Identification of Piwil2-Like (PL2L) Proteins that Promote Tumorigenesis

PIWIL2, a member of PIWI/AGO gene family, is expressed in the germline stem cells (GSCs) of testis for gametogenesis but not in adult somatic and stem cells. It has been implicated to play an important role in tumor development. We have previously reported that precancerous stem cells (pCSCs) constitutively express Piwil2 transcripts to promote their proliferation. Here we show that these transcripts de facto represent Piwil2-like (PL2L) proteins. We have identified several PL2L proteins including PL2L80, PL2L60, PL2L50 and PL2L40, using combined methods of Gene-Exon-Mapping Reverse Transcription Polymerase Chain Reaction (GEM RT-PCR), bioinformatics and a group of novel monoclonal antibodies. Among them, PL2L60 rather than Piwil2 and other PL2L proteins is predominantly expressed in various types of human and mouse tumor cells. It promotes tumor cell survival and proliferation in vitro through up-regulation of Stat3 and Bcl2 gene expressions, the cell cycle entry from G0/1 into S-phase, and the nuclear expression of NF-κB, which contribute to the tumorigenicity of tumor cells in vivo. Consistently, PL2L proteins rather than Piwil2 are predominantly expressed in the cytoplasm or cytoplasm and nucleus of euchromatin-enriched tumor cells in human primary and metastatic cancers, such as breast and cervical cancers. Moreover, nuclear PL2L proteins are always co-expressed with nuclear NF-κB. These results reveal that PL2L60 can coordinate with NF-κB to promote tumorigenesis and might mediate a common pathway for tumor development without tissue restriction. The identification of PL2L proteins provides a novel insight into the mechanisms of cancer development as well as a novel bridge linking cancer diagnostics and anticancer drug development

Improved general regression network for protein domain boundary prediction

Background: Protein domains present some of the most useful information that can be used to understand protein structure and functions. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques, such as Artificial Neural Networks and Support Vector Machines. In this study, we propose a new machine learning model (IGRN) that can achieve accurate and reliable classification, with significantly reduced computations. The IGRN was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. Results: The proposed model achieved average prediction accuracy of 67% on the Benchmark_2 dataset for domain boundary identification in multi-domains proteins and showed superior predictive performance and generalisation ability among the most widely used neural network models. With the CASP7 benchmark dataset, it also demonstrated comparable performance to existing domain boundary predictors such as DOMpro, DomPred, DomSSEA, DomCut and DomainDiscovery with 70.10% prediction accuracy. Conclusion: The performance of proposed model has been compared favourably to the performance of other existing machine learning based methods as well as widely known domain boundary predictors on two benchmark datasets and excels in the identification of domain boundaries in terms of model bias, generalisation and computational requirements. © 2008 Yoo et al; licensee BioMed Central Ltd

An Adaptive Fuzzy Logic System for the Compensation of Nonlinear Distortion in Wireless Power Amplifiers

Computational intelligent systems are becoming an increasingly attractive solution for power amplifier (PA) behavioural modelling, due to their excellent approximation capability. This paper utilizes an adaptive fuzzy logic system (AFLS) for the modelling of the highly nonlinear MIMIX CFH2162-P3 PA. Moreover, PA’s inverse model based also on AFLS has been developed in order to act as a pre-distorter unit. Driving an LTE 1.4 MHz 64 QAM signal at 880 MHz as centre frequency at PA’s input, very good modelling performance was achieved, for both PA’s forward and inverse dynamics. A comparative study of AFLS and neural networks (NN) has been carried out to establish AFLS as an effective, robust, and easy-to-implement baseband model, which is suitable for inverse modelling of PAs and capable to be used as an effective digital pre-distorter. Pre-distortion system based on AFLS, achieved distortion suppression of 84.2%, compared to the 48.4% gained using the NN-based equivalent schem

Biomechanical spinal growth modulation and progressive adolescent scoliosis – a test of the 'vicious cycle' pathogenetic hypothesis: Summary of an electronic focus group debate of the IBSE

There is no generally accepted scientific theory for the causes of adolescent idiopathic scoliosis (AIS). As part of its mission to widen understanding of scoliosis etiology, the International Federated Body on Scoliosis Etiology (IBSE) introduced the electronic focus group (EFG) as a means of increasing debate on knowledge of important topics. This has been designated as an on-line Delphi discussion. The text for this debate was written by Dr Ian A Stokes. It evaluates the hypothesis that in progressive scoliosis vertebral body wedging during adolescent growth results from asymmetric muscular loading in a "vicious cycle" (vicious cycle hypothesis of pathogenesis) by affecting vertebral body growth plates (endplate physes). A frontal plane mathematical simulation tested whether the calculated loading asymmetry created by muscles in a scoliotic spine could explain the observed rate of scoliosis increase by measuring the vertebral growth modulation by altered compression. The model deals only with vertebral (not disc) wedging. It assumes that a pre-existing scoliosis curve initiates the mechanically-modulated alteration of vertebral body growth that in turn causes worsening of the scoliosis, while everything else is anatomically and physiologically 'normal' The results provide quantitative data consistent with the vicious cycle hypothesis. Dr Stokes' biomechanical research engenders controversy. A new speculative concept is proposed of vertebral symphyseal dysplasia with implications for Dr Stokes' research and the etiology of AIS. What is not controversial is the need to test this hypothesis using additional factors in his current model and in three-dimensional quantitative models that incorporate intervertebral discs and simulate thoracic as well as lumbar scoliosis. The growth modulation process in the vertebral body can be viewed as one type of the biologic phenomenon of mechanotransduction. In certain connective tissues this involves the effects of mechanical strain on chondrocytic metabolism a possible target for novel therapeutic intervention

On the interaction between feature selection and parameter determination in fuzzy modelling

\u3cp\u3eNowadays the amount of data that is collected in various settings is growing rapidly. These elaborate data records enable the training of machine learning models that can be used to extract insights and for making better informed decisions. When doing the data mining task, on one hand, feature selection is often used to reduce the dimensionality of the data. On the other hand, we need to decide the structure (parameters) of the model when building the model. However, feature selection and the parameters of the model may interact and affect the performance of the model. Therefore, it is difficult to decide the optimal parameter and the optimal feature subset without an exhaustive search of all the combination of the parameters and the feature subsets which is time-consuming. In this paper, we study how the interaction between feature selection and the parameters of a model affect the performance of the model through experiments on four data sets.\u3c/p\u3