Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing single cell fermentation flux and intercellular exchange networks via pH-microenvironment sensing and inverse modeling

The homeostatic control of their environment is an essential task of living cells. It has been hypothesized that when microenvironmental pH inhomogeneities are induced by high cellular metabolic activity, diffusing protons act as signaling molecules, driving the establishment of cross-feeding networks sustained by the cell-to-cell shuttling of overflow products such as lactate. Despite their fundamental role, the extent and dynamics of such networks is largely unknown due to the lack of methods in single cell flux analysis. In this study we provide direct experimental characterization of such exchange networks. We devise a method to quantify single cell fermentation fluxes over time by integrating high-resolution pH microenvironment sensing via ratiometric nanofibers with constraint-based inverse modeling. We apply our method to cell cultures with mixed populations of cancer cells and fibroblasts. We find that the proton trafficking underlying bulk acidification is strongly heterogeneous, with maximal single cell fluxes exceeding typical values by up to 3 orders of magnitude. In addition, a crossover in time from a networked phase sustained by densely connected “hubs” (corresponding to cells with high activity) to a sparse phase dominated by isolated dipolar motifs (i.e. by pair-wise cell-to-cell exchanges) is uncovered, which parallels the time course of bulk acidification. Our method promises to shed light on issues ranging from the homeostatic function of proton exchange to the metabolic coupling of cells with different energetic demands, and paves the way for real-time non-invasive single cell metabolic flux analysis.
Motor and Visual Plasticity interact in Adult Humans

Neuroplasticity is maximal during development and declines in adulthood, especially for sensory cortices. On the contrary, the motor cortex retains plasticity throughout the lifespan. This difference has led to a modular view of plasticity in which different brain regions have their own plasticity mechanisms that do not depend or translate on others. Recent evidence indicates that visual and motor plasticity share common neural mechanisms (e.g. GABAergic inhibition), indicating a possible link between these different forms of plasticity, however the interaction between visual and motor plasticity has never been tested directly. Here we show that when visual and motor plasticity are elicited at the same time in adult humans, visual plasticity is impaired, while motor plasticity is spared. This unilateral interaction between visual and motor plasticity demonstrates a clear link between these two forms of plasticity. We conclude that local neuroplasticity in separate systems might be regulated globally, to preserve overall homeostasis in the brain.
Single trial variability in neural activity during a working memory task: A window into multiple distinct information processing sequences

Successful encoding, maintenance, and retrieval of information stored in working memory requires persistent coordination of activity among multiple brain regions. It is generally assumed that the pattern of such coordinated activity remains consistent for a given task. Thus, to separate this task-relevant signal from noise, multiple trials of the same task are completed, and the neural response is averaged across trials to generate an event-related potential (ERP). However, from trial to trial, the neuronal activity recorded with electroencephalogram (EEG) is actually spatially and temporally diverse, conflicting with the assumption of a single pattern of activity for a given task. Here, we show that variability in neuronal activity among single time-locked trials arises from the presence of multiple forms of stimulus dependent synchronized activity (i.e., distinct ERPs). We develop a data-driven classification method based on community detection to identify three discrete spatio-temporal clusters, or subtypes, of trials with different patterns of activation that are further associated with differences in decision-making processes. These results demonstrate that differences in the patterns of neural activity during working memory tasks represent fluctuations in the engagement of distinct brain networks and cognitive processes, suggesting that the brain can choose from multiple mechanisms to perform a given task. Significance Statement Working memory is a complex cognitive ability requiring coordinated activity among multiple brain regions to encode, maintain, and retrieve information. It is generally assumed that the pattern of coordination among brain regions remains consistent and one can average data across multiple trials of the same task. We instead show that there is significant variability in the patterns of brain activity among trials of the same task and develop a method to classify brain activity into distinct subtypes of responses, each with a different spatial and temporal pattern. The subtypes are associated with differences in decision-making processes, suggesting that the brain can use multiple mechanisms to perform a given task.
PathGPS: Discover shared genetic architecture using biobank data

The increasing availability and scale of Genome Wide Association Studies (GWAS) bring new horizons for understanding biological mechanisms. PathGPS is an exploratory method that discovers genetic architecture using GWAS summary data. It can separate genetic components from unobserved environmental factors and extract clusters of genes and traits associated with the same biological pathways. When applying to a metabolomics dataset and the UK Biobank, PathGPS confirms several known gene-trait clusters and suggest many new hypotheses for future investigations.
State-dependent coupling of hippocampal oscillations

Oscillations occurring simultaneously in a given area represent a physiological unit of brain states. They allow for temporal segmentation of spikes and support distinct behaviors. To establish how multiple oscillatory components co-varies simultaneously and influence neuronal firing during sleep and wakefulness, we describe a multi-variate analytical framework for constructing the state space of hippocampal oscillations. Examining the co-occurrence patterns of oscillations on the state space, across species, uncovered the presence of network constraints and distinct set of cross-frequency interactions during wakefulness as compared to sleep. We demonstrated how the state space can be used as a canvas to map the neural firing and found that distinct neurons during navigation were tuned to different set of simultaneously occurring oscillations during sleep. This multivariate analytical framework provides a window to move beyond classical bivariate pipelines, for investigating oscillations and neuronal firing, thereby allowing to factor-in the complexity of oscillation-population interactions.
An actomyosin-polarized membrane reservoir mediates the unequal divisions of Drosophila neural stem cells

Summary The asymmetric divisions of Drosophila neural stem cells (NSCs) produce unequally sized siblings, with most volume directed into the sibling that retains the NSC fate. Sibling size asymmetry results from preferential expansion of the NSC sibling surface during division. Here we show that a polarized membrane reservoir constructed by the NSC in early mitosis provides the source for expansion. The reservoir is formed from membrane domains that contain folds and microvilli that become polarized by apically-directed cortical flows of actomyosin early in mitosis. When furrow ingression begins and internal pressure increases, the stores of membrane within the apical reservoir are rapidly consumed. Expansion is significantly diminished in NSCs that lack a reservoir, and membrane expansion equalizes when the reservoir is not polarized. Our results suggest that the cortical flows that remodel the plasma membrane during asymmetric cell division function to satisfy the dynamic surface area requirements of unequally dividing cells.
Fewer but higher quality of pollinator visits determines plant invasion success in simulated plant-pollinator networks

1. Invasive plants often use mutualisms to establish and spread in their new habitats. These plants tend to be well-integrated into plant-pollinator networks in terms of being visited by resident pollinators similarly or more frequently than the native plants. 2. The long-term persistence of non-native plants, however, not only depends on the quantity of visits they receive but also on the visit quality and the resulting reproductive success, which have been rarely studied in a network context. 3. We evaluated the potential of non-native plants to invade and impact plant-pollinator networks based on their ability to attract pollinators via production of floral rewards, to produce and attach pollen on pollinators, and the consequent seed production and population growth. 4. We simulated the introduction of different types of non-native plants into thousands of different networks using a model that includes population dynamics of plants and and pollinators’ adaptive foraging. 5. We found the counterintuitive result that introduced plants visited by fewer pollinators were more successful at securing the high-quality visits necessary to invade. These plants did not experience the depletion of floral rewards that plants visited by too many pollinators experienced, which caused pollinators to reassign their visits to other plants and, therefore, dilute the conspecific pollen they carried. 6. Native pollinators increased their abundance with the plant invasions, but the reallocated foraging effort concentrated on invaders reduced the quantity and quality of visits to native plants and made the networks’ visitation structure more modular and nested. These effects were buffered by plant richness. 7. Interestingly, the significant changes in visitation structure only caused a minimal decline in native plant abundance and no extinctions. 8. Our results call for evaluating the impact of invasive plants not only on visitation rates and network structure, but also on the demographics of native plants, which depend on other processes beyond pollination including seed production and recruitment.
Clair3-trio: high-performance Nanopore long-read variant calling in family trios with trio-to-trio deep neural networks

Accurate identification of genetic variants from family child-mother-father trio sequencing data is important in genomics. However, state-of-the-art approaches treat variant calling from trios as three independent tasks, which limits their calling accuracy for Nanopore long-read sequencing data. For better trio variant calling, we introduce Clair3-Trio, the first variant caller tailored for family trio data from Nanopore long-reads. Clair3-Trio employs a Trio-to-Trio deep neural network model, which allows it to input the trio sequencing information and output all of the trio’s predicted variants within a single model to improve variant calling. We also present MCVLoss, a novel loss function tailor-made for variant calling in trios, leveraging the explicit encoding of the Mendelian inheritance. Clair3-Trio showed comprehensive improvement in experiments. It predicted far fewer Mendelian inheritance violation variations than current state-of-the-art methods. We also demonstrated that our Trio-to-Trio model is more accurate than competing architectures. Clair3-Trio is accessible as a free, open-source project at https://github.com/HKU-BAL/Clair3-Trio.
Retinoic acid-gated BDNF synthesis in neuronal dendrites drives presynaptic homeostatic plasticity

Homeostatic synaptic plasticity is a non-Hebbian synaptic mechanism that adjusts synaptic strength to maintain network stability while achieving optimal information processing. Among the molecular mediators shown to regulate this form of plasticity, synaptic signaling through retinoic acid (RA) and its receptor, RARα, has been shown to be critically involved in the homeostatic adjustment of synaptic transmission in both hippocampus and sensory cortices. In this study, we explore the molecular mechanism through which postsynaptic RA and RARα regulates presynaptic neurotransmitter release during prolonged synaptic inactivity at excitatory synapses. We show that RARα binds to a subset of dendritically sorted brain-derived neurotrophic factor (BDNF) mRNA splice isoforms and represses their translation. The RA-mediated translational de-repression of postsynaptic BDNF results in the retrograde activation of presynaptic Tropomyosin receptor kinase B (TrkB) receptors, facilitating presynaptic homeostatic compensation through enhanced presynaptic release. Together, our study illustrates a RA-mediated retrograde synaptic signaling pathway through which postsynaptic protein synthesis during synaptic inactivity drives compensatory changes at presynaptic site.
Organic Convolution Model of Ventral Visual Path Reproduces the Fine Structure of Shape Tuning in Area V4

This modeling study investigates whether an orderly convergence of neuronal selectivities from cortical areas V1 and V2 can produce curvature selective receptive fields found in area V4. A model of the ventral visual pathway from V1 to V4 is composed of approximately 500,000 individual integrate and fire units. The V1 and V2 models are based on recent findings about the composition of V2 receptive fields. A novel proposal is made for how V4 neurons may create selectivity for varying degrees of local curvature through the orderly convergence of afferent inputs from V2. The study employs a novel method for simulating individual spikes in large numbers of model neurons using tensor programming and GPU hardware: Assuming that convergent functional micro-architectural patterns repeat in topographically organized visual space, the details of individual unit depolarization and spike time is modeled using convolution operations combined with a model for the time course of post-synaptic potentials. The few parameters in the model are set manually, and this is sufficient to qualitatively reproduce the V4 recordings. This demonstrates that convolution technologies can be used for the simulation of large numbers of neurons at an intermediate level of biological realism.
“Tap Stimulation”: An Alternative To Vibrations To Convey The Apparent Haptic Motion Illusion

Vibrotactile sensations can be used to elicit apparent haptic motion illusions, which consist in using discrete vibration patterns to convey an illusory continuous moving sensation across the skin. However, experiencing prolonged vibrations is also known to increase the cognitive load. This study investigates whether continuous mechanical stimulations, or taps, that are activated in sequence, can also create a convincing illusion of haptic motion across the skin. Moreover, we also test whether an increased curvature of the contact surface impacts the quality of the felt illusion. We conducted a comparative psychophysical experiment enrolling 18 participants showing that the proposed “tap” stimulation was as efficient as a 120 Hz vibrotactile stimulation in conveying the haptic motion illusion. Moreover, results showed that the curvature of the contact surface had little effect on the quality of the sensation. Thus, using continuous mechanical stimulations that do not vibrate can be a good alternative to vibrations for rendering haptic sensations in hand-held devices in a lot of applications including navigation guidance.
A Public Repository to Improve Replicability and Collaboration in Deep Learning for HAR*

Deep learning methods have become an almost default choice of machine learning approach for human activity recognition (HAR) systems that operate on time series data, such as those of wearable sensors. However, the implementations of such methods suffer from complex package dependencies, obsolescence, and subtleties in the implementation which are sometimes not well documented. In order to accelerate research and minimise any discrepancies between (re-)implementations, we introduce a curated, open-source repository which (1) contains complete data loading and preprocessing pipelines for 6 well-established HAR datasets, (2) supports several popular HAR deep learning architectures, and (3) provides necessary functionalities to train and evaluate said models. We welcome contributions from the fellow researcher to this repository, made available through: https://github.com/STRCSussex-UbiCompSiegen/dl_har_public
Vibration-based communication for deafblind people

Deafblind people have both hearing and visual impairments, which makes communication with other people often dependent on expensive technologies e.g., Braille displays, or on caregivers acting as interpreters. This paper presents Morse I/O (MIO), a vibrotactile interface for Android, evaluated through experiments and interviews with deafblind participants. MIO was shown to enable consistent text entry and recognition after only a few hours of practice. The participants were willing to continue using the interface, although there were perceived difficulties in learning to use it. Overall, MIO is a cost-effective, portable interface for deafblind people without access to Braille displays or similar.
Asynchronous finite-time exponentially extended dissipativity for stochastic bilinear Markov jump systems

In this paper, we are concerned with the finite-time exponentially extended dissipativity (FTEED) for uncertain stochastic bilinear Markov jump systems based on the hidden Markov model (HMM). A more general performance index named as FTEED is given to make the concept of extended dissipativity in infinite-time domain suitable for the finite-time case. With the help of the recursive technique and the stochastic Lyapunov functional method, sufficient conditions are established such that the closed-loop uncertain stochastic bilinear hidden Markov jump systems are finite-time stochastic bounded while satisfying the finite-time exponentially extended dissipative performance. The obtained results can be regarded as corresponding extensions of finite-time H∞,L2 - L∞, passive, and dissipative control. Furthermore, an asynchronous and robust resilient controller is successfully derived for all the admissible bounded perturbations in terms of linear matrix inequalities (LMIs). Finally, a numerical example is used to demonstrate the effectiveness of proposed methods.
Gas bubble disease in resident fish below Grand Coulee Dam

Archive tags recording pressure (i.e., depth) and temperature were implanted in adult fish within the reservoir downstream from Grand Coulee Dam during 1999, 2000 and 2001 to determine their relative exposures to total dissolved gas supersaturation (TDGS), the causative agent of gas bubble disease. Triploid steelhead (Oncorhynchus mykiss; STH) reared in net pens at a commercial fish farm and wild bridgelip sucker (Catostomus columbianus; BLS), largescale sucker (C. macrocheilus; LSS), longnose sucker (C. catostomus; LNS), northern pikeminnow (Ptycocheilus oregonensis; NPM) and walleye (Stizostedion vitreum; WAL) from the reservoir were implanted with tags programmed to record pressure and temperature every 15 min. Tags from 7 net pen fish and 17 wild fish were recovered after data collection ranging from 16 to 156 d. The data indicated abrupt changes in depths of all fish near sunrise and sunset. Most fish were deeper during the night than in the day, but the longnose suckers and some walleye were shallowest during the night. The median depths of each species, in ascending order, were STH (1.6 m), NPM (2.0 m), BLS (2.8 m), WAL (3.7 m), LNS (5.2 m) and LSS (6.8 m). The TDGS during the study period was less than levels known to cause gas bubble disease in resident fish, so the relative exposure to TDGS was evaluated by comparing the time and distance shallower and deeper than the hydrostatic compensation depth at a hypothetical TDGS of 130%. The hydrostatic compensation depth is the depth at which the hydrostatic pressure equals the total gas pressure and below which gas bubble disease does not typically occur. The relative exposures, in ascending order of severity, were LNS, LSS, WAL, BLS, NPM and STH. Based on these results, the STH from the net pens are expected to show signs and mortality due to gas bubble disease prior to several of the resident species tested, though species-specific tolerances to TDGS should also be considered.
A hybrid eigenmode restoration algorithm for computational lithography problems based on mode matching principle

The mode matching method is an efficient solution to conventional microwave waveguide problems for its dimensionality advantages. It is theoretically extendable to computational lithography problems in nano scales with periodic boundary condition. The high computational complexity of conventional mode matching limits its application significance in highly complicated nano scale problems. This paper introduces an optimized efficient mode matching method for computational lithography problems whose complexity is O(N1.5). This bottleneck is breached by transforming governing equations with Lanczos algorithm. A novel hybrid eigenmode restoration algorithm is proposed to solve the eigenmode accuracy loss by involving the Arnoldi method. Benchmark cases verify the accuracy of the restored eigenmode and mode matching method with from microwave to nano scale problems. The efficiency is further optimized by exploring the relevance between iteration step and structure complexity. The non-uniform iteration step is introduced for efficiency optimization. A real mask component is modelled as a multi-junctional waveguide structure with periodic boundary condition. Simulation results validate the effectiveness of the proposed method and efficiency benefits are discussed through comparison with other solvers including HFSS and RCWA. Results indicate that the proposed method possesses good flexibility and application significance for computational lithography problems with high complexity.
A Singularity-Free Terminal Sliding Mode Control of an Uncertain Paediatric Exoskeleton System

The coupled human-exoskeleton design holds the problem of uncertain model parameters and disturbances, which vary for different age groups and related gait pathological levels. These uncertainties become more dominant in the case of pediatric subjects. Therefore, in this work, a robust singularity-free terminal sliding mode (SFTSM) control is proposed for an uncertain pediatric exoskeleton system in passive-assist mode. At first, the dynamic model is presented using Lagrangian principle. Thereafter, the non-singular sliding surface is considered to derive the required control law. The boundary layer approach is considered to avoid chattering behavior. The novelty of this work lies in the Lyapunov stability analysis with upper-bound criterion to ensure the finite-time convergence of the gait tracking error. Finally, it is observed from the simulation runs that the proposed SFTSM control outperforms different contrast control schemes for the passive-assist gait rehabilitation.
Harmonic Current Compensation Based on Three-phase Shunt Active Filter using Model Predictive Current Control

The increase use of non-linear devices introduces harmonics into electrical networks which cause distortion of current and voltage signals and damage the quality of electrical power. However, in electrical systems, the elimination of harmonics is a great importance. This paper presents an approach to remove harmonics, to reduce the total harmonic distortion and to improve the power factor in power distribution networks using shunt active power filters based on a robust controls as a model predictive control. In our study, we tested the conventional theory (p-q theory) to detect the harmonics present in the electrical system. In addition, for the DC link voltage control, the conventional regulator PI is used. The robustness of this proposed command is tested for three different non-linear load cases, also before and after filtering. The simulation results show that this control technique satisfies all standards with minimum current total Harmonic distortion.