An application of kernel methods to gene cluster temporal meta-analysis

The application of various clustering techniques for large-scale gene-expression measurement experiments is a well-established method in bioinformatics. Clustering is also usually accompanied by functional characterization of gene sets by assessing statistical enrichments of structured vocabularies, such as the gene ontology (GO) [Gene Ontology Consortium. The gene ontology (GO) project in 2006. Nucleic Acids Research (Database issue), vol. 34; 2006. p. D322–6]. If different clusters are generated for correlated experiments, a machine learning step termed cluster meta-analysis may be performed, in order to discover relations among the components of such sets. Several approaches have been proposed: in particular, kernel methods may be used to exploit the graphical structure of typical ontologies such as GO. Following up the formulation of such approach [Merico D, Zoppis I, Antoniotti M, Mauri G. Evaluating graph kernel methods for relation discovery in GO-annotated clusters. In: KES-2007/WIRN-2007, Part IV, Lecture notes in artificial intelligence, vol. 4694. Berlin: Springer; 2007. p. 892–900; Zoppis I, Merico D, Antoniotti M, Mishra B, Mauri G. Discovering relations among GO-annotated clusters by graph kernel methods. In: Proceedings of the 2007 international symposium on bioinformatics research and applications. Lecture notes in computer science, vol. 4463. Berlin: Springer; 2007], in this paper we discuss, from an information-theoretic point of view, further results about its applicability and its performance.     

On the Covering Radius of Long 5-ary BCH Codes with Minimum Distance 7

 We study S. D. Cohen’s method of estimating the covering radius of long primitive non-binary BCH codes with prime field alphabet. As an application we show that the covering radius is at most 7 for long 5-ary BCH codes with minimum distance seven. Received: November 13, 1996; revised version: March 11, 1997     

Hyperplasia of lymphatic vessels in VEGF-C transgenic mice

No growth factors specific for the lymphatic vascular system have yet been described. Vascular endothelial growth factor (VEGF) regulates vascular permeability and angiogenesis, but does not promote lymphangiogenesis. Overexpression of VEGF-C, a ligand of the VEGF receptors VEGFR-3 and VEGFR-2, in the skin of transgenic mice resulted in lymphatic, but not vascular, endothelial proliferation and vessel enlargement. Thus, VEGF-C induces selective hyperplasia of the lymphatic vasculature, which is involved in the draining of interstitial fluid and in immune function, inflammation, and tumor metastasis. VEGF-C may play a role in disorders involving the lymphatic system and may be of potential use in therapeutic lymphangiogenesis.     

Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes

Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this sense, error-correcting output codes (ECOC) show to robustly combine binary classifiers to solve multi-class problems. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different sub-sets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers. Furthermore, the combination of RF and texture-based features also shows improvements over the state-of-the-art approaches.     

Time and frequency domain estimates of spontaneous baroreflex sensitivity provide early detection of autonomic dysfunction in diabetes mellitus

Diabetic autonomic dysfunction is associated with a high risk of mortality which makes its early identification clinically important. The aim of our study was to compare the detection of autonomic dysfunction provided by classical laboratory autonomic function tests with that obtained through computer assessment of the spontaneous sensitivity of the baroreceptor-heart rate reflex (BRS) by time domain and frequency domain techniques. In 20 normotensive diabetic patients (mean age +/- SD 41.9 +/- 8.1 years) with no evidence of autonomic dysfunction on laboratory autonomic testing (D0) blood pressure (BP) and ECG were continuously monitored over 15 min in the supine position. BRS was assessed as the slope of the regression line between spontaneous increases or reductions in systolic BP and linearly related lengthening or shortening in RR interval over sequences of at least 4 consecutive beats (sequence method), or as the squared ratio between RR interval and systolic BP spectral powers around 0.1 Hz. We compared the results with those of 32 age-matched normotensive diabetic patients with abnormal autonomic function tests (D1) and with those of 24 healthy age-matched control subjects with normal autonomic function tests (C). Compared to C, BRS was markedly less in D1 when assessed by both the slope of the two types of sequences (data pooled) and by the spectral method (-71.3% and -60.2% respectively, both p < 0.01). However, BRS was consistently although somewhat less markedly reduced in D0, the reduction being clearly evident for all the estimates (-57.0% and -43.5%, both p < 0.01). The effects were more evident than those obtained by the simple quantification of the RR interval variability. These data suggest that time and frequency domain estimates of spontaneous BRS allow earlier detection of diabetic autonomic dysfunction than classical laboratory autonomic tests. The estimates can be obtained by short non-invasive recording of the BP and RR interval signals in the supine patient, i.e. under conditions suitable for routine outpatient evaluation.     

Molecular Genetics of GLUT1DS Italian Pediatric Cohort: 10 Novel Disease-Related Variants and Structural Analysis

GLUT1 deficiency syndrome (GLUT1DS1; OMIM #606777) is a rare genetic metabolic disease, characterized by infantile-onset epileptic encephalopathy, global developmental delay, progressive microcephaly, and movement disorders (e.g., spasticity and dystonia). It is caused by heterozygous mutations in the SLC2A1 gene, which encodes the GLUT1 protein, a glucose transporter across the blood-brain barrier (BBB). Most commonly, these variants arise de novo resulting in sporadic cases, although several familial cases with AD inheritance pattern have been described. Twenty-seven Italian pediatric patients, clinically suspect of GLUT1DS from both sporadic and familial cases, have been enrolled. We detected by trios sequencing analysis 25 different variants causing GLUT1DS. Of these, 40% of the identified variants (10 out of 25) had never been reported before, including missense, frameshift, and splice site variants. Their structural mapping on the X-ray structure of GLUT1 strongly suggested the potential pathogenic effects of these novel disease-related mutations, broadening the genotypic spectrum heterogeneity found in the SLC2A1 gene. Moreover, 24% is located in a vulnerable region of the GLUT1 protein that involves transmembrane 4 and 5 helices encoded by exon 4, confirming a mutational hotspot in the SLC2A1 gene. Lastly, we investigated possible correlations between mutation type and clinical and biochemical data observed in our GLUT1DS cohort, revealing that splice site and frameshift variants are related to a more severe phenotype and low CSF parameters.     

GTV<Emphasis Type="SmallCaps">cut</Emphasis> for neuro-radiosurgery treatment planning: an MRI brain cancer seeded image segmentation method based on a cellular automata model

Despite of the development of advanced segmentation techniques, achieving accurate and reproducible gross tumor volume (GTV) segmentation results is still an important challenge in neuro-radiosurgery. Nowadays, magnetic resonance imaging (MRI) is the most prominent modality in radiation therapy for soft-tissue anatomical districts. Gamma Knife stereotactic neuro-radiosurgery is a minimally invasive technology for dealing with inaccessible or insufficiently treated tumors with traditional surgery or radiotherapy. During a treatment planning phase, the GTV is generally contoured by experienced neurosurgeons and radiation oncologists using fully manual segmentation procedures on MR images. Unfortunately, this operative methodology is definitely time-expensive and operator-dependent. Delineation result repeatability, in terms of both intra- and inter-operator reliability, can be achieved only by using computer-assisted approaches. In this paper a novel semi-automatic seeded image segmentation method, based on a cellular automata model, for MRI brain cancer detection and delineation is proposed. This approach, called GTVcut, employs an adaptive seed selection strategy and helps to segment the GTV, by identifying the target volume to be treated using the Gamma Knife device. The accuracy of GTVcut was evaluated on a dataset composed of 32 brain cancers, using both spatial overlap-based and distance-based metrics. The achieved experimental results are very reproducible, showing the effectiveness and the clinical feasibility of the proposed approach.     

Postprandial triglyceride response in visceral obesity in men

Although metabolic disturbances are often observed in obese patients, increased accumulation of visceral adipose tissue (AT) has been shown to be more closely associated with high fasting triglyceride (TG) and insulin levels as well as with low HDL cholesterol concentrations than with excess body fatness per se. Interestingly, the fasting concentration of plasma TGs has been shown to be an important determinant of the magnitude and duration of the postprandial TG response. Yet little is known about the respective contributions of obesity versus excess visceral AT to the variation in postprandial TG clearance. In the present study, we examined potential differences in postprandial triglyceride-rich lipoprotein (TRL) responses in subjects characterized by high versus low levels of visceral AT. In a sample of 43 men (mean age: 41.3 +/- 9.6 years), we found that both excess body fat and visceral obesity were associated with increased postprandial TG responses in total TRL (r = 0.33-0.45). We also found a strong relationship between fasting plasma TG levels and postprandial total TRL-TG concentrations (r = 0.79, P < 0.0001). When matched for total body fat mass, individuals with high levels of visceral AT (> or =130 cm2; n = 10) as assessed by computed tomography were characterized by increased medium- and small-TRL-TG responses (P < 0.05) compared with subjects with low visceral AT accumulation (<130 cm2; n = 10). Moreover, this elevated response of small-TRL triglycerides noted in men with high levels of visceral AT was not accompanied by a concomitant increased retinyl palmitate response in this TRL fraction, suggesting that visceral obesity in men is accompanied by higher postprandial VLDL production than is found in obese men with lower levels of visceral AT. Increased postprandial insulin and free fatty acid (FFA) responses were also noted in men with high levels of visceral AT. Finally, postheparin plasma lipoprotein lipase activity was negatively correlated with the total-TRL-TG response in a subsample of 32 individuals (r = -0.37, P < 0.05). The results of the present study suggest that visceral obesity is associated with an impaired postprandial TG clearance. Furthermore, the exaggerated postprandial FFA response observed in subjects with high visceral AT suggests that visceral obesity may contribute to fasting and postprandial hypertriglyceridemia by altering FFA metabolism in the postprandial state.