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OBJECTIVE. Hearing loss is a major public health epidemic, and the primary treatment for hearing loss is hearing aids. Hearing aid uptake among people would could potentially benefit from hearing aid use, however, is very low. One primary reason for the low rate of hearing aid uptake is a lack of perceived benefit, particularly in noisy situations where people with hearing loss struggle to hear in the most. Many hearing aid technologies that are efficacious in laboratory tests of speech in noise perception are not effective in real-world noisy environments. One reason might be that the types of tests and noise stimuli typically used in audiology and hearing science research bare little resemblance to real-world noisy soundscapes. Improving our understanding of factors that affect listening in real-world noisy environments and using that knowledge to improve the ecological validity of laboratory and clinical assessments in audiology will improve hearing aid technologies, clinical decision making, and outcome assessment. To that end, the broad purpose of this study was to improve our understanding of real-world noise and its effects on speech perception for listeners with normal hearing and listeners with hearing loss. To do this, we measured the complexity of different real-world soundscapes using entropy, a way to quantify the amount of information in a system based on the probability structure of a system parameter. Using simulated real-world soundscapes in the lab, we characterized the effects of time-domain and frequency-domain entropy in real-world noise on speech perception among listeners with normal hearing and listeners with hearing loss in aided and unaided conditions. Then, participants collected data in real-world noisy environments they encountered in their daily lives by recording the environments and completing surveys while in the environment. We then investigated the effects of entropy in real-world soundscapes on speech intelligibility, listening effort, and perceived soundscape complexity., DESIGN. Laboratory experiments: the complexity of noise recordings from eight real-world soundscapes were systematically quantified by their entropy in the time and frequency domains. Then, 50 segments the length of IEEE sentences were extracted from each environment. Twenty-five segments were extracted that varied from low to high entropy in the time domain and 25 segments were extracted that varied from low to high entropy in the frequency domain. Then, using a trial-by-trial design, participants repeated back IEEE sentences embedded in each segment in two blocks (time-domain entropy and frequency-domain entropy). For each trial, a random IEEE sentence was drawn, convolved with the room impulse response of the noise environment, and then combined with the noise at a fixed signal-to-noise ratio. Then, all trials were randomized and presented to the participant. Twenty-five participants with normal hearing and 25 participants with hearing loss participated. Participants with hearing loss completed the study twice: once without hearing aids and once with hearing aids, using the the Portable Hearing Aid Lab running the Open Master Hearing Aid software. Using linear mixed effects models, we characterized the effects of entropy on number of keywords correct. Real-world experiments: participants were asked to seek out 5-10 complex listening environments they encounter in their daily lives. Once in the environment, participants wore the Portable Hearing Hid lab and completed two ecological momentary assessments on a smartphone. The assessments asked participants questions about their environment as well as perception (e.g., speech intelligibility, listening effort, perceived complexity). The portable hearing aid lab also recorded their environment. For participants with normal hearing, the Portable Hearing Aid Lab provided no gain. For participants with hearing loss, the hearing aid switched from an unaided (acoustically transparent) to aided condition between the two momentary assessments. Using linear mixed effects models, we characterized the effects of entropy in the real-world on self-reported speech intelligibility, listening effort, and perceived soundscape complexity., RESULTS. Laboratory experiments: 15,000 trials for the energy entropy block and 15,000 trials for the spectral entropy block were collected and analyzed. Entropy had significant effects on speech perception, with higher entropy resulting in poorer speech perception. Effects were larger for entropy in the time domain than the frequency domain. Effects were larger for participants with normal hearing than participants with hearing loss, and larger for participants with hearing loss in the aided than unaided condition. Speech perception differed significantly between most environments for all participants. Real-world experiments: effects of entropy in the real-world were more equivocal, but generally mirrored the findings of the laboratory experiments, with clearer effects of entropy in the time domain than frequency domain, for participants with normal hearing than hearing loss, and for participants with hearing loss in the aided than unaided condition. For speech listening, greater time-domain entropy generally yielded greater listening effort and higher perceived complexity for speech listening. Greater frequency-domain entropy yielded greater listening effort for participants with hearing loss in the aided condition. For overall environment perception, greater time-domain entropy was associated with higher overall ratings of environment complexity and listening effort, but only for participants with normal hearing and participants with hearing loss in the aided condition. For both laboratory and real-world experiments, speech perception was not different in the aided and unaided conditions for participants with hearing loss. Implications for ecological validity in hearing science and audiology research as well as clinical implications are discussed., CONCLUSION. Entropy affected speech perception for listeners in a controlled laboratory experiment, with greater entropy resulting in poorer speech perception. Effects were larger for time-domain than frequency-domain entropy, and larger for listeners with normal hearing than listeners with hearing loss. Effects of entropy on listening performance in the real-world were more equivocal, with clearest effects for listening effort and complexity perception for listeners with normal hearing. Entropy should be considered when designing ecologically valid tests for speech perception and hearing aid benefit.

Colonization plays an important role during assembly of the gut microbiome and influences microbiome composition throughout the life of the host, but determinants of colonization success are not yet fully understood. The size of introduced populations and frequency of their introduction can be important drivers of colonization success for introduced species. However, the effect of these factors, which are known together as “propagule pressure,” varies across environments and can be modulated by the strength of competition between introduced and resident species. In vitro microbial communities provide a tractable, high-throughput experimental system to interrogate the influence of propagule pressure on colonization. To quantify the importance of propagule size during colonization in gut microbial communities, we derived in vitro communities from human stool samples and systematically mixed eight pairs of communities at seven ratios ranging from 1:1000 to 1000:1. We compared our results to the predictions of a neutral model in which the relative abundance of each species remains proportional to its inoculation dose, a measure of propagule size. We observed that community co-cultures maintained similar levels of species diversity to the parent communities regardless of mixture ratio, suggesting that community diversity may be limited by resource competition even when many species are initially present. Likewise, community composition was generally maintained across mixture ratios, in contrast to neutral predictions. However, certain bacterial taxa showed varying levels of dose-dependence across mixtures, suggesting that the effect of propagule size depends on community composition and may be affected by the strength of competitive interactions in each mixture. Our results highlight the importance of both propagule pressure and competition in determining colonization success in gut microbial communities.

Freshwater mussels play several important roles for stream ecology. They actively filter organic matter from the water column to obtain food and burrow part of their shell into the substrate to prevent being dislocated by the flows. However, the interactions between mussels and the hydrodynamic conditions inside their habitat were poorly understood. The primary goal of this study is to understand flow, turbulence and mass transport processes around mussels, to describe the main mechanisms that are responsible for the dislocation of mussels from the substrate and the entrainment of particles from the bed, and how these mechanisms are affected by the main physical parameters associated with isolated mussels and mussel bed. Obtaining such knowledge is necessary to improve mussel habitat conditions. A fully 3-D eddy-resolving numerical model based on the Detached Eddy Simulation approach is developed to investigate flow in a flat-bed channel containing active filtering, partially burrowed freshwater mussels. Simulations conducted for isolated mussels show that the interactions between the incoming flow and the exposed part of the mussel generate large-scale coherent structures. These large-scale coherent structures play a very significant role in controlling the near-bed flow. They affect the drag force acting on the mussels, the capacity of the flow to induce sediment entrainment from the bed and the transport processes around mussels., To investigate the effect of mussel-to-mussel interactions on flow hydrodynamics, mussel bed stability and the scalar concentration field characterizing mixing between the filtered water originating in the excurrent siphons and the water in the incoming flow, several series of simulations were performed in open channels containing large arrays of mussels. Even for cases with relatively high mussel bed density, the flow structure around the mussels in the array bears many similarities with that observed around an isolated mussel. Mussel clustering increases the capacity of the mussels to resist dislodgement by the overflow and lowers the bed erosion potential., The present study provides detailed information on the structure and the spatial development of the (width-averaged) turbulent boundary layer induced by the interaction of the incoming flow with the emerged parts of the mussels. The vertical profiles of width-averaged streamwise velocity, scalar concentration and turbulent kinetic energy (TKE) inside the boundary layer are analyzed. With proper nondimensionalization, the streamwise velocity, scalar concentration and TKE profiles are found to be self-similar inside the outer region extending from the top of the mussels to the top edge of the boundary layer. Analytical models are proposed to describe the self-similar profiles of these variables. In addition, an analytical model containing three subzones is proposed to characterize the velocity profile from the channel bed to the top of the boundary layer.

Steady-state visual evoked potential (SSVEP) is one of the most commonly used control signal in the brain-computer interface (BCI) systems. However, the conventional spatial filtering methods for SSVEP classification highly depend on the subject-specific calibration data. The need for the methods that can alleviate the demand for the calibration data become urgent. In recent years, developing the methods that can work in inter-subject classification scenario has become a promising new direction. As the popular deep learning model nowadays, Transformer has excellent performance and has been used in EEG signal classification tasks. Therefore, in this study, we propose a deep learning model for SSVEP classification based on Transformer structure in inter-subject classification scenario, termed as SSVEPformer, which is the first application of the transformer to the classification of SSVEP. Inspired by previous studies, the model adopts the frequency spectrum of SSVEP data as input, and explores the spectral and spatial domain information for classification. Furthermore, to fully utilize the harmonic information, an extended SSVEPformer based on the filter bank technology (FB-SSVEPformer) is proposed to further improve the classification performance. Experiments were conducted using two open datasets (Dataset 1: 10 subjects, 12-class task; Dataset 2: 35 subjects, 40-class task) in the inter-subject classification scenario. The experimental results show that the proposed models could achieve better results in terms of classification accuracy and information transfer rate, compared with other baseline methods. The proposed model validates the feasibility of deep learning models based on Transformer structure for SSVEP classification task, and could serve as a potential model to alleviate the calibration procedure in the practical application of SSVEP-based BCI systems.

The Susceptible-Exposed-Infectious-Recovered (SEIR) model is applied in several countries to ascertain the spread of the coronavirus disease 2019 (COVID-19). We consider discrete-time SEIR epidemic model in a closed system which does not account for births or deaths, total population size under consideration is constant. This dynamical system generated by a non-linear evolution operator depending on four parameters. Under some conditions on parameters we reduce the evolution operator to a quadratic stochastic operator (QSO) which maps 3-dimensional simplex to itself. We show that the QSO has uncountable set of fixed points (all laying on the boundary of the simplex). It is shown that all trajectories of the dynamical system (generated by the QSO) of the SEIR model are convergent (i.e. the QSO is regular). Moreover, we discuss the efficiency of the model for Uzbekistan., Comment: 13 pages, 4 figures

In many areas of the brain, neural spiking activity covaries with features of the external world, such as sensory stimuli or an animal's movement. Experimental findings suggest that the variability of neural activity changes over time and may provide information about the external world beyond the information provided by the average neural activity. To flexibly track time-varying neural response properties, here we developed a dynamic model with Conway-Maxwell Poisson (CMP) observations. The CMP distribution can flexibly describe firing patterns that are both under- and over-dispersed relative to the Poisson distribution. Here we track parameters of the CMP distribution as they vary over time. Using simulations, we show that a normal approximation can accurately track dynamics in state vectors for both the centering and shape parameters ($\lambda$ and $\nu$). We then fit our model to neural data from neurons in primary visual cortex and "place cells" in the hippocampus. We find that this method out-performs previous dynamic models based on the Poisson distribution. The dynamic CMP model provides a flexible framework for tracking time-varying non-Poisson count data and may also have applications beyond neuroscience., Comment: 6 figures

While noise is generally associated with uncertainties and often has a negative connotation in engineering, living organisms have evolved to adapt to (and even exploit) such uncertainty to ensure the survival of a species or implement certain functions that would have been difficult or even impossible otherwise. In this article, we review the role and impact of noise in systems and synthetic biology, with a particular emphasis on its role in the genetic control of biological systems, an area we refer to as Cybergenetics. The main modeling paradigm is that of stochastic reaction networks, whose applicability goes beyond biology, as these networks can represent any population dynamics system, including ecological, epidemiological, and opinion dynamics networks. We review different ways to mathematically represent these systems, and we notably argue that the concept of ergodicity presents a particularly suitable way to characterize their stability. We then discuss noise-induced properties and show that noise can be both an asset and a nuisance in this setting. Finally, we discuss recent results on (stochastic) Cybergenetics and explore their relationships to noise. Along the way, we detail the different technical and biological constraints that need to be respected when designing synthetic biological circuits. Finally, we discuss the concepts, problems, and solutions exposed in the article; raise criticisms and concerns about current ideas and approaches; suggest current (open) problems with potential solutions; and provide some ideas for future research directions., 36 pages; 6 Figures

Switch-like motifs are among the basic building blocks of biochemical networks. A common motif that can serve as an ultrasensitive switch consists of two enzymes acting antagonistically on a substrate, one making and the other removing a covalent modification. To work as a switch, such covalent modification cycles must be held out of thermodynamic equilibrium by continuous expenditure of energy. Here, we exploit the linear framework for timescale separation to establish tight bounds on the performance of any covalent-modification switch, in terms of the chemical potential difference driving the cycle. The bounds apply to arbitrary enzyme mechanisms, not just Michaelis-Menten, with arbitrary rate constants, and thereby reflect fundamental physical constraints on covalent switching., 29 pages, 6 figures

Nuclear pore complexes (NPCs) mediate the exchange of materials between the nucleoplasm and cytoplasm, playing a key role in the separation of nucleic acids and proteins into their required compartments. The static structure of the NPC is relatively well defined by recent cryo-EM and other studies. The functional roles of dynamic components in the pore of the NPC, phenylalanyl-glycyl (FG) repeat rich nucleoporins, is less clear because of our limited understanding of highly dynamic protein systems. These proteins form a ‘restrained concentrate’ which interacts with and concentrates nuclear transport factors (NTRs) to provide facilitated nucleocytoplasmic transport of cargoes. Very rapid on- and off-rates among FG repeats and NTRs supports extremely fast facilitated transport, close to the rate of macromolecular diffusion in cytoplasm, while complexes without specific interactions are entropically excluded, though details on several aspects of the transport mechanism and FG repeat behaviors remain to be resolved. However, as discussed here, new technical approaches combined with more advanced modeling methods will likely provide an improved dynamic description of NPC transport, potentially at the atomic level in the near future. Such advances are likely to be of major benefit in comprehending the roles the malfunctioning NPC plays in cancer, ageing, viral diseases, and neurodegeneration.

Contains fulltext : 292135.pdf (Publisher’s version ) (Open Access) Recent artificial neural networks that process natural language achieve unprecedented performance in tasks requiring sentence-level understanding. As such, they could be interesting models of the integration of linguistic information in the human brain. We review works that compare these artificial language models with human brain activity and we assess the extent to which this approach has improved our understanding of the neural processes involved in natural language comprehension. Two main results emerge. First, the neural representation of word meaning aligns with the context-dependent, dense word vectors used by the artificial neural networks. Second, the processing hierarchy that emerges within artificial neural networks broadly matches the brain, but is surprisingly inconsistent across studies. We discuss current challenges in establishing artificial neural networks as process models of natural language comprehension. We suggest exploiting the highly structured representational geometry of artificial neural networks when mapping representations to brain data. 18 april 2023 19 p.

We consider a spatial model of cancer in which cells are points on the d-dimensional torus $\mathcal{T}=[0,L]^d$ , and each cell with $k-1$ mutations acquires a kth mutation at rate $\mu_k$ . We assume that the mutation rates $\mu_k$ are increasing, and we find the asymptotic waiting time for the first cell to acquire k mutations as the torus volume tends to infinity. This paper generalizes results on waiting for $k\geq 3$ mutations in Foo et al. (2020), which considered the case in which all of the mutation rates $\mu_k$ are the same. In addition, we find the limiting distribution of the spatial distances between mutations for certain values of the mutation rates.

The massive amount of genomic data appearing for SARS-CoV-2 since the beginning of the COVID-19 pandemic has challenged traditional methods for studying its dynamics. As a result, new methods such as Pangolin, which can scale to the millions of samples of SARS-CoV-2 currently available, have appeared. Such a tool is tailored to take as input assembled, aligned and curated full-length sequences, such as those found in the GISAID database. As high-throughput sequencing technologies continue to advance, such assembly, alignment and curation may become a bottleneck, creating a need for methods which can process raw sequencing reads directly. In this paper, we propose Reads2Vec, an alignment-free embedding approach that can generate a fixed-length feature vector representation directly from the raw sequencing reads without requiring assembly. Furthermore, since such an embedding is a numerical representation, it may be applied to highly optimized classification and clustering algorithms. Experiments on simulated data show that our proposed embedding obtains better classification results and better clustering properties contrary to existing alignment-free baselines. In a study on real data, we show that alignment-free embeddings have better clustering properties than the Pangolin tool and that the spike region of the SARS-CoV-2 genome heavily informs the alignment-free clusterings, which is consistent with current biological knowledge of SARS-CoV-2.

With the rapid spread of COVID-19 worldwide, viral genomic data is available in the order of millions of sequences on public databases such as GISAID. This Big Data creates a unique opportunity for analysis towards the research of effective vaccine development for current pandemics, and avoiding or mitigating future pandemics. One piece of information that comes with every such viral sequence is the geographical location where it was collected -- the patterns found between viral variants and geographical location surely being an important part of this analysis. One major challenge that researchers face is processing such huge, highly dimensional data to obtain useful insights as quickly as possible. Most of the existing methods face scalability issues when dealing with the magnitude of such data. In this paper, we propose an approach that first computes a numerical representation of the spike protein sequence of SARS-CoV-2 using $k$-mers (substrings) and then uses several machine learning models to classify the sequences based on geographical location. We show that our proposed model significantly outperforms the baselines. We also show the importance of different amino acids in the spike sequences by computing the information gain corresponding to the true class labels., Accepted at Journal of Computational Biology (JCB)

Traumatic brain injury is a global public health problem associated with chronic neurological complications and long-term disability. Biomarkers that map onto the underlying brain pathology driving these complications are urgently needed to identify individuals at risk for poor recovery and to inform design of clinical trials of neuroprotective therapies. Neuroinflammation and neurodegeneration are two endophenotypes potentially associated with increases in brain extracellular water content, but the nature of extracellular free water abnormalities after neurotrauma and its relationship to measures typically thought to reflect traumatic axonal injury are not well characterized. The objective of this study was to describe the relationship between a neuroimaging biomarker of extracellular free water content and the clinical features of a cohort with primarily complicated mild traumatic brain injury. We analyzed a cohort of 59 adult patients requiring hospitalization for non-penetrating traumatic brain injury of all severities as well as 36 healthy controls. Patients underwent brain MRI at two-weeks (n = 59) and six-months (n = 29) post-injury period, and controls underwent a single MRI. Of the participants with TBI, 50 underwent clinical neuropsychological assessment at two weeks and 28 at six months. For each subject, we derived a summary score representing deviations in whole brain white matter (1) extracellular free water volume fraction (VF) and (2) free water-corrected fractional anisotropy (fw-FA). The summary specific anomaly score (SAS) for VF was significantly higher in TBI patients at two-weeks and six-months post-injury relative to controls. SAS for VF exhibited moderate correlation with neuropsychological functioning, particularly on measures of executive function. These findings indicate abnormalities in whole brain white matter extracellular water fraction in patients with TBI and are an important step toward identifying and validating noninvasive biomarkers that map onto the pathology driving disability after TBI.

Biomolecular binding kinetics including the association (kon) and dissociation (koff) rates are critical parameters for therapeutic design of small-molecule drugs, peptides and antibodies. Notably, drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling and Machine Learning have been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.

Electroporation method is a useful tool for delivering drugs into various diseased tissues in the human body. As a result of an applied electric field, drug particles enter the intracellular compartment through the temporarily permeabilized cell membrane. Consequently, electroporation method allows better penetration of the drug into the diseased tissue and improves treatment clinically. In this study, a more generalized model of drug transport in a single-cell is proposed. The model is able to capture non-homogeneous drug transport in the cell due to non-uniform cell membrane permeabilization. Several numerical experiments are conducted to understand the effects of electric field and drug permeability on drug uptake into the cell. Through investigation, the appropriate electric field and drug permeability are identified that lead to sufficient drug uptake into the cell. This model can be used by experimentalists to get information prior to conduct any experiment, and it may help reduce the number of actual experiments that might be conducted otherwise., 18 pages

The search for biosignatures necessitates developing our understanding of life under different conditions. If life can influence the climate evolution of its planet then understanding the behaviour of life-climate feedbacks under extreme conditions is key to determine the ‘edges’ of the habitable zone. Additionally understanding the behaviour of a temperature limited biosphere will help towards formulating biosignature predictions for alien life living under conditions very different to those on Earth. Towards this aim, we extend the ‘ExoGaia Model’ – an abstract model of microbial life living on a highly simplified zero-dimensional planet. Via their metabolisms, microbes influence the atmospheric composition and therefore the temperature of the planet and emergent feedback loops allow microbes to regulate their climate and maintain long-term habitability. Here, we adapt the ExoGaia model to include temperature adaptation of the microbes by allowing different species to have different temperature ‘preferences’. We find that rather than adapting towards the planet’s abiotic conditions the biosphere tends to more strongly influence the climate of its planet, suggesting that the surface temperature of an inhabited planet might be significantly different from that predicted using abiotic models. We find that the success rate for microbial establishment on planets is improved when adaptation is allowed. However, planetary abiotic context is important for determining whether overall survival prospects for life will be improved or degraded. These results indicate the necessity to develop an understanding of life living under different limiting regimes to form predictions for the boundaries of the habitable zone.

Searching for extrasolar biosignatures is important to understand life on Earth and its origin. Astronomical observations of exoplanets may find such signatures, but it is difficult and may be impossible to claim unambiguous detection of life by remote sensing of exoplanet atmospheres. Here, another approach is considered: collecting grains ejected by asteroid impacts from exoplanets in the Milky Way and then traveling to the Solar System. The optimal grain size for this purpose is around 1 $\mu$m, and though uncertainty is large, about $10^5$ such grains are expected to be accreting on Earth every year, which may contain biosignatures of life that existed on their home planets. These grains may be collected by detectors placed in space, or extracted from Antarctic ice or deep-sea sediments, depending on future technological developments., Comment: 5 pages, no figure. Accepted for publication in International Journal of Astrobiology

Neurons in the brain are wired into adaptive networks that exhibit a range of collective dynamics. Oscillations, for example, are paradigmatic synchronous patterns of neural activity with a defined temporal scale. Neuronal avalanches, in contrast, are scale-free cascades of neural activity, often considered as evidence of brain tuning to criticality. While models have been developed to account for oscillations or avalanches separately, they typically do not explain both phenomena, are too complex to analyze analytically, or intractable to infer from data rigorously. Here we propose a non-equilibrium feedback-driven Ising like class of neural networks that simultaneously and quantitatively captures scale-free avalanches and scale-specific oscillations. In the most simple yet fully microscopic model version we can analytically compute the phase diagram and make direct contact with human brain resting-state activity recordings via tractable inference of the model's two essential parameters. The inferred model quantitatively captures the dynamics over a broad range of scales, from single sensor oscillations and collective behaviors of nearly-synchronous extreme events on multiple sensors, to neuronal avalanches unfolding over multiple sensors across multiple time bins. Importantly, the inferred parameters correlate with model-independent signatures of "closeness to criticality", indicating that the coexistence of scale-specific (neural oscillations) and scale-free (neuronal avalanches) dynamics in brain activity occurs close to a non-equilibrium critical point at the onset of self-sustained oscillations., Comment: 29 pages, 20 figures

Filtering for stochastic reaction networks (SRNs) is an important problem in systems/synthetic biology aiming to estimate the state of unobserved chemical species. A good solution to it can provide scientists valuable information about the hidden dynamic state and enable optimal feedback control. Usually, the model parameters need to be inferred simultaneously with state variables, and a conventional particle filter can fail to solve this problem accurately due to sample degeneracy. In this case, the regularized particle filter (RPF) is preferred to the conventional ones, as the RPF can mitigate sample degeneracy by perturbing particles with artificial noise. However, the artificial noise introduces an additional bias to the estimate, and, thus, it is questionable whether the RPF can provide reliable results for SRNs. In this paper, we aim to identify conditions under which the RPF converges to the exact filter in the filtering problem determined by a bimolecular network. First, we establish computationally efficient RPFs for SRNs on different scales using different dynamical models, including the continuous-time Markov process, tau-leaping model, and piecewise deterministic process. Then, by parameter sensitivity analyses, we show that the established RPFs converge to the exact filters if all reactions leading to an increase of the molecular population have linearly growing propensities and some other mild conditions are satisfied simultaneously. This ensures the performance of the RPF for a large class of SRNs, and several numerical examples are presented to illustrate our results., Comment: 28 pages, 6 figures

We review the literature on swimming in complex fluids. A classification is proposed by comparing the length and time scales of a swimmer with those of nearby obstacles, interpreted broadly, extending from rigid or soft confining boundaries to molecules which confer the bulk fluid with complex stresses. A third dimension in the classification is the concentration of swimmers, which incorporates fluids whose complexity arises purely by the collective motion of swimming organisms. For each of the eight system classes which we identify we provide a background and describe modern research findings. While some classes have seen a great deal of attention for decades, others remain uncharted waters still open and awaiting exploration., Comment: 31 pages, 13 figures, to appear in Annual Review of Condensed Matter Physics

Ordinary differential equation (ODE) models are widely used to describe chemical or biological processes. This article considers the estimation and assessment of such models on the basis of time-course data. Due to experimental limitations, time-course data are often noisy and some components of the system may not be observed. Furthermore, the computational demands of numerical integration have hindered the widespread adoption of time-course analysis using ODEs. To address these challenges, we explore the efficacy of the recently developed MAGI (MAnifold-constrained Gaussian process Inference) method for ODE inference. First, via a range of examples we show that MAGI is capable of inferring the parameters and system trajectories, including unobserved components, with appropriate uncertainty quantification. Second, we illustrate how MAGI can be used to assess and select different ODE models with time-course data based on MAGI's efficient computation of model predictions. Overall, we believe MAGI is a useful method for the analysis of time-course data in the context of ODE models, which bypasses the need for any numerical integration., Comment: 26 pages, 8 figures, with code supplement

Genomic selection has increased genetic gain in several livestock species, but due to the complicated genetics and reproduction biology not yet in honey bees. Recently, 2 970 queens were genotyped to gather a reference population. For the application of genomic selection in honey bees, this study analyses the predictive ability and bias of pedigree-based and genomic breeding values for honey yield, three workability traits and two traits for resistance against the parasite Varroa destructor. For breeding value estimation, we use a honey bee-specific model with maternal and direct effects, to account for the contributions of the workers and the queen of a colony to the phenotypes. We conducted a validation for the last generation and a five-fold cross-validation. In the validation for the last generation, the predictive ability of pedigree-based estimated breeding values was 0.06 for honey yield, and ranged from 0.2 to 0.41 for the workability traits. The inclusion of genomic marker data improved these predictive abilities to 0.11 for honey yield, and a range from 0.22 to 0.44 for the workability traits. The inclusion of genomic data did not improve the predictive ability for the disease related traits. Traits with high heritability for maternal effects compared to the heritability for direct effects showed the most promising results. Across all traits, the bias with genomic methods was close to the bias with pedigree-based BLUP. The results show that genomic selection can successfully be applied to honey bees., Comment: 31 pages, 4 figures, submitted to Heredity

As genomic scale datasets motivate research on species tree inference, simulators of the multispecies coalescent (MSC) process are essential for the testing and evaluation of new inference methods. However, the simulators themselves must be tested to ensure they give valid samples from the coalescent process. In this work we develop several statistical tools using summary statistics to evaluate the fit of a simulated gene tree sample to the MSC model. Using these tests on samples from four published simulators, we uncover flaws in several. The tests are implemented as an R package, so that both developers and users will be able to easily check proper performance of future simulators., Comment: 7 pages + 26 pages supplementary materials

Recent advances in high-resolution microscopy have allowed scientists to better understand the underlying brain connectivity. However, due to the limitation that biological specimens can only be imaged at a single timepoint, studying changes to neural projections is limited to general observations using population analysis. In this paper, we introduce NeuRegenerate, a novel end-to-end framework for the prediction and visualization of changes in neural fiber morphology within a subject, for specified age-timepoints.To predict projections, we present neuReGANerator, a deep-learning network based on cycle-consistent generative adversarial network (cycleGAN) that translates features of neuronal structures in a region, across age-timepoints, for large brain microscopy volumes. We improve the reconstruction quality of neuronal structures by implementing a density multiplier and a new loss function, called the hallucination loss.Moreover, to alleviate artifacts that occur due to tiling of large input volumes, we introduce a spatial-consistency module in the training pipeline of neuReGANerator. We show that neuReGANerator has a reconstruction accuracy of 94% in predicting neuronal structures. Finally, to visualize the predicted change in projections, NeuRegenerate offers two modes: (1) neuroCompare to simultaneously visualize the difference in the structures of the neuronal projections, across the age timepoints, and (2) neuroMorph, a vesselness-based morphing technique to interactively visualize the transformation of the structures from one age-timepoint to the other. Our framework is designed specifically for volumes acquired using wide-field microscopy. We demonstrate our framework by visualizing the structural changes in neuronal fibers within the cholinergic system of the mouse brain between a young and old specimen., Comment: Accepted for publication in IEEE Transactions on Visualization and Computer Graphics

Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements., Comment: 64 pages, 11 figures

International audience; Assistive devices for indivuals with upper-limb movement often lack controllability and intuitiveness, in particular for grasping function. In this work, we introduce a novel user interface for grasping movement control in which the user delegates the grasping task decisions to the device, only moving their (potentially prosthetic) hand toward the targeted object.

Optimization of vaccine allocations among different segments of a heterogeneous population is important for enhancing the effectiveness of vaccination campaigns in reducing the burden of epidemics. Intuitively, it would seem that allocations designed to minimize infections should prioritize those with the highest risk of being infected and infecting others. This prescription is well supported by vaccination theory, e.g., when the vaccination campaign aims to reach herd immunity. In this work, we show, however, that for vaccines providing partial protection (leaky vaccines) and for sufficiently high values of the basic reproduction number, intuition is overturned: the optimal allocation for minimizing the number of infections prioritizes the vaccination of those who are least likely to be infected. Furthermore, we show that this phenomenon occurs at a range of basic reproduction numbers relevant for the currently circulating strains of SARS-CoV-19. The work combines numerical investigations, asymptotic analysis for a general model, and complete mathematical analysis in a simple two-group model. The results point to important considerations in managing vaccination campaigns for infections with high transmissibility.

Homochirality is a generic and unique property of all biochemical life and is considered a universal and agnostic biosignature. Upon interaction with unpolarized light, homochirality induces fractional circular polarization in the light that is scattered from it, which can be sensed remotely. As such, it can be a prime candidate biosignature in the context of future life-detection missions and observatories. The linear polarizance of vegetation is also sometimes envisaged as a biosignature, although it does not share the molecular origin as circular polarization. It is known that the linear polarization of surfaces is strongly dependent on the phase angle. The relation between the phase angle and circular polarization stemming from macromolecular assemblies, such as in vegetation, however, remained unclear. We demonstrate in this study using the average of 27 different species that the circular polarization phase angle dependency of vegetation induces relatively small changes in spectral shape and mostly affects the signal magnitude. With these results we underline the use of circular spectropolarimetry as a promising agnostic biosignature complementary to the use of linear spectropolarimetry and scalar reflectance., Comment: 37 pages, 12 figures

This paper proposes a data-driven machine learning framework for parameter estimation and uncertainty quantification in epidemic models based on two key ingredients: (i) prior parameters learning via the cross-entropy method and (ii) update of the model calibration and uncertainty propagation through approximate Bayesian computation. The effectiveness of the new methodology is illustrated with the aid of actual data from COVID-19 epidemic at Rio de Janeiro city in Brazil, employing an ordinary differential equation based model with a generalized SEIR-type mechanistic structure that includes time-dependent transmission rate, asymptomatics, and hospitalizations. A minimization problem with two cost terms (number of hospitalizations and deaths) is formulated, and twelve parameters are identified. The calibrated model provides a consistent description of the available data, able to extrapolate forecasts over a few weeks, which makes the proposed methodology very appealing for use in the context of real-time epidemic modeling.

Drug addiction or drug overdose is a global public health crisis, and the design of anti-addiction drugs remains a major challenge due to intricate mechanisms. Since experimental drug screening and optimization are too time-consuming and expensive, there is urgent need to develop innovative artificial intelligence (AI) methods for addressing the challenge. We tackle this challenge by topology-inferred drug addiction learning (TIDAL) built from integrating topological Laplacian, deep bidirectional transformer, and ensemble-assisted neural networks (EANNs). The topological Laplacian is a novel algebraic topology tool that embeds molecular topological invariants and algebraic invariants into its harmonic spectra and non-harmonic spectra, respectively. These invariants complement sequence information extracted from a bidirectional transformer. We validate the proposed TIDAL framework on 22 drug addiction related, 4 hERG, and 12 DAT datasets, showing that TIDAL is a state-of-the-art framework for the modeling and analysis of drug addiction data. We carry out cross-target analysis of the current drug addiction candidates to alert their side effects and identify their repurposing potentials, revealing drugmediated linear and bilinear target correlations. Finally, TIDAL is applied to shed light on relative efficacy, repurposing potential, and potential side effects of 12 existing anti-addiction medications. Our results suggest that TIDAL provides a new computational strategy for pressingly-needed anti-substance addiction drug development.

Immunoaffinity-based liquid biopsies of circulating tumor cells (CTCs) hold great promise for cancer management, but typically suffer from low throughput, relative complexity and post-processing limitations. Here we address these issues simultaneously by decoupling and independently optimizing the nano-, micro- and macro-scales of an enrichment device that is simple to fabricate and operate. Unlike other affinity-based devices, our scalable mesh approach enables optimum capture conditions at any flow rate, as demonstrated with constant capture efficiencies, above 75% between 50-200 uL/min. The device achieved 96% sensitivity and 100% specificity when used to detect CTCs in the blood of 79 cancer patients and 20 healthy controls. We demonstrate its post processing capacity with the identification of potential responders to immune checkpoint inhibition therapy and the detection of HER2 positive breast cancer. The results compare well with other assays, including clinical standards. This suggests that our approach, which overcomes major limitations associated with affinity-based liquid biopsies, could help improve cancer management., Comment: 19 pages, 5 figures (+ supplementary materials: 16 pages, 10 figures)

When searching for inhabited exoplanets, understanding the boundaries of the habitable zone around the parent star is the key. If life can strongly influence its global environment, then we would expect the boundaries of the habitable zone to be influenced by the presence of life. Here, using a simple abstract model of ‘tangled-ecology’ where life can influence a global parameter, labelled as temperature, we investigate the boundaries of the habitable zone of our model system. As with other models of life-climate interactions, the species act to regulate the temperature. However, the system can also experience ‘punctuations’, where the system’s state jumps between different equilibria. Despite this, an ensemble of systems still tends to sustain or even improve conditions for life on average, a feature we call Entropic Gaia. The mechanism behind this is sequential selection with memory which is discussed in detail. With this modelling framework we investigate questions about how Gaia can affect and ultimately extend the habitable zone to what we call the Gaian habitable zone. This generates concrete predictions for the size of the habitable zone around stars, suggests directions for future work on the simulation of exoplanets and provides insight into the Gaian bottleneck hypothesis and the habitability/inhabitance paradox.

In recent years, the field of neuroscience has gone through rapid experimental advances and extensive use of quantitative and computational methods. This accelerating growth has created a need for methodological analysis of the role of theory and the modeling approaches currently used in this field. Toward that end, we start from the general view that the primary role of science is to solve empirical problems, and that it does so by developing theories that can account for phenomena within their domain of application. We propose a commonly-used set of terms - descriptive, mechanistic, and normative - as methodological designations that refer to the kind of problem a theory is intended to solve. Further, we find that models of each kind play distinct roles in defining and bridging the multiple levels of abstraction necessary to account for any neuroscientific phenomenon. We then discuss how models play an important role to connect theory and experiment, and note the importance of well-defined translation functions between them. Furthermore, we describe how models themselves can be used as a form of experiment to test and develop theories. This report is the summary of a discussion initiated at the conference Present and Future Theoretical Frameworks in Neuroscience, which we hope will contribute to a much-needed discussion in the neuroscientific community., deposited on: https://arxiv.org/abs/2003.13825

1. The popularity of Machine learning (ML), Deep learning (DL), and Artificial intelligence (AI) has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML and DL algorithms are often perceived as opaque, and their relationship to classical data analysis tools remains debated. 2. Although it is often assumed that ML and DL excel primarily at making predictions, ML and DL can also be used for analytical tasks traditionally addressed with statistical models. Moreover, most recent discussions and reviews on ML focus mainly on DL, missing out on synthesizing the wealth of ML algorithms with different advantages and general principles. 3. Here, we provide a comprehensive overview of the field of ML and DL, starting by summarizing its historical developments, existing algorithm families, differences to traditional statistical tools, and universal ML principles. We then discuss why and when ML and DL models excel at prediction tasks and where they could offer alternatives to traditional statistical methods for inference, highlighting current and emerging applications for ecological problems. Finally, we summarize emerging trends such as scientific and causal ML, explainable AI, and responsible AI that may significantly impact ecological data analysis in the future. 4. We conclude that ML and DL are powerful new tools for predictive modeling and data analysis. The superior performance of ML and DL algorithms compared to statistical models can be explained by their higher flexibility and automatic data-dependent complexity optimization. However, their use for causal inference is still disputed as the focus of ML and DL methods on predictions creates challenges for the interpretation of these models. Nevertheless, we expect ML and DL to become an indispensable tool in E&E, comparable to other traditional statistical tools., 62 pages, 10 figures

In this paper we study the long term dynamics of two prey species and one predator species. In the deterministic setting, if we assume the interactions are of Lotka-Volterra type (competition or predation), the long term behavior of this system is well known. However, nature is usually not deterministic. All ecosystems experience some type of random environmental fluctuations. We incorporate these into a natural framework as follows. Suppose the environment has two possible states. In each of the two environmental states the dynamics is governed by a system of Lotka-Volterra ODE. The randomness comes from spending an exponential amount of time in each environmental state and then switching to the other one. We show how this random switching can create very interesting phenomena. In some cases the randomness can facilitate the coexistence of the three species even though coexistence is impossible in each of the two environmental states. In other cases, even though there is coexistence in each of the two environmental states, switching can lead to the loss of one or more species. We look into how predators and environmental fluctuations can mediate coexistence among competing species., 20 pages, 2 figures

We study two fringe subtree counting statistics, the number of cherries and that of pitchforks for Ford's $\alpha$ model, a one-parameter family of random phylogenetic tree models that includes the uniform and the Yule models, two tree models commonly used in phylogenetics. Based on a nonuniform version of the extended P\'olya urn models in which negative entries are permitted for their replacement matrices, we obtain the strong law of large numbers and the central limit theorem for the joint distribution of these two count statistics for the Ford model. Furthermore, we derive a recursive formula for computing the exact joint distribution of these two statistics. This leads to exact formulas for their means and higher order asymptotic expansions of their second moments, which allows us to identify a critical parameter value for the correlation between these two statistics. That is, when $n$ is sufficiently large, they are negatively correlated for $0\le \alpha \le 1/2$ and positively correlated for $1/2, Comment: 23 pages, 2 figures

Upright stance tested with a superposition of support surface tilt and translation. Steady state response is characterized by frequency response function. Interaction between two stimuli absent in most of the cases. Larger stimuli may create interaction. Simulations suggest that the observed effects can be due to joint stiffness modulation., Comment: 10 pages, 7 figures

All-atom simulations have become increasingly popular to study conformational and dynamical properties of nucleic acids as they are accurate and provide high spatial and time resolutions. This high resolution however comes at a heavy computational cost and within the time scales of simulations nucleic acids weakly fluctuate around their ideal structure exploring a limited set of conformations. We introduce the RBB-NA algorithm which is capable of controlling rigid base parameters in all-atom simulations of Nucleic Acids. With suitable biasing potentials this algorithm can "force" a DNA or RNA molecule to assume specific values of the six rotational (tilt, roll, twist, buckle, propeller, opening) and/or the six translational parameters (shift, slide, rise, shear, stretch, stagger). The algorithm enables the use of advanced sampling techniques to probe the structure and dynamics of locally strongly deformed Nucleic Acids. We illustrate its performance showing some examples in which DNA is strongly twisted, bent or locally buckled. In these examples RBB-NA reproduces well the unconstrained simulations data and other known features of DNA mechanics, but it also allows one to explore the anharmonic behavior characterizing the mechanics of nucleic acids in the high deformation regime., 12 pages, 6 figures

Simultaneous identification and metabolic analysis of microbes with single-cell resolution and high throughput is necessary to answer the question of "who eats what, when, and where" in complex microbial communities. Here, we present a mid-infrared photothermal-fluorescence in situ hybridization (MIP-FISH) platform that enables direct bridging of genotype and phenotype. Through multiple improvements of MIP imaging, the sensitive detection of isotopically-labelled compounds incorporated into proteins of individual bacterial cells became possible, while simultaneous detection of FISH labelling with rRNA-targeted probes enabled the identification of the analyzed cells. In proof-of-concept experiments, we showed that the clear spectral red shift in the protein amide I region due to incorporation of $^{13}$C atoms originating from $^{13}$C-labelled-glucose can be exploited by MIP-FISH to discriminate and identify $^{13}$C-labelled bacterial cells within a complex human gut microbiome sample. The presented methods open new opportunities for single-cell structure-function analyses for microbiology.

Background: A therapeutic intervention in psychiatry can be viewed as an attempt to influence the brain's large-scale, dynamic network state transitions underlying cognition and behavior. Building on connectome-based graph analysis and control theory, Network Control Theory is emerging as a powerful tool to quantify network controllability - i.e., the influence of one brain region over others regarding dynamic network state transitions. If and how network controllability is related to mental health remains elusive. Methods: From Diffusion Tensor Imaging data, we inferred structural connectivity and inferred calculated network controllability parameters to investigate their association with genetic and familial risk in patients diagnosed with major depressive disorder (MDD, n=692) and healthy controls (n=820). Results: First, we establish that controllability measures differ between healthy controls and MDD patients while not varying with current symptom severity or remission status. Second, we show that controllability in MDD patients is associated with polygenic scores for MDD and psychiatric cross-disorder risk. Finally, we provide evidence that controllability varies with familial risk of MDD and bipolar disorder as well as with body mass index. Conclusions: We show that network controllability is related to genetic, individual, and familial risk in MDD patients. We discuss how these insights into individual variation of network controllability may inform mechanistic models of treatment response prediction and personalized intervention-design in mental health., Comment: 24 pages, 1 figure

Coherence resonance (CR), stochastic synchronization (SS), and spike-timing-dependent plasticity (STDP) are ubiquitous dynamical processes in biological neural networks. Whether there exists an optimal network and STDP configuration at which CR and SS are both pronounced is a fundamental question of interest that is still elusive. We expect such a configuration to enable the brain to make synergistic and optimal use of these phenomena to process information efficiently. This paper considers a small-world network of excitable Hodgkin-Huxley neurons driven by channel noise and STDP with an asymmetric Hebbian time window. Numerical results indicate specific network topology and STDP parameter intervals in which CR and SS can be simultaneously enhanced. Our results imply that an optimally tuned inherent background noise, STDP rule, and network topology can play a constructive role in enhancing both the time precision of firing and the synchronization in neural systems., 17 pages, 12 figures, 71 references

In the analysis of binary disease classification, single biomarkers might not have significant discriminating power and multiple biomarkers from a large set of biomarkers should be selected. Numerous approaches exist, but they merely work well for mean differences in biomarkers between cases and controls. Biological processes are however much more heterogeneous, and differences could also occur in other distributional characteristics (e.g. variances, skewness). Many machine learning techniques are better capable of utilizing these higher order distributional differences, sometimes at cost of explainability. In this study we propose quantile based prediction (QBP), a binary classification method that is based on the selection of multiple continuous biomarkers. QBP generates a single score using the tails of the biomarker distributions for cases and controls. This single score can then be evaluated by ROC analysis to investigate its predictive power. The performance of QBP is compared to supervised learning methods using extensive simulation studies, and two case studies: major depression disorder and trisomy. Simultaneously, the classification performance of the existing techniques in relation to each other is assessed. The key strengths of QBP are the opportunity to select relevant biomarkers and the outstanding classification performance in the case biomarkers predominantly show variance differences between cases and controls. When only shifts in means were present in the biomarkers, QBP obtained an inferior performance. Lastly, QBP proved to be unbiased in case of absence of disease relevant biomarkers and outperformed the other methods on the MDD case study. More research is needed to further optimize QBP, since it has several opportunities to improve its performance. Here we wanted to introduce the principle of QBP and show its potential., Comment: 26 pages, 5 figures

Phylogenetically informed k-mers, or phylo-k-mers for short, are k-mers that are predicted to appear within a given genomic region at predefined locations of a fixed phylogeny. Given a reference alignment for this genomic region and assuming a phylogenetic model of sequence evolution, we can compute a probability score for any given k-mer at any given tree node. The k-mers with sufficiently high probabilities can later be used to perform alignment-free phylogenetic classification of new sequences-a procedure recently proposed for the phylogenetic placement of metabarcoding reads and the detection of novel virus recombinants. While computing phylo-k-mers, we need to consider large numbers of k-mers at each tree node, which warrants the development of efficient enumeration algorithms. We consider a formal definition of the problem of phylo-k-mer computation: How to efficiently find all k-mers whose probability lies above a user-defined threshold for a given tree node? We describe and analyze algorithms for this problem, relying on branch-and-bound and divideand-conquer techniques. We exploit the redundancy of adjacent windows of the alignment and the structure of the probability matrix to save on computation. Besides computational complexity analyses, we provide an empirical evaluation of the relative performance of their implementations on real-world and simulated data. The divide-and-conquer algorithms, which to the best of our knowledge are novel, are found to be clear improvements over the branch-and-bound approach, especially when a large number of phylo-k-mers are found.

The functioning of double-stranded (ds) nucleic acids (NAs) in cellular processes is strongly mediated by their elastic response. These processes involve proteins that interact with dsDNA or dsRNA and distort their structures. The perturbation of the elasticity of NAs arising from these deformations is not properly considered by most theoretical frameworks. In this work, we introduce a novel method to assess the impact of mechanical stress on the elastic response of dsDNA and dsRNA through the analysis of the fluctuations of the double helix. Application of this approach to atomistic simulations reveals qualitative differences in the force dependence of the mechanical properties of dsDNA with respect to those of dsRNA, which we relate to structural features of these molecules by means of physically-sound minimalistic models., Comment: 6 pages, 2 figures

Rapid discovery of new reactions and molecules in recent years has been facilitated by the advancements in high throughput screening, accessibility to a much more complex chemical design space, and the development of accurate molecular modeling frameworks. A holistic study of the growing chemistry literature is, therefore, required that focuses on understanding the recent trends and extrapolating them into possible future trajectories. To this end, several network theory-based studies have been reported that use a directed graph representation of chemical reactions. Here, we perform a study based on representing chemical reactions as hypergraphs where the hyperedges represent chemical reactions and nodes represent the participating molecules. We use a standard reactions dataset to construct a hypernetwork and report its statistics such as degree distributions, average path length, assortativity or degree correlations, PageRank centrality, and graph-based clusters (or communities). We also compute each statistic for an equivalent directed graph representation of reactions to draw parallels and highlight differences between the two. To demonstrate the AI applicability of hypergraph reaction representation, we generate dense hypergraph embeddings and use them in the reaction classification problem. We conclude that the hypernetwork representation is flexible, preserves reaction context, and uncovers hidden insights that are otherwise not apparent in a traditional directed graph representation of chemical reactions.

Electrical neuromodulation as a palliative treatment has been increasingly used in the control of epilepsy. However, current neuromodulations commonly implement predetermined actuation strategies and lack the capability of self-adaptively adjusting stimulation inputs. In this work, rooted in optimal control theory, we propose a Koopman-MPC framework for real-time closed-loop electrical neuromodulation in epilepsy, which integrates i) a deep Koopman operator based dynamical model to predict the temporal evolution of epileptic EEG with an approximate finite-dimensional linear dynamics and ii) a model predictive control (MPC) module to design optimal seizure suppression strategies. The Koopman operator based linear dynamical model is embedded in the latent state space of the autoencoder neural network, in which we can approximate and update the Koopman operator online. The linear dynamical property of the Koopman operator ensures the convexity of the optimization problem for subsequent MPC control. The proposed deep Koopman operator model shows greater predictive capability than the baseline models (e.g., vector autoregressive model, kernel based method and recurrent neural network (RNN)) in both synthetic and real epileptic EEG data. Moreover, compared with the RNN-MPC framework, our Koopman-MPC framework can suppress seizure dynamics with better computational efficiency in both the Jansen-Rit model and the Epileptor model. Koopman-MPC framework opens a new window for model-based closed-loop neuromodulation and sheds light on nonlinear neurodynamics and feedback control policies.

The branching of an RNA molecule is an important structural characteristic yet difficult to predict correctly, especially for longer sequences. Using plane trees as a combinatorial model for RNA folding, we consider the thermodynamic cost, known as the barrier height, of transitioning between branching configurations. Using branching skew as a coarse energy approximation, we characterize various types of paths in the discrete configuration landscape. In particular, we give sufficient conditions for a path to have both minimal length and minimal branching skew. The proofs offer some biological insights, notably the potential importance of both hairpin stability and domain architecture to higher resolution RNA barrier height analyses., 15 pages, 2 figures

Real-time deformability cytometry (RT-DC) is an established method that quantifies features like size, shape, and stiffness for whole cell populations on a single-cell level in real time. To extract the cell stiffness, a lookup table (LUT) disentangles the experimentally derived steady state cell deformation and the projected area, yielding the Young's modulus. So far, two lookup tables exists, but are limited to simple linear material models and cylindrical channel geometries. Here, we present two new lookup tables for RT-DC based on a neo-Hookean hyperelastic material numerically derived by simulations based on the finite element method in square and cylindrical channel geometries. At the same time, we quantify the influence of the shear-thinning behaviour of the surrounding medium on the stationary deformation of cells in RT-DC and discuss the applicability and impact of the proposed LUTs regarding past and future RT-DC data analysis. Additionally, we provide insights about the cell strain and stresses, as well as the influence resulting from the rotational symmetric assumption on the cell deformation and volume estimation. The new lookup tables as well as the numerical cell shapes are made freely available.

Objective: Murmurs are abnormal heart sounds, identified by experts through cardiac auscultation. The murmur grade, a quantitative measure of the murmur intensity, is strongly correlated with the patient's clinical condition. This work aims to estimate each patient's murmur grade (i.e., absent, soft, loud) from multiple auscultation location phonocardiograms (PCGs) of a large population of pediatric patients from a low-resource rural area. Methods: The Mel spectrogram representation of each PCG recording is given to an ensemble of 15 convolutional residual neural networks with channel-wise attention mechanisms to classify each PCG recording. The final murmur grade for each patient is derived based on the proposed decision rule and considering all estimated labels for available recordings. The proposed method is cross-validated on a dataset consisting of 3456 PCG recordings from 1007 patients using a stratified ten-fold cross-validation. Additionally, the method was tested on a hidden test set comprised of 1538 PCG recordings from 442 patients. Results: The overall cross-validation performances for patient-level murmur gradings are 86.3% and 81.6% in terms of the unweighted average of sensitivities and F1-scores, respectively. The sensitivities (and F1-scores) for absent, soft, and loud murmurs are 90.7% (93.6%), 75.8% (66.8%), and 92.3% (84.2%), respectively. On the test set, the algorithm achieves an unweighted average of sensitivities of 80.4% and an F1-score of 75.8%. Conclusions: This study provides a potential approach for algorithmic pre-screening in low-resource settings with relatively high expert screening costs. Significance: The proposed method represents a significant step beyond detection of murmurs, providing characterization of intensity which may provide a enhanced classification of clinical outcomes.