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.

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.

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.

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.

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.

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.

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.

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%.

Meta-analysis of genomic variants in power and endurance sports to decode the impact of genomics on athletic performance and success.

Association between genomic variants and athletic performance has seen a high degree of controversy, as there is often conflicting data as far as the association of genomic variants with endurance, speed and strength is concerned. Here, findings from a thorough meta-analysis from 4228 articles exploring the association of genomic variants with athletic performance in power and endurance sports are summarized, aiming to confirm or overrule the association of genetic variants with athletic performance of all types. From the 4228 articles, only 107 were eligible for further analysis, including 37 different genes. From these, there were 21 articles for the ACE gene, 29 articles for the ACTN3 gene and 8 articles for both the ACE and ACTN3 genes, including 54,382 subjects in total, from which 11,501 were endurance and power athletes and 42,881 control subjects. These data show that there is no statistically significant association between genomic variants and athletic performance either for endurance or power sports, underlying the fact that it is highly risky and even unethical to make such genetic testing services for athletic performance available to the general public. Overall, a strict regulatory monitoring should be exercised by health and other legislative authorities to protect the public from such services from an emerging discipline that still lacks the necessary scientific evidence and subsequent regulatory approval.

Aperiodic and periodic components of oscillatory brain activity in relation to cognition and symptoms in pediatric ADHD.

Children with attention-deficit/hyperactivity disorder show deficits in processing speed, as well as aberrant neural oscillations, including both periodic (oscillatory) and aperiodic (1/f-like) activity, reflecting the pattern of power across frequencies. Both components were suggested as underlying neural mechanisms of cognitive dysfunctions in attention-deficit/hyperactivity disorder. Here, we examined differences in processing speed and resting-state-Electroencephalogram neural oscillations and their associations between 6- and 12-year-old children with (n = 33) and without (n = 33) attention-deficit/hyperactivity disorder. Spectral analyses of the resting-state EEG signal using fast Fourier transform revealed increased power in fronto-central theta and beta oscillations for the attention-deficit/hyperactivity disorder group, but no differences in the theta/beta ratio. Using the parameterization method, we found a higher aperiodic exponent, which has been suggested to reflect lower neuronal excitation-inhibition, in the attention-deficit/hyperactivity disorder group. While fast Fourier transform-based theta power correlated with clinical symptoms for the attention-deficit/hyperactivity disorder group only, the aperiodic exponent was negatively correlated with processing speed across the entire sample. Finally, the aperiodic exponent was correlated with fast Fourier transform-based beta power. These results highlight the different and complementary contribution of periodic and aperiodic components of the neural spectrum as metrics for evaluation of processing speed in attention-deficit/hyperactivity disorder. Future studies should further clarify the roles of periodic and aperiodic components in additional cognitive functions and in relation to clinical status.

Comparative Analysis of Single-Cell RNA Sequencing Methods.

Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.

The psychology of separation: Border walls, soft power, and international neighborliness.

This study assesses the impact of international border walls on evaluations of countries and on beliefs about bilateral relationships between states. Using a short video, we experimentally manipulate whether a border wall image appears in a broader description of the history and culture of a little-known country. In a third condition, we also indicate which bordering country built the wall. Demographically representative samples from the United States, Ireland, and Turkey responded similarly to these experimental treatments. Compared to a control group, border walls lowered evaluations of the bordering countries. They also signified hostile international relationships to third-party observers. Furthermore, the government of the country responsible for building the wall was evaluated especially negatively. Reactions were consistent regardless of people's predispositions toward walls in their domestic political context. Our findings have important implications for a country's attractiveness, or "soft power," an important component of nonmilitary influence in international relations.

Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference.

Inference in neuroimaging typically occurs at the level of focal brain areas or circuits. Yet, increasingly, well-powered studies paint a much richer picture of broad-scale effects distributed throughout the brain, suggesting that many focal reports may only reflect the tip of the iceberg of underlying effects. How focal versus broad-scale perspectives influence the inferences we make has not yet been comprehensively evaluated using real data. Here, we compare sensitivity and specificity across procedures representing multiple levels of inference using an empirical benchmarking procedure that resamples task-based connectomes from the Human Connectome Project dataset (∼1,000 subjects, 7 tasks, 3 resampling group sizes, 7 inferential procedures). Only broad-scale (network and whole brain) procedures obtained the traditional 80% statistical power level to detect an average effect, reflecting >20% more statistical power than focal (edge and cluster) procedures. Power also increased substantially for false discovery rate- compared with familywise error rate-controlling procedures. The downsides are fairly limited; the loss in specificity for broad-scale and FDR procedures was relatively modest compared to the gains in power. Furthermore, the broad-scale methods we introduce are simple, fast, and easy to use, providing a straightforward starting point for researchers. This also points to the promise of more sophisticated broad-scale methods for not only functional connectivity but also related fields, including task-based activation. Altogether, this work demonstrates that shifting the scale of inference and choosing FDR control are both immediately attainable and can help remedy the issues with statistical power plaguing typical studies in the field.

Conformational dynamics of the Hsp70 chaperone throughout key steps of its ATPase cycle.

The 70 kDa heat shock proteins (Hsp70s) are highly versatile molecular chaperones that assist in a wide variety of protein-folding processes. They exert their functions by continuously cycling between states of low and high affinity for client polypeptides, driven by ATP-binding and hydrolysis. This cycling is tuned by cochaperones and clients. Although structures for the high and low client affinity conformations of Hsp70 and Hsp70 domains in complex with various cochaperones and peptide clients are available, it is unclear how structural rearrangements in the presence of cochaperones and clients are orchestrated in space and time. Here, we report insights into the conformational dynamics of the prokaryotic model Hsp70 DnaK throughout its adenosine-5'-triphosphate hydrolysis (ATPase) cycle using proximity-induced fluorescence quenching. Our data suggest that ATP and cochaperone-induced structural rearrangements in DnaK occur in a sequential manner and resolve hitherto unpredicted cochaperone and client-induced structural rearrangements. Peptides induce large conformational changes in DnaK·ATP prior to ATP hydrolysis, whereas a protein client induces significantly smaller changes but is much more effective in stimulating ATP hydrolysis. Analysis of the enthalpies of activation for the ATP-induced opening of the DnaK lid in the presence of clients indicates that the lid does not exert an enthalpic pulling force onto bound clients, suggesting entropic pulling as a major mechanism for client unfolding. Our data reveal important insights into the mechanics, allostery, and dynamics of Hsp70 chaperones. We established a methodology for understanding the link between dynamics and function, Hsp70 diversity, and activity modulation.

Communication by means of modulated Johnson noise.

We present the design of a passive wireless communication method that does not rely on ambient or generated RF sources. Instead, the method modulates the Johnson (thermal) noise of a resistor to transmit information bits wirelessly. By selectively connecting or disconnecting a matched resistor to an antenna, the system can achieve data rates of up to 26 bps and distances of up to 7.3 m. This communication method operates at very low power, similar to that of an RFID tag, with the advantage of not requiring a preexisting RF signal to reflect.