The Wnt/beta-catenin pathway posteriorizes neural tissue in <i>Xenopus </i>by an indirect mechanism requiring FGF signalling

AbstractIn order to identify factors involved in posteriorization of the central nervous system, we undertook a functional screen in Xenopus animal cap explants which involved coinjecting noggin RNA together with pools of RNA from a chick somite cDNA library. In the course of this screen, we isolated a clone encoding a truncated form of β-catenin, which induced posterior neural and dorsal mesodermal markers when coinjected with noggin in animal caps. Similar results were obtained with Xwnt-8 and Xwnt-3a, suggesting that these effects are a consequence of activating the canonical Wnt signalling pathway. To investigate whether the activation of posterior neural markers requires mesoderm induction, we performed experiments using a chimeric inducible form of β-catenin. Activation of this protein during blastula stages resulted in the induction of both posterior neural and mesodermal markers, while activation during gastrula stages induced only posterior neural markers. We show that this posteriorizing activity occurs by an indirect and noncell-autonomous mechanism requiring FGF signalling

Pollen mother cells of Tradescantia clone 4430 and Tradescantia pallida var. purpurea are equally sensitive to the clastogenic effects of X-rays.

The Tradescantia micronucleus test is a sensitive bioassay for mutagenesis that may be employed both under field and laboratory conditions. This test has been standardized mostly on the basis of the results obtained with clone 4430. However, this clone is not well adapted to tropical weather, frequently showing problems with growth and flowering. In addition, it is attacked by parasites and insects, a fact that limits its use in field studies aiming at the biomonitoring of air pollution. In the city of São Paulo, Tradescantia pallida (Rose) Hunt. var. purpurea Boom is widely distributed as an ornamental plant in gardens and along roadsides and streets, mostly because of its natural resistance and its easy propagation. In this report, we present dose-response curves indicating that the sensitivity of T. pallida and clone 4430 to X-radiation (1, 10, 25 and 50 cGy) is similar. The results confirm our previous suggestion that T. pallida represents a good alternative for in situ mutagenesis testing in tropical regions, especially biomonitoring studies in which the exposure conditions may not be fully controllable

Moving Toward a Strategy for Addressing Climate Displacement of Marine Resources: A Proof-of-Concept

Realistic predictions of climate change effects on natural resources are central to adaptation policies that try to reduce these impacts. However, most current forecasting approaches do not incorporate species-specific, process-based biological information, which limits their ability to inform actionable strategies. Mechanistic approaches, incorporating quantitative information on functional traits, can potentially predict species- and population-specific responses that result from the cumulative impacts of small-scale processes acting at the organismal level, and can be used to infer population-level dynamics and inform natural resources management. Here we present a proof-of-concept study using the European anchovy as a model species that shows how a trait-based, mechanistic species distribution model can be used to explore the vulnerability of marine species to environmental changes, producing quantitative outputs useful for informing fisheries management. We crossed scenarios of temperature and food to generate quantitative maps of selected mechanistic model outcomes (e.g., Maximum Length and Total Reproductive Output). These results highlight changing patterns of source and sink spawning areas as well as the incidence of reproductive failure. This study demonstrates that model predictions based on functional traits can reduce the degree of uncertainty when forecasting future trends of fish stocks. However, to be effective they must be based on high spatial- and temporal resolution environmental data. Such a sensitive and spatially explicit predictive approach may be used to inform more effective adaptive management strategies of resources in novel climatic conditions

Epidemic processes in complex networks

In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio

Suppression of a charge density wave ground state in high magnetic fields: spin and orbital mechanisms

The charge density wave (CDW) transition temperature in the quasi-one dimensional (Q1D) organic material of (Per)$_2$Au(mnt)$_2$ is relatively low (TCDW = 12 K). Hence in a mean field BCS model, the CDW state should be completely suppressed in magnetic fields of order 30 - 40 T. To explore this possibility, the magnetoresistance of (Per)$_2$Au(mnt)$_2$ was investigated in magnetic fields to 45 T for 0.5 K < T < 12 K. For fields directed along the Q1D molecular stacking direction, TCDW decreases with field, terminating at about ~ 37 T for temperatures approaching zero. Results for this field orientation are in general agreement with theoretical predictions, including the field dependence of the magnetoresistance and the energy gap, $\Delta_{CDW}$. However, for fields tilted away from the stacking direction, orbital effects arise above 15 T that may be related to the return of un-nested Fermi surface sections that develop as the CDW state is suppressed. These findings are consistent with expectations that quasi-one dimensional metallic behavior will return outside the CDW phase boundary.Comment: 12 pages, 5 figure

Solar Irradiance Forecasting Using Dynamic Ensemble Selection

Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is an important task because it can improve the planning and operation of photovoltaic systems, resulting in economic advantages. Traditionally, single models are employed in this task. However, issues regarding the selection of an inappropriate model, misspecification, or the presence of random fluctuations in the solar irradiance series can result in this approach underperforming. This paper proposes a heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance. For each unseen test pattern, HetDS chooses the most suitable forecasting model based on a pool of seven well-known literature methods: ARIMA, support vector regression (SVR), multilayer perceptron neural network (MLP), extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The experimental evaluation was performed with four data sets of hourly solar irradiance measurements in Brazil. The proposed model attained an overall accuracy that is superior to the single models in terms of five well-known error metrics

Care Systematization in Pediatric 1ursing Applying Case-based Reasoning

Abstract--It is very difficult to find and collect nursing diagnoses in hospitals, where various clinical records and procedures are done by hand and manually stored on paper form. This condition impairs the readability of hospital process documents, and the archival method makes the information recovery very slow, which ultimately frustrates the search which could result in important information to improve the decision making process. The aim of this paper is to present an application to help the nurses in the clinical reasoning, keeping their experiences as a collection of cases for future research. The process is to scan diagnoses of pediatric nursing, and insert them into a case database, in a structure that provides for recovery, adaptation, indexing and comparison of cases, to be used to evaluate the effectiveness of the prototype application in handling these cases. This article presents a computational tool for health care support, employing techniques of case based reasoning, whose performance was satisfactory in the location of cases directly related to the presented test case. This fact suggests that the prototype presented is able to recover diagnoses made previously and it is of great importance for decision-making and improvement of diagnoses

Evaluation of machine-learning methods for ligand-based virtual screening

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed