HTself2: Combining p-values to Improve Classification of Differential Gene Expression in HTself

HTself is a web-based bioinformatics tool designed to deal with the classification of differential gene expression for low replication microarray studies. It is based on a statistical test that uses self-self experiments to derive intensity-dependent cutoffs. The method was previously described in Vêncio et al, (DNA Res. 12: 211- e 214, 2005). In this work we consider an extension of HTself by calculating p-values instead of using a fixed credibility level α. As before, the statistic used to compute single spots p-values is obtained from the gaussian Kernel Density Estimator method applied to self-self data. Different spots corresponding to the same biological gene (replicas) give rise to a set of independent p-values which can be combined by well known statistical methods. The combined p-value can be used to decide whether a gene can be considered differentially expressed or not. HTself2 is a new version of HTself that uses the idea of p-values combination. It was implemented as a user-friendly desktop application to help laboratories without a bioinformatics infrastructure

Cross-Lingual Classification of Crisis Data

Many citizens nowadays flock to social media during crises to share or acquire the latest information about the event. Due to the sheer volume of data typically circulated during such events, it is necessary to be able to efficiently filter out irrelevant posts, thus focusing attention on the posts that are truly relevant to the crisis. Current methods for classifying the relevance of posts to a crisis or set of crises typically struggle to deal with posts in different languages, and it is not viable during rapidly evolving crisis situations to train new models for each language. In this paper we test statistical and semantic classification approaches on cross-lingual datasets from 30 crisis events, consisting of posts written mainly in English, Spanish, and Italian. We experiment with scenarios where the model is trained on one language and tested on another, and where the data is translated to a single language. We show that the addition of semantic features extracted from external knowledge bases improve accuracy over a purely statistical model

Classifying Crises-Information Relevancy with Semantics

Social media platforms have become key portals for sharing and consuming information during crisis situations. However, humanitarian organisations and affected communities often struggle to sieve through the large volumes of data that are typically shared on such platforms during crises to determine which posts are truly relevant to the crisis, and which are not. Previous work on automatically classifying crisis information was mostly focused on using statistical features. However, such approaches tend to be inappropriate when processing data on a type of crisis that the model was not trained on, such as processing information about a train crash, whereas the classifier was trained on floods, earthquakes, and typhoons. In such cases, the model will need to be retrained, which is costly and time-consuming. In this paper, we explore the impact of semantics in classifying Twitter posts across same, and different, types of crises. We experiment with 26 crisis events, using a hybrid system that combines statistical features with various semantic features extracted from external knowledge bases. We show that adding semantic features has no noticeable benefit over statistical features when classifying same-type crises, whereas it enhances the classifier performance by up to 7.2% when classifying information about a new type of crisis

Probing effective field theory operators in the associated production of top quarks with a Z boson in multilepton final states at √s = 13 TeV

A search for new top quark interactions is performed within the framework of an effective field theory using the associated production of either one or two top quarks with a Z boson in multilepton final states. The data sample corresponds to an integrated luminosity of 138 fb−1 of proton-proton collisions at √ s = 13 TeV collected by the CMS ex periment at the LHC. Five dimension-six operators modifying the electroweak interactions of the top quark are considered. Novel machine-learning techniques are used to enhance the sensitivity to effects arising from these operators. Distributions used for the signal extrac tion are parameterized in terms of Wilson coefficients describing the interaction strengths of the operators. All five Wilson coefficients are simultaneously fit to data and 95% confidence level intervals are computed. All results are consistent with the SM expectations

Grain Sorghum Response to Band Applied Zinc Fertilizer

Zinc (Zn) is one of the micronutrients found to be deficient in Kansas. The objective of this study was to evaluate the response of grain sorghum to Zn fertilization using strip trials. The experiment was set up in Manhattan, KS, in 2015. The experimental design consisted of two strips, one with Zn fertilizer and the other without, with five replications. Zn fertilizer was applied as starter in combination with ammonium polyphosphate at the rate of 0.5 lb Zn/a. Plant tissue samples were collected to determine Zn content. Grain yield was recorded by combine equipped with yield monitor. No significant differences were found for sorghum grain yield. Grain Zn content increased with Zn fertilization. Zn fertilization may be considered for future studies in food biofortification

Reflexiones sobre el Consejo de la Magistratura del Poder Judicial de la Nación

El autor se avoca a realizar un análisis sobre algunos aspectos del Órgano Constitucional que se encuentran cuestionados, utilizando para ello las recientes decisiones jurisprudenciales, los antecedentes legislativos y el derecho comparado

Reflexiones sobre el Consejo de la Magistratura del Poder Judicial de la Nación.

El autor se avoca a realizar un análisis sobre algunos aspectos del Órgano Constitucional que se encuentran cuestionados, utilizando para ello las recientes decisiones jurisprudenciales, los antecedentes legislativos y el derecho comparado

Calibração de psicrômetros para avaliações de potencial hídrico foliar.

bitstream/item/71982/1/ID-30949.pd

Temozolomide is additive with cytotoxic effect of irradiation in canine glioma cell lines

Background: Similar to human glioblastoma patients, glial tumours in dogs have high treatment resistance and a guarded prognosis. In human medicine, the addition of temozolomide to radiotherapy leads to a favourable outcome in vivo as well as a higher antiproliferative effect on tumour cells in vitro. Objectives: The aim of the study was to determine the radio- and temozolomide-sensitivity of three canine glial tumour cell lines and to investigate a potential additive cytotoxic effect in combined treatment. Additionally, we wanted to detect the level of MGMT promoter methylation in these cell lines and to investigate a potential association between MGMT promoter methylation and treatment resistance. Methods: Cells were treated with various concentrations of temozolomide and/or irradiated with 4 and 8 Gy. Radiosensitization by temozolomide was evaluated using proliferation assay and clonogenic assay, and MGMT DNA methylation was investigated using bisulfite next-generation sequencing. Results: In all tested canine cell lines, clonogenicity was inhibited significantly in combined treatment compared to radiation alone. All canine glial cell lines tested in this study were found to have high methylation levels of MGMT promoter. Conclusions: Hence, an additive effect of combined treatment in MGMT negative canine glial tumour cell lines in vitro was detected. This motivates to further investigate the association between treatment resistance and MGMT, such as MGMT promoter methylation status