Participant Perceptions of Twitter Research Ethics

Social computing systems such as Twitter present new research sites that have provided billions of data points to researchers. However, the availability of public social media data has also presented ethical challenges. As the research community works to create ethical norms, we should be considering users’ concerns as well. With this in mind, we report on an exploratory survey of Twitter users’ perceptions of the use of tweets in research. Within our survey sample, few users were previously aware that their public tweets could be used by researchers, and the majority felt that researchers should not be able to use tweets without consent. However, we find that these attitudes are highly contextual, depending on factors such as how the research is conducted or disseminated, who is conducting it, and what the study is about. The findings of this study point to potential best practices for researchers conducting observation and analysis of public data

Role of SPAK-NKCC1 signaling cascade in the choroid plexus blood-CSF barrier damage after stroke.

This is the final version. Available from BMC via the DOI in this record. Availability of data and materials: Supporting data and information about used material are available from the corresponding author on reasonable request.BACKGROUND: The mechanisms underlying dysfunction of choroid plexus (ChP) blood-cerebrospinal fluid (CSF) barrier and lymphocyte invasion in neuroinflammatory responses to stroke are not well understood. In this study, we investigated whether stroke damaged the blood-CSF barrier integrity due to dysregulation of major ChP ion transport system, Na+-K+-Cl- cotransporter 1 (NKCC1), and regulatory Ste20-related proline-alanine-rich kinase (SPAK). METHODS: Sham or ischemic stroke was induced in C57Bl/6J mice. Changes on the SPAK-NKCC1 complex and tight junction proteins (TJs) in the ChP were quantified by immunofluorescence staining and immunoblotting. Immune cell infiltration in the ChP was assessed by flow cytometry and immunostaining. Cultured ChP epithelium cells (CPECs) and cortical neurons were used to evaluate H2O2-mediated oxidative stress in stimulating the SPAK-NKCC1 complex and cellular damage. In vivo or in vitro pharmacological blockade of the ChP SPAK-NKCC1 cascade with SPAK inhibitor ZT-1a or NKCC1 inhibitor bumetanide were examined. RESULTS: Ischemic stroke stimulated activation of the CPECs apical membrane SPAK-NKCC1 complex, NF-κB, and MMP9, which was associated with loss of the blood-CSF barrier integrity and increased immune cell infiltration into the ChP. Oxidative stress directly activated the SPAK-NKCC1 pathway and resulted in apoptosis, neurodegeneration, and NKCC1-mediated ion influx. Pharmacological blockade of the SPAK-NKCC1 pathway protected the ChP barrier integrity, attenuated ChP immune cell infiltration or neuronal death. CONCLUSION: Stroke-induced pathological stimulation of the SPAK-NKCC1 cascade caused CPECs damage and disruption of TJs at the blood-CSF barrier. The ChP SPAK-NKCC1 complex emerged as a therapeutic target for attenuating ChP dysfunction and lymphocyte invasion after stroke.National Institutes of HealthVeterans AdministrationVA Research Career Scientist awardUPMC Endowed Chair professorship for Brain Disorders Researc

Hyper-parameter Optimisation by Restrained Stochastic Hill Climbing

Abstract. Machine learning practitioners often refer to hyper-parameter optimisation (HPO) as an art form and a skill that requires intuition and experience; Neuroevolution (NE) typically employs a combination of manual and evolutionary approaches for HPO. This paper explores the integration of a stochastic hill climbing approach for HPO within a NE algorithm. We empirically show that HPO by restrained stochastic hill climbing (HORSHC) is more effective than manual and pure evolutionary HPO. Empirical evidence is derived from a comparison of: (1) a NE algorithm that solely optimises hyper-parameters through evolution and (2) a number of derived algorithms with random search optimisation integration for optimising the hyper-parameters of a Neural Network. Through statistical analysis of the experimental results it has been revealed that random initialisation of hyper-parameters does not significantly affect the final performance of the Neural Networks evolved. However, HORSHC, a novel optimisation approach proposed in this paper has been proven to significantly out-perform the NE control algorithm. HORSHC presents itself as a solution that is computationally comparable in terms of both time and complexity as well as outperforming the control algorithm

Taxonomy based on science is necessary for global conservation

Peer reviewe

Layered Graphs with a Maximum Number of Edges

This document has been published in Circuit Theory and Design 93, Editor: Herv'e Dedieu, part I, pages 403--408. Elsevier, Amsterdam, The Netherlands, 1993

Connectivity Maximization of Layered Neural Networks for Supervised Learning

One of the main problems in current artificial neural network engineering is the lack of design rules for layered neural network topologies, namely how many hidden layers and how many neurons per hidden layer to choose for a neural network. This paper offers a theoretical basis for approaching this problem. Formally proven theories are developed which maximize the interconnection topology of layered neural networks, which use supervised learning, to obtain a maximum number of interconnections and therefore allow a maximum potential storage capacity. The results presented here depend only on the neural network statics and are therefore learning rule independent. Keywords: (artificial) neural network, connectionism, neural network topology, neural network statics, neural network connectivity, neural network architecture, neural network capacity, neural network taxonomy, high(er) order neural network, Sigma-Pi neural network 1 Introduction Although the field of artificial neural network..