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Due to the complexity of construction sequence and the extended duration required to construct super large section tunnels, the selection of excavation method critically influences the stability of the surrounding rock and support structures. In this work, the Xiaoyuan Tunnel project in Jiangxi Province serves as the research background for employing ABAQUS software to simulate the variations in displacement and stress within the rock and support structures under three different excavation methods. The simulated results are subsequently compared and verified against monitoring data. The findings indicate that the three-benching seven-step method releases more stress (maximum principal stress value reaches 0.621 MPa) from the surrounding rock and support structures than the other methods, resulting in stress concentrations. Therefore, it is of vital significance to complete the initial support in time and seal the tunnel opening quickly. The maximum principal stress values caused by three excavation methods all appear at the arch foot position, highlighting the need for prompt reinforcement of stability support there. Compared to the CRD method and the three-benching seven-step method, the tunnel vault’s settlement value caused by the double-side drift method is reduced by 14% and 19%, respectively. Furthermore, the largest disturbance of the surrounding rock occurs under the CRD method, while the double-side drift method minimizes such disturbances, making it the preferred choice for the construction of super large section tunnels. These insights are invaluable for guiding the selection and optimization of construction methods for such tunnels.

A large number of experimental studies have demonstrated that the permeability and damage of rock are not constant but rather functionally dependent on stresses or stress-induced deformation. Neglecting the influence of damage and permeability evolution on rock mechanics and sealing properties can result in an overestimation of the safety and stability of underground engineering, leading to an incomplete assessment of the risks associated with surrounding rock failure. To address this, the damage and permeability evolution functions of rock under compression were derived through a combination of experimental results and theoretical analysis, unifying the relationship between porosity and permeability in both porous media flow and fractured flow. Based on this, a fluid–solid coupled seepage model considering rock damage and permeability evolution was proposed. More importantly, this model was utilized to investigate the behavior of deformation, damage, and permeability, as well as their coupled effects. The model’s validity was verified by comparing its numerical results with experimental data. The analysis results show that the evolution of permeability and porosity resulted from a competitive interaction between effective mean stress and stress-induced damage. When the effective mean stress was dominant, the permeability tended to decrease; otherwise, it followed an increasing trend. The damage evolution was primarily related to stress- and pressure-induced crack growth and irreversible deformation. Additionally, the influence of the seepage pressure on the strength, damage, and permeability of the investigated rock was evaluated. The model results reveal the damage and permeability evolution of the rock under compression, which has a certain guiding significance for the stability and safety analysis of rock in underground engineering.

The biocurrent generated by soil extracellular electron transfer (EET) partly drives biogeochemical cycles and controls soil quality. However, it is unclear how the soil abiotic and biotic conditions affect the biocurrent conduction. In this study, the response relationship of soil microenvironment and in-situ biocurrent was studied. The results showed that red soil exhibited the optimal electron transfer efficiency, as evidenced by the maximum current density and accumulated charge output, with increments of 56–93 % and 80–2800 %, respectively, compared with the other five types of soils. Soil physicochemical properties were the most important factor on the biocurrent generation, and further the quantity and bioavailability of dissolved organic matter, NH4+-N content, and lower pH were predictive indicators for the exoelectrogenic processes of soils. In addition, the high soil biocurrent was likely determined by a complex synergistic network of the transformation of carbon and nitrogen, electroactive bacteria involving the functions of cell wall/membrane and cytochrome enzyme metabolism and transport related EET process. Overall, we provide an insight into the relationship among soil biocurrent conduction, physicochemical properties, bacteria community and metabolic function, and a support for bioelectrochemical technology application.

Deep learning-based 3D target detection methods need to solve the problem of insufficient 3D target detection accuracy. In this paper, the KM3D network is selected as the benchmark network after the experimental comparison of current mainstream algorithms, and the IAE-KM3D network algorithm based on the KM3D network is proposed. First, the Resnet V2 network is introduced, and the residual module is redesigned to improve the training capability of the new residual module with higher generalization. IBN NET is then introduced to carefully integrate instance normalization and batch normalization as building blocks to improve the model’s detection accuracy in hue- and brightness-changing scenarios without increasing time loss. Then, a parameter-free attention mechanism, Simam, is introduced to improve the detection accuracy of the model. After that, the elliptical Gaussian kernel is introduced to improve the algorithm’s ability to detect 3D targets. Finally, a new key point loss function is proposed to improve the algorithm’s ability to train. Experiments using the KITTI dataset conclude that the IAE-KM3D network model significantly improves detection accuracy and outperforms the KM3D algorithm regarding detection performance compared to the original KM3D network. The improvements for AP2D, AP3D, and APBEV are 5%, 12.5%, and 8.3%, respectively, and only a tiny amount of time loss and network parameters are added. Compared with other mainstream target detection algorithms, Monn3D, 3DOP, GS3D, and FQNet, the improved IAE-KM3D network in this paper significantly improves AP3D and APBEV, with fewer network parameters and shorter time consumption.

In this study, 2-t-butyl-4-arylimino-2,3-dihydroacridylnickel dibromides were synthesized by nickel-template one-pot condensation, and well characterized along with the single-crystal X-ray diffraction to one representative complex, revealing a distorted tetrahedral geometry around nickel. When activated with modified methylaluminoxane (MMAO), all nickel complexes exhibited high activities (up to 1.91 × 106 g mol−1 (Ni) h−1) toward major trimerization of ethylene. When activated with ethylaluminum dichloride (EtAlCl2), however, the title complexes performed good activities (up to 1.05 × 106 g mol−1 (Ni) h−1) for selective dimerization of ethylene. In comparison to analogous nickel complexes, higher activities were achieved with the substituent of t-butyl group, especially in the rare case of nickel complexes performing trimerization of ethylene.

With the rapid development of today’s society, the traffic environment has become more and more complex. As an essential part of intelligent vehicles, trajectory tracking has attracted significant attention for its stability and safety. It is prone to poor accuracy and instability in extreme working conditions like high speed. In this paper, a trajectory tracking control strategy to ensure lateral stability at a high speed and low attachment limit conditions is proposed for distributed drive vehicles. The model predictive controller (MPC) was used to control the front wheel angle, and the particle swarm optimization (PSO) algorithm was designed to optimize the MPC control parameters adaptively. The sliding mode controller controls the rear wheel angle, and the vehicle instability degree is judged by analyzing the β − β˙ phase plane. The controllers of different instability degrees are designed herein. Finally, a torque divider is designed to consider the actuation anti-slip. The designed controller is verified by Carsim and MATLAB-Simulink co-simulation. The results show that the trajectory tracking controller designed in this paper effectively improves the tracking accuracy under the premise of ensuring stability.

Through the implementation of a one-pot strategy, five examples of non-symmetrical [N,N-diaryl-11-phenyl-1,2,3,7,8,9,10-heptahydrocyclohepta[b]quinoline-4,6-diimine]iron(II) chloride complexes (aryl = 2,6-Me2Ph Fe1, 2,6-Et2Ph Fe2, 2,6-i-Pr2Ph Fe3, 2,4,6-Me3Ph Fe4, and 2,6-Et2-4-MePh Fe5), incorporating fused six- and seven-membered carbocyclic rings and appended with a remote para-phenyl group, were readily prepared. The molecular structures of Fe2 and Fe3 emphasize the variation in fused ring size and the skewed disposition of the para-phenyl group present in the N′,N,N″-ligand support. Upon activation with MAO or MMAO, Fe1–Fe5 all showed high catalytic activity for ethylene polymerization, with an exceptional level of 35.92 × 106 g (PE) mol−1 (Fe) h−1 seen for mesityl-substituted Fe4/MMAO operating at 60 °C. All catalysts generated highly linear polyethylene with good control of the polymer molecular weight achievable via straightforward manipulation of run temperature. Typically, low molecular weight polymers with narrow dispersity (Mw/Mn = 1.5) were produced at 80 °C (MMAO: 3.7 kg mol−1 and MAO: 4.9 kg mol−1), while at temperatures between 40 °C and 50 °C, moderate molecular weight polymers were obtained (MMAO: 35.6–51.6 kg mol−1 and MAO: 72.4–95.5 kg mol−1). Moreover, analysis of these polyethylenes by 1H and 13C NMR spectroscopy highlighted the role played by both β-H elimination and chain transfer to aluminum during chain termination, with the highest rate of β-H elimination seen at 60 °C for the MMAO-activated system and 70 °C for the MAO system.

Presenilin, a transmembrane protein primarily known for its role in Alzheimer’s disease (AD) as part of the γ-secretase complex, has garnered increased attention due to its multifaceted functions in various cellular processes. Recent investigations have unveiled a plethora of functions beyond its amyloidogenic role. This review aims to provide a comprehensive overview of presenilin’s diverse roles in AD and other neurodegenerative disorders. It includes a summary of well-known substrates of presenilin, such as its involvement in amyloid precursor protein (APP) processing and Notch signaling, along with other functions. Additionally, it highlights newly discovered functions, such as trafficking function, regulation of ferritin expression, apolipoprotein E (ApoE) secretion, the interaction of ApoE and presenilin, and the Aβ42-to-Aβ40-converting activity of ACE. This updated perspective underscores the evolving landscape of presenilin research, emphasizing its broader impact beyond established pathways. The incorporation of these novel findings accentuates the dynamic nature of presenilin’s involvement in cellular processes, further advancing our comprehension of its multifaceted roles in neurodegenerative disorders. By synthesizing evidence from a range of studies, this review sheds light on the intricate web of presenilin functions and their implications in health and disease.

In this study, we prepared a single M-type strontium ferrite phase with different iron contents using the traditional ceramic method and studied the effect of iron deficiency on the crystalline structure and magnetic properties of M-type hexaferrite strontium has been studied. With the decrease in iron content, the saturation magnetization of M-type strontium ferrite SrO·nFe2O3 powders (5.49 ≤ n ≤ 5.95) decreases by 5.5% and 4.7% at 300 K and 5 K, respectively. The change in Fe3+ occupancy was determined using X-ray diffraction spectroscopy. The occupancy of Fe3+ in sublattices 2a, 12k and 4f1 decreased with decreasing Fe content, and the decreased amplitude was in the order 2a > 4f1 > 12k. In addition, the (4f1 site) Fe3-O2-Fe5 (12k site) and (4f1 site) Fe3O2 bond length decreased and increased, respectively, with decreasing Fe content. These results are of great significance for improving the magnetism of SrM by regulating iron vacancies in its structure.

The aim of this research is to study the lateral deformation characteristics of the pile foundation in front of a shield construction and accurately predict its displacement in the shield tunneling direction. The impact of deep-shield construction on the pile foundations was analyzed using the Mindlin elastic solution to determine the lateral displacement. The Kerr foundation model and other factors, like additional shield thrust and uneven shell friction, were considered. The study assessed the impact of the incision distance, shield outer diameter, and additional thrust on pile displacement. The theoretical and numerical solutions of lateral displacement at various shield construction stages were compared to determine the variation law. The results indicate that the theoretical method is reliable, considering its good agreement with the numerical solutions. The buried depth of the shield means that the upper part of the pile is less affected by the additional thrust, leading to less deformation at the top. We recommend using a smaller shield thrust and outer diameter to control the pile’s end and top displacement.

Information on the temporal and spatial scale of cellular molecules in biological systems is crucial for estimating life processes and may be conducive to an improved understanding of disease progression. This intracellular and extracellular information is often difficult to obtain at the same time due to the limitations of accessibility and sensing throughput. DNA is an excellent material for in vivo and in vitro applications, and can be used to build functional modules that can transform bio-information (input) into ATCG sequence information (output). Due to their small volume and highly amenable programming, DNA-based functional modules provide an opportunity to monitor a range of information, from transient molecular events to dynamic biological processes. Over the past two decades, with the advent of customized strategies, a series of functional modules based on DNA networks have been designed to gather different information about molecules, including the identity, concentration, order, duration, location, and potential interactions; the action of these modules are based on the principle of kinetics or thermodynamics. This paper summarizes the available DNA-based functional modules that can be used for biomolecular signal sensing and transformation, reviews the available designs and applications of these modules, and assesses current challenges and prospects.

Deep learning-based object detection methods address the problem of how to trade off the object detection accuracy and detection speed of the model. This paper proposes the PBA-YOLOv7 network algorithm, which is based on the YOLOv7 network, and first introduces the PConv, which lightens the ELAN module in the backbone network structure and reduces the number of parameters to improve the detection speed of the network and then designs and introduces the BiFusionNet network, which better aggregates the high-level semantic features and the low-level semantic features; and finally, on this basis, the coordinate attention mechanism is introduced to make the network focus on more critical features without increasing the model complexity. The coordinate attention mechanism is introduced to make the network focus more on important feature information and improve the feature expression ability of the network without increasing the model complexity. Experiments on the publicly available KITTI’s dataset show that the PBA-YOLOv7 network model significantly improves both detection accuracy and detection speed compared to the original YOLOv7 model, with 4% and 7.8% improvement in mAP0.5 and mAP0.5:0.95, respectively, and six frames improvement in FPS. The improved algorithm in this paper weighs the model’s detection accuracy and detection speed in the detection task. It performs well compared to other algorithms, such as YOLOv7 and YOLOv5l.

Various plant species contain terpene secondary metabolites, which disrupt insect growth and development by affecting the activity of juvenile hormone-degrading enzymes, and the juvenile hormone (JH) titers maintained in insects. Nerolidol, a natural sesquiterpenol belonging to the terpenoid group, exhibits structural similarities to insect JHs. However, the impact of nerolidol on insect growth and development, as well as its underlying molecular mechanism, remains unclear. Here, the effects of nerolidol on Spodoptera exigua were investigated under treatment at various sub-lethal doses (4.0 mg/mL, 1.0 mg/mL, 0.25 mg/mL). We found that a higher dose (4.0 mg/mL) of nerolidol significantly impaired the normal growth, development, and population reproduction of S. exigua, although a relatively lower dose (0.25 mg/mL) of nerolidol had no significant effect on this growth and development. Combined transcriptome sequencing and gene family analysis further revealed that four juvenile hormone esterase (JHE)-family genes that are involved in juvenile hormone degradation were significantly altered in S. exigua larvae after nerolidol treatment (4.0 mg/mL). Interestingly, the juvenile hormone esterase-like (JHEL) gene Sexi006721, a critical element responsive to nerolidol stress, was closely linked with the significant augmentation of JHE activity and JH titer in S. exigua (R2 = 0.94, p < 0.01). Taken together, we speculate that nerolidol can function as an analog of JH by modulating the expression of the enzyme genes responsible for degrading JH, resulting in JH disorders and ultimately disrupting the development of insect larvae. This study ultimately provides a theoretical basis for the sustainable control of S. exigua in the field whilst proposing a new perspective for the development of novel biological pesticides.

Abstract Surface‐enhanced Raman spectroscopy (SERS) has shown strength in non‐invasive, rapid, trace analysis and has been used in many fields in medicine. Machine learning (ML) is an algorithm that can imitate human learning styles and structure existing content with the knowledge to effectively improve learning efficiency. Integrating SERS and ML can have a promising future in the medical field. In this review, we summarize the applications of SERS combined with ML in recent years, such as the recognition of biological molecules, rapid diagnosis of diseases, developing of new immunoassay techniques, and enhancing SERS capabilities in semi‐quantitative measurements. Ultimately, the possible opportunities and challenges of combining SERS with ML are addressed.

Most anticancer treatments only induce the death of ordinary cancer cells, while cancer stem cells (CSCs) in the quiescent phase of cell division are difficult to kill, which eventually leads to cancer drug resistance, metastasis, and relapse. Therefore, CSCs are also important in targeted cancer therapy. Herein, we developed dual-targeted and glutathione (GSH)-responsive novel nanoparticles (SSBPEI–DOX@siRNAs/iRGD–PEG–HA) to efficiently and specifically deliver both doxorubicin and small interfering RNA cocktails (siRNAs) (survivin siRNA, Bcl-2 siRNA and ABCG2 siRNA) to ovarian CSCs. They are fabricated via electrostatic assembly of anionic siRNAs and cationic disulfide bond crosslinking-branched polyethyleneimine-doxorubicin (SSBPEI–DOX) as a core. Interestingly, the SSBPEI–DOX could be degraded into low-cytotoxic polyethyleneimine (PEI). Because of the enrichment of glutathione reductase in the tumor microenvironment, the disulfide bond (–SS–) in SSBPEI–DOX can be specifically reduced to promote the controlled release of siRNA and doxorubicin (DOX) in the CSCs. siRNA cocktails could specifically silence three key genes in CSCs, which, in combination with the traditional chemotherapy drug DOX, induces apoptosis or necrosis of CSCs. iRGD peptides and “sheddable” hyaluronic acid (HA) wrapped around the core could mediate CSC targeting by binding with neuropilin-1 (NRP1) and CD44 to enhance delivery. In summary, the multifunctional delivery system SSBPEI–DOX@siRNAs/iRGD–PEG–HA nanoparticles displays excellent biocompatibility, accurate CSC-targeting ability, and powerful anti-CSC ability, which demonstrates its potential value in future treatments to overcome ovarian cancer metastasis and relapse. To support this work, as exhaustive search was conducted for the literature on nanoparticle drug delivery research conducted in the last 17 years (2007–2023) using PubMed, Web of Science, and Google Scholar.

Two one-dimensional oxalate-bridged Cu(II) ammonium salts, [(CH3)3NH]2[Cu(μ-C2O4)(C2O4)]·2.5H2O (I) and [(C2H5)3NH]2[Cu(μ-C2O4)(C2O4)]·H2O (II) were obtained and characterized. They were composed of ammonium: (CH3)3NH+ in (I), (C2H5)3NH+ in (II), [Cu(μ-C2O4)(C2O4)2−]n and H2O. The Jahn–Teller-distorted Cu(II) is octahedrally coordinated by six O atoms from three oxalates and forms a one-dimensional zigzag chain. The hydrogen bonds between ammonium, the anion and H2O form a three-dimensional network. There is no hydrogen bond between the anion chains. They were insulated at 20 °C with a relative humidity of 40%. Ferromagnetic and weak-ferromagnetic behaviors were observed in I and II, separately. No long-range ordering was observed above 2 K.

The Baeyer–Villiger oxidation (BVO) of ketone and aldehyde can produce ester and formate, which both have wide applications in many areas. In this work, a series of Sn-containing silicates were prepared through the sol-gel process by using structure-directing and crystallizing agents and post-synthetic coordinated modification of binaphthol. Characterizations revealed that loading of (L)-sodium lactate as the crystallizing agent decreased the crystal size of the synthesized catalyst, and there were SnO2 nanoparticles with sizes of 17–19 nm on the catalyst. Furthermore, quite differently from the 3D mesoporous structure of classical Sn-beta zeolites, the synthesized catalysts had a silt-like mesoporous structure. In the catalysis, when cyclic aliphatic ketones were used as the substrate, only BVO-type products and corresponding ring-opening products were obtained. BVO of aliphatic aldehyde produced both an aerobic oxidation product (carboxylic acid) and a BVO-type product. The presented transformation of aromatic aldehyde (benzaldehyde) only gave an aerobic oxidation product (benzoic acid). The post-synthetic coordinating attachment of (S)-binaphthol to the Sn-containing silicate backbone worsened the BVO of aliphatic ketones but improved the BVO of aliphatic aldehyde and the aerobic oxidation of aromatic aldehyde. In addition, this work also developed two new routes for the synthesis of high-value-added 6-hydroxyhexanoic acid and cyclohexylformate under catalytic BVO conditions.

The C-3 functionalization of 1H-indazole could produce a lot of highly valuable pharmaceutical precursors, which could be used for the treatment of cancer and many other inflammatory diseases. This work was focused on the C-3 functionalization of 1H-indazole through Suzuki–Miyaura cross-coupling of 3-iodo-1H-indazole with organoboronic acids, catalyzed by various palladium catalysts immobilized over imidazolium ionic liquids, as well as catalyst recycling. A series of reaction parameters, including the substrate, catalyst, and ionic liquid, were fully investigated. It is significant to note that the yields of the present Suzuki–Miyaura cross-coupling were mainly determined by the catalyst and the solvent used, more than the chemical structure of the substrate. Furthermore, ferrocene-based divalent palladium complexes showed better catalytic outputs compared to simple palladium salts. Moreover, using two imidazolium ionic liquids, BMImX (BMIm+ = 1-n-butyl-3-methylimidazolium, X− = BF4−, PF6−) not only improved the yields of cross-coupled products, but also avoided the formation of Pd(0) black, as compared to the non-ionic liquid facilitated reactions, and simultaneously making catalyst recycling more effective. On average, BMImBF4 performed better than BMImPF6. Additionally, scientific calculations revealed that 1,1′-bis(diphenylphosphino)ferrocene-palladium(II)dichloride dichloromethane complex (PdCl2(dppf)) showed a lower energy barrier in the formation of intermediates than [1,1′-bis(di-tert-butylphosphino)ferrocene]dichloropalladium(II) (PdCl2(dtbpf)), leading to higher catalytic outputs. This work may contribute to the development of 1H-indazole-derived new pharmaceuticals.

A glucose biosensor was layer-by-layer assembled on a modified glassy carbon electrode (GCE) from a nanocomposite of NAD(P)+-dependent glucose dehydrogenase, aminated polyethylene glycol (mPEG), carboxylic acid-functionalized multi-wall carbon nanotubes (fMWCNTs), and ionic liquid (IL) composite functional polymers. The electrochemical electrode was denoted as NF/IL/GDH/mPEG-fMWCNTs/GCE. The composite polymer membranes were characterized by cyclic voltammetry, ultraviolet-visible spectrophotometry, electrochemical impedance spectroscopy, scanning electron microscopy, and transmission electron microscopy. The cyclic voltammogram of the modified electrode had a pair of well-defined quasi-reversible redox peaks with a formal potential of −61 mV (vs. Ag/AgCl) at a scan rate of 0.05 V s−1. The heterogeneous electron transfer constant (ks) of GDH on the composite functional polymer-modified GCE was 6.5 s−1. The biosensor could sensitively recognize and detect glucose linearly from 0.8 to 100 µM with a detection limit down to 0.46 μM (S/N = 3) and a sensitivity of 29.1 nA μM−1. The apparent Michaelis–Menten constant (Kmapp) of the modified electrode was 0.21 mM. The constructed electrochemical sensor was compared with the high-performance liquid chromatography method for the determination of glucose in commercially available glucose injections. The results demonstrated that the sensor was highly accurate and could be used for the rapid and quantitative determination of glucose concentration.

The Baeyer-Villiger Oxidation (BVO) of ketones and aldehydes produce lactones and formates, while aerobic carboxylation of aldehydes manufactures carboxylic acids, both having high added value. This work prepared a series of Al-containing silicates modified with organic ligands and SnO2 nanoparticles, which were then employed as catalyst in BVO and carboxylation. Characterizations revealed the morphology of the synthesized catalyst was changed from micron-sized thin sheets to smaller blocks, and then to uniform nanoparticles (size of 50 nm) having the doped SnO2 nanoparticles with a size of 29 nm. All catalysts showed high BET surface areas featuring silt-like mesopores. In determining the priority of BVO and carboxylation, an influence evaluation of the parameters showed the order to be substrate > oxidant > solvent > catalyst. Cyclic aliphatic ketones were suitable for BVO, but linear aliphatic and aromatic aldehydes for carboxylation. Coordination of (S)-binaphthol or doping of Sn into catalyst showed little influence on BVO under m-CPBA, but the Sn-doped catalyst largely increased BVO under (NH4)2S2O8 and H2O2. Calculations revealed that the catalyst containing both Al and Sn could give BVO intermediates lower energies than the Sn-beta zeolite model. The present system exhibited merits including wider substrate scope, innocuous catalytic metal, greener oxidant, as well as lower catalyst cost.

The cobalt(II) chloride N,N,N-pincer complexes, [2-{(2,4-(C15H13)2-6-FC6H2)N=CMe}-6-(ArN=CMe)C5H3N]CoCl2 (Ar = 2,6-Me2C6H3) (Co1), 2,6-Et2C6H3 (Co2), 2,6-i-Pr2C6H3 (Co3), 2,4,6-Me3C6H2 (Co4), 2,6-Et2-4-MeC6H2 (Co5), and [2,6-{(2,4-(C15H13)2-6-FC6H2)N=CMe}2C5H3N]CoCl2 (Co6), each containing at least one N-2,4-bis(dibenzosuberyl)-6-fluorophenyl group, were synthesized in good yield from their corresponding unsymmetrical (L1–L5) and symmetrical bis(imino)pyridines (L6). The molecular structures of Co1 and Co2 spotlighted their distorted square pyramidal geometries (τ5 value range: 0.23–0.29) and variations in steric hindrance offered by the dissimilar N-aryl groups. On activation with either MAO or MMAO, Co1–Co6 all displayed high activities for ethylene polymerization, with levels falling in the order: Co1 > Co4 > Co5 > Co2 > Co3 > Co6. Indeed, the least sterically hindered 2,6-dimethyl Co1 in combination with MAO exhibited a very high activity of 1.15 × 107 g PE mol−1 (Co) h−1 at the operating temperature of 70 °C, which dropped by only 15% at 80 °C and 43% at 90 °C. Vinyl-terminated polyethylenes of high linearity and narrow dispersity were generated by all catalysts, with the most sterically hindered, Co3 and Co6, producing the highest molecular weight polymers [Mw range: 30.26–33.90 kg mol−1 (Co3) and 42.90–43.92 kg mol−1 (Co6)]. In comparison with structurally related cobalt catalysts, it was evident that the presence of the N-2,4-bis(dibenzosuberyl)-6-fluorophenyl groups had a limited effect on catalytic activity but a marked effect on thermal stability.

Herein, we developed a dual-activated prodrug, BTC, that contains three functional components: a glutathione (GSH)-responsive BODIPY-based photosensitizer with a photoinduced electron transfer (PET) effect between BODIPY and the 2,4-dinitrobenzenesulfonate (DNBS) group, and an ROS-responsive thioketal linker connecting BODIPY and the chemotherapeutic agent camptothecin (CPT). Interestingly, CPT displayed low toxicity because the active site of CPT was modified by the BODIPY-based macrocycle. Additionally, BTC was encapsulated with the amphiphilic polymer DSPE-mPEG2000 to improve drug solubility and tumor selectivity. The resulting nano-prodrug passively targeted tumor cells through enhanced permeability and retention (EPR) effects, and then the photosensitizing ability of the BODIPY dye was restored by removing the DNBS group with the high concentration of GSH in tumor cells. Light-triggered ROS from activated BODIPY can not only induce apoptosis or necrosis of tumor cells but also sever the thioketal linker to release CPT, achieving the combination treatment of selective photodynamic therapy and chemotherapy. The antitumor activity of the prodrug has been demonstrated in mouse mammary carcinoma 4T1 and human breast cancer MCF-7 cell lines and 4T1 tumor-bearing mice.

Due to their unprecedented ability to flexibly manipulate the parameters of light, metasurfaces offer a new approach to integrating multiple functions in a single optical element. In this paper, based on a single-celled metasurface composed of chiral umbrella-shaped metal–insulator–metal (MIM) unit cells, a strategy for simultaneous multiple polarization generation and wavefront shaping is proposed. The unit cells can function as broadband and high-performance polarization-preserving mirrors. In addition, by introducing a chiral-assisted Aharonov–Anandan (AA) geometric phase, the phase profile and phase retardation of two spin-flipped orthogonal circular polarized components can be realized simultaneously and independently with a single-celled metasurface via two irrelevant parameters. Benefiting from this flexible phase manipulation ability, a vectorial hologram generator and metalens array with spatially varying polarizations were demonstrated. This work provides an effective approach to avoid the pixel and efficiency losses caused by the intrinsic symmetry of the PB geometric phase, and it may play an important role in the miniaturization and integration of multipolarization-involved displays, real-time imaging, and spectroscopy systems.

In this study, the authors simulate a polygonal discrete fracture network (DFN) in rock masses. The probability models of the relevant geological parameters, including the orientation, trace length, volume density, and coordinates of the centroid, are firstly developed as fractures are in the shape of rectangles. In the process, the probability distribution of rectangular fractures with side lengths as random variables is introduced and described in terms of mean trace lengths on the basis of the probability model of disk-shaped fracture with the diameter as the random variable. The relationship between the volume density and the linear density of rectangular fractures is given for a negative exponential distribution. Following this, the coordinates of the vertices of fractures are derived based on spatial algebraic geometry, and the data for the three-dimensional DFN model are generated using the Monte-Carlo technique. The resulting three-dimensional DFN is visualized by calling the Open GL graphics database in the environment of Visual C, and the process of implementation of the DFN simulation is given. Finally, the validity of the simulation is verified by applying it to engineering practice.

Optimizing heat conduction layout is essential during engineering design, especially for sensible thermal products. However, when the optimization algorithm iteratively evaluates different loading cases, the traditional numerical simulation methods usually lead to a substantial computational cost. To effectively reduce the computational effort, data-driven approaches are used to train a surrogate model as a mapping between the prescribed external loads and various geometry. However, the existing model is trained by data-driven methods, which require intensive training samples from numerical simulations and do not effectively solve the problem. Choosing the steady heat conduction problems as examples, this paper proposes a physics-driven convolutional neural networks (PD-CNNs) method to infer the physical field solutions for randomly varied loading cases. After that, the particle swarm optimization (PSO) algorithm is used to optimize the sizes, and the positions of the hole masks in the prescribed design domain and the average temperature value of the entire heat conduction field is minimized. The goal of reducing heat transfer is achieved. Compared with the existing data-driven approaches, the proposed PD-CNN optimization framework predicts field solutions that are highly consistent with conventional simulation results. However, the proposed method generates the solution space without pre-obtained training data. We obtained thermal intensity results for holes 1, hole 2, hole 3, and hole 4 with 0.3948, 0.007, 0.0044, and 0.3939, respectively, by optimization PD-CNN model.

A series of 2-(arylimino)benzylidene-8-arylimino-5,6,7-trihydroquinoline cobalt(II) chlorides (Co1–Co6) containing a fused ring and a more inert phenyl group as the substituent at the imino-C atom has been synthesized using a one-pot synthesis method and fully characterized by FT-IR and elemental analysis. The molecular structures of Co2 and Co5 have been confirmed by X-ray diffraction as having a distorted square pyramidal geometry around a cobalt core with a tridentate N,N,N-chelating ligand and two chlorides. On activation with either methylaluminoxane (MAO) or modified methylaluminoxane (MMAO), Co1–Co6 exhibited high activities for ethylene polymerization. The least sterically hindered Co2 showed a maximum activity of 16.51 × 106 g (PE) mol−1 (Co) h−1 at a moderate temperature 50 °C. Additionally, ortho-fluoride Co6 was able to maintain a high activity not only at 70 °C but also after 60 min at 50 °C, highlighting its excellent thermal-stability and long catalytic lifetime. The resultant polyethylene showed clearly narrower molecular weight distribution (PDI: 1.3–3.1) than those produced by structurally related cobalt counterparts, indicating the positive influence of benzhydryl substitution on the catalysis. Moreover, the molecular weight (1.7–386.6 kg mol−1) of vinyl- or n-propyl-terminated polyethylene can be finely regulated by controlling polymerization parameters.

Four examples of para-nitro substituted 2-(arylimino)pyridine-nickel(II) bromide complexes of general formula, [2-{(2,6-R-4-NO2C6H2)N=CMe}C5H4N]NiBr2, but differentiable by the steric/electronic properties displayed by the ortho-groups [R = i-Pr (Ni1), Et (Ni2), CHPh2 (Ni3), CH(4-FPh)2 (Ni4)], have been prepared in good yield. For comparative purposes, the meta-nitro complex, [2-{(2,6-i-Pr2-3-NO2-4-(4-FPh)2C6H)N=CMe}C5H4N]NiBr2 (Ni5), has also been synthesized. The molecular structures of mononuclear Ni3·xH2O (x = 2, 3) and bromide-bridged dinuclear Ni4 and Ni5 are disclosed. Upon activation with either ethylaluminum dichloride (EtAlCl2) or modified methylaluminoxane (MMAO), all precatalysts displayed good catalytic performance at operating temperatures between 30 °C and 60 °C with higher activities generally seen using EtAlCl2 [up to 4.7 × 106 g PE (mol of Ni)−1 h−1]: Ni2 ~ Ni5 > Ni1 ~ Ni4 > Ni3. In terms of the resultant polyethylene (PE), Ni4/EtAlCl2 formed the highest molecular weight of the series (Mw up to 1.4 × 105 g mol−1) with dispersities (Mw/Mn) ranging from narrow to broad (Mw/Mn range: 2.2–24.4). Moreover, the melting temperatures (Tm) of the polymers generated via EtAlCl2 activation fell in a narrow range, 117.8–126.0 °C, which resembles that seen for industrial-grade linear-low density polyethylene (LLDPE). Indeed, their 13C NMR spectra revealed significant amounts of uniformly distributed long-chain branches (LCBs), while internal vinylene groups constituted the major type of chain unsaturation [vinylene:vinyl = 5.3:1 (EtAlCl2) and 9.9:1 (MMAO)].

Raman spectroscopy is a well-established and powerful tool for in situ biomolecular evaluation. Type 2 crystal nephropathies are characterized by the deposition of crystalline materials in the tubular lumen, resulting in rapid onset of acute kidney injury without specific symptoms. Timely crystal identification is essential for its diagnosis, mechanism exploration and therapy, but remains challenging. This study aims to develop a Raman spectroscopy-based method to assist pathological diagnosis of type 2 crystal nephropathies. Unknown crystals in renal tissue slides from a victim suffered extensive burn injury were detected by Raman spectroscopy, and the inclusion of crystals was determined by comparing Raman data with established database. Multiple crystals were scanned to verify the reproducibility of crystal in situ. Raman data of 20 random crystals were obtained, and the distribution and uniformity of substances in crystals were investigated by Raman imaging. A mouse model was established to mimic the crystal nephropathy to verify the availability of Raman spectroscopy in frozen biopsy. All crystals on the human slides were identified to be calcium oxalate dihydrate, and the distribution and content of calcium oxalate dihydrate on a single crystal were uneven. Raman spectroscopy was further validated to be available in identification of calcium oxalate dihydrate crystals in the biopsy specimens. Here, a Raman spectroscopy-based method for in situ identification of unknown crystals in both paraffin-embedded tissues and biopsy specimens was established, providing an effective and promising method to analyze unknown crystals in tissues and assist the precise pathological diagnosis in both clinical and forensic medicine.

Trigeminal neuralgia (TN) is a common type of peripheral neuralgia in clinical practice, which is usually difficult to cure. Common analgesic drugs are difficult for achieving the desired analgesic effect. Syb-prII-1 is a β-type scorpion neurotoxin isolated from the scorpion venom of Buthus martensi Karsch (BmK). It has an important influence on the voltage-gated sodium channel (VGSCs), especially closely related to Nav1.8 and Nav1.9. To explore whether Syb-prII-1 has a good analgesic effect on TN, we established the Sprague Dawley (SD) rats’ chronic constriction injury of the infraorbital nerve (IoN-CCI) model. Behavioral, electrophysiological, Western blot, and other methods were used to verify the model. It was found that Syb-prII-1 could significantly relieve the pain behavior of IoN-CCI rats. After Syb-prII-1 was given, the phosphorylation level of the mitogen-activated protein kinases (MAPKs) pathway showed a dose-dependent decrease after IoN-CCI injury. Moreover, Syb-prII-1(4.0 mg/kg) could significantly change the steady-state activation and inactivation curves of Nav1.8. The steady-state activation and inactivation curves of Nav1.9 were similar to those of Nav1.8, but there was no significant difference. It was speculated that it might play an auxiliary role. The binding mode, critical residues, and specific interaction type of Syb-prII-1 and VSD2rNav1.8 were clarified with computational simulation methods. Our results indicated that Syb-prII-1 could provide a potential treatment for TN by acting on the Nav1.8 target.

With the warming global climate, drought stress is considered to be the most important abiotic factor limiting plant growth and yield in the world. Drought stress has serious impacts on crop production. Many researchers have studied the influences of drought stress on crop production and plant physiology; however, few researchers have combined root exudates with root-associated microbiomes for their mutual effects under drought conditions. In this review, we systematically illustrate the impact of drought stress on root exudates and root-associated microbiomes, and then we discuss the mutual regulation of root-associated microbiomes and the host plant in helping the plant adapt to drought. Finally, we construct a framework for the mutual connections between the plant, root exudates, and the microbiome. We hope this review can provide some significant guidelines to promote the study of drought resistance in plants in association with the rhizosphere microbiota.

The aqueous room temperature mono-dehydration of sugar alcohols (D-sorbitol and D-mannitol) was conducted using functionalized yttrium oxide nanocatalysts prepared via sol-gel methods. Materials exhibited high selectivity to mono-dehydration products. Solvent and catalyst effects were also investigated and discussed. The introduction of titanium into the yttrium oxide framework would decrease both substrate conversion and mono-dehydration efficiency. In addition, studies of the catalytic mechanism indicate high mono-dehydration efficiency may come from the stability of the formed intermediate during catalysis. This work provides a highly efficient and benign system for catalytic mono-dehydration of sugar alcohols.

Metolachlor is extensively used and the most persistent chloroacetamide herbicide, thereby which its metabolites have been frequently detected in soils and surface and groundwaters. Microbial degradation is predominantly responsible for the removal of metolachlor from soil and water. However, few microbial strains reported previously are highly efficient in degrading potentials for metolachlor. We isolated the fungal strain MET-F-1 from an activated sludge, characterized as Penicillium oxalicum, which could degrade 88.6% of 50 mg/L metolachlor coupled with 0.1% glucose plus 0.1% yeast extract within 384 h under optimal conditions. Compared with metabolites produced by previously isolated microorganisms, different degradation products, i.e., MOXA, M2H, and MDES, detected by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS), were produced through hydrolytic and reductive dechlorination by MET-F-1. This is the first report on the degradation of metolachlor by Penicillium oxalicum sp. Furthermore, field plot experiments using the wheat bran inoculum method were performed and demonstrated good metolachlor-degrading activity of this strain. This study serves as a steppingstone to promote MET-F-1 strain usage as a promising agent for metolachlor-contaminated soil remediation.

The main purpose is to figure out the involved synergistic effects by combining sono-Fenton using in situ generated H2O2 and the photocatalytic process of P25 under visible light (Vis/P25). Two emerging contaminants, dimethyl phthalate (DMP) and diethyl phthalate (DEP), with similar structure but different properties were selected to examine the influence of hydrophilic and hydrophobic properties of target pollutants. Results show that there is synergy between sono-Fenton and Vis/P25, and more significant synergy can be obtained with low dose of Fe3+ or Fe2+ (0.02 mM) and for more hydrophilic DMP. Based on systematic analysis, the primary mechanism of the synergy is found to be the fast regeneration of Fe2+ by photo-electrons from P25 photocatalysis, which plays the dominant role when the Fe3+/Fe2+ concentration is low (0.02 mM). However, at high Fe3+/Fe2+ concentration (0.5 mM), the photoreduction of Fe(III) to Fe2+ can play a key role with relatively low efficiency. By studying the degradation intermediates of both DMP and DEP, the degradation pathways can be determined as the hydroxylation of aromatic ring and the oxidation of the aliphatic chain. Better mineralization performance is achieved for DMP than that for DEP due to the enhanced utilization efficiency of H2O2 by accelerating Fe2+ regeneration.

Nucleotide binding, leucine-rich-repeat (NLR) proteins are the major class of resistance (R) proteins used by plants to defend against pathogen infection. The recognition between NLRs and their cognate pathogen effectors usually triggers a rapid localized cell death, termed the hypersensitive response (HR). Flavone synthase I (FNSI) is one of the key enzymes in the flavone biosynthesis pathway. It also displays salicylic acid (SA) 5-hydroxylase (S5H) activity. A close homolog of FNSI/S5H displays SA 3-hydroxylase (S3H) activity. Both FNSI/S5H and S3H play important roles in plant innate immunity. However, the underlying molecular mechanisms and the relationship between S5H and S3H with the NLR-mediated HR are not known in any plant species. In this study, we identified three genes encoding ZmFNSI-1, ZmFNSI-2 and ZmS3H that are significantly upregulated in a maize line carrying an autoactive NLR Rp1-D21 mutant. Functional analysis showed that ZmFNSI-1 and ZmFNSI-2, but not ZmS3H, suppressed HR conferred by Rp1-D21 and its signaling domain CCD21 when transiently expressed in N. benthamiana. ZmFNSI-1 and ZmFNSI-2 physically interacted with CCD21. Furthermore, ZmFNSI-1 and ZmFNSI-2 interacted with HCT, a key enzyme in lignin biosynthesis pathway, which can also suppress Rp1-D21-mediated HR. These results lay the foundation for the further functional analysis of the roles of FNSI in plant innate immunity.

In this paper, a graphene/ITO nanorod metamaterial/U-bent-annealing (Gr/ITO-NM/U-bent-A)-based U-bent optical fiber local surface plasmon resonance (LSPR) sensor is presented and demonstrated for DNA detection. The proposed sensor, compared with other conventional sensors, exhibits higher sensitivity, lower cost, as well as better biological affinity and oxidize resistance. Besides, it has a structure of an original Indium Tin Oxides (ITO) nanocolumn array coated with graphene, allowing the sensor to exert significant bulk plasmon resonance effect. Moreover, for its discontinuous structure, a larger specific surface area is created to accommodate more biomolecules, thus maximizing the biological properties. The fabricated sensors exhibit great performance (690.7 nm/RIU) in alcohol solution testing. Furthermore, it also exhibits an excellent linear response (R2 = 0.998) to the target DNA with respective concentrations from 0.1 to 100 nM suggesting the promising medical applications of such sensors.

Uridine diphosphate glycosyltransferases (UGTs) are multifunctional detoxification enzymes, which are involved in metabolizing various chemicals and contribute to the development of insecticide resistance. However, the possible roles of UGTs in chlorantraniliprole resistance in Chilo suppressalis have rarely been studied in detail. Based on genome data, 24 UGT genes in C. suppressalis belonging to 11 families were identified, which were designated by the UGT nomenclature committee. Synergism assay data suggested that UGTs are potentially involved in chlorantraniliprole resistance in C. suppressalis. CsUGT40AL1 and CsUGT33AG3 were significantly overexpressed in the chlorantraniliprole resistant strain (12.36- and 5.34-fold, respectively). The two UGTs were highly expressed in the larval Malpighian tubules, fat body, and midgut; however, expression was lowest in the head. Injection of individual dsRNAs reduced the expression of the two target genes (by 69.34% and 48.74%, respectively) and caused significant higher larval mortality (81.33% and 54.67%, respectively). Overexpression of CsUGT40AL1 and CsUGT33AG3 was potentially involved in chlorantraniliprole resistance in C. suppressalis, as confirmed by the RNAi assay. Our findings suggest that overexpression of UGTs may contribute to chlorantraniliprole resistance in C. suppressalis.

When inverse synthetic aperture radar (ISAR) imaging maneuvers targets, the azimuth echo of the target scattering point causes a Doppler frequency time-varying problem, which leads to the blurring and defocusing of the imaging results. Traditional imaging methods struggle to meet the imaging requirements for maneuvering targets due to a poor imaging effect or low efficiency. Given these challenges, a modified chirp Fourier transform (MCFT) imaging method is proposed in this paper, based on the specific relationship between the target rotation parameters and the radar echo signal parameters. Firstly, discrete chirp Fourier transform is used to quickly estimate the target’s coarse rotation ratio. Then, the minimum entropy function and gradient descent method are used to calculate the target’s accurate rotation ratio. Finally, the azimuth focusing image is accomplished by performing MCFT once on the azimuth echo signal using the accurate rotation ratio. This method avoids estimating and separating the sub-echo components one-by-one, considerably improves the imaging speed, and guarantees the best imaging quality by applying the global minimum entropy principle. The experimental results show that the proposed method effectively achieves the two-dimensional, high-quality, and fast imaging of maneuvering targets.

Phylogeographical analyses on Squalidus argentatus samples from thirteen localities within mainland China and Taiwan were conducted for biogeographic studies, as their dispersal strictly depends on geological evolution of the landmasses. A total of 95 haplotypes were genotyped for mtDNA cyt b gene in 160 specimens from nine river systems. Relatively high levels of haplotype diversity (h = 0.984) and low levels of nucleotide diversity (π = 0.020) were detected in S. argentatus. Two major phylogenetic haplotype groups, A and B, were revealed via phylogenetic analysis. The degree of intergroup divergence (3.96%) indicates that these groups diverged about 4.55 myr (million years) ago. Haplotype network and population analyses indicated significant genetic structure (FST = 0.775), largely concordant with the geographical location of the populations. According to SAMOVA analysis, we divided these populations into four units: Yangtze-Pearl, Qiantang-Minjiang, Jiulong-Beijiang and Taiwan groups. Mismatch distribution analysis, neutrality tests and Bayesian skyline plots indicated a significant population expansion for lineage A and B, approximately dated 0.35 and 0.04 myr ago, respectively. We found strong geographical organization of the haplotype clades across different geographic scales that can be explained by episodes of dispersal and population expansion followed by population fragmentation and restricted gene flow.

Squalidus argentatus (Sauvage and Dabry de Thiersant 1874) is a small-sized freshwater fish which is distributed in Mainland China, Hainan Island and Taiwan. The populations of S. argentatus have dropped sharply probably due to overharvesting and water pollution recently. Eleven polymorphic microsatellite markers were developed for the cyprinid fish S. argentatus. These new markers were tested on 43 individuals collected from Yangtze River and Qiantang River. The number of alleles, observed and expected heterozygosity per locus, in two populations ranged from 3 to 14, from 0.333 to 0.954 and from 0.480 to 0.928, respectively. Only two loci are significantly deviated from Hardy–Weinberg expectations due to the heterozygote deficiency. No significant linkage disequilibrium was detected between the pairwise comparisons of these loci. These polymorphic microsatellite loci will enable us to study the genetic variation, population structure, and conservation genetics of this species in the future.

The prioritization of capability gaps for weapon system of systems is the basis for design and capability planning in the system of systems development process. In order to address input information uncertainties, the prioritization of capability gaps is computed in two steps using the conditional evidential network method. First, we evaluated the belief distribution of degree of required satisfaction for capabilities, and then calculated the reverse conditional belief function between capability hierarchies. We also provided verification for the feasibility and effectiveness of the proposed method through a prioritization of capability gaps calculation using an example of a spatial-navigation-and-positioning system of systems.

Using Pareto optimization in Multi-Objective Reinforcement Learning (MORL) leads to better learning results for network defense games. This is particularly useful for network security agents, who must often balance several goals when choosing what action to take in defense of a network. If the defender knows his preferred reward distribution, the advantages of Pareto optimization can be retained by using a scalarization algorithm prior to the implementation of the MORL. In this paper, we simulate a network defense scenario by creating a multi-objective zero-sum game and using Pareto optimization and MORL to determine optimal solutions and compare those solutions to different scalarization approaches. We build a Pareto Defense Strategy Selection Simulator (PDSSS) system for assisting network administrators on decision-making, specifically, on defense strategy selection, and the experiment results show that the Satisficing Trade-Off Method (STOM) scalarization approach performs better than linear scalarization or GUESS method. The results of this paper can aid network security agents attempting to find an optimal defense policy for network security games.

Improving network security is a difficult problem that requires balancing several goals, such as defense cost and need for network efficiency, in order to achieve proper results. In this paper, we devise method of modeling network attack in a zero-sum multi-objective game and attempt to find the best defense against such an attack. We combined Pareto optimization and Q-learning methods to determine the most harmful attacks and consequently to find the best defense against those attacks. The results should help network administrators in search of a hands-on method of improving network security.

This paper attempts to study and optimize the affinity partitioning conditions of papain in an aqueous two-phase system (ATPS). The effect of the amount of ionic liquids (ILs), the concentration of K2HPO4, temperature, pH, and the volume of papain solution were discussed concretely. The optimum conditions were determined as ionic liquid was 1.4 g and K2HPO4 was 1.4 g, the extraction efficiency of papain could reach 98.33% with pH unadjusted. The temperature and the pH of the solution are major parameters that influence the partitioning of protein in ILs-based ATPSs. The partition of papain to the IL-rich phase was enhanced by increasing the amount of ILs, the concentration of K2HPO4, and temperature, especially at its isoelectric point.