Macrophage migration inhibitory factor (MIF) modulates trophic signaling through interaction with serine protease HTRA1

Macrophage migration inhibitory factor (MIF), a small conserved protein, is abundant in the immune- and central nervous system (CNS). MIF has several receptors and binding partners that can modulate its action on a cellular level. It is upregulated in neurodegenerative diseases and cancer although its function is far from clear. Here, we report the finding of a new binding partner to MIF, the serine protease HTRA1. This enzyme cleaves several growth factors, extracellular matrix molecules and is implicated in some of the same diseases as MIF. We show that the function of the binding between MIF and HTRA1 is to inhibit the proteolytic activity of HTRA1, modulating the availability of molecules that can change cell growth and differentiation. MIF is therefore the first endogenous inhibitor ever found for HTRA1. It was found that both molecules were present in astrocytes and that the functional binding has the ability to modulate astrocytic activities important in development and disease of the CNS.     

Learning deficits, but normal development and tumor predisposition, in mice lacking exon 23a of Nf1

Neurofibromatosis type 1 (NF1) is a commonly inherited autosomal dominant disorder. Previous studies indicated that mice homozygous for a null mutation in Nf1 exhibit mid-gestation lethality, whereas heterozygous mice have an increased predisposition to tumors and learning impairments. Here we show that mice lacking the alternatively spliced exon 23a, which modifies the GTPase-activating protein (GAP) domain of Nf1, are viable and physically normal, and do not have an increased tumor predisposition, but show specific learning impairments. Our findings have implications for the development of a treatment for the learning disabilities associated with NF1 and indicate that the GAP domain of NF1 modulates learning and memory.     

Nuclear localization or inclusion body formation of ataxin-2 are not necessary for SCA2 pathogenesis in mouse or human

Instability of CAG DNA trinucleotide repeats is the mutational mechanism for several neurodegenerative diseases resulting in the expansion of a polyglutamine (polyQ) tract. Proteins with long polyQ tracts have an increased tendency to aggregate, often as truncated fragments forming ubiquitinated intranuclear inclusion bodies. We examined whether similar features define spinocerebellar ataxia type 2 (SCA2) pathogenesis using cultured cells, human brains and transgenic mouse lines. In SCA2 brains, we found cytoplasmic, but not nuclear, microaggregates. Mice expressing ataxin-2 with Q58 showed progressive functional deficits accompanied by loss of the Purkinje cell dendritic arbor and finally loss of Purkinje cells. Despite similar functional deficits and anatomical changes observed in ataxin-1[Q80] transgenic lines, ataxin-2[Q58] remained cytoplasmic without detectable ubiquitination.     

An Ensemble Model of Arima and Ann with Restricted Boltzmann Machine Based on Decomposition of Discrete Wavelet Transform for Time Series Forecasting

Time series forecasting research area mainly focuses on developing effective forecasting models to improve prediction accuracy. An ensemble model composed of autoregressive integrated moving average (ARIMA), artificial neural network (ANN), restricted Boltzmann machines (RBM), and discrete wavelet transform (DWT) is presented in this paper. In the proposed model, DWT first decomposes time series into approximation and detail. Then Khashei and Bijari’s model, which is an ensemble model of ARIMA and ANN, is applied to the approximation and detail to extract their both linear and nonlinear components and fit the relationship between the components as a function instead of additive relationship. Furthermore, RBM is used to perform pre-training for generating initial weights and biases based on inputs feature for ANN. Finally, the forecasted approximation and detail are combined to obtain final forecasting. The forecasting capability of the proposed model is tested with three well-known time series: sunspot, Canadian lynx, exchange rate time series. The prediction performance is compared to the other six forecasting models. The results indicate that the proposed model gives the best performance in all three data sets and all three measures (i.e. MSE, MAE and MAPE).     

De novo somatic mutations in components of the PI3K-AKT3-mTOR pathway cause hemimegalencephaly

De novo somatic mutations in focal areas are well documented in diseases such as neoplasia but are rarely reported in malformation of the developing brain. Hemimegalencephaly (HME) is characterized by overgrowth of either one of the two cerebral hemispheres. The molecular etiology of HME remains a mystery. The intractable epilepsy that is associated with HME can be relieved by the surgical treatment hemispherectomy, allowing sampling of diseased tissue. Exome sequencing and mass spectrometry analysis in paired brain-blood samples from individuals with HME (n = 20 cases) identified de novo somatic mutations in 30% of affected individuals in the PIK3CA, AKT3 and MTOR genes. A recurrent PIK3CA c.1633G>A mutation was found in four separate cases. Identified mutations were present in 8-40% of sequenced alleles in various brain regions and were associated with increased neuronal S6 protein phosphorylation in the brains of affected individuals, indicating aberrant activation of mammalian target of rapamycin (mTOR) signaling. Thus HME is probably a genetically mosaic disease caused by gain of function in phosphatidylinositol 3-kinase (PI3K)-AKT3-mTOR signaling.     

Interaction of two isomers, N-nitrosomethylphenylamine, N-nitroso derivative, and p-nitrosomethylaniline with microorganisms]

There exists a relationship between the chemical structure C--NO or N--NO and biological effects. p-nitrosomethylaniline, a C-nitroso compound, in contrast to its isomer, N-nitrosomethylphenylamine, a N-nitroso derivate, shows a marked action against microoganisms and is, alone, reduced enzymatically.     

A proportional 3-tuple fuzzy linguistic representation model for screening new product projects

It is critically important for companies to screen new product projects before they are launched to the market. So far, many approaches have been developed for tackling the process of screening product innovations. Due to uncertain, vague and incomplete information as well as dynamically complex process regarding to new product development (NPD), a fuzzy linguistic approach employed linguistic assessments and the fuzzy-set-based computation is reasonable for screening new products. However, such a fuzzy linguistic approach faces with various defects and limitations, such as loss of information, failing in considering the aspects related to human nature on uncertain subjective judgments etc. These defects and limitations lead to a dilemma, i.e., it’s very difficult to screen new product projects reasonably and precisely. In this paper, we propose a notion of proportional 3-tuple to represent a linguistic assessment and related ignoring information, and a preference-preserving proportional 3-tuple transformation for the unification of linguistic assessments represented by proportional 3-tuples between two different linguistic term sets. On this basis, a proportional 3-tuple fuzzy linguistic representation model for screening new product projects is developed. It is shown that the proposed model is flexible to handle uncertain, vague and incomplete information related to screening new product projects. It not only allows evaluators to express their subjective judgments with different confidence levels, but is also able to deal with incomplete linguistic assessments. Ultimately, the proposed model also improves the precision and reasonability of the screening result.