Hysteresis in memristors produces conduction inductance and conduction capacitance effects

Memristors are devices in which the conductance state can be alternately switched between a high and a low value by means of a voltage scan. In general, systems involving a chemical inductor mechanism as solar cells, asymmetric nanopores in electrochemical cells, transistors, and solid state memristive devices, exhibit a current increase and decrease over time that generates hysteresis. By performing small signal ac impedance spectroscopy, we show that memristors, or any other system with hysteresis relying on the conductance modulation effect, display intrinsic dynamic inductor-like and capacitance-like behaviours in specific input voltage ranges. Both the conduction inductance and the conduction capacitance originate in the same delayed conduction process linked to the memristor dynamics and not in electromagnetic or polarization effects. A simple memristor model reproduces the main features of the transition from capacitive to inductive impedance spectroscopy spectra, which causes a nonzero crossing of current-voltage curves.

Hardware implementation of memristor-based artificial neural networks

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.

Revealing the quantum nature of the voltage-induced conductance changes in oxygen engineered yttrium oxide-based RRAM devices

In this work, the quasi-analog to discrete transition occurring in the current-voltage characteristic of oxygen engineered yttrium oxide-based resistive random-access memory (RRAM) devices is investigated in detail. In particular, the focus of our research is not on the absolute conductance values of this characteristic but on the magnitude of its conductance changes occurring during the reset process of the device. It is found that the detected changes correspond to conductance values predominantly of the order of the quantum unit of conductance G0 = 2e2/h, where e is the electron charge and h the Planck constant. This feature is observed even at conductance levels far above G0, i.e. where electron transport is seemingly diffusive. It is also observed that such behavior is reproducible across devices comprising yttrium oxide layers with different oxygen concentrations and measured under different voltage sweep rates. While the oxygen deficiency affects the total number of quantized conductance states, the magnitude of the changes in conductance, close to 1 G0, is invariant to the oxygen content of the functional layer.

Multinational comparison study of aircraft pilot healthcare avoidance behaviour

BACKGROUND

US and Canadian pilots are required to meet medical standards to secure their active flying status, but a subgroup exhibit healthcare avoidance behaviour due to fear of loss of that status. This phenomenon has the potential to impact pilot health, aeromedical screening and aviation safety. No international comparison study of pilot healthcare avoidance currently exists between US and Canadian pilots.

AIMS

To compare the rate and subtypes of healthcare avoidance behaviour secondary to fear for loss of flying status between US and Canadian pilots.

METHODS

A comparison analysis of data collected during two independent, non-probabilistic, cross-sectional internet surveys including any individual certified to perform flying duties in the USA (US survey) or Canada (Canadian survey).

RESULTS

There were 4320 US pilots and 1415 Canadian pilots who completed informed consent and 3765 US pilots and 1405 Canadian pilots were included in the results. There were 56% of US pilots who reported a history of healthcare avoidance behaviour compared to 55% of Canadian pilots (P = 0.578). A multivariable logistic regression that included age, pilot type and gender showed that US pilots were slightly more likely than Canadian pilots to report this behaviour (odds ratio 1.22, 95% confidence interval 1.06-1.4).

CONCLUSIONS

Healthcare avoidance behaviour due to fear of loss of flying status has a relatively high prevalence in both US and Canadian pilot populations.

Sex-Related Mortality After Burns: A Scoping Review in the Sadc-Region

Findings on mortality by sex after burns in low- and middle-income countries (LMICs) are contradictory and, where differences have been described, the reasons are often based on speculation and not on the analysis of factors that could have affected the outcome, such as patient or injury characteristics or provided care. Since the paucity of studies on burns from single LMICs is notorious, merging data from neighboring countries with similar socio-economic backgrounds might provide a larger dataset, contributing to identifying recurrent causes. This scoping review aimed therefore to analyze differences in mortality after burns between the sexes, as well as to identify aspects that could explain possible differences, in countries belonging to the South African Development Community (SADC) region. Studies in English published between 2010 and 2020 were identified according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines by searching PubMed and/or Medline, Clinical Trials and Cochrane Library, and using the screening tool "Covidence". The 13 included studies could not consistently show association between sex and mortality after burns, but contradictory findings. In the case of differences in outcome between the sexes, explanations were mainly based on speculation (e.g., hormonal differences, self-harm intention), while rarely burn specific factors were reported and included in the analysis of the mortality risk. This study indicates the need for prospective burn specific data collection in LMICs that would contribute to identifying factors associated with death.

Experimental and Modeling Study of Metal-Insulator Interfaces to Control the Electronic Transport in Single Nanowire Memristive Devices

Memristive devices relying on redox-based resistive switching mechanisms represent promising candidates for the development of novel computing paradigms beyond von Neumann architecture. Recent advancements in understanding physicochemical phenomena underlying resistive switching have shed new light on the importance of an appropriate selection of material properties required to optimize the performance of devices. However, despite great attention has been devoted to unveiling the role of doping concentration, impurity type, adsorbed moisture, and catalytic activity at the interfaces, specific studies concerning the effect of the counter electrode in regulating the electronic flow in memristive cells are scarce. In this work, the influence of the metal-insulator Schottky interfaces in electrochemical metallization memory (ECM) memristive cell model systems based on single-crystalline ZnO nanowires (NWs) is investigated following a combined experimental and modeling approach. By comparing and simulating the electrical characteristics of single NW devices with different contact configurations and by considering Ag and Pt electrodes as representative of electrochemically active and inert electrodes, respectively, we highlight the importance of an appropriate choice of electrode materials by taking into account the Schottky barrier height and interface chemistry at the metal-insulator interfaces. In particular, we show that a clever choice of metal-insulator interfaces allows to reshape the hysteretic conduction characteristics of the device and to increase the device performance by tuning its resistance window. These results obtained from single NW-based devices provide new insights into the selection criteria for materials and interfaces in connection with the design of advanced ECM cells.

Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks

In this paper, the use of Artificial Neural Networks (ANNs) in the form of Convolutional Neural Networks (AlexNET) for the fast and energy-efficient fitting of the Dynamic Memdiode Model (DMM) to the conduction characteristics of bipolar-type resistive switching (RS) devices is investigated. Despite an initial computationally intensive training phase the ANNs allow obtaining a mapping between the experimental Current-Voltage (I-V) curve and the corresponding DMM parameters without incurring a costly iterative process as typically considered in error minimization-based optimization algorithms. In order to demonstrate the fitting capabilities of the proposed approach, a complete set of I-Vs obtained from Y₂O₃-based RRAM devices, fabricated with different oxidation conditions and measured with different current compliances, is considered. In this way, in addition to the intrinsic RS variability, extrinsic variation is achieved by means of external factors (oxygen content and damage control during the set process). We show that the reported method provides a significant reduction of the fitting time (one order of magnitude), especially in the case of large data sets. This issue is crucial when the extraction of the model parameters and their statistical characterization are required.

Quantum Conductance in Memristive Devices: Fundamentals, Developments, and Applications

Quantum effects in novel functional materials and new device concepts represent a potential breakthrough for the development of new information processing technologies based on quantum phenomena. Among the emerging technologies, memristive elements that exhibit resistive switching, which relies on the electrochemical formation/rupture of conductive nanofilaments, exhibit quantum conductance effects at room temperature. Despite the underlying resistive switching mechanism having been exploited for the realization of next-generation memories and neuromorphic computing architectures, the potentialities of quantum effects in memristive devices are still rather unexplored. Here, a comprehensive review on memristive quantum devices, where quantum conductance effects can be observed by coupling ionics with electronics, is presented. Fundamental electrochemical and physicochemical phenomena underlying device functionalities are introduced, together with fundamentals of electronic ballistic conduction transport in nanofilaments. Quantum conductance effects including quantum mode splitting, stability, and random telegraph noise are analyzed, reporting experimental techniques and challenges of nanoscale metrology for the characterization of memristive phenomena. Finally, potential applications and future perspectives are envisioned, discussing how memristive devices with controllable atomic-sized conductive filaments can represent not only suitable platforms for the investigation of quantum phenomena but also promising building blocks for the realization of integrated quantum systems working in air at room temperature.

Polymerogenic neuroserpin causes mitochondrial alterations and activates NFκB but not the UPR in a neuronal model of neurodegeneration FENIB

The neurodegenerative condition FENIB (familiar encephalopathy with neuroserpin inclusion bodies) is caused by heterozygous expression of polymerogenic mutant neuroserpin (NS), with polymer deposition within the endoplasmic reticulum (ER) of neurons. We generated transgenic neural progenitor cells (NPCs) from mouse fetal cerebral cortex stably expressing either the control protein GFP or human wild type, polymerogenic G392E or truncated (delta) NS. This cellular model makes it possible to study the toxicity of polymerogenic NS in the appropriated cell type by in vitro differentiation to neurons. Our previous work showed that expression of G392E NS in differentiated NPCs induced an adaptive response through the upregulation of several genes involved in the defence against oxidative stress, and that pharmacological reduction of the antioxidant defences by drug treatments rendered G392E NS neurons more susceptible to apoptosis than control neurons. In this study, we assessed mitochondrial distribution and found a higher percentage of perinuclear localisation in G392E NS neurons, particularly in those containing polymers, a phenotype that was enhanced by glutathione chelation and rescued by antioxidant molecules. Mitochondrial membrane potential and contact sites between mitochondria and the ER were reduced in neurons expressing the G392E mutation. These alterations were associated with a pattern of ER stress that involved the ER overload response but not the unfolded protein response. Our results suggest that intracellular accumulation of NS polymers affects the interaction between the ER and mitochondria, causing mitochondrial alterations that contribute to the neuronal degeneration seen in FENIB patients.

Nano-Memristors with 4 mV Switching Voltage Based on Surface-Modified Copper Nanoparticles

The development of memristors operating at low switching voltages <50 mV can be very useful to avoid signal amplification in many types of circuits, such as those used in bioelectronic applications to interact with neurons and nerves. Here, it is reported that 400 nm-thick films made of dalkyl-dithiophosphoric (DDP) modified copper nanoparticles (CuNPs) exhibit volatile threshold-type resistive switching (RS) at ultralow switching voltage of ≈4 mV. The RS is observed in small nanocells with a lateral size of <50 nm^-2 , during hundreds of cycles, and with an ultralow variability. Atomistic calculations reveal that the switching mechanism is related to the modification of the Schottky barriers and insulator-to-metal transition when ionic movement is induced via external bias. The devices are also used to model integrate-and-fire neurons for spiking neural networks and it is concluded that circuits employing DDP-CuNPs consume around ten times less power than similar neurons implemented with a memristor that switches at 40 mV.

SPICE Implementation of the Dynamic Memdiode Model for Bipolar Resistive Switching Devices

This paper reports the fundamentals and the SPICE implementation of the Dynamic Memdiode Model (DMM) for the conduction characteristics of bipolar-type resistive switching (RS) devices. Following Prof. Chua’s memristive devices theory, the memdiode model comprises two equations, one for the electron transport based on a heuristic extension of the quantum point-contact model for filamentary conduction in thin dielectrics and a second equation for the internal memory state related to the reversible displacement of atomic species within the oxide film. The DMM represents a breakthrough with respect to the previous Quasi-static Memdiode Model (QMM) since it describes the memory state of the device as a balance equation incorporating both the snapback and snapforward effects, features of utmost importance for the accurate and realistic simulation of the RS phenomenon. The DMM allows simple setting of the initial memory condition as well as decoupled modeling of the set and reset transitions. The model equations are implemented in the LTSpice simulator using an equivalent circuital approach with behavioral components and sources. The practical details of the model implementation and its modes of use are also discussed.

Standards for the Characterization of Endurance in Resistive Switching Devices

Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to variability and reliability issues, which are usually evaluated through switching endurance tests. However, we note that most studies that claimed high endurances >10⁶ cycles were based on resistance versus cycle plots that contain very few data points (in many cases even <20), and which are collected in only one device. We recommend not to use such a characterization method because it is highly inaccurate and unreliable (i.e., it cannot reliably demonstrate that the device effectively switches in every cycle and it ignores cycle-to-cycle and device-to-device variability). This has created a blurry vision of the real performance of RS devices and in many cases has exaggerated their potential. This article proposes and describes a method for the correct characterization of switching endurance in RS devices; this method aims to construct endurance plots showing one data point per cycle and resistive state and combine data from multiple devices. Adopting this recommended method should result in more reliable literature in the field of RS technologies, which should accelerate their integration in commercial products.

On the Thermal Models for Resistive Random Access Memory Circuit Simulation

Resistive Random Access Memories (RRAMs) are based on resistive switching (RS) operation and exhibit a set of technological features that make them ideal candidates for applications related to non-volatile memories, neuromorphic computing and hardware cryptography. For the full industrial development of these devices different simulation tools and compact models are needed in order to allow computer-aided design, both at the device and circuit levels. Most of the different RRAM models presented so far in the literature deal with temperature effects since the physical mechanisms behind RS are thermally activated; therefore, an exhaustive description of these effects is essential. As far as we know, no revision papers on thermal models have been published yet; and that is why we deal with this issue here. Using the heat equation as the starting point, we describe the details of its numerical solution for a conventional RRAM structure and, later on, present models of different complexity to integrate thermal effects in complete compact models that account for the kinetics of the chemical reactions behind resistive switching and the current calculation. In particular, we have accounted for different conductive filament geometries, operation regimes, filament lateral heat losses, the use of several temperatures to characterize each conductive filament, among other issues. A 3D numerical solution of the heat equation within a complete RRAM simulator was also taken into account. A general memristor model is also formulated accounting for temperature as one of the state variables to describe electron device operation. In addition, to widen the view from different perspectives, we deal with a thermal model contextualized within the quantum point contact formalism. In this manner, the temperature can be accounted for the description of quantum effects in the RRAM charge transport mechanisms. Finally, the thermometry of conducting filaments and the corresponding models considering different dielectric materials are tackled in depth.

Role of the left coronary artery geometry configuration in atherosusceptibility: CFD simulations considering sPTT model for blood

The achievement of clinically viable methodologies to simulate the hemodynamics in patient-specific coronary arteries is still a major challenge. Therefore, the novelty of this work is attained by the introduction of the viscoelastic property of blood in the numerical simulations, to study the role of the left coronary artery (LCA) geometry configuration in the atherosusceptibility. Apparently healthy patients were used and four different methodologies were tested. The methodology giving the most accurate results at the same time of having the lowest computational time is the one considering the viscoelastic property of blood and computational fluid dynamics. A Pearson correlation analysis was used to highlight relationships between geometric configuration and hemodynamic descriptors based on the simulated wall shear stress (WSS). The left main stem (LMS) has the greatest atherosusceptibility followed by the left anterior descending artery (LAD) since the relative residence time (RRT) average values are 3.81 and 3.70 Pa^-1, respectively. The geometric parameters with relevant contribution to directional flow change are the cross-sectional areas, especially the one of LMS segment (A_LMS), and the curvature of LMS segment. For LMS and LAD segments, when A_LMS increases, blood flow disturbance (r = 0.81 in LMS and r = 0.74 in LAD) and atherosusceptibility (r = 0.84 in LMS and r = 0.85 in LAD) increases. When the LMS curvature decreases, the WSS magnitude (r = 0.80 in LMS and r = 0.83 in LAD) decreases, and disturbance (r=-0.80 in LMS and r=-0.91 in LAD) and atherosusceptibility (r=-0.74 in LMS and r=-0.74 in LAD) increases.

Kinetics and drainage index in function of pH, in the dewatering of arsenic iron sludge

Water for human consumption containing arsenic from natural and anthropogenic sources is a public health problem worldwide. Therefore, different technologies must be used to remove it from the water (coagulation-filtration assisted with ferric chloride, adsorption, membranes, etc.). While these technologies produce water that is free from arsenic, they also produce toxic residuals with high arsenic concentrations, which must be treated in order to decrease their volume and thereby facilitate transport and final disposal. Thus, the main purpose of this investigation was to study the physical and chemical properties of arsenic iron sludge in thickening, chemical conditioning, and dewatering processes, as well as to propose new kinetic criteria for obtaining the drainage index (E_g) based on polymer dose, mesh permeability, specific resistance to filtration, and pH. We found a significant improvement in the physical and chemical properties when thickening the sludge, in particular, floc size increased and specific resistance to filtration and Z-potential decreased due to weakened repulsive forces, resulting in more sedimentation. The polymer AN913VHM (PF2) had the best behavior in the chemical conditioning and dewatering tests, with an optimal dose of 8 mg/L and a mesh permeability of 80-100 ft³/min ft², which made it possible to retain more flocs (60% in 3.74 min). The pH affected the amount of total solids in the cake, with more solids obtained at a pH of 6 (25.93 g/L) and 6.5 (21.81 g/L), and with rapid drainage of surface water (69.28%). Furthermore, new kinetic criteria were obtained for a drainage time of 120 s in order to eliminate 60% of the total volume of the filtrate mass, with E_g of 3.05 at a pH of 6. This value is considered medium drainage for this type of sludge, which is difficult to treat.

A Study of the Set of Probability Measures Compatible with Comparative Judgements

We consider a set of comparative probability judgements over a finite possibility space and study the structure of the set of probability measures that are compatible with them. We relate the existence of some compatible probability measure to Walley’s behavioural theory of imprecise probabilities, and introduce a graphical representation that allows us to bound, and in some cases determine, the extreme points of the set of compatible measures. In doing this, we generalise some earlier work by Miranda and Destercke on elementary comparisons.

On the Application of a Diffusive Memristor Compact Model to Neuromorphic Circuits

Memristive devices have found application in both random access memory and neuromorphic circuits. In particular, it is known that their behavior resembles that of neuronal synapses. However, it is not simple to come by samples of memristors and adjusting their parameters to change their response requires a laborious fabrication process. Moreover, sample to sample variability makes experimentation with memristor-based synapses even harder. The usual alternatives are to either simulate or emulate the memristive systems under study. Both methodologies require the use of accurate modeling equations. In this paper, we present a diffusive compact model of memristive behavior that has already been experimentally validated. Furthermore, we implement an emulation architecture that enables us to freely explore the synapse-like characteristics of memristors. The main advantage of emulation over simulation is that the former allows us to work with real-world circuits. Our results can give some insight into the desirable characteristics of the memristors for neuromorphic applications.

Goal-directed fluid therapy on laparoscopic colorectal surgery within enhanced recovery after surgery program

INTRODUCTION

Enhanced recovery after surgery protocols (ERAS) are used in peri-operative care to reduce the stress response to surgical aggression. As fluid overload has been associated with increased morbidity and delayed hospital discharge, a major aspect of this is fluid management. Intra-operative goal-directed fluid protocols have been shown to reduce post-operative complications, particularly in high risk patients.?.

OBJECTIVE

To compare 2fluid therapy models (zero-balance versus goal-directed fluid therapy) in patients who were scheduled for laparoscopic colorectal surgery within an ERAS program, recording the rate of complications such as surgical site infection, ileus, post-operative náusea and vomiting, and variability of the estimated glomerular filtration rate (eGFR).

MATERIALS AND METHODS

An observational, retrospective study was conducted including adults who were scheduled for elective laparoscopic colorectal surgery within an ERAS program, and to investigate the postoperative complication rate.

RESULTS

A total of 128 patients were included in this study; 43 (33.6%) in the zero-balance group and 85 (66.4%) in the goal-directed fluid therapy group. The total fluids administered was lower in the goal-directed fluid therapy group, as well as the incidence of post-operative complications (surgical site infection, anastomotic leak, ileus, and postoperative náusea and vomiting). No significant differences were found for length of stay, intra-operative urine output, and variability of the eGFR.?.

CONCLUSION

The results of this study show that by using a goal-directed fluid therapy algorithm, the total amount of fluids administered can be reduced, as well as obtaining a lower incidence of post-operative complications.

Extreme Points of the Core of Possibility Measures and Maxitive p-Boxes

Under an epistemic interpretation, an upper probability can be regarded as equivalent to the set of probability measures it dominates, sometimes referred to as its core. In this paper, we study the properties of the number of extreme points of the core of a possibility measure, and investigate in detail those associated with (uni- and bi-)variate p-boxes, that model the imprecise information about a cumulative distribution function.

Shapley and Banzhaf Values as Probability Transformations

We investigate the role of some game solutions, such the Shapley and the Banzhaf values, as probability transformations. The first one coincides with the pignistic transformation proposed in the Transferable Belief Model; the second one is not efficient in general, leading us to consider its normalized version. We study a number of particular models of lower probabilities: minitive measures, coherent lower probabilities, as well as the lower probabilities induced by comparative or distortion models. For them, we provide some alternative expressions of the Shapley and Banzhaf values and study under which conditions they belong to the core of the lower probability.

Silicon Oxide (SiOx ): A Promising Material for Resistance Switching?

Interest in resistance switching is currently growing apace. The promise of novel high-density, low-power, high-speed nonvolatile memory devices is appealing enough, but beyond that there are exciting future possibilities for applications in hardware acceleration for machine learning and artificial intelligence, and for neuromorphic computing. A very wide range of material systems exhibit resistance switching, a number of which-primarily transition metal oxides-are currently being investigated as complementary metal-oxide-semiconductor (CMOS)-compatible technologies. Here, the case is made for silicon oxide, perhaps the most CMOS-compatible dielectric, yet one that has had comparatively little attention as a resistance-switching material. Herein, a taxonomy of switching mechanisms in silicon oxide is presented, and the current state of the art in modeling, understanding fundamental switching mechanisms, and exciting device applications is summarized. In conclusion, silicon oxide is an excellent choice for resistance-switching technologies, offering a number of compelling advantages over competing material systems.

Singular fibres of the Gelfand-Cetlin system on 𝔲(n)

In this paper, we show that every singular fibre of the Gelfand-Cetlin system on co-adjoint orbits of unitary groups is a smooth isotropic submanifold which is diffeomorphic to a two-stage quotient of a compact Lie group by free actions of two other compact Lie groups. In many cases, these singular fibres can be shown to be homogeneous spaces or even diffeomorphic to compact Lie groups. We also give a combinatorial formula for computing the dimensions of all singular fibres, and give a detailed description of these singular fibres in many cases, including the so-called (multi-)diamond singularities. These (multi-)diamond singular fibres are degenerate for the Gelfand-Cetlin system, but they are Lagrangian submanifolds diffeomorphic to direct products of special unitary groups and tori. Our methods of study are based on different ideas involving complex ellipsoids, Lie groupoids and also general ideas coming from the theory of singularities of integrable Hamiltonian systems.This article is part of the theme issue 'Finite dimensional integrable systems: new trends and methods'.

UPR activation specifically modulates glutamate neurotransmission in the cerebellum of a mouse model of autism

An increasing number of rare mutations linked to autism spectrum disorders have been reported in genes encoding for proteins involved in synapse formation and maintenance, such as the post-synaptic cell adhesion proteins neuroligins. Most of the autism-linked mutations in the neuroligin genes map on the extracellular protein domain. The autism-linked substitution R451C in Neuroligin3 (NLGN3) induces a local misfolding of the extracellular domain, causing defective trafficking and retention of the mutant protein in the endoplasmic reticulum (ER). The activation of the unfolded protein response (UPR), due to misfolded proteins accumulating in the ER, has been implicated in pathological and physiological conditions of the nervous system. It was previously shown that the over-expression of R451C NLGN3 in a cellular system leads to the activation of the UPR. Here, we have investigated whether this protective cellular response is detectable in the knock-in mouse model of autism endogenously expressing R451C NLGN3. Our data showed up-regulation of UPR markers uniquely in the cerebellum of the R451C mice compared to WT littermates, at both embryonic and adult stages, but not in other brain regions. Miniature excitatory currents in the Purkinje cells of the R451C mice showed higher frequency than in the WT, which was rescued inhibiting the PERK branch of UPR. Taken together, our data indicate that the R451C mutation in neuroligin3 elicits UPR in vivo, which appears to trigger alterations of synaptic function in the cerebellum of a mouse model expressing the R451C autism-linked mutation.

Tuning the Hydrolytic Stability of Next Generation Maleimide Cross-Linkers Enables Access to Albumin-Antibody Fragment Conjugates and tri-scFvs

We describe investigations to expand the scope of next generation maleimide cross-linkers for the construction of homogeneous protein-protein conjugates. Diiodomaleimides are shown to offer the ideal properties of rapid bioconjugation with reduced hydrolysis, allowing the cross-linking of even sterically hindered systems. The optimized linkers are exploited to link human serum albumin to antibody fragments (Fab or scFv) as a prospective half-life extension platform, with retention of antigen binding and robust serum stability. Finally, a triprotein conjugate is formed, by linking scFv antibody fragments targeting carcinoembryonic antigen. This tri-scFv is shown to infer a combination of greater antigen avidity and increased in vivo half-life, representing a promising platform for antibody therapeutic development.

Axiomatising Incomplete Preferences through Sets of Desirable Gambles

We establish the equivalence of two very general theories: the first is the decision-theoretic formalisation of incomplete preferences based on the mixture independence axiom; the second is the theory of coherent sets of desirable gambles (bounded variables) developed in the context of imprecise probability and extended here to vector-valued gambles. Such an equivalence allows us to analyse the theory of incomplete preferences from the point of view of desirability. Among other things, this leads us to uncover an unexpected and clarifying relation: that the notion of `state independence'---the traditional assumption that we can have separate models for beliefs (probabilities) and values (utilities)---coincides with that of `strong independence' in imprecise probability; this connection leads us also to propose much weaker, and arguably more realistic, notions of state independence. Then we simplify the treatment of complete beliefs and values by putting them on a more equal footing. We study the role of the Archimedean condition---which allows us to actually talk of expected utility---, identify some weaknesses and propose alternatives that solve these. More generally speaking, we show that desirability is a valuable alternative foundation to preferences for decision theory that streamlines and unifies a number of concepts while preserving great generality. In addition, the mentioned equivalence shows for the first time how to extend the theory of desirability to imprecise non-linear utility, thus enabling us to formulate one of the most powerful self-consistent theories of reasoning and decision-making available today.