Differences in the consolidation by spontaneous and evoked ripples in the presence of active dendrites.

Ripples are a typical form of neural activity in hippocampal neural networks associated with the replay of episodic memories during sleep as well as sleep-related plasticity and memory consolidation. The emergence of ripples has been observed both dependent as well as independent of input from other brain areas and often coincides with dendritic spikes. Yet, it is unclear how input-evoked and spontaneous ripples as well as dendritic excitability affect plasticity and consolidation. Here, we use mathematical modeling to compare these cases. We find that consolidation as well as the emergence of spontaneous ripples depends on a reliable propagation of activity in feed-forward structures which constitute memory representations. This propagation is facilitated by excitable dendrites, which entail that a few strong synapses are sufficient to trigger neuronal firing. In this situation, stimulation-evoked ripples lead to the potentiation of weak synapses within the feed-forward structure and, thus, to a consolidation of a more general sequence memory. However, spontaneous ripples that occur without stimulation, only consolidate a sparse backbone of the existing strong feed-forward structure. Based on this, we test a recently hypothesized scenario in which the excitability of dendrites is transiently enhanced after learning, and show that such a transient increase can strengthen, restructure and consolidate even weak hippocampal memories, which would be forgotten otherwise. Hence, a transient increase in dendritic excitability would indeed provide a mechanism for stabilizing memories.

Artificial intelligence-driven shimming for parallel high field nuclear magnetic resonance.

Rapid drug development requires a high throughput screening technology. NMR could benefit from parallel detection but is hampered by technical obstacles. Detection sites must be magnetically shimmed to ppb uniformity, which for parallel detection is precluded by commercial shimming technology. Here we show that, by centering a separate shim system over each detector and employing deep learning to cope with overlapping non-orthogonal shimming fields, parallel detectors can be rapidly calibrated. Our implementation also reports the smallest NMR stripline detectors to date, based on an origami technique, facilitating further upscaling in the number of detection sites within the magnet bore.

The mode of action of tapasin on major histocompatibility class I (MHC-I) molecules.

Tapasin (Tsn) plays a critical role in antigen processing and presentation by major histocompatibility complex class I (MHC-I) molecules. The mechanism of Tsn-mediated peptide loading and exchange hinges on the conformational dynamics governing the interaction of Tsn and MHC-I with recent structural and functional studies pinpointing the critical sites of direct or allosteric regulation. In this review, we highlight these recent findings and relate them to the extensive molecular and cellular data that are available for these evolutionary interdependent proteins. Furthermore, allotypic differences of MHC-I with regard to the editing and chaperoning function of Tsn are reviewed and related to the mechanistic observations. Finally, evolutionary aspects of the mode of action of Tsn will be discussed, a short comparison with the Tsn-related molecule TAPBPR (Tsn-related protein) will be given, and the impact of Tsn on noncanonical MHC-I molecules will be described.

Acquisitions with random shim values enhance AI-driven NMR shimming.

Shimming is still an unavoidable, time-consuming and cumbersome burden that precedes NMR experiments, and aims to achieve a homogeneous magnetic field distribution, which is required for expressive spectroscopy measurements. This study presents multiple enhancements to AI-driven shimming. We achieve fast, quasi-iterative shimming on multiple shims simultaneously via a temporal history that combines spectra and past shim actions. Moreover, we enable efficient data collection by randomized dataset acquisition, allowing scalability to higher-order shims. Application at a low-field benchtop magnet reduces the linewidth in 87 of 100 random distortions from ∼ 4 Hz to below 1 Hz, within less than 10 NMR acquisitions. Compared to, and combined with, traditional methods, we significantly enhance both the speed and performance of shimming algorithms. In particular, AI-driven shimming needs roughly 1/3 acquisitions, and helps to avoid local minima in 96% of the cases. Our dataset and code is publicly available.

Deep regression with ensembles enables fast, first-order shimming in low-field NMR.

Shimming in the context of nuclear magnetic resonance aims to achieve a uniform magnetic field distribution, as perfect as possible, and is crucial for useful spectroscopy and imaging. Currently, shimming precedes most acquisition procedures in the laboratory, and this mostly semi-automatic procedure often needs to be repeated, which can be cumbersome and time-consuming. The paper investigates the feasibility of completely automating and accelerating the shimming procedure by applying deep learning (DL). We show that DL can relate measured spectral shape to shim current specifications and thus rapidly predict three shim currents simultaneously, given only four input spectra. Due to the lack of accessible data for developing shimming algorithms, we also introduce a database that served as our DL training set, and allows inference of changes to 1H NMR signals depending on shim offsets. In situ experiments of deep regression with ensembles demonstrate a high success rate in spectral quality improvement for random shim distortions over different neural architectures and chemical substances. This paper presents a proof-of-concept that machine learning can simplify and accelerate the shimming problem, either as a stand-alone method, or in combination with traditional shimming methods. Our database and code are publicly available.

The biophysical basis underlying the maintenance of early phase long-term potentiation.

The maintenance of synaptic changes resulting from long-term potentiation (LTP) is essential for brain function such as memory and learning. Different LTP phases have been associated with diverse molecular processes and pathways, and the molecular underpinnings of LTP on the short, as well as long time scales, are well established. However, the principles on the intermediate time scale of 1-6 hours that mediate the early phase of LTP (E-LTP) remain elusive. We hypothesize that the interplay between specific features of postsynaptic receptor trafficking is responsible for sustaining synaptic changes during this LTP phase. We test this hypothesis by formalizing a biophysical model that integrates several experimentally-motivated mechanisms. The model captures a wide range of experimental findings and predicts that synaptic changes are preserved for hours when the receptor dynamics are shaped by the interplay of structural changes of the spine in conjunction with increased trafficking from recycling endosomes and the cooperative binding of receptors. Furthermore, our model provides several predictions to verify our findings experimentally.

Characterizing spreading dynamics of subsampled systems with nonstationary external input.

Many systems with propagation dynamics, such as spike propagation in neural networks and spreading of infectious diseases, can be approximated by autoregressive models. The estimation of model parameters can be complicated by the experimental limitation that one observes only a fraction of the system (subsampling) and potentially time-dependent parameters, leading to incorrect estimates. We show analytically how to overcome the subsampling bias when estimating the propagation rate for systems with certain nonstationary external input. This approach is readily applicable to trial-based experimental setups and seasonal fluctuations as demonstrated on spike recordings from monkey prefrontal cortex and spreading of norovirus and measles.

Information governance in NHS's NPfIT: a case for policy specification.

PURPOSE: The National Health Service's (NHS's) National Programme for Information Technology (NPfIT) in the UK with its proposed nation-wide online health record service poses serious technical challenges, especially with regard to access control and patient confidentiality. The complexity of the confidentiality requirements and their constantly evolving nature (due to changes in law, guidelines and ethical consensus) make traditional technologies such as role-based access control (RBAC) unsuitable. Furthermore, a more formal approach is also needed for debating about and communicating on information governance, as natural-language descriptions of security policies are inherently ambiguous and incomplete. Our main goal is to convince the reader of the strong benefits of employing formal policy specification in nation-wide electronic health record (EHR) projects. APPROACH: Many difficulties could be alleviated by specifying the requirements in a formal authorisation policy language such as Cassandra. The language is unambiguous, declarative and machine-enforceable, and is based on distributed constrained Datalog. Cassandra is interpreted within a distributed Trust Management environment, where digital credentials are used for establishing mutual trust between strangers. RESULTS: To demonstrate how policy specification can be applied to NPfIT, we translate a fragment of natural-language NHS specification into formal Cassandra rules. In particular, we present policy rules pertaining to the management of Clinician Sealed Envelopes, the mechanism by which clinical patient data can be concealed in the nation-wide EHR service. Our case study exposes ambiguities and incompletenesses in the informal NHS documents. CONCLUSIONS: We strongly recommend the use of trust management and policy specification technology for the implementation of nation-wide EHR infrastructures. Formal policies can be used for automatically enforcing confidentiality requirements, but also for specification and communication purposes. Formalising the requirements also reveals ambiguities and missing details in the currently used informal specification documents.