A midbrain-thalamus-cortex circuit reorganizes cortical dynamics to initiate movement.

Motor behaviors are often planned long before execution but only released after specific sensory events. Planning and execution are each associated with distinct patterns of motor cortex activity. Key questions are how these dynamic activity patterns are generated and how they relate to behavior. Here, we investigate the multi-regional neural circuits that link an auditory "Go cue" and the transition from planning to execution of directional licking. Ascending glutamatergic neurons in the midbrain reticular and pedunculopontine nuclei show short latency and phasic changes in spike rate that are selective for the Go cue. This signal is transmitted via the thalamus to the motor cortex, where it triggers a rapid reorganization of motor cortex state from planning-related activity to a motor command, which in turn drives appropriate movement. Our studies show how midbrain can control cortical dynamics via the thalamus for rapid and precise motor behavior.

Large-scale two-photon calcium imaging in freely moving mice.

We developed a miniaturized two-photon microscope (MINI2P) for fast, high-resolution, multiplane calcium imaging of over 1,000 neurons at a time in freely moving mice. With a microscope weight below 3 g and a highly flexible connection cable, MINI2P allowed stable imaging with no impediment of behavior in a variety of assays compared to untethered, unimplanted animals. The improved cell yield was achieved through a optical system design featuring an enlarged field of view (FOV) and a microtunable lens with increased z-scanning range and speed that allows fast and stable imaging of multiple interleaved planes, as well as 3D functional imaging. Successive imaging across multiple, adjacent FOVs enabled recordings from more than 10,000 neurons in the same animal. Large-scale proof-of-principle data were obtained from cell populations in visual cortex, medial entorhinal cortex, and hippocampus, revealing spatial tuning of cells in all areas.

Fundamental Biological Features of Spaceflight: Advancing the Field to Enable Deep-Space Exploration.

Research on astronaut health and model organisms have revealed six features of spaceflight biology that guide our current understanding of fundamental molecular changes that occur during space travel. The features include oxidative stress, DNA damage, mitochondrial dysregulation, epigenetic changes (including gene regulation), telomere length alterations, and microbiome shifts. Here we review the known hazards of human spaceflight, how spaceflight affects living systems through these six fundamental features, and the associated health risks of space exploration. We also discuss the essential issues related to the health and safety of astronauts involved in future missions, especially planned long-duration and Martian missions.

Comprehensive Multi-omics Analysis Reveals Mitochondrial Stress as a Central Biological Hub for Spaceflight Impact.

Spaceflight is known to impose changes on human physiology with unknown molecular etiologies. To reveal these causes, we used a multi-omics, systems biology analytical approach using biomedical profiles from fifty-nine astronauts and data from NASA's GeneLab derived from hundreds of samples flown in space to determine transcriptomic, proteomic, metabolomic, and epigenetic responses to spaceflight. Overall pathway analyses on the multi-omics datasets showed significant enrichment for mitochondrial processes, as well as innate immunity, chronic inflammation, cell cycle, circadian rhythm, and olfactory functions. Importantly, NASA's Twin Study provided a platform to confirm several of our principal findings. Evidence of altered mitochondrial function and DNA damage was also found in the urine and blood metabolic data compiled from the astronaut cohort and NASA Twin Study data, indicating mitochondrial stress as a consistent phenotype of spaceflight.

Saltatory Conduction along Myelinated Axons Involves a Periaxonal Nanocircuit.

The propagation of electrical impulses along axons is highly accelerated by the myelin sheath and produces saltating or "jumping" action potentials across internodes, from one node of Ranvier to the next. The underlying electrical circuit, as well as the existence and role of submyelin conduction in saltatory conduction remain, however, elusive. Here, we made patch-clamp and high-speed voltage-calibrated optical recordings of potentials across the nodal and internodal axolemma of myelinated neocortical pyramidal axons combined with electron microscopy and experimentally constrained cable modeling. Our results reveal a nanoscale yet conductive periaxonal space, incompletely sealed at the paranodes, which separates the potentials across the low-capacitance myelin sheath and internodal axolemma. The emerging double-cable model reproduces the recorded evolution of voltage waveforms across nodes and internodes, including rapid nodal potentials traveling in advance of attenuated waves in the internodal axolemma, revealing a mechanism for saltation across time and space.

Corticoamygdala Transfer of Socially Derived Information Gates Observational Learning.

Observational learning is a powerful survival tool allowing individuals to learn about threat-predictive stimuli without directly experiencing the pairing of the predictive cue and punishment. This ability has been linked to the anterior cingulate cortex (ACC) and the basolateral amygdala (BLA). To investigate how information is encoded and transmitted through this circuit, we performed electrophysiological recordings in mice observing a demonstrator mouse undergo associative fear conditioning and found that BLA-projecting ACC (ACC→BLA) neurons preferentially encode socially derived aversive cue information. Inhibition of ACC→BLA alters real-time amygdala representation of the aversive cue during observational conditioning. Selective inhibition of the ACC→BLA projection impaired acquisition, but not expression, of observational fear conditioning. We show that information derived from observation about the aversive value of the cue is transmitted from the ACC to the BLA and that this routing of information is critically instructive for observational fear conditioning. VIDEO ABSTRACT.

Super-Resolution Imaging of the Extracellular Space in Living Brain Tissue.

The extracellular space (ECS) of the brain has an extremely complex spatial organization, which has defied conventional light microscopy. Consequently, despite a marked interest in the physiological roles of brain ECS, its structure and dynamics remain largely inaccessible for experimenters. We combined 3D-STED microscopy and fluorescent labeling of the extracellular fluid to develop super-resolution shadow imaging (SUSHI) of brain ECS in living organotypic brain slices. SUSHI enables quantitative analysis of ECS structure and reveals dynamics on multiple scales in response to a variety of physiological stimuli. Because SUSHI produces sharp negative images of all cellular structures, it also enables unbiased imaging of unlabeled brain cells with respect to their anatomical context. Moreover, the extracellular labeling strategy greatly alleviates problems of photobleaching and phototoxicity associated with traditional imaging approaches. As a straightforward variant of STED microscopy, SUSHI provides unprecedented access to the structure and dynamics of live brain ECS and neuropil.

Parvalbumin and Somatostatin Interneurons Control Different Space-Coding Networks in the Medial Entorhinal Cortex.

The medial entorhinal cortex (MEC) contains several discrete classes of GABAergic interneurons, but their specific contributions to spatial pattern formation in this area remain elusive. We employed a pharmacogenetic approach to silence either parvalbumin (PV)- or somatostatin (SOM)-expressing interneurons while MEC cells were recorded in freely moving mice. PV-cell silencing antagonized the hexagonally patterned spatial selectivity of grid cells, especially in layer II of MEC. The impairment was accompanied by reduced speed modulation in colocalized speed cells. Silencing SOM cells, in contrast, had no impact on grid cells or speed cells but instead decreased the spatial selectivity of cells with discrete aperiodic firing fields. Border cells and head direction cells were not affected by either intervention. The findings point to distinct roles for PV and SOM interneurons in the local dynamics underlying periodic and aperiodic firing in spatially modulated cells of the MEC. VIDEO ABSTRACT.

The perivascular space is a conduit for cerebrospinal fluid flow in humans: A proof-of-principle report.

The glymphatic pathway was defined in rodents as a network of perivascular spaces (PVSs) that facilitates organized distribution of cerebrospinal fluid (CSF) into the brain parenchyma. To date, perivascular CSF and cerebral interstitial fluid exchange has not been shown in humans. Using intrathecal gadolinium contrast-enhanced MRI, we show that contrast-enhanced CSF moves through the PVS into the parenchyma, supporting the existence of a glymphatic pathway in humans.

Stochastic machine learning via sigma profiles to build a digital chemical space.

This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate the sigma profile digital space, quickly converging to local extrema of target physicochemical properties. While this requires the availability of pretrained GP models on existing datasets, such limitations are eliminated with the implementation of BO, which can find global extrema with a limited number of iterations. A remarkable example of this is that of BO toward boiling temperature optimization. Holding no knowledge of chemistry except for the sigma profile and boiling temperature of carbon monoxide (the worst possible initial guess), BO finds the global maximum of the available boiling temperature dataset (over 1,000 molecules encompassing more than 40 families of organic and inorganic compounds) in just 15 iterations (i.e., 15 property measurements), cementing sigma profiles as a powerful digital chemical space for molecular optimization and discovery, particularly when little to no experimental data is initially available.

Circadian and environmental signal integration in a natural population of Arabidopsis.

Plants sense and respond to environmental cues during 24 h fluctuations in their environment. This requires the integration of internal cues such as circadian timing with environmental cues such as light and temperature to elicit cellular responses through signal transduction. However, the integration and transduction of circadian and environmental signals by plants growing in natural environments remains poorly understood. To gain insights into 24 h dynamics of environmental signaling in nature, we performed a field study of signal transduction from the nucleus to chloroplasts in a natural population of Arabidopsis halleri. Using several modeling approaches to interpret the data, we identified that the circadian clock and temperature are key regulators of this pathway under natural conditions. We identified potential time-delay steps between pathway components, and diel fluctuations in the response of the pathway to temperature cues that are reminiscent of the process of circadian gating. We found that our modeling framework can be extended to other signaling pathways that undergo diel oscillations and respond to environmental cues. This approach of combining studies of gene expression in the field with modeling allowed us to identify the dynamic integration and transduction of environmental cues, in plant cells, under naturally fluctuating diel cycles.

High-resolution stereolithography: Negative spaces enabled by control of fluid mechanics.

Stereolithography enables the fabrication of three-dimensional (3D) freeform structures via light-induced polymerization. However, the accumulation of ultraviolet dose within resin trapped in negative spaces, such as microfluidic channels or voids, can result in the unintended closing, referred to as overcuring, of these negative spaces. We report the use of injection continuous liquid interface production to continuously displace resin at risk of overcuring in negative spaces created in previous layers with fresh resin to mitigate the loss of Z-axis resolution. We demonstrate the ability to resolve 50-µm microchannels, breaking the historical relationship between resin properties and negative space resolution. With this approach, we fabricated proof-of-concept 3D free-form microfluidic devices with improved design freedom over device material selection and resulting properties.

Using JADES NIRCam photometry to investigate the dependence of stellar mass inferences on the IMF in the early universe.

The detection of numerous and relatively bright galaxies at redshifts z > 9 has prompted new investigations into the star-forming properties of high-redshift galaxies. Using local forms of the initial mass function (IMF) to estimate stellar masses of these galaxies from their light output leads to galaxy masses that are at the limit allowed for the state of the Lambda Cold Dark Matter (ΛCDM) Universe at their redshift. We explore how varying the IMF assumed in studies of galaxies in the early universe changes the inferred values for the stellar masses of these galaxies. We infer galaxy properties with the spectral energy distribution (SED) fitting code Prospector using varying IMF parameterizations for a sample of 102 galaxies with photometry from the James Webb Space Telescope, JWST Advanced Deep Extragalactic Survey that are spectroscopically confirmed to be at [Formula: see text], with additional photometry from the JWST Extragalactic Medium Band Survey for twenty-one of the galaxies. We demonstrate that models with stellar masses reduced by a factor of three or more do not affect the modeled SED.

Space wandering in the rodent default mode network.

The default mode network (DMN) is a large-scale brain network known to be suppressed during a wide range of cognitive tasks. However, our comprehension of its role in naturalistic and unconstrained behaviors has remained elusive because most research on the DMN has been conducted within the restrictive confines of MRI scanners. Here, we use multisite GCaMP (a genetically encoded calcium indicator) fiber photometry with simultaneous videography to probe DMN function in awake, freely exploring rats. We examined neural dynamics in three core DMN nodes-the retrosplenial cortex, cingulate cortex, and prelimbic cortex-as well as the anterior insula node of the salience network, and their association with the rats' spatial exploration behaviors. We found that DMN nodes displayed a hierarchical functional organization during spatial exploration, characterized by stronger coupling with each other than with the anterior insula. Crucially, these DMN nodes encoded the kinematics of spatial exploration, including linear and angular velocity. Additionally, we identified latent brain states that encoded distinct patterns of time-varying exploration behaviors and found that higher linear velocity was associated with enhanced DMN activity, heightened synchronization among DMN nodes, and increased anticorrelation between the DMN and anterior insula. Our findings highlight the involvement of the DMN in collectively and dynamically encoding spatial exploration in a real-world setting. Our findings challenge the notion that the DMN is primarily a "task-negative" network disengaged from the external world. By illuminating the DMN's role in naturalistic behaviors, our study underscores the importance of investigating brain network function in ecologically valid contexts.

Cascaded momentum-space polarization filters enabled label-free black-field microscopy for single nanoparticles analysis.

Label-free optical imaging of single-nanometer-scale matter is extremely important for a variety of biomedical, physical, and chemical investigations. One central challenge is that the background intensity is much stronger than the intensity of the scattering light from single nano-objects. Here, we propose an optical module comprising cascaded momentum-space polarization filters that can perform vector field modulation to block most of the background field and result in an almost black background; in contrast, only a small proportion of the scattering field is blocked, leading to obvious imaging contrast enhancement. This module can be installed in various optical microscopies to realize a black-field microscopy. Various single nano-objects with dimensions smaller than 20 nm appear distinctly in the black-field images. The chemical reactions occurring on single nanocrystals with edge lengths of approximately 10 nm are in situ real-time monitored by using the black-field microscopy. This label-free black-field microscopy is highly promising for a wide range of future multidisciplinary science applications.

Expanding the field of view - a simple approach for interactive visualisation of electron microscopy data.

The unparalleled resolving power of electron microscopy is both a blessing and a curse. At 30,000× magnification, 1 µm corresponds to 3 cm in the image and the field of view is only a few micrometres or less, resulting in an inevitable reduction in the spatial data available in an image. Consequently, the gain in resolution is at the cost of loss of the contextual 'reference space', which is crucial for understanding the embedded structures of interest. This problem is particularly pronounced in immunoelectron microscopy, where the detection of a gold particle is crucial for the localisation of specific molecules. The common solution of presenting high-magnification and overview images side by side often insufficiently represents the cellular environment. To address these limitations, we propose here an interactive visualization strategy inspired by digital maps and GPS modules which enables seamless transitions between different magnifications by dynamically linking virtual low magnification overview images with primary high-resolution data. By enabling dynamic browsing, it offers the potential for a deeper understanding of cellular landscapes leading to more comprehensive analysis of the primary ultrastructural data.

DrugMetric: quantitative drug-likeness scoring based on chemical space distance.

The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.

The macroeconomic spillovers from space activity.

The resurgence in space activities we are witnessing may provide opportunities for new technologies to generate potential spillovers to the real economy. To address this view, we propose a macroeconomic model with endogenous growth and a space sector. The model describes the relationship between space investment and technological spillovers, which support persistent economic growth. Our estimates indicate that space activities provide growth spillovers that peak from the late 1960s to the early 1980s. Recent space activities have a much lower economic impact. Finally, extensive experiments quantify the economic relevance of our results.

Expanding economic activity in space may offer a solution to secular stagnation.

In this speculative article, I argue that the expansion of economic activity in space may offer a uniquely promising way to escape indefinitely from what economists call "secular stagnation," a state of self-fulfilling, persistently sluggish economic growth that has increasingly threatened high-income countries. Economists have pointed to both supply-side and demand-side drivers of secular stagnation, and space as a focal point for investment can-at least in principle-address both. On the supply side, space is an unlimited frontier that, as have frontiers in the past, may inspire the individualism, innovation, and world-building needed to sustainably increase productivity and population growth. On the demand side, public investment toward increased economic activity in space could meaningfully add to aggregate demand if it reached historical peak benchmarks in the United States.

Mining in space could spur sustainable growth.

Growth models with resources and environmental externalities typically assume that planet Earth is a closed economy. However, private firms like Blue Origin and SpaceX have reduced the cost of rocket launches by a factor of 20 over the last decade. What if these costs continue to decline, making mining from asteroids or the moon feasible? What would be the implications for economic growth and the environment? This paper provides stylized facts about cost trends, geology, and the environmental impact of mining on Earth and potentially in Space. We extend a neoclassical growth model to investigate the transition from mining on Earth to Space. We find that such a transition could potentially allow for continued growth of metal use, while limiting environmental and social costs on Earth. Acknowledging the high uncertainty around the topic, our paper provides a starting point for research on how Space mining could contribute to sustainable growth on Earth.