Optimal decision-making in mammals : insights from a robot study of rodent texture discrimination

Texture perception is studied here in a physical model of the rat whisker system consisting of a robot equipped with a biomimetic vibrissal sensor. Investigations of whisker motion in rodents have led to several explanations for texture discrimination, such as resonance or stick-slips. Meanwhile, electrophysiological studies of decision-making in monkeys have suggested a neural mechanism of evidence accumulation to threshold for competing percepts, described by a probabilistic model of Bayesian sequential analysis. For our robot whisker data, we find that variable reaction-time decision-making with sequential analysis performs better than the fixed response-time maximum-likelihood estimation. These probabilistic classifiers also use whatever available features of the whisker signals aid the discrimination, giving improved performance over a single-feature strategy, such as matching the peak power spectra of whisker vibrations. These results cast new light on how the various proposals for texture discrimination in rodents depend on the whisker contact mechanics and suggest the possibility of a common account of decision-making across mammalian species

The Nakayama automorphism of the almost Calabi-Yau algebras associated to SU(3) modular invariants

We determine the Nakayama automorphism of the almost Calabi-Yau algebra A associated to the braided subfactors or nimrep graphs associated to each SU(3) modular invariant. We use this to determine a resolution of A as an A-A bimodule, which will yield a projective resolution of A.Comment: 46 pages which constitutes the published version, plus an Appendix detailing some long calculations. arXiv admin note: text overlap with arXiv:1110.454

Polyelectrolyte Complex Templated Synthesis of Monodisperse, Sub-100 nm Porous Silica Nanoparticles for Cancer Targeted and Stimuli-Responsive Drug Delivery

Porous silica nanoparticles (PSiNPs) have long attracted interest in drug delivery research. However, conventional synthesis methods for sub-100 nm, functionalised PSiNPs typically give poor monodispersity, reproducibility, or involve complex synthetic protocols. We report a facile, reproducible, and cost-effective one-pot method for the synthesis of cancer targeting and pH responsive PSiNPs in this size range, without the need for post-synthetic modification. This was achieved by using monodisperse L-arginine (Arg)/ poly(acrylic acid) (PAA) polyelectrolyte complexes (PECs) as soft templates for silane hydrolysis and condensation. Highly uniform PSiNPs with tunable size control between 42 and 178 nm and disordered pore structure (1.1–2.7 nm) were obtained. Both PAA and Arg were retained within the PSiNPs, which enabled a high doxorubicin hydrochloride (Dox) loading capacity (22% w/w) and a 4-fold increase in drug release under weakly acidic pH compared to physiological pH. The surface presentation of Arg conferred significantly higher intracellular accumulation of Arg/PAA-PSiNPs in patient-derived glioblastoma cells compared to non-tumorigenic neural progenitor cells, which effectively translated to lower IC50 values for Dox-loaded Arg/PAA-PSiNPs than non-functionalised PSiNPs. This work brings forward new insights for the development of monodisperse PSiNPs with highly desirable built-in functionalities for biomedical applications

Active sensorimotor control for tactile exploration

In this paper, we present a novel and robust Bayesian approach for autonomous active exploration of unknown objects using tactile perception and sensorimotor control. Despite recent advances in tactile sensing, robust active exploration remains a challenging problem, which is a major hurdle to the practical deployment of tactile sensors in robots. Our proposed approach is based on a Bayesian perception method that actively controls the sensor with local small repositioning movements to reduce perception uncertainty, followed by explorative movements based on the outcome of each perceptual decision making step. Two sensorimotor control strategies are proposed for improving the accuracy and speed of the active exploration that weight the evidence from previous exploratory steps through either a weighted prior or weighted posterior. The methods are validated both off-line and in real-time on a contour following exploratory procedure. Results clearly demonstrate improvements in both accuracy and exploration time when using the proposed active methods compared to passive perception. Our work demonstrates that active perception has the potential to enable robots to perform robust autonomous tactile exploration in natural environments

Prediction of mechanistic subtypes of Parkinson’s using patient-derived stem cell models

Parkinson’s disease is a common, incurable neurodegenerative disorder that is clinically heterogeneous: it is likely that different cellular mechanisms drive the pathology in different individuals. So far it has not been possible to define the cellular mechanism underlying the neurodegenerative disease in life. We generated a machine learning-based model that can simultaneously predict the presence of disease and its primary mechanistic subtype in human neurons. We used stem cell technology to derive control or patient-derived neurons, and generated different disease subtypes through chemical induction or the presence of mutation. Multidimensional fluorescent labelling of organelles was performed in healthy control neurons and in four different disease subtypes, and both the quantitative single-cell fluorescence features and the images were used to independently train a series of classifiers to build deep neural networks. Quantitative cellular profile-based classifiers achieve an accuracy of 82%, whereas image-based deep neural networks predict control and four distinct disease subtypes with an accuracy of 95%. The machine learning-trained classifiers achieve their accuracy across all subtypes, using the organellar features of the mitochondria with the additional contribution of the lysosomes, confirming the biological importance of these pathways in Parkinson’s. Altogether, we show that machine learning approaches applied to patient-derived cells are highly accurate at predicting disease subtypes, providing proof of concept that this approach may enable mechanistic stratification and precision medicine approaches in the future

α-Synuclein-Confocal Nanoscanning (ASYN-CONA), a Bead-Based Assay for Detecting Early-Stage α-Synuclein Aggregation

α-Synuclein fibrils are considered a hallmark of Parkinson’s disease and other synucleinopathies. However, small oligomers that formed during the early stages of α-synuclein aggregation are thought to be the main toxic species causing disease. The formation of α-synuclein oligomers has proven difficult to follow, because of the heterogeneity and transient nature of the species formed. Here, a novel bead-based aggregation assay for monitoring the earliest stages of α-synuclein oligomerization, α-Synuclein–Confocal Nanoscanning (ASYN-CONA), is presented. The α-synuclein A91C single cysteine mutant is modified with a trifunctional chemical tag, which allows simultaneous fluorescent labeling with a green dye (tetramethylrhodamine, TMR) and attachment to microbeads. Beads with bound TMR-labeled α-synuclein are then incubated with a red dye (Cy5)-labeled variant of α-synuclein A91C, and EtOH (20%) to induce aggregation. Aggregation is detected by confocal scanning imaging, below the equatorial plane of the beads, which is known as the CONA technique. On-bead TMR-labeled α-synuclein and aggregated Cy5-labeled α-synuclein from the solution are quantitatively monitored in parallel by detection of fluorescent halos or “rings”. α-Synuclein on-bead oligomerization results in a linear increase of red bead ring fluorescence intensity over a period of 5 h. Total internal reflection fluorescence microscopy was performed on oligomers cleaved from the beads, and it revealed that (i) oligomers are sufficiently stable in solution to investigate their composition, consisting of 6 ± 1 monomer units, and (ii) oligomers containing a mean of 15 monomers bind Thioflavin-T. Various known inhibitors of α-synuclein aggregation were used to validate the ASYN-CONA assay for drug screening. Baicalein, curcumin, and rifampicin showed concentration-dependent inhibition of the α-synuclein aggregation and the IC₅₀ (the concentration of the compound at which the maxiumum intensity was reduced by one-half) were calculated

Prediction of Choice from Competing Mechanosensory and Choice-Memory Cues during Active Tactile Decision Making

Perceptual decision making is an active process where animals move their sense organs to extract task-relevant information. To investigate how the brain translates sensory input into decisions during active sensation, we developed a mouse active touch task where the mechanosensory input can be precisely measured and that challenges animals to use multiple mechanosensory cues. Male mice were trained to localize a pole using a single whisker and to report their decision by selecting one of three choices. Using high-speed imaging and machine vision, we estimated whisker–object mechanical forces at millisecond resolution. Mice solved the task by a sensory-motor strategy where both the strength and direction of whisker bending were informative cues to pole location. We found competing influences of immediate sensory input and choice memory on mouse choice. On correct trials, choice could be predicted from the direction and strength of whisker bending, but not from previous choice. In contrast, on error trials, choice could be predicted from previous choice but not from whisker bending. This study shows that animal choices during active tactile decision making can be predicted from mechanosensory and choice-memory signals, and provides a new task well suited for the future study of the neural basis of active perceptual decisions

The remarkably low affinity of CD4/peptide-major histocompatibility complex class II protein interactions

The αβ T-cell co-receptor CD4 enhances immune responses more than one million-fold in some assays, and yet the affinity of CD4 for its ligand, peptide-major histocompatibility class II (pMHC II) on antigen-presenting cells, is so weak that it was previously unquantifiable. Here, we report that a soluble form of CD4 failed to bind detectably to pMHC II in surface plasmon resonance-based assays, establishing a new upper limit for the solution affinity at 2.5 mM. However, when presented multivalently on magnetic beads, soluble CD4 bound pMHC II-expressing B cells, confirming that it is active and allowing mapping of the native co-receptor binding site on pMHC II. Whereas binding was undetectable in solution, the affinity of the CD4/pMHC II interaction could be measured in two dimensions (2D) using CD4- and adhesion molecule-functionalized, supported lipid bilayers, yielding a 2D dissociation constant, Kd, of ~5000 molecules/μm2. This value is 2-3 orders of magnitude higher than previously measured 2D Kd values for interacting leukocyte surface proteins. Calculations indicated, however, that CD4/pMHC II binding would increase rates of T-cell receptor (TCR) complex phosphorylation by three-fold via the recruitment of Lck, with only a small, 2-20% increase in the effective affinity of the TCR for pMHC II. The affinity of CD4/pMHC II therefore appears to be set at a value that increases T-cell sensitivity by enhancing phosphorylation, without compromising ligand discrimination.This work was supported by the Wellcome Trust and the UK Medical Research Council. PJ was supported by grants from the Swedish Research Council (number: 623-2014- 6387 and 621-2014-3907). OD is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number: 098363)

Protein aggregation and calcium dysregulation are hallmarks of familial Parkinson's disease in midbrain dopaminergic neurons

Mutations in the SNCA gene cause autosomal dominant Parkinson’s disease (PD), with loss of dopaminergic neurons in the substantia nigra, and aggregation of α-synuclein. The sequence of molecular events that proceed from an SNCA mutation during development, to end-stage pathology is unknown. Utilising human-induced pluripotent stem cells (hiPSCs), we resolved the temporal sequence of SNCA-induced pathophysiological events in order to discover early, and likely causative, events. Our small molecule-based protocol generates highly enriched midbrain dopaminergic (mDA) neurons: molecular identity was confirmed using single-cell RNA sequencing and proteomics, and functional identity was established through dopamine synthesis, and measures of electrophysiological activity. At the earliest stage of differentiation, prior to maturation to mDA neurons, we demonstrate the formation of small β-sheet-rich oligomeric aggregates, in SNCA-mutant cultures. Aggregation persists and progresses, ultimately resulting in the accumulation of phosphorylated α-synuclein aggregates. Impaired intracellular calcium signalling, increased basal calcium, and impairments in mitochondrial calcium handling occurred early at day 34–41 post differentiation. Once midbrain identity fully developed, at day 48–62 post differentiation, SNCA-mutant neurons exhibited mitochondrial dysfunction, oxidative stress, lysosomal swelling and increased autophagy. Ultimately these multiple cellular stresses lead to abnormal excitability, altered neuronal activity, and cell death. Our differentiation paradigm generates an efficient model for studying disease mechanisms in PD and highlights that protein misfolding to generate intraneuronal oligomers is one of the earliest critical events driving disease in human neurons, rather than a late-stage hallmark of the disease