Dimensions of Animal Consciousness.

How does consciousness vary across the animal kingdom? Are some animals 'more conscious' than others? This article presents a multidimensional framework for understanding interspecies variation in states of consciousness. The framework distinguishes five key dimensions of variation: perceptual richness, evaluative richness, integration at a time, integration across time, and self-consciousness. For each dimension, existing experiments that bear on it are reviewed and future experiments are suggested. By assessing a given species against each dimension, we can construct a consciousness profile for that species. On this framework, there is no single scale along which species can be ranked as more or less conscious. Rather, each species has its own distinctive consciousness profile

Review of the Evidence of Sentience in Cephalopod Molluscs and Decapod Crustaceans

Birch and colleagues have developed an important and useful framework for evaluating the evidence for sentience – namely, the capacity to experience pain, distress and suffering, in cephalopod molluscs (cuttlefish, octopods and squid) and decapod crustaceans (including crabs, crayfish, lobsters, prawns, shrimps). Birch and colleagues develop eight criteria in their evaluation framework which are used to weigh the evidence from over 300 scientific publications. The report also investigates the potential welfare implications of current commercial practices on these species. The report concludes that cephalopods and decapod crustaceans are probably sentient

Animal sentience research: Synthesis and proposals

Most commentaries on our target article broadly support our approach to evaluating evidence of animal sentience. In this Response, we clarify the framework’s purpose and address criticisms of our criteria. A recurring theme is that a framework to synthesise current evidence of sentience is not the same as an agenda for future directions in animal sentience research. Although future directions are valuable, our framework aims to evaluate existing evidence and inform animal welfare legislation

Review of the evidence of sentience in cephalopod molluscs and decapod crustaceans

Sentience is the capacity to have feelings, such as feelings of pain, pleasure, hunger, thirst, warmth, joy, comfort and excitement. It is not simply the capacity to feel pain, but feelings of pain, distress or harm, broadly understood, have a special significance for animal welfare law. Drawing on over 300 scientific studies, we have evaluated the evidence of sentience in two groups of invertebrate animals: the cephalopod molluscs or, for short, cephalopods (including octopods, squid and cuttlefish) and the decapod crustaceans or, for short, decapods (including crabs, lobsters and crayfish). We have also evaluated the potential welfare implications of current commercial practices involving these animals

Deep Model for Improved Operator Function State Assessment

A deep learning framework is presented for engagement assessment using EEG signals. Deep learning is a recently developed machine learning technique and has been applied to many applications. In this paper, we proposed a deep learning strategy for operator function state (OFS) assessment. Fifteen pilots participated in a flight simulation from Seattle to Chicago. During the four-hour simulation, EEG signals were recorded for each pilot. We labeled 20- minute data as engaged and disengaged to fine-tune the deep network and utilized the remaining vast amount of unlabeled data to initialize the network. The trained deep network was then used to assess if a pilot was engaged during the four-hour simulation

Depletion of B-cells with rituximab improves endothelial function and reduces inflammation among individuals with rheumatoid arthritis.

BackgroundIndividuals with rheumatoid arthritis (RA) are at increased risk for cardiovascular disease, partly due to systemic inflammation and endothelial dysfunction. B-cells play an important pathogenic role in the inflammatory process that drives RA disease activity. Rituximab, a chimeric murine/human monoclonal antibody that depletes B-cells, is an effective therapy for RA. The purpose of this study was to determine whether B-cell depletion with rituximab reduces systemic inflammation and improves macrovascular (brachial artery flow-mediated dilation, FMD) and microvascular (reactive hyperemia) endothelial function in RA patients.Methods and resultsRA patients received a single course of rituximab (1000 mg IV infusion at baseline and on day 15). FMD, reactive hyperemia, inflammatory markers, and clinical assessments were performed at baseline, week 12, and week 24. Twenty patients (95% female, median age 54 years) completed the study. Following treatment, FMD improved from a baseline of 4.5±0.4% to 6.4±0.6% at 12 weeks (mean±SE; P<0.0001), followed by a decline at week 24; a similar pattern was observed for hyperemic velocity. Significant decreases in RA disease scores, high-sensitivity C-reactive protein, erythrocyte sedimentation rate, and circulating CD19+ B-cells were sustained through week 24. Cholesterol and triglycerides became significantly although modestly elevated during the study.ConclusionsDepletion of B-cells with rituximab improved macrovascular and microvascular endothelial function and reduced systemic inflammation, despite modest elevation in lipids. Given these results, rituximab should be evaluated in the future for its possible role in reducing excess cardiovascular risk in RA.Clinical trial registrationURL http://ClinicalTrials.gov. Unique identifier: NCT00844714

Deep Models for Engagement Assessment With Scarce Label Information

Task engagement is defined as loadings on energetic arousal (affect), task motivation, and concentration (cognition) [1]. It is usually challenging and expensive to label cognitive state data, and traditional computational models trained with limited label information for engagement assessment do not perform well because of overfitting. In this paper, we proposed two deep models (i.e., a deep classifier and a deep autoencoder) for engagement assessment with scarce label information. We recruited 15 pilots to conduct a 4-h flight simulation from Seattle to Chicago and recorded their electroencephalograph (EEG) signals during the simulation. Experts carefully examined the EEG signals and labeled 20 min of the EEG data for each pilot. The EEG signals were preprocessed and power spectral features were extracted. The deep models were pretrained by the unlabeled data and were fine-tuned by a different proportion of the labeled data (top 1%, 3%, 5%, 10%, 15%, and 20%) to learn new representations for engagement assessment. The models were then tested on the remaining labeled data. We compared performances of the new data representations with the original EEG features for engagement assessment. Experimental results show that the representations learned by the deep models yielded better accuracies for the six scenarios (77.09%, 80.45%, 83.32%, 85.74%, 85.78%, and 86.52%), based on different proportions of the labeled data for training, as compared with the corresponding accuracies (62.73%, 67.19%, 73.38%, 79.18%, 81.47%, and 84.92%) achieved by the original EEG features. Deep models are effective for engagement assessment especially when less label information was used for training

Review of the Evidence of Sentience in Cephalopod Molluscs and Decapod Crustaceans

Sentience is the capacity to have feelings, such as feelings of pain, pleasure, hunger, thirst, warmth, joy, comfort and excitement. It is not simply the capacity to feel pain, but feelings of pain, distress or harm, broadly understood, have a special significance for animal welfare law. Drawing on over 300 scientific studies, we evaluate the evidence of sentience in two groups of invertebrate animals: the cephalopod molluscs or, for short, cephalopods (including octopods, squid and cuttlefish) and the decapod crustaceans or, for short, decapods (including crabs, lobsters and crayfish). We also evaluate the potential welfare implications of current commercial practices involving these animals

The human Na⁺/H⁺ exchanger 1 is a membrane scaffold protein for extracellular signal-regulated kinase 2

Background Extracellular signal-regulated kinase 2 (ERK2) is an S/T kinase with more than 200 known substrates, and with critical roles in regulation of cell growth and differentiation and currently no membrane proteins have been linked to ERK2 scaffolding. Methods and results Here, we identify the human Na+/H+ exchanger 1 (hNHE1) as a membrane scaffold protein for ERK2 and show direct hNHE1-ERK1/2 interaction in cellular contexts. Using nuclear magnetic resonance (NMR) spectroscopy and immunofluorescence analysis we demonstrate that ERK2 scaffolding by hNHE1 occurs by one of three D-domains and by two non-canonical F-sites located in the disordered intracellular tail of hNHE1, mutation of which reduced cellular hNHE1-ERK1/2 co-localization, as well as reduced cellular ERK1/2 activation. Time-resolved NMR spectroscopy revealed that ERK2 phosphorylated the disordered tail of hNHE1 at six sites in vitro, in a distinct temporal order, with the phosphorylation rates at the individual sites being modulated by the docking sites in a distant dependent manner. Conclusions This work characterizes a new type of scaffolding complex, which we term a “shuffle complex”, between the disordered hNHE1-tail and ERK2, and provides a molecular mechanism for the important ERK2 scaffolding function of the membrane protein hNHE1, which regulates the phosphorylation of both hNHE1 and ERK2

Improving and accelerating the differentiation and functional maturation of human stem cell-derived neurons: role of extracellular calcium and GABA

Neurons differentiated from pluripotent stem cells using established neural culture conditions often exhibit functional deficits. Recently, we have developed enhanced media which both synchronize the neurogenesis of pluripotent stem cell-derived neural progenitors and accelerate their functional maturation; together these media are termed SynaptoJuice. This pair of media are pro-synaptogenic and generate authentic, mature synaptic networks of connected forebrain neurons from a variety of induced pluripotent and embryonic stem cell lines. Such enhanced rate and extent of synchronized maturation of pluripotent stem cell-derived neural progenitor cells generates neurons which are characterized by a relatively hyperpolarized resting membrane potential, higher spontaneous and induced action potential activity, enhanced synaptic activity, more complete development of a mature inhibitory GABAA receptor phenotype and faster production of electrical network activity when compared to standard differentiation media. This entire process – from pre-patterned neural progenitor to active neuron – takes 3 weeks or less, making it an ideal platform for drug discovery and disease modelling in the fields of human neurodegenerative and neuropsychiatric disorders, such as Huntington's disease, Parkinson's disease, Alzheimer's disease and Schizophrenia