Role of SOCS-1 Gene on Melanoma Cell Growth and Tumor Development

Melanoma is the most aggressive form of skin cancer, and its incidence has increased dramatically over the years. The murine B16F10 melanoma in syngeneic C57Bl/6 mice has been used as a highly aggressive model to investigate tumor development. Presently, we demonstrate in the B16F10-Nex2 subclone that silencing of SOCS-1, a negative regulator of Jak/Stat pathway, leads to reversal of the tumorigenic phenotype and inhibition of melanoma cell metastasis. SOCS-1 silencing with short hairpin RNA affected tumor growth and cell cycle regulation with arrest at the S phase with large-sized nuclei, reduced cell motility, and decreased melanoma cell invasion through Matrigel. A clonogenic assay showed that SOCS-1 acted as a modulator of resistance to anoikis. In addition, downregulation of SOCS-1 decreased the expression of epidermal growth factor receptor (mainly the phosphorylated-R), Ins-Rα, and fibroblast growth factor receptor. In vivo, silencing of SOCS-1 inhibited subcutaneous tumor growth and metastatic development in the lungs. Because SOCS-1 is expressed in most melanoma cell lines and bears a relation with tumor invasion, thickness, and stage of disease, the present results on the effects of SOCS-1 silencing in melanoma suggest that this regulating protein can be a target of cancer therapy

Age differences in the use of implicit visual cues in a response time task

Background: Many activities require a complex interrelationship between a performer and stimuli available in the environment without explicit perception, but many aspects regarding developmental changes in the use of implicit cues remain unknown. Aim: To investigate the use of implicit visual precueing presented at different time intervals in children, adolescents, and adults. Method: Seventy-two people, male and female, constituted four age groups: 8-, 10- and 12-year-olds and adults. Participants performed 32 trials, four-choice-time task across four conditions: no precue and a 43 ms centralized dot appearing in the stimulus circle at 43, 86 or 129 ms prior the stimulus. Response times were obtained for each trial and pooled into each condition. Results: Response times for 8-year-olds were longer than for 12-year-olds and adults and for 10-year-olds were longer than for adults. Response times were longer in the no precue condition compared to when precues were presented at 86 and 129 ms before the stimulus. Response times were longer when precue was presented at 43 ms compared presented at 129 ms before the stimulus. Interpretation: Implicit precues reduce response time in children, adolescents and adults, but young children benefit less from implicit precues than adolescents and adults.</jats:p

Perceptual Compressive Sensing

Compressive sensing (CS) works to acquire measurements at sub-Nyquist rate and recover the scene images. Existing CS methods always recover the scene images in pixel level. This causes the smoothness of recovered images and lack of structure information, especially at a low measurement rate. To overcome this drawback, in this paper, we propose perceptual CS to obtain high-level structured recovery. Our task no longer focuses on pixel level. Instead, we work to make a better visual effect. In detail, we employ perceptual loss, defined on feature level, to enhance the structure information of the recovered images. Experiments show that our method achieves better visual results with stronger structure information than existing CS methods at the same measurement rate.Comment: Accepted by The First Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2018). This is a pre-print version (not final version

An integrated remote-sensing and GIS approach for mapping past tin mining landscapes in northwest Iberia

This is the final version. Available from MDPI via the DOI in this record. Data Availability Statement: Restrictions apply to the availability of the presented data.Northwest Iberia can be considered as one of the main areas where tin was exploited in antiquity. However, the location of ancient tin mining and metallurgy, their date and the intensity of tin production are still largely uncertain. The scale of mining activity and its socio-economical context have not been truly assessed, nor its evolution over time. With the present study, we intend to present an integrated, multiscale, multisensor and interdisciplinary methodology to tackle this problem. The integration of airborne LiDAR and historic aerial imagery has enabled us to identify and map ancient tin mining remains on the Tinto valley (Viana do Castelo, northern Portugal). The combination with historic mining documentation and literature review allowed us to confirm the impact of modern mining and define the best-preserved ancient mining areas for further archaeological research. After data processing and mapping, subsequent ground-truthing involved field survey and geological sampling that confirmed cassiterite exploitation as the key feature of the mining works. This non-invasive approach is of importance for informing future research and management of these landscapes.European CommissionFEDERFCTFCT/ES

A general approach to simultaneous model fitting and variable elimination in response models for biological data with many more variables than observations

<p>Abstract</p> <p>Background</p> <p>With the advent of high throughput biotechnology data acquisition platforms such as micro arrays, SNP chips and mass spectrometers, data sets with many more variables than observations are now routinely being collected. Finding relationships between response variables of interest and variables in such data sets is an important problem akin to finding needles in a haystack. Whilst methods for a number of response types have been developed a general approach has been lacking.</p> <p>Results</p> <p>The major contribution of this paper is to present a unified methodology which allows many common (statistical) response models to be fitted to such data sets. The class of models includes virtually any model with a linear predictor in it, for example (but not limited to), multiclass logistic regression (classification), generalised linear models (regression) and survival models. A fast algorithm for finding sparse well fitting models is presented. The ideas are illustrated on real data sets with numbers of variables ranging from thousands to millions. R code implementing the ideas is available for download.</p> <p>Conclusion</p> <p>The method described in this paper enables existing work on response models when there are less variables than observations to be leveraged to the situation when there are many more variables than observations. It is a powerful approach to finding parsimonious models for such datasets. The method is capable of handling problems with millions of variables and a large variety of response types within the one framework. The method compares favourably to existing methods such as support vector machines and random forests, but has the advantage of not requiring separate variable selection steps. It is also works for data types which these methods were not designed to handle. The method usually produces very sparse models which make biological interpretation simpler and more focused.</p

New approaches to measuring anthelminthic drug efficacy: parasitological responses of childhood schistosome infections to treatment with praziquantel

By 2020, the global health community aims to control and eliminate human helminthiases, including schistosomiasis in selected African countries, principally by preventive chemotherapy (PCT) through mass drug administration (MDA) of anthelminthics. Quantitative monitoring of anthelminthic responses is crucial for promptly detecting changes in efficacy, potentially indicative of emerging drug resistance. Statistical models offer a powerful means to delineate and compare efficacy among individuals, among groups of individuals and among populations.; We illustrate a variety of statistical frameworks that offer different levels of inference by analysing data from nine previous studies on egg counts collected from African children before and after administration of praziquantel.; We quantify responses to praziquantel as egg reduction rates (ERRs), using different frameworks to estimate ERRs among population strata, as average responses, and within strata, as individual responses. We compare our model-based average ERRs to corresponding model-free estimates, using as reference the World Health Organization (WHO) 90 % threshold of optimal efficacy. We estimate distributions of individual responses and summarize the variation among these responses as the fraction of ERRs falling below the WHO threshold.; Generic models for evaluating responses to anthelminthics deepen our understanding of variation among populations, sub-populations and individuals. We discuss the future application of statistical modelling approaches for monitoring and evaluation of PCT programmes targeting human helminthiases in the context of the WHO 2020 control and elimination goals

Using the Juvenile Arthritis Disease Activity Score based on erythrocyte sedimentation rate or C-reactive protein level: results from the Portuguese register

Our aims were to evaluate the correlation between Juvenile Arthritis Disease Activity Score 27-joint reduced count (JADAS27) with erythrocyte sedimentation rate (ESR) and JADAS27 with C-reactive protein (CRP) scores and to test the agreement of both scores on classifying each disease activity state. We also aimed at verifying the correlation of the 2 scores across juvenile idiopathic arthritis (JIA) categories and to check the correlation between JADAS27-ESR and clinical JADAS27 (JADAS27 without ESR)