PTEN Phosphatase-Independent Maintenance of Glandular Morphology in a Predictive Colorectal Cancer Model System

Organotypic models may provide mechanistic insight into colorectal cancer (CRC) morphology. Three-dimensional (3D) colorectal gland formation is regulated by phosphatase and tensin homologue deleted on chromosome 10 (PTEN) coupling of cell division cycle 42 (cdc42) to atypical protein kinase C (aPKC). This study investigated PTEN phosphatase-dependent and phosphatase-independent morphogenic functions in 3D models and assessed translational relevance in human studies. Isogenic PTEN-expressing or PTEN-deficient 3D colorectal cultures were used. In translational studies, apical aPKC activity readout was assessed against apical membrane (AM) orientation and gland morphology in 3D models and human CRC. We found that catalytically active or inactive PTEN constructs containing an intact C2 domain enhanced cdc42 activity, whereas mutants of the C2 domain calcium binding region 3 membrane-binding loop (M-CBR3) were ineffective. The isolated PTEN C2 domain (C2) accumulated in membrane fractions, but C2 M-CBR3 remained in cytosol. Transfection of C2 but not C2 M-CBR3 rescued defective AM orientation and 3D morphogenesis of PTEN-deficient Caco-2 cultures. The signal intensity of apical phospho-aPKC correlated with that of Na/H exchanger regulatory factor-1 (NHERF-1) in the 3D model. Apical NHERF-1 intensity thus provided readout of apical aPKC activity and associated with glandular morphology in the model system and human colon. Low apical NHERF-1 intensity in CRC associated with disruption of glandular architecture, high cancer grade, and metastatic dissemination. We conclude that the membrane-binding function of the catalytically inert PTEN C2 domain influences cdc42/aPKC-dependent AM dynamics and gland formation in a highly relevant 3D CRC morphogenesis model system.

FLT1 Genetic Variation Predisposes to Neovascular AMD in Ethnically Diverse Populations and Alters Systemic FLT1 Expression

Purpose: Current understanding of the genetic risk factors for age-related macular degeneration (AMD) is not sufficiently predictive of the clinical course. The VEGF pathway is a key therapeutic target for treatment of neovascular AMD; however, risk attributable to genetic variation within pathway genes is unclear. We sought to identify single nucleotide polymorphisms (SNPs) associated with AMD within the VEGF pathway.
Methods: Using a tagSNP, direct sequencing and meta-analysis approach within four ethnically diverse cohorts, we identified genetic risk present in FLT1, though not within other VEGF pathway genes KDR, VEGFA, or VASH1. We used ChIP and ELISA in functional analysis.
Results: The FLT1 SNPs rs9943922, rs9508034, rs2281827, rs7324510, and rs9513115 were significantly associated with increased risk of neovascular AMD. Each association was more significant after meta-analysis than in any one of the four cohorts. All associations were novel, within noncoding regions of FLT1 that do not tag for coding variants in linkage disequilibrium. Analysis of soluble FLT1 demonstrated higher expression in unaffected individuals homozygous for the FLT1 risk alleles rs9943922 (P = 0.0086) and rs7324510 (P = 0.0057). In silico analysis suggests that these variants change predicted splice sites and RNA secondary structure, and have been identified in other neovascular pathologies. These data were supported further by murine chromatin immunoprecipitation demonstrating that FLT1 is a target of Nr2e3, a nuclear receptor gene implicated in regulating an AMD pathway.
Conclusions: Although exact variant functions are not known, these data demonstrate relevancy across ethnically diverse genetic backgrounds within our study and, therefore, hold potential for global efficacy.

Performance analysis of switch-and-stay combining in two-way relay systems with analog network coding and time-division broadcast protocols

In this paper, weconsider switch-and-stay combining (SSC) in two-way relay systems with two amplify-and-forward relays, one of which is activated to assist the information exchange between the two sources. The system operates in either analog network coding (ANC) protocol where the communication is only achieved with the help of the active relay or timedivision broadcast (TDBC) protocol where the direct link between two sources can be utilized to exploit more diversity gain. In both cases, we study the outage probability and bit error rate (BER) for Rayleigh fading channels. In particular, we derive closed-form lower bounds for the outage probability and the average BER, which remain tight for different fading conditions. We also present asymptotic analysis for both the outage probability and the average BER at high signalto-noise ratio. It is shown that SSC can achieve the full diversity order in two-way relay systems for both ANC and TDBC protocols with proper switching thresholds. Copyright © 2014 John Wiley & Sons, Ltd.

Efficient predictive demand response using laguerre functions

Predictive Demand Response (DR) algorithms allow schedulable loads in power systems to be shifted to off-peak times. However, the size of the optimisation problems associated with predictive DR can grow very large and so efficient implementations of algorithms are desirable. In this paper Laguerre functions are used to significantly reduce the size of the optimisation needed to implement predictive DR, thus significantly increasing the efficiency of the implementation. © 2013 IEEE.

A predictive maintenance system for integral type faults based on support vector machines:An application to ion implantation

In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset. © 2013 IEEE.

Machine Learning for Predictive Maintenance: A Multiple Classifiers Approach

In this paper a multiple classifier machine learning methodology for Predictive Maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating so called ’health factors’ or quantitative indicators of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance trade-offs in terms of frequency of unexpected breaks and unexploited lifetime and then employing this information in an operating cost based maintenance decision system to minimise expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.