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.

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.

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.

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.

Balanced NOx- and Proton Adsorption for Efficient Electrocatalytic NOx- to NH3 Conversion.

Electrocatalytic nitrate (NO3-)/nitrite (NO2-) reduction reaction (eNOx-RR) to ammonia under ambient conditions presents a green and promising alternative to the Haber-Bosch process. Practically available NOx- sources, such as wastewater or plasma-enabled nitrogen oxidation reaction (p-NOR), typically have low NOx- concentrations. Hence, electrocatalyst engineering is important for practical eNOx-RR to obtain both high NH3 Faradaic efficiency (FE) and high yield rate. Herein, we designed balanced NOx- and proton adsorption by properly introducing Cu sites into the Fe/Fe2O3 electrocatalyst. During the eNOx-RR process, the H adsorption is balanced, and the good NOx- affinity is maintained. As a consequence, the designed Cu-Fe/Fe2O3 catalyst exhibits promising performance, with an average NH3 FE of ∼98% and an average NH3 yield rate of 15.66 mg h-1 cm-2 under the low NO3- concentration (32.3 mM) of typical industrial wastewater at an applied potential of -0.6 V versus reversible hydrogen electrode (RHE). With low-power direct current p-NOR generated NOx- (23.5 mM) in KOH electrolyte, the Cu-Fe/Fe2O3 catalyst achieves an FE of ∼99% and a yield rate of 15.1 mg h-1 cm-2 for NH3 production at -0.5 V (vs RHE). The performance achieved in this study exceeds industrialization targets for NH3 production by exploiting two available low-concentration NOx- sources.

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.

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.

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.

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.

Hepatocellular carcinoma: Novel understandings and therapeutic strategies based on bile acids (Review).

Bile acids (BAs) are the major components of bile and products of cholesterol metabolism. Cholesterol is catalyzed by a variety of enzymes in the liver to form primary BAs, which are excreted into the intestine with bile, and secondary BAs are formed under the modification of the gut microbiota. Most of the BAs return to the liver via the portal vein, completing the process of enterohepatic circulation. BAs have an important role in the development of hepatocellular carcinoma (HCC), which may participate in the progression of HCC by recognizing receptors such as farnesoid X receptor (FXR) and mediating multiple downstream pathways. Certain BAs, such as ursodeoxycholic acid and obeticholic acid, were indicated to be able to delay liver injury and HCC progression. In the present review, the structure and function of BAs were introduced and the metabolism of BAs and the process of enterohepatic circulation were outlined. Furthermore, the mechanisms by which BAs participate in the development of HCC were summarized and possible strategies for targeting BAs and key sites of their metabolic processes to treat HCC were suggested.

Allicin in Digestive System Cancer: From Biological Effects to Clinical Treatment.

Allicin is the main active ingredient in freshly-crushed garlic and some other allium plants, and its anticancer effect on cancers of digestive system has been confirmed in many studies. The aim of this review is to summarize epidemiological studies and in vitro and in vivo investigations on the anticancer effects of allicin and its secondary metabolites, as well as their biological functions. In epidemiological studies of esophageal cancer, liver cancer, pancreatic cancer, and biliary tract cancer, the anticancer effect of garlic has been confirmed consistently. However, the results obtained from epidemiological studies in gastric cancer and colon cancer are inconsistent. In vitro studies demonstrated that allicin and its secondary metabolites play an antitumor role by inhibiting tumor cell proliferation, inducing apoptosis, controlling tumor invasion and metastasis, decreasing angiogenesis, suppressing Helicobacter pylori, enhancing the efficacy of chemotherapeutic drugs, and reducing the damage caused by chemotherapeutic drugs. In vivo studies further demonstrate that allicin and its secondary metabolites inhibit cancers of the digestive system. This review describes the mechanisms against cancers of digestive system and therapeutic potential of allicin and its secondary metabolites.

Hepatocellular Carcinoma: How the Gut Microbiota Contributes to Pathogenesis, Diagnosis, and Therapy.

The gut microbiota is gaining increasing attention, and the concept of the "gut-liver axis" is gradually being recognized. Leaky gut resulting from injury and/or inflammation can cause the translocation of flora to the liver. Microbiota-associated metabolites and components mediate the activation of a series of signalling pathways, thereby playing an important role in the development of hepatocellular carcinoma (HCC). For this reason, targeting the gut microbiota in the diagnosis, prevention, and treatment of HCC holds great promise. In this review, we summarize the gut microbiota and the mechanisms by which it mediates HCC development, and the characteristic alterations in the gut microbiota during HCC pathogenesis. Furthermore, we propose several strategies to target the gut microbiota for the prevention and treatment of HCC, including antibiotics, probiotics, faecal microbiota transplantation, and immunotherapy.

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.

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.

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.

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.