Selecting electrical billing attributes: big data preprocessing improvements

The attribute selection is a very relevant activity of data preprocessing when discovering knowledge on databases. Its main objective is to eliminate irrelevant and/or redundant attributes to obtain computationally treatable issues, without affecting the quality of the solution. Various techniques are proposed, mainly from two approaches: wrapper and ranking. This article evaluates a novel approach proposed by Bradley and Mangasarian (Machine learning ICML. Morgan Kaufmann, Sn Fco, CA, pp. 82–90, 1998 [1]) which uses concave programming for minimizing the classification error and the number of attributes required to perform the task. The technique is evaluated using the electric service billing database in Colombia. The results are compared against traditional techniques for evaluating: attribute reduction, processing time, discovered knowledge size, and solution quality

Validation of the Chronic Pain Acceptance Questionnaire-8 in an Australian pain clinic sample

Background: Recently, an 8-item short-form version of the Chronic Pain Acceptance Questionnaire (CPAQ-8) was developed predominantly in an internet sample. Further investigation of the factor structure in a multidisciplinary pain clinic sample is required. Investigation of the concurrent validity of the CPAQ-8 after accounting for the effects of variables commonly measured in the pain clinic setting is also necessary. Purpose: This study examines the factor structure and concurrent validity of the CPAQ-8 in a sample of treatmentseeking patients who attended a multidisciplinary pain clinic. Methods: Participants were 334 patients who attended an Australian multidisciplinary pain service. Participants completed the CPAQ, a demographic questionnaire, and measures of patient adjustment and functioning. Results: Confirmatory factor analysis identified a two-factor 8-item model consisting of Activity Engagement and Pain Willingness factors (SRMR=0.039, RMSEA=0.063, CFI=0.973, TLI=0.960) was superior to both the CPAQ and CPAQ with an item removed. The CPAQ and CPAQ-8 total scores were highly correlated (r=0.93). After accounting for pain intensity, the CPAQ-8 was a significant predictor of depression, anxiety, stress, and disability. The subscales of the CPAQ-8 were both unique contributors to depression and disability in regression analyses, after accounting for pain intensity and kinesiophobia, and after accounting for pain intensity and catastrophizing. Conclusions: The CPAQ-8 has a sound factor structure and similar psychometric properties to the CPAQ; it may have clinical utility as a measure of pain acceptance in treatmentseeking, chronic pain patients

Predicting short-term electricity demand through artificial neural network

Forecasting the consumption of electric power on a daily basis allows considerable money savings for the supplying companies, by reducing the expenses in generation and operation. Therefore, the cost of forecasting errors can be of such magnitude that many studies have focused on minimizing the forecasting error, which makes this topic as an integral part of planning in many companies of various kinds and sizes, ranging from generation, transmission, and distribution to consumption, by requiring reliable forecasting systems

Rubber Impact on 3D Textile Composites

A low velocity impact study of aircraft tire rubber on 3D textile-reinforced composite plates was performed experimentally and numerically. In contrast to regular unidirectional composite laminates, no delaminations occur in such a 3D textile composite. Yarn decohesions, matrix cracks and yarn ruptures have been identified as the major damage mechanisms under impact load. An increase in the number of 3D warp yarns is proposed to improve the impact damage resistance. The characteristic of a rubber impact is the high amount of elastic energy stored in the impactor during impact, which was more than 90% of the initial kinetic energy. This large geometrical deformation of the rubber during impact leads to a less localised loading of the target structure and poses great challenges for the numerical modelling. A hyperelastic Mooney-Rivlin constitutive law was used in Abaqus/Explicit based on a step-by-step validation with static rubber compression tests and low velocity impact tests on aluminium plates. Simulation models of the textile weave were developed on the meso- and macro-scale. The final correlation between impact simulation results on 3D textile-reinforced composite plates and impact test data was promising, highlighting the potential of such numerical simulation tools

Patient satisfaction in neurological second opinions and tertiary referrals

Although the number of neurological second opinions (SOs) and tertiary referrals (TRs) is increasing, only little is known about expectations and patient satisfaction in this group of patients. Therefore, the purpose of this study was to explore expectations of patients who get a neurological SO or TR and to assess patient satisfaction in these groups of patients. All new patients attending an academic neurological day-care clinic in a 6-month period were investigated. Demographic characteristics, duration of symptoms, expectations and motivation, new diagnoses and treatment consequences were studied, and patient satisfaction with the previous physician and the day-care clinic physician was assessed. Three hundred consecutive patients (183 SOs and 117 TRs) were evaluated. SO patients were younger (47 years vs. 51 years), and their duration of symptoms was longer (24 vs. 13 months) than TR patients. Most patients expected a new diagnosis or treatment (60%). SO patients were equally as satisfied with the day-care clinic consultation as TR patients (overall satisfaction using a VAS-score ranging 0–10: 7.4 vs. 7.5; p = 0.81), and significantly less satisfied with the referring physician (overall satisfaction: 5.6 vs. 7.0; p < 0.001). SO patients, in particular, were more satisfied with the degree of information and emotional support provided by the consulting neurologist as compared to the referring physician. Receiving a new diagnosis and/or treatment advice did not influence satisfaction. A day-care admission for neurological SO and TR leads to an increase of patient satisfaction, irrespective of making a new diagnosis or initiation of a new treatment

Predator-Induced Vertical Behavior of a Ctenophore

Although many studies have focused on Mnemiopsis leidyi predation, little is known about the role of this ctenophore as prey when abundant in native and invaded pelagic systems. We examined the response of the ctenophore M. leidyi to the predatory ctenophore Beroe ovata in an experiment in which the two species could potentially sense each other while being physically separated. On average, M. leidyi responded to the predator’s presence by increasing variability in swimming speeds and by lowering their vertical distribution. Such behavior may help explain field records of vertical migration, as well as stratified and near-bottom distributions of M. leidyi

FOXP1 suppresses immune response signatures and MHC class II expression in activated B-cell-like diffuse large B-cell lymphomas.

The FOXP1 (forkhead box P1) transcription factor is a marker of poor prognosis in diffuse large B-cell lymphoma (DLBCL). Here microarray analysis of FOXP1-silenced DLBCL cell lines identified differential regulation of immune response signatures and major histocompatibility complex class II (MHC II) genes as some of the most significant differences between germinal center B-cell (GCB)-like DLBCL with full-length FOXP1 protein expression versus activated B-cell (ABC)-like DLBCL expressing predominantly short FOXP1 isoforms. In an independent primary DLBCL microarray data set, multiple MHC II genes, including human leukocyte antigen DR alpha chain (HLA-DRA), were inversely correlated with FOXP1 transcript expression (P<0.05). FOXP1 knockdown in ABC-DLBCL cells led to increased cell-surface expression of HLA-DRA and CD74. In R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone)-treated DLBCL patients (n=150), reduced HLA-DRA (<90% frequency) expression correlated with inferior overall survival (P=0.0003) and progression-free survival (P=0.0012) and with non-GCB subtype stratified by the Hans, Choi or Visco-Young algorithms (all P<0.01). In non-GCB DLBCL cases with <90% HLA-DRA, there was an inverse correlation with the frequency (P=0.0456) and intensity (P=0.0349) of FOXP1 expression. We propose that FOXP1 represents a novel regulator of genes targeted by the class II MHC transactivator CIITA (MHC II and CD74) and therapeutically targeting the FOXP1 pathway may improve antigen presentation and immune surveillance in high-risk DLBCL patients

Adverse prognostic and predictive significance of low DNA-dependent protein kinase catalytic subunit (DNA-PKcs) expression in early-stage breast cancers

Background: DNA-dependent protein kinase catalytic subunit (DNA-PKcs), a serine threonine kinase belonging to the PIKK family (phosphoinositide 3-kinase-like-family of protein kinase), is a critical component of the non-homologous end joining (NHEJ) pathway required for the repair of DNA double strand breaks. DNA-PKcs may be involved in breast cancer pathogenesis. Methods: We evaluated clinicopathological significance of DNA-PKcs protein expression in 1161 tumours and DNA-PKcs mRNA expression in 1950 tumours. We correlated DNA-PKcs to other markers of aggressive phenotypes, DNA repair, apoptosis and cell cycle regulation. Results: Low DNA-PKcs protein expression was associated with higher tumour grade, higher mitotic index, tumour de-differentiation and tumour type (ps<0.05). Absence of BRCA1, low XRCC1/SMUG1/APE1/Polβ were also more likely in low DNA-PKcs expressing tumours (ps<0.05). Low DNA-PKcs protein expression was significantly associated with worse breast cancer specific survival (BCCS) in univariate and multivariate analysis (ps<0.01). At the mRNA level, low DNA-PKcs was associated with PAM50.Her2 and PAM50.LumA molecular phenotypes (ps<0.01) and poor BCSS. In patients with ER positive tumours who received endocrine therapy, low DNA-PKcs (protein and mRNA) was associated with poor survival. In ER negative patients, low DNA-PKcs mRNA remains significantly associated with adverse outcome. Conclusions: Our study suggests that low DNA-PKcs expression may have prognostic and predictive significance in breast cancers

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR