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Low-end LiDAR sensor provides an alternative for depth measurement and object recognition for lightweight devices. However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extraction. Therefore, a classification method which could receive sparse input, while providing ample leverage for the classification process to accurately differentiate objects within limited computing capability is required. To achieve reliable feature extraction from a sparse LiDAR point cloud, this paper proposes a novel Clustered Extraction and Centroid Based Clustered Extraction Method (CE-CBCE) method for feature extraction followed by a convolutional neural network (CNN) object classifier. The integration of the CE-CBCE and CNN methods enable us to utilize lightweight actuated LiDAR input and provides low computing means of classification while maintaining accurate detection. Based on genuine LiDAR data, the final result shows reliable accuracy of 97% through the method proposed.

BackgroundFew studies demonstrate delayed recovery after exercise in children and adults with heart disease. We assess the recovery patterns of gas exchange parameters and heart rate (HR) in children with repaired Tetralogy of Fallot (rToF) compared to healthy peers and investigate the correlation with ventricular function and QRS duration.Methods45 children after rToF and 45 controls performed a maximal incremental cardiopulmonary exercise test. In the subsequent recovery period, patterns of VO2, VCO2 and HR were analysed. Half-life time (T1/2) of the exponential decay and drop per minute (Recmin) were compared between groups. In the rToF group, correlations were examined between the recovery parameters and QRS-duration and ventricular function, described by fractional shortening (FS) and tricuspid annular plane systolic excursion (TAPSE) measured at baseline prior to exercise.ResultsRecovery of VO2 and VCO2 was delayed in rToF patients, half-life time values were higher compared to controls (T1/2VO2 52.51 ±11.29 s vs. 44.31 ± 10.47 s; p = 0.001 and T1/2VCO2 68.28 ± 13.84 s vs. 59.41 ± 12.06 s; p = 0.002) and percentage drop from maximal value was slower at each minute of recovery (pConclusionsChildren after rToF show a delayed gas exchange recovery after exercise. This delay correlates to ventricular function, demonstrating its importance in recovery after physical activity.

Schema therapy (ST) has been found to be effective in the treatment of borderline personality disorder (BPD). However very little is known about how the therapy is experienced by individuals with BPD including which specific elements of ST are helpful or unhelpful from their perspectives. The aim of this study is to explore BPD patients' experiences of receiving ST, in intensive group or combined group-individual format. Qualitative data were collected through semi-structured interviews with 36 individuals with a primary diagnosis of BPD (78% females) who received ST for at least 12 months. Participants were recruited as part of an international, multicenter randomized controlled trial (RCT). Interview data (11 Australian, 12 Dutch, 13 German) were analyzed following the procedures of qualitative content analysis. Patients' perceptions of the benefits gained in ST included improved self-understanding, and better awareness and management of their own emotional processes. While some aspects of ST, such as experiential techniques were perceived as emotionally confronting, patient narratives informed that this was necessary. Some recommendations for improved implementation of ST include the necessary adjunct of individual sessions to group ST and early discussion of therapy termination. Implications of the findings are also discussed, in particular the avenues for assessing the suitability of patients for group ST; management of group conflict and the optimal format for delivering treatment in the intensive group versus combined group-individual formats.

Children with Autism Spectrum Disorders (ASD) orient less to socially salient stimuli, such as dynamic social images, than typically developing children. In turn, this lack of social orienting is thought to impair affected individuals' socio communicative development. Here, we aim to explore the relationship between time spent on dynamic social images and ASD behaviors, such as joint attention and communication, in preschoolers on the autism spectrum. In this study, social orienting is measured using eye-tracking during a task consisting of side-by-side presentations of dynamic social images and dynamic geometric images. The side of the screen where each type of video was presented alternated between items to avoid visual perseveration from influencing the location of participants' first fixations. Visual exploration patterns recorded during the task from 33 preschoolers with ASD were compared with those of 27 typical developing (TD) children. Additionally, we quantified joint attention behaviors and used standardized parent reports to measure communication. We observed reduced orienting to dynamic social images in preschoolers with ASD compared to TD children. Also, ASD participants went to the dynamic social images less frequently for their first fixations. However, we observed great heterogeneity within the ASD group. ASD preschoolers who spent more time on the dynamic social images also presented more pronounced visual engagement with the dynamic social images (longer mean fixation duration and fewer saccades per second). Moreover, in the ASD group, more time spent on dynamic social images correlated with increased frequency of joint attention behaviors, which in turn correlated with improved communication skills. Our results support reduced social orienting in children with ASD, which correlated with their visual exploration patterns. Further, reduced orienting to the social world in young children with ASD is related to socio communicative deficits and should, therefore, be a focus of intervention programs as early as possible.

Diffusion processes in social networks often cause the emergence of global phenomena from individual behavior within a society. The study of those global phenomena and the simulation of those diffusion processes frequently require a good model of the global network. However, survey data and data from online sources are often restricted to single social groups or features, such as age groups, single schools, companies, or interest groups. Hence, a modeling approach is required that extrapolates the locally restricted data to a global network model. We tackle this Missing Data Problem using Link-Prediction techniques from social network research, network generation techniques from the area of Social Simulation, as well as a combination of both. We found that techniques employing less information may be more adequate to solve this problem, especially when data granularity is an issue. We validated the network models created with our techniques on a number of real-world networks, investigating degree distributions as well as the likelihood of links given the geographical distance between two nodes.

To study metabolic/inflammatory biomarker risk profiles in women with PCOS and PCOS offspring.Cross-sectional comparison of serum biomarkers.University Medical Center Utrecht.Hyperandrogenic PCOS women (HA-PCOS, n = 34), normoandrogenic PCOS women (NA-PCOS, n = 34), non-PCOS reference population (n = 32), PCOS offspring (n = 14, age 6-8 years), and a paedriatic reference population (n = 30).Clustering profile of adipocytokines (IL-1b, IL-6, IL-13, IL-17, IL-18, TNF-α, adiponectin, adipsin, leptin, chemerin, resistin, RBP4, DPP-IV/sCD26, CCL2/MCP-1), growth factors (PIGF, VEGF, sVEGF-R1), soluble cell adhesion molecules (sICAM-1/sCD54, sVCAM-1/sCD106), and other inflammatory related proteases (MMP-9, S100A8, Cathepsin S). Differences in median biomarker concentrations between groups, and associations with the free androgen index (FAI; Testosterone/SHBG x100).The cluster analysis identified leptin, RBP-4, DPP-IV and adiponectin as potential discriminative markers for HA-PCOS with a specifically strong correlation in cases with increased BMI. Leptin (R2 = 0.219) and adiponectin (R2 = 0.182) showed the strongest correlation with the FAI. When comparing median protein concentrations adult PCOS women with or without hyperandrogenemia, the most profound differences were observed for leptin (P < 0.001), DPP-IV (P = 0.005), and adiponectin (P < 0.001). Adjusting for age, BMI and multiple testing attenuated all differences. In PCOS offspring, MMP-9 (P = 0.001) and S100A8 (P < 0.001) concentrations were significantly higher compared to a healthy matched reference population, even after correcting for age and BMI and adjustment for multiple testing.In this preliminary investigation we observed significant differences in adipocytokines between women with or without hyperandrogenic PCOS and non-PCOS controls, mostly influenced by BMI. Leptin and adiponectin showed the strongest correlation with the FAI in adult women with PCOS. In PCOS offspring other inflammatory biomarkers (MMP-9, S100A8) were increased, suggesting that these children may exhibit increased chronic low-grade inflammation. Additional research is required to confirm results of the current exploratory investigation.

BACKGROUND:Neuroblastoma is a pediatric cancer that exhibits a wide clinical spectrum ranging from spontaneous regression in low-risk patients to fatal disease in high-risk patients. The identification of single nucleotide polymorphisms (SNPs) may help explain the heterogeneity of neuroblastoma and assist in identifying patients at higher risk for poor survival. SNPs in the TP53 pathway are of special importance, as several studies have reported associations between TP53 pathway SNPs and cancer. Of note, less than 2% of neuroblastoma tumors have a TP53 mutation at diagnosis. PATIENTS AND METHODS:We selected 21 of the most frequently studied SNPs in the TP53 pathway and evaluated their association with outcome in 500 neuroblastoma patients using TaqMan allelic discrimination assays. RESULTS AND CONCLUSION:We investigated the impact of 21 SNPs on overall survival, event-free survival, age at diagnosis, MYCN status, and stage of the disease in 500 neuroblastoma patients. A missense SNP in exon 10 of the CASP8 gene SNP D302H was associated with worse overall and event-free survival in patients with MYCN-amplified neuroblastoma tumors.

CONTEXT: Febrile children in primary care have a low risk for serious infection. Although several alarming signs and symptoms are proposed to have predictive value for serious infections, most are based on research in secondary care. The frequency of alarming signs/symptoms has not been established in primary care; however, in this setting differences in occurrence may influence their predictive value for serious infections. OBJECTIVE: To determine the frequency of alarming signs/symptoms in febrile children in primary care. DESIGN: Observational cohort study. Clinical information was registered in a semi-structured way and manually recoded. SETTING: General practitioners' out-of-hours service. SUBJECTS: Face-to-face patient contacts concerning children (aged ≤16 years) with fever were eligible for inclusion. MAIN OUTCOME MEASURES: Frequency of 18 alarming signs and symptoms as reported in the literature. RESULTS: A total of 10,476 patient contacts were included. The frequency of alarming signs/symptoms ranged from n = 1 (ABC instability; 40°C as reported by the parents; 12.9%) to 8,647 contacts (parental concern; 82.5%). CONCLUSION: Although the prevalence of specific alarming signs/symptoms is low in primary care, ≥50% of children have one or more alarming signs/symptoms. There is a need to determine the predictive value of alarming signs/symptoms not only for serious infections in primary care, but as well for increased risk of a complicated course of the illness.

BACKGROUND: Asian populations have an increased risk of developing cardiometabolic disorders at a lower body mass index (BMI) than other ethnic groups. Therefore, lower adult BMI cut-offs to determine overweight and obesity are recommended to assess the associated health risks for Asian (23 and 27.5 kg/m(2) respectively) and Asian Indian (23, 25 kg/m(2)) populations. The objective of this study was to develop BMI cut-offs for thinness, overweight, and obesity for South Asian children in the Netherlands, and to compare the BMI cut-offs and distribution with an Asian Indian reference, the WHO Child Growth Reference, and universal BMI cut-offs. METHODS: A reference cohort of 546 Surinamese South Asian boys and 521 girls, born between 1974-1976 (during the pre-obesity era) with 3408 and 3267 BMI measurements respectively, was retrospectively analysed. BMI-for-age charts were created with the LMS method. BMI centile curves passing through the cut-off points of 15 (thinness), 23 (overweight), 25 and 27.5 kg/m(2) (obesity) at 18y were drawn as cut-off levels. RESULTS: The BMI of Surinamese South Asian children had a similar distribution to the Asian Indian reference, apart from a lower mean and less variation. The BMI distribution differed considerably from the WHO reference and universal BMI criteria. The calculated BMI cut-offs corresponding to a BMI of 15, 23, 25, and 27.5 kg/m(2) at 18y were at the 7.1, 81.1, 89.8, and 95.5 percentile respectively in boys, and at the 2.7, 79.5, 89.2, and 95.2 percentile in girls. CONCLUSIONS: This is the first study proposing BMI cut-offs for South Asian children based on measurements from a prosperous population unaffected by the obesity epidemic. We recommend the use of these cut-offs in South Asian children in the Netherlands as these better reflect the health risks associated with thinness, overweight and obesity, and therefore may prevent the development of cardiometabolic disorders.

BACKGROUND: Although the throughput of next generation sequencing is increasing and at the same time the cost is substantially reduced, for the majority of laboratories whole genome sequencing of large cohorts of cancer samples is still not feasible. In addition, the low number of genomes that are being sequenced is often problematic for the downstream interpretation of the significance of the variants. Targeted resequencing can partially circumvent this problem; by focusing on a limited number of candidate cancer genes to sequence, more samples can be included in the screening, hence resulting in substantial improvement of the statistical power. In this study, a successful strategy for prioritizing candidate genes for targeted resequencing of cancer genomes is presented. RESULTS: Four prioritization strategies were evaluated on six different cancer types: genes were ranked using these strategies, and the positive predictive value (PPV) or mutation rate within the top-ranked genes was compared to the baseline mutation rate in each tumor type. Successful strategies generate gene lists in which the top is enriched for known mutated genes, as evidenced by an increase in PPV. A clear example of such an improvement is seen in colon cancer, where the PPV is increased by 2.3 fold compared to the baseline level when 100 top fitSNP genes are sequenced. CONCLUSIONS: A gene prioritization strategy based on the fitSNP scores appears to be most successful in identifying mutated cancer genes across different tumor entities, with variance of gene expression levels as a good second best.

Despite improvements in terms of sequence quality and price per basepair, Sanger sequencing remains restricted to screening of individual disease genes. The development of massively parallel sequencing (MPS) technologies heralded an era in which molecular diagnostics for multigenic disorders becomes reality. Here, we outline different PCR amplification based strategies for the screening of a multitude of genes in a patient cohort. We performed a thorough evaluation in terms of set-up, coverage and sequencing variants on the data of 10 GS-FLX experiments (over 200 patients). Crucially, we determined the actual coverage that is required for reliable diagnostic results using MPS, and provide a tool to calculate the number of patients that can be screened in a single run. Finally, we provide an overview of factors contributing to false negative or false positive mutation calls and suggest ways to maximize sensitivity and specificity, both important in a routine setting. By describing practical strategies for screening of multigenic disorders in a multitude of samples and providing answers to questions about minimum required coverage, the number of patients that can be screened in a single run and the factors that may affect sensitivity and specificity we hope to facilitate the implementation of MPS technology in molecular diagnostics.

BACKGROUND: Type 2 diabetes mellitus and muscle insulin resistance have been associated with reduced capacity of skeletal muscle mitochondria, possibly as a result of increased intake of dietary fat. Here, we examined the hypothesis that a prolonged high-fat diet consumption (HFD) increases the saturation of muscle mitochondrial membrane phospholipids causing impaired mitochondrial oxidative capacity and possibly insulin resistance. METHODOLOGY: C57BL/6J mice were fed an 8-week or 20-week low fat diet (10 kcal%; LFD) or HFD (45 kcal%). Skeletal muscle mitochondria were isolated and fatty acid (FA) composition of skeletal muscle mitochondrial phospholipids was analyzed by thin-layer chromatography followed by GC. High-resolution respirometry was used to assess oxidation of pyruvate and fatty acids by mitochondria. Insulin sensitivity was estimated by HOMA-IR. PRINCIPAL FINDINGS: At 8 weeks, mono-unsaturated FA (16∶1n7, 18∶1n7 and 18∶1n9) were decreased (-4.0%, p

Low-end LiDAR sensor provides an alternative for depth measurement and object recognition for lightweight devices. However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extraction. Therefore, a classification method which could receive sparse input, while providing ample leverage for the classification process to accurately differentiate objects within limited computing capability is required. To achieve reliable feature extraction from a sparse LiDAR point cloud, this paper proposes a novel Clustered Extraction and Centroid Based Clustered Extraction Method (CE-CBCE) method for feature extraction followed by a convolutional neural network (CNN) object classifier. The integration of the CE-CBCE and CNN methods enable us to utilize lightweight actuated LiDAR input and provides low computing means of classification while maintaining accurate detection. Based on genuine LiDAR data, the final result shows reliable accuracy of 97% through the method proposed., Competing Interests: The authors have declared that no competing interests exist.

Diffusion processes in social networks often cause the emergence of global phenomena from individual behavior within a society. The study of those global phenomena and the simulation of those diffusion processes frequently require a good model of the global network. However, survey data and data from online sources are often restricted to single social groups or features, such as age groups, single schools, companies, or interest groups. Hence, a modeling approach is required that extrapolates the locally restricted data to a global network model. We tackle this Missing Data Problem using Link-Prediction techniques from social network research, network generation techniques from the area of Social Simulation, as well as a combination of both. We found that techniques employing less information may be more adequate to solve this problem, especially when data granularity is an issue. We validated the network models created with our techniques on a number of real-world networks, investigating degree distributions as well as the likelihood of links given the geographical distance between two nodes.