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

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

The Usability of E-learning Platforms in Higher Education: A Systematic Mapping Study

The use of e-learning in higher education has increased significantly in recent years, which has led to several studies being conducted to investigate the usability of the platforms that support it. A variety of different usability evaluation methods and attributes have been used, and it has therefore become important to start reviewing this work in a systematic way to determine how the field has developed in the last 15 years. This paper describes a systematic mapping study that performed searches on five electronic libraries to identify usability issues and methods that have been used to evaluate e-learning platforms. Sixty-one papers were selected and analysed, with the majority of studies using a simple research design reliant on questionnaires. The usability attributes measured were mostly related to effectiveness, satisfaction, efficiency, and perceived ease of use. Furthermore, several research gaps have been identified and recommendations have been made for further work in the area of the usability of online learning

New insights into the pathophysiology of gestational diabetes mellitus: possible role of human leukocyte antigen-G.

Diabetes can develop in up to 10% of pregnant women who have not previously had the condition. This condition which usually begins in the second half of the pregnancy is called gestational diabetes mellitus (GDM). In most cases, all diabetic symptoms disappear following delivery. However, women with GDM have an increased risk of developing type 2 diabetes mellitus (DM) later in life, especially if they were overweight before the pregnancy. The cause of GDM is unknown. Although hormones present in the pregnancy, especially human placental lactogen, are thought to be responsible for the development of this condition, many questions remain to be answered. It is still not known why GDM develops in a subgroup of pregnant women. It may be possible that events leading to the development of GDM are triggered by an antigenic load which is the fetus itself. Human leukocyte antigen-G (HLA-G) expression that functions to protect the fetus from immune attack by down-regulating cytotoxic T cell responses to fetal trophoblast antigens is postulated to protect the islet cells of the pancreatic tissue also. HLA-G and nuclear factor-kappaB (NF-kappaB) interaction is suggested to be central in the events leading to GDM development. An analogy between the development of DM in some transplant patients and GDM development in a proportion of pregnancies is postulated, so that an antigenic load triggers the diabetogenic process. Further support of this hypothesis with new studies may lead to the possibility that recombinant HLA-G can be used for the prevention of diabetes in high risk patients