Background
Previous studies have suggested that etiologic heterogeneity may complicate epidemiologic analyses designed to identify risk factors for birth defects Case classification uses knowledge of embryologic and pathogenetic mechanisms to make case groups more homogeneous and is important to the success of birth defects studies

METHODS
The goal of the National Birth Defects Prevention Study (NBDPS), an ongoing multi-site case–control study, is to identify environmental and genetic risk factors for birth defects Information on environmental risk factors is collected through an hour-long maternal interview, and DNA is collected from the infant and both parents for evaluation of genetic risk factors Clinical data on infants are reviewed by clinical geneticists to ensure they meet the detailed case definitions developed specifically for the study To standardize the methods of case classification for the study, an algorithm has been developed to guide NBDPS clinical geneticists in this process

RESULTS
Methods for case classification into isolated, multiple, and syndrome categories are described Defects considered minor for the purposes of case classification are defined Differences in the approach to case classification for studies of specific defects and of specific exposures are noted

CONCLUSIONS
The case classification schema developed for the NBDPS may be of value to other clinicians working on epidemiologic studies of birth defects etiology Consideration of these guidelines will lead to more comparable case groups, an important element of careful studies aimed at identifying risk factors for birth defects Birth Defects Research (Part A) 67:193–201, 2003 © 2003 Wiley-Liss, Inc

Guidelines for case classification for the National Birth Defects Prevention Study.

BACKGROUND AND PURPOSE: Because of improved visualization of posterior fossa structures with MR imaging, cerebellar malformations are recognized with increasing frequency. Herein we attempt to describe and propose a rational classification of cerebellar malformations. METHODS: MR images obtained in 70 patients with cerebellar malformations were retrospectively reviewed. The cerebellar malformations were initially divided into those with hypoplasia and those with dysplasia. They were then divided into focal and diffuse malformations. Finally, they were separated according to other features, such as brain stem involvement and cerebral involvement. RESULTS: All patients with diffuse cerebellar dysplasia (muscular dystrophy [n = 10], cytomegalovirus [n = 6], lissencephaly [n = 3],) had abnormalities of the cerebrum. Patients with focal cerebellar dysplasia of the Joubert (n = 12) and rhombencephalosynapsis (n = 8) types had variable cerebral dysplasia. Patients with nonsyndromic focal cerebellar dysplasia (isolated focal cerebellar cortical dysplasia [n = 2], cerebellar heterotopia with cerebellar cortical dysplasia [n = 1], idiopathic diffuse cerebellar dysplasia [n = 1], Lhermitte-Duclos syndrome [n = 1]) and those with cerebellar hypoplasia (isolated cerebellar hypoplasia [n = 6], pontocerebellar hypoplasia type 1 [n = 1]) had normal cerebra. Patients with features of Dandy-Walker malformation (n = 19) had both hypoplasia and dysplasia of the cerebellum. No notable difference was found between the cerebella of patients with large fourth ventricle cysts (Dandy-Walker malformations) and those without large fourth ventricle cysts (isolated cerebellar hypoplasia). Therefore, the Dandy-Walker malformation seems to be heterogeneous. CONCLUSION: Use of this classification system helps in the segregation and understanding of the relationship among cerebellar malformations. Although it will undoubtedly require revisions, this classification is a first step in combining imaging with molecular biology to facilitate understanding of cerebellar development and maldevelopment.

http://www.ajnr.org/content/ajnr/23/7/1074.full.pdf

Analysis and Classification of Cerebellar Malformations

Dandy-Walker malformation (DWM; OMIM #220200) is a common but poorly understood congenital cerebellar malformation in humans. Through physical mapping of 3q2 interstitial deletions in several individuals with DWM, we defined the first critical region associated with DWM, encompassing two adjacent Zinc finger in cerebellum genes, ZIC1 and ZIC4. Mice with a heterozygous deletion of these two linked genes have a phenotype that closely resembles DWM, providing a mouse model for this malformation.

/pdf/heterozygous-deletion-of-the-linked-genes-zic1-and-zic4-is-3oyrgrjczp.pdf

Heterozygous deletion of the linked genes ZIC1 and ZIC4 is involved in Dandy-Walker malformation.

Human malformations of the midbrain and hindbrain: review and proposed classification scheme.

The human ZIC gene family is comprised of five members encoding zinc-finger transcription factors, which are the vertebrate homologs of the Drosophila odd-paired gene. Mutations in ZIC genes in humans have recently been implicated in a wide variety of congenital malformations, including Dandy-Walker malformation, holoprosencephaly, neural tube defects, and heterotaxy. Mutant analysis of these genes in mice has underscored the conserved developmental roles of these genes. Further, this analysis has begun to elucidate the molecular and developmental mechanisms underlying these important birth defects.

The ZIC gene family in development and disease

Deep clustering utilizes representation learning to learn features in an unsupervised setting. Although successful, the current models rely on the assumption of the centralized dataset, which due to the privacy concerns is becoming less realistic. To address this challenge, we propose a federated deep convolutional embedded clustering framework. Our framework relies on a federated server to orchestrate the training between workers where each participant individually trains the model with the objective of decreasing clustering loss using Kullback–Leibler divergence. To avoid feature space being distorted by the clustering loss, each worker maintains their own local decoder which for privacy reasons is not shared with the federated server. Empirical results with both IID and non-IID client data on benchmark datasets demonstrates the feasibility of our federated training when compared to the centralized counterpart. We also evaluate our model on a real world application of community detection using GPS traces and measure the computational complexity and energy consumption on a smartphone.

Deep Embedded Clustering of Urban Communities Using Federated Learning

The Dandy-Walker malformation (DWM) includes hydrocephalus, incomplete cerebellar vermis and a posterior fossa cyst. Genetic influences and recurrence risks for DWM have not been well characterized. We report a retrospective study of 21 autopsy-proven cases of DWM and review the literature regarding an additional 92 subjects. DWM represents marked genetic and etiologic heterogeneity. Recurrence risk for siblings may be high when DWM is associated with a single gene disorder such as the autosomal recessive Warburg and Meckel-Gruber syndromes. DWM may also result from chromosomal anomalies or environmental factors. When the evidence suggests that DWM has not occurred as part of a Mendelian or chromosomal disorder then the recurrence risk is relatively low (on the order of 1 to 5%). There appears to be an increased frequency of the association of DWM with congenital heart disease, cleft lip/palate and neural tube defects. Based on our study we provide guidelines for the genetic counseling of families having a child with DWM.

Dandy-Walker malformation: etiologic heterogeneity and empiric recurrence risks.

With mobile phone penetration rates reaching 90%, Consumer Proprietary Network Information (CPNI) can offer extremely valuable information to different sectors, including policymakers. Indeed, as part of CPNI, Call Detail Records have been successfully used to provide real-time traffic information, to improve our understanding of the dynamics of people's mobility and so to allow prevention and measures in fighting infectious diseases, and to offer population statistics. While there is no doubt of the usefulness of CPNI data, privacy concerns regarding sharing individuals' data have prevented it from being used to its full potential. Traditional de-anonymization measures, such as pseudonymization and standard de-identification, have been shown to be insufficient to protect privacy. This has been specifically shown on mobile phone datasets. As an example, researchers have shown that with only four data points of approximate place and time information of a user, 95% of users could be re-identified in a dataset of 1.5 million mobile phone users. In this landscape paper, we will discuss the state-of-the-art anonymization techniques and their shortcomings.

Privacy Preserving Techniques Applied to CPNI Data: Analysis and Recommendations.

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Deep Embedded Clustering of Urban Communities Using Federated Learning

Dandy-Walker malformation: etiologic heterogeneity and empiric recurrence risks.

Privacy Preserving Techniques Applied to CPNI Data: Analysis and Recommendations.