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While hearing loss is the greatest risk factor associated with developing tinnitus, some tinnitus patients exhibit no hearing loss on conventional pure-tone audiometry (PTA).This study was developed to assess whether tinnitus patients with normal hearing as measuredIn total, 102 tinnitus patients were separated into unilateral and bilateral tinnitus groups. Age- and sex-matched controls without tinnitus were enrolled.No significant differences were observed when comparing EHF audiometry results in the 9-14 kHz range between controls and tinnitus patients, with only left-sided tinnitus ears exhibiting higher hearing thresholds than contralateral ears at 9, 10, and 14 kHz. Relative to normal controls, the hearing thresholds in the 2-8 kHz range for tinnitus ears were significantly increased.Relative to controls, tinnitus patients with normal hearing did not exhibit any significant hearing loss in the EHF range. Unexpectedly, tinnitus patients with normal hearing exhibited significant hidden hearing loss in the conventional frequency range rather than in the EHF range. For patients with normal hearing, it appears to be unnecessary to conduct EHF examinations to detect hearing loss in the EHF range.

Semiconductors with high thermal conductivity and electron-hole mobility are of great importance for electronic and photonic devices as well as for fundamental studies. Among the ultrahigh–thermal conductivity materials, cubic boron arsenide (c-BAs) is predicted to exhibit simultaneously high electron and hole mobilities of >1000 centimeters squared per volt per second. Using the optical transient grating technique, we experimentally measured thermal conductivity of 1200 watts per meter per kelvin and ambipolar mobility of 1600 centimeters squared per volt per second at the same locations on c-BAs samples at room temperature despite spatial variations. Ab initio calculations show that lowering ionized and neutral impurity concentrations is key to achieving high mobility and high thermal conductivity, respectively. The high ambipolar mobilities combined with the ultrahigh thermal conductivity make c-BAs a promising candidate for next-generation electronics.

Understanding the brain’s perception algorithm is a highly intricate problem, as the inherent complexity of sensory inputs and the brain’s nonlinear processing make characterizing sensory representations difficult. Recent studies have shown that functional models—capable of predicting large-scale neuronal activity in response to arbitrary sensory input—can be powerful tools for characterizing neuronal representations by enabling high-throughputin silicoexperiments. However, accurately modeling responses to dynamic and ecologically relevant inputs like videos remains challenging, particularly when generalizing to new stimulus domains outside the training distribution. Inspired by recent breakthroughs in artificial intelligence, where foundation models—trained on vast quantities of data— have demonstrated remarkable capabilities and generalization, we developed a “foundation model” of the mouse visual cortex: a deep neural network trained on large amounts of neuronal responses to ecological videos from multiple visual cortical areas and mice. The model accurately predicted neuronal responses not only to natural videos but also to various new stimulus domains, such as coherent moving dots and noise patterns, underscoring its generalization abilities. The foundation model could also be adapted to new mice with minimal natural movie training data. We applied the foundation model to the MICrONS dataset: a study of the brain that integrates structure with function at unprecedented scale, containing nanometer-scale morphology, connectivity with >500,000,000 synapses, and function of >70,000 neurons within a ∼ 1mm3volume spanning multiple areas of the mouse visual cortex. This accurate functional model of the MICrONS data opens the possibility for a systematic characterization of the relationship between circuit structure and function. By precisely capturing the response properties of the visual cortex and generalizing to new stimulus domains and mice, foundation models can pave the way for a deeper understanding of visual computation.

A defining characteristic of intelligent systems, whether natural or artificial, is the ability to generalize and infer behaviorally relevant latent causes from high-dimensional sensory input, despite significant variations in the environment. To understand how brains achieve generalization, it is crucial to identify the features to which neurons respond selectively and invariantly. However, the high-dimensional nature of visual inputs, the non-linearity of information processing in the brain, and limited experimental time make it challenging to systematically characterize neuronal tuning and invariances, especially for natural stimuli. Here, we extended “inception loops” — a paradigm that iterates between large-scale recordings, neural predictive models, andin silicoexperiments followed byin vivoverification — to systematically characterize single neuron invariances in the mouse primary visual cortex. Using the predictive model we synthesized Diverse Exciting Inputs (DEIs), a set of inputs that differ substantially from each other while each driving a target neuron strongly, and verified these DEIs’ efficacyin vivo. We discovered a novel bipartite invariance: one portion of the receptive field encoded phase-invariant texturelike patterns, while the other portion encoded a fixed spatial pattern. Our analysis revealed that the division between the fixed and invariant portions of the receptive fields aligns with object boundaries defined by spatial frequency differences present in highly activating natural images. These findings suggest that bipartite invariance might play a role in segmentation by detecting texture-defined object boundaries, independent of the phase of the texture. We also replicated these bipartite DEIs in the functional connectomics MICrONs data set, which opens the way towards a circuit-level mechanistic understanding of this novel type of invariance. Our study demonstrates the power of using a data-driven deep learning approach to systematically characterize neuronal invariances. By applying this method across the visual hierarchy, cell types, and sensory modalities, we can decipher how latent variables are robustly extracted from natural scenes, leading to a deeper understanding of generalization.

A key role of sensory processing is integrating information across space. Neuronal responses in the visual system are influenced by both local features in the receptive field center and contextual information from the surround. While center-surround interactions have been extensively studied using simple stimuli like gratings, investigating these interactions with more complex, ecologically-relevant stimuli is challenging due to the high dimensionality of the stimulus space. We used large-scale neuronal recordings in mouse primary visual cortex to train convolutional neural network (CNN) models that accurately predicted center-surround interactions for natural stimuli. These models enabled us to synthesize surround stimuli that strongly suppressed or enhanced neuronal responses to the optimal center stimulus, as confirmed byin vivoexperiments. In contrast to the common notion that congruent center and surround stimuli are suppressive, we found that excitatory surrounds appeared to complete spatial patterns in the center, while inhibitory surrounds disrupted them. We quantified this effect by demonstrating that CNN-optimized excitatory surround images have strong similarity in neuronal response space with surround images generated by extrapolating the statistical properties of the center, and with patches of natural scenes, which are known to exhibit high spatial correlations. Our findings cannot be explained by theories like redundancy reduction or predictive coding previously linked to contextual modulation in visual cortex. Instead, we demonstrated that a hierarchical probabilistic model incorporating Bayesian inference, and modulating neuronal responses based on prior knowledge of natural scene statistics, can explain our empirical results. We replicated these center-surround effects in the multi-area functional connectomics MICrONS dataset using natural movies as visual stimuli, which opens the way towards understanding circuit level mechanism, such as the contributions of lateral and feedback recurrent connections. Our data-driven modeling approach provides a new understanding of the role of contextual interactions in sensory processing and can be adapted across brain areas, sensory modalities, and species.

To understand how the neocortex underlies our ability to perceive, think, and act, it is important to study the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feedback connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron’s tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron’s receptive field is located). We show that the feature, but not the spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the “like-to-like” connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and function.

Metamers, images that are perceived as equal, are a useful tool to study representations of natural images in biological and artificial vision systems. We synthesized metamers for the mouse visual system by inverting a deep encoding model to find an image that matched the observed neural activity to the original presented image. When testing the resulting images in physiological experiments we found that they most closely reproduced the neural activity of the original image when compared to other decoding methods, even when tested in a different animal whose neural activity was not used to produce the metamer. This demonstrates that deep encoding models do capture general characteristic properties of biological visual systems and can be used to define a meaningful perceptual loss for the visual system.

The interference calculation is essential to the flight dynamic analysis especially for multi-rotors configurations. To analyze the flight dynamic characteristics of a small tandem helicopter precisely, a vortex method is utilized to obtain the aerodynamic interaction between the tandem rotors and the fuselage. Specially, the unsteady air loads and the wake of the rotors are developed using a lifting surface method and the viscous vortex particle theory, respectively, and the fuselage is modeled by the panel method to simulate the blocking effect on the rotors. Then the aerodynamic interference of the small tandem helicopter is analyzed under the conditions of different vertical or horizontal distance between the rear and front rotors. Subsequently, to save calculation time during flight dynamic analysis, a hybrid optimization trimming method combining the equilibrium optimizer and the improved delta method is proposed. At last, the flight dynamic analysis for the small tandem helicopter with different configurations are carried out and the results indicate that configuration in which the rear rotor is located higher than the front rotor and the one with larger longitudinal distance between tandem rotors are beneficial to increase the stability in lateral and longitudinal directions, respectively.

Compared with high-income countries, Low- and Middle-income Countries (LMICs) are characterized by large and dense populations with low standards of living. During major public health events, medical resources are often strained in LMICs. The COVID-19 pandemic has upended health and living standards around the world. This paper analyzes the impact of COVID-19 pandemic on medical resources in LMICs using multimodal data, including static attributes and epidemic time series data of countries. Specifically, we use the dynamic models to predict the maximum hospital demand and number of infections for the LMICs in the COVID-19 pandemic. The epidemic is estimated to impose health care burden excessively exceeding the current capacity of hospitals in many LMICs, especially in Honduras, Central African Republic, and Colombia. The reasons for the shortage of medical resources in these areas are further analyzed. We provide suggestion on coping with the shortage of medical resources in the COVID-19 era, as well as preparations for preventing major public health events in the future.

We present a joint deep neural system identification model for two major sources of neural variability: stimulus-driven and stimulus-conditioned fluctuations. To this end, we combine (1) state-of-the-art deep networks for stimulus-driven activity and (2) a flexible, normalizing flow-based generative model to capture the stimulus-conditioned variability including noise correlations. This allows us to train the model end-to-end without the need for sophisticated probabilistic approximations associated with many latent state models for stimulus-conditioned fluctuations. We train the model on the responses of thousands of neurons from multiple areas of the mouse visual cortex to natural images. We show that our model outperforms previous state-of-the-art models in predicting the distribution of neural population responses to novel stimuli, including shared stimulus-conditioned variability. Furthermore, it successfully learns known latent factors of the population responses that are related to behavioral variables such as pupil dilation, and other factors that vary systematically with brain area or retinotopic location. Overall, our model accurately accounts for two critical sources of neural variability while avoiding several complexities associated with many existing latent state models. It thus provides a useful tool for uncovering the interplay between different factors that contribute to variability in neural activity.

The core issue of sustainable development refers to the coordinated development of economic-social-environmental issues. In the present study, by complying with the China Sustainable Development Indicator System (CSDIS) concept, a comprehensive index system was built; besides, Natural Breaks (Jenks) Classification Method, Exploratory Spatial Data Analysis and Geographic Detector Analysis were conducted to delve into the sustainability and coordinated degree at city level in China from 2007 to 2017. The achieved results are presented as follows. First, for spatial differentiation, the overall spatial distribution pattern was characterized by the high-value units in eastern China and the low-value units in western China from 2007 to 2017. To be specific, the high-value units were radiated along the Beijing-Guangdong Axis (Jing-Guang Axis) centered on the core of Beijing-Tianjin-Hebei Region, the middle-value units were distributed in strips along the coast, and the low-value units were vastly gathered in western China and gradually break via the Hu Huanyong line (Hu Line) in south China from 2007 to 2017. More specifically, based on the five subsystems, the pattern of each system was consistent with the whole, whereas the degree of concentration was different. Second, for spatial correlation, the significant High-High (HH) areas were primarily distributed in the core of Beijing-Tianjin-Hebei, Yangtze River Delta and Pearl River Delta Regions. The significant Low-Low (LL) areas were continuously distributed in the southwest China and broke through the Hu Line from 2007 to 2017. There were insufficient number of significant High-Low (HL) and significant Low-High (LH) areas, whereas the spatial agglomeration of them was less obvious. Third, for internal coupling coordination, the spatial differentiation between the coupling degree and the coupling coordinated degree was significantly consistent in 2007 and 2017. The Beijing-Tianjin-Hebei, Yangtze River Delta and Pearl River Delta Regions have demonstrated a high level of coordinated evolution, and the pattern of western mountainous areas exhibited a low degree of coordinated growth. Lastly, based on the combination of quantitative and qualitative, its factors were underpinned by robust economic strength, the vitality support of the information level and the basic support function of the topography, active guidance of national policies and path dependence and industrial transfer.

Bismuth-Yttrium Oxide Bi3YO6 (BYO) is well-known among researchers for its potential applications in different fields but its use as an electrode for asymmetric supercapacitor (ASC) has not yet been discussed and uncovered. As high energy and power densities could be well achieved with the aid of optimum potential. In the current scenario, BYO is used as an electrode material for ASCs for the first time. The material is prepared by a solid-state reaction method and its structural, morphological, and electrochemical properties have been explored by using different characterization tools. Structural analysis declared the flourite based structure of BYO having a cubic phase without any visible impurity. In a morphological analysis, a mesoporous thin film with a high surface area (~ 36.6 m2 g−1) with a narrow pore width of (~ 5–18 nm) has been observed. The as-prepared BYO employed as an electrode material for SCs delivers a high specific capacitance of (388 F g−1 at 100 m Vs−1 in the three-electrode system) with excellent cycling stability (5% loss after 6000 cycles). Essentially, an ASC is assembled using BYO and rGO as a positive and negative electrodes operating in a stable and wide potential window up to 2.2 V. Moreover, a high energy density of 11 Wh kg−1 along with a power density of 648 W kg−1 is obtained and maintained a high power density of 888 W kg−1 with the downfall of energy density to 3.7 Wh kg−1. The results prove electrode material as an alternative promising candidate for ASCs.

More than a dozen excitatory cell types have been identified in the mouse primary visual cortex (V1) based on transcriptomic, morphological and in vitro electrophysiological features. However, the functional landscape of excitatory neurons with respect to their responses to visual stimuli is currently unknown. Here, we combined large-scale two-photon imaging and deep learning neural predictive models to study the functional organization of mouse V1 using digital twins. Digital twins enable exhaustive in silico functional characterization providing a bar code summarizing the input-output function of each neuron. Clustering the bar codes revealed a continuum of function with around 30 modes. Each mode represented a group of neurons that exhibited a specific combination of stimulus selectivity and nonlinear response properties such as cross-orientation inhibition, size-contrast tuning and surround suppression. These non-linear properties were expressed independently spanning all possible combinations across the population. This combinatorial code provides the first large-scale, data-driven characterization of the functional organization of V1. This powerful approach based on digital twins is applicable to other brain areas and to complex non-linear systems beyond the brain.

Objective: To investigate the effect of aortic esophageal fistula treatment after thoracic aortic endovascular repair (TEVAR) with artificial vessel bypass. Methods: The clinical data of 6 consecutive patients who received surgical treatment at Shanghai Deda Hospital from September 2019 to June 2021 due to aortic esophageal fistula after TEVAR were retrospectively analyzed. There were 6 males, aged (47.7±8.2) years old (range: 35-56 years old). All patients had recurrent fever, and 4 patients had positive blood cultures. According to the specific conditions of the patients, all patients underwent artificial blood vessel bypass and jejunostomy under general anesthesia without extracorporeal circulation. One case underwent artificially infected vascular segment resection and esophageal repair at the same time. 5 cases underwent artificial infection vascular resection, 4 of them underwent esophageal repair, and 1 case had a large intraoperative fistula and local resection of the esophagus. Sensitive antibacterial drugs were continued after the operation for 6 to 8 weeks. Results: There were 2 deaths in hospital, 1 case of large cerebral infarction early postoperatively, and 1 case of septic shock. The remaining 4 patients recovered well after the operation and were discharged. The follow-up period was 2 to 23 months. During the follow-up period, the remaining patients had no recurrence of infection and esophageal fistula. Conclusion ：In patients with aortic esophageal fistula after TEVAR, the establishment of artificial vascular bypass, the resection of the infected vascular segment, contemporaneous or staged esophageal repair, regular anti-infective treatment can obtain a good prognosis.

Doping is central for solid-state devices from transistors to thermoelectric energy converters. The interaction between electrons and dopants plays a pivotal role in carrier transport. Conventional theory suggests that the Coulomb field of the ionized dopants limits the charge mobility at high carrier densities, and that either the atomic details of the dopants are unimportant or the mobility can only be further degraded, while experimental results often show that dopant choice affects mobility. In practice, the selection of dopants is still mostly a trial-and-error process. Here we demonstrate, via first-principles simulation and comparison with experiments, that a large short-range perturbation created by selected dopants can in fact counteract the long-range Coulomb field, leading to electron transport that is nearly immune to the presence of dopants. Such “cloaking” of dopants leads to enhanced mobilities at high carrier concentrations close to the intrinsic electron–phonon scattering limit. We show that the ionic radius can be used to guide dopant selection in order to achieve such an electron-cloaking effect. Our finding provides guidance to the selection of dopants for solid-state conductors to achieve high mobility for electronic, photonic, and energy conversion applications.

Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The challenge is aggravated in crystalline solids due to crystallographic symmetry considerations and data scarcity. Here, the direct prediction of phonon density‐of‐states (DOS) is demonstrated using only atomic species and positions as input. Euclidean neural networks are applied, which by construction are equivariant to 3D rotations, translations, and inversion and thereby capture full crystal symmetry, and achieve high‐quality prediction using a small training set of ≈103 examples with over 64 atom types. The predictive model reproduces key features of experimental data and even generalizes to materials with unseen elements, and is naturally suited to efficiently predict alloy systems without additional computational cost. The potential of the network is demonstrated by predicting a broad number of high phononic specific heat capacity materials. The work indicates an efficient approach to explore materials' phonon structure, and can further enable rapid screening for high‐performance thermal storage materials and phonon‐mediated superconductors., A machine‐learning‐based predictive model allows a direct output of phonon density‐of‐states (DOS) by inputting atomic coordinates. The implemented Euclidean neural network preserves crystallographic symmetry and enables high‐quality prediction with small training data. The model allows for fast DOS calculation, reaching ab initio accuracy for 70–80% materials with much lowered computational cost, facilitating materials design especially in alloy systems.