Neuromodulation of Brain State and Behavior.

Neural activity and behavior are both notoriously variable, with responses differing widely between repeated presentation of identical stimuli or trials. Recent results in humans and animals reveal that these variations are not random in their nature, but may in fact be due in large part to rapid shifts in neural, cognitive, and behavioral states. Here we review recent advances in the understanding of rapid variations in the waking state, how variations are generated, and how they modulate neural and behavioral responses in both mice and humans. We propose that the brain has an identifiable set of states through which it wanders continuously in a nonrandom fashion, owing to the activity of both ascending modulatory and fast-acting corticocortical and subcortical-cortical neural pathways. These state variations provide the backdrop upon which the brain operates, and understanding them is critical to making progress in revealing the neural mechanisms underlying cognition and behavior.

A disinhibitory circuit mechanism explains a general principle of peak performance during mid-level arousal.

Perceptual decision-making is highly dependent on the momentary arousal state of the brain, which fluctuates over time on a scale of hours, minutes, and even seconds. The textbook relationship between momentary arousal and task performance is captured by an inverted U-shape, as put forward in the Yerkes-Dodson law. This law suggests optimal performance at moderate levels of arousal and impaired performance at low or high arousal levels. However, despite its popularity, the evidence for this relationship in humans is mixed at best. Here, we use pupil-indexed arousal and performance data from various perceptual decision-making tasks to provide converging evidence for the inverted U-shaped relationship between spontaneous arousal fluctuations and performance across different decision types (discrimination, detection) and sensory modalities (visual, auditory). To further understand this relationship, we built a neurobiologically plausible mechanistic model and show that it is possible to reproduce our findings by incorporating two types of interneurons that are both modulated by an arousal signal. The model architecture produces two dynamical regimes under the influence of arousal: one regime in which performance increases with arousal and another regime in which performance decreases with arousal, together forming an inverted U-shaped arousal-performance relationship. We conclude that the inverted U-shaped arousal-performance relationship is a general and robust property of sensory processing. It might be brought about by the influence of arousal on two types of interneurons that together act as a disinhibitory pathway for the neural populations that encode the available sensory evidence used for the decision.

Dynamic defects boost in-situ H2O2 piezocatalysis for water cleanup.

Creating efficient catalysts for simultaneous H2O2 generation and pollutant degradation is vital. Piezocatalytic H2O2 synthesis offers a promising alternative to traditional methods but faces challenges like sacrificial reagents, harsh conditions, and low activity. In this study, we introduce a cobalt-loaded ZnO (CZO) piezocatalyst that efficiently generates H2O2 from H2O and O2 under ultrasonic (US) treatment in ambient aqueous conditions. The catalyst demonstrates exceptional performance with ~50.9% TOC removal of phenol and in situ generation of 1.3 mM H2O2, significantly outperforming pure ZnO. Notably, the CZO piezocatalyst maintains its H2O2 generation capability even after multiple cycles, showing continuous improvement (from 1.3 mM to 1.8 mM). This is attributed to the piezoelectric electrons promoting the generation of dynamic defects under US conditions, which in turn promotes the adsorption and activation of oxygen, thereby facilitating efficient H2O2 production, as confirmed by EPR spectrometry, XPS analysis, and DFT calculations. Moreover, the CZO piezocatalysts maintain outstanding performance in pollutant degradation and H2O2 production even after long periods of inactivity, and the deactivated catalyst due to metal ion dissolution could be rejuvenated by pH adjustment, offering a sustainable solution for wastewater purification.

Ecological marginalization is widespread and increases extinction risk in mammals.

Human land-use results in widespread range change across taxa. Anthropogenic pressures can result in species' realized niches expanding, shifting, or contracting. Marginalization occurs when contraction constrains species to the geographic or ecological extremes of their historic niche. Using 4,785 terrestrial mammal species, we show that range contraction results in niche space and habitat diversity loss. Additionally, ecological marginalization is a common consequence of range contraction caused by human land use change. Remnant populations become located in the climatic and topographic extremes of their historic niche that are more likely to be at the periphery of their historic niche at greater distances from historic niche centroids. This ecological marginalization is associated with poor performance and increased extinction risk independent of geographic range loss. Range loss and marginalization may create a "double whammy" in vulnerable groups, such as large-bodied species and species with small geographical range size. Our results reveal a hitherto unrecognized conservation threat that is vital to incorporate into conservation assessment and management.

Insights from high-fidelity modeling of industrial rotary bell atomization.

The global automotive industry sprayed over 2.6 billion liters of paint in 2018, much of which through electrostatic rotary bell atomization, a highly complex process involving the fluid mechanics of rapidly rotating thin films tearing apart into micrometer-thin filaments and droplets. Coating operations account for 65% of the energy usage in a typical automotive assembly plant, representing 10,000s of gigawatt-hours each year in the United States alone. Optimization of these processes would allow for improved robustness, reduced material waste, increased throughput, and significantly reduced energy usage. Here, we introduce a high-fidelity mathematical and algorithmic framework to analyze rotary bell atomization dynamics at industrially relevant conditions. Our approach couples laboratory experiment with the development of robust non-Newtonian fluid models; devises high-order accurate numerical methods to compute the coupled bell, paint, and gas dynamics; and efficiently exploits high-performance supercomputing architectures. These advances have yielded insight into key dynamics, including i) parametric trends in film, sheeting, and filament characteristics as a function of fluid rheology, delivery rates, and bell speed; ii) the impact of nonuniform film thicknesses on atomization performance; and iii) an understanding of spray composition via primary and secondary atomization. These findings result in coating design principles that are poised to improve energy- and cost-efficiency in a wide array of industrial and manufacturing settings.

Superhuman artificial intelligence can improve human decision-making by increasing novelty.

How will superhuman artificial intelligence (AI) affect human decision-making? And what will be the mechanisms behind this effect? We address these questions in a domain where AI already exceeds human performance, analyzing more than 5.8 million move decisions made by professional Go players over the past 71 y (1950 to 2021). To address the first question, we use a superhuman AI program to estimate the quality of human decisions across time, generating 58 billion counterfactual game patterns and comparing the win rates of actual human decisions with those of counterfactual AI decisions. We find that humans began to make significantly better decisions following the advent of superhuman AI. We then examine human players' strategies across time and find that novel decisions (i.e., previously unobserved moves) occurred more frequently and became associated with higher decision quality after the advent of superhuman AI. Our findings suggest that the development of superhuman AI programs may have prompted human players to break away from traditional strategies and induced them to explore novel moves, which in turn may have improved their decision-making.

Multidisciplinary learning through collective performance favors decentralization.

Many models of learning in teams assume that team members can share solutions or learn concurrently. However, these assumptions break down in multidisciplinary teams where team members often complete distinct, interrelated pieces of larger tasks. Such contexts make it difficult for individuals to separate the performance effects of their own actions from the actions of interacting neighbors. In this work, we show that individuals can overcome this challenge by learning from network neighbors through mediating artifacts (like collective performance assessments). When neighbors' actions influence collective outcomes, teams with different networks perform relatively similarly to one another. However, varying a team's network can affect performance on tasks that weight individuals' contributions by network properties. Consequently, when individuals innovate (through "exploring" searches), dense networks hurt performance slightly by increasing uncertainty. In contrast, dense networks moderately help performance when individuals refine their work (through "exploiting" searches) by efficiently finding local optima. We also find that decentralization improves team performance across a battery of 34 tasks. Our results offer design principles for multidisciplinary teams within which other forms of learning prove more difficult.

Striatal Dopamine Contributions to Skilled Motor Learning.

Coordinated multijoint limb and digit movements-"manual dexterity"-underlie both specialized skills (e.g., playing the piano) and more mundane tasks (e.g., tying shoelaces). Impairments in dexterous skill cause significant disability, as occurs with motor cortical injury, Parkinson's disease, and a range of other pathologies. Clinical observations, as well as basic investigations, suggest that corticostriatal circuits play a critical role in learning and performing dexterous skills. Furthermore, dopaminergic signaling in these regions is implicated in synaptic plasticity and motor learning. Nonetheless, the role of striatal dopamine signaling in skilled motor learning remains poorly understood. Here, we use fiber photometry paired with a genetically encoded dopamine sensor to investigate striatal dopamine release in both male and female mice as they learn and perform a skilled reaching task. Dopamine rapidly increases during a skilled reach and peaks near pellet consumption. In the dorsolateral striatum, dopamine dynamics are faster than in the dorsomedial and ventral striatum. Across training, as reaching performance improves, dopamine signaling shifts from pellet consumption to cues that predict pellet availability, particularly in medial and ventral areas of the striatum. Furthermore, performance prediction errors are present across the striatum, with reduced dopamine release after an unsuccessful reach. These findings show that dopamine dynamics during skilled motor behaviors change with learning and are differentially regulated across striatal subregions.

Gray and White Matter Metrics Demonstrate Distinct and Complementary Prediction of Differences in Cognitive Performance in Children: Findings from ABCD (N = 11,876).

Individual differences in cognitive performance in childhood are a key predictor of significant life outcomes such as educational attainment and mental health. Differences in cognitive ability are governed in part by variations in brain structure. However, studies commonly focus on either gray or white matter metrics in humans, leaving open the key question as to whether gray or white matter microstructure plays distinct or complementary roles supporting cognitive performance. To compare the role of gray and white matter in supporting cognitive performance, we used regularized structural equation models to predict cognitive performance with gray and white matter measures. Specifically, we compared how gray matter (volume, cortical thickness, and surface area) and white matter measures (volume, fractional anisotropy, and mean diffusivity) predicted individual differences in cognitive performance. The models were tested in 11,876 children (ABCD Study; 5,680 female, 6,196 male) at 10 years old. We found that gray and white matter metrics bring partly nonoverlapping information to predict cognitive performance. The models with only gray or white matter explained respectively 15.4 and 12.4% of the variance in cognitive performance, while the combined model explained 19.0%. Zooming in, we additionally found that different metrics within gray and white matter had different predictive power and that the tracts/regions that were most predictive of cognitive performance differed across metrics. These results show that studies focusing on a single metric in either gray or white matter to study the link between brain structure and cognitive performance are missing a key part of the equation.

A critical assessment of clustering algorithms to improve cell clustering and identification in single-cell transcriptome study.

Cell clustering is typically the initial step in single-cell RNA sequencing (scRNA-seq) analyses. The performance of clustering considerably impacts the validity and reproducibility of cell identification. A variety of clustering algorithms have been developed for scRNA-seq data. These algorithms generate cell label sets that assign each cell to a cluster. However, different algorithms usually yield different label sets, which can introduce variations in cell-type identification based on the generated label sets. Currently, the performance of these algorithms has not been systematically evaluated in single-cell transcriptome studies. Herein, we performed a critical assessment of seven state-of-the-art clustering algorithms including four deep learning-based clustering algorithms and commonly used methods Seurat, Cosine-based Tanimoto similarity-refined graph for community detection using Leiden's algorithm (CosTaL) and Single-cell consensus clustering (SC3). We used diverse evaluation indices based on 10 different scRNA-seq benchmarks to systematically evaluate their clustering performance. Our results show that CosTaL, Seurat, Deep Embedding for Single-cell Clustering (DESC) and SC3 consistently outperformed Single-Cell Clustering Assessment Framework and scDeepCluster based on nine effectiveness scores. Notably, CosTaL and DESC demonstrated superior performance in clustering specific cell types. The performance of the single-cell Variational Inference tools varied across different datasets, suggesting its sensitivity to certain dataset characteristics. Notably, DESC exhibited promising results for cell subtype identification and capturing cellular heterogeneity. In addition, SC3 requires more memory and exhibits slower computation speed compared to other algorithms for the same dataset. In sum, this study provides useful guidance for selecting appropriate clustering methods in scRNA-seq data analysis.

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.

Epilepsy in childhood and school performance: a nation-wide cohort study.

Childhood epilepsy has been linked to poor academic performance, but large-scale studies are lacking. In this nation-wide study of school-aged children, we examined the association between childhood epilepsy and school performance in standardized tests according to phenotypic and treatment-related characteristics. We performed a matched register-based cohort study of children born in Denmark (1997-2009) who participated in the Danish National School Test Programme between 2010 and 2019. We used population and health registers to identify children with epilepsy and a randomly sampled sex- and age-matched reference cohort without epilepsy (ratio 1:10). Norm-based test scores from language and mathematics reflecting performance as a percentile of the nation-wide distribution of scores (scale 1-100) were used to assess academic performance. Adjusted differences in mean standardized scores between children with and without epilepsy were estimated using linear regression models. Among 582 840 children participating in the School Test Programme, we identified 4659 (0.8%) children with epilepsy (52.8% males) and 46 590 matched reference children. Median age at epilepsy onset was 7.5 years (interquartile range: 4.0-10.6). Childhood epilepsy was associated with poorer school performance overall (mean score = 48.2 versus references = 56.7; adjusted difference = -6.7, 95% CI: -7.4 to -6.0), and worse performance was found in all epilepsy subgroups, including in 3534 children with uncomplicated epilepsy (i.e. no other pre-existing neurologic or intellectual disabilities and no identified possible cause for epilepsy; adjusted difference = -6.0, 95% CI: -6.8 to -5.2). No major variation by sex, age or subject was observed, but larger score differences were seen in children using antiseizure medication at time of testing (e.g. valproate monotherapy, adjusted difference = -9.3, 95% CI: -11.5 to -7.0 and lamotrigine monotherapy, adjusted difference = -13.1, 95% CI: -15.0 to -11.3) and in children with psychiatric comorbidity, especially epilepsy with comorbid intellectual disability (adjusted difference = -27.0, 95% CI: -30.0 to -23.9) and epilepsy with comorbid attention deficit/hyperactivity disorder (adjusted difference = -15.7, 95% CI: -19.0 to -12.4). Children with epilepsy scored significantly lower than their unaffected siblings (adjusted difference = -6.2, 95% CI: -7.1 to -5.4). In conclusion, childhood epilepsy was associated with impaired academic performance throughout schooling, which suggest that there is a widespread need for educational support of children with epilepsy, even when the child has no other comorbidities and when the epilepsy appears well-managed.

Putaminal dopamine modulates movement motivation in Parkinson's disease.

The relative inability to produce effortful movements is the most specific motor sign of Parkinson's disease, which is primarily characterized by loss of dopaminergic terminals in the putamen. The motor motivation hypothesis suggests that this motor deficit may not reflect a deficiency in motor control per se, but a deficiency in cost-benefit considerations for motor effort. For the first time, we investigated the quantitative effect of dopamine depletion on the motivation of motor effort in Parkinson's disease. A total of 21 early-stage, unmedicated patients with Parkinson's disease and 26 healthy controls were included. An incentivized force task was used to capture the amount of effort participants were willing to invest for different monetary incentive levels and dopamine transporter depletion in the bilateral putamen was assessed. Our results demonstrate that patients with Parkinson's disease applied significantly less grip force than healthy controls, especially for low incentive levels. Congruously, decrease of motor effort with greater loss of putaminal dopaminergic terminals was most pronounced for low incentive levels. This signifies that putaminal dopamine is most critical to motor effort when the trade-off with the benefit is poor. Taken together, we provide direct evidence that the reduction of effortful movements in Parkinson's disease depends on motivation and that this effect is associated with putaminal dopaminergic degeneration.

Neural consequences of binaural beat stimulation on auditory sentence comprehension: an EEG study.

A growing literature has shown that binaural beat (BB)-generated by dichotic presentation of slightly mismatched pure tones-improves cognition. We recently found that BB stimulation of either beta (18 Hz) or gamma (40 Hz) frequencies enhanced auditory sentence comprehension. Here, we used electroencephalography (EEG) to characterize neural oscillations pertaining to the enhanced linguistic operations following BB stimulation. Sixty healthy young adults were randomly assigned to one of three listening groups: 18-Hz BB, 40-Hz BB, or pure-tone baseline, all embedded in music. After listening to the sound for 10 min (stimulation phase), participants underwent an auditory sentence comprehension task involving spoken sentences that contained either an object or subject relative clause (task phase). During the stimulation phase, 18-Hz BB yielded increased EEG power in a beta frequency range, while 40-Hz BB did not. During the task phase, only the 18-Hz BB resulted in significantly higher accuracy and faster response times compared with the baseline, especially on syntactically more complex object-relative sentences. The behavioral improvement by 18-Hz BB was accompanied by attenuated beta power difference between object- and subject-relative sentences. Altogether, our findings demonstrate beta oscillations as a neural correlate of improved syntactic operation following BB stimulation.

The effect of masks on cognitive performance.

The use of face masks has been a key response to the COVID-19 pandemic in almost every country. However, despite widespread use of masks in classrooms and offices around the world, almost nothing is known about their effects on cognitive performance. Using a natural experiment, I show that mandatory mask wearing has a negative causal effect on the cognitive performance of competitive chess players. I analyzed the quality of almost 3 million chess moves played by 8,531 individuals (ages 5-98 y) in 18 countries before and during the pandemic. Wearing a mask decreased the quality of players' decisions-a measure of their cognitive performance-by approximately one-third of an SD. However, the disruptive effect of masks is relatively short-lived, gradually weakening such that there is no measurable disadvantage from wearing a mask after roughly 4 h of play. The mask effect is driven by a large, negative effect for experts, with minimal change in performance at lower levels, and is stronger in high-incentive competitions. I provide support for a distraction mechanism whereby masks interfere with performance when working memory load is high.

Antimicrobial second skin using copper nanomesh.

The functional support and advancement of our body while preserving inherent naturalness is one of the ultimate goals of bioengineering. Skin protection against infectious pathogens is an application that requires common and long-term wear without discomfort or distortion of the skin functions. However, no antimicrobial method has been introduced to prevent cross-infection while preserving intrinsic skin conditions. Here, we propose an antimicrobial skin protection platform copper nanomesh, which prevents cross-infectionmorphology, temperature change rate, and skin humidity. Copper nanomesh exhibited an inactivation rate of 99.99% for Escherichia coli bacteria and influenza virus A within 1 and 10 min, respectively. The thin and porous nanomesh allows for conformal coating on the fingertips, without significant interference with the rate of skin temperature change and humidity. Efficient cross-infection prevention and thermal transfer of copper nanomesh were demonstrated using direct on-hand experiments.

High-performance seawater oxidation by a homogeneous multimetallic layered double hydroxide electrocatalyst.

SignificanceSeawater is one of the most abundant resources on Earth. Direct electrolysis of seawater is a transformative technology for sustainable hydrogen production without causing freshwater scarcity. However, this technology is severely impeded by a lack of robust and active oxygen evolution reaction (OER) electrocatalysts. Here, we report a highly efficient OER electrocatalyst composed of multimetallic layered double hydroxides, which affords superior catalytic performance and long-term durability for high-performance seawater electrolysis. To the best of our knowledge, this catalyst is among the most active for OER and it advances the development of seawater electrolysis technology.

A high-performance computational workflow to accelerate GATK SNP detection across a 25-genome dataset.

BACKGROUND: Single-nucleotide polymorphisms (SNPs) are the most widely used form of molecular genetic variation studies. As reference genomes and resequencing data sets expand exponentially, tools must be in place to call SNPs at a similar pace. The genome analysis toolkit (GATK) is one of the most widely used SNP calling software tools publicly available, but unfortunately, high-performance computing versions of this tool have yet to become widely available and affordable. RESULTS: Here we report an open-source high-performance computing genome variant calling workflow (HPC-GVCW) for GATK that can run on multiple computing platforms from supercomputers to desktop machines. We benchmarked HPC-GVCW on multiple crop species for performance and accuracy with comparable results with previously published reports (using GATK alone). Finally, we used HPC-GVCW in production mode to call SNPs on a "subpopulation aware" 16-genome rice reference panel with ~ 3000 resequenced rice accessions. The entire process took ~ 16 weeks and resulted in the identification of an average of 27.3 M SNPs/genome and the discovery of ~ 2.3 million novel SNPs that were not present in the flagship reference genome for rice (i.e., IRGSP RefSeq). CONCLUSIONS: This study developed an open-source pipeline (HPC-GVCW) to run GATK on HPC platforms, which significantly improved the speed at which SNPs can be called. The workflow is widely applicable as demonstrated successfully for four major crop species with genomes ranging in size from 400 Mb to 2.4 Gb. Using HPC-GVCW in production mode to call SNPs on a 25 multi-crop-reference genome data set produced over 1.1 billion SNPs that were publicly released for functional and breeding studies. For rice, many novel SNPs were identified and were found to reside within genes and open chromatin regions that are predicted to have functional consequences. Combined, our results demonstrate the usefulness of combining a high-performance SNP calling architecture solution with a subpopulation-aware reference genome panel for rapid SNP discovery and public deployment.

A Bioinspired Gradient Design Strategy for Cellulose-Based Electromagnetic Wave Absorbing Structural Materials.

Cellulose nanofiber (CNF) possesses excellent intrinsic properties, and many CNF-based high-performance structural and functional materials have been developed recently. However, the coordination of the mechanical properties and functionality is still a considerable challenge. Here, a CNF-based structural material is developed by a bioinspired gradient structure design using hollow magnetite nanoparticles and the phosphorylation-modified CNF as building blocks, which simultaneously achieves a superior mechanical performance and electromagnetic wave absorption (EMA) ability. Benefiting from the gradient design, the flexural strength of the structural material reached ∼205 MPa. Meanwhile, gradient design improves impedance matching, contributing to the high EMA ability (-59.5 dB) and wide effective absorption width (5.20 GHz). Besides, a low coefficient of thermal expansion and stable storage modulus was demonstrated as the temperature changes. The excellent mechanical, thermal, and EMA performance exhibited great potential for application in stealth equipment and electromagnetic interference protecting electronic packaging materials.

Enhancing Field Emission in Air via Ultrascaled Nanorod Electrodes.

Enhancing field emission in ultrascaled electronics improves the device performance and energy efficiency. Conventional lithography defines electrodes with a few-nanometer spacing on the cost of strengthened electron scattering and the reduced field enhancement factor, thus presenting challenges to enhance field emission at a small bias. Here, we used self-assembled nanorods with sub-5 nm spacing as electrodes to overcome these challenges. Intrinsic ballistic transport through high-crystallinity solution-synthesized nanorods minimized charge scattering; meanwhile ultrascaled anisotropic morphologies concentrated local electric fields and thereby lowered the barrier height. Enabled by these structural features, we demonstrated field emission density up to 4.1 × 104 A cm-2 at 1 V in air, more than 10-fold higher than typical molecular and vacuum electronics at similar conditions, and constructed an air-operating electron source with an on/off ratio of 105 at the collector electrode. Energy-efficient high-conductance electron emission suggested the potential of using solution-synthesized nanomaterials in ultrascaled electronics.