Agenesis of human pancreas due to decreased half-life of insulin promoter factor 1.

Neonatal diabetes mellitus can be transient or permanent. The severe form of permanent neonatal diabetes mellitus can be associated with pancreas agenesis. Normal pancreas development is controlled by a cascade of transcription factors, where insulin promoter factor 1 (IPF1) plays a crucial role. Here, we describe two novel mutations in the IPF1 gene leading to pancreas agenesis. Direct sequence analysis of exons 1 and 2 of the IPF1 gene revealed two point mutations within the homeobox in exon 2. Genetic analysis of the parents showed that each mutation was inherited from one parent. Mutations localized in helices 1 and 2, respectively, of the homeodomain, decreased the protein half-life significantly, leading to intracellular IPF1 levels of 36% and 27% of wild-type levels. Both mutant forms of IPF1 were normally translocated to the nucleus, and their DNA binding activity on different known target promoters was similar to that of the wild-type protein. However, transcriptional activity of both mutant IPF1 proteins, alone or in combination with HNF3 beta/Foxa2, Pbx1, or the heterodimer E47-beta 2 was reduced, findings accounted for by decreased IPF1 steady state levels and not by impaired protein-protein interactions. We conclude that the IPF1 level is critical for human pancreas formation.

Energy expenditure by doubly labeled water: validation in humans and proposed calculation.

To further validate the doubly labeled water method for measurement of CO2 production and energy expenditure in humans, we compared it with near-continuous respiratory gas exchange in nine healthy young adult males. Subjects were housed in a respiratory chamber for 4 days. Each received 2H2(18)O at either a low (n = 6) or a moderate (n = 3) isotope dose. Low and moderate doses produced initial 2H enrichments of 5 and 10 X 10(-3) atom percent excess, respectively, and initial 18O enrichments of 2 and 2.5 X 10(-2) atom percent excess, respectively. Total body water was calculated from isotope dilution in saliva collected at 4 and 5 h after the dose. CO2 production was calculated by the two-point method using the isotopic enrichments of urines collected just before each subject entered and left the chamber. Isotope enrichments relative to predose samples were measured by isotope ratio mass spectrometry. At low isotope dose, doubly labeled water overestimated average daily energy expenditure by 8 +/- 9% (SD) (range -7 to 22%). At moderate dose the difference was reduced to +4 +/- 5% (range 0-9%). The isotope elimination curves for 2H and 18O from serial urines collected from one of the subjects showed expected diurnal variations but were otherwise quite smooth. The overestimate may be due to approximations in the corrections for isotope fractionation and isotope dilution. An alternative approach to the corrections is presented that reduces the overestimate to 1%.

Autosomal-Recessive Posterior Microphthalmos Is Caused by Mutations in PRSS56, a Gene Encoding a Trypsin-Like Serine Protease.

Posterior microphthalmos (MCOP) is a rare isolated developmental anomaly of the eye characterized by extreme hyperopia due to short axial length. The population of the Faroe Islands shows a high prevalence of an autosomal-recessive form (arMCOP) of the disease. Based on published linkage data, we refined the position of the disease locus (MCOP6) in an interval of 250 kb in chromosome 2q37.1 in two large Faroese families. We detected three different mutations in PRSS56. Patients of the Faroese families were either homozygous for c.926G>C (p.Trp309Ser) or compound heterozygous for c.926G>C and c.526C>G (p.Arg176Gly), whereas a homozygous 1 bp duplication (c.1066dupC) was identified in five patients with arMCOP from a consanguineous Tunisian family. In one patient with MCOP from the Faroe Islands and in another one from Turkey, no PRSS56 mutation was detected, suggesting nonallelic heterogeneity of the trait. Using RT-PCR, PRSS56 transcripts were detected in samples derived from the human adult retina, cornea, sclera, and optic nerve. The expression of the mouse ortholog could be first detected in the eye at E17 and was maintained into adulthood. The predicted PRSS56 protein is a 603 amino acid long secreted trypsin-like serine peptidase. The c.1066dupC is likely to result in a functional null allele, whereas the two point mutations predict the replacement of evolutionary conserved and functionally important residues. Molecular modeling of the p.Trp309Ser mutant suggests that both the affinity and reactivity of the enzyme toward in vivo protein substrates are likely to be substantially reduced.

Transcriptional control of the hydrogen cyanide biosynthetic genes hcnABC by the anaerobic regulator ANR and the quorum-sensing regulators LasR and RhlR in Pseudomonas aeruginosa.

Virulence factors of Pseudomonas aeruginosa include hydrogen cyanide (HCN). This secondary metabolite is maximally produced at low oxygen tension and high cell densities during the transition from exponential to stationary growth phase. The hcnABC genes encoding HCN synthase were identified on a genomic fragment complementing an HCN-deficient mutant of P. aeruginosa PAO1. The hcnA promoter was found to be controlled by the FNR-like anaerobic regulator ANR and by the quorum-sensing regulators LasR and RhlR. Primer extension analysis revealed two transcription starts, T1 and T2, separated by 29 bp. Their function was confirmed by transcriptional lacZ fusions. The promoter sequence displayed an FNR/ANR box at -42.5 bp upstream of T2 and a lux box centered around -42.5 bp upstream of T1. Expression of the hcn genes was completely abolished when this lux box was deleted or inactivated by two point mutations in conserved nucleotides. The lux box was recognized by both LasR [activated by N-(oxododecanoyl)-homoserine lactone] and RhlR (activated by N-butanoyl-homoserine lactone), as shown by expression experiments performed in quorum-sensing-defective P. aeruginosa mutants and in the N-acyl-homoserine lactone-negative heterologous host P. fluorescens CHA0. A second, less conserved lux box lying 160 bp upstream of T1 seems to account for enhanced quorum-sensing-dependent expression. Without LasR and RhlR, ANR could not activate the hcn promoter. Together, these data indicate that expression of the hcn promoter from T1 can occur under quorum-sensing control alone. Enhanced expression from T2 appears to rely on a synergistic action between LasR, RhlR, and ANR.

Genetic heterogeneity of the Coppock-like cataract: a mutation in CRYBB2 on chromosome 22q11.2.

PURPOSE: To identify the genetic defect for the Coppock-like cataract (CCL) affecting a Swiss family, which defect was unlinked to the chromosome 2q33-35 CCL locus. METHODS: A large family was characterized for linkage analysis by slit lamp examination or by the review of drawings made before cataract extraction. The affection status was attributed before genotyping, and the genotyping was masked to the affection status. Two-point and multipoint linkage analyses were performed using the MLINK and the LINKMAP components of the LINKAGE program package (ver. 5.1), respectively. Mutational analysis of candidate genes was performed by a combination of direct cycle sequencing and an amplification refractory mutation system assay. RESULTS: Ten individuals were affected with the CCL phenotype. The disease was autosomal dominant and appeared to be fully penetrant. A new CCL locus was identified on chromosome 22q11.2 within a 11.67-cM interval (maximum lod score [Zmax] = 4.14; theta = 0). Mutational analysis of the CRYBB2 candidate gene identified a disease-causing mutation in exon 6. This sequence change was identical with that previously described to be associated with the cerulean cataract, a clinically distinct entity. CONCLUSIONS: The CCL phenotype is genetically heterogeneous with a second gene on chromosome 22q11.2, CRYBB2. The CCL and the cerulean cataract are two distinct clinical entities associated with the same genetic defect. This work provides evidence for a modifier factor that influences cataract formation and that remains to be identified.

Uncertainty Quantification with Support Vector Regression Prediction Models

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. The paper considers a data driven approach in modelling uncertainty in spatial predictions. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic features and describe stochastic variability and non-uniqueness of spatial properties. It is able to capture and preserve key spatial dependencies such as connectivity, which is often difficult to achieve with two-point geostatistical models. Semi-supervised SVR is designed to integrate various kinds of conditioning data and learn dependences from them. A stochastic semi-supervised SVR model is integrated into a Bayesian framework to quantify uncertainty with multiple models fitted to dynamic observations. The developed approach is illustrated with a reservoir case study. The resulting probabilistic production forecasts are described by uncertainty envelopes.

The effectiveness of a standardised preoperative preparation in reducing child and parent anxiety: a single-blind randomised controlled trial.

AIMS: To evaluate the effect of a structured preoperative preparation on child and parent state anxiety, child behavioural change and parent satisfaction. BACKGROUND: It is estimated that around 50-70% of hospitalised children experience severe anxiety and distress prior to surgery. Children who are highly anxious and distressed preoperatively are likely to be distressed on awakening and have negative postoperative behaviour. Although education before surgery has been found to be useful mostly in North America, the effectiveness of preoperative preparation programme adapted to the Australian context remains to be tested. DESIGN: This single-blind randomised controlled study was conducted at a tertiary referral hospital for children in Western Australia. METHODS: Following ethics approval and parental consent, 73 children and one of their carers (usually a parent) were randomly assigned into two groups. The control group had standard practice with no specific preoperative education and the experimental group received a preoperative preparation, including a photo file, demonstration of equipment using a role-modelling approach and a tour. RESULTS: The preoperative preparation reduced parent state anxiety significantly (-2·32, CI -4·06 to -0·56, p = 0·009), but not child anxiety (-0·59, CI -1·23 to 0·06, p = 0·07). There was no significant difference in child postoperative behaviour or parent satisfaction between the groups. There was a significant two-point pain score reduction in the preoperative preparation group, when compared with the control group median 2 (IQR 5) and 4 (IQR 4), respectively (p = 0·001).¦CONCLUSIONS: Preoperative preparation was more efficient on parent than child. Although the preoperative preparation had limited effect on child anxiety, it permitted to decrease pain experience in the postoperative period.¦RELEVANCE TO CLINICAL PRACTICE: Parents should be actively involved in their child preoperative preparation.

A genomewide scan for early-onset coronary artery disease in 438 families: the GENECARD Study.

A family history of coronary artery disease (CAD), especially when the disease occurs at a young age, is a potent risk factor for CAD. DNA collection in families in which two or more siblings are affected at an early age allows identification of genetic factors for CAD by linkage analysis. We performed a genomewide scan in 1,168 individuals from 438 families, including 493 affected sibling pairs with documented onset of CAD before 51 years of age in men and before 56 years of age in women. We prospectively defined three phenotypic subsets of families: (1) acute coronary syndrome in two or more siblings; (2) absence of type 2 diabetes in all affected siblings; and (3) atherogenic dyslipidemia in any one sibling. Genotypes were analyzed for 395 microsatellite markers. Regions were defined as providing evidence for linkage if they provided parametric two-point LOD scores >1.5, together with nonparametric multipoint LOD scores >1.0. Regions on chromosomes 3q13 (multipoint LOD = 3.3; empirical P value <.001) and 5q31 (multipoint LOD = 1.4; empirical P value <.081) met these criteria in the entire data set, and regions on chromosomes 1q25, 3q13, 7p14, and 19p13 met these criteria in one or more of the subsets. Two regions, 3q13 and 1q25, met the criteria for genomewide significance. We have identified a region on chromosome 3q13 that is linked to early-onset CAD, as well as additional regions of interest that will require further analysis. These data provide initial areas of the human genome where further investigation may reveal susceptibility genes for early-onset CAD.

Etude clinique et analyse de liaison au locus 3q28 de deux familles suisses avec atrophie optique dominante de Kjer (OPA1) [Clinical study and genetic 3q28 locus linkage in 2 Swiss families with Kjer dominant optic atrophy (OPA1)]

METHODS: We examined 20 patients from 2 unrelated Swiss families to describe their clinical phenotype. In addition, a linkage analysis was performed in an attempt to confirm the reported genetic homogeneity of this condition as well as to refine its genomic localization. RESULTS: Two point analysis provided a cumulative LOD-score of 3.03 with marker D3S 2305. The absence of recombination precluded further refinement of the disease interval. CONCLUSIONS: Our data confirm the genetic homogeneity and the extreme variability of expression, occasionally mimicking low tension glaucoma.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Analysis of a finite volume method for a cross-diffusion model in population dynamics

The main goal of this paper is to propose a convergent finite volume method for a reactionâeuro"diffusion system with cross-diffusion. First, we sketch an existence proof for a class of cross-diffusion systems. Then the standard two-point finite volume fluxes are used in combination with a nonlinear positivity-preserving approximation of the cross-diffusion coefficients. Existence and uniqueness of the approximate solution are addressed, and it is also shown that the scheme converges to the corresponding weak solution for the studied model. Furthermore, we provide a stability analysis to study pattern-formation phenomena, and we perform two-dimensional numerical examples which exhibit formation of nonuniform spatial patterns. From the simulations it is also found that experimental rates of convergence are slightly below second order. The convergence proof uses two ingredients of interest for various applications, namely the discrete Sobolev embedding inequalities with general boundary conditions and a space-time $L^1$ compactness argument that mimics the compactness lemma due to Kruzhkov. The proofs of these results are given in the Appendix.

Influence of heart motion on cardiac output estimation by means of electrical impedance tomography: a case study.

Electrical impedance tomography (EIT) is a non-invasive imaging technique that can measure cardiac-related intra-thoracic impedance changes. EIT-based cardiac output estimation relies on the assumption that the amplitude of the impedance change in the ventricular region is representative of stroke volume (SV). However, other factors such as heart motion can significantly affect this ventricular impedance change. In the present case study, a magnetic resonance imaging-based dynamic bio-impedance model fitting the morphology of a single male subject was built. Simulations were performed to evaluate the contribution of heart motion and its influence on EIT-based SV estimation. Myocardial deformation was found to be the main contributor to the ventricular impedance change (56%). However, motion-induced impedance changes showed a strong correlation (r = 0.978) with left ventricular volume. We explained this by the quasi-incompressibility of blood and myocardium. As a result, EIT achieved excellent accuracy in estimating a wide range of simulated SV values (error distribution of 0.57 ± 2.19 ml (1.02 ± 2.62%) and correlation of r = 0.996 after a two-point calibration was applied to convert impedance values to millilitres). As the model was based on one single subject, the strong correlation found between motion-induced changes and ventricular volume remains to be verified in larger datasets.

Managing the Sick Child in the Era of Declining Malaria Transmission: Development of ALMANACH, an Electronic Algorithm for Appropriate Use of Antimicrobials.

OBJECTIVE: To review the available knowledge on epidemiology and diagnoses of acute infections in children aged 2 to 59 months in primary care setting and develop an electronic algorithm for the Integrated Management of Childhood Illness to reach optimal clinical outcome and rational use of medicines. METHODS: A structured literature review in Medline, Embase and the Cochrane Database of Systematic Review (CDRS) looked for available estimations of diseases prevalence in outpatients aged 2-59 months, and for available evidence on i) accuracy of clinical predictors, and ii) performance of point-of-care tests for targeted diseases. A new algorithm for the management of childhood illness (ALMANACH) was designed based on evidence retrieved and results of a study on etiologies of fever in Tanzanian children outpatients. FINDINGS: The major changes in ALMANACH compared to IMCI (2008 version) are the following: i) assessment of 10 danger signs, ii) classification of non-severe children into febrile and non-febrile illness, the latter receiving no antibiotics, iii) classification of pneumonia based on a respiratory rate threshold of 50 assessed twice for febrile children 12-59 months; iv) malaria rapid diagnostic test performed for all febrile children. In the absence of identified source of fever at the end of the assessment, v) urine dipstick performed for febrile children <2 years to consider urinary tract infection, vi) classification of 'possible typhoid' for febrile children >2 years with abdominal tenderness; and lastly vii) classification of 'likely viral infection' in case of negative results. CONCLUSION: This smartphone-run algorithm based on new evidence and two point-of-care tests should improve the quality of care of <5 year children and lead to more rational use of antimicrobials.

Winter habitat selection by two sympatric forest grouse in western Switzerland: implications for conservation

Two endangered tetraonids, the capercaillie (Tetrao urogallus) and the hazel grouse (Bonasa bonasia rupestris), are sympatric throughout part of their distribution range in central Europe. Precise information on their specific habitat requirements is needed if the coexistence of both species in exploited forests is to be maintained. We quantified winter habitat selection for both species in the upper part (1100-1600 m) of the Jura mountains (Switzerland). No preference for altitude or exposure could be detected. Capercaillie preferred open forests (including grazed forests) with a sparse canopy dominated by spruce (Picea abies) and fir (Abies alba), and avoided dense undercanopy and understorey, especially when dominated by spruce and beech (Fagus sylvatica). By contrast, hazel grouse preferred feeding sites with a dense understorey of rowan (Sorbus aucuparia), willow (Salix sp.), beech and spruce. These preferences can be related to the feeding habits and predator avoidance behaviour of both species. Coexistence thus requires a mosaic distribution of habitat types, with a matrix of open forests (30% canopy cover) where fir is favoured, and understorey kept sparse (20%). Group-cuts of mature trees should allow regeneration patches, where a dense understorey (50% cover) should provide suitable habitats for hazel grouse

46, XY gonadal dysgenesis: new SRY point mutation in two siblings with paternal germ line mosaicism.

Stoppa-Vaucher S, Ayabe T, Paquette J, Patey N, Francoeur D, Vuissoz J-M, Deladoëy J, Samuels ME, Ogata T, Deal CL. 46, XY gonadal dysgenesis: new SRY point mutation in two siblings with paternal germ line mosaicism. Familial recurrence risks are poorly understood in cases of de novo mutations. In the event of parental germ line mosaicism, recurrence risks can be higher than generally appreciated, with implications for genetic counseling and clinical practice. In the course of treating a female with pubertal delay and hypergonadotropic hypogonadism, we identified a new missense mutation in the SRY gene, leading to somatic feminization of this karyotypically normal XY individual. We tested a younger sister despite a normal onset of puberty, who also possessed an XY karyotype and the same SRY mutation. Imaging studies in the sister revealed an ovarian tumor, which was removed. DNA from the father's blood possessed the wild type SRY sequence, and paternity testing was consistent with the given family structure. A brother was 46, XY with a wild type SRY sequence strongly suggesting paternal Y-chromosome germline mosaicism for the mutation. In disorders of sexual development (DSDs), early diagnosis is critical for optimal psychological development of the affected patients. In this case, preventive karyotypic screening allowed early diagnosis of a gonadal tumor in the sibling prior to the age of normal puberty. Our results suggest that cytological or molecular diagnosis should be applied for siblings of an affected DSD individual.