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Cortical neural dynamics mediate information processing for the cerebral cortex, which is implicated in fundamental biological processes such as vision and olfaction, in addition to neurological and psychiatric diseases. Spontaneous pain is a key feature of human neuropathic pain. Whether spontaneous pain pushes the cortical network into an aberrant state and, if so, whether it can be brought back to a 'normal' operating range to ameliorate pain are unknown. Using a clinically relevant mouse model of neuropathic pain with spontaneous pain-like behavior, we report that orofacial spontaneous pain activated a specific area within the primary somatosensory cortex (S1), displaying synchronized neural dynamics revealed by intravital two-photon calcium imaging. This synchronization was underpinned by local GABAergic interneuron hypoactivity. Pain-induced cortical synchronization could be attenuated by manipulating local S1 networks or clinically effective pain therapies. Specifically, both chemogenetic inhibition of pain-related c-Fos-expressing neurons and selective activation of GABAergic interneurons significantly attenuated S1 synchronization. Clinically effective pain therapies including carbamazepine and nerve root decompression could also dampen S1 synchronization. More important, restoring a 'normal' range of neural dynamics through attenuation of pain-induced S1 synchronization alleviated pain-like behavior. These results suggest that spontaneous pain pushed the S1 regional network into a synchronized state, whereas reversal of this synchronization alleviated pain., Introduction Although the functions of cortical circuits vary dramatically between brain regions and species, general principles have been found that constrain neural dynamics to stable activity regimes. For example, sleep, [...]

Herein we report on the formation of the adduct salts [Xe(OTeF5)(pyF)][Al(OTeF5)4] (pyF = C5F5N, C5H3F2N) by abstraction of an –OTeF5 group from Xe(OTeF5)2 with the Lewis superacid Al(OTeF5)3 and subsequent adduct formation of the generated [XeOTeF5]+ cation with fluorinated pyridines. These salts represent the first xenonium cations with the weakly coordinating [Al(OTeF5)4]− anion. The strong oxidizing property of these compounds is further assessed. [ABSTRACT FROM AUTHOR]

Privacy-utility tradeoff remains as one of the fundamental issues of differentially private machine learning. This paper introduces a geometrically inspired kernel-based approach to mitigate the accuracy-loss issue in classification. In this approach, a representation of the affine hull of given data points is learned in Reproducing Kernel Hilbert Spaces (RKHS). This leads to a novel distance measure that hides privacy-sensitive information about individual data points and improves the privacy-utility tradeoff via significantly reducing the risk of membership inference attacks. The effectiveness of the approach is demonstrated through experiments on MNIST dataset, Freiburg groceries dataset, and a real biomedical dataset. It is verified that the approach remains computationally practical. The application of the approach to federated learning is considered and it is observed that the accuracy-loss due to data being distributed is either marginal or not significantly high. [ABSTRACT FROM AUTHOR]

The straightforward synthesis of polyamide-derived statistical copolymers with catechol, amine, amide and hydroxy residues via free radical polymerization is presented. In particular, catechol, amine and amide residues are present in natural mussel foot proteins, enabling strong underwater adhesion due to synergistic effects where cationic residues displace hydration and ion layers, followed by strong short-rang hydrogen bonding between the catechol or primary amides and SiO2 surfaces. The present study is aimed at investigating whether such synergistic effects also exist for statistical copolymer systems that lack the sequence-defined positioning of functional groups in mussel foot proteins. A series of copolymers is established and the adsorption in saline solutions on SiO2 is determined by quartz crystal microbalance measurements and ellipsometry. These studies confirm a synergy between cationic amine groups with catechol units and primary amide groups via an increased adsorptivity and increased polymer layer thicknesses. Therefore, the free radical polymerization of catechol, amine and amide monomers as shown here may lead to simplified mussel-inspired adhesives that can be prepared with the readily scalable methods required for large-scale applications. [ABSTRACT FROM AUTHOR]

In order to develop machine learning and deep learning models that take into account the guidelines and principles of trustworthy AI, a novel information theoretic approach is introduced in this article. A unified approach to privacy-preserving interpretable and transferable learning is considered for studying and optimizing the trade-offs between the privacy, interpretability, and transferability aspects of trustworthy AI. A variational membership-mapping Bayesian model is used for the analytical approximation of the defined information theoretic measures for privacy leakage, interpretability, and transferability. The approach consists of approximating the information theoretic measures by maximizing a lower-bound using variational optimization. The approach is demonstrated through numerous experiments on benchmark datasets and a real-world biomedical application concerned with the detection of mental stress in individuals using heart rate variability analysis. [ABSTRACT FROM AUTHOR]

Herein, we successfully fabricated porous nanocomposite membranes decorated with high loadings of nickel nanoparticles (25–45 wt %) by using chelating additives (sodium dodecyl sulfate (SDS) and Fumion) for the film casting cum phase separation approach. These additives decreased the nickel agglomeration and promoted the presentation of particles at the pore surface of the final membranes, leading to increased water permeances (450 to 1200–2400 L/m2 hbar) and porosities (56% to 70–78%) in comparison to a nanocomposite membrane made without additives. The hydrophobic ionomer Fumion, incorporated into the polyethersulfone matrix during solidification, further allowed us to tune the membrane hydrophobicity. We discovered that the same nickel nanoparticles exhibited a 5× increased H2 generation rate during the catalytic hydrogen formation from ammonia borane when presented in a membrane with a higher water contact angle (85° compared to 60°). We could attribute this improvement to the lower capillary pressure in more hydrophobic pores, which facilitates the release of H2 bubbles. The nanocomposite membranes were further employed as flow-through reactors for the catalytic hydrogenation of p-nitrophenol (p-NP) in the presence of ammonia borane. Membranes with an additive-directed nickel nanoparticle presentation at the pore surface demonstrated 6 to 7× higher p-NP turnover frequencies (TOFs). We also observed that the formation of hydrogen gas from ammonia borane results in a permeance decrease during the flow-through p-NP conversion. However, this effect was mitigated in more hydrophilic membranes with an anisotropic pore structure. [ABSTRACT FROM AUTHOR]

Magnetic gels and elastomers are promising candidates to construct reversibly excitable soft actuators, triggered from outside by magnetic fields. These magnetic fields induce or alter the magnetic interactions between discrete rigid particles embedded in a soft elastic polymeric matrix, leading to overall deformations. It is a major challenge in theory to correctly predict from the discrete particle configuration the type of deformation resulting for a finite-sized system. Considering an elastic sphere, we here present such an approach. The method is in principle exact, at least within the framework of linear elasticity theory and for large enough interparticle distances. Different particle arrangements are considered. We find, for instance, that regular simple cubic configurations show elongation of the sphere along the magnetization if oriented along a face or space diagonal of the cubic unit cell. Contrariwise, with the magnetization along the edge of the cubic unit cell, they contract. The opposite is true in this geometry for body- and face-centered configurations. Remarkably, for the latter configurations but the magnetization along a face or space diagonal of the unit cell, contraction was observed to revert to expansion with decreasing Poisson ratio of the elastic material. Randomized configurations were considered as well. They show a tendency of elongating the sphere along the magnetization, which is more pronounced for compressible systems. Our results can be tested against actual experiments for spherical samples. Moreover, our approach shall support the search of optimal particle distributions for a maximized effect of actuation. [ABSTRACT FROM AUTHOR]

Cortical neural dynamics mediate information processing for the cerebral cortex, which is implicated in fundamental biological processes such as vision and olfaction, in addition to neurological and psychiatric diseases. Spontaneous pain is a key feature of human neuropathic pain. Whether spontaneous pain pushes the cortical network into an aberrant state and, if so, whether it can be brought back to a "normal" operating range to ameliorate pain are unknown. Using a clinically relevant mouse model of neuropathic pain with spontaneous pain-like behavior, we report that orofacial spontaneous pain activated a specific area within the primary somatosensory cortex (S1), displaying synchronized neural dynamics revealed by intravital two-photon calcium imaging. This synchronization was underpinned by local GABAergic interneuron hypoactivity. Pain-induced cortical synchronization could be attenuated by manipulating local S1 networks or clinically effective pain therapies. Specifically, both chemogenetic inhibition of pain-related c-Fos-expressing neurons and selective activation of GABAergic interneurons significantly attenuated S1 synchronization. Clinically effective pain therapies including carbamazepine and nerve root decompression could also dampen S1 synchronization. More important, restoring a "normal" range of neural dynamics through attenuation of pain-induced S1 synchronization alleviated pain-like behavior. These results suggest that spontaneous pain pushed the S1 regional network into a synchronized state, whereas reversal of this synchronization alleviated pain. [ABSTRACT FROM AUTHOR]

Although not labeled as such, the three research centers presented in this article have been performing translational computer science (TCS) for more than 20 years. SBA Research, Software Competence Center Hagenberg, and VRVis Zentrum fuer Virtual Reality und Visualisierung, all funded by the Austrian COMET Competence Centers for Excellent Technologies program, each selected one science–industry partnership to exemplify how they do TCS in their day-to-day work. We show how the COMET funding program provides a framework for establishing long-term research relationships with industry, thus facilitating TCS: research done by the COMET centers is directly put in the practical context of the industry partners, and successes and failures are immediately reported back. This constant feedback loop positively impacts the know-how at the involved companies, while allowing the computer scientists to continuously test, improve, and publish their research based on real-world scenarios. [ABSTRACT FROM AUTHOR]

We present an electrical mobility classifier for mass–mobility measurements of atmospheric ions. Size segregation coupled with mass spectrometric detection of naturally occurring ions in the atmosphere is challenging due to the low ion concentration. Conventional electrical mobility classifying devices were not yet coupled with mass spectrometry to resolve natural ion composition. This is due to either the insufficient transmission efficiency or design concepts being incompatible with this application, e.g. using high electric fields close to the inlets to push ions from high to low electric potential. Here, we introduce an axial ion mobility classifier, termed AMC, with the aim to achieve higher transmission efficiencies to segregate natural ions at reasonable sizing resolution. Similar to the recently introduced principle of the high-pass electrical mobility filter (HP-EMF) presented by Bezantakos et al. (2015) and Surawski et al. (2017), ions are classified via an electric field that is opposed to the gas flow direction carrying the ions. Compared to the HP-EMF concept, we make use of sheath flows to improve the size resolution in the sub-3 nm range. With our new design we achieve a sizing resolution of 7 Z/ΔZ with a transmission efficiency of about 70 %. [ABSTRACT FROM AUTHOR]

In the last decade, industry's demand for deep learning (DL) has increased due to its high performance in complex scenarios. Due to the DL method's complexity, experts and non-experts rely on blackbox software packages such as Tensorflow and Pytorch. The frameworks are constantly improving, and new versions are released frequently. As a natural process in software development, the released versions contain improvements/changes in the methods and their implementation. Moreover, versions may be bug-polluted, leading to the model performance decreasing or stopping the model from working. The aforementioned changes in implementation can lead to variance in obtained results. This work investigates the effect of implementation changes in different major releases of these frameworks on the model performance. We perform our study using a variety of standard datasets. Our study shows that users should consider that changing the framework version can affect the model performance. Moreover, they should consider the possibility of a bug-polluted version before starting to debug source code that had an excellent performance before a version change. This also shows the importance of using virtual environments, such as Docker, when delivering a software product to clients. [ABSTRACT FROM AUTHOR]