Biophysics-inspired spike rate adaptation for computationally efficient phenomenological nerve modeling.

This study introduces and evaluates the PHAST+ model, part of a computational framework designed to simulate the behavior of auditory nerve fibers in response to the electrical stimulation from a cochlear implant. PHAST+ incorporates a highly efficient method for calculating accommodation and adaptation, making it particularly suited for simulations over extended stimulus durations. The proposed method uses a leaky integrator inspired by classic biophysical nerve models. Through evaluation against single-fiber animal data, our findings demonstrate the model's effectiveness across various stimuli, including short pulse trains with variable amplitudes and rates. Notably, the PHAST+ model performs better than its predecessor, PHAST (a phenomenological model by van Gendt et al.), particularly in simulations of prolonged neural responses. While PHAST+ is optimized primarily on spike rate decay, it shows good behavior on several other neural measures, such as vector strength and degree of adaptation. The future implications of this research are promising. PHAST+ drastically reduces the computational burden to allow the real-time simulation of neural behavior over extended periods, opening the door to future simulations of psychophysical experiments and multi-electrode stimuli for evaluating novel speech-coding strategies for cochlear implants.

Method Development for the Prediction of Melt Quality in the Extrusion Process.

Simulation models are used to design extruders in the polymer processing industry. This eliminates the need for prototypes and reduces development time for extruders and, in particular, extrusion screws. These programs simulate, among other process parameters, the temperature and pressure curves in the extruder. At present, it is not possible to predict the resulting melt quality from these results. This paper presents a simulation model for predicting the melt quality in the extrusion process. Previous work has shown correlations between material and thermal homogeneity and the screw performance index. As a result, the screw performance index can be used as a target value for the model to be developed. The results of the simulations were used as input variables, and with the help of artificial intelligence-more precisely, machine learning-a linear regression model was built. Finally, the correlation between the process parameters and the melt quality was determined, and the quality of the model was evaluated.

Asymmetric Synthesis with Organoselenium Compounds - The Past Twelve Years.

The discovery and synthetic applications of novel organoselenium compounds and their reactions proceeded rapidly during the past fifty years and such processes are now carried out routinely in many laboratories. At the same time, the growing demand for new enantioselective processes provided new challenges. The convergence of selenium chemistry and asymmetric synthesis led to key developments in the 1970s, although the majority of early work was based on stoichiometric processes. More recently, greater emphasis has been placed on greener catalytic variations, along with the discovery of novel reactions and a deeper understanding of their mechanisms. The present review covers the literature in this field from 2010 to early 2023 and encompasses asymmetric reactions mediated by chiral selenium-based reagents, auxiliaries, and especially, catalysts. Protocols based on achiral selenium compounds in conjunction with other species of chiral catalysts, as well as reactions that are controlled by chiral substrates, are also included.

Reinforcement learning assisted recursive QAOA.

In recent years, variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems. It is, however, known that at low depth, certain locality constraints of QAOA limit its performance. To go beyond these limitations, a non-local variant of QAOA, namely recursive QAOA (RQAOA), was proposed to improve the quality of approximate solutions. The RQAOA has been studied comparatively less than QAOA, and it is less understood, for instance, for what family of instances it may fail to provide high-quality solutions. However, as we are tackling NP-hard problems (specifically, the Ising spin model), it is expected that RQAOA does fail, raising the question of designing even better quantum algorithms for combinatorial optimization. In this spirit, we identify and analyze cases where (depth-1) RQAOA fails and, based on this, propose a reinforcement learning enhanced RQAOA variant (RL-RQAOA) that improves upon RQAOA. We show that the performance of RL-RQAOA improves over RQAOA: RL-RQAOA is strictly better on these identified instances where RQAOA underperforms and is similarly performing on instances where RQAOA is near-optimal. Our work exemplifies the potentially beneficial synergy between reinforcement learning and quantum (inspired) optimization in the design of new, even better heuristics for complex problems.

Synthesis of the Marine Alkaloid Cylindricine C and Serendipitous Synthesis of Its 2,13-Di-epi Stereoisomer.

A new approach to the marine alkaloid cylindricine C afforded its previously unreported (±)-2,13-di-epi stereoisomer as the major product along with a minor amount of the racemic parent alkaloid. Key steps included a stereoselective dianion alkylation of a monoester of 1,2-cyclohexanedicarboxylic acid and an annulation based on the tandem conjugate addition of a primary amine to an acetylenic sulfone, followed by intramolecular acylation of the resulting sulfone-stabilized carbanion. The cis-azadecalin moiety thus formed, comprising the cyclohexane A-ring and enaminone B-ring of the products, was further elaborated by the selenenyl chloride-induced cyclofunctionalization of a pendant butenyl substituent with the enaminone moiety, followed by a seleno-Pummerer reaction. Desulfonylation and enaminone reduction afforded the final products. Molecular modeling and X-ray crystallography provided further insight into these processes.

IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics.

We present IOHexperimenter, the experimentation module of the IOHprofiler project. IOHexperimenter aims at providing an easy-to-use and customizable toolbox for benchmarking iterative optimization heuristics such as local search, evolutionary and genetic algorithms, and Bayesian optimization techniques. IOHexperimenter can be used as a stand-alone tool or as part of a benchmarking pipeline that uses other modules of the IOHprofiler environment. IOHexperimenter provides an efficient interface between optimization problems and their solvers while allowing for granular logging of the optimization process. Its logs are fully compatible with existing tools for interactive data analysis, which significantly speeds up the deployment of a benchmarking pipeline. The main components of IOHexperimenter are the environment to build customized problem suites and the various logging options that allow users to steer the granularity of the data records.

Evolutionary Algorithms for Parameter Optimization-Thirty Years Later.

Thirty years, 1993-2023, is a huge time frame in science. We address some major developments in the field of evolutionary algorithms, with applications in parameter optimization, over these 30 years. These include the covariance matrix adaptation evolution strategy and some fast-growing fields such as multimodal optimization, surrogate-assisted optimization, multiobjective optimization, and automated algorithm design. Moreover, we also discuss particle swarm optimization and differential evolution, which did not exist 30 years ago, either. One of the key arguments made in the paper is that we need fewer algorithms, not more, which, however, is the current trend through continuously claiming paradigms from nature that are suggested to be useful as new optimization algorithms. Moreover, we argue that we need proper benchmarking procedures to sort out whether a newly proposed algorithm is useful or not. We also briefly discuss automated algorithm design approaches, including configurable algorithm design frameworks, as the proposed next step toward designing optimization algorithms automatically, rather than by hand.

Chemistry Related to the Catalytic Cycle of the Antioxidant Ebselen.

The antioxidant drug ebselen has been widely studied in both laboratories and in clinical trials. The catalytic mechanism by which it destroys hydrogen peroxide via reduction with glutathione or other thiols is complex and has been the subject of considerable debate. During reinvestigations of several key steps, we found that the seleninamide that comprises the first oxidation product of ebselen underwent facile reversible methanolysis to an unstable seleninate ester and two dimeric products. In its reaction with benzyl alcohol, the seleninamide produced a benzyl ester that reacted readily by selenoxide elimination, with formation of benzaldehyde. Oxidation of ebselen seleninic acid did not afford a selenonium seleninate salt as previously observed with benzene seleninic acid, but instead generated a mixture of the seleninic and selenonic acids. Thiolysis of ebselen with benzyl thiol was faster than oxidation by ca. an order of magnitude and produced a stable selenenyl sulfide. When glutathione was employed, the product rapidly disproportionated to glutathione disulfide and ebselen diselenide. Oxidation of the S-benzyl selenenyl sulfide, or thiolysis of the seleninamide with benzyl thiol, afforded a transient thiolseleninate that also readily underwent selenoxide elimination. The S-benzyl derivative disproportionated readily when catalyzed by the simultaneous presence of both the thiol and triethylamine. The phenylthio analogue disproportionated when exposed to ambient or UV (360 nm) light by a proposed radical mechanism. These observations provide additional insight into several reactions and intermediates related to ebselen.

A Gain-of-function Mutation in the Gating Domain of ITPR1 Impairs Motor Movement and Increases Thermal and Mechanical Sensitivity.

Inositol 1,4,5-trisphosphate receptor type 1 (ITPR1) is an intracellular Ca2+ release channel important for a number of fundamental cellular functions. Consistent with its critical physiological significance, mutations in ITPR1 are associated with disease. Surprisingly, nearly all the disease-associated ITPR1 mutations characterized to date are loss of function. Despite the paucity of ITPR1 gain-of-function (GOF) mutations, enhanced ITPR1 function as a result of dysregulation by ITPR1 interacting proteins is thought to be associated with ataxia, learning and memory impairments, Alzheimer's disease (AD) progression, and chronic pain. However, direct evidence for the role of ITPR1 GOF in disease is lacking. To determine whether GOF in ITPR1 itself has pathological ramifications, we employed a newly developed mouse model expressing an ITPR1 mutation in the gating domain of the channel, D2594K, that markedly increased the channel's sensitivity to activation by IP3. Behavioral studies showed that the ITPR1-D2594K+/- mutant mice displayed motor deficits and reduced muscle strength. However, the ITPR1-D2594K+/- mutation did not significantly alter hippocampal learning and memory and did not change learning and memory impairments when crossed with the 5xFAD AD model mice. On the other hand, ITPR1-D2594K+/- mice exhibited increased sensitivity to thermal and mechanical stimulation compared to WT. Interestingly, R-carvedilol treatment attenuated the enhanced thermal and mechanical nociception in ITPR1-D2594K+/- mice. Thus, the ITPR1-D2594K+/- mutation in the channel's gating domain has a marked impact on motor movements and pain perception, but little effect on hippocampal learning and memory.

Optimizing stimulus energy for cochlear implants with a machine learning model of the auditory nerve.

Performing simulations with a realistic biophysical auditory nerve fiber model can be very time-consuming, due to the complex nature of the calculations involved. Here, a surrogate (approximate) model of such an auditory nerve fiber model was developed using machine learning methods, to perform simulations more efficiently. Several machine learning models were compared, of which a Convolutional Neural Network showed the best performance. In fact, the Convolutional Neural Network was able to emulate the behavior of the auditory nerve fiber model with extremely high similarity (R2>0.99), tested under a wide range of experimental conditions, whilst reducing the simulation time by five orders of magnitude. In addition, a method for randomly generating charge-balanced waveforms using hyperplane projection is introduced. In the second part of this paper, the Convolutional Neural Network surrogate model was used by an Evolutionary Algorithm to optimize the shape of the stimulus waveform in terms of energy efficiency. The resulting waveforms resemble a positive Gaussian-like peak, preceded by an elongated negative phase. When comparing the energy of the waveforms generated by the Evolutionary Algorithm with the commonly used square wave, energy decreases of 8%-45% were observed for different pulse durations. These results were validated with the original auditory nerve fiber model, which demonstrates that the proposed surrogate model can be used as its accurate and efficient replacement.

Unexpected Formation and Potent Antioxidant Activity of Macrocyclic Dimers Containing Disulfide and Selenide Groups.

During attempts to prepare spirodithiaselenuranes as GPx mimetics, a series of unexpected dimeric macrocycles was obtained, each containing two selenide and two disulfide moieties in rings ranging from 18- to 26-membered. The products showed potent GPx-like activity in an NMR assay based on their ability to catalyze the reduction of hydrogen peroxide with benzyl thiol. The high catalytic activity was attributed to transannular effects during selenide to selenoxide oxidation. This redox process was also characterized by an induction period that indicated autocatalysis in the formation of an intermediate selenoxide from the oxidation of the corresponding selenide.

Synthesis, Catalytic GPx-like Activity, and SET Reactions of Conformationally Constrained 2,7-Dialkoxy-Substituted Naphthalene-1,8-peri-diselenides.

Several 2,7-dialkoxy-substituted naphthalene-1,8-peri-diselenides were prepared and tested for catalytic antioxidant activity in an NMR-based assay employing the reduction of hydrogen peroxide with stoichiometric amounts of benzyl thiol. Acidic conditions enhanced their catalytic activity, whereas basic conditions suppressed it. The highest activity was observed with a 2,7-bis(triethyleneglycol) derivative. These compounds serve as mimetics of the antioxidant selenoenzyme glutathione peroxidase. Studies based on NMR peak-broadening effects and EPR spectroscopy indicated that a thiol-dependent SET reaction occurs under the conditions of the assay, which can be reversed by the addition of triethylamine. In contrast, peak broadening induced by proton-catalyzed electron transfer during the treatment of naphthalene-1,8-peri-diselenides with trifluoroacetic acid can be suppressed by the addition of excess thiol. These observations provide new insights into the redox mechanisms of these processes.

Quantum annealing for industry applications: introduction and review.

Quantum annealing (QA) is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies have enabled the development of small- and intermediate-scale quantum processors that implement the QA algorithm for programmable use. Specifically, QA processors produced by D-Wave systems have been studied and tested extensively in both research and industrial settings across different disciplines. In this paper we provide a literature review of the theoretical motivations for QA as a heuristic quantum optimization algorithm, the software and hardware that is required to use such quantum processors, and the state-of-the-art applications and proofs-of-concepts that have been demonstrated using them. The goal of our review is to provide a centralized and condensed source regarding applications of QA technology. We identify the advantages, limitations, and potential of QA for both researchers and practitioners from various fields.

Using forecasting to evaluate the impact of COVID-19 on passenger air transport demand.

The COVID-19 pandemic caused a drastic drop in passenger air transport demand due to two forces: supply restriction and demand depression. In order for airlines to recover, the key is to identify which force they are fighting against. We propose a method for separating the two forces of COVID-19 and evaluating the respective impact on demand. Our method involves dividing passengers into different segments based on passenger characteristics, simulating different scenarios, and predicting demand for each passenger segment in each scenario. Comparing the predictions with each other and with the real situation, we quantify the impact of COVID-19 associated with the two forces, respectively. We apply our method to a dataset from Air France-KLM and show that from March 1st to May 31st 2020, the pandemic caused demand at the airline to drop 40.3% on average for passengers segmented based on age and purpose of travel. The 57.4% of this decline is due to demand depression, whereas the other 42.6% is due to supply restriction. In addition, we find that the impact of COVID-19 associated with each force varies between passenger segments. The demand depression force impacted business passengers between age 41 and 60 the most, and it impacted leisure passengers between age 20 and 40 the least. The opposite result holds for the supply restriction force. We give suggestions on how airlines can plan their recovery using our results and how other industries can use our evaluation method.

Limiting RyR2 open time prevents Alzheimer's disease-related deficits in the 3xTG-AD mouse model.

Increasing evidence suggests that Alzheimer's disease (AD) progression is driven by a vicious cycle of soluble ß-amyloid (Aß)-induced neuronal hyperactivity. Thus, breaking this vicious cycle by suppressing neuronal hyperactivity may represent a logical approach to stopping AD progression. In support of this, we have recently shown that genetically and pharmacologically limiting ryanodine receptor 2 (RyR2) open time prevented neuronal hyperactivity, memory impairment, dendritic spine loss, and neuronal cell death in a rapid, early onset AD mouse model (5xFAD). Here, we assessed the impact of limiting RyR2 open time on AD-related deficits in a relatively late occurring, slow developing AD mouse model (3xTG-AD) that bears more resemblance (compared to 5xFAD) to that of human AD. Using behavioral tests, long-term potentiation recordings, and Golgi and Nissl staining, we found that the RyR2-E4872Q mutation, which markedly shortens the open duration of the RyR2 channel, prevented learning and memory impairment, defective long-term potentiation, dendritic spine loss, and neuronal cell death in the 3xTG-AD mice. Furthermore, pharmacologically shortening the RyR2 open time with R-carvedilol rescued these AD-related deficits in 3xTG mice. Therefore, limiting RyR2 open time may offer a promising, neuronal hyperactivity-targeted anti-AD strategy.

One-Pot Synthesis of Aryl Selenonic Acids and Some Unexpected Byproducts.

The synthesis of aryl selenonic acids was achieved from diverse aryl bromides via a one-pot method involving metalation, selenation, and oxidation with hydrogen peroxide followed by ion exchange to afford the pure products in 77-90% yield. An o-hydroxymethyl derivative was found to dehydrate readily, affording the first example of a cyclic selenonic ester, while two minor byproducts were isolated and shown by X-ray crystallography to be mixed salts of aryl selenonic acids with either the corresponding aryl seleninic or selenious acid.

Genetically and pharmacologically limiting RyR2 open time prevents neuronal hyperactivity of hippocampal CA1 neurons in brain slices of 5xFAD mice.

Neuronal hyperactivity is an early, common manifestation of Alzheimer's disease (AD), and is believed to drive AD progression. Neuronal hyperactivity in the form of baseline activity (or spontaneous Ca2+ transients) has consistently been demonstrated in mouse models of AD using two-photon in vivo Ca2+ imaging of cortical or hippocampal neurons in anesthetized animals. Notably, these AD-related spontaneous Ca2+ transients were hardly detected in acute hippocampal slices, probably due to neuronal damage during brain slicing. To better preserve neuronal activity, we employed the N-methyl-D-glucamine (NMDG) protective brain slicing protocol. We performed confocal in vitro Ca2+ imaging of hippocampal CA1 neurons in optimized hippocampal slices. Consistent with previous in vivo studies, our in vitro studies using optimized brain slices also showed that limiting the open duration of the ryanodine receptor 2 (RyR2) by the RyR2 mutation E4872Q or by the R-carvedilol enantiomer prevented and rescued neuronal hyperactivity of hippocampal CA1 neurons from 5xFAD mice. Thus, genetically and pharmacologically limiting RyR2 open time prevented and rescued AD-related neuronal hyperactivity in vitro in optimized brain slices in the absence of anesthetics' influence. Our data also suggest that the NMDG protective brain slicing preparation offers an alternative means to study neuronal hyperactivity of various cell types in different brain regions, especially in regions that are not readily accessible to two-photon in vivo Ca2+ imaging.

Preoperative Electroencephalography-Based Machine Learning Predicts Cognitive Deterioration After Subthalamic Deep Brain Stimulation.

BACKGROUND: Subthalamic deep brain stimulation (STN DBS) may relieve refractory motor complications in Parkinson's disease (PD) patients. Despite careful screening, it remains difficult to determine severity of alpha-synucleinopathy involvement which influences the risk of postoperative complications including cognitive deterioration. Quantitative electroencephalography (qEEG) reflects cognitive dysfunction in PD and may provide biomarkers of postoperative cognitive decline. OBJECTIVE: To develop an automated machine learning model based on preoperative EEG data to predict cognitive deterioration 1 year after STN DBS. METHODS: Sixty DBS candidates were included; 42 patients had available preoperative EEGs to compute a fully automated machine learning model. Movement Disorder Society criteria classified patients as cognitively stable or deteriorated at 1-year follow-up. A total of 16,674 EEG-features were extracted per patient; a Boruta algorithm selected EEG-features to reflect representative neurophysiological signatures for each class. A random forest classifier with 10-fold cross-validation with Bayesian optimization provided class-differentiation. RESULTS: Tweny-five patients were classified as cognitively stable and 17 patients demonstrated cognitive decline. The model differentiated classes with a mean (SD) accuracy of 0.88 (0.05), with a positive predictive value of 91.4% (95% CI 82.9, 95.9) and negative predictive value of 85.0% (95% CI 81.9, 91.4). Predicted probabilities between classes were highly differential (hazard ratio 11.14 [95% CI 7.25, 17.12]); the risk of cognitive decline in patients with high probabilities of being prognosticated as cognitively stable (>0.5) was very limited. CONCLUSIONS: Preoperative EEGs can predict cognitive deterioration after STN DBS with high accuracy. Cortical neurophysiological alterations may indicate future cognitive decline and can be used as biomarkers during the DBS screening. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Limiting RyR2 Open Time Prevents Alzheimer's Disease-Related Neuronal Hyperactivity and Memory Loss but Not ß-Amyloid Accumulation.

Neuronal hyperactivity is an early primary dysfunction in Alzheimer's disease (AD) in humans and animal models, but effective neuronal hyperactivity-directed anti-AD therapeutic agents are lacking. Here we define a previously unknown mode of ryanodine receptor 2 (RyR2) control of neuronal hyperactivity and AD progression. We show that a single RyR2 point mutation, E4872Q, which reduces RyR2 open time, prevents hyperexcitability, hyperactivity, memory impairment, neuronal cell death, and dendritic spine loss in a severe early-onset AD mouse model (5xFAD). The RyR2-E4872Q mutation upregulates hippocampal CA1-pyramidal cell A-type K+ current, a well-known neuronal excitability control that is downregulated in AD. Pharmacologically limiting RyR2 open time with the R-carvedilol enantiomer (but not racemic carvedilol) prevents and rescues neuronal hyperactivity, memory impairment, and neuron loss even in late stages of AD. These AD-related deficits are prevented even with continued ß-amyloid accumulation. Thus, limiting RyR2 open time may be a hyperactivity-directed, non-ß-amyloid-targeted anti-AD strategy.

The Unexpected Role of SeVI Species in Epoxidations with Benzeneseleninic Acid and Hydrogen Peroxide.

Benzeneperoxyseleninic acid has been proposed as the key intermediate in the widely used epoxidation of alkenes with benzeneseleninic acid and hydrogen peroxide. However, it reacts sluggishly with cyclooctene and instead rapidly decomposes in solution to a mixed selenonium-selenonate salt that was identified by X-ray absorption and 77 Se NMR spectroscopy, as well as by single crystal X-ray diffraction. This process includes a selenoxide elimination of the peroxyseleninic acid with liberation of oxygen and additional redox steps. The salt is relatively stable in the solid state, but generates the corresponding selenonic acid in the presence of hydrogen peroxide. The selenonic acid is inert towards cyclooctene on its own; however, rapid epoxidation occurs when hydrogen peroxide is added. This shows that the selenonic acid must first be activated through further oxidation, presumably to the heretofore unknown benzeneperoxyselenonic acid. The latter is the principal oxidant in this epoxidation.