Synaptic-like transmission between neural axons and arteriolar smooth muscle cells drives cerebral neurovascular coupling.

Neurovascular coupling (NVC) is important for brain function and its dysfunction underlies many neuropathologies. Although cell-type specificity has been implicated in NVC, how active neural information is conveyed to the targeted arterioles in the brain remains poorly understood. Here, using two-photon focal optogenetics in the mouse cerebral cortex, we demonstrate that single glutamatergic axons dilate their innervating arterioles via synaptic-like transmission between neural-arteriolar smooth muscle cell junctions (NsMJs). The presynaptic parental-daughter bouton makes dual innervations on postsynaptic dendrites and on arteriolar smooth muscle cells (aSMCs), which express many types of neuromediator receptors, including a low level of glutamate NMDA receptor subunit 1 (Grin1). Disruption of NsMJ transmission by aSMC-specific knockout of GluN1 diminished optogenetic and whisker stimulation-caused functional hyperemia. Notably, the absence of GluN1 subunit in aSMCs reduced brain atrophy following cerebral ischemia by preventing Ca2+ overload in aSMCs during arteriolar constriction caused by the ischemia-induced spreading depolarization. Our findings reveal that NsMJ transmission drives NVC and open up a new avenue for studying stroke.

Applicability of Nuclear Magnetic Resonance Experiment in Analyzing Pore and Fluid Distribution Characteristics of Tight Sandstone: A Case Study in the Julu Area, Bohai Bay Basin, China.

Characterizing the pore and fluid distribution is critical for evaluating the reservoir potential of new areas. Nuclear magnetic resonance (NMR) is considered as an experimental method capable of full-scale characterization of pore characteristics. However, the T2 spectrum of a saturated sample is affected by a combination of sample and experimental parameters, and it is important to confirm whether the T2 spectrum fully reflects the sample pore information. Eight tight sandstone samples from the Julu area were selected for thin section identification, mercury intrusion porosimetry (MIP), NMR, NMR cryoporometry (NMRC), and centrifugation experiments to critically analyze the applicability of the NMR results. Two methods, the similarity method and Kozeny's equation method, were used to calculate surface relaxivity, a critical parameter for converting NMR T2 signals into pore information. The discussion focuses on the applicability of the calculated surface relaxivity and the phenomenon of T2 signal changes in a short relaxation range after centrifugation. The main results are as follows: The surface relaxivity values calculated using the different methods differed significantly. The surface relaxivity calculated using the same method reflected the relative magnitude of the true surface relaxivity of the samples. For the samples with large surface relaxivity, there may be partial misses of the short relaxation signal, the NMR porosity was smaller than the gas-measured porosity, there was a variation in the T2 spectrum in the short relaxation range after centrifugation, and the calculated surface relaxivity was small. The surface relaxivity calculated using Kozeny's equation was nearly accurate, but perhaps smaller than the true value. The T2 spectra mainly reflected macropore information. This study suggests that PSDs converted from T2 spectra of saturated samples should be judged with relative caution rather than solely based on the peak or range correspondence between the two curves, and the minimum centrifugal radius can be used as a constraint.

Neural control of lexical tone production in human laryngeal motor cortex.

In tonal languages, which are spoken by nearly one-third of the world's population, speakers precisely control the tension of vocal folds in the larynx to modulate pitch in order to distinguish words with completely different meanings. The specific pitch trajectories for a given tonal language are called lexical tones. Here, we used high-density direct cortical recordings to determine the neural basis of lexical tone production in native Mandarin-speaking participants. We found that instead of a tone category-selective coding, local populations in the bilateral laryngeal motor cortex (LMC) encode articulatory kinematic information to generate the pitch dynamics of lexical tones. Using a computational model of tone production, we discovered two distinct patterns of population activity in LMC commanding pitch rising and lowering. Finally, we showed that direct electrocortical stimulation of different local populations in LMC evoked pitch rising and lowering during tone production, respectively. Together, these results reveal the neural basis of vocal pitch control of lexical tones in tonal languages.

Selenite affected photosynthesis of Oryza sativa L. exposed to antimonite: Electron transfer, carbon fixation, pigment synthesis via a combined analysis of physiology and transcriptome.

Selenium (Se) is a microelement that can counteract (a)biotic stresses in plants. Excess antimony (Sb) will inhibit plant photosynthesis, which can be alleviated by appropriate doses of Se but the associated mechanisms at the molecular levels have not been fully explored. Here, a rice variety (Yongyou 9) was exposed to selenite [Se(IV), 0.2 and 0.8 mg L-1] alone or combined with antimonite [Sb(III), 5 and 10 mg L-1]. When compared to the 10 mg L-1 Sb treatment alone, addition of Se in a dose-dependent manner 1) reduced the heat dissipation efficiency resulting from the inhibited donors, Sb concentrations in shoots and roots, leaf concentrations of fructose, H2O2 and O2•-; 2) enhanced heat dissipation efficiency resulting from the inhibited accepters value, concentrations of Chl a, sucrose and starch, and the enzyme activity of adenosine diphosphate glucose pyrophosphorylase, sucrose phosphate synthase, and sucrose synthase; but 3) did not alter gas exchange parameters, concentrations of Chl b and total Chl, enzyme activity of soluble acid invertase, and values of maximum P700 signal, photochemical efficiency of PSI and electron transport rate of PSI. Se alleviated the damage caused by Sb to the oxygen-evolving complex and promoted the transfer of electrons from QA to QB. When compared to the 10 mg L-1 Sb treatment alone, addition of Se 1) up-regulated genes correlated to synthesis pathways of Chl, carotenoid, sucrose and glucose; 2) disturbed signal transduction pathway of abscisic acid; and 3) upregulated gene expression correlated to photosynthetic complexes (OsFd1, OsFER1 and OsFER2).

Decoding and synthesizing tonal language speech from brain activity.

Recent studies have shown that the feasibility of speech brain-computer interfaces (BCIs) as a clinically valid treatment in helping nontonal language patients with communication disorders restore their speech ability. However, tonal language speech BCI is challenging because additional precise control of laryngeal movements to produce lexical tones is required. Thus, the model should emphasize the features from the tonal-related cortex. Here, we designed a modularized multistream neural network that directly synthesizes tonal language speech from intracranial recordings. The network decoded lexical tones and base syllables independently via parallel streams of neural network modules inspired by neuroscience findings. The speech was synthesized by combining tonal syllable labels with nondiscriminant speech neural activity. Compared to commonly used baseline models, our proposed models achieved higher performance with modest training data and computational costs. These findings raise a potential strategy for approaching tonal language speech restoration.

The Cell Tracking Challenge: 10 years of objective benchmarking.

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.

FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement.

Latent fingerprint enhancement is an essential preprocessing step for latent fingerprint identification. Most latent fingerprint enhancement methods try to restore corrupted gray ridges/valleys. In this paper, we propose a new method that formulates latent fingerprint enhancement as a constrained fingerprint generation problem within a generative adversarial network (GAN) framework. We name the proposed network FingerGAN. It can enforce its generated fingerprint (i.e, enhanced latent fingerprint) indistinguishable from the corresponding ground truth instance in terms of the fingerprint skeleton map weighted by minutia locations and the orientation field regularized by the FOMFE model. Because minutia is the primary feature for fingerprint recognition and minutia can be retrieved directly from the fingerprint skeleton map, we offer a holistic framework that can perform latent fingerprint enhancement in the context of directly optimizing minutia information. This will help improve latent fingerprint identification performance significantly. Experimental results on two public latent fingerprint databases demonstrate that our method outperforms the state of the arts significantly. The codes will be available for non-commercial purposes from https://github.com/HubYZ/LatentEnhancement.

A comparison of manual and automated neural architecture search for white matter tract segmentation.

Segmentation of white matter tracts in diffusion magnetic resonance images is an important first step in many imaging studies of the brain in health and disease. Similar to medical image segmentation in general, a popular approach to white matter tract segmentation is to use U-Net based artificial neural network architectures. Despite many suggested improvements to the U-Net architecture in recent years, there is a lack of systematic comparison of architectural variants for white matter tract segmentation. In this paper, we evaluate multiple U-Net based architectures specifically for this purpose. We compare the results of these networks to those achieved by our own various architecture changes, as well as to new U-Net architectures designed automatically via neural architecture search (NAS). To the best of our knowledge, this is the first study to systematically compare multiple U-Net based architectures for white matter tract segmentation, and the first to use NAS. We find that the recently proposed medical imaging segmentation network UNet3+ slightly outperforms the current state of the art for white matter tract segmentation, and achieves a notably better mean Dice score for segmentation of the fornix (+ 0.01 and + 0.006 mean Dice increase for left and right fornix respectively), a tract that the current state of the art model struggles to segment. UNet3+ also outperforms the current state of the art when little training data is available. Additionally, manual architecture search found that a minor segmentation improvement is observed when an additional, deeper layer is added to the U-shape of UNet3+. However, all networks, including those designed via NAS, achieve similar results, suggesting that there may be benefit in exploring networks that deviate from the general U-Net paradigm.

A Compound Loss Function With Shape Aware Weight Map for Microscopy Cell Segmentation.

Microscopy cell segmentation is a crucial step in biological image analysis and a challenging task. In recent years, deep learning has been widely used to tackle this task, with promising results. A critical aspect of training complex neural networks for this purpose is the selection of the loss function, as it affects the learning process. In the field of cell segmentation, most of the recent research in improving the loss function focuses on addressing the problem of inter-class imbalance. Despite promising achievements, more work is needed, as the challenge of cell segmentation is not only the inter-class imbalance but also the intra-class imbalance (the cost imbalance between the false positives and false negatives of the inference model), the segmentation of cell minutiae, and the missing annotations. To deal with these challenges, in this paper, we propose a new compound loss function employing a shape aware weight map. The proposed loss function is inspired by Youden's J index to handle the problem of inter-class imbalance and uses a focal cross-entropy term to penalize the intra-class imbalance and weight easy/hard samples. The proposed shape aware weight map can handle the problem of missing annotations and facilitate valid segmentation of cell minutiae. Results of evaluations on all ten 2D+time datasets from the public cell tracking challenge demonstrate 1) the superiority of the proposed loss function with the shape aware weight map, and 2) that the performance of recent deep learning-based cell segmentation methods can be improved by using the proposed compound loss function.

Toxicity of different forms of antimony to rice plants: Photosynthetic electron transfer, gas exchange, photosynthetic efficiency, and carbon assimilation combined with metabolome analysis.

Antimony (Sb) is a toxic metalloid, and excess Sb causes damage to the plant photosynthetic system. However, the underlying mechanisms of Sb toxicity in the plant photosynthetic system are not clear. Hydroponic culture experiments were conducted to illustrate the toxicity differences of antimonite [Sb(III)] and antimonate [Sb(V)] to the photosynthetic system in a rice plant (Yangdao No. 6). The results showed that Sb(III) showed a higher toxicity than Sb(V), judging from (1) lower shoot and root biomass, leaf water moisture content, water use efficiency, stomatal conductance, net photosynthetic rate, and transpiration rate; (2) higher water vapor deficit, soluble sugar content, starch content, and oligosaccharide content (sucrose, stachyose, and 1-kestose). To further analyze the direction of the photosynthetic products, we conducted a metabonomic analysis. More glycosyls were allocated to the synthesis pathways of oligosaccharides (sucrose, stachyose, and 1-kestose), anthocyanins, salicylic acid, flavones, flavonols, and lignin under Sb stress to quench excess oxygen free radicals (ROS), strengthen the cell wall structure, rebalance the cell membrane, and/or regulate cell permeability. This study provides a complete mechanism to elucidate the toxicity differences of Sb(III) and Sb(V) by exploring their effects on photosynthesis, saccharide synthesis, and the subsequent flow directions of glycosyls.

Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey.

Cognitive biometrics is an emerging branch of biometric technology. Recent research has demonstrated great potential for using cognitive biometrics in versatile applications, including biometric recognition and cognitive and emotional state recognition. There is a major need to summarize the latest developments in this field. Existing surveys have mainly focused on a small subset of cognitive biometric modalities, such as EEG and ECG. This article provides a comprehensive review of cognitive biometrics, covering all the major biosignal modalities and applications. A taxonomy is designed to structure the corresponding knowledge and guide the survey from signal acquisition and pre-processing to representation learning and pattern recognition. We provide a unified view of the methodological advances in these four aspects across various biosignals and applications, facilitating interdisciplinary research and knowledge transfer across fields. Furthermore, this article discusses open research directions in cognitive biometrics and proposes future prospects for developing reliable and secure cognitive biometric systems.

Effects of Rapid Igneous Intrusion Heating on the Geochemistry, Petrography, and Microcrystalline Structure of Coals from Huainan, China.

Igneous intrusion into coal-bearing strata may change the geochemical, petrographic, and microcrystalline structural characteristics of coal. Here, a series of coal samples affected by igneous intrusion were analyzed by petrography, geochemistry, and X-ray diffraction. In addition, the trend observed in altered coal with normal burial maturity is compared to evaluate whether the intrusive coal follows another maturity path. A petrographic analysis shows that the R 0 value increased rapidly and lost the ability to distinguish liptinite. Pyrolytic carbon and isotropic and anisotropic coke with a fine-grained circular mosaic structure are formed at the intrusion. Moreover, the degree of structural order of coal samples increases in an approach to the intrusion. There are transition phases with different structural orders due to different degrees of metamorphism. Petrographic and geochemical data indicate that intrusive coals may follow a maturation pathway other than that from normal burial maturation, which may be related to the rapid geological thermal event related to the intrusion. However, the results of XRD data suggest that the microcrystalline structure of igneous intrusion coals is consistent with a growth in the trend of normal burial. This study of geochemical petrography and microcrystalline structure of surrounding coal seams by rapid intrusive heating of igneous intrusions not only greatly improves the natural coke industrial utilization but also provides an important theoretical basis for the generation and enrichment of coalbed methane in igneous thermal abnormal coal reservoirs.

Distinct spatiotemporal patterns of syntactic and semantic processing in human inferior frontal gyrus.

Human languages are based on syntax, a set of rules which allow an infinite number of meaningful sentences to be constructed from a finite set of words. A theory associated with Chomsky and others holds that syntax is a mind-internal, universal structure independent of semantics. This theory, however, has been challenged by studies of the Chinese language showing that syntax is processed under the semantic umbrella, and is secondary and not independent. Here, using intracranial high-density electrocorticography, we find distinct spatiotemporal patterns of neural activity in the left inferior frontal gyrus that are specifically associated with syntactic and semantic processing of Chinese sentences. These results suggest that syntactic processing may occur before semantic processing. Our findings are consistent with the view that the human brain implements syntactic structures in a manner that is independent of semantics.

Therapeutic effect of indirubin-loaded bovine serum albumin nanoparticules on ulcerative colitis.

Indirubin is considered to have promising potential in the treatment of ulcerative colitis (UC). However, poor aqueous solubility and low bioavailability limit its clinical application. We produced indirubin-loaded bovine serum albumin nanoparticles (INPs) and characterized their drug encapsulation efficiency, drug-loading capacity, capacity to release indirubin in vitro and short-term physical stability. We also investigated the pharmacokinetics of INPs in mice. We then compared the curative effects of INPs and indirubin against dextran sulfate sodium-induced colitis in mice and 3D cultured biopsies from patients with UC. In the mouse model, the outcomes of INP treatment, including the disease activity index and serous levels of interleukin (IL)-1ß and IL-10, were significantly different from those of indirubin treatment. Similarly, when we administered INPs and indirubin to the ex vivo colonic tissues of patients with UC, the effect of INPs was stronger than that of indirubin for most antioxidant and anti-inflammatory biomarkers. The results of both the animal trial and ex vivo experiment indicate that the therapeutic effect of indirubin was further enhanced by the carrier system, making it a highly promising medical candidate for UC.

Strong Electron-Phonon Coupling in ß-Ga2O3: A Huge Broadening of Self-Trapped Exciton Emission and a Significant Red Shift of the Direct Bandgap.

The temperature dependence of self-trapped exciton (STE) emission and the optical absorption edge of monoclinic gallium oxide (ß-Ga2O3) has been carefully studied. According to this research, it is found that as temperature increases (from 10 to 300 K), the STE and the direct bandgap of ß-Ga2O3 exhibit a huge broadening (∼120 meV) and a significant red shift (∼250 meV), respectively. Combined with theoretical analysis, these temperature-dependent change trends are found related to the strong electron-phonon coupling (EPC) effect in crystal, which is caused by the high localization (i.e., self-trapping) of carriers in ß-Ga2O3. This finding further indicates that in the transition process of carriers' absorbing and releasing photons, the influence of lattice vibration needs to be considered and described by the configuration coordinate model. The strength of EPC can be measured by the Huang-Rhys factor, which is about S ≈ 9 for ß-Ga2O3 with the polar longitudinal optical phonon mode of lower energy (∼31 meV) being involved.

Anomalous Blue Shift of Exciton Luminescence in Diamond.

Generally speaking, for a semiconductor, the temperature dependence of excitonic emission corresponds to that of its band gap. However, an anomalous behavior is exhibited by the excitonic luminescence of diamond where as the temperature increases (from 10 to 300 K), its indirect exciton luminescence peak displays a spectral-distinguishable blue shift, whereas the indirect band-gap absorption shows a weak red shift. According to experimental high-resolution deep-ultraviolet spectra and theoretical analysis, the weak red shift of its indirect band gap is ascribed to its large Debye temperature (ΘD ≈ 2220 K), which makes the lattice constant change comparatively little in a large temperature range, so the change of its band gap is relatively small; in this case, as the temperature rises, the thermal population of valence-band holes that moves to a high-energy state far away from the Fermi surface contributes to the macroscopic blue shift of its excitonic emission.

Nanosecond and Highly Sensitive Scintillator Based on All-Inorganic Perovskite Single Crystals.

The scintillator is a unique class of luminescent materials, which is of great significance in clinical diagnosis, security inspection, and radiation detection. Herein, an all-inorganic Cs4PbI6 single crystals (SCs) as a nanosecond and an efficient X-ray and α particle scintillator is described. The radioluminescence (RL) spectrum of Cs4PbI6 SCs under X-ray excitation consists of a band gap emission at 310 nm and a broadband emission at 552 nm at room temperature. Furthermore, Cs4PbI6 SCs demonstrate nanosecond decay times of 0.95 and 6.86 ns, a high sensitivity to low-energy X-ray (30 keV) with a low detection limit (187 nGyair/s), and a favorable linearity detection range, potentially enabling their broad application in X-ray imaging. Under 237Np α particle irradiation, the light yield of Cs4PbI6 SCs is about 49.5% of that of a BGO scintillator with an energy resolution of 35% at 4.78 MeV. Our results demonstrate the potential of Cs4PbI6 SCs as a nanosecond and low-cost scintillator in radiation detection applications.

Deep Learning in Diverse Intelligent Sensor Based Systems.

Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted. With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holistic view. This survey paper aims to contribute to this by systematically investigating deep learning models/methods and their applications across diverse sensor systems. It also provides a comprehensive summary of deep learning implementation tips and links to tutorials, open-source codes, and pretrained models, which can serve as an excellent self-contained reference for deep learning practitioners and those seeking to innovate deep learning in this space. In addition, this paper provides insights into research topics in diverse sensor systems where deep learning has not yet been well-developed, and highlights challenges and future opportunities. This survey serves as a catalyst to accelerate the application and transformation of deep learning in diverse sensor systems.

Overexpression of the rice ORANGE gene OsOR negatively regulates carotenoid accumulation, leads to higher tiller numbers and decreases stress tolerance in Nipponbare rice.

The ORANGE (OR) gene has been reported to regulate chromoplast differentiation and enhance carotenoid biosynthesis in many dicotyledonous plants. However, the function of the OR gene in monocotyledons, especially rice, is poorly known. Here, the OR gene from rice, OsOR, was isolated and characterized by generating overexpressing and genome editing mutant lines. The OsOR-overexpressing plants exhibited pleiotropic phenotypes, such as alternating transverse green and white sectors on leaves at the early tillering stage, that were due to changes in thylakoid development and reduced carotenoid content. In addition, the number of tillers significantly increased in OsOR-overexpressing plants but decreased in osor mutant lines, a result similar to that previously reported for the carotenoid isomerase mutant mit3. The expression of the DWARF3 and DWARF53 genes that are involved in the strigolactone signalling pathway were similarly downregulated in OsOR-overexpressing plants but upregulated in osor mutants. Moreover, the OsOR-overexpressing plants exhibited greater sensitivity to salt and cold stress, and had lower total chlorophyll and higher MDA contents. All results suggest that the OsOR gene plays an important role not only in carotenoid accumulation but also in tiller number regulation and in responses to environmental stress in rice.