Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students

European consensus meeting of ARM-Net members concerning diagnosis and early management of newborns with anorectal malformations.

The ARM-Net (anorectal malformation network) consortium held a consensus meeting in which the classification of ARM and preoperative workup were evaluated with the aim of improving monitoring of treatment and outcome. The Krickenbeck classification of ARM and preoperative workup suggested by Levitt and Peña, used as a template, were discussed, and a collaborative consensus was achieved. The Krickenbeck classification is appropriate in describing ARM for clinical use. The preoperative workup was slightly modified. In males with a visible fistula, no cross-table lateral X-ray is needed and an anoplasty or (mini-) posterior sagittal anorectoplasty can directly be performed. In females with a small vestibular fistula (Hegar size 5 mm, and in the meantime, gentle painless dilatations can be performed. In both male and female perineal fistula and either a low birth weight (<2,000 g) or severe associated congenital anomalies, prolonged preoperative painless dilatations might be indicated to decrease perioperative morbidity caused by general anesthesia. The Krickenbeck classification is appropriate in describing ARM for clinical use. Some minor modifications to the preoperative workup by Levitt and Peña have been introduced in order to refine terminology and establish a comprehensive preoperative workup

The Exstrophy-epispadias complex

Exstrophy-epispadias complex (EEC) represents a spectrum of genitourinary malformations ranging in severity from epispadias (E) to classical bladder exstrophy (CEB) and exstrophy of the cloaca (EC). Depending on severity, EEC may involve the urinary system, musculoskeletal system, pelvis, pelvic floor, abdominal wall, genitalia, and sometimes the spine and anus. Prevalence at birth for the whole spectrum is reported at 1/10,000, ranging from 1/30,000 for CEB to 1/200,000 for EC, with an overall greater proportion of affected males. EEC is characterized by a visible defect of the lower abdominal wall, either with an evaginated bladder plate (CEB), or with an open urethral plate in males or a cleft in females (E). In CE, two exstrophied hemibladders, as well as omphalocele, an imperforate anus and spinal defects, can be seen after birth. EEC results from mechanical disruption or enlargement of the cloacal membrane; the timing of the rupture determines the severity of the malformation. The underlying cause remains unknown: both genetic and environmental factors are likely to play a role in the etiology of EEC. Diagnosis at birth is made on the basis of the clinical presentation but EEC may be detected prenatally by ultrasound from repeated non-visualization of a normally filled fetal bladder. Counseling should be provided to parents but, due to a favorable outcome, termination of the pregnancy is no longer recommended. Management is primarily surgical, with the main aims of obtaining secure abdominal wall closure, achieving urinary continence with preservation of renal function, and, finally, adequate cosmetic and functional genital reconstruction. Several methods for bladder reconstruction with creation of an outlet resistance during the newborn period are favored worldwide. Removal of the bladder template with complete urinary diversion to a rectal reservoir can be an alternative. After reconstructive surgery of the bladder, continence rates of about 80% are expected during childhood. Additional surgery might be needed to optimize bladder storage and emptying function. In cases of final reconstruction failure, urinary diversion should be undertaken. In puberty, genital and reproductive function are important issues. Psychosocial and psychosexual outcome depend on long-term multidisciplinary care to facilitate an adequate quality of life

Utilization of neurophysiological data to classify player immersion to distract from pain

Painful experiences during clinical procedures can have detrimental effects on the physical and mental health of a patient. Current pain reduction methods can be effective in reducing pain, however these methods are not without fault. Active distraction via computer games have been proven to effectively reduce the experience of pain. However, the potential of this distraction to effectively alleviate pain is dependent on players’ engagement with the game, which is determined by the difficulty of the game and the skill of the player. This paper aims to model and classify immersion through increasingly difficult levels of game play, in the presence of pain, using functional Near Infrared Spectroscopy (fNIRS) and heart rate data. Twenty people participated in a study wherein fNIRS data (4 channels located at the prefrontal cortex, four channels located at the somatosensory cortex) and heart rate data were collected whilst participants were subjected to experimental pain, via the Cold Pressor Test (CPT). Participants played a computer game at varying difficultly levels as a distraction. Data were then pre-processed using an Acceleration Based Movement Artefact Reduction Algorithm (AMARA) and Correlation Based Signal Improvement (CBSI). Classification was subsequently undertaken using Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and Recursive Partitioning (rPart). The results demonstrate a maximum accuracy of 99.2% for the binary detection of immersion in the presence of pain