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Author(s): Baodong Liu (corresponding author) [1]; Xin Ye (corresponding author) [2]; Weijun Fan (corresponding author) [3]; Xiuyi Zhi (corresponding author) [1]; Haitao Ma [4]; Jun Wang [2]; Peng Wang [5]; [...], This expert consensus reviews current literature and provides clinical practice guidelines for the diagnosis and treatment of multiple ground glass nodule-like lung cancer. The main contents of this review include the following: ① follow-up strategies, ② differential diagnosis, ③ diagnosis and staging, ④ treatment methods, and ⑤ post-treatment follow-up. Keywords: Multiple ground glass nodule-like lung cancer, multiple primary lung cancer, pulmonary ground glass nodule

Memristors can be introduced into neural networks for convenient chaos generation even combined with regulating firing activities. This article presents a no-equilibrium memristive ring neural network with dual memristors, one of which is used to mimic neural synapses and the other to describe the effects of external electromagnetic radiation. Initial conditions of different variables bring the chance to arise bistability and tristability. Initial offset-boosted coexisting hidden attractors are arranged by offset boosting, where homogeneous and heterogeneous multistabilities are captured simultaneously. When homogeneous attractors break barriers and connect together, the growth of attractors becomes apparent, allowing for flexible regulation over time and effective self-reproduction through varying initial values. Interestingly, abundant hidden firing patterns are well regulated by memristor-related parameters or the external current. Bursting and spiking firings are switched flexibly by proper knobs. In addition, infinitely many coexisting bursting and spiking firing patterns are also triggered by the initial values of the memristor acting as the coupled synapse or considered as an external electromagnetic radiation. Finally, a digital circuit based on CH32 is constructed to verify the characteristics of various coexisting hidden attractors and abundant firing patterns, thus confirming these new findings.

This work proposes a bio-inspired decision-making memristive circuit drawing on Hull’s secondary learning system. This circuit can not only mimic the decision-making initiated by secondary drive stimuli and shaped and guided by secondary reinforcers via integrating classical conditioning (CC) and operant conditioning (OC), but also consider the factors that influence decision-making, such as demand states, incentive motivation, and habit strength. These bionic functions have not yet been implemented by existing memristive circuits. Our circuit primarily includes CC module, drive regulation module, habit memory module, incentive generation module, and winner-takes-all module, which is designed through a modular hierarchical circuit design method. Memristors play a core role in our circuit and enable the circuit to perform brain-like online learning in an in-memory computing way, which has power and area advantages. The PSPICE-based simulations in various scenarios show that our circuit has a strong adaptive decision-making ability since more bionic features are considered. The proposed circuit can be applied to a bionic intelligent robot, enabling the robot capable of autonomous associative learning abilities to perform complex tasks such as detection and rescue. Note to Practitioners—This work is motivated by the problem of neuromorphic circuit design for bio-inspired learning and decision-making. To realize brain-like online in-situ learning in an in-memory computing way and enhance the adaptability of the circuit in dynamic environment, a memristive circuit integrating CC and OC is proposed. Referring to Hull’s secondary learning system, the proposed circuit takes into account factors that affect decision-making, such as demand states, incentive motivation, and habit strength, as well as the fact that decision-making processes can be evoked by secondary drive stimuli and shaped by secondary reinforcers, which is unaddressed by existing memristor-based works. The bionic foraging simulations in various scenarios show that our circuit can make favorable adaptive decisions based on various cues categorized by associative memories when engaging with their environment. Such a bio-inspired memristive circuit system can be applied to bionic robots or rescue detection robots through large-scale integration to achieve adaptive learning and decision-making of complex tasks with low power consumption.

Nowadays, severe traffic accidents attributed to driver drowsiness have become increasingly frequent, prompting a widespread concern among researchers in electroencephalography (EEG)-based driver drowsiness detection. However, due to the significant differences in EEG signals between participants, the prevalence of redundant information in multichannel EEG data, and the computational burden in combining channel selection with neural networks, achieving an accurate and efficient real-time driver drowsiness recognition remains challenging. To overcome these limitations, this article proposes a novel deep learning framework that utilizes a separable convolutional neural network (CNN) to mine the intricate spatiotemporal information in EEG signals, combined with the channel selection layer to jointly optimize EEG channels and network parameters. This layer employs an efficient embedded Gumbel-Softmax technique for discrete sampling and differentiable approximation. To prevent the introduction of duplicate channels, we impose penalties on the row sums of the selection matrix to encourage the selection neurons to learn distinct channels, enabling the neural network to train in an end-to-end manner. The proposed model achieves an average accuracy of 80.84% and an F1 score of 79.65% in cross-subject drowsiness identification for 11 subjects on the publicly available sustained-attention driving task dataset. Compared to the results of recent relevant works, our model exhibits superior performance, surpassing state-of-the-art (SOTA) deep learning methods by 1.47%. Furthermore, building upon the model’s advantages, we have further actualized a real-time driver drowsiness detection graphical user interface (GUI), providing a practical reference for real-world applications.

Thanks to the biomimetic properties of synaptic plasticity, memristors are often utilized to mimic biological neuronal synapses. This article presents a new memristor synapse coupling (MSC) approach for producing multidirectional multidouble-scroll attractors. Through adopting flux-controlled hyperbolic memristor synapses to couple a Hopfield neural network, a novel multidirectional multidouble-scroll Hopfield neural network (MDMDSHNN) is constructed. Theoretical results and numerical calculations indicate that MDMDSHNN is capable of producing any desired amount of multidirectional multidouble-scroll attractors, including unidirectional (1-D), bidirectional (2-D), and three-directional (3-D) multidouble-scroll attractors. Furthermore, an infinite amount of initial offset-boosted coexisting multidouble-scroll chaotic attractors possessing identical shapes but different positions, i.e., homogeneous extreme multistability are also found via switching the memristor initial values. Furthermore, to validate the physical implementability and practicality of MDMDSHNN, the digital hardware platform is performed. Finally, to investigate MDMDSHNN in practical application, an image encryption scheme with superior security performance is given by employing the homogeneous multidouble-scroll chaotic sequences, further illustrating good superiority and effectiveness of the present MSC method.

The large utilization of red mud (RM) presents many challenges due to its high alkalinity, which is attributed to the presence of CaO and Na2O. High phosphorus oolitic hematite (HPOH) is a typical refractory ore for Fe and P separation. During the direct reduction of HPOH to produce direct reduced iron (DRI) and achieve simultaneous dephosphorization, sodium or calcium salts are typically added as dephosphorization agents. This study investigated the feasibility of using RM as a green dephosphorization agent. Co-reduction of RM with HPOH was conducted to prepare DRI. By adding 60 % RM and using a reductant dosage of 17 %, a DRI with 90.89 % Fe and 0.095 % P was obtained. The corresponding total iron recovery was 87.79 %. This indicated that the addition of RM facilitated effective simultaneous iron extraction and dephosphorization. The co-reduction mechanisms were analyzed using X-ray diffraction (XRD), thermodynamic analysis, and scanning electron microscopy combined with energy dispersive spectroscopy (SEM–EDS). In the absence of RM, the reduction of fluoroapatite occured, leading to the incorporation of P into metallic iron. However, with adding RM, albite and anorthite were generated, hindering the reduction behavior of fluoroapatite and reducing P content in DRI. Thus, RM effectively served as a green dephosphorization agent in the co-reduction process.

Studying molecular recognition in aqueous environments is crucial for understanding biological processes. Herein, we present a series of N-doped endo-functionalized anthracene-based cavities by introducing inward-directed pyridine units. The subtle modifications can significantly affect the binding properties of these receptors, which is similar to the mutations in biological systems. Combining theoretical calculations and molecular recognition experiments demonstrated that these “mutations” have an impact on the hydrophobicity and charge distribution of the hosts, consequently significantly influencing molecular recognition. This is crucial for understanding the intricate mechanisms of molecular-level interactions in biological receptors.

Worldwide forest fires have occurred frequently in recent years, a result of which may be the emission of so-called “legacy” organochlorine pollutants (OCPs) accumulated in forests. However, few studies have measured the emission factors (EFs) of the toxicity of the OCPs from forest fires. In this study, the EFs of vegetation burning were observed in forests along the altitudinal gradient from 1000 to 4200 m, and the EFs of ∑DDTs (dechlorodiphenylthrichloroethanes), HCB (hexachlorobenzene), ∑HCHs (hexachlorocyclohexanes), and ∑PCBs (polychlorinated biphenyls) were 2050 ± 1175, 379 ± 409, 48 ± 51, and 65 ± 59 ng/kg, respectively. Re-evaporation was the primary mechanism of the emission of OCP from forest fires. The masses of HCB, β-HCH, o,p′-DDD, p,p′-DDD, and PCB-28 in smoke increased 3–7 times compared with those in unburnt vegetation, suggesting the formation of these pollutants by the pyrolysis of biomass or other pollutants. Based on the observed EFs, previously estimated quantities of fire-emitted OCPs in the Himalayan regions were revisited. The DDT emissions from the Himalayan forest fires increased ∼70% compared with the previous estimation (from 19 to 32 kg/year). This highlighted that the EF observations could decrease the uncertainties of estimating OCP emissions from forest fires, which is helpful in revealing the potential roles of forest fires on global POP cycling.

Histone lysine-specific demethylase 1 (LSD1) is hyperactive in breast cancer, which is associated with the metastasis of the tumor. Current irreversible LSD1 inhibitors are all synthesized by covalently binding to the flavin adenine dinucleotide cofactor, which often have side effects due to the high affinity for a variety of targets. Here, we identified isoforsythiaside (IFA), a natural phenylpropanoid glycoside isolated from Forsythia suspensa, as a novel covalent inhibitor of LSD1. The target ligand fishing technique and LC–MS/MS analysis identified that IFA could covalently bind to the Ser817 residue of LSD1 by α,β-unsaturated ketone moiety to block the amine oxidase-like domain of LSD1. Moreover, RBMS3/Twist1/MMP2, the downstream signaling pathway of LSD1, was activated after IFA treatment to inhibit the metastasis of MDA-MB-231 cells in vitroand in vivo. This study provided novel molecular templates for development of LSD1 covalence-binding inhibitor and laid a foundation for developing agents against breast carcinoma metastasis for targeting LSD1.

Anomalous X-ray diffraction (AXD) and neutron diffraction can be used to crystallographically distinguish between metals of similar electron density. Despite the use of AXD for structural characterization in mixed metal clusters, there are no benchmark studies evaluating the accuracy of AXD toward assessing elemental occupancy in molecules with comparisons with what is determined via neutron diffraction. We collected resonant diffraction data on several homo and heterometallic clusters and refined their anomalous scattering components to determine metal site occupancies. Theoretical resonant scattering terms for Fe0, Co0, and Zn0were compared against experimental values, revealing theoretical values are ill-suited to serve as references for occupancy determination. The cluster featuring distinct cation and anion metal compositions [CoCp2*][(tbsL)Fe3(μ3–NAr)] was used to assess the accuracy of different f′ references for occupancy determination (f′theoretical± 15–17%; f′experimental± 10%). This methodology was applied toward calculating the occupancy of three different clusters: (tbsL)Fe2Zn(py) (6), (tbsL)Fe2Zn(μ3–NAr)(py) (7), and [CoCp*2][(tbsL)Fe2Zn(μ3–NAr)] (8). The first two clusters maintain 100% Fe/Zn site isolation, whereas 8showed metal mixing within the sites. The large crystal size of 8enabled collection of neutron diffraction data which was compared against the results found with AXD. The ability of AXD to replicate the metal occupancies as determined by neutron diffraction supports the AXD occupancy methodology developed herein. Furthermore, the advantages innate to AXD (e.g., smaller crystal sizes, shorter collection times, and greater availability of synchrotron resources) versus neutron diffraction further support the need for its development as a standard technique.

The generation of human emotion is often influenced by memory and personality. However, few reported circuits of emotion generation have considered this view. This work proposes a memristive emotion generation circuit with the functions of emotional memory and retrieval, and personality traits deployment. It mimics the emotional circuit of the brain and consists of six modules, which are input module, sensory cortex module, amygdala module, orbitofrontal module, hippocampus module, and personality module. When inputting multimodal signals, the proposed design can not only generate 2-D continuous emotions but also perform emotional memory and retrieval. Meanwhile, it provides various personality traits to mimic individual differences amid emotion generation based on the Big Five model. All in all, the designed memristive circuit provides a brain-like hierarchical and parallel emotion-generating method. It offers advantages in lower power consumption, area, and in-memory processing. In addition, the nonideality analysis shows the circuit’s robustness. This work could provide a reference for the implementation of human-like emotional robots.

This article mainly explores the fixed-time control problem of fuzzy semi-Markov systems with uncertainties and unknown transition rates. Firstly, the T-S fuzzy semi-Markov system is established by using the membership relation of fuzzy logic and Markov probability property. Then, unlike the existing fixed-time control strategies, this work uses a hyperbolic sine function to replace the traditional multiple powers fixed-time control method and construct a novel fixed-time adaptive integral sliding mode control strategy, which reduces the complexity of the controller and adaptive law while optimizing the sliding mode surface and improving the fixed-time convergence performance of the system. Moreover, compared with current methods that require the assumption that the unknown function satisfies the Lipschitz condition or is bounded, the fully connected neural network is introduced to approximate the unknown nonlinear function in the system, improving the intelligence and practicality of the controller. Finally, the theoretical results are verified through numerical simulation, showing the superior performance of achieving fixed-time stability through the proposed control scheme, the gap in the study of fixed-time control using hyperbolic sine functions and fully connected neural networks is filled.

Cyclohexanol is a key intermediate in the preparation of nylon materials. However, commercial catalysts for the preparation of cyclohexanol have low catalytic activity. We developed a method for the production of HZSM-5 zeolites by hydrothermal synthesis using the structural additive poly(ethylene glycol) (PEG) as a modifier. The amount of PEG added had an effect on the specific surface area and Bro̷nsted acid sites of the zeolites. The HZSM-5 zeolite prepared by PEG addition of 1.5 g had a large specific surface area, more Bro̷nsted acid sites, and a rich pore structure, which facilitated the exposure of more Bro̷nsted acid sites to HZSM-5 and the diffusion process of cyclohexene and cyclohexanol. Under the optimized reaction conditions of a reaction temperature of 130 °C, a reaction pressure of 0.95 MPa, a reaction time of 4 h, a cyclohexene to water volume ratio of 1:1, a stirring speed of 600 rpm, and a catalyst dosage of 35 wt %, the catalytic performance of 1.5 g of PEG-modified HZSM-5 was better, with 15.93% cyclohexene conversion and 99.35% cyclohexanol selectivity. The PEG-modified HZSM-5 zeolite still maintained a high catalytic performance after five reuse cycles.

Two-dimensional (2D) hybrid organic–inorganic perovskites (HOIPs) offer an outstanding opportunity for spin-related technologies owing in part to their tunable structural symmetry breaking and distortions driven by organic–inorganic hydrogen (H) bonds. However, understanding how H-bonds tailor inorganic symmetry and distortions and therefore enhance spin splitting for more effective spin manipulation remains imprecise due to challenges in measuring H atom positions using X-ray diffraction. Here, we report a thermally induced structural transition (at ∼209 K) for a 2D HOIP, (2-BrPEA)2PbI4[2-BrPEA = 2-(2-bromophenyl)ethylammonium], which induces inversion asymmetry and a strong spin splitting (ΔE> 30 meV). While X-ray diffraction generally establishes heavy atom coordinates, we utilize neutron diffraction for accurate H atom position determination, demonstrating that the structural transition-induced rearrangement of H-bonds with distinct bond strengths asymmetrically shifts associated iodine atom positions. Consequences of this shift include an increased structural asymmetry, an enhanced difference between adjacent interoctahedra distortions (i.e., Pb–I–Pb bond angles), and therefore significant spin splitting. We further show that H-only density-functional theory (DFT) relaxation of the X-ray structure shifts H atoms to positions that are consistent with the neutron experimental data, validating a convenient pathway to more generally improve upon HOIP H-bonding analyses derived from quicker/less-expensive X-ray data.

Conduct disorder is associated with the highest burden of any mental disorder in childhood, yet its neurobiology remains unclear. Inconsistent findings limit our understanding of the role of brain structure alterations in conduct disorder. This study aims to identify the most robust and replicable brain structural correlates of conduct disorder.

Discrete memristors have been employed in discrete maps for the purpose of chaos generation and regulation. In this paper, a novel universal model for discrete memristors is proposed to generate multi-attractors. The classical Hénon map and Rulkov neuron are chosen as two examples to verify the effectiveness of the proposed memristor. Coexisting homogeneous attractors are identified in the phase space by memristor-induced offset boosting. An arbitrarily desired number of coexisting attractors is extracted by the appropriate feedback strength of the memristor. What adds further interest to this case is that the amplitude is rescaled by a memristor-related parameter that works well over an infinite range. Number-related parameters are extracted to rescale the oscillation range of the chaotic signals. Moreover, CH32-based circuit implementation is built, which aligns with numerical simulation results. Finally, coexisting homogeneous chaotic signals are tested to explore their robust performance in the application of pseudo-random number generator.

Catalysts for the hydrogenation of adiponitrile (ADN) have been extensively studied, but achieving high selectivity of hexamethylenediamine (HMDA) in the absence of additional alkali inhibitors still poses many challenges. Herein, we fabricated nickel-based catalysts modified with Na additives for the liquid-phase hydrogenation of ADN in a fixed-bed reactor. The results showed that suitable weak acid sites are more conducive to enhancing the selectivity for HMDA, while stronger acid sites tend to promote the formation of higher amines. Moreover, the introduction of an appropriate amount of Na additive facilitates the dispersion of nickel, increasing the content of Ni0species. Its electron-donating capacity to nickel aids in hydrogen adsorption and dissociation, thereby enhancing hydrogenation activity and favoring the selectivity for HMDA. Under optimal conditions of 120 °C and 4 MPa, improved catalytic performance with 100% ADN conversion and 82.07% HMDA selectivity were achieved over the Ni-0.15Na/Al2O3catalyst.

Covalently cross-linked rubber is indispensable in many fields, such as automotive tires, gaskets, and sports equipment. It is of great significance to endow rubber with an efficient recycling performance. Herein, a tetrafunctional nitroxide radical cross-linked agent (QT) was synthesized and introduced into nitrile rubber (NBR) to prepare thermoreversible nitrile rubber (QNBR) by conventional rubber processing technology without additional additives. The nitroxide radicals in QT have the ability to react with the unsaturated carbon–carbon double bonds in the molecular chain of NBR to generate thermoreversible alkoxyamine (C-ON) bonds, which can dissociate and reform at the corresponding temperature. The study indicates that the recyclability and mechanical properties of QNBR are excellent and can meet the needs of practical applications. Furthermore, with the addition of the carbon black, reinforced nitrile rubber composite (QcNBR) also has high mechanical strength (up to 23.79 ± 1.76 MPa) and reversible efficiency (up to 67%). This study demonstrates that constructing a thermoreversible alkoxyamine cross-linking network is an effective method for the sustainable application of rubber materials.

China is experiencing rapid population aging. The one contributing factor affecting senior citizens’ lives is the disconnect between the built environment in urban and rural areas and the behavioral preferences of older adults. However, research on the relation between the built environment and the behavior of older individuals has been limited. Thus, this paper uses the most recent health tracking data on factors influencing aging in China released in 2020 (China Senior Health Survey Tracking Survey). Applying traditional regression, least absolute shrinkage and selection operator regression, and two decision tree optimization models from machine learning, a comprehensive comparative study is carried out to investigate the correlation between the built environment and the physical activity, dietary habits, and social interactions of older age groups. The findings reveal that built environment variables most significantly impact physical activity, accounting for 52.525%, followed by social interaction behaviors at 50.202% and dietary intake at 47.991%. Furthermore, the authors identify population density and greenness rate as the built environment factors having considerable effects on the behavior of older adults. Thus, this study establishes a theoretical foundation for developing age-friendly community environments for older adults.

MiRNA (MicroRNA) can target and regulate mRNA, thereby influencing the biological processes of tumors. The extract of Tibetan medicine Rhodiolademonstrates anti-tumor effects by targeting and regulating cancer-related signaling molecules and pathways. This review aims to pave the way for integrating salidroside into clinical practice, fully leverage its multi-effective and multi-targeted biological effects, and innovate approaches to tumor prevention and management, thus enhancing cancer prognosis. We initially delve into the occurrence and mechanism of action of miRNA, as well as the anti-tumor mechanism of the miRNA-mRNA signaling axis. Additionally, it was described that salidroside can hinder cancer cell proliferation by inducing autophagy and apoptosis, inhibiting cell metastasis and invasion, and modulating the tumor microenvironment and cell cycle. Moreover, examples from lung cancer, gastric cancer, and nasopharyngeal carcinoma confirm that salidroside regulates the onset and progression of tumors through the miRNA-mRNA signaling axis. This groundbreaking discovery carries significant implications for cancer diagnosis and treatment, offering a novel pathway for therapeutic intervention.

The use of sulfuric acid to catalyze the synthesis of methyl chloroacetate from chloroacetic acid and methanol leads to equipment corrosion and separation difficulties. To solve the above problems and treat chloroacetic acid efficiently, the esterification of chloroacetic acid was catalyzed with a cation exchange resin in this work. The effects of the alcohol/acid molar ratio, catalyst dosage, reaction temperature, and reaction time were investigated by single-factor and orthogonal experiments. The excellent catalytic performance was reached with 70.11% chloroacetic acid conversion under the optimal reaction conditions of 1.4:1 alcohol/acid molar ratio, 3 wt % catalyst dosage, 70 °C reaction temperature, and 2 h reaction time. In addition, a power-law kinetic model for methyl chloroacetate was developed over a cation exchange resin. The activation energy and the pre-exponential factor were calculated by the Arrhenius equation: Ea+= 38.83 kJ/mol, Ea–= 53.64 kJ/mol, A+= 1.71 × 105L0.84mol–0.84h–1, and A–= 1.15 × 105L0.54mol–0.54h–1. After verification, the kinetic model can accurately predict the esterification reaction results of chloroacetic acid and methanol under experimental conditions, which provides theoretical guidance for the simulation of the chloroacetic acid esterification process.

Reservoir computing (RC) contains two significant variants: wide RC and deep RC. The hybrid wide-deep architecture absorbs their strengths with a powerful parallel processing capability while enhancing the memory capacity of reservoirs. However, the fully analog RC circuit combining the two structures has yet to be proposed, mainly due to unmanageable hierarchical signal processing. Here we report a full-analog memristive RC circuit with a hybrid wide-deep architecture comprising an input module, mask module, reservoir module, and readout module. The input module can generate continuous voltages with temporal sequences. The mask module provides parallel mask processes, laying the foundation for implementing a wide RC structure. The reservoir module includes dynamic memristors and postprocessing circuits. Dynamic memristors can produce high-dimensional reservoir states, and postprocessing circuits allow memristive reservoir circuits to be cascaded to achieve a deep RC structure. The readout module mainly consists of a nonvolatile memristor crossbar and an analog integrator, enabling an efficient multiplication-and-accumulation operation. The simulation results in LTspice illustrate that the memory capacity of the proposed circuit is 91.6% higher than that of wide RC. Moreover, it can efficiently perform temporal tasks, obtaining a high accuracy of 98.99% in arrhythmia detection.

Atmospheric phosphorus is a vital nutrient for ecosystems whose sources and fate are still debated in the fragile Himalayan region, hindering our comprehension of its local ecological impact. This study provides novel insights into atmospheric phosphorus based on the study of total suspended particulate matter at the Qomolangma station. Contrary to the prevailing assumptions, we show that biomass burning (BB), not mineral dust, dominates total dissolved phosphorus (TDP, bioavailable) deposition in this arid region, especially during spring. While total phosphorus is mainly derived from dust (77% annually), TDP is largely affected by the transport of regional biomass-burning plumes from South Asia. During BB pollution episodes, TDP causing springtime TDP fluxes alone accounts for 43% of the annual budget. This suggests that BB outweighs dust in supplying bioavailable phosphorus, a critical nutrient, required to sustain Himalayas’ ecological functions. Overall, this first-hand field evidence refines the regional and global phosphorus budget by demonstrating that BB emission, while still unrecognized, is a significant source of P, even in the remote mountains of the Himalayas. It also reveals the heterogeneity of atmospheric phosphorus deposition in that region, which will help predict changes in the impacted ecosystems as the deposition patterns vary.

The magnetohydrodynamic (MHD) flow has been concerned widely for its widespread adoption in the field of astrophysics, electronics and many other industries over the years. The purpose of this article is to introduce the variable and distributed order space fractional models to characterize the MHD flow and heat transfer of heterogeneous viscoelastic fluids in a parallel plates. Based on the central difference approximation of Riesz space fractional derivative, the Crank–Nicolson difference scheme for the governing equations is established, and the effectiveness of the algorithm is verified by two numerical examples. We examine the effects of fractional-order model parameters on the velocity and temperature, our investigation indicates that for the constant fractional model, the larger the fractional order parameter, the smaller the velocity and temperature. The variable space fractional method can be used to describe dynamic behavior with time and space dependence, while the distributed space fractional model can describe various phenomena in which the number of differential orders varies over a certain range, characterizing their complex processes over space, and it is also more suitable for simulating the fluid flow and thermal behavior of complex viscoelastic magnetic fluid. [ABSTRACT FROM AUTHOR]

The intrinsic fluorescence of berberine is very weak, which can be enhanced by its interaction with specific aptamers. A simple and sensitive DNA sensor for visual detection of berberine has here been established. When using this sensor, there was a good linear relationship between the change in fluorescence intensity of berberine and the concentration of berberine in the range of 16–2000 nM, with a detection limit of 5.1 nM. The change in fluorescence intensity was caused by the addition of aptamers. A detection limit of 170.1 nM was acquired by reading the RGB values of fluorescent images with a smartphone for the quantification of berberine. Common antibiotics did not interfere with the measurement of the berberine concentration. The molecular ion peaks of the complexes formed by the aptamer and berberine could be clearly observed by electrospray ionization mass spectrometry. The UV-vis absorption spectra, circular dichroism spectra, and fluorescence spectra indicated a strong interaction between berberine and the aptamer. The dissociation constant (Kd) between berberine and the aptamer was 1.91 μM. This sensor was both simple and sensitive, requiring only a 21-base oligonucleotide. It realized a visual quantitative analysis with a smartphone. This method could also be used for similar fluorescence visualization determination of aptamer-based drug molecules.

The rheological properties of polymer solutions are extremely complex and it leads to a specific power-law behavior in response to creep experiments, which can not be better characterized by the classical viscoelastic constitutive models. The renowned viscoelastic fluid models with fractional derivatives have shown astonishing advantages in characterizing the broad relaxation process of the multiple materials with power-law response behavior. In this paper, we use the fractional Maxwell constitutive model to simulate the power-law responses for several polymer solutions through experimental measuring, and better fitting results than classical models can be found.

Abstract: The activation of T lymphocytes plays a very important role in T-cell-mediated immune response. Though there are many related literatures, the changes of membrane surface nanostructures and adhesion property of T lymphocytes at different activation stages have not been reported yet. However, these investigations will help us further understand the biophysical and immunologic function of T lymphocytes in the context of activation. In the present study, the membrane architectures of peripheral blood T lymphocytes were obtained by AFM, and adhesion force of the cell membrane were measured by acquiring force–distance curves. The results indicated that the cell volume increased with the increases of activation time, whereas membrane surface adhesion force decreased, even though the local stiffness for resting and activated cells is similar. The results provided complementary and important data to further understand the variation of biophysical properties of T lymphocytes in the context of in vitro activation.

In this contribution, the problem of fixed-time stabilization in multi-weighted complex networks via the novel adaptive pinning nonchattering control based on the linear matrix inequality (LMI) method, as well as its application to image protection is addressed. Different from the traditional methods, a novel fixed-time stable form is proposed and the convergence time is estimated based on beta function. Next, utilizing the designed continuous adaptive control strategy, a sufficient LMI condition is presented to ensure the fixed-time stabilization of multi-weighted complex networks. Furthermore, the novel nonchattering adaptive pinning control protocol is given to guarantee the fixed-time stabilization of the system only by controlling a small number of nodes. Note that a scheme of how to select the number of control nodes is put forward accordingly. Finally, the effectiveness of the proposed method is verified by the actual financial model. Meanwhile, a number of encryption experiments are carried out based on three networks, and the mean and variance of the encryption performance are calculated to show the stability and robustness of image encryption different from the existing research works.

In this article, a bionic localization memristive circuit is proposed, which mainly consists of head direction cell module, grid cell module, place cell module and decoding module. This work modifies the two-dimensional Continuous Attractor Network (CAN) model of grid cells into two one-dimensional models in X and Y directions. The head direction cell module utilizes memristors to integrate angular velocity and represents the real orientation of an agent. The grid cell module uses memristors to sense linear velocity and orientation signals, which are both self-motion cues, and encodes the position in space by firing in a periodic mode. The place cell module receives the grid cell module's output and fires in a specific position. The decoding module decodes the angle or place information and transfers the neuron state to a ‘one-hot’ code. This proposed circuit completes the localizing task in space and realizes in-memory computing due to the use of memristors, which can shorten the execution time. The functions mentioned above are implemented in LTSPICE. The simulation results show that the proposed circuit can realize path integration and localization. Moreover, it is shown that the proposed circuit has good robustness and low area overhead. This work provides a possible application idea in a prospective robot platform to help the robot localize and build maps.

This article investigates the quasi-synchronization for fractional multiweighted coupled neural networks (FMCNNs) with discontinuous activation functions and mismatched parameters. First, under the generalized Caputo fractional-order derivative operator, a novel piecewise fractional differential inequality is established to study the convergence of fractional systems, which significantly extends some related published results. Subsequently, by exploiting the new inequality and Lyapunov stability theory, some sufficient quasi-synchronization conditions of FMCNNs are presented by aperiodic intermittent control. Meanwhile, the exponential convergence rate and synchronization error’s bound are given explicitly. Finally, the validity of theoretical analysis is confirmed by numerical examples and simulations.

This study aims to identify dynamic patterns within the spatiotemporal feature space that are specific to nonpsychotic major depression (NPMD), psychotic major depression (PMD), and schizophrenia (SCZ). The study also evaluates the effectiveness of machine learning algorithms based on these network manifestations in differentiating individuals with NPMD, PMD, and SCZ. A total of 579 participants were recruited, including 152 patients with NPMD, 45 patients with PMD, 185 patients with SCZ, and 197 healthy controls (HCs). A dynamic functional connectivity (DFC) approach was employed to estimate the principal FC states within each diagnostic group. Incremental proportions of data (ranging from 10% to 100%) within each diagnostic group were used for variability testing. DFC metrics, such as proportion, mean duration, and transition number, were examined among the four diagnostic groups to identify disease-related neural activity patterns. These patterns were then used to train a two-layer classifier for the four groups (HC, NPMD, PMD, and SCZ). The four principal brain states (i.e., states 1,2,3, and 4) identified by the DFC approach were highly representative within and across diagnostic groups. Between-group comparisons revealed significant differences in network metrics of state 2 and state 3, within delta, theta, and gamma frequency bands, between healthy individuals and patients in each diagnostic group (p< 0.01, FDR corrected). Moreover, the identified key dynamic network metrics achieved an accuracy of 73.1 ± 2.8% in the four-way classification of HC, NPMD, PMD, and SCZ, outperforming the static functional connectivity (SFC) approach (p< 0.001). These findings suggest that the proposed DFC approach can identify dynamic network biomarkers at the single-subject level. These biomarkers have the potential to accurately differentiate individual subjects among various diagnostic groups of psychiatric disorders or healthy controls. This work may contribute to the development of a valuable EEG-based diagnostic tool with enhanced accuracy and assistive capabilities.

Achieving unity quantum efficiency in single-photon emitters (SPEs) is a holy grail in quantum information science. Through plasmonic coupling it is possible to increase the quantum efficiency of SPEs by increasing the radiative decay rate, but to approach unity quantum efficiency, non-radiative decay must be mitigated. Here we show that non-radiative decay in two-dimensional WSe2quantum emitters can be electrically suppressed through charge depletion by using dual gate configurations under a large electric field. In this condition, for site-controlled SPEs in WSe2coupled to gold nanogaps, the SPE transition quantum efficiency after gating is increased to 76.4 ± 14.6% on average, with some SPEs reaching near-unity (more than 90%) quantum efficiency. This study provides a new approach for tuning SPEs with an applied gate voltage and motivates further theoretical and experimental studies of SPE enhancement on vertically aligned nanogaps.

Abstract: In this paper, the existence, uniqueness and uniform stability of solutions for a class of impulsive fractional functional differential equations are investigated by applying new boundedness condition and Lipschitz condition. An example is given to illustrate the main results.

This work explores the fixed-time stabilization of fuzzy uncertain systems based on conditional integral sliding mode control. The difficulties in solving such problems include the integral windup problem and the severe chattering problem when the system converges. Moreover, the design of the traditional fixed-time controller algorithm in the integral sliding mode control becomes complicated. In order to overcome these difficulties: 1) a piecewise function which can bring distant states closer to the sliding surface is considered in the integral function to prevent the integral windup problem, 2) a class of state variable controllers with odd power is designed to solve the chattering problem due to there is no sign function, 3) a fixed-time control algorithm is introduced into the integral function and the controller to guarantee that both the reachability and sliding motion phases are fixed-time stable. Furthermore, based on 1) and 3), an appropriate fixed-time conditional integral sliding mode controller is constructed to ensure the fixed-time reachability of the designed sliding mode surface. Besides, combining 1), 2) and 3), a class of chattering-free fixed-time integral sliding-mode controllers is established to obtain the fixed-time reachability of the sliding mode surface and the fixed-time stability of sliding mode dynamics. This method dramatically reduces the chattering problem and can adjust integral windup. Finally, two numerical simulations are presented to verify the theoretical results, showing the robustness of the designed overshoot factor and the chattering-free scheme for sliding mode control.

AbstractCOVID-19 has slowed the growth of, the global economy, which has certain practical significance. Consequently, this study seeks to analyze the investment opportunities in the medical sector before and after the COVID-19 outbreak. In this study, the Markowitz mean–variance (MV) model, capital asset pricing model (CAPM), and correlation models are constructed based on the principle of Markowitz MV and correlation analysis. Simultaneously, statistical analysis is used to verify the analysis, and the MATLAB statistical tool is used to build the model. The results show that the actual expected yield of China’s medical sector is significantly higher than that calculated by the CAPM before and after the pandemic, and that the investment value of the medical sector is undervalued by the market. From the perspective of risk, China’s medical sector has a stable systemic risk premium. Based on the above analysis, when building investment portfolios in the post-pandemic era, investors should appropriately allocate stocks in the medical and pharmaceutical sectors to improve the portfolio income and diversify the investment risk.

Methyl methacrylate (MMA) is frequently produced via the aldol condensation process involving methyl propionate (MP) and formaldehyde (FA), which is believed to be an intriguing and effective approach. Here, acid–base bifunctional catalysts were developed, and a strategy combining Cs with various acid promoters on an Al2O3support was proposed to enhance catalytic activity and stability. Experimental results demonstrated that the Cs–Nb combination exhibited the highest catalytic performance because of its optimal distribution of acid–base sites. A relationship between the activity of the catalyst and the acid–base properties was identified. Likewise, the Cs–Nb/Al2O3catalyst under optimal loading exhibited the maximum MMA yield owing to the highest density of medium acid sites. Moreover, a plausible acid–base synergetic reaction pathway for MMA synthesis over the Cs–Nb/Al2O3catalyst was proposed. Lower activity was observed on the Cs–Nb/Al2O3catalyst due to carbonaceous deposition during the reaction. However, heat treatment in the atmosphere at 400 °C can readily revive the activity of the catalyst, demonstrating remarkable reusability.

Permafrost in the polar regions potentially stores large amounts of toxic chemicals, including organic compounds bound with halogens. The release of such halogenated organic chemicals (HOC) from thawing permafrost represents a potential global concern with climate change. However, the exact inventory of HOC remains uncertain because conventional analytical techniques largely overlook nonextractable residues. Here we present an inventory of HOC in permafrost soils sampled from the Tibetan Plateau using stepwise chemical treatment following conventional solvent extraction to release and analyse the nonextractable residues. We identify more than 270 types of HOC, with total mean concentration of 310,000 ng g−1, of which 180,000 ng g−1are naturally sourced based on their molecular structures. We also find unexpectedly high fractions of the nonextractable residues, contributing more than 99% of the total HOC, much higher than those reported for other soils and sediments. Up to 85% of the nonextractable residues are physically entrapped in soils rather than chemically bound, such that they could readily be remobilized if soil properties change. We suggest that this substantial stock of HOC in Tibetan Plateau permafrost poses potentially important future risks to local ecosystems in a warming climate.

Thanks to their distinct synaptic plasticity and memory effects, memristors not only can mimic biological neuronal synapses but also can describe the influence of external electromagnetic radiation. This article proposes a novel memristive autapse-coupled neuron model (MACNM) using a locally active memristor as an autapse and simultaneously introducing a flux-controlled piecewise-nonlinear memristor to describe the external electromagnetic radiation. Theoretical analysis and numerical simulation results show that the MACNM is able to generate multiple numbers of grid multiscroll hidden attractors. Moreover, it can exhibit rich and complex hidden firing dynamics, including periodic spiking/bursting firing, chaotic spiking/bursting firing, as well as firing patterns transition. In particular, hidden firing multistability of five coexisting homogeneous chaotic bursting firing patterns with different offsets along the boosting route is discovered, giving raise to the interesting phenomenon of hidden homogeneous multistability. Finally, a circuit is designed to verify the physical feasibility of the abundant electrical activities in the proposed MACNM.

IMPORTANCE: The benefit of neoadjuvant camrelizumab plus chemotherapy for resectable stage IIIA or IIIB non–small cell lung cancer (NSCLC) remains unknown. OBJECTIVE: To assess the efficacy and safety of neoadjuvant camrelizumab plus chemotherapy vs chemotherapy alone for patients with resectable stage IIIA or IIIB NSCLC. DESIGN, SETTING, AND PARTICIPANTS: In this randomized phase 2 clinical trial conducted at 2 hospitals in China, patients aged 18 to 70 years with resectable stage IIIA or IIIB (T3N2) NSCLC were enrolled between April 7, 2020, and January 12, 2022. INTERVENTIONS: Patients were randomly assigned to receive 3 cycles of camrelizumab (200 mg) plus chemotherapy (nab-paclitaxel, 130 mg/m2, and platinum [cisplatin, 75 mg/m2; carboplatin, area under the curve, 5; or nedaplatin, 100 mg/m2]) or chemotherapy alone, followed by surgery after 4 to 6 weeks. MAIN OUTCOMES AND MEASURES: The primary end point was the pathologic complete response (pCR) rate. Secondary end points included the major pathologic response (MPR) rate, objective response rate (ORR), event-free survival (EFS), and safety. Disease-free survival (DFS, defined as the time from surgery to disease recurrence or death from any cause) was analyzed post hoc. Efficacy was assessed on a modified intention-to-treat basis. RESULTS: Ninety-four Chinese patients were randomized, and 88 (93.6%; median age, 61 years [IQR, 54-65 years]; 74 men [84.1%]) received allocated neoadjuvant treatment (43 received camrelizumab plus chemotherapy, and 45 received chemotherapy alone). Among these 88 patients, the pCR rate was 32.6% (14 of 43; 95% CI, 19.1%-48.5%) with camrelizumab plus chemotherapy vs 8.9% (4 of 45; 95% CI, 2.5%-21.2%) with chemotherapy alone (odds ratio, 4.95; 95% CI, 1.35-22.37; P = .008). The MPR rates were 65.1% (95% CI, 49.1%-79.0%) with camrelizumab plus chemotherapy and 15.6% (95% CI, 6.5%-29.5%) with chemotherapy alone. The radiographic ORRs were 72.1% (95% CI, 56.3%-84.7%) with camrelizumab plus chemotherapy and 53.3% (95% CI, 37.9%-68.3%) with chemotherapy alone. With a median follow-up of 14.1 months (IQR, 9.2-20.9 months), the median EFS and DFS were not reached in either group. The most common neoadjuvant treatment-related adverse events of grade 3 or higher were decreased white blood cell count (6 of 43 [14.0%] in the camrelizumab plus chemotherapy group vs 2 of 45 [4.4%] in the chemotherapy group) and decreased neutrophil count (3 of 43 [7.0%] in the camrelizumab plus chemotherapy group vs 5 of 45 [11.1%] in the chemotherapy group). No treatment-related deaths were reported. CONCLUSIONS AND RELEVANCE: This randomized clinical trial found that among patients with resectable stage IIIA or IIIB (T3N2) NSCLC, camrelizumab plus chemotherapy, compared with chemotherapy alone, significantly improved the pCR rate with manageable toxic effects. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04338620

Accurate structural models are of paramount importance for elucidating structure–property relationships in functional materials. Spinels (AB2O4) form a highly important family of materials with complex crystal structures, and subtle structural details have a critical bearing on understanding their physical properties. In some spinels, the space group symmetry is debated, and in general, point defects such as cation inversion and interstitials add complexity. Most studies of spinels concern powder materials, and this challenges deep structural characterization. In fact, most published spinel structures have dubious atomic displacement parameters (ADPs), which is a typical sign of problematic structural description in the refinement of diffraction data. Here, we use various X-ray and neutron diffraction techniques to establish a benchmark crystal structure for the essentially defect-free spinel ferrite ZnFe2O4, which is a widely studied frustrated magnet. It is shown that the appearance of Fd3̅mforbidden reflections in the ZnFe2O4single-crystal neutron diffraction data is an artifact of multiple scattering rather than the loss of inversion symmetry. We then provide benchmark ADPs and demonstrate how strongly these parameters affect the refined cation inversion. The ADPs reported here may be used as reference data to test the soundness of refined structural models, possibly to constrain those based on suboptimal data quality, and thereby provide a more accurate fundamental understanding of the structure–property relationship in spinel-type materials.

Three hitherto unknown eight-electron rhodium/silver alloy nanoclusters, [RhAg21{S2P(OnPr)2}12] (1), [RhHAg20{S2P(OnPr)2}12] (2), and [RhH2Ag19{S2P(OnPr)2}12] (3), have been isolated and fully characterized. Cluster 1contains a regular Rh@Ag12icosahedral core, whereas 2and 3exhibit distorted RhH@Ag12and RhH2@Ag12icosahedral cores. The single-crystal neutron structure of 2located the encapsulated hydride at the center of an enlarged RhAg3tetrahedron. A similar position was found by neutron diffraction for one of the hydrides in 3, whereas the other hydride is trigonally coordinated to Rh and an elongated Ag–Ag edge. The solid-state structures of 1–3possess C1symmetry due to the asymmetric arrangement of the surrounding capping Ag atoms. Our investigation shows that the insertion of one hydride dopant provokes the elimination of one capping silver atom on the cluster surface, resulting in the general formula [RhHx@Ag21–x{S2P(OnPr)2}12] (x= 0–2), which maintains the same number of cluster electrons as well as neutral charge. Clusters 1–3exhibit an intense emission band in the NIR region. Contrarily to their PdAg21and PdHAg20relatives, the 4d orbitals of the encapsulated heterometal are somewhat involved in the optical processes.

Unraveling the evolution mechanism of metal nanoclusters is of great importance in understanding the formation and evolution of metallic condensed matters. In this work, the specific evolution process between a pair of gold nanocluster (Au NC) isomers is completely revealed by introducing hydride ligands to simplify the research system. A hydride-containing Au NC, Au22(SR)15H, was synthesized by kinetic control, and the positions of the hydrides were then confirmed by combining X-ray diffraction, neutron diffraction, and DFT calculations. Importantly, a reversible structural isomerization was found to occur on this Au22(SR)15H. By combining the crystal structures and theoretical calculations, the focus was placed on the hydride-binding site, and a [Au–H] migration mechanism of this isomerization process is clearly shown. Furthermore, this [Au–H] migration mechanism is confirmed by the subsequent capture and structural determination of theoretically predicted intermediates. This work provides insight into the dynamic behavior of hydride ligands in nanoclusters and a strategy to study the evolution mechanism of nanoclusters by taking the hydride ligand as the breakthrough point.

RNase9plays a reproductive function and has been recognized as an important member of the ribonuclease (RNase) A superfamily, a gene family that is widely used as a model for molecular evolutionary studies. Here, we identified 178 RNase9genes from 95 Cetartiodactyla species that represent all four lineages and 21 families of this clade. Unexpectedly, RNase9experienced an evolutionary scenario of “birth and death” in Ruminantia, and expression analyses showed that duplicated RNase9Aand RNase9Bgenes are expressed in reproductive tissues (epididymis, vas deferens or prostate). This expression pattern combined with the estimate that these genes duplicated during the middle Eocene, a time when Ruminantia become a successful lineage, suggests that the RNase9gene duplication might have been advantageous for promoting sperm motility and male fertility as an adaptation to climate seasonality changes of this period. In contrast, all RNase9genes were lost in the Cetacean lineage, which might be associated with their high levels of prostatic lesions and lower reproductive rates as adaptations to a fully aquatic environment and a balance to the demands of ocean resources. This study reveals a complex and intriguing evolutionary history and functional divergence for RNase9in Cetartiodactyla, providing new insights into the evolution of the RNaseA superfamily and molecular mechanisms for organismal adaptations to the environment.

Introduction:Due to the wide variation in the prognosis of autosomal dominant polycystic kidney disease (ADPKD), prediction of risk of renal survival in ADPKD patients is a tough challenge. We aimed to establish a nomogram for the prediction of renal survival in ADPKD patients. Methods:We conducted a retrospective observational cohort study in 263 patients with ADPKD. The patients were randomly assigned to a training set (N= 198) and a validation set (N= 65), and demographic and statistical data at baseline were collected. The total kidney volume was measured using stereology. A clinical prediction nomogram was developed based on multivariate Cox regression results. The performance and clinical utility of the nomogram were assessed by calibration curves, the concordance index (C-index), and decision curve analysis (DCA). The nomogram was compared with the height-adjusted total kidney volume (htTKV) model by receiver operating characteristic curve analysis and DCA. Results:The five independent factors used to construct the nomogram for prognosis prediction were age, htTKV, estimated glomerular filtration rate, hypertension, and hemoglobin. The calibration curve of predicted probabilities against observed renal survival indicated excellent concordance. The model showed very good discrimination with a C-index of 0.91 (0.83–0.99) and an area under the curve of 0.94, which were significantly higher than those of the htTKV model. Similarly, DCA demonstrated that the nomogram had a better net benefit than the htTKV model. Conclusion:The risk prediction nomogram, incorporating easily assessable clinical parameters, was effective for the prediction of renal survival in ADPKD patients. It can be a useful clinical adjunct for clinicians to evaluate the prognosis of ADPKD patients and provide individualized decision-making.

Existing studies have not provided robust evidence about the CVD risk of non-smoking patients with restrictive spirometric pattern (RSP) or airflow obstruction (AFO), and how the risk is modified by body shape. We aimed to bridge the gap.

Pickering emulsions as a kind of emulsified fracturing fluids can enhance the efficacy of shale fracturing. Investigation of the rheological properties of Pickering emulsions yields valuable insights for their in-situ fracturing applications. In this work, nano-clay particles and heavy crude oil were employed to form oil-in-water (O/W) Pickering emulsions. The impact of salinity, pH, and temperature on the rheological properties, including viscosity property and viscoelastic property, of Pickering emulsions were tested and analyzed. The results showed that the viscosity of heavy O/W Pickering emulsions were significantly affected by the change of salinity and pH values, with higher salinity or lower pH resulting in increased viscosity. Under ultra-high salinities (>5 wt% NaCl), the coalescence stability of Pickering emulsion strengthens and creaming stability declines. The massive formation of clay clusters leads to a reduction in the number of nano-clay particles used to generate the network, causing instability in the emulsion creaming. The heavy O/W Pickering emulsions exhibited high stability and high viscosity characteristics, which were mainly caused by the change of surface charge of nano-clay particles and the corresponding change of particle adsorption on droplet surface and particle-particle interactions. At a salinity of 2.5% NaCl and a pH of 4.6, the emulsion system remained stable at a temperature up to 120 °C, holding a viscosity reaching 74.1 mPa s at a shear rate of 6.81 s−1. Besides, heavy O/W Pickering emulsions were viscoelastic fluids, with both noticeable viscous property and elastic property at the same time. Elastic modulus (G′) and viscous modulus (G″) of the emulsion increased with increasing salinity and decreasing pH, enhancing great deformation capacity. Remarkably, the Pickering emulsions could maintain its viscoelastic behavior at varying temperature conditions. For the first time, rheological study of Pickering emulsions formed by nano-clay nanoparticles as a single emulsifier to heavy oil. Our study underscores the emulsifying ability of nano-clay particles to heavy crude oils, under a wide range of salinity and pH, yielding Pickering emulsions with exceptional stability and rheological properties. This novel emulsion system, possessing high resistance to temperature and salinity, holds promise for a high-performance fracturing fluid design.

Ficus hirta, a well-known medicinal and edible fruit crop widely distributed in the temperate and subtropical areas of China, is rich of flavonoids. However, up to now, the genetic and molecular mechanisms of flavonoid biosynthesis in F. hirtaare still unclear. In this study, we assembled the transcriptome of F. hirtausing the third-generation sequencing technique single molecule real-time sequencing (SMRT-seq). Then, comparative transcriptome analysis was performed to investigate the fruit gene expression profiling differences among four F. hirtavarieties with different lobed leaves (undivided (ud), three-lobed-finger (tf), five-lobed-finger (ff) and seven-lobed-finger (sf)) using RNA-seq. Totally, we obtained 534,375 full-length non-chimeric (FLNC) reads with an average length of 2178 bp by using SMRT-Seq, and 22,782,329, 22,361,639, 21,982,877, and 22,479,690 clean reads for each F. hirtafruit by using RNA-Seq, respectively. Of the assembled 48,399 transcripts, 21,971, 19,183, 21,834, 14,277, 16,255, 16,255 and 21,089 were successfully annotated by NR, SwissProt, KEGG, KOG, Pfam, GO and NT, respectively. And 96 and 21 transcripts were identified to be involved in the phenylpropanoid and flavonoid biosynthesis, respectively. Furthermore, we identified 6, 5, 7, 8, 13 and 14 flavonoid biosynthesis related DEGs in comparison of ud versus tf, ud versus sf, ud versus sf, tf versus ff, tf versus sf and ff versus sf, respectively. And the differential expression of 6 DEGs were confirmed by using quantitative real time PCR. Our study can provide basis for understanding metabolism differences of the bioactive phytochemicals especially flavonoids among different F. hirtavarieties.