Natural clusters of tuberous sclerosis complex (TSC)-associated neuropsychiatric disorders (TAND): new findings from the TOSCA TAND research project.

BACKGROUND: Tuberous sclerosis complex (TSC)-associated neuropsychiatric disorders (TAND) have unique, individual patterns that pose significant challenges for diagnosis, psycho-education, and intervention planning. A recent study suggested that it may be feasible to use TAND Checklist data and data-driven methods to generate natural TAND clusters. However, the study had a small sample size and data from only two countries. Here, we investigated the replicability of identifying natural TAND clusters from a larger and more diverse sample from the TOSCA study. METHODS: As part of the TOSCA international TSC registry study, this embedded research project collected TAND Checklist data from individuals with TSC. Correlation coefficients were calculated for TAND variables to generate a correlation matrix. Hierarchical cluster and factor analysis methods were used for data reduction and identification of natural TAND clusters. RESULTS: A total of 85 individuals with TSC (female:male, 40:45) from 7 countries were enrolled. Cluster analysis grouped the TAND variables into 6 clusters: a scholastic cluster (reading, writing, spelling, mathematics, visuo-spatial difficulties, disorientation), a hyperactive/impulsive cluster (hyperactivity, impulsivity, self-injurious behavior), a mood/anxiety cluster (anxiety, depressed mood, sleep difficulties, shyness), a neuropsychological cluster (attention/concentration difficulties, memory, attention, dual/multi-tasking, executive skills deficits), a dysregulated behavior cluster (mood swings, aggressive outbursts, temper tantrums), and an autism spectrum disorder (ASD)-like cluster (delayed language, poor eye contact, repetitive behaviors, unusual use of language, inflexibility, difficulties associated with eating). The natural clusters mapped reasonably well onto the six-factor solution generated. Comparison between cluster and factor solutions from this study and the earlier feasibility study showed significant similarity, particularly in cluster solutions. CONCLUSIONS: Results from this TOSCA research project in an independent international data set showed that the combination of cluster analysis and factor analysis may be able to identify clinically meaningful natural TAND clusters. Findings were remarkably similar to those identified in the earlier feasibility study, supporting the potential robustness of these natural TAND clusters. Further steps should include examination of larger samples, investigation of internal consistency, and evaluation of the robustness of the proposed natural clusters

Flexible and self-adaptive sense-and-compress for sub-microWatt always-on sensory recording

Miniaturized sensory systems for IoT applications experience a severe power burden from their wireless link and/or embedded storage system. Compressive sensing techniques target data compression before storage and transmission to save power, while minimizing information loss. This work proposes a self-adaptive sense-and-compress system, which consumes only 45-884n W while continuously recording and compressing signals with a bandwidth up to 5kHz. The flexible system uses a combination of off-line Evolutionary Algorithms, and on-line self-adaptivity to constantly adapt to the incoming sensory data statistics, and the current application quality requirements. The 0.27mm2 sense-and-compress interface is integrated in a 65nm CMOS technology, together with an on-board temperature sensor, or can interface with any external sensor. The scalable, self-adaptive system is moreover heavily optimized for low-power and low-leakage, resulting in a tiny, efficient, yet flexible interface allowing always-on sensory monitoring, while consuming 2.5X less power compared to the current State-of-the-Art

Flexible, Self-Adaptive Sense-and-Compress SoC for Sub-microWatt Always-On Sensory Recording

We present a 5-sensor, fully integrated sensing system with interchangeable sensors and programmable configuration to create a sub-microWatt multisensor node that can tackle a wide range of sensing applications. Furthermore, the sensor node is capable of autonomously adapting its configuration to the application requirements hence minimizing system power. Such self-reconfiguration is enabled at low overhead by developing an automated offline optimization strategy, in combination with an autonomous embedded configuration controller, using the concept of behavioral trees (BTs). The resulting fully integrated platform consumes a maximum of 321 nW when sampling at 500 Hz and 3025 nW at 8 kHz. Furthermore, we demonstrate the end-to-end autonomous optimization flow for two different applications exploiting different sensors: 1) human activity recognition using accelerometers and 2) machine listening using a microphone. Both use cases demonstrate that the introduced system and methodology reduces the power by more than a factor 2 without losing significant application detection accuracy

Exploring the unknown through successive generations of low power and low resource versatile agents

The Phoenix1 project aims to develop a new approach to explore unknown environments, based on multiple measurement campaigns carried out by extremely tiny devices, called agents, that gather data through multiple sensors. These low power and low resource agents are configured specifically for each measurement campaign to achieve the exploration goal in the smallest number of iterations. Thus, the main design challenge is to build agents as much reconfigurable as possible. This paper introduces the Phoenix project in more details, and presents first developments in the agent design

Very Low Birth Weight Is an Independent Risk Factor for Emergency Surgery in Premature Infants with Inguinal Hernia

BACKGROUND: Common surgical knowledge is that inguinal hernia repair in premature infants should be postponed until they reach a certain weight or age. Optimal management, however, is still under debate. The objective of this study was to collect evidence for the optimal management of inguinal hernia repair in premature infants. STUDY DESIGN: In the period between 2010 and 2013, data for all premature infants with inguinal hernia who underwent hernia correction within 3 months after birth in the Erasmus MC-Sophia Children's Hospital, Rotterdam were analyzed. Primary outcomes measures were the incidences of incarceration and subsequent emergency surgery. In a multivariate analysis, Cox proportional hazards model served to identify independent risk factors for incarceration requiring an emergency procedure. RESULTS: A total of 142 premature infants were included in the analysis. Median follow-up was 28 months (range 15 to 39 months). Seventy-nine premature infants (55.6%) presented with a symptomatic inguinal hernia; emergency surgery was performed in 55.7%. Complications occurred in 27.3% of emergency operations vs 10.2% after elective repair; recurrences occurred in 13.6% vs 2.0%, respectively. Very low birth weight (<= 1,500 g) was an independent risk factor for emergency surgery, with a hazard ratio of 2.7 in the Cox proportional hazards model. CONCLUSIONS: More than half of premature infants with an inguinal hernia have incarceration. Those with very low birth weight have a 3-fold greater risk of requiring an emergency procedure than heavier premature infants. Emergency repair results in higher recurrence rates and more complications. Elective hernia repair is recommended, particularly in very low birth weight premature infants. (C) 2015 by the American College of Surgeon

Data from: Individual variation in winter supplementary food consumption and its consequences for reproduction in wild birds.

The provision of wild birds with supplementary food has increased substantially over recent decades. While it is assumed that provisioning birds is beneficial, supplementary feeding can have detrimental ‘carry-over’ effects on reproductive traits. Due to difficulties in monitoring individual feeding behaviour, assessing how individuals within a population vary in their exploitation of supplementary food resources has been limited. Quantifying individual consumption of supplementary food is necessary to understand the operation of carry-over effects at the individual level. We used Radio Frequency Identification (RFID) technology and automated feeders to estimate individual consumption of supplementary winter food in a large wild population of great tits Parus major and blue tits Cyanistes caeruleus. Using these data, we identified demographic factors that explained individual variation in levels of supplementary food consumption. We also tested for carry-over effects of supplementary food consumption on recruitment, reproductive success and a measure of survival. Individual variation in consumption of supplementary food was explained by differences between species, ages, sexes and years. Individuals were consistent across time in their usage of supplementary resources. We found no strong evidence that the extent of supplementary food consumption directly influenced subsequent fitness parameters. Such effects may instead result from supplementary food influencing population demographics by enhancing the survival and subsequent breeding of less competitive individuals, which reduce average breeding parameters and increase density-dependent competition. Carry-over effects of supplementary feeding are not universal and may depend upon the temporal availability of the food provided. Our study demonstrates how RFID systems can be used to examine individual-level behaviour with minimal effects on fitness