Frequency dependence of human thresholds - both perceptual and vestibulo-ocular reflex (VOR) thresholds

While perceptual thresholds have been widely studied, VOR thresholds have received less attention, so the relationship between vestibulo-ocular reflex (VOR) and perceptual thresholds remains unclear. We compared the frequency dependence of human VOR thresholds to human perceptual thresholds for yaw head rotation in both upright ("yaw rotation") and supine ("yaw tilt") positions using the same human subjects and motion device. VOR thresholds were generally a little smaller than perceptual thresholds. We also found that horizontal VOR thresholds for both yaw rotation about an earth-vertical axis and yaw tilt (yaw rotation about an earth-horizontal axis) were relatively constant across 4 frequencies (0.2, 0.5, 1 and 2 Hz) with little difference between yaw rotation and yaw tilt VOR thresholds. For yaw tilt stimuli, perceptual thresholds were slightly lower at the lowest frequency and nearly constant at all other (higher) frequencies. However, for yaw rotation, perceptual thresholds increased significantly at the lowest frequency (0.2Hz). We conclude: (i) that VOR thresholds were relatively constant across frequency for both yaw rotation and yaw tilt, (ii) that the known contributions of velocity storage to the VOR likely yielded these VOR thresholds that were similar for yaw rotation and yaw tilt for all frequencies tested, and (iii) that the integration of otolith and horizontal canal signals during yaw tilt when supine contributes to stable perceptual thresholds, especially relative to the low frequency perceptual thresholds recorded during yaw rotation.

Quantum network utility: A framework for benchmarking quantum networks

The central aim of quantum networks is to facilitate user connectivity via quantum channels, but there is an open need for benchmarking metrics to compare diverse quantum networks. Here, we propose a general framework for quantifying the performance of a quantum network by estimating the value created by connecting users through quantum channels. In this framework, we define the quantum network utility metric [Formula: see text] to capture the social and economic value of quantum networks. The proposed framework accommodates a variety of applications from secure communications to distributed sensing. As a case study, we investigate the example of distributed quantum computing in detail. We determine the scaling laws of quantum network utility, which suggest that distributed edge quantum computing has more potential for success than its classical equivalent. We believe the proposed utility-based framework will serve as a foundation for guiding and assessing the development of quantum network technologies and designs.

Oxazolone mediated peptide chain extension and homochirality in aqueous microdroplets

Peptide formation from amino acids is thermodynamically unfavorable but a recent study provided evidence that the reaction occurs at the air/solution interfaces of aqueous microdroplets. Here, we show that i) the suggested amino acid complex in microdroplets undergoes dehydration to form oxazolone; ii) addition of water to oxazolone forms the dipeptide; and iii) reaction of oxazolone with other amino acids forms tripeptides. Furthermore, the chirality of the reacting amino acids is preserved in the oxazolone product, and strong chiral selectivity is observed when converting the oxazolone to tripeptide. This last fact ensures that optically impure amino acids will undergo chain extension to generate pure homochiral peptides. Peptide formation in bulk by wet-dry cycling shares a common pathway with the microdroplet reaction, both involving the oxazolone intermediate.

Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators

We address the challenge of acoustic simulations in three-dimensional (3D) virtual rooms with parametric source positions, which have applications in virtual/augmented reality, game audio, and spatial computing. The wave equation can fully describe wave phenomena such as diffraction and interference. However, conventional numerical discretization methods are computationally expensive when simulating hundreds of source and receiver positions, making simulations with parametric source positions impractical. To overcome this limitation, we propose using deep operator networks to approximate linear wave-equation operators. This enables the rapid prediction of sound propagation in realistic 3D acoustic scenes with parametric source positions, achieving millisecond-scale computations. By learning a compact surrogate model, we avoid the offline calculation and storage of impulse responses for all relevant source/listener pairs. Our experiments, including various complex scene geometries, show good agreement with reference solutions, with root mean squared errors ranging from 0.02 to 0.10 Pa. Notably, our method signifies a paradigm shift as-to our knowledge-no prior machine learning approach has achieved precise predictions of complete wave fields within realistic domains.

Functional aggregation of cell-free proteins enables fungal ice nucleation

Biological ice nucleation plays a key role in the survival of cold-adapted organisms. Several species of bacteria, fungi, and insects produce ice nucleators (INs) that enable ice formation at temperatures above -10 °C. Bacteria and fungi produce particularly potent INs that can promote water crystallization above -5 °C. Bacterial INs consist of extended protein units that aggregate to achieve superior functionality. Despite decades of research, the nature and identity of fungal INs remain elusive. Here, we combine ice nucleation measurements, physicochemical characterization, numerical modeling, and nucleation theory to shed light on the size and nature of the INs from the fungus Fusarium acuminatum. We find ice-binding and ice-shaping activity of Fusarium IN, suggesting a potential connection between ice growth promotion and inhibition. We demonstrate that fungal INs are composed of small 5.3 kDa protein subunits that assemble into ice-nucleating complexes that can contain more than 100 subunits. Fusarium INs retain high ice-nucleation activity even when only the ~12 kDa fraction of size-excluded proteins are initially present, suggesting robust pathways for their functional aggregation in cell-free aqueous environments. We conclude that the use of small proteins to build large assemblies is a common strategy among organisms to create potent biological INs.

Network Communications Flexibly Predict Visual Contents That Enhance Representations for Faster Visual Categorization

Models of visual cognition generally assume that brain networks predict the contents of a stimulus to facilitate its subsequent categorization. However, understanding prediction and categorization at a network level has remained challenging, partly because we need to reverse engineer their information processing mechanisms from the dynamic neural signals. Here, we used connectivity measures that can isolate the communications of a specific content to reconstruct these network mechanisms in each individual participant (N = 11, both sexes). Each was cued to the spatial location (left vs right) and contents [low spatial frequency (LSF) vs high spatial frequency (HSF)] of a predicted Gabor stimulus that they then categorized. Using each participant's concurrently measured MEG, we reconstructed networks that predict and categorize LSF versus HSF contents for behavior. We found that predicted contents flexibly propagate top down from temporal to lateralized occipital cortex, depending on task demands, under supervisory control of prefrontal cortex. When they reach lateralized occipital cortex, predictions enhance the bottom-up LSF versus HSF representations of the stimulus, all the way from occipital-ventral-parietal to premotor cortex, in turn producing faster categorization behavior. Importantly, content communications are subsets (i.e., 55-75%) of the signal-to-signal communications typically measured between brain regions. Hence, our study isolates functional networks that process the information of cognitive functions.SIGNIFICANCE STATEMENT An enduring cognitive hypothesis states that our perception is partly influenced by the bottom-up sensory input but also by top-down expectations. However, cognitive explanations of the dynamic brain networks mechanisms that flexibly predict and categorize the visual input according to task-demands remain elusive. We addressed them in a predictive experimental design by isolating the network communications of cognitive contents from all other communications. Our methods revealed a Prediction Network that flexibly communicates contents from temporal to lateralized occipital cortex, with explicit frontal control, and an occipital-ventral-parietal-frontal Categorization Network that represents more sharply the predicted contents from the shown stimulus, leading to faster behavior. Our framework and results therefore shed a new light of cognitive information processing on dynamic brain activity.