No association between polymorphisms in the BDNF gene and age at onset in Huntington disease

BACKGROUND: Recent evidence suggests that brain-derived neurotrophic factor (BDNF) is an attractive candidate for modifying age at onset (AO) in Huntington disease (HD). In particular, the functional Val66Met polymorphism appeared to exert a significant effect. Here we evaluate BDNF variability with respect to AO of HD using markers that represent the entire locus. METHODS: Five selected tagging polymorphisms were genotyped across a 65 kb region comprising the BDNF gene in a well established cohort of 250 unrelated German HD patients. RESULTS: Addition of BDNF genotype variations or one of the marker haplotypes to the effect of CAG repeat lengths did not affect the variance of the AO. CONCLUSION: We were unable to verify a recently reported association between the functional Val66Met polymorphism in the BDNF gene and AO in HD. From our findings, we conclude that neither sequence variations in nor near the gene contribute significantly to the variance of AO

An Integrated Model for User Attribute Discovery: A Case Study on Political Affiliation Identification

Discovering user demographic attributes from social media is a problem of considerable interest. The problem setting can be generalized to include three components - users, topics and behaviors. In recent studies on this problem, however, the behavior between users and topics are not effectively incorporated. In our work, we proposed an integrated unsupervised model which takes into consideration all the three components integral to the task. Furthermore, our model incorporates collaborative filtering with probabilistic matrix factorization to solve the data sparsity problem, a computational challenge common to all such tasks. We evaluated our method on a case study of user political affiliation identification, and compared against state-of-the-art baselines. Our model achieved an accuracy of 70.1% for user party detection task. ? 2014 Springer International Publishing.EI

NMDA receptor genotypes associated with the vulnerability to develop dyskinesia

Dyskinesias are involuntary muscle movements that occur spontaneously in Huntington's disease (HD) and after long-term treatments for Parkinson's disease (levodopa-induced dyskinesia; LID) or for schizophrenia (tardive dyskinesia, TD). Previous studies suggested that dyskinesias in these three conditions originate from different neuronal pathways that converge on overstimulation of the motor cortex. We hypothesized that the same variants of the N-methyl--aspartate receptor gene that were previously associated with the age of dyskinesia onset in HD were also associated with the vulnerability for TD and not LID. Genotyping patients with LID and TD revealed, however, that these two variants were dose-dependently associated with susceptibility to LID, but not TD. This suggested that LID, TD and HD might arise from the same neuronal pathways, but TD results from a different mechanism

NMDA receptor gene variations as modifiers in Huntington disease: a replication study.

Several candidate modifier genes which, in addition to the pathogenic CAG repeat expansion, influence the age at onset (AO) in Huntington disease (HD) have already been described. The aim of this study was to replicate association of variations in the N-methyl D-aspartate receptor subtype genes GRIN2A and GRIN2B in the "REGISTRY" cohort from the European Huntington Disease Network (EHDN). The analyses did replicate the association reported between the GRIN2A rs2650427 variation and AO in the entire cohort. Yet, when subjects were stratified by AO subtypes, we found nominally significant evidence for an association of the GRIN2A rs1969060 variation and the GRIN2B rs1806201 variation. These findings further implicate the N-methyl D-aspartate receptor subtype genes as loci containing variation associated with AO in HD

Bi-allelic JAM2 Variants Lead to Early-Onset Recessive Primary Familial Brain Calcification.

Primary familial brain calcification (PFBC) is a rare neurodegenerative disorder characterized by a combination of neurological, psychiatric, and cognitive decline associated with calcium deposition on brain imaging. To date, mutations in five genes have been linked to PFBC. However, more than 50% of individuals affected by PFBC have no molecular diagnosis. We report four unrelated families presenting with initial learning difficulties and seizures and later psychiatric symptoms, cerebellar ataxia, extrapyramidal signs, and extensive calcifications on brain imaging. Through a combination of homozygosity mapping and exome sequencing, we mapped this phenotype to chromosome 21q21.3 and identified bi-allelic variants in JAM2. JAM2 encodes for the junctional-adhesion-molecule-2, a key tight-junction protein in blood-brain-barrier permeability. We show that JAM2 variants lead to reduction of JAM2 mRNA expression and absence of JAM2 protein in patient's fibroblasts, consistent with a loss-of-function mechanism. We show that the human phenotype is replicated in the jam2 complete knockout mouse (jam2 KO). Furthermore, neuropathology of jam2 KO mouse showed prominent vacuolation in the cerebral cortex, thalamus, and cerebellum and particularly widespread vacuolation in the midbrain with reactive astrogliosis and neuronal density reduction. The regions of the human brain affected on neuroimaging are similar to the affected brain areas in the myorg PFBC null mouse. Along with JAM3 and OCLN, JAM2 is the third tight-junction gene in which bi-allelic variants are associated with brain calcification, suggesting that defective cell-to-cell adhesion and dysfunction of the movement of solutes through the paracellular spaces in the neurovascular unit is a key mechanism in CNS calcification

Autosomal dominant hereditary spastic paraplegia: Novel mutations in the REEP1 gene (SPG31)

<p>Abstract</p> <p>Background</p> <p>Mutations in the <it>SPG4 </it>gene (spastin) and in the <it>SPG3A </it>gene (atlastin) account for the majority of 'pure' autosomal dominant form of hereditary spastic paraplegia (HSP). Recently, mutations in the <it>REEP1 </it>gene were identified to cause autosomal dominant HSP type SPG31. The purpose of this study was to determine the prevalence of <it>REEP1 </it>mutations in a cohort of 162 unrelated Caucasian index patients with 'pure' HSP and a positive family history (at least two persons per family presented symptoms).</p> <p>Methods</p> <p>162 patients were screened for mutations by, both, DHPLC and direct sequencing.</p> <p>Results</p> <p>Ten mutations were identified in the <it>REEP1 </it>gene, these included eight novel mutations comprising small insertions/deletions causing frame shifts and subsequently premature stop codons, one nonsense mutation and one splice site mutation as well as two missense mutations. Both missense mutations and the splice site mutation were not identified in 170 control subjects.</p> <p>Conclusion</p> <p>In our HSP cohort we found pathogenic mutations in 4.3% of cases with autosomal dominant inheritance. Our results confirm the previously observed mutation range of 3% to 6.5%, respectively, and they widen the spectrum of <it>REEP1 </it>mutations.</p

Technologies for the global energy transition

The availability of reliable, affordable and mature technologies is at the basis of an effective decarbonization strategy, that should be in turn supported by timely and accurate policies. Due to the large differences across sectors and countries, there is no silver bullet to support decarbonization, but a combination of multiple technologies will be required to reach the challenging goal of decarbonizing the energy sector. This chapter presents a focus on the current technological solutions that are available in four main sectors: power generation, industry, transport and buildings. The aim of this work is to highlight the main strengths and weaknesses of the current technologies, to help the reader in understanding which are the main opportunities and challenges related to the development and deployment of each of them, as well as their potential contribution to the decarbonization targets. The chapter also provides strategies and policy recommendations from a technology point of view on how to decarbonize the global energy systems by mid-century and of the necessity to take a systems approach

Genomic NGFB variation and multiple sclerosis in a case control study

<p>Abstract</p> <p>Background</p> <p>Nerve growth factor β (NGFB) is involved in cell proliferation and survival, and it is a mediator of the immune response. ProNGF, the precursor protein of NGFB, has been shown to induce cell death via interaction with the p75 neurotrophin receptor. In addition, this neurotrophin is differentially expressed in males and females. Hence NGFB is a good candidate to influence the course of multiple sclerosis (MS), much like in the murine model of experimental autoimmune encephalomyelitis (EAE).</p> <p>Methods</p> <p>Ten single nucleotide polymorphisms (SNPs) were genotyped in the <it>NGFB </it>gene in up to 1120 unrelated MS patients and 869 controls. Expression analyses were performed for selected MS patients in order to elucidate the possible functional relevance of the SNPs.</p> <p>Results</p> <p>Significant association of NGFB variations with MS is evident for two SNPs. <it>NGFB </it>mRNA seems to be expressed in sex- and disease progression-related manner in peripheral blood mononuclear cells.</p> <p>Conclusion</p> <p>NGFB variation and expression levels appear as modulating factors in the development of MS.</p

Outlier detection and classification in sensor data streams for proactive decision support systems

A paper has a deal with the problem of quality assessment in sensor data streams accumulated by proactive decision support systems. The new problem is stated where outliers need to be detected and to be classified according to their nature of origin. There are two types of outliers defined; the first type is about misoperations of a system and the second type is caused by changes in the observed system behavior due to inner and external influences. The proposed method is based on the data-driven forecast approach to predict the values in the incoming data stream at the expected time. This method includes the forecasting model and the clustering model. The forecasting model predicts a value in the incoming data stream at the expected time to find the deviation between a real observed value and a predicted one. The clustering method is used for taxonomic classification of outliers. Constructive neural networks models (CoNNS) and evolving connectionists systems (ECS) are used for prediction of sensors data. There are two real world tasks are used as case studies. The maximal values of accuracy are 0.992 and 0.974, and F1 scores are 0.967 and 0.938, respectively, for the first and the second tasks. The conclusion contains findings how to apply the proposed method in proactive decision support systems