Age and growth of four-spotted megrim (Lepidorhombus boscii Risso, 1810) from Saros Bay (Northern Aegean Sea, Turkey)

In this study, the growth parameters of the four-spotted megrim, (Lepidorhombus boscii Risso, 1810), were studied in Saros Bay, which had been closed to bottom trawl fishery since 2000. The sex ratio of females to males was 1:0.42. Length-weight relationships were W=0.0032L3.31 and W=0.0069L3.04 for females and males, respectively. Growth parameters of the populations were L∞=49.8 cm, k=0.09 year-1, t0=-2.15 year for females; L∞=39.1 cm, k=0.11 year-1, t0=-2.59 year for males. The growth performance index (Φ’) was found to be 2.35 and 2.23 for females and males, respectively

MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study.

Background: Diffuse intrinsic pontine gliomas (DIPGs) are lethal pediatric brain tumors. Presently, MRI is the mainstay of disease diagnosis and surveillance. We identify clinically significant computational features from MRI and create a prognostic machine learning model. Methods: We isolated tumor volumes of T1-post-contrast (T1) and T2-weighted (T2) MRIs from 177 treatment-naïve DIPG patients from an international cohort for model training and testing. The Quantitative Image Feature Pipeline and PyRadiomics was used for feature extraction. Ten-fold cross-validation of least absolute shrinkage and selection operator Cox regression selected optimal features to predict overall survival in the training dataset and tested in the independent testing dataset. We analyzed model performance using clinical variables (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variables. Results: All selected features were intensity and texture-based on the wavelet-filtered images (3 T1 gray-level co-occurrence matrix (GLCM) texture features, T2 GLCM texture feature, and T2 first-order mean). This multivariable Cox model demonstrated a concordance of 0.68 (95% CI: 0.61-0.74) in the training dataset, significantly outperforming the clinical-only model (C = 0.57 [95% CI: 0.49-0.64]). Adding clinical features to radiomics slightly improved performance (C = 0.70 [95% CI: 0.64-0.77]). The combined radiomics and clinical model was validated in the independent testing dataset (C = 0.59 [95% CI: 0.51-0.67], Noether's test P = .02). Conclusions: In this international study, we demonstrate the use of radiomic signatures to create a machine learning model for DIPG prognostication. Standardized, quantitative approaches that objectively measure DIPG changes, including computational MRI evaluation, could offer new approaches to assessing tumor phenotype and serve a future role for optimizing clinical trial eligibility and tumor surveillance

Searching for serial refreshing in working memory:Using response times to track the content of the focus of attention over time

One popular idea is that, to support maintenance of a set of elements over brief periods of time, the focus of attention rotates among the different elements thereby serially refreshing the content of Working Memory (WM). In the research reported here, probe letters were presented between to-be-remembered letters. Response times to these probes were used to infer the status of the different items in WM. If the focus of attention cycles from one item to the next, its content should be different at different points in time and this should be reflected in a change in the response time patterns over time. Across a set of four experiments, we demonstrate a striking pattern of invariance in the response time patterns over time, suggesting that either the content of the focus of attention did not change over time or that response times cannot be used to infer the content of the focus of attention. We discuss how this pattern constrains models of WM, attention, and human information processing

Radiomic signatures of posterior fossa ependymoma: Molecular subgroups and risk profiles

BACKGROUND: The risk profile for posterior fossa ependymoma (EP) depends on surgical and molecular status [Group A (PFA) versus Group B (PFB)]. While subtotal tumor resection is known to confer worse prognosis, MRI-based EP risk-profiling is unexplored. We aimed to apply machine learning strategies to link MRI-based biomarkers of high-risk EP and also to distinguish PFA from PFB. METHODS: We extracted 1800 quantitative features from presurgical T2-weighted (T2-MRI) and gadolinium-enhanced T1-weighted (T1-MRI) imaging of 157 EP patients. We implemented nested cross-validation to identify features for risk score calculations and apply a Cox model for survival analysis. We conducted additional feature selection for PFA versus PFB and examined performance across three candidate classifiers. RESULTS: For all EP patients with GTR, we identified four T2-MRI-based features and stratified patients into high- and low-risk groups, with 5-year overall survival rates of 62% and 100%, respectively (p < 0.0001). Among presumed PFA patients with GTR, four T1-MRI and five T2-MRI features predicted divergence of high- and low-risk groups, with 5-year overall survival rates of 62.7% and 96.7%, respectively (p = 0.002). T1-MRI-based features showed the best performance distinguishing PFA from PFB with an AUC of 0.86. CONCLUSIONS: We present machine learning strategies to identify MRI phenotypes that distinguish PFA from PFB, as well as high- and low-risk PFA. We also describe quantitative image predictors of aggressive EP tumors that might assist risk-profiling after surgery. Future studies could examine translating radiomics as an adjunct to EP risk assessment when considering therapy strategies or trial candidacy

Firms cash management, adjustment cost and its impact on firms’ speed of adjustment-A cross country analysis

We investigate the firms’ specific attributes that determine the difference in speed of adjustment (SOA) towards the cash holdings target in the Scandinavian countries: Denmark, Norway and Sweden. We examine whether Scandinavian firms maintain an optimal level of cash holdings and determine if the active cash holdings management is associated with the firms’ higher SOA and lower adjustment costs. Our findings substantiate that a higher level of off-target cost induces professional managers to rebalance their cash level towards the optimal balance of cash holdings. Our results reveal that Scandinavian firms accelerate SOA towards cash targets primarily for the precautionary motive. Moreover, our results show that SOA is heterogeneous across Scandinavian firms based on adjustment cost and deviate cash holdings towards the target mainly with the support of internal financing. Furthermore, our empirical findings show that the SOA of Norwegian firms is significantly higher than the Danish and Swedish firms

Agile manufacturing practices: the role of big data and business analytics with multiple case studies

The purpose of this study was to examine the role of big data and business analytics (BDBA) in agile manufacturing practices. Literature has discussed the benefits and challenges related to the deployment of big data within operations and supply chains, but there has not been a study of the facilitating roles of BDBA in achieving an enhanced level of agile manufacturing practices. As a response to this gap, and drawing upon multiple qualitative case studies undertaken among four U.K. organizations, we present and validate a framework for the role of BDBA within agile manufacturing. The findings show that market turbulence has negative universal effects and that agile manufacturing enablers are being progressively deployed and aided by BDBA to yield better competitive and business performance objectives. Further, the level of intervention was found to differ across companies depending on the extent of deployment of BDBA, which accounts for variations in outcomes

Pretransplant Prediction of Posttransplant Survival for Liver Recipients with Benign End-Stage Liver Diseases: A Nonlinear Model

Background: The scarcity of grafts available necessitates a system that considers expected posttransplant survival, in addition to pretransplant mortality as estimated by the MELD. So far, however, conventional linear techniques have failed to achieve sufficient accuracy in posttransplant outcome prediction. In this study, we aim to develop a pretransplant predictive model for liver recipients ’ survival with benign end-stage liver diseases (BESLD) by a nonlinear method based on pretransplant characteristics, and compare its performance with a BESLD-specific prognostic model (MELD) and a generalillness severity model (the sequential organ failure assessment score, or SOFA score). Methodology/Principal Findings: With retrospectively collected data on 360 recipients receiving deceased-donor transplantation for BESLD between February 1999 and August 2009 in the west China hospital of Sichuan university, we developed a multi-layer perceptron (MLP) network to predict one-year and two-year survival probability after transplantation. The performances of the MLP, SOFA, and MELD were assessed by measuring both calibration ability and discriminative power, with Hosmer-Lemeshow test and receiver operating characteristic analysis, respectively. By the forward stepwise selection, donor age and BMI; serum concentration of HB, Crea, ALB, TB, ALT, INR, Na +; presence of pretransplant diabetes; dialysis prior to transplantation, and microbiologically proven sepsis were identified to be the optimal input features. The MLP, employing 18 input neurons and 12 hidden neurons, yielded high predictive accuracy, wit

Recent Advances in Polymeric Materials Used as Electron Mediators and Immobilizing Matrices in Developing Enzyme Electrodes

Different classes of polymeric materials such as nanomaterials, sol-gel materials, conducting polymers, functional polymers and biomaterials have been used in the design of sensors and biosensors. Various methods have been used, for example from direct adsorption, covalent bonding, crossing-linking with glutaraldehyde on composites to mixing the enzymes or use of functionalized beads for the design of sensors and biosensors using these polymeric materials in recent years. It is widely acknowledged that analytical sensing at electrodes modified with polymeric materials results in low detection limits, high sensitivities, lower applied potential, good stability, efficient electron transfer and easier immobilization of enzymes on electrodes such that sensing and biosensing of environmental pollutants is made easier. However, there are a number of challenges to be addressed in order to fulfill the applications of polymeric based polymers such as cost and shortening the long laboratory synthetic pathways involved in sensor preparation. Furthermore, the toxicological effects on flora and fauna of some of these polymeric materials have not been well studied. Given these disadvantages, efforts are now geared towards introducing low cost biomaterials that can serve as alternatives for the development of novel electrochemical sensors and biosensors. This review highlights recent contributions in the development of the electrochemical sensors and biosensors based on different polymeric material. The synergistic action of some of these polymeric materials and nanocomposites imposed when combined on electrode during sensing is discussed