Regulation of circadian behavioral output via a MicroRNA-JAK/STAT circuit.

Although molecular components of the circadian clock are known, mechanisms that transmit signals from the clock and produce rhythmic behavior are poorly understood. We find that the microRNA miR-279 regulates the JAK/STAT pathway to drive rest:activity rhythms in Drosophila. Overexpression of microRNA miR-279 or miR-279 deletion attenuates rest:activity rhythms. Oscillations of the clock protein PERIOD are normal in pacemaker neurons lacking miR-279, suggesting that miR-279 acts downstream of the clock. We identify the JAK/STAT ligand, Upd, as a target of miR-279 and show that knockdown of Upd rescues the behavioral phenotype of miR-279 mutants. Manipulations of the JAK/STAT pathway also disrupt circadian rhythms. In addition, central clock neurons project in the vicinity of Upd-expressing neurons, providing a possible physical connection by which the central clock could regulate JAK/STAT signaling to control rest:activity rhythms.

Enhanced Charge Transfer via S-Scheme Heterojunction Interface Engineering of Supramolecular SubPc-Br/UiO-66 Arrays for Efficient Photocatalytic Oxidation.

Constructing heterojunction of supramolecular arrays self-assembled on metal-organic frameworks (MOFs) with elaborate charge transfer mechanisms is a promising strategy for the photocatalytic oxidation of organic pollutants. Herein, H12 SubPcB-Br (SubPc-Br) and UiO-66 are used to obtain the step-scheme (S-scheme) heterojunction SubPc-Br/UiO-66 for the first time, which is then applied in the photocatalytic oxidation of minocycline. Atomic-level B-O-Zr charge-transfer channels and van der Waals force connections synergistically accelerated the charge transfer at the interface of the SubPc-Br/UiO-66 heterojunction, while the establishment of the B-O-Zr bonds also led to the directional transfer of charge from SubPc-Br to UiO-66. The synergy is the key to improving the photocatalytic activity and stability of SubPc-Br/UiO-66, which is also verified by various characterization methods and theoretical calculations. The minocycline degradation efficiency of supramolecular SubPc-Br/UiO-66 arrays reach 90.9% within 30 min under visible light irradiation. The molecular dynamics simulations indicate that B-O-Zr bonds and van der Waals force contribute significantly to the stability of the SubPc-Br/UiO-66 heterojunction. This work reveals an approach for the rational design of semiconducting MOF-based heterojunctions with improved properties.

Difference frequency coherent matched autoproduct processing for source localization in deep ocean.

Matched autoproduct processing (MAP) refers to a matched field processing (MFP) style array signal processing technique for passive source localization, which interrogates frequency-difference autoproduct instead of genuine acoustic pressure. Due to frequency downshifting, MAP is less sensitive to environmental mismatch, but it suffers from low spatial resolution and a low peak-to-sidelobe ratio of ambiguity surface. These source localization metrics are herein improved with coherent approaches. Specifically, the coherent normalized MFP is extended to coherent matched autoproduct processing (CMAP), a difference frequency coherent algorithm that exploits correlations among the autoproducts at various difference frequencies and eliminates the phase factor of the source spectrum for passive source localization. Phase-only coherent matched autoproduct processing is a CMAP derivation technique that only uses phase information. Through simulations in a Munk sound-speed profile environment, sensitivity analysis in the South China Sea environment, and high signal-to-noise ratio experimental measurements, these two algorithms are validated as compared to the conventional MFP and incoherent MAP. Simulation investigations demonstrate that difference frequency coherent algorithms can suppress sidelobes while simultaneously enhancing the localization resolution and robustness. The experimental results generally support the findings of the simulations.

Cluster-aware channel estimation with deep learning method in deep-water acoustic communications.

In underwater acoustic (UWA) communications, channels often exhibit a clustered-sparse structure, wherein most of the channel impulse responses are near zero, and only a small number of nonzero taps assemble to form clusters. Several algorithms have used the time-domain sparse characteristic of UWA channels to reduce the complexity of channel estimation and improve the accuracy. Employing the clustered structure to enhance channel estimation performance provides another promising research direction. In this work, a deep learning-based channel estimation method for UWA orthogonal frequency division multiplexing (OFDM) systems is proposed that leverages the clustered structure information. First, a cluster detection model based on convolutional neural networks is introduced to detect the cluster of UWA channels. This method outperforms the traditional Page test algorithm with better accuracy and robustness, particularly in low signal-to-noise ratio conditions. Based on the cluster detection model, a cluster-aware distributed compressed sensing channel estimation method is proposed, which reduces the noise-induced errors by exploiting the joint sparsity between adjacent OFDM symbols and limiting the search space of channel delay spread. Numerical simulation and sea trial results are provided to illustrate the superior performance of the proposed approach in comparison with existing sparse UWA channel estimation methods.

Normal mode energy estimation based on reconstructing the incoherent beamformed outputs from a horizontal array.

The acoustic pressure field in many underwater environments is well described by a superposition of normal modes. The normal modes can be used for source localization and environmental inversion. However, the wavenumber resolution of traditional normal mode filtering methods for a small-aperture horizontal array is usually not sufficient to identify individual modes in a shallow water waveguide. This paper proposes an original method of normal mode energy estimation to remove the energy leakage between modes. The modal energy is defined as the square of the modal amplitude. This method is to reconstruct the incoherent beamformed outputs in wavenumber domain for a horizontally moving source. The adaptive beamforming is used to suppress interference and improve output signal-to-noise ratio. The uncertainty of modal phase velocity has also been considered in this method. The proposed method can provide more accurate estimates of modal energy for a small-aperture horizontal array than the traditional mode filtering methods, such as the matched filter, the least squares mode filter, the regularized-least squares mode filter, and the maximum a posteriori mode filter, in simulations and experiments.

Synthetic adaptive matched field processing for moving source with a horizontal line array.

Using adaptive matched field processing (AMFP) to search for targets in shallow water is challenged by source motion. For AMFP, a relatively large number of samples is required to minimize the variance of the covariance matrix. For a fast moving target, direct integrating over a large number of data snapshots will blur the sound interference structure and, hence, degrade the ability of AMFP to produce a sharp main peak. This paper presents a source motion mitigation technique for broadband moving targets. By applying the waveguide invariant theory, the covariance matrix can be reformulated by frequency and phase shifting according to a single scalar parameter hypothesis. When the hypothesis parameter is in accordance with the true value, the moving target can be considered stationary. The technique is applied to experimental data acquired by a bottom mounted horizontal line array and demonstrates an increase in detection ranges.

Classification of odontocete echolocation clicks using convolutional neural network.

A method based on a convolutional neural network for the automatic classification of odontocete echolocation clicks is presented. The proposed convolutional neural network comprises six layers: three one-dimensional convolutional layers, two fully connected layers, and a softmax classification layer. Rectified linear units were chosen as the activation function for each convolutional layer. The input to the first convolutional layer is the raw time signal of an echolocation click. Species prediction was performed for groups of m clicks, and two strategies for species label prediction were explored: the majority vote and maximum posterior. Two datasets were used to evaluate the classification performance of the proposed algorithm. Experiments showed that the convolutional neural network can model odontocete species from the raw time signal of echolocation clicks. With the increase in m, the classification accuracy of the proposed method improved. The proposed method can be employed in passive acoustic monitoring to classify different delphinid species and facilitate future studies on odontocetes.

Measurement and modeling of sound propagation over continental slope in the South China Sea.

Measurements along two ship tracks were obtained in an experiment to investigate the properties of acoustic propagation over the continental slope in the South China Sea. The measured data show a notable difference in transmission loss about 35 dB as sound crosses different geodesic paths. Numerical simulations indicate that the range and azimuth-dependent geological properties control the level of the transmission loss and lead to this large transmission loss fluctuation. In addition, the model also suggests some small-scale features of horizontal refraction effect caused by irregular topography, but they are not observed in the measured data.

Seasonally-invariant head wave speed extracted from ocean noise cross-correlation.

Ambient noise was recorded continuously for 9 months by two horizontal arrays deployed in shallow water with a horizontal separation of approximately 0.5 km. Stable empirical Green's functions (EGFs) were extracted from ambient noise correlations between the two arrays. The EGFs have three distinct envelopes which correspond to the head waves, direct waves, and surface-reflected waves. The arrival time of the head wave was almost constant with season. Corresponding simulations were carried out, and implied that the relatively small penetration depth of heat flow is the main reason for the seasonally-invariant head wave speed.

Convolutional neural network for detecting odontocete echolocation clicks.

In this work, a convolutional neural network based method is proposed to automatically detect odontocetes echolocation clicks by analyzing acoustic data recordings from a passive acoustic monitoring system. The neural network was trained to distinguish between click and non-click clips and was subsequently converted to a full-convolutional network. The performance of the proposed network was evaluated using synthetic data and real audio recordings. The experimental results indicate that the proposed method works stably with echolocation clicks of different species.

Passive ocean acoustic tomography in shallow water.

It has been demonstrated that an estimate of an empirical Green's function (EGF) can be extracted from the ocean ambient noise cross-correlation functions, which can provide an alternative method for ocean acoustic tomography. However, the requirement for a long recording time to obtain EGFs with a high signal-to-noise ratio limits the application. This article focuses on using array signal processing to accelerate the convergence rate of EGFs between two horizontally separated arrays. With the extracted EGFs and data assimilation, ocean sound speed profiles (SSPs) can be inverted every 2 h in shallow water. The experimental results indicate that the variation in ocean SSPs can be reconstructed with reasonable agreement using an average variance of 1.14 m/s over three months.

Observations of wind-generated noise by the tropical cyclone.

An analysis of the measured ocean noise during the tropical cyclone period is presented. While the observed noise is highly correlated with wind, this study reveals the dispersion of noise spectra. A wind-driven noise model within the framework of the bubble oscillation is developed. The noise spectrum for frequencies from hundreds of hertz to kilohertz due to the effective bubble oscillation within the bubble cloud is assumed instead of the collective bubble oscillation. The proposed model addresses the arbitrarily-shaped bubble clouds in a stratified ocean and the relation between the wind speed and the noise level, and these aspects develop from the existing models in the literature. The wind-driven ocean noise spectrum is estimated as a function of frequency and wind speed based on available information of the bubble creation rate. The comparison shows that the proposed model with optimized oceanographic parameters could fit the noise spectra of data for the frequencies from about 0.5 to 4 kHz, which indicates that the modified model based on the bubble oscillations within the bubble clouds could account for the mechanism of wind-generated noise data.

Tumor immune microenvironment-based therapies in pancreatic ductal adenocarcinoma: time to update the concept.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal solid tumors. The tumor immune microenvironment (TIME) formed by interactions among cancer cells, immune cells, cancer-associated fibroblasts (CAF), and extracellular matrix (ECM) components drives PDAC in a more immunosuppressive direction: this is a major cause of therapy resistance and poor prognosis. In recent years, research has advanced our understanding of the signaling mechanism by which TIME components interact with the tumor and the evolution of immunophenotyping. Through revolutionary technologies such as single-cell sequencing, we have gone from simply classifying PDACs as "cold" and "hot" to a more comprehensive approach of immunophenotyping that considers all the cells and matrix components. This is key to improving the clinical efficacy of PDAC treatments. In this review, we elaborate on various TIME components in PDAC, the signaling mechanisms underlying their interactions, and the latest research into PDAC immunophenotyping. A deep understanding of these network interactions will contribute to the effective combination of TIME-based therapeutic approaches, such as immune checkpoint inhibitors (ICI), adoptive cell therapy, therapies targeting myeloid cells, CAF reprogramming, and stromal normalization. By selecting the appropriate integrated therapies based on precise immunophenotyping, significant advances in the future treatment of PDAC are possible.

The role of NF-κB signaling pathway in reactive astrocytes among neurodegeneration after methamphetamine exposure by integrated bioinformatics.

BACKGROUND: Methamphetamine (METH) is a highly addictive stimulant that has become one of the top five risk substances cause deaths from substance abuse. METH exposure increases the risk of neurodegenerative disease (ND), such as Parkinson's disease (PD), leading to disability and death. Activation of reactive astrocytes is an essential factor in neurodegeneration, and their complex role in METH exposure remains unclear. This study explored the role of reactive astrocyte overactivation in neurodegeneration after METH exposure. METHODS: METH bulk RNA sequencing data (GSE107015 and GSE98793) and single-cell RNA sequencing data (GSE119861) were obtained from the GEO database. We performed immune infiltration analysis on the bulk RNA data. After cell clustering using the single-cell RNA data, astrocytes were extracted for downstream analysis. Differentially expressed genes (DEGs) were identified from the bulk and single-cell RNA sequencing datasets, and GO, KEGG, and GSEA pathway analyses were performed. The PPI network and random forest methods were performed on the overlapping genes of the DEGs to screen hub genes. To explore the common ground between METH exposure and neurodegenerative diseases, we applied a random forest algorithm to PD chip data (GSE99039 and GSE72267) to establish a diagnostic model using the hub genes in METH. New object recognition and the Morris water maze were used to examine cognitive function in mice exposed to METH for 14 days in vivo. Astrocytes were cocultured with neurons for the detection of intercellular crosstalk. RESULTS: DEGs in the METH group significantly enriched pathways related to NDs, inflammation, and the NF-κB signaling pathway. Immune infiltration analysis revealed significantly increased infiltration of monocytes, T cells, and NK cells and decreased infiltration of neutrophils in the METH group. An intersection of 44 hub genes was screened based on the PPI network and random forest algorithm. These genes suggest that there might be similar pathogenesis between METH exposure and PD. METH exposure resulted in learning memory impairment, hippocampal astrocyte activation, and upregulation of NF-κB expression in mice. Activation of reactive astrocytes cocultured with neurons causes neural damage. CONCLUSIONS: This study explored the crosstalk between astrocytes and neurons in METH exposure, providing a potential pathogenesis to explore the altered immune microenvironment involving reactive astrocytes after METH exposure.

Single-cell RNA-sequencing analysis reveals α-syn induced astrocyte-neuron crosstalk-mediated neurotoxicity.

Accumulation of alpha-synuclein (α-syn) is a key pathological hallmark of synucleinopathies and has been shown to negatively impact neuronal function and activity. α-syn is an important factor contributing to astrocyte overactivation, though the effect of astrocyte overactivation on neurons remains unclear. Single-cell RNA sequencing data of mouse brain frontal cortex and midbrain from Hua-Syn (A53T) and wild type mice were utilized from the GEO database. Enrichment analysis, protein-protein interaction networks, and cell-cell interaction networks all indicated enhanced communication between astrocytes and neurons, along with the involvement of TNF and inflammation-related signaling pathways. In vitro experiments were performed to further explore the mechanism of neurotoxicity in astrocyte-neuron crosstalk. Astrocytes were treated by α-syn, neuronal TNFR1 receptors were antagonized by R-7050, and the cells were co-cultured after 24 h treatment. ELISA results revealed that cytokines such as TNF-α and IL-6 were significantly upregulated in astrocytes following the endocytosis of α-syn. Immunofluorescence (IF) showed neuronal dendritic reduction, axon elongation and increased co-localisation of TNFR1 receptor expression. Western blot showed up-regulation of PKR, P-eIF2α and ATF4 protein expression. Conversely, after antagonizing neuronal TNFR1 receptors with the R-7050 chemical inhibitor, neuronal synaptic structure was significantly restored and the expression of PKR, P-eIF2α and ATF4 was down-regulated. In summary, TNF-α acts as a signaling molecule mediating the up-regulated astrocyte-neuron crosstalk, providing new insights into the pathogenesis of α-syn-related neurological disorders.

Cascade Electrocatalytic Nitrate Reduction Reaching 100% Nitrate-N to Ammonia-N Conversion over Cu2O@CoO Yolk-Shell Nanocubes.

The electroreduction of nitrate to ammonia via a selective eight-electron transfer nitrate reduction reaction offers a promising, low energy consumption, pollution-free, green NH3 synthesis strategy alternative to the Haber-Bosch method. However, it remains a great challenge to achieve high NH4+ selectivity and complete conversion from NO3--N to NH4+-N. Herein, we report ingredients adjustable Cu2O@CoO yolk-shell nanocubes featured with tunable inner void spaces and diverse activity centers, favoring the rapid cascade conversion of NO3- into NO2- on Cu2O and NO2- into NH4+ on CoO. Cu2O@CoO yolk-shell nanocubes exhibit super NH4+ Faradaic efficiencies (>99%) over a wide potential window (-0.2 V to -0.9 V versus RHE) with a considerable NH4+ yield rate of 15.27 mg h-1 cm-2 and fantastic cycling stability and long-term chronoamperometric durability. Cu2O@CoO yolk-shell nanocubes exhibited glorious NO3--N to NH4+-N conversion efficiency in both dilute (500 ppm) and highly concentrated (0.1 and 1 M) NO3- electrolytes, respectively. The nitrate electrolysis membrane electrode assembly system equipped with Cu2O@CoO yolk-shell nanocubes delivers over 99.8% NH4+ Faradaic efficiency at cell voltages of 1.9-2.3 V.

Alloying Pd with Ru enables electroreduction of nitrate to ammonia with ∼100% faradaic efficiency over a wide potential window.

Electrocatalytic nitrate (NO3-) reduction reaction (eNO3-RR) to ammonia under ambient conditions is deemed a sustainable route for wastewater treatment and a promising alternative to the Haber-Bosch process. However, there is still a lack of efficient electrocatalysts to achieve high NH3 production performance at wastewater-relevant low NO3- concentrations. Herein, we report a Pd74Ru26 bimetallic nanocrystal (NC) electrocatalyst capable of exhibiting an average NH3 FE of ∼100% over a wide potential window from 0.1 to -0.3 V (vs. reversible hydrogen electrode, RHE) at a low NO3- concentration of 32.3 mM. The average NH3 yield rate at -0.3 V can reach 16.20 mg h-1 cm-2. Meanwhile, Pd74Ru26 also demonstrates excellent electrocatalytic stability for over 110 h. Experimental investigations and density functional theory (DFT) calculations suggest that the electronic structure modulation between Pd and Ru favors the optimization of NO3- transport with respect to single components. Along the *NO3 reduction pathway, the synergy between Pd and Ru can also lower the energy barrier of the rate-determining steps (RDSs) on Ru and Pd, which are the protonation of *NO2 and *NO, respectively. Finally, this unique alloying design achieves a high-level dynamic equilibrium of adsorption and coupling between *H and various nitrogen intermediates during eNO3-RR.

Radial-torsional vibration conversion ultrasonic transducer based on radial chute disc.

Using the stress wave generated by radial excitation, an ultrasonic transducer with pure torsional output based on a radial chute is developed. Based on the reflection principle of the stress wave from the radial chute, the mechanical model of the radial wave entering the chute disc then synthesizing the circumferential wave is established, and the stress state of the stress wave after the radial wave acts on the chute is deduced. On this basis, the influence of the chute angle on the circumferential wave is obtained. Theoretical analysis shows that there is an optimal chute angle for the synthesis of the circumferential wave. Then, the optimal inclined chute disc and ultrasonic transducer are selected for modal analysis. In the simulation, the radial wave generated by the excitation is evenly distributed at the disc and effectively converted into a circumferential wave. The converted circumferential wave is transmitted to the output end through the amplitude transformer of pure torsional mode, and the ultrasonic transducer realizes pure in-plane torsional output. When measured, the circumferential amplitude of the output rod is 5.22 times of the radial amplitude of the chute disc.

The effects of GPER on age-associated memory impairment induced by decreased estrogen levels.

Estrogen, as a pleiotropic endocrine hormone, not only regulates the physiological functions of peripheral tissues but also exerts vital neuroregulatory effects in the central nervous system (CNS), such as the development of neurons and the formation of neural network connections, wherein rapid estrogen-mediated reactions positively stimulate spinogenesis and regulate synaptic plasticity and synaptic transmission to facilitate cognitive and memory performance. These fast non-genomic effects can be initiated by membrane-bound estrogen receptors (ERs), three best known of which are ERα, ERß, and G protein-coupled estrogen receptor (GPER). To date, the effects of ERα and ERß have been well studied in age-associated memory impairment, whereas there is still a lack of attention to the role of GPER in age-associated memory impairment, and there are still disputes about whether GPER indeed functions as an ER to enhance learning and memory. In this review, we provide a systematic overview of the role of GPER in age-associated memory impairment based on its expression, distribution, and signaling pathways, which might bring some inspiration for translational drugs targeting GPER for age-related diseases and update knowledge on the role of estrogen and its receptor system in the brain.

Ethical perspective on AI hazards to humans: A review.

This article explores the potential ethical hazards of artificial intelligence (AI) on society from an ethical perspective. We introduce the development and application of AI, emphasizing its potential benefits and possible negative impacts. We particularly examine the application of AI in the medical field and related ethical and legal issues, and analyze potential hazards that may exist in other areas of application, such as autonomous driving, finance, and security. Finally, we offer recommendations to help policymakers, technology companies, and society as a whole address the potential hazards of AI. These recommendations include strengthening regulation and supervision of AI, increasing public understanding and awareness of AI, and actively exploring how to use the advantages of AI to achieve a more just, equal, and sustainable social development. Only by actively exploring the advantages of AI while avoiding its negative impacts can we better respond to future challenges.