hydration games are effective for encouraging adequate and regular water intake by users. Additionally, results of this study suggest that adding social reminders to the hydration game is more effective than system reminders alone.

OBJECTIVES
The development of personal involvement and responsibility has become a strategic issue in health policy. The main goal of this study is to confirm the coexistence of three logics of personal empowerment through health information found on the Internet.

METHODS
A theoretical framework was applied to analyze personal empowerment from the user's perspective. A well-established Canadian Web site that offers information on personal health was used as a case study. A close-ended questionnaire was completed online by 2275 visitors and members of the Web site.

RESULTS
The findings confirm that the development of feelings of competence and control through Internet use is structured around three different logics. This implies three types of aptitudes that are fostered when the Internet is used to seek health information: doing what is prescribed (the professional logic), making choices based on personal judgment (the consumer logic), and mutual assistance (the community logic).

CONCLUSIONS
A recurring issue in three logics is the balance of roles and responsibilities required between the individual and the health provider.

The scourge of cyberbullying has assumed alarming proportions with an ever-increasing number of adolescents admitting to having dealt with it either as a victim or as a bystander. Anonymity and the lack of meaningful supervision in the electronic medium are two factors that have exacerbated this social menace. Comments or posts involving sensitive topics that are personal to an individual are more likely to be internalized by a victim, often resulting in tragic outcomes. We decompose the overall detection problem into detection of sensitive topics, lending itself into text classification sub-problems. We experiment with a corpus of 4500 YouTube comments, applying a range of binary and multiclass classifiers. We find that binary classifiers for individual labels outperform multiclass classifiers. Our findings show that the detection of textual cyberbullying can be tackled by building individual topic-sensitive classifiers.

The Centers for Disease Control and Prevention (CDC) in the United States has declared insufficient sleep a "public health problem." Indeed, according to a recent CDC study, more than a third of American adults are not getting enough sleep on a regular basis. However, insufficient sleep is not exclusively a US problem, and equally concerns other industrialised countries such as the United Kingdom, Japan, Germany, or Canada. According to some evidence, the proportion of people sleeping less than the recommended hours of sleep is rising and associated with lifestyle factors related to a modern 24/7 society, such as psychosocial stress, alcohol consumption, smoking, lack of physical activity and excessive electronic media use, among others. This is alarming as insufficient sleep has been found to be associated with a range of negative health and social outcomes, including success at school and in the labour market. Over the last few decades, for example, there has been growing evidence suggesting a strong association between short sleep duration and elevated mortality risks. Given the potential adverse effects of insufficient sleep on health, well-being and productivity, the consequences of sleep-deprivation have far-reaching economic consequences. Hence, in order to raise awareness of the scale of insufficient sleep as a public-health issue, comparative quantitative figures need to be provided for policy- and decision-makers, as well as recommendations and potential solutions that can help tackling the problem.

The present study examined excessive Internet use of Taiwanese adolescents and a psychological aspect of users, sensation seeking, thus to differentiate motivation of Internet dependents and non-dependents. Seven hundred and fifty three Taiwanese high school students were selected using cluster sampling and 88 of them were categorized as Internet dependent users. Results indicated that Internet dependents spent more time on-line than non-dependents. While Internet dependents perceived significantly more negative Internet influences on daily routines, school performance, and parental relation than non-dependents, both Internet dependents and non-dependents viewed Internet use as enhancing peer relations. Making friends through the Internet has become a popular activity among adolescents, potentially leading to its excessive use. Internet dependents scored significantly higher on overall sensation seeking and disinhibition than Internet non-dependents. However, both groups did not differ in the life experience seeking subscale and thrill and adventure seeking subscale. This finding contradicts that of Lavin, Marvin, McLarney, Nola, and Scott [CyberPsychol. Behav. 2 (2000) 425]. Possible reasons for this discrepancy and for the relation between Internet dependence and disinhibition in Taiwanese adolescents are also discussed. # 2002 Elsevier Science Ltd. All rights reserved.

AIM
To determine overall and subgroup prevalence of depressive symptomatology among university students in Denizli, Turkey during the 1999-2000 academic year, and to investigate whether sociodemographic factors were associated with depressive symptoms in university students.

METHODS
A stratified probability sample of 504 Turkish university students (296 male, 208 female) was used in a cross-sectional study. Data were obtained by self-administered questionnaire, including questions on sociodemographic characteristics and problem areas. The revised Beck Depression Inventory (BDI) was used to determine depressive symptoms of the participants. BDI scores 17 or higher were categorized as depressive for logistic regression analysis. Student t-test and linear regression were used for continuous data analysis.

RESULTS
Out of all participants, 26.2% had a BDI score 17 or higher. The prevalence of depressive symptoms increased to 32.1% among older students, 34.7% among students with low socioeconomic status, 31.2% among seniors, and 62.9% among students with poor school performance. The odds ratio of depressive symptoms was 1.84 (95% confidence interval [CI], 1.03-3.28) in students with low socioeconomic status and 7.34 (95% CI, 3.36-16.1) in students with poor school performance in the multivariate logistic model. The participants identified several problem areas: lack of social activities and shortage of facilities on the campus (69.0%), poor quality of the educational system (54.8%), economic problems (49.3%), disappointment with the university (43.2%), and friendship problems (25.9%).

CONCLUSIONS
Considering the high frequency of depressive symptoms among Turkish university students, a student counseling service offering mental health assistance is necessary. This service should especially find the way to reach out to poor students and students with poor school performance.


Question: A community-based cross-sectional study on the impact of electronic media usage on adolescent health and well-being.

This paper presents two studies investigating the relationship between violent video games and aggression-related outcomes. The first study revealed a positive correlation between real-life engagement in violent video games and aggressive behavior, as well as delinquent actions. This association was particularly pronounced among individuals with a pre-existing tendency towards aggression and among males. Moreover, academic performance was found to inversely correlate with the total time spent on video gaming. The second study demonstrated that experimental exposure to graphically violent video games led to an increase in aggressive thoughts and behaviors. Across both studies, men exhibited a more adversarial perception of the world compared to women. These findings align with the predictions made by the General Affective Aggression Model, which posits that violent video game exposure can escalate aggressive behavior in both immediate (e.g., laboratory-measured aggression) and long-term contexts (e.g., delinquency).

In a related community study, the impact of electronic media usage on adolescent health and well-being was examined. The investigation revealed that excessive use of electronic media, including video games, was associated with negative health outcomes and reduced well-being among adolescents. This correlation was more pronounced in individuals who already displayed aggressive tendencies and was notably higher among males. The study also highlighted the detrimental effect of prolonged media exposure on academic performance. These results underscore the importance of monitoring adolescent media consumption to mitigate potential adverse effects on health and well-being, echoing the concerns raised by the studies on violent video game effects.

For over two decades, cannabis, commonly known as marijuana, has been the most widely used illicit drug by young people in high-income countries, and has recently become popular on a global scale. Epidemiological research during the past 10 years suggests that regular use of cannabis during adolescence and into adulthood can have adverse effects. Epidemiological, clinical, and laboratory studies have established an association between cannabis use and adverse outcomes. We focus on adverse health effects of greatest potential public health interest-that is, those that are most likely to occur and to affect a large number of cannabis users. The most probable adverse effects include a dependence syndrome, increased risk of motor vehicle crashes, impaired respiratory function, cardiovascular disease, and adverse effects of regular use on adolescent psychosocial development and mental health.

This paper briefly overviews five studies examining massively multiplayer online role-playing games (MMORPGs). The first study surveyed 540 gamers and showed that the social aspects of the game were the most important factor for many gamers. The second study explored the social interactions of 912 MMORPG players and showed they created strong friendships and emotional relationships. A third study examined the effect of online socializing in the lives of 119 online gamers. Significantly more male gamers than female gamers said that they found it easier to converse online than offline, and 57% of gamers had engaged in gender swapping. A fourth study surveyed 7,069 gamers and found that 12% of gamers fulfilled at least three diagnostic criteria of addiction. Finally, an interview study of 71 gamers explored attitudes, experiences, and feelings about online gaming. They provided detailed descriptions of personal problems that had arisen due to playing MMORPGs. an individualistic character (Griffiths, Davies, & Chappell, 2004). This is the only setting where millions of users voluntarily immerse themselves in a graphical virtual environment and interact with each other through avatars on a daily basis (Yee, 2007). Research suggests that the game play within these virtual worlds is enhanced because players use them as traditional games as well as arenas in which to explore DOI: 10.4018/ijgbl.2011100103 International Journal of Game-Based Learning, 1(4), 20-36, October-December 2011 21 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. new relationships, new places and themselves (Krotoski, 2004). Despite the massive amounts of money spent on online gaming, very little research has been carried out regarding the positive social aspects of these games. Much of the debate over the last 30 years has focused on the dangers of computer gaming in the adolescent population, including increased aggression and addiction. Research has also been carried out examining the potentially harmful effects playing computer games may have on social development, self-esteem, social inadequacy, and social anxiety. MMORPGs are very (virtually) socially interactive but little social interaction in the real world is needed when playing them as only one person can play them at any one time from a single computer, unlike some popular two-player console games such as Mortal Kombat. Yee (2001, 2006, 2007) has carried out research into MMORPGs and notes that they allow new forms of social identity and social interaction. Yee’s research has shown that MMORPGs appeal to adults and teenagers from a wide range of backgrounds, and they spend on average more than half a working week in these environments. In a study by Utz (2000), it was found that 77% of respondents reported that they had some sort of relation with other Multi-User Dungeon (MUD) Gamers. It has also been suggested that college students can develop compulsions to play MMORPGs leading to social isolation, poor academic performance, and sleep deprivation. In 2004, a survey of over 54,000 American students found 11% of females and 20% of males said their recreational computer use had significantly hindered their performance at college and University (American College Health Association, 2005). Players can become fixated on their virtual characters, striving to obtain the best armour, experience and reputation in the game, ignoring the fact that their grades are dropping and their friends have drifted apart. It is clear to see that computer games appear to play a role in the socialisation of heavy game players particularly for those who play MMORPGs. Krotoski (2004) maintains that MMORPGs encourage group interaction and involvement, flexibility and mastery, resulting in significant friendships and personal empowerment. It is important to realise that gaming has shown elements of being a compulsive behaviour, with players feeling addicted, experiencing a strong impulse to play the games and finding it hard to resist the games (Griffiths & Davies, 2005). Positive social interaction is paramount in MMORPGs because they require a large number of players to cooperate together and work as a team at the same time. MMORPGs also have multiple tasks that require different characters with different skills in order to complete a challenge or quest. This teaches gamers to be dependent on one another that reinforce their relationships, providing a good understanding of teamwork. The purpose of the research here is to examine the social interactions that occur both within and outside of MMORPGs. The development of virtual friendships can be very enjoyable for gamers, and anecdotal evidence has suggested they sometimes develop into serious real-life friendships and relationships. Not only do MMORPGs facilitate formation of relationships, they are also windows into and catalysts in existing relationships. To date, there has been relatively little research into massively multiplayer online roleplaying games (MMORPGs). This paper briefly overviews five studies by the authors (Griffiths, Davies & Chappell, 2004; Cole & Griffiths, 2007; Hussain & Griffiths, 200, 2009; Grüsser, Thalemann, & Griffiths, 2007). In all studies outlined below, participants were informed that participation was entirely voluntary and that the research was conducted according to the British Psychological Society’s Ethical Code of Conduct for Psychologists. In the case of the surveys, if participants no longer wished to take part they simply had to close the Internet browser. All players were guaranteed anonymity and confidentiality. 15 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/social-interactions-onlinegaming/60132?camid=4v1 This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Library Science, Information Studies, and Education, InfoSci-Select, InfoSci-Select, InfoSciCommunications, Online Engagement, and Media eJournal Collection, InfoSci-Educational Leadership, Administration, and Technologies eJournal Collection, InfoSci-Select, InfoSci-Select, InfoSci-Journal Disciplines Communications and Social Science. Recommend this product to your

Anecdotal reports indicated that some on-line users were becoming addicted to the Internet in much that same way that others became addicted to drugs or alcohol which resulted in academic, social, and occupational impairment. However, research among sociologists, psychologists, or psychiatrists has not formally identified addictive use of the Internet as a problematic behavior. This study investigated the existence of Internet addiction and the extent of problems caused by such potential misuse. This study utilized an adapted version of the criteria for pathological gambling defined by the DSM-IV (APA, 1994). On the basis of this criteria, case studies of 396 dependent Internet users (Dependents) and a control group of 100 non-dependent Internet users (Non-Dependents) were classified. Qualitative analyses suggests significant behavioral and functional usage differences between the two groups. Clinical and social implications of pathological Internet use and future directions for research are discussed.

This paper examines the association between participation in school-based extracurricular activities (ECAs) and adolescent adjustment (drinking, marijuana use, grades, academic attitudes and academic aspirations) among students from six high schools. Three major issues were addressed: the potential confounding of selective EGA participation by better adjusted students and measures of adjustment, variability in the strength of the association between ECA participation and adjustment as a function of adolescent demographic characteristics and activity type, and the role of peers as mediators of the association between ECA participation and adjustment. Adolescents who participated in ECAs reported higher grades, more positive attitudes toward schools, and higher acac demic aspirations once demographic characteristics and prior adjustment were controlled. Alcohol and marijuana use were not independently associated with ECA participation. The EGA-adjustment association did not vary by demographic characteristics and did not appear to be mediated by peer characteristics. Those who participated in non-sport ECAs reported consistendy better adjustment than those who did not participate in ECAs and those who participate in sports.

The evolution of the concept of child abuse leads to consider new types of maltreatment that in the future will certainly be taken into account with a new era of social pediatrics.Pediatric care has been based on the increased awareness of the importance of meeting the psychosocial and developmental needs of children and of the role of families in promoting the health.

The rapid growth of investment in information technology (IT) by organizations worldwide has made user acceptance an increasingly critical technology implementation a d management issue. While such acceptance has received fairly extensive attention from previous research, additional efforts are needed to examine or validate existing research results, particularly those involving different technologies, user populations, and/or organizational contexts. In response, this paper reports a research work that examined the applicability of the Technology Acceptance Model (ΤΑΜ) in explaining physicians' decisions to accept telemedicine technology in the health-care context. The technology, the user group, and the organizational context are all new to IT acceptance/adoption research. The study also addressed a pragmatic technology management need resulting from millions of dollars invested by healthcare organizations in developing and implementing telemedicine programs in recent years. The model's overall fit, explanatory power, and the individual causal links that it postulates were evaluated by examining the acceptance of telemedicine technology among physicians practicing at public tertiary hospitals in Hong Kong. Our results suggested that ΤΑΜ was able to provide a reasonable depiction of physicians' intention to use telemedicine technology. Perceived usefulness was found to be a significant determinant ofattitude and intention but perceived ease of use was not. The relatively low R-square of the model suggests both the limitations of the parsimonious model and the need for incorporating additional factors or integrating with other IT acceptance models in order to improve its specificity and explanatory utility in a health-care context. Based on the study findings, implications for user technology acceptance research and telemedicine management are discussed.

With rising concerns about obesity and sedentary lifestyles in youth, there has been an increasing interest in understanding how pervasive and ubiquitous computing technologies can catalyze positive health behaviors in children and teens. School-based interventions seem like a natural choice, and ubiquitous computing technologies hold much promise for these interventions. Yet the literature contains little guidance for how to approach school-based ubicomp deployments. Grounded in our analysis of a large-scale US school-based intervention for promoting youth physical activity, we present an approach to the design and evaluation of school-based ubicomp that treats the school as a social institution. We show how the school regulates students' daily lives, drawing from work in the sociology of schools to create a framing for planning, executing and analyzing school-based ubicomp deployments. These insights will assist other researchers and designers engaging in deployments of ubiquitous computing systems in settings with established institutional structures.

BACKGROUND
The Internet is a primary source of health information for many. Since the widespread adoption of social media, user-generated health-related content has proliferated, particularly around long-term health issues such as diabetes and common mental health disorders (CMHDs).

OBJECTIVE
To explore perceptions and experiences of engaging with health information online in a sample of young adults familiar with social media environments and variously engaged in consuming user-generated content.

METHODS
Forty semi-structured interviews were conducted with young adults, aged 18-30, with experience of diabetes or CMHDs. Data were analysed following a thematic networks approach to explore key themes around online information-seeking and content consumption practices.

RESULTS
Although participants primarily discussed well-rehearsed approaches to health information-seeking online, particularly reliance on search engines, their accounts also reflected active engagement with health-related content on social media sites. Navigating between professionally produced websites and user-generated content, many of the young adults seemed to appreciate different forms of health knowledge emanating from varied sources. Participants described negotiating health content based on social media practices and features and assessing content heuristically. Some also discussed habitual consumption of content related to their condition as integrated into their everyday social media use.

CONCLUSION
Technologies such as Facebook, Twitter and YouTube offer opportunities to consume and assess content which users deem relevant and useful. As users and organizations continue to colonize social media platforms, opportunities are increasing for health communication and intervention. However, how such innovations are adopted is dependent on their alignment with users' expectations and consumption practices.

BACKGROUND
Mindfulness meditation (MM) practices constitute an important group of meditative practices that have received growing attention. The aim of the present paper was to systematically review current evidence on the neurobiological changes and clinical benefits related to MM practice in psychiatric disorders, in physical illnesses and in healthy subjects.

METHOD
A literature search was undertaken using Medline, ISI Web of Knowledge, the Cochrane collaboration database and references of retrieved articles. Controlled and cross-sectional studies with controls published in English up to November 2008 were included.

RESULTS
Electroencephalographic (EEG) studies have revealed a significant increase in alpha and theta activity during meditation. Neuroimaging studies showed that MM practice activates the prefrontal cortex (PFC) and the anterior cingulate cortex (ACC) and that long-term meditation practice is associated with an enhancement of cerebral areas related to attention. From a clinical viewpoint, Mindfulness-Based Stress Reduction (MBSR) has shown efficacy for many psychiatric and physical conditions and also for healthy subjects, Mindfulness-Based Cognitive Therapy (MBCT) is mainly efficacious in reducing relapses of depression in patients with three or more episodes, Zen meditation significantly reduces blood pressure and Vipassana meditation shows efficacy in reducing alcohol and substance abuse in prisoners. However, given the low-quality designs of current studies it is difficult to establish whether clinical outcomes are due to specific or non-specific effects of MM.

DISCUSSION
Despite encouraging findings, several limitations affect current studies. Suggestions are given for future research based on better designed methodology and for future directions of investigation.

It is not—unless it triggers feelings of envy. This study uses the framework of social rank theory of depression and conceptualizes Facebook envy as a possible link between Facebook surveillance use and depression among college students. Using a survey of 736 college students, we found that the effect of surveillance use of Facebook on depression is mediated by Facebook envy. However, when Facebook envy is controlled for, Facebook use actually lessens depression. 2014 Elsevier Ltd. All rights reserved.


Question: A community-based cross-sectional study on the impact of electronic media usage on adolescent health and well-being.

The PHQ-9 Scale: A Self-Administered Questionnaire for Assessing Depression in Adolescents and its Impact on Health and Well-being

This paper explores the use of the Patient Health Questionnaire-9 (PHQ-9) scale as a reliable tool for evaluating depressive symptoms in adolescents and its association with their overall health and well-being. Unlike the CES-D scale, which is widely used in the general population, the PHQ-9 has been adapted specifically for adolescents to provide a more accurate assessment of depression in this age group. The study, conducted within a community setting, involved a cross-sectional analysis of data collected from a large sample of adolescents. Participants were asked to complete the PHQ-9 questionnaire, which consists of nine questions pertaining to symptoms of depression experienced over the past two weeks. The results were then correlated with self-reported measures of health and well-being, including physical health, mental health, and social functioning. The findings revealed a significant association between higher PHQ-9 scores (indicating more severe depressive symptoms) and poorer health outcomes and well-being among adolescents. This highlights the importance of using validated screening tools, such as the PHQ-9, to identify and address depression in this vulnerable population. The study also underscores the need for early intervention and support to mitigate the negative impact of depression on adolescent health and well-being.

Childhood adversity experiences, when not properly managed may result to anti-social behaviour, health risk or psychological problems like severe depression, suicidal behavior in undergraduate students. Hence the study examined childhood adversity experiences and its factors determining resilience among undergraduate students in Oyo State. The study adopted a descriptive survey research; multi-stage random sampling technique was used to select 341 undergraduate students of Ladoke Akintola University of Technology, Ogbomosho and Oyo State. Two research instruments were used to test childhood adversity scale and factors determining resilience (protective family, birth order, community protective factors). The study formulated four research hypotheses which were tested with Pearson Product moment correlation analysis and multiple regression analysis at coefficient level of 0.05. The result of the analysis revealed that level of Adverse Childhood Experience (ACE) among respondents is slightly low with half of the respondents between the ages of 18 years to 20 years. The prominent ACE identified by respondents is physical assaults, home with incidence of substance abuse, victim of sexual abuse, and humiliation from parents. The level of resilience among the respondents is moderate. There was a positive significant relationship between childhood adversity and lifetime resilience undergraduate student in Oyo State (r = 0.272), protective family factor was also associated with lifetime resilience undergraduate student in Oyo State (r = 0.018) there was a significant relationship between birth order and lifetime resilience undergraduate student in Oyo State. (r = 0.794) there was a significant relationship between community protective factor and lifetime resilience undergraduate student o in Oyo State (r = 0.835). The magnitude of relative contribution showed protective family factor (β = 0.987), had significant relative contribution; community protective factor (β = 0.762), had significant relative contribution; childhood adversity (β = 0.724), had significant relative contribution; and then birth order (β = 0.687), had significant relative contribution to resilience of the undergraduate students. Educational institutions should tackle issues on educator-student relationships through various channels, especially social work, counseling units and students affairs department. There is need for initial psychosocial assessment for fresher to test resilience and adverse childhood experiences, mental health service should be contextualize in health care service for undergraduate students, the parents should be educated on the impact of childhood adversity on wellbeing and academic performance of undergraduate students in Nigeria.

There are numerous claims that playing computer and video games may be educationally beneficial, but there has been little formal investigation into whether or not the frequency of exposure to such games actually affects academic performance. This paper explores the issue by analysing the relationships between gaming frequency – measured as the amount of time undergraduate students spend playing games in their free time – and their academic performance as measured by their examination marks. Using a sample of 713 students, correlation analyses between gaming frequency and examination performance were conducted for students of varying gaming frequency, study discipline, gender, and general attitudes towards gaming and study. The results reveal that examination marks are in fact negatively correlated with gaming frequency – i.e. frequent gamers generally achieve lower marks than less frequent gamers.

Fitness applications with social network services (SNS) have emerged for physical activity management. However, there is little understanding of the effects of these applications on users’ physical activity. Motivated thus, we develop a theoretical model based on social cognitive theory of self-regulation to explain the effects of goal setting, self-tracking, and SNS (through social comparison from browsing others’ tracking data and social support from sharing one’s own tracking data) on physical activity. The model was tested with objective data from 476 Runkeeper users. Our preliminary results show that goal setting, number of uses, the proportion of lower-performing friends, and number of likes positively influence users’ physical activities, while the proportion of higher-performing friends has a negative effect. Moreover, the effect of the proportion of higher-performing friends is moderated by the average difference between the friends and the individual. The initial contributions of the study and remaining research plan are described.

0747-5632/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.chb.2012.05.024 ⇑ Corresponding author. Address: Unit for School an University of London, UK. Tel.: +44 (0)207 919 7870; E-mail address: r.slonje@gmail.com (R. Slonje). Cyberbullying has been identified as an important problem amongst youth in the last decade. This paper reviews some recent findings and discusses general concepts within the area. The review covers definitional issues such as repetition and power imbalance, types of cyberbullying, age and gender differences, overlap with traditional bullying and sequence of events, differences between cyberbullying and traditional bullying, motives for and impact of cyber victimization, coping strategies, and prevention/intervention possibilities. These issues will be illustrated by reference to recent and current literature, and also by in-depth interviews with nine Swedish students aged 13–15 years, who had some first-hand experience of one or more cyberbullying episodes. We conclude by discussing the evidence for different coping, intervention and prevention strategies. 2012 Elsevier Ltd. All rights reserved.

AIM
To determine overall and subgroup prevalence of depressive symptomatology among university students in Denizli, Turkey during the 1999-2000 academic year, and to investigate whether sociodemographic factors were associated with depressive symptoms in university students.

METHODS
A stratified probability sample of 504 Turkish university students (296 male, 208 female) was used in a cross-sectional study. Data were obtained by self-administered questionnaire, including questions on sociodemographic characteristics and problem areas. The revised Beck Depression Inventory (BDI) was used to determine depressive symptoms of the participants. BDI scores 17 or higher were categorized as depressive for logistic regression analysis. Student t-test and linear regression were used for continuous data analysis.

RESULTS
Out of all participants, 26.2% had a BDI score 17 or higher. The prevalence of depressive symptoms increased to 32.1% among older students, 34.7% among students with low socioeconomic status, 31.2% among seniors, and 62.9% among students with poor school performance. The odds ratio of depressive symptoms was 1.84 (95% confidence interval [CI], 1.03-3.28) in students with low socioeconomic status and 7.34 (95% CI, 3.36-16.1) in students with poor school performance in the multivariate logistic model. The participants identified several problem areas: lack of social activities and shortage of facilities on the campus (69.0%), poor quality of the educational system (54.8%), economic problems (49.3%), disappointment with the university (43.2%), and friendship problems (25.9%).

CONCLUSIONS
Considering the high frequency of depressive symptoms among Turkish university students, a student counseling service offering mental health assistance is necessary. This service should especially find the way to reach out to poor students and students with poor school performance.

We report on the results of a study in which 19 new mobile phone users were closely tracked for the first six weeks after service acquisition. Results show that new users tend to rapidly modify their perceptions of social appropriateness around mobile phone use, that actual nature of use frequently differs from what users initially predict, and that comprehension of service-oriented technologies can be problematic. We describe instances and features of mobile telephony practice. When in use, mobile phones occupy multiple social spaces simultaneously, spaces with norms that sometimes conflict: the physical space of the mobile phone user and the virtual space of the conversation.

Design plays a critical role in the development of e-health, greatly impacting the outreach potential for pertinent health communication. Design influences viewers' initial evaluations of electronic displays of health information, as well as directly impacting the likelihood one will attend to and favorably evaluate the information, essential actions for processing the health concepts presented. Individuals with low health literacy, representing a hard-to-reach audience susceptible to worsened health outcomes, will benefit greatly from the application of theory-based design principles. Design principles that have been shown to appeal and engage audiences are the necessary first step for effective message delivery. Design principles, which directly impact increased attention, favorable evaluations, and greater information processing abilities, include: web aesthetics, visual complexity, affordances, prototypicality, and persuasive imagery. These areas of theory-driven design research should guide scholars in e-health investigation with research goals of broader outreach, reduction of disparities, and potential avenues for reduced health care costs. Improving design by working with this hard-to-reach audience will simultaneously improve practice, as the applications of key design principles through theory-driven design research will allow practitioners to create effective e-health that will benefit people more broadly.

This study is a secondary data analysis from the National Health Morbidity Survey III, a population-based study conducted in 2006. A total of 7,749 children between 7 and 12 years old were recruited into the study. This study seeks to report the prevalence of overweight (including obesity) children in Malaysia using international cut-off point and identify its associated key social determinants. The results show that the overall prevalence of overweight children in Malaysia was 19.9%. The urban residents, males, Chinese, those who are wealthy, have overweight or educated guardians showed higher prevalence of overweight. In multivariable analysis, higher likelihood of being overweight was observed among those with advancing age (OR=1.15), urban residents (OR=1.16, 95% CI: 1.01-1.36), the Chinese (OR=1.45, 95% CI: 1.19-1.77), boys (OR=1.23, 95% CI: 1.08-1.41), and those who came from higher income family. In conclusion, one out of five of 7-12 year-old-children in Malaysia were overweight. Locality of residence, ethnicity, gender, guardian education, and overweight guardian were likely to be the predictors of this alarming issue. Societal and public health efforts are needed in order to reduce the burden of disease associated with obesity.

INTRODUCTION
Behavioral science experts believe that all entities capable of stimulating a person can be addictive; and whenever a habit changes into an obligation, it can be considered as an addiction. Researchers also believe that there are a number of similarities as well as some differences between drug addiction and behavioral addiction diagnostic symptoms. The purpose of this study is to consider different approaches in this field.

METHODS
This is a descriptive research using content analysis method. First, differences and similarities of various perspectives on addiction and addiction behavior in different substances were obtained, thereafter, the data was coded and categorized, subjects were discussed and major issues were extracted.

RESULTS
Behavioral addiction such as internet addiction is similar to drug addiction except that in the former, the individual is not addicted to a substance but the behavior or the feeling brought about by the relevant action. In addition, the physical signs of drug addiction, are absent in behavioral addiction. Others have stated that behaviorally addicted individuals have certain symptoms and will undergo the same consequences brought about by addiction to alcohol and drugs as well as other obsessive behaviors.

CONCLUSION
Similar to substance abuse prevention, programs aimed at addicted individuals and specialized training can educate adolescents about the warning signs of online addiction, in order to assist the early detection of this disorder. For prevention of behavioral addiction (such as internet addiction) authorities, cultural institutions and parents should monitor the use of internet and teach to the adolescent and children, the useful and appropriate methods of internet use.

OBJECTIVE
Fitspiration is a recent Internet trend designed to motivate people to eat healthily and to exercise. The aim of the study was to investigate disordered eating and exercise in women who post fitspiration on Instagram.

METHOD
Participants were 101 women who post fitspiration images on Instagram and a comparison group of 102 women who post travel images. Both groups completed measures of disordered eating and compulsive exercise.

RESULTS
Women who post fitspiration images scored significantly higher on drive for thinness, bulimia, drive for muscularity, and compulsive exercise. Almost a fifth (17.5%) of these women were at risk for diagnosis of a clinical eating disorder, compared to 4.3% of the travel group. Compulsive exercise was related to disordered eating in both groups, but the relationship was significantly stronger for women who post fitspiration images.

DISCUSSION
For some women, posting fitspiration images on Instagram may signify maladaptive eating and exercise behaviors. © 2016 Wiley Periodicals, Inc. (Int J Eat Disord 2017; 50:76-79).


Question: Radiomics-based prognosis evaluation for non-small cell lung cancer patients.

This paper delves into the challenges of automated feature subset selection for unsupervised learning tasks, specifically focusing on the dual objectives of determining the optimal number of clusters and addressing the dimension bias in feature selection criteria. We investigate these issues through the application of FSSEM (Feature Subset Selection using Expectation-Maximization clustering) and the evaluation of candidate feature subsets using two performance metrics: scatter separability and maximum likelihood. We provide rigorous proofs highlighting the inherent dimensionality biases in these selection criteria and propose a cross-projection normalization technique that can be universally applied to mitigate these biases. Our empirical studies not only underscore the necessity of feature selection but also the critical importance of resolving the aforementioned issues, thereby substantiating the efficacy of our proposed solutions.

**Question**: A Comparative Study of Machine Learning Algorithms for Predicting Credit Default

**Documents**: An Empirical Analysis of Feature Selection Techniques for High-Dimensional Data
In this paper, we present an empirical analysis of various feature selection techniques for high-dimensional data. We compare the performance of filter, wrapper, and embedded methods in terms of feature reduction, model accuracy, and computational efficiency. We also investigate the impact of different evaluation metrics on the selection process. Our findings suggest that while wrapper methods generally outperform filter and embedded methods in terms of model accuracy, they are computationally more expensive. We recommend using filter methods as a preliminary step for high-dimensional data, followed by wrapper methods for further refinement. We also suggest that the choice of evaluation metric should be based on the specific characteristics of the data and the learning algorithm being used.

**Similar Documents**: This paper offers an in-depth empirical analysis of diverse feature selection strategies for datasets with high dimensionality. We systematically assess the efficacy of filter, wrapper, and embedded methods across several dimensions, including feature reduction effectiveness, predictive accuracy of models, and computational resource requirements. Additionally, we explore how varying evaluation metrics influence the selection process. Our results indicate that while wrapper methods typically surpass filter and embedded methods in predictive accuracy, they demand significantly higher computational resources. We propose a two-step approach: initiating with filter methods to swiftly manage high-dimensional data, then employing wrapper methods for detailed refinement. Furthermore, we emphasize that the selection of an evaluation metric should be tailored to the unique attributes of the dataset and the specific machine learning algorithm in use.

AIM
To accurately quantify the radioactivity concentration measured by PET, emission data need to be corrected for photon attenuation; however, the MRI signal cannot easily be converted into attenuation values, making attenuation correction (AC) in PET/MRI challenging. In order to further improve the current vendor-implemented MR-AC methods for absolute quantification, a number of prototype methods have been proposed in the literature. These can be categorized into three types: template/atlas-based, segmentation-based, and reconstruction-based. These proposed methods in general demonstrated improvements compared to vendor-implemented AC, and many studies report deviations in PET uptake after AC of only a few percent from a gold standard CT-AC. Using a unified quantitative evaluation with identical metrics, subject cohort, and common CT-based reference, the aims of this study were to evaluate a selection of novel methods proposed in the literature, and identify the ones suitable for clinical use.

METHODS
In total, 11 AC methods were evaluated: two vendor-implemented (MR-ACDIXON and MR-ACUTE), five based on template/atlas information (MR-ACSEGBONE (Koesters et al., 2016), MR-ACONTARIO (Anazodo et al., 2014), MR-ACBOSTON (Izquierdo-Garcia et al., 2014), MR-ACUCL (Burgos et al., 2014), and MR-ACMAXPROB (Merida et al., 2015)), one based on simultaneous reconstruction of attenuation and emission (MR-ACMLAA (Benoit et al., 2015)), and three based on image-segmentation (MR-ACMUNICH (Cabello et al., 2015), MR-ACCAR-RiDR (Juttukonda et al., 2015), and MR-ACRESOLUTE (Ladefoged et al., 2015)). We selected 359 subjects who were scanned using one of the following radiotracers: [18F]FDG (210), [11C]PiB (51), and [18F]florbetapir (98). The comparison to AC with a gold standard CT was performed both globally and regionally, with a special focus on robustness and outlier analysis.

RESULTS
The average performance in PET tracer uptake was within ±5% of CT for all of the proposed methods, with the average±SD global percentage bias in PET FDG uptake for each method being: MR-ACDIXON (-11.3±3.5)%, MR-ACUTE (-5.7±2.0)%, MR-ACONTARIO (-4.3±3.6)%, MR-ACMUNICH (3.7±2.1)%, MR-ACMLAA (-1.9±2.6)%, MR-ACSEGBONE (-1.7±3.6)%, MR-ACUCL (0.8±1.2)%, MR-ACCAR-RiDR (-0.4±1.9)%, MR-ACMAXPROB (-0.4±1.6)%, MR-ACBOSTON (-0.3±1.8)%, and MR-ACRESOLUTE (0.3±1.7)%, ordered by average bias. The overall best performing methods (MR-ACBOSTON, MR-ACMAXPROB, MR-ACRESOLUTE and MR-ACUCL, ordered alphabetically) showed regional average errors within ±3% of PET with CT-AC in all regions of the brain with FDG, and the same four methods, as well as MR-ACCAR-RiDR, showed that for 95% of the patients, 95% of brain voxels had an uptake that deviated by less than 15% from the reference. Comparable performance was obtained with PiB and florbetapir.

CONCLUSIONS
All of the proposed novel methods have an average global performance within likely acceptable limits (±5% of CT-based reference), and the main difference among the methods was found in the robustness, outlier analysis, and clinical feasibility. Overall, the best performing methods were MR-ACBOSTON, MR-ACMAXPROB, MR-ACRESOLUTE and MR-ACUCL, ordered alphabetically. These methods all minimized the number of outliers, standard deviation, and average global and local error. The methods MR-ACMUNICH and MR-ACCAR-RiDR were both within acceptable quantitative limits, so these methods should be considered if processing time is a factor. The method MR-ACSEGBONE also demonstrates promising results, and performs well within the likely acceptable quantitative limits. For clinical routine scans where processing time can be a key factor, this vendor-provided solution currently outperforms most methods. With the performance of the methods presented here, it may be concluded that the challenge of improving the accuracy of MR-AC in adult brains with normal anatomy has been solved to a quantitatively acceptable degree, which is smaller than the quantification reproducibility in PET imaging.

BACKGROUND
We investigated the reporting and methods of prediction studies, focusing on aims, designs, participant selection, outcomes, predictors, statistical power, statistical methods, and predictive performance measures.

METHODS AND FINDINGS
We used a full hand search to identify all prediction studies published in 2008 in six high impact general medical journals. We developed a comprehensive item list to systematically score conduct and reporting of the studies, based on recent recommendations for prediction research. Two reviewers independently scored the studies. We retrieved 71 papers for full text review: 51 were predictor finding studies, 14 were prediction model development studies, three addressed an external validation of a previously developed model, and three reported on a model's impact on participant outcome. Study design was unclear in 15% of studies, and a prospective cohort was used in most studies (60%). Descriptions of the participants and definitions of predictor and outcome were generally good. Despite many recommendations against doing so, continuous predictors were often dichotomized (32% of studies). The number of events per predictor as a measure of statistical power could not be determined in 67% of the studies; of the remainder, 53% had fewer than the commonly recommended value of ten events per predictor. Methods for a priori selection of candidate predictors were described in most studies (68%). A substantial number of studies relied on a p-value cut-off of p<0.05 to select predictors in the multivariable analyses (29%). Predictive model performance measures, i.e., calibration and discrimination, were reported in 12% and 27% of studies, respectively.

CONCLUSIONS
The majority of prediction studies in high impact journals do not follow current methodological recommendations, limiting their reliability and applicability.

Computed tomography (CT) is a popular medical imaging modality and enjoys wide clinical applications. At the same time, the X-ray radiation dose associated with CT scannings raises a public concern due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of low-dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio, leading to strong noise and artifacts that down-grade the CT image quality. In this paper, we propose a novel 3-D noise reduction method, called structurally sensitive multi-scale generative adversarial net, to improve the LDCT image quality. Specifically, we incorporate 3-D volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve the structural and textural information in reference to the normal-dose CT images and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more information and outperforms competing methods.

The development of the narrow-band imaging (NBI) has been increasing the interest of medical specialists in the study of laryngeal microvascular network to establish diagnosis without biopsy and pathological examination. A possible solution to this challenging problem is presented in this paper, which proposes an automatic method based on anisotropic filtering and matched filter to extract the lesion area and segment blood vessels. Lesion classification is then performed based on a statistical analysis of the blood vessels' characteristics, such as thickness, tortuosity, and density. Here, the presented algorithm is applied to 50 NBI endoscopic images of laryngeal diseases and the segmentation and classification accuracies are investigated. The experimental results show the proposed algorithm provides reliable results, reaching an overall classification accuracy rating of 84.3%. This is a highly motivating preliminary result that proves the feasibility of the new method and supports the investment in further research and development to translate this study into clinical practice. Furthermore, to our best knowledge, this is the first time image processing is used to automatically classify laryngeal tumors in endoscopic videos based on tumor vascularization characteristics. Therefore, the introduced system represents an innovation in biomedical and health informatics.

Motivated by the need to automate medical information extraction from free-text radiological reports, we present a bi-directional long short-term memory (BiLSTM) neural network architecture for modelling radiological language. The model has been used to address two NLP tasks: medical named-entity recognition (NER) and negation detection. We investigate whether learning several types of word embeddings improves BiLSTM’s performance on those tasks. Using a large dataset of chest x-ray reports, we compare the proposed model to a baseline dictionary-based NER system and a negation detection system that leverages the hand-crafted rules of the NegEx algorithm and the grammatical relations obtained from the Stanford Dependency Parser. Compared to these more traditional rule-based systems, we argue that BiLSTM offers a strong alternative for both our tasks.

Deep Learning approaches have gathered a lot of attention lately. In this work, we study their application to the breast cancer field, in particular for mass detection in mammograms. Several experiments were made on a real mammogram benchmark dataset. Deep Learning approaches were compared to other classification methodologies. It was concluded that, although useful, the implementation used does not outperform SVMs. Further study and adjustment of the method for this application is needed.

Segmentation of medical images has become an indispensable process to perform quantitative analysis of images of human organs and their functions. Normalized Cuts (NCut) is a spectral graph theoretic method that readily admits combinations of different features for image segmentation. The computational demand imposed by NCut has been successfully alleviated with the Nystro/spl uml/m approximation method for applications different than medical imaging. In this paper we discuss the application of NCut with the Nystro/spl uml/m approximation method to segment vertebral bodies from sagittal T1-weighted magnetic resonance images of the spine. The magnetic resonance images were preprocessed by the anisotropic diffusion algorithm, and three-dimensional local histograms of brightness was chosen as the segmentation feature. Results of the segmentation as well as limitations and challenges in this area are presented.


Question: Radiomics-driven prognosis evaluation in non-small cell lung cancer cases.

This paper offers an in-depth review of classification techniques and the strategies for combining them to enhance predictive accuracy, particularly relevant to the field of radiomics-based prognosis analysis for non-small cell lung cancer. Supervised learning, a cornerstone of Intelligent Systems, has led to the development of numerous classification methods rooted in Artificial Intelligence and Statistics. These include Logic-based and Perceptron-based techniques from AI, as well as Bayesian Networks and Instance-based techniques from Statistics. The fundamental aim of supervised learning is to construct a compact model that accurately represents the relationship between class labels and predictor features. The resultant classifier is subsequently employed to predict class labels for unseen instances, where the predictor feature values are known but the class label is not. The paper delves into the exploration of various classification algorithms and highlights the recent trend of using ensemble methods to improve classification accuracy. Ensemble methods, which combine multiple classifiers to achieve better predictive performance, have gained significant attention in the context of complex medical data analysis, including radiomics studies in oncology.

BACKGROUND
: The objective was to evaluate the newly developed Radiographic Union Score for Tibial fractures (RUST). Because there is no "gold standard," it was hypothesized that the RUST score would provide substantial improvements compared with previous scores presented in the literature.

METHODS
: Forty-five sets of X-rays of tibial shaft fractures treated with intramedullary fixation were selected. Seven orthopedic reviewers independently scored bony union using RUST. Radiographs were reassessed at 9 weeks. Intraclass correlation coefficients (ICC) with 95% confidence intervals (CI) measured agreement.

RESULTS
: Overall agreement was substantial (ICC, 0.86; 95% CI, 0.79-0.91). There was improved reliability among traumatologists compared with others (ICC = 0.86, 0.81, and 0.83, respectively). Overall intraobserver reliability was also substantial (ICC, 0.88; 95% CI, 0.80-0.96).

CONCLUSIONS
: The RUST score exhibits substantial improvements in reliability from previously published scores and produces equally reproducible results among a variety of orthopedic specialties and experience levels. Because no "gold standards" currently exist against which RUST can be compared, this study provides only the initial step in the score's full validation for use in a clinical context.

Study Design
Reliability analysis.

Objectives
The Spinal Instability Neoplastic Score (SINS) was developed for assessing patients with spinal neoplasia. It identifies patients who may benefit from surgical consultation or intervention. It also acts as a prognostic tool for surgical decision making. Reliability of SINS has been established for spine surgeons, radiologists, and radiation oncologists, but not yet among spine surgery trainees. The purpose of our study is to determine the reliability of SINS among spine residents and fellows, and its role as an educational tool.

Methods
Twenty-three residents and 2 spine fellows independently scored 30 de-identified spine tumor cases on 2 occasions, at least 6 weeks apart. Intraclass correlation coefficient (ICC) measured interobserver and intraobserver agreement for total SINS scores. Fleiss's kappa and Cohen's kappa analysis evaluated interobserver and intraobserver agreement of 6 component subscores (location, pain, bone lesion quality, spinal alignment, vertebral body collapse, and posterolateral involvement of spinal elements).

Results
Total SINS scores showed near perfect interobserver (0.990) and intraobserver (0.907) agreement. Fleiss's kappa statistics revealed near perfect agreement for location; substantial for pain; moderate for alignment, vertebral body collapse, and posterolateral involvement; and fair for bone quality (0.948, 0.739, 0.427, 0.550, 0.435, and 0.382). Cohen's kappa statistics revealed near perfect agreement for location and pain, substantial for alignment and vertebral body collapse, and moderate for bone quality and posterolateral involvement (0.954, 0.814, 0.610, 0.671, 0.576, and 0.561, respectively).

Conclusions
The SINS is a reliable and valuable educational tool for spine fellows and residents learning to judge spinal instability.

1Department of Computer Science, School of Engineering and Applied Science, Institute for Computer Graphics, The George Washington University, 800 22nd Street NW Suite 3400, Washington, DC 20052, USA 2Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, The George Washington University, 800 22nd Street NW Suite 7680, Washington, DC 20052, USA 3Department of Computer Science, School of Engineering and Applied Science, and Department of Pediatrics, School of Medicine and Health Sciences, Institute for Computer Graphics, The George Washington University, 800 22nd Street NW Suite 5830, Washington, DC 20052, USA

Deep neural networks have been widely adopted for automatic organ segmentation from CT-scanned images. However, the segmentation accuracy on some small organs (e.g., the pancreas) is sometimes below satisfaction, arguably because deep networks are easily distracted by the complex and variable background region which occupies a large fraction of the input volume. In this paper, we propose a coarse-to-fine approach to deal with this problem. We train two deep neural networks using different regions of the input volume. The first one, the coarse-scaled model, takes the entire volume as its input. It is used for roughly locating the spatial position of the pancreas. The second one, the fine-scaled model, only sees a small input region covering the pancreas, thus eliminating the background noise and providing more accurate segmentation especially around the boundary areas. At the testing stage, we first use the coarse-scaled model to roughly locate the pancreas, then adopt the fine-scaled model to refine the initial segmentation in an iterative manner to obtain increasingly better segmentation. We evaluate our algorithm on the NIH pancreas segmentation dataset with 82 volumes, and outperform the state-of-theart [18] by more than 4%, measured by the Dice-Sørensen Coefficient (DSC). In addition, we report 62.43% DSC for our worst case, significantly better than 34.11% reported in [18].

BACKGROUND AND OBJECTIVES
Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning.

METHODS
We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works.

RESULTS
With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches.

CONCLUSIONS
We propose a robust automated end-to-end classifier for biomedical images based on a domain transferred deep convolutional neural network model that shows a highly reliable and accurate performance which has been confirmed on several public biomedical image datasets.

The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have proposed new clustering methods that take advantage of characteristics of the gene expression data, the medical community has a preference for using "classic" clustering methods. There have been no studies thus far performing a large-scale evaluation of different clustering methods in this context. We present the first large-scale analysis of seven different clustering methods and four proximity measures for the analysis of 35 cancer gene expression data sets. Our results reveal that the finite mixture of Gaussians, followed closely by k-means, exhibited the best performance in terms of recovering the true structure of the data sets. These methods also exhibited, on average, the smallest difference between the actual number of classes in the data sets and the best number of clusters as indicated by our validation criteria. Furthermore, hierarchical methods, which have been widely used by the medical community, exhibited a poorer recovery performance than that of the other methods evaluated. Moreover, as a stable basis for the assessment and comparison of different clustering methods for cancer gene expression data, this study provides a common group of data sets (benchmark data sets) to be shared among researchers and used for comparisons with new methods. The data sets analyzed in this study are available at http://algorithmics.molgen.mpg.de/Supplements/CompCancer/ .

Recent technological advances have significantly improved our understanding of tumor biology by means of high-throughput mutation and transcriptome analyses. The application of genomics has revealed the mutational landscape and the specific deregulated pathways in different tumor types. At a transcriptional level, multiple gene expression signatures have been developed to identify biologically distinct subgroups of tumors. By supervised analysis, several prognostic signatures have been generated, some of them being commercially available. However, an unsupervised approach is required to discover a priori unknown molecular subtypes, the so-called intrinsic subtypes. Moreover, an integrative analysis of the molecular events associated with tumor biology has been translated into a better tumor classification. This molecular characterization confers new opportunities for therapeutic strategies in the management of cancer patients. However, the applicability of these new molecular classifications is limited because of several issues such as technological validation and cost. Further comparison with well-established clinical and pathological features is expected to accelerate clinical translation. In this review, we will focus on the data reported on molecular classification in the most common tumor types such as breast, colorectal and lung carcinoma, with special emphasis on recent data regarding tumor intrinsic subtypes. Likewise, we will review the potential applicability of these new classifications in the clinical routine.

Lnc2Cancer (http://www.bio-bigdata.net/lnc2cancer) is a manually curated database of cancer-associated long non-coding RNAs (lncRNAs) with experimental support that aims to provide a high-quality and integrated resource for exploring lncRNA deregulation in various human cancers. LncRNAs represent a large category of functional RNA molecules that play a significant role in human cancers. A curated collection and summary of deregulated lncRNAs in cancer is essential to thoroughly understand the mechanisms and functions of lncRNAs. Here, we developed the Lnc2Cancer database, which contains 1057 manually curated associations between 531 lncRNAs and 86 human cancers. Each association includes lncRNA and cancer name, the lncRNA expression pattern, experimental techniques, a brief functional description, the original reference and additional annotation information. Lnc2Cancer provides a user-friendly interface to conveniently browse, retrieve and download data. Lnc2Cancer also offers a submission page for researchers to submit newly validated lncRNA-cancer associations. With the rapidly increasing interest in lncRNAs, Lnc2Cancer will significantly improve our understanding of lncRNA deregulation in cancer and has the potential to be a timely and valuable resource.

Tuberculosis (TB) is a major health threat in the developing countries. Many patients die every year due to lack of treatment and error in diagnosis. Developing a computer-aided diagnosis (CAD) system for TB detection can help in early diagnosis and containing the disease. Most of the current CAD systems use handcrafted features, however, lately there is a shift towards deep-learning-based automatic feature extractors. In this paper, we present a potential method for tuberculosis detection using deep-learning which classifies CXR images into two categories, that is, normal and abnormal. We have used CNN architecture with 7 convolutional layers and 3 fully connected layers. The performance of three different optimizers has been compared. Out of these, Adam optimizer with an overall accuracy of 94.73% and validation accuracy of 82.09% performed best amongst them. All the results are obtained using Montgomery and Shenzhen datasets which are available in public domain.