Insertion of Hemin into Metal-Organic Frameworks: Mimicking Natural Peroxidase Microenvironment for the Rapid Ultrasensitive Detection of Uranium.

Constructing enzyme-like active sites in mimic enzyme systems is critical for achieving catalytic performances comparable to natural enzymes and can shed light on the natural development of enzymes. In this study, we described a specific hemin-based mimetic enzyme, which was facilely synthesized by the assembly of zeolitic imidazolate framework-l (ZIF-l) and hemin. The obtained hemin-based mimetic enzyme (denoted as ZIF-l-hemin) displayed enhanced peroxidase activity compared to free hemin in solution. Such excellent activity originated from the ZIF-l framework mimicking the active site cavity microenvironment of horseradish peroxidase in terms of axially coordinated histidine and distal histidine. Additionally, the constructed peroxidase mimetic was extremely resistant to a variety of severe circumstances that would normally denature natural enzymes. These characteristics made ZIF-l-hemin a potential platform for the colorimetric sensor of uranium (UO22+) with wide linear ranges (0.25-40 µM) and low limits of detection (0.079 µM). Moreover, the detection mechanism demonstrated that the coordination of uranyl ion with imidazole of ZIF-l-hemin reduced the catalytic efficiency of ZIF-l-hemin. The current work not only proposed a novel approach for fabricating artificial peroxidase but also offered facile colorimetric methods for selective radionuclide detection.

Mental Health and Psychological Responses During the Coronavirus Disease 2019 Epidemic: A Comparison Between Wuhan and Other Areas in China.

OBJECTIVE: This study aimed to compare the mental health and psychological responses in Wuhan, a severely affected area, and other areas of China during the coronavirus disease 2019 (COVID-19) epidemic. METHODS: This cross-sectional study was conducted on February 10-20, 2020. A set of online questionnaires was used to measure mental health and responses. A total of 1397 participants from Wuhan (age, 36.4 ± 10.7 years; male, 36.1%) and 2794 age- and sex-matched participants from other areas of China (age, 35.9 ± 9.9 years; male, 39.0%) were recruited. RESULTS: Compared with their counterparts, participants from Wuhan had a significantly higher prevalence of any mental health problems (46.6% versus 32.2%; adjusted odds ratio [OR] = 1.89, 95% confidence interval [CI] = 1.65-2.17), anxiety (15.2% versus 6.2%; adjusted OR = 2.65, 95% CI = 2.14-3.29), depression (18.3% versus 9.7%; adjusted OR = 2.11, 95% CI = 1.74-2.54), suicidal ideation (10.5% versus 7.1%; adjusted OR = 1.60, 95% CI = 1.28-2.02), and insomnia (38.6% versus 27.6%; adjusted OR = 1.70, 95% CI = 1.48-1.96). Participants from Wuhan had a slightly higher rate of help-seeking behavior (7.1% versus 4.2%; adjusted OR = 1.76, 95% CI = 1.12-2.77) but similar rate of treatment (3.5% versus 2.7%; adjusted OR = 1.23, 95% CI = 0.68-2.24) for mental problems than did their counterparts. In addition, compared with their counterparts, participants from Wuhan gave higher proportions of responses regarding "fearful" (52% versus 36%, p < .001), "discrimination against COVID-19 cases" (64% versus 58%, p = .006), "strictly comply with preventive behaviors" (98.7% versus 96%, p = .003), and "fewer living and medical supplies" (<2 weeks: 62% versus 57%, p = .015). CONCLUSIONS: The COVID-19 epidemic has raised enormous challenges regarding public mental health and psychological responses, especially in the highly affected Wuhan area. The present findings provide important information for developing appropriate strategies for the prevention and management of mental health problems during COVID-19 and other epidemics.

Emotion Recognition from Multiband EEG Signals Using CapsNet.

Emotion recognition based on multi-channel electroencephalograph (EEG) signals is becoming increasingly attractive. However, the conventional methods ignore the spatial characteristics of EEG signals, which also contain salient information related to emotion states. In this paper, a deep learning framework based on a multiband feature matrix (MFM) and a capsule network (CapsNet) is proposed. In the framework, the frequency domain, spatial characteristics, and frequency band characteristics of the multi-channel EEG signals are combined to construct the MFM. Then, the CapsNet model is introduced to recognize emotion states according to the input MFM. Experiments conducted on the dataset for emotion analysis using EEG, physiological, and video signals (DEAP) indicate that the proposed method outperforms most of the common models. The experimental results demonstrate that the three characteristics contained in the MFM were complementary and the capsule network was more suitable for mining and utilizing the three correlation characteristics.

HIF-1α induces immune escape of prostate cancer by regulating NCR1/NKp46 signaling through miR-224.

BACKGROUND: Metastasis of prostate cancer (PCa) is largely affected by natural killer (NK) cells. This study aimed to clarify the mechanisms underlying tumor cells escaping from NK cells mediated by HIF-1α. METHODS: MiR-224 expression in lymphocytes and HIF-1α protein level in NK cells were determined by qRT-PCR and western blot, respectively. The amount of NKp46+ NK cells was detected with flow cytometry. The IFN-γ level was examined by enzyme linked immunosorbent assay (ELISA). NK cells were tested for cytolytic activity with a Non-Radioactive Cytotoxicity Assay, and treated with oxygenglucose deprivation (OGD) for hypoxia simulation. Interaction between miR-224 and NCR1 was evaluated with dual luciferase reporter assay. RESULTS: MiR-224 was down-regulated in lymphocytes isolated from prostate cancer tissues (n = 10). Overexpression of miR-224 protected prostate cancer from NK cells. HIF-1α increased miR-224 to inhibit the killing capability of NK cells on prostate cancer. MiR-224 controlled the expression of NCR1. Overexpression of miR-224 protected prostate cancer from NK cells through NCR1/NKp46 signaling. Suppression of HIF-1α enhanced the cytotoxicity of NK cells on prostate cancer via miR-224/NCR1 pathway. CONCLUSION: HIF-1α inhibits NCR1/NKp46 pathway through up-regulating miR-224, which affects the killing capability of NK cells on prostate cancer, thus inducing immune escape of tumor cells.

Prediction of thermodynamically stable Li-B compounds at ambient pressure.

To clarify controversial structures and phase stability in the Li-B system, we predicted energetically favorable compounds and crystal structures of the Li-B binary system at ambient pressure, mainly including Li6B5, LiB2, and LiB3, from ab initio evolutionary structure simulations and further investigated physical properties of stable Li-B compounds using first-principles methods. Metallic Li6B5, predicted in our simulations, has trigonal symmetry with space group R32 and contains linear B chains, but its superconducting Tc is low according to the electron-phonon coupling calculations. Orthorhombic LiB2 (Pnma) and tetragonal LiB3 (P4/mbm) are zero-gap semiconductors; LiB2 is a Dirac semimetal, and both LiB2 and LiB3 are promising thermoelectric materials.

BaC: a thermodynamically stable layered superconductor.

To predict all stable compounds in the Ba-C system, we perform a comprehensive study using first-principles variable-composition evolutionary algorithm USPEX. We find that at 0 K the well-known compound BaC2 is metastable in the whole pressure range 0-40 GPa, while intercalated graphite phase BaC6 is stable at 0-19 GPa. A hitherto unknown layered orthorhombic Pbam phase of BaC has structure consisting of alternating layers of Ba atoms and layers of stoichiometry Ba2C3 containing linear C3 groups and is predicted to be stable in the pressure range 3-32 GPa. From our electron-phonon coupling calculations, the newly found BaC compound is a phonon-mediated superconductor and has a critical superconductivity temperature Tc of 4.32 K at 5 GPa. This compound is dynamically stable at 0 GPa and therefore may be quenchable under normal conditions.

Pressure-induced stabilization and insulator-superconductor transition of BH.

Diborane (B(2)H(6)), a high energy density material, was believed to be stable in a wide P, T interval. A systematic investigation of the B-H system using the ab initio variable-composition evolutionary simulations shows that boron monohydride (BH) is thermodynamically stable and can coexist with solid B, H(2), and B(2)H(6) in a wide pressure range above 50 GPa. B(2)H(6) becomes unstable and decomposes into the Ibam phase of BH and H(2) (C2/c) at 153 GPa. The semiconducting layered Ibam structure of BH at 168 GPa transforms into a metallic phase with space group P6/mmm and a 3D topology with strong B-B and B-H covalent bonds. The Ibam-P6/mmm transformation pathway suggests the possibility of obtaining the metastable Pbcm phase on cold decompression of the P6/mmm phase. The electron-phonon coupling calculations indicate that P6/mmm-BH is a phonon-mediated superconductor with a critical temperature of superconductivity (T(c)) of 14.1-21.4 K at 175 GPa.

Sensing performance of precisely ordered TiO2 nanowire gas sensors fabricated by electron-beam lithography.

In this study, electron beam lithography, rather than the most popular method, chemical synthesis, is used to construct periodical TiO(2) nanowires for a gas sensor with both robust and rapid performance. The effects of temperature on the sensing response and reaction time are analyzed at various operation temperatures ranging from 200 to 350 °C. At the optimized temperature of 300 °C, the proposed sensor repeatedly obtained a rise/recovery time (ΔR: 0.9 R(0) to 0.1 R(0)) of 3.2/17.5 s and a corresponding sensor response (ΔR/R(0)) of 21.7% at an ethanol injection mass quantity of 0.2 µg.

Multi-channel EEG emotion recognition through residual graph attention neural network.

In this paper, a novel EEG emotion recognition method based on residual graph attention neural network is proposed. The method constructs a three-dimensional sparse feature matrix according to the relative position of electrode channels, and inputs it into the residual network to extract high-level abstract features containing electrode spatial position information. At the same time, the adjacency matrix representing the connection relationship of electrode channels is constructed, and the time-domain features of multi-channel EEG are modeled using graph. Then, the graph attention neural network is utilized to learn the intrinsic connection relationship between EEG channels located in different brain regions from the adjacency matrix and the constructed graph structure data. Finally, the high-level abstract features extracted from the two networks are fused to judge the emotional state. The experiment is carried out on DEAP data set. The experimental results show that the spatial domain information of electrode channels and the intrinsic connection relationship between different channels contain salient information related to emotional state, and the proposed model can effectively fuse these information to improve the performance of multi-channel EEG emotion recognition.

Corrigendum: Multi-channel EEG emotion recognition using Residual Graph Attention Neural Network.

[This corrects the article DOI: 10.3389/fnins.2023.1135850.].

Template-Free In Situ Encapsulation of Enzymes in Hollow Covalent Organic Framework Capsules for the Electrochemical Analysis of Biomarkers.

Although capsule-like materials as host carriers for enzyme encapsulation have been a hot topic in recent years, creating an ideal microenvironment for enhanced enzymatic performance is still a formidable challenge. Herein, we created a template-free method to in situ encapsulate natural enzymes in hollow covalent organic framework (COF) capsules at room temperature. The COF crystallites migrated from the inner core and self-assembled at the outside walls during the inside-out Ostwald ripening process, retaining the enzymes in the cavity. The adjustable hollow structure of the enzyme@COF capsule allowed the basic vibration of the enzyme to maintain a certain degree of freedom, thus significantly enhancing the enzymatic bioactivity. The hollow enzyme@COF capsule has large mesoporous tunnels allowing the efficient transport. In addition, the enzyme encapsulated in the capsule showed superior activity and ultrahigh stability under various extreme conditions that may lead to enzyme inactivation, such as high temperature, organic solvents, chelates, and the denaturing agent. Finally, the prepared hollow GOx@COF capsule was used for electrochemical sensing of glucose in human serum, and the electrochemical sensor exhibited high selectivity and satisfactory test results. This research not only provides a new way for COFs to encapsulate enzymes but also has potential applications in biocatalysis and biosensing, making artificial organelles possible.

Surfactant-Modified CdS/CdCO3 Composite Photocatalyst Morphology Enhances Visible-Light-Driven Cr(VI) Reduction Performance.

The surfactant modification of catalyst morphology is considered as an effective method to improve photocatalytic performance. In this work, the visible-light-driven composite photocatalyst was obtained by growing CdS nanoparticles in the cubic crystal structure of CdCO3, which, after surfactant modification, led to the formation of CdCO3 elliptical spheres. This reasonable composite-structure-modification design effectively increased the specific surface area, fully exposing the catalytic-activity check point. Cd2+ from CdCO3 can enter the CdS crystal structure to generate lattice distortion and form hole traps, which productively promoted the separation and transfer of CdS photogenerated electron-hole pairs. The prepared 5-CdS/CdCO3@SDS exhibited excellent Cr(VI) photocatalytic activity with a reduction efficiency of 86.9% within 30 min, and the reduction rate was 0.0675 min-1, which was 15.57 and 14.46 times that of CdS and CdCO3, respectively. Finally, the main active substances during the reduction process, the photogenerated charge transfer pathways related to heterojunctions and the catalytic mechanism were proposed and analyzed.

First identification of two co-existing genome-wide significant sex quantitative trait loci (QTL) in red tilapia using integrative QTL mapping.

Red tilapia ( Oreochromis spp .) is one of the most popular fish in China due to its bright red appearance, fast growth rate, and strong adaptability. Understanding the sex determination mechanisms is of vital importance for the selection of all-male lines to increase aquacultural production of red tilapia. In this research, the genetic architecture for sex from four mapping populations ( n=1 090) of red tilapia was analyzed by quantitative trait loci (QTL)-seq, linkage-based QTL mapping, and linkage disequilibrium (LD)-based genome-wide association studies. Two genome-wide significant QTL intervals associated with sex were identified on ChrLG1 (22.4-23.9 Mb) and ChrLG23 (32.0-35.9 Mb), respectively. The QTL on ChrLG1 was detected in family 1 (FAM1), FAM2, and FAM4, and the other QTL on ChrLG23 was detected in FAM3 and FAM4. Four microsatellite markers located within the QTL were successfully developed for marker-assisted selection. Interestingly, three ( lpp, sox14, and amh) of the 12 candidate genes located near or on the two QTL intervals were abundantly expressed in males, while the remaining genes were more highly expressed in females. Seven genes ( scly, ube3a, lpp, gpr17, oca2, cog4, and atp10a) were significantly differentially expressed between the male and female groups. Furthermore, LD block analysis suggested that a cluster of genes on ChrLG23 may participate in regulating sex development in red tilapia. Our study provides important information on the genetic architecture of sex in red tilapia and should facilitate further exploration of sex determination mechanisms in this species.

Oligonucleotide-Functionalized Enzymes Chemisorbing on Magnetic Layered Double Hydroxides: A Multimodal Catalytic Platform with Boosted Activity for Ultrasensitive Glucose Detection.

A reasonable design of multifarious chemo- and biocatalytic functions within individual nano/microunits is urgently desired for high-performance cascade reactions but has heretofore remained elusive. Herein, glucose oxidase was functionalized with oligonucleotides and steadily chemisorbed on magnetic layered double hydroxides (mLDHs) to construct a multimodal catalytic platform for realizing divergent reactions with heterogeneous and biocatalytic steps. The flowerlike mLDHs served both as an enzyme support and a peroxidase mimic cooperating with enzymes for tandem catalysis. Oligo-DNA connected the enzymes to mLDHs like a bridge, and a stepwise ligand-exchange-assisted coordination mechanism was proposed to explain the robust interaction between DNA and mLDHs. More importantly, DNA significantly improved the bioactivity of the whole system. The acceleration mechanism was attributed to the diffusion tunnels for the substrate/product and enhanced substrates binding on mLDHs. The multimodal catalytic platform was applied for colorimetric and electrochemical sensing of glucose with a low limit of detection and high selectivity. The practical analysis capability of the ultrasensitive sensor was evaluated by detecting glucose in human serum and sweat, showing reliable results, satisfactory recovery, and excellent stability. The strategy of combining mLDHs and enzymes for cascade catalysis provides a universal approach to prepare chemo-enzyme hybrids with high performance, which holds great promise for applications in biosensors and industrial catalysis.

Ionic Carbazole-Based Water-Soluble Two-Photon Photoinitiator and the Fabrication of Biocompatible 3D Hydrogel Scaffold.

Two-photon polymerization of a three-dimensional (3D) hydrogel structure has been widely applied in biological tissue engineering. For improving the biocompatibility of hydrogel structures, a new kind of ionic carbazole water-soluble photoinitiator was prepared to realize the fabrication of a 3D hydrogel structure in aqueous phase. 3,6-Bis[2-(1-methyl-pyridinium)vinyl]-9-methyl-carbazole diiodide (BMVMC) and cucurbit[7]uril (CB7) have been employed to generate a complex with better water solubility by host-guest interactions. The binding ratio of the complex was demonstrated to be 1:1 through the characterization of isothermal titration calorimetry (ITC). The two-photon absorption (TPA) cross section of the complex increases to 2500 GM compared with the 750 GM of the BMVMC molecule. Then, an aqueous-phase photoresist was obtained using the CB7/BMVMC complex as the photoinitiator and poly(ethylene glycol) diacrylate (PEGda) as the hydrogel monomer. Two-photon fabrication capability in aqueous phase has been studied using the as-prepared photoresist. A low laser threshold of 3.7 mW as well as a high resolution of 180 nm are achieved. Benefiting from the fluorescence properties of the photoinitiator, we can achieve the confocal fluorescence images without any assistance of fluorescent probes. Subsequently, a 3D engineered hydrogel scaffold microstructure was fabricated by the two-photon polymerization technology, whose biocompatibility was demonstrated by culturing the structure with living cells of L929. The BMVMC-CB7 complex and the as-prepared photoresist are demonstrated to have good biocompatibility, which is prospective for further application in tissue engineering.

Marker-assisted selection of YY supermales from a genetically improved farmed tilapia-derived strain.

Genetically improved farmed tilapia (GIFT) and GIFT-derived strains account for the majority of farmed tilapia worldwide. As male tilapias grow much faster than females, they are often considered more desirable in the aquacultural industry. Sex reversal of females to males using the male sex hormone 17-α-methyltestosterone (MT) is generally used to induce phenotypic males during large-scale production of all male fingerlings. However, the widespread use of large quantities of sex reversal hormone in hatcheries may pose a health risk to workers and ecological threats to surrounding environments. Breeding procedures to produce genetically all-male tilapia with limited or no use of sex hormones are therefore urgently needed. In this study, by applying marker-assisted selection (MAS) for the selection of YY supermales from a GIFT-derived strain, we identified 24 XY pseudofemale and 431 YY supermale tilapias. Further performance evaluation on the progenies of the YY supermales resulted in male rates of 94.1%, 99.5% and 99.6%, respectively, in three populations, and a daily increase in body weight of 1.4 g at 3 months (n=997). Our study established a highly effective MAS procedure in the selection of YY supermales from a GIFT-derived strain. Furthermore, the development of MAS-selected YY supermales will help reduce the utilization of hormones for controlling sex in the tilapia aquaculture.

Crystal structure prediction of LiBeH3 using ab initio total-energy calculations and evolutionary simulations.

The stable crystal structure of LiBeH(3) is predicted on the basis of ab initio total-energy calculations using density-functional theory and an extended database of candidate structures and using global optimizations based on an evolutionary algorithm. At the level of density-functional theory, a CaSiO(3)_1-type structure with space group P2(1)/c, containing BeH(4) tetrahedra linked in chains, is the ground-state structure of LiBeH(3) (alpha-LiBeH(3)). It is found to be lower in energy than the structures proposed in previous studies. The analysis of the electronic structure shows that alpha-LiBeH(3) is an insulator with a band gap of about 4.84 eV and exhibits strong covalent bonding in the BeH(4) tetrahedral complexes. Calculations at finite temperatures and high pressures suggest that at T=408 K and ambient pressure a structural transition from alpha-LiBeH(3) (CaSiO(3)-type) to a YBO(3)-type structure with space group Cmcm occurs and that at a pressure of 7.1 GPa alpha-LiBeH(3) undergoes a pressure-induced structural transition from the alpha-phase to a MgSiO(3)-type structure with space group C2/c. The calculated enthalpies of formation (-45.36 and -30.12 kJ/mol H(2) without and with zero-point energy corrections) are in good agreement with the experimental result, indicating that LiBeH(3) is a potential hydrogen storage material with low activation barriers for hydrogen desorption.

Recognition of Emotions Using Multichannel EEG Data and DBN-GC-Based Ensemble Deep Learning Framework.

Fusing multichannel neurophysiological signals to recognize human emotion states becomes increasingly attractive. The conventional methods ignore the complementarity between time domain characteristics, frequency domain characteristics, and time-frequency characteristics of electroencephalogram (EEG) signals and cannot fully capture the correlation information between different channels. In this paper, an integrated deep learning framework based on improved deep belief networks with glia chains (DBN-GCs) is proposed. In the framework, the member DBN-GCs are employed for extracting intermediate representations of EEG raw features from multiple domains separately, as well as mining interchannel correlation information by glia chains. Then, the higher level features describing time domain characteristics, frequency domain characteristics, and time-frequency characteristics are fused by a discriminative restricted Boltzmann machine (RBM) to implement emotion recognition task. Experiments conducted on the DEAP benchmarking dataset achieve averaged accuracy of 75.92% and 76.83% for arousal and valence states classification, respectively. The results show that the proposed framework outperforms most of the above deep classifiers. Thus, potential of the proposed framework is demonstrated.

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance.

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.