A coupled bulk-surface model for cell polarisation

Several cellular activities, such as directed cell migration, are coordinated by an intricate network of biochemical reactions which lead to a polarised state of the cell, in which cellular symmetry is broken, causing the cell to have a well defined front and back. Recent work on balancing biological complexity with mathematical tractability resulted in the proposal and formulation of a famous minimal model for cell polarisation, known as the wave pinning model. In this study, we present a three-dimensional generalisation of this mathematical framework through the maturing theory of coupled bulk-surface semilinear partial differential equations in which protein compartmentalisation becomes natural. We show how a local perturbation over the surface can trigger propagating reactions, eventually stopped in a stable profile by the interplay with the bulk component. We describe the behaviour of the model through asymptotic and local perturbation analysis, in which the role of the geometry is investigated. The bulk-surface finite element method is used to generate numerical simulations over simple and complex geometries, which confirm our analysis, showing pattern formation due to propagation and pinning dynamics. The generality of our mathematical and computational framework allows to study more complex biochemical reactions and biomechanical properties associated with cell polarisation in multi-dimensions

Self-assembled monolayers of bisphosphonates: Influence of side chain steric hindrance

Bisphosphonates form self-assembled monolayers (SAMs) spontaneously on stainless steel, silicon, and titanium oxidized surfaces. We used contact angle measurements, atomic force microscopy, and X-ray reflectivity analysis to study the formation of SAMs on a model surface of ultraflat titanium (rms=0.2 nm). The results were extended to standard materials (mechanically polished titanium, stainless steel, and silicon) and showed that water-soluble bisphosphonic perfluoropolyether can easily form SAMs, with 100% surface coverage and a layer thickness of less than 3 nm. Hydrophobic (water contact angle >110° on stainless steel or titanium) and lipophobic (methylene iodide contact angle >105° on titanium) properties are discussed in terms of industrial applications

Green Production of Anionic Surfactant Obtained from Pea Protein

A pea protein isolate was hydrolyzed by a double enzyme treatment method in order to obtain short peptide sequences used as raw materials to produce lipopeptides-based surfactants. Pea protein hydrolysates were prepared using the combination of Alcalase and Flavourzyme. The influence of the process variables was studied to optimize the proteolytic degradation to high degrees of hydrolysis. The average peptide chain lengths were obtained at 3–5 amino acid units after a hydrolysis of 30 min with the mixture of enzymes. Then, N-acylation in water, in presence of acid chloride (C12 and C16), carried out with a conversion rate of amine functions of 90%, allowed to obtain anionic surfactant mixtures (lipopeptides and sodium fatty acids). These two steps were performed in water, in continuous and did not generate any waste. This process was therefore in line with green chemistry principles. The surface activities (CMC, foaming and emulsifying properties) of these mixtures were also studied. These formulations obtained from natural renewable resources and the reactions done under environmental respect, could replace petrochemical based surfactants for some applications

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Background: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods: In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window". Results: The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions: Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.FCT under the Neuroclinomics2 project [PTDC/EEI-SII/1937/2014, SFRH/BD/95846/2013]; INESC-ID plurianual [UID/CEC/50021/2013]; LASIGE Research Unit [UID/CEC/00408/2013

Ultrahigh-power micrometre-sized supercapacitors based on onion-like carbon

Electrochemical capacitors, also called supercapacitors, store energy in two closely spaced layers with opposing charges, and are used to power hybrid electric vehicles, portable electronic equipment and other devices¹. By offering fast charging and discharging rates, and the ability to sustain millions of ²⁻⁵, electrochemical capacitors bridge the gap between batteries, which offer high energy densities but are slow, and conventional electrolytic capacitors, which are fast but have low energy densities. Here, we demonstrate microsupercapacitors with powers per volume that are comparable to electrolytic capacitors, capacitances that are four orders of magnitude higher, and energies per volume that are an order of magnitude higher. We also measured discharge rates of up to 200 V s⁻¹, which is three orders of magnitude higher than conventional supercapacitors. The microsupercapacitors are produced by the electrophoretic deposition of a several micrometre-thick layer of nanostructured carbon onions⁶‚⁷ with diameters of 6-7 nm. Integration of these nanoparticles in a microdevice with a high surface-to-volume ratio, without the use of organic binders and polymer separators, improves performance because of the ease with which ions can access the active material. Increasing the energy density and discharge rates of supercapacitors will enable them to compete with batteries and conventional electrolytic capacitors in a number of applications

Diffusion in low-dimensional lipid membranes

The diffusion behavior of biological components in cellular membranes is vital to the function of cells. By collapsing the complexity of planar 2D membranes down to one dimension, fundamental investigations of bimolecular behavior become possible in one dimension. Here we develop lipid nanolithography methods to produce membranes, under fluid, with widths as low as 6 nm but extending to microns in length. We find reduced lipid mobility, as the width is reduced below 50 nm, suggesting different lipid packing in the vicinity of boundaries. The insertion of a membrane protein, M2, into these systems, allowed characterization of protein diffusion using high-speed AFM to demonstrate the first membrane protein 1D random walk. These quasi-1D lipid bilayers are ideal for testing and understanding fundamental concepts about the roles of dimensionality and size on physical properties of membranes from energy transfer to lipid packing

On Distant Speech Recognition for Home Automation

The official version of this draft is available at Springer via http://dx.doi.org/10.1007/978-3-319-16226-3_7International audienceIn the framework of Ambient Assisted Living, home automation may be a solution for helping elderly people living alone at home. This study is part of the Sweet-Home project which aims at developing a new home automation system based on voice command to improve support and well-being of people in loss of autonomy. The goal of the study is vocal order recognition with a focus on two aspects: distance speech recognition and sentence spotting. Several ASR techniques were evaluated on a realistic corpus acquired in a 4-room flat equipped with microphones set in the ceiling. This distant speech French corpus was recorded with 21 speakers who acted scenarios of activities of daily living. Techniques acting at the decoding stage, such as our novel approach called Driven Decoding Algorithm (DDA), gave better speech recognition results than the baseline and other approaches. This solution which uses the two best SNR channels and a priori knowledge (voice commands and distress sentences) has demonstrated an increase in recognition rate without introducing false alarms

The PACE Study: A randomised clinical trial of cognitive activity (CA) for older adults with mild cognitive impairment (MCI)

<p>Abstract</p> <p>Background</p> <p>Research evidence from observational studies suggests that cognitive activity reduces the risk of cognitive impairment in later life as well as the rate of cognitive decline of people with dementia. The Promoting Healthy Ageing with Cognitive Exercise (PACE) study has been designed to determine whether a cognitive activity intervention decreases the rate of cognitive decline amongst older adults with mild cognitive impairment (MCI).</p> <p>Methods/Design</p> <p>The study will recruit 160 community-dwelling men and women aged 65 years of age or over with mild cognitive impairment (MCI). Participants will be randomly allocated to two treatment groups: non-specific education and cognitive activity. The intervention will consist of ten 90-minute sessions delivered twice per week over a period of five weeks. The primary outcome measure of the study is the change from baseline in the total score on the Cambridge Cognitive Score (CAMCOG). Secondary outcomes of interest include changes in memory, attention, executive functions, mood and quality of life. Primary endpoints will be collected 12, 52 and 104 weeks after the baseline assessment.</p> <p>Discussion</p> <p>The proposed project will produce the best available evidence on the merits of increased cognitive activity as a strategy to prevent cognitive decline among older adults with MCI. We anticipate that the results of this study will have implications for the development of evidence-based preventive strategies to reduce the rate of cognitive decline amongst older people at risk of dementia.</p> <p>Trial registration</p> <p>ACTRN12608000556347</p

Capacitive energy storage from -50 to 100 °C using an ionic liquid electrolyte

Relying on redox reactions, most batteries are limited in their ability to operate at very low or very high temperatures. While performance of electrochemical capacitors is less dependent on the temperature, present-day devices still cannot cover the entire range needed for automotive and electronics applications under a variety of environmental conditions. We show that the right combination of the exohedral nanostructured carbon (nanotubes and onions) electrode and a eutectic mixture of ionic liquids can dramatically extend the temperature range of electrical energy storage, thus defying the conventional wisdom that ionic liquids can only be used as electrolytes above room temperature. We demonstrate electrical double layer capacitors able to operate from -50 to 100 °C over a wide voltage window (up to 3.7 V) and at very high charge/discharge rates of up to 20 V/s

Making effective use of healthcare data using data-to-text technology

Healthcare organizations are in a continuous effort to improve health outcomes, reduce costs and enhance patient experience of care. Data is essential to measure and help achieving these improvements in healthcare delivery. Consequently, a data influx from various clinical, financial and operational sources is now overtaking healthcare organizations and their patients. The effective use of this data, however, is a major challenge. Clearly, text is an important medium to make data accessible. Financial reports are produced to assess healthcare organizations on some key performance indicators to steer their healthcare delivery. Similarly, at a clinical level, data on patient status is conveyed by means of textual descriptions to facilitate patient review, shift handover and care transitions. Likewise, patients are informed about data on their health status and treatments via text, in the form of reports or via ehealth platforms by their doctors. Unfortunately, such text is the outcome of a highly labour-intensive process if it is done by healthcare professionals. It is also prone to incompleteness, subjectivity and hard to scale up to different domains, wider audiences and varying communication purposes. Data-to-text is a recent breakthrough technology in artificial intelligence which automatically generates natural language in the form of text or speech from data. This chapter provides a survey of data-to-text technology, with a focus on how it can be deployed in a healthcare setting. It will (1) give an up-to-date synthesis of data-to-text approaches, (2) give a categorized overview of use cases in healthcare, (3) seek to make a strong case for evaluating and implementing data-to-text in a healthcare setting, and (4) highlight recent research challenges.Comment: 27 pages, 2 figures, book chapte