Mechanistic study of a low-power bacterial maintenance state using high-throughput electrochemistry.

Mechanistic studies of life's lower metabolic limits have been limited due to a paucity of tractable experimental systems. Here, we show that redox-cycling of phenazine-1-carboxamide (PCN) by Pseudomonas aeruginosa supports cellular maintenance in the absence of growth with a low mass-specific metabolic rate of 8.7 × 10-4 W (g C)-1 at 25°C. Leveraging a high-throughput electrochemical culturing device, we find that non-growing cells cycling PCN tolerate conventional antibiotics but are susceptible to those that target membrane components. Under these conditions, cells conserve energy via a noncanonical, facilitated fermentation that is dependent on acetate kinase and NADH dehydrogenases. Across PCN concentrations that limit cell survival, the cell-specific metabolic rate is constant, indicating the cells are operating near their bioenergetic limit. This quantitative platform opens the door to further mechanistic investigations of maintenance, a physiological state that underpins microbial survival in nature and disease.

An intrinsically disordered region-mediated confinement state contributes to the dynamics and function of transcription factors.

Transcription factors (TFs) regulate gene expression by binding to specific consensus motifs within the local chromatin context. The mechanisms by which TFs navigate the nuclear environment as they search for binding sites remain unclear. Here, we used single-molecule tracking and machine-learning-based classification to directly measure the nuclear mobility of the glucocorticoid receptor (GR) in live cells. We revealed two distinct and dynamic low-mobility populations. One accounts for specific binding to chromatin, while the other represents a confinement state that requires an intrinsically disordered region (IDR), implicated in liquid-liquid condensate subdomains. Further analysis showed that the dwell times of both subpopulations follow a power-law distribution, consistent with a broad distribution of affinities on the GR cistrome and interactome. Together, our data link IDRs with a confinement state that is functionally distinct from specific chromatin binding and modulates the transcriptional output by increasing the local concentration of TFs at specific sites.

ATP-induced conformational change of axonemal outer dynein arms revealed by cryo-electron tomography.

Axonemal outer dynein arm (ODA) motors generate force for ciliary beating. We analyzed three states of the ODA during the power stroke cycle using in situ cryo-electron tomography, subtomogram averaging, and classification. These states of force generation depict the prepower stroke, postpower stroke, and intermediate state conformations. Comparison of these conformations to published in vitro atomic structures of cytoplasmic dynein, ODA, and the Shulin-ODA complex revealed differences in the orientation and position of the dynein head. Our analysis shows that in the absence of ATP, all dynein linkers interact with the AAA3/AAA4 domains, indicating that interactions with the adjacent microtubule doublet B-tubule direct dynein orientation. For the prepower stroke conformation, there were changes in the tail that is anchored on the A-tubule. We built models starting with available high-resolution structures to generate a best-fitting model structure for the in situ pre- and postpower stroke ODA conformations, thereby showing that ODA in a complex with Shulin adopts a similar conformation as the active prepower stroke ODA in the axoneme.

Accelerating transmission capacity expansion by using advanced conductors in existing right-of-way.

As countries pursue decarbonization goals, the rapid expansion of transmission capacity for renewable energy (RE) integration poses a significant challenge due to hurdles such as permitting and cost allocation. However, we find that large-scale reconductoring with advanced composite-core conductors can cost-effectively double transmission capacity within existing right-of-way, with limited additional permitting. This strategy unlocks a high availability of increasingly economically viable RE resources in close proximity to the existing network. We implement reconductoring in a model of the US power system, showing that reconductoring can help meet over 80% of the new interzonal transmission needed to reach over 90% clean electricity by 2035 given restrictions on greenfield transmission build-out. With $180 billion in system cost savings by 2050, reconductoring presents a cost-effective and time-efficient, yet underutilized, opportunity to accelerate global transmission expansion.

Quantum computation of stopping power for inertial fusion target design.

Stopping power is the rate at which a material absorbs the kinetic energy of a charged particle passing through it-one of many properties needed over a wide range of thermodynamic conditions in modeling inertial fusion implosions. First-principles stopping calculations are classically challenging because they involve the dynamics of large electronic systems far from equilibrium, with accuracies that are particularly difficult to constrain and assess in the warm-dense conditions preceding ignition. Here, we describe a protocol for using a fault-tolerant quantum computer to calculate stopping power from a first-quantized representation of the electrons and projectile. Our approach builds upon the electronic structure block encodings of Su et al. [PRX Quant. 2, 040332 (2021)], adapting and optimizing those algorithms to estimate observables of interest from the non-Born-Oppenheimer dynamics of multiple particle species at finite temperature. We also work out the constant factors associated with an implementation of a high-order Trotter approach to simulating a grid representation of these systems. Ultimately, we report logical qubit requirements and leading-order Toffoli costs for computing the stopping power of various projectile/target combinations relevant to interpreting and designing inertial fusion experiments. We estimate that scientifically interesting and classically intractable stopping power calculations can be quantum simulated with roughly the same number of logical qubits and about one hundred times more Toffoli gates than is required for state-of-the-art quantum simulations of industrially relevant molecules such as FeMoco or P450.

A generic approach to infer community-level fitness of microbial genes.

The gene content in a metagenomic pool defines the function potential of a microbial community. Natural selection, operating on the level of genomes or genes, shapes the evolution of community functions by enriching some genes while depriving the others. Despite the importance of microbiomes in the environment and health, a general metric to evaluate the community-wide fitness of microbial genes remains lacking. In this work, we adapt the classic neutral model of species and use it to predict how the abundances of different genes will be shaped by selection, regardless of at which level the selection acts. We establish a simple metric that quantitatively infers the average survival capability of each gene in a microbiome. We then experimentally validate the predictions using synthetic communities of barcoded Escherichia coli strains undergoing neutral assembly and competition. We further show that this approach can be applied to publicly available metagenomic datasets to gain insights into the environment-function interplay of natural microbiomes.

Cortical miR-709 links glutamatergic signaling to NREM sleep EEG slow waves in an activity-dependent manner.

MicroRNAs (miRNAs) are key post-transcriptional regulators of gene expression that have been implicated in a plethora of neuronal processes. Nevertheless, their role in regulating brain activity in the context of sleep has so far received little attention. To test their involvement, we deleted mature miRNAs in post-mitotic neurons at two developmental ages, i.e., in early adulthood using conditional Dicer knockout (cKO) mice and in adult mice using an inducible conditional Dicer cKO (icKO) line. In both models, electroencephalographic (EEG) activity was affected and the response to sleep deprivation (SD) altered; while the rapid-eye-movement sleep (REMS) rebound was compromised in both, the increase in EEG delta (1 to 4 Hz) power during non-REMS (NREMS) was smaller in cKO mice and larger in icKO mice compared to controls. We subsequently investigated the effects of SD on the forebrain miRNA transcriptome and found that the expression of 48 miRNAs was affected, and in particular that of the activity-dependent miR-709. In vivo inhibition of miR-709 in the brain increased EEG power during NREMS in the slow-delta (0.75 to 1.75 Hz) range, particularly after periods of prolonged wakefulness. Transcriptome analysis of primary cortical neurons in vitro revealed that miR-709 regulates genes involved in glutamatergic neurotransmission. A subset of these genes was also affected in the cortices of sleep-deprived, miR-709-inhibited mice. Our data implicate miRNAs in the regulation of EEG activity and indicate that miR-709 links neuronal activity during wakefulness to brain synchrony during sleep through the regulation of glutamatergic signaling.

A unified framework for perceived magnitude and discriminability of sensory stimuli.

The perception of sensory attributes is often quantified through measurements of sensitivity (the ability to detect small stimulus changes), as well as through direct judgments of appearance or intensity. Despite their ubiquity, the relationship between these two measurements remains controversial and unresolved. Here, we propose a framework in which they arise from different aspects of a common representation. Specifically, we assume that judgments of stimulus intensity (e.g., as measured through rating scales) reflect the mean value of an internal representation, and sensitivity reflects a combination of mean value and noise properties, as quantified by the statistical measure of Fisher information. Unique identification of these internal representation properties can be achieved by combining measurements of sensitivity and judgments of intensity. As a central example, we show that Weber's law of perceptual sensitivity can coexist with Stevens' power-law scaling of intensity ratings (for all exponents), when the noise amplitude increases in proportion to the representational mean. We then extend this result beyond the Weber's law range by incorporating a more general and physiology-inspired form of noise and show that the combination of noise properties and sensitivity measurements accurately predicts intensity ratings across a variety of sensory modalities and attributes. Our framework unifies two primary perceptual measurements-thresholds for sensitivity and rating scales for intensity-and provides a neural interpretation for the underlying representation.

Spatially resolved land and grid model of carbon neutrality in China.

China has committed to achieve net carbon neutrality by 2060 to combat global climate change, which will require unprecedented deployment of negative emissions technologies, renewable energies (RE), and complementary infrastructure. At terawatt-scale deployment, land use limitations interact with operational and economic features of power systems. To address this, we developed a spatially resolved resource assessment and power systems planning optimization that models a full year of power system operations, sub-provincial RE siting criteria, and transmission connections. Our modeling results show that wind and solar must be expanded to 2,000 to 3,900 GW each, with one plausible pathway leading to 300 GW/yr combined annual additions in 2046 to 2060, a three-fold increase from today. Over 80% of solar and 55% of wind is constructed within 100 km of major load centers when accounting for current policies regarding land use. Large-scale low-carbon systems must balance key trade-offs in land use, RE resource quality, grid integration, and costs. Under more restrictive RE siting policies, at least 740 GW of distributed solar would become economically feasible in regions with high demand, where utility-scale deployment is limited by competition with agricultural land. Effective planning and policy formulation are necessary to achieve China's climate goals.

Power in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues.

The growing scale and declining cost of single-cell RNA-sequencing (RNA-seq) now permit a repetition of cell sampling that increases the power to detect rare cell states, reconstruct developmental trajectories, and measure phenotype in new terms such as cellular variance. The characterization of anatomy and developmental dynamics has not had an equivalent breakthrough since groundbreaking advances in live fluorescent microscopy. The new resolution obtained by single-cell RNA-seq is a boon to genetics because the novel description of phenotype offers the opportunity to refine gene function and dissect pleiotropy. In addition, the recent pairing of high-throughput genetic perturbation with single-cell RNA-seq has made practical a scale of genetic screening not previously possible.

Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization.

Therapeutic peptides are therapeutic agents synthesized from natural amino acids, which can be used as carriers for precisely transporting drugs and can activate the immune system for preventing and treating various diseases. However, screening therapeutic peptides using biochemical assays is expensive, time-consuming, and limited by experimental conditions and biological samples, and there may be ethical considerations in the clinical stage. In contrast, screening therapeutic peptides using machine learning and computational methods is efficient, automated, and can accurately predict potential therapeutic peptides. In this study, a k-nearest neighbor model based on multi-Laplacian and kernel risk sensitive loss was proposed, which introduces a kernel risk loss function derived from the K-local hyperplane distance nearest neighbor model as well as combining the Laplacian regularization method to predict therapeutic peptides. The findings indicated that the suggested approach achieved satisfactory results and could effectively predict therapeutic peptide sequences.

Cell survival following direct executioner-caspase activation.

Executioner-caspase activation has been considered a point-of-no-return in apoptosis. However, numerous studies report survival from caspase activation after treatment with drugs or radiation. An open question is whether cells can recover from direct caspase activation without pro-survival stress responses induced by drugs. To address this question, we engineered a HeLa cell line to express caspase-3 inducibly and combined it with a quantitative caspase activity reporter. While high caspase activity levels killed all cells and very low levels allowed all cells to live, doses of caspase activity sufficient to kill 15 to 30% of cells nevertheless allowed 70 to 85% to survive. At these doses, neither the rate, nor the peak level, nor the total amount of caspase activity could accurately predict cell death versus survival. Thus, cells can survive direct executioner-caspase activation, and variations in cellular state modify the outcome of potentially lethal caspase activity. Such heterogeneities may underlie incomplete tumor cell killing in response to apoptosis-inducing cancer treatments.

A universal description of stochastic oscillators.

Many systems in physics, chemistry, and biology exhibit oscillations with a pronounced random component. Such stochastic oscillations can emerge via different mechanisms, for example, linear dynamics of a stable focus with fluctuations, limit-cycle systems perturbed by noise, or excitable systems in which random inputs lead to a train of pulses. Despite their diverse origins, the phenomenology of random oscillations can be strikingly similar. Here, we introduce a nonlinear transformation of stochastic oscillators to a complex-valued function [Formula: see text](x) that greatly simplifies and unifies the mathematical description of the oscillator's spontaneous activity, its response to an external time-dependent perturbation, and the correlation statistics of different oscillators that are weakly coupled. The function [Formula: see text] (x) is the eigenfunction of the Kolmogorov backward operator with the least negative (but nonvanishing) eigenvalue λ1 = µ1 + iω1. The resulting power spectrum of the complex-valued function is exactly given by a Lorentz spectrum with peak frequency ω1 and half-width µ1; its susceptibility with respect to a weak external forcing is given by a simple one-pole filter, centered around ω1; and the cross-spectrum between two coupled oscillators can be easily expressed by a combination of the spontaneous power spectra of the uncoupled systems and their susceptibilities. Our approach makes qualitatively different stochastic oscillators comparable, provides simple characteristics for the coherence of the random oscillation, and gives a framework for the description of weakly coupled oscillators.

A submillimeter bundled microtubular flow battery cell with ultrahigh volumetric power density.

Flow batteries are a promising energy storage solution. However, the footprint and capital cost need further reduction for flow batteries to be commercially viable. The flow cell, where electron exchange takes place, is a central component of flow batteries. Improving the volumetric power density of the flow cell (W/Lcell) can reduce the size and cost of flow batteries. While significant progress has been made on flow battery redox, electrode, and membrane materials to improve energy density and durability, conventional flow batteries based on the planar cell configuration exhibit a large cell size with multiple bulky accessories such as flow distributors, resulting in low volumetric power density. Here, we introduce a submillimeter bundled microtubular (SBMT) flow battery cell configuration that significantly improves volumetric power density by reducing the membrane-to-membrane distance by almost 100 times and eliminating the bulky flow distributors completely. Using zinc-iodide chemistry as a demonstration, our SBMT cell shows peak charge and discharge power densities of 1,322 W/Lcell and 306.1 W/Lcell, respectively, compared with average charge and discharge power densities of <60 W/Lcell and 45 W/Lcell, respectively, of conventional planar flow battery cells. The battery cycled for more than 220 h corresponding to >2,500 cycles at off-peak conditions. Furthermore, the SBMT cell has been demonstrated to be compatible with zinc-bromide, quinone-bromide, and all-vanadium chemistries. The SBMT flow cell represents a device-level innovation to enhance the volumetric power of flow batteries and potentially reduce the size and cost of the cells and the entire flow battery.

An integrated RF energy-harvesting system with broad input voltage range and high power conversion efficiency.

By 2050, countries around the world are expected to be gradually phasing out fossil fuels and implementing greener energy technologies. In this work, we present a system employing Energy harvesting, a self-powered technology that can recycle energy from the surrounding environment. A high-efficiency radio frequency (RF) energy-harvesting chip was designed and fabricated. With an off-chip antenna and rectifier, the system scavenges ambient RF energy and converts it into usable energy, which is then stored in energy storage elements (such as a supercapacitor or a rechargeable battery). The system can further be implemented as an energy source for charging smart devices. The system-on-chip design consists of a cold start block, a boost converter with maximum power point tracking functionalities, and a charging block. The chip was fabricated using AMS 350 nm technology. Although the system was optimized for harvesting RF energy, it can be easily adapted to harvest other energy sources (i.e., mechanical and thermal energy sources). Using an optimized cold start architecture, the circuit has a cold start voltage of 380 mV. With an improved control strategy of power conversion, the system is capable of continuously charging up to 4.5 V with a broad input voltage range of 100 mV to 10 V and has a peak charging efficiency of 82%.

The metabolic rate of the biosphere and its components.

We assessed the relationship between rates of biological energy utilization and the biomass sustained by that energy utilization, at both the organism and biosphere level. We compiled a dataset comprising >10,000 basal, field, and maximum metabolic rate measurements made on >2,900 individual species, and, in parallel, we quantified rates of energy utilization, on a biomass-normalized basis, by the global biosphere and by its major marine and terrestrial components. The organism-level data, which are dominated by animal species, have a geometric mean among basal metabolic rates of 0.012 W (g C)-1 and an overall range of more than six orders of magnitude. The biosphere as a whole uses energy at an average rate of 0.005 W (g C)-1 but exhibits a five order of magnitude range among its components, from 0.00002 W (g C)-1 for global marine subsurface sediments to 2.3 W (g C)-1 for global marine primary producers. While the average is set primarily by plants and microorganisms, and by the impact of humanity upon those populations, the extremes reflect systems populated almost exclusively by microbes. Mass-normalized energy utilization rates correlate strongly with rates of biomass carbon turnover. Based on our estimates of energy utilization rates in the biosphere, this correlation predicts global mean biomass carbon turnover rates of ~2.3 y-1 for terrestrial soil biota, ~8.5 y-1 for marine water column biota, and ~1.0 y-1 and ~0.01 y-1 for marine sediment biota in the 0 to 0.1 m and >0.1 m depth intervals, respectively.

Decreased Beta Power and OFC-STN Phase Synchronization during Reactive Stopping in Freely Behaving Rats.

During natural behavior, an action often needs to be suddenly stopped in response to an unexpected sensory input-referred to as reactive stopping. Reactive stopping has been mostly investigated in humans, which led to hypotheses about the involvement of different brain structures, in particular the hyperdirect pathway. Here, we directly investigate the contribution and interaction of two key regions of the hyperdirect pathway, the orbitofrontal cortex (OFC) and subthalamic nucleus (STN), using dual-area, multielectrode recordings in male rats performing a stop-signal task. In this task, rats have to initiate movement to a go-signal, and occasionally stop their movement to the go-signal side after a stop-signal, presented at various stop-signal delays. Both the OFC and STN show near-simultaneous field potential reductions in the beta frequency range (12-30 Hz) compared with the period preceding the go-signal and the movement period. These transient reductions (∼200 ms) only happen during reactive stopping, which is when the stop-signal was received after action initiation, and are well timed after stop-signal onset and before the estimated time of stopping. Phase synchronization analysis also showed a transient attenuation of synchronization between the OFC and STN in the beta range during reactive stopping. The present results provide the first direct quantification of local neural oscillatory activity in the OFC and STN and interareal synchronization specifically timed during reactive stopping.

Role of the Locus Coeruleus Arousal Promoting Neurons in Maintaining Brain Criticality across the Sleep-Wake Cycle.

Sleep control depends on a delicate interplay among brain regions. This generates a complex temporal architecture with numerous sleep-stage transitions and intermittent fluctuations to micro-states and brief arousals. These temporal dynamics exhibit hallmarks of criticality, suggesting that tuning to criticality is essential for spontaneous sleep-stage and arousal transitions. However, how the brain maintains criticality remains not understood. Here, we investigate θ- and δ-burst dynamics during the sleep-wake cycle of rats (Sprague-Dawley, adult male) with lesion in the wake-promoting locus coeruleus (LC). We show that, in control rats, θ- and δ-bursts exhibit power-law (θ-bursts, active phase) and exponential-like (δ-bursts, quiescent phase) duration distributions, as well as power-law long-range temporal correlations (LRTCs)-typical of non-equilibrium systems self-organizing at criticality. Furthermore, consecutive θ- and δ-bursts durations are characterized by anti-correlated coupling, indicating a new class of self-organized criticality that emerges from underlying feedback between neuronal populations and brain areas involved in generating arousals and sleep states. In contrast, we uncover that LC lesion leads to alteration of θ- and δ-burst critical features, with change in duration distributions and correlation properties, and increase in θ-δ coupling. Notably, these LC-lesion effects are opposite to those observed for lesions in the sleep-promoting ventrolateral preoptic (VLPO) nucleus. Our findings indicate that critical dynamics of θ- and δ-bursts arise from a balanced interplay of LC and VLPO, which maintains brain tuning to criticality across the sleep-wake cycle-a non-equilibrium behavior in sleep micro-architecture at short timescales that coexists with large-scale sleep-wake homeostasis.

Cardiac-Sympathetic Contractility and Neural Alpha-Band Power: Cross-Modal Collaboration during Approach-Avoidance Conflict.

As evidence mounts that the cardiac-sympathetic nervous system reacts to challenging cognitive settings, we ask if these responses are epiphenomenal companions or if there is evidence suggesting a more intertwined role of this system with cognitive function. Healthy male and female human participants performed an approach-avoidance paradigm, trading off monetary reward for painful electric shock, while we recorded simultaneous electroencephalographic and cardiac-sympathetic signals. Participants were reward sensitive but also experienced approach-avoidance "conflict" when the subjective appeal of the reward was near equivalent to the revulsion of the cost. Drift-diffusion model parameters suggested that participants managed conflict in part by integrating larger volumes of evidence into choices (wider decision boundaries). Late alpha-band (neural) dynamics were consistent with widening decision boundaries serving to combat reward sensitivity and spread attention more fairly to all dimensions of available information. Independently, wider boundaries were also associated with cardiac "contractility" (an index of sympathetically mediated positive inotropy). We also saw evidence of conflict-specific "collaboration" between the neural and cardiac-sympathetic signals. In states of high conflict, the alignment (i.e., product) of alpha dynamics and contractility were associated with a further widening of the boundary, independent of either signal's singular association. Cross-trial coherence analyses provided additional evidence that the autonomic systems controlling cardiac-sympathetics might influence the assessment of information streams during conflict by disrupting or overriding reward processing. We conclude that cardiac-sympathetic control might play a critical role, in collaboration with cognitive processes, during the approach-avoidance conflict in humans.

Neurophysiology of Effortful Listening: Decoupling Motivational Modulation from Task Demands.

In demanding listening situations, a listener's motivational state may affect their cognitive investment. Here, we aim to delineate how domain-specific sensory processing, domain-general neural alpha power, and pupil size as a proxy for cognitive investment encode influences of motivational state under demanding listening. Participants (male and female) performed an auditory gap-detection task while the pupil size and the magnetoencephalogram were simultaneously recorded. Task demand and a listener's motivational state were orthogonally manipulated through changes in gap duration and monetary-reward prospect, respectively. Whereas task difficulty impaired performance, reward prospect enhanced it. The pupil size reliably indicated the modulatory impact of an individual's motivational state. At the neural level, the motivational state did not affect auditory sensory processing directly but impacted attentional postprocessing of an auditory event as reflected in the late evoked-response field and alpha-power change. Both pregap pupil dilation and higher parietal alpha power predicted better performance at the single-trial level. The current data support a framework wherein the motivational state acts as an attentional top-down neural means of postprocessing the auditory input in challenging listening situations.