Overcoming barriers to effective early parenting interventions for attention-deficit hyperactivity disorder (ADHD): parent and practitioner views

BackgroundThe importance of early intervention approaches for the treatment of attention-deficit hyperactivity disorder (ADHD) has been increasingly acknowledged. Parenting programmes (PPs) are recommended for use with preschool children with ADHD. However, low take-up' and high drop-out' rates compromise the effectiveness of such programmes within the community. MethodsThis qualitative study examined the views of 25 parents and 18 practitioners regarding currently available PPs for preschool children with ADHD-type problems in the UK. Semi-structured interviews were undertaken to identify both barriers and facilitators associated with programme access, programme effectiveness, and continued engagement. Results and conclusionsMany of the themes mirrored previous accounts relating to generic PPs for disruptive behaviour problems. There were also a number of ADHD-specific themes. Enhancing parental motivation to change parenting practice and providing an intervention that addresses the parents' own needs (e.g. in relation to self-confidence, depression or parental ADHD), in addition to those of the child, were considered of particular importance. Comparisons between the views of parents and practitioners highlighted a need to increase awareness of parental psychological barriers among practitioners and for better programme advertising generally. Clinical implications and specific recommendations drawn from these findings are discussed and presented

Bandit Models of Human Behavior: Reward Processing in Mental Disorders

Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for multi-armed bandit problem, which extends the standard Thompson Sampling approach to incorporate reward processing biases associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain. We demonstrate empirically that the proposed parametric approach can often outperform the baseline Thompson Sampling on a variety of datasets. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions.Comment: Conference on Artificial General Intelligence, AGI-1

Fostering collective intelligence education

New educational models are necessary to update learning environments to the digitally shared communication and information. Collective intelligence is an emerging field that already has a significant impact in many areas and will have great implications in education, not only from the side of new methodologies but also as a challenge for education. This paper proposes an approach to a collective intelligence model of teaching using Internet to combine two strategies: idea management and real time assessment in the class. A digital tool named Fabricius has been created supporting these two elements to foster the collaboration and engagement of students in the learning process. As a result of the research we propose a list of KPI trying to measure individual and collective performance. We are conscious that this is just a first approach to define which aspects of a class following a course can be qualified and quantified.Postprint (published version

Behavioral Changes in Aging but Not Young Mice after Neonatal Exposure to the Polybrominated Flame Retardant DecaBDE

BACKGROUND: After several decades of commercial use, the flame-retardant chemicals polybrominated diphenyl ethers (PBDEs) and their metabolites are pervasive environmental contaminants and are detected in the human body. Decabrominated diphenyl ether (decaBDE) is currently the only PBDE in production in the United States. OBJECTIVES: Little is known about the health effects of decaBDE. In the present study we examined the effects of neonatal decaBDE exposure on behavior in mice at two ages. METHODS: Neonatal male and female C57BL6/J mice were exposed to a daily oral dose of 0, 6, or 20 mg/kg decaBDE from postnatal days 2 through 15. Two age groups were examined: a cohort that began training during young adulthood and an aging cohort of littermates that began training at 16 months of age. Both cohorts were tested on a series of operant procedures that included a fixed-ratio I schedule of reinforcement, a fixed-interval (FI) 2-min schedule, and a light-dark visual discrimination. RESULTS: We observed minimal effects on the light-dark discrimination in the young cohort, with no effects on the other tasks. The performance of the aging cohort was significantly affected by decaBDE. On the FI schedule, decaBDE exposure increased the overall response rate. On the light-dark discrimination, older treated mice learned the task more slowly, made fewer errors on the first-response choice of a trial but more perseverative errors after an initial error, and had lower latencies to respond compared with controls. Effects were observed in both dose groups and sexes on various measures. CONCLUSIONS: These findings suggest that neonatal decaBDE exposure produces effects on behavioral tasks in older but not younger animals. The behavioral mechanisms responsible for the pattern of observed effects may include increased impulsivity, although further research is required

Microwave enhanced ion-cut silicon layer transfer

Microwave heating has been used to decrease the time required for exfoliation of thin single-crystalline silicon layers onto insulator substrates using ion-cut processing. Samples exfoliated in a 2.45 GHz, 1300 W cavity applicator microwave system saw a decrease in incubation times as compared to conventional anneal processes. Rutherford backscattering spectrometry, cross sectional scanning electron microscopy, cross sectional transmission electron microscopy, and selective aperture electron diffraction were used to determine the transferred layer thickness and crystalline quality. The surface quality was determined by atomic force microscopy. Hall measurements were used to determine electrical properties as a function of radiation repair anneal times. Results of physical and electrical characterizations demonstrate that the end products of microwave enhanced ion-cut processing do not appreciably differ from those using more traditional means of exfoliation. © 2007 American Institute of Physics

Comparing computer-generated and pathologist-generated tumour segmentations for immunohistochemical scoring of breast tissue microarrays

BACKGROUND: Tissue microarrays (TMAs) have become a valuable resource for biomarker expression in translational research. Immunohistochemical (IHC) assessment of TMAs is the principal method for analysing large numbers of patient samples, but manual IHC assessment of TMAs remains a challenging and laborious task. With advances in image analysis, computer-generated analyses of TMAs have the potential to lessen the burden of expert pathologist review. METHODS: In current commercial software computerised oestrogen receptor (ER) scoring relies on tumour localisation in the form of hand-drawn annotations. In this study, tumour localisation for ER scoring was evaluated comparing computer-generated segmentation masks with those of two specialist breast pathologists. Automatically and manually obtained segmentation masks were used to obtain IHC scores for thirty-two ER-stained invasive breast cancer TMA samples using FDA-approved IHC scoring software. RESULTS: Although pixel-level comparisons showed lower agreement between automated and manual segmentation masks (κ=0.81) than between pathologists' masks (κ=0.91), this had little impact on computed IHC scores (Allred; [Image: see text]=0.91, Quickscore; [Image: see text]=0.92). CONCLUSIONS: The proposed automated system provides consistent measurements thus ensuring standardisation, and shows promise for increasing IHC analysis of nuclear staining in TMAs from large clinical trials

Deep residual networks for automatic segmentation of laparoscopic videos of the liver

MOTIVATION: For primary and metastatic liver cancer patients undergoing liver resection, a laparoscopic approach can reduce recovery times and morbidity while offering equivalent curative results; however, only about 10% of tumours reside in anatomical locations that are currently accessible for laparoscopic resection. Augmenting laparoscopic video with registered vascular anatomical models from pre-procedure imaging could support using laparoscopy in a wider population. Segmentation of liver tissue on laparoscopic video supports the robust registration of anatomical liver models by filtering out false anatomical correspondences between pre-procedure and intra-procedure images. In this paper, we present a convolutional neural network (CNN) approach to liver segmentation in laparoscopic liver procedure videos. METHOD: We defined a CNN architecture comprising fully-convolutional deep residual networks with multi-resolution loss functions. The CNN was trained in a leave-one-patient-out cross-validation on 2050 video frames from 6 liver resections and 7 laparoscopic staging procedures, and evaluated using the Dice score. RESULTS: The CNN yielded segmentations with Dice scores ≥0.95 for the majority of images; however, the inter-patient variability in median Dice score was substantial. Four failure modes were identified from low scoring segmentations: minimal visible liver tissue, inter-patient variability in liver appearance, automatic exposure correction, and pathological liver tissue that mimics non-liver tissue appearance. CONCLUSION: CNNs offer a feasible approach for accurately segmenting liver from other anatomy on laparoscopic video, but additional data or computational advances are necessary to address challenges due to the high inter-patient variability in liver appearance

Low Friction Flows of Liquids at Nanopatterned Interfaces

With the recent important development of microfluidic systems, miniaturization of flow devices has become a real challenge. Microchannels, however, are characterized by a large surface to volume ratio, so that surface properties strongly affect flow resistance in submicrometric devices. We present here results showing that the concerted effect of wetting . properties and surface roughness may considerably reduce friction of the fluid past the boundaries. The slippage of the fluid at the channel boundaries is shown to be drastically increased by using surfaces that are patterned at the nanometer scale. This effect occurs in the regime where the surface pattern is partially dewetted, in the spirit of the 'superhydrophobic' effects that have been recently discovered at the macroscopic scales. Our results show for the first time that, in contrast to the common belief, surface friction may be reduced by surface roughness. They also open the possibility of a controlled realization of the 'nanobubbles' that have long been suspected to play a role in interfacial slippag

Alzheimer's disease: using gene/protein network machine learning for molecule discovery in olive oil

Alzheimer's disease (AD) poses a profound human, social, and economic burden. Previous studies suggest that extra virgin olive oil (EVOO) may be helpful in preventing cognitive decline. Here, we present a network machine learning method for identifying bioactive phytochemicals in EVOO with the highest potential to impact the protein network linked to the development and progression of the AD. A balanced classification accuracy of 70.3 ± 2.6% was achieved in fivefold cross-validation settings for predicting late-stage experimental drugs targeting AD from other clinically approved drugs. The calibrated machine learning algorithm was then used to predict the likelihood of existing drugs and known EVOO phytochemicals to be similar in action to the drugs impacting AD protein networks. These analyses identified the following ten EVOO phytochemicals with the highest likelihood of being active against AD: quercetin, genistein, luteolin, palmitoleate, stearic acid, apigenin, epicatechin, kaempferol, squalene, and daidzein (in the order from the highest to the lowest likelihood). This in silico study presents a framework that brings together artificial intelligence, analytical chemistry, and omics studies to identify unique therapeutic agents. It provides new insights into how EVOO constituents may help treat or prevent AD and potentially provide a basis for consideration in future clinical studies