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Abstract Bitter gourd is an economically important horticultural crop for its edible and medicinal value. However, the regulatory mechanisms of bitter gourd in response to cold stress are still poorly elucidated. In this study, phytohormone determination and comparative transcriptome analyses in XY (cold-tolerant) and QF (cold-sensitive) after low temperature treatment were conducted. Under cold stress, the endogenous contents of abscisic acid (ABA), jasmonic acid (JA) and salicylic acid (SA) in XY were significantly increased at 24 h after treatment (HAT), indicating that ABA, JA and SA might function in regulating cold resistance. RNA-seq results revealed that more differentially expressed genes were identified at 6 HAT in QF and 24 HAT in XY, respectively. KEGG analysis suggested that the plant hormone signal transduction pathway was significantly enriched in both genotypes at all the time points. In addition, transcription factors showing different expression patterns between XY and QF were identified, including CBF3, ERF2, NAC90, WRKY51 and WRKY70. Weighted gene co-expression network analysis suggested MARK1, ERF17, UGT74E2, GH3.1 and PPR as hub genes. These results will deepen the understanding of molecular mechanism of bitter gourd in response to cold stress and the identified genes may help to facilitate the genetic improvement of cold-resistant cultivars.

IntroductionHyperuricaemia (HUA), one of chronic diseases, has an increased prevalence and is related to diseases such as gout, arthritis, infectious diseases, etc. Antimicrobial resistance (AMR) in the gut is considered as an atypical chronic disease, and poses risk to human health. The gut microbiome has been proved to be a reservoir for AMR and play an important role in HUA patients. The microbial characteristics of the gut in individuals with HUA have been previously explored, however, the characteristics of the resistome in individuals with HUA have remained largely unexplored.MethodsThus, we investigated the landscape of the AMR in individuals with HUA and without HUA, and the potentially influential factors in a case-control study using metagenomics-based approaches.ResultsWe found that drinking juice and abnormal stool were risk factors associated with HUA. The taxonomic diversity of gut microbiota in individuals with HUA was lower than that in non-HUA individuals. Notably, a higher abundance and diversity of the resistome (entire antimicrobial resistance genes) was observed in individuals with HUA (median: 1.10 vs. 0.76, P = 0.039, U-test), especially in tetracycline resistance genes (median: 0.46 vs. 0.20, P < 0.001, U-test), which are associated with more complex mobile genetic elements (MGEs) in individuals with HUA. Furthermore, we found that a higher abundance of the resistome was positively correlated with uric acid (UA) levels and affected by several host-associated factors (mainly dietary habits). Specifically, pork consumption and the consumption of root and tuber vegetables were identified as contributing factors. We also found a higher abundance of virulence genes (VGs), mostly related to adherence, antimicrobial activity, competitive advantage, and exoenzymes, in the gut microbial community of individuals with HUA.DiscussionAll findings revealed higher activity of the resistome and pathogenicity of the microbiota in individuals with HUA, indicating a higher health risk in the elderly HUA population.

1-3 piezoelectric composites have been widely used in transmitting transducers, medical devices, navigation, aerospace, etc. However, due to poor thermal conduction of inside piezoelectric composites, performance degradation and service life shortening of transmitting transducers are easily caused while working under high-power or continuously operated states. In this paper, a solution is provided by designing and creating highly efficient thermally conductive paths in 1-1-3 piezoelectric composite. This novel design resulted in two-fold increase in heat dissipation rate compared with traditional 1–3 piezoelectric composites, while maintaining high piezoelectric properties. Furthermore, we designed and fabricated an efficient heat dissipation transducer (EHDT) with the novel 1-1-3 piezoelectric composite as the core material, which can relief heat accumulation effectively compared with conventional transducers (CT). The EHDT can achieve three times more power output than the CT at the same temperature threshold of 90 °C.

Converting solar energy to electricity efficiently requires an accurate model with well-estimated parameters in a photovoltaic (PV) system. However, identifying parameters for the PV model remains challenging attributed to the nonlinearity and multimodality inherent in diode circuit models. In this paper, we propose a dual-feedback adaptive clone selection algorithm with a golden sine search. The clonal selection algorithm (CSA), known for its inherent diversity maintenance ability, offers an advantage in handling multimodal optimization problems. The critical factor is the cloning operator, particularly the determination of clone size, crucial for balancing exploration and exploitation. To address the conflict between clone size and convergence speed, we introduce a dual feedback adaptive cloning strategy. This strategy considers both the fitness difference between the hypermutated population and the original population and the harmonic mean distance of the population. Additionally, to improve accuracy, we introduce an elite learning strategy based on the golden sine mechanism. The proposed algorithm’s effectiveness is assessed through comprehensive experiments on PV cells and three PV modules, considering both single diode and double diode models, respectively. The experimental results demonstrate that the accuracy of parameter identification achieved by the proposed algorithm exceeds 99.9% for all three types of PV modules. Moreover, we validate the algorithm’s performance using manufacturer’s datasheets with varying irradiations and temperatures. All experiments consistently indicate the stability and effectiveness of the proposed method in accurately identifying the parameters of PV models.

We propose a cost-effective and memory-aware end-to-end learning scheme utilizing bi-directional gated recurrent unit (bi-GRU) for geometric constellation shaping (GCS) under the first-order regular perturbation (FRP) auxiliary channel. The performance of the proposed system has been numerically verified at a 32 GBd 5-channel wavelength division multiplexing (WDM) 64 quadrature amplitude modulation (QAM) 800 km optical coherent communication system. Results show that the proposed bi-GRU based GCS scheme can achieve a performance gain over square 64QAM in mutual information (MI) with 0.12 bits/symbol and a Q-factor gain of 0.4 dB at optimal launched optical power. When transmission distance is extended to 1280 km, a generalized mutual information (GMI) gain of 0.136 bits/symbol is observed. Additionally, compared with the bi-directional long short-term memory (bi-LSTM) based GCS, the proposed bi-GRU scheme has lower computation complexity with similar system performance.

Abstract The H3 methyltransferases ATXR5 and ATXR6 deposit H3.1K27me1 to heterochromatin to prevent genomic instability and transposon re-activation. Here, we report that atxr5 atxr6 mutants display robust resistance to Geminivirus. The viral resistance is correlated with activation of DNA repair pathways, but not with transposon re-activation or heterochromatin amplification. We identify RAD51 and RPA1A as partners of virus-encoded Rep protein. The two DNA repair proteins show increased binding to heterochromatic regions and defense-related genes in atxr5 atxr6 vs wild-type plants. Consequently, the proteins have reduced binding to viral DNA in the mutant, thus hampering viral amplification. Additionally, RAD51 recruitment to the host genome arise via BRCA1, HOP2, and CYCB1;1, and this recruitment is essential for viral resistance in atxr5 atxr6. Thus, Geminiviruses adapt to healthy plants by hijacking DNA repair pathways, whereas the unstable genome, triggered by reduced H3.1K27me1, could retain DNA repairing proteins to suppress viral amplification in atxr5 atxr6.

Foxp3+ TREG cells have been at the focus of intense investigation for their recognized roles in preventing autoimmunity, facilitating tissue recuperation following injury, and orchestrating a tolerance to innocuous non-self-antigens. To perform these critical tasks, TREG cells undergo deep epigenetic, transcriptional, and post-transcriptional changes that allow them to adapt to conditions found in tissues both at steady-state and during inflammation. The path leading TREG cells to express these tissue-specialized phenotypes begins during thymic development, and is further driven by epigenetic and transcriptional modifications following TCR engagement and polarizing signals in the periphery. However, this process is highly regulated and requires TREG cells to adopt strategies to avoid losing their regulatory program altogether. Here, we review the origins of tissue-resident TREG cells, from their thymic and peripheral development to the transcriptional regulators involved in their tissue residency program. In addition, we discuss the distinct signalling pathways that engage the inflammatory adaptation of tissue-resident TREG cells, and how they relate to their ability to recognize tissue and pathogen-derived danger signals.

This paper addresses the challenge of enhancing the presentation style of visual exhibitions within museum settings, where the traditional approach can be perceived as unengaging. The study focuses on improving the performance of the ORB algorithm, a feature detection and matching method. Specifically, we introduce a novel technique that incorporates scale invariance into the algorithm by borrowing the detection concept from the BRISK algorithm. Furthermore, we integrate an improved PROSAC algorithm into the matching stage to enhance the quality of matched points. Our experimental results demonstrate significant improvements in the accuracy of the ORB algorithm. In comparison to the SIFT and GMS algorithms, the ORB algorithm exhibits enhanced matching accuracy, making it a compelling choice for feature-based computer vision applications. Moreover, the proposed approach creates a captivating illusion of seamlessly merging reality and virtual elements, meeting the expectations of system designers. This integration enhances the overall visitor experience within museum settings, fostering engagement and interaction.

Dynamic liquid level monitoring and measurement in oil wells is essential in ensuring the safe and efficient operation of oil extraction machinery and formulating rational extraction policies that enhance the productivity of oilfields. This paper presents an intelligent infrasound-based measurement method for oil wells’ dynamic liquid levels; it is designed to address the challenges of conventional measurement methods, including high costs, low precision, low robustness and inadequate real-time performance. Firstly, a novel noise reduction algorithm is introduced to effectively mitigate both periodic and stochastic noise, thereby significantly improving the accuracy of dynamic liquid level detection. Additionally, leveraging the PyQT framework, a software platform for real-time dynamic liquid level monitoring is engineered, capable of generating liquid level profiles, computing the sound velocity and liquid depth and visualizing the monitoring data. To bolster the data storage and analytical capabilities, the system incorporates an around-the-clock unattended monitoring approach, utilizing Internet of Things (IoT) technology to facilitate the transmission of the collected dynamic liquid level data and computed results to the oilfield’s central data repository via LoRa and 4G communication modules. Field trials on dynamic liquid level monitoring and measurement in oil wells demonstrate a measurement range of 600 m to 3000 m, with consistent and reliable results, fulfilling the requirements for oil well dynamic liquid level monitoring and measurement. This innovative system offers a new perspective and methodology for the computation and surveillance of dynamic liquid level depths.

Abstract In Jupiter's middle magnetosphere, debate remains on the controlling physics of electron acceleration to the ultrarelativistic regime. Some local‐time asymmetric processes regulating MeV electrons may have their hold across electron spectra, leaving imprints down to 10–100s keV energies. Spectra at these lower energies can be measured with finite energy resolution, which is hardly achieved for MeV electrons. We investigate 10–100s keV electron spectra using Galileo measurements. We show that at 15–20 RJ, the power‐law exponents exhibit a previously unexpected dawn‐dusk asymmetry. This asymmetry is more prominent for 100s‐keV spectra, persistent but enhanced from 1996 to 2001. These match the theory of transport driven by a dawn‐to‐dusk electric field. Contrary to past expectations of dawn‐to‐dusk electric field at Io plasma torus (IPT) extending outward from 6 RJ, the origin of the field we infer may be completely different, despite its orientation and impacts on electron energization similar with the IPT one.

Maintaining the storage stability of polymer-modified asphalt is crucial to its optimal performance. The phase separation in styrene–butadiene–styrene (SBS) -modified asphalt during high-temperature storage has not been solved due to the lack of fundamental understanding of intermolecular interaction. This study aims to reveal the formation of molecular nano-micelles and endogenous π-π interaction in the SBS-modified asphalt. Molecular simulation indicates that incorporating more than 7 % SBS into bitumen forms SBS self-aggregates that entangle with asphaltenes, causing a reorganization of the colloidal structure. The SBS aggregation is dominated by the π – π conjugate interactions and steric hindrance of confused aromatic. In low-content SBS-modified asphalt (7%), SBS interlocks parallel benzene rings and selectively adsorbs maltene from neat bitumen. High concentrations of SBS in the modified asphalt increase inter-SBS attraction but decrease asphaltene-SBS interaction, causing SBS self-aggregation and segregation from asphaltene-rich phase. Atomic force microscope and fluorescence microscope tests demonstrate the selective maltene adsorption and phase migration in high-content SBS-modified asphalt, and macro performance exhibits severe phase separation and degradation. This study provides a new perspective on improving the storage stability of high-content SBS-modified asphalt.

Abstract Background Ankylosing spondylitis (AS) is one of the most common immune-mediated arthritic diseases worldwide. Despite considerable efforts to elucidate its pathogenesis, the molecular mechanisms underlying AS are still not fully understood. Methods To identify candidate genes involved in AS progression, the researchers downloaded the microarray dataset GSE25101 from the Gene Expression Omnibus (GEO) database. They identified differentially expressed genes (DEGs) and functionally enriched them for analysis. They also constructed a protein–protein interaction network (PPI) using STRING and performed cytoHubba modular analysis, immune cell and immune function analysis, functional analysis and drug prediction.The results showed that DEGs were mainly associated with histone modifications, chromatin organisation, transcriptional coregulator activity, transcriptional co-activator activity, histone acetyltransferase complexes and protein acetyltransferase complexes. Results The researchers analysed the differences in expression between the CONTROL and TREAT groups in terms of immunity to determine their effect on TNF-α secretion. By obtaining hub genes, they predicted two therapeutic agents, AY 11–7082 and myricetin. Conclusion The DEGs, hub genes and predicted drugs identified in this study contribute to our understanding of the molecular mechanisms underlying the onset and progression of AS. They also provide candidate targets for the diagnosis and treatment of AS.

Accurate inferior alveolar nerve (IAN) canal segmentation has been considered a crucial task in dentistry. Failing to accurately identify the position of the IAN canal may lead to nerve injury during dental procedures. While IAN canals can be detected from dental cone beam computed tomography, they are usually difficult for dentists to precisely identify as the canals are thin, small, and span across many slices. This paper focuses on improving accuracy in segmenting the IAN canals. By integrating our proposed frequency-domain attention mechanism in UNet, the proposed frequency attention UNet (FAUNet) is able to achieve 75.55% and 81.35% in the Dice and surface Dice coefficients, respectively, which are much higher than other competitive methods, by adding only 224 parameters to the classical UNet. Compared to the classical UNet, our proposed FAUNet achieves a 2.39% and 2.82% gain in the Dice coefficient and the surface Dice coefficient, respectively. The potential advantage of developing attention in the frequency domain is also discussed, which revealed that the frequency-domain attention mechanisms can achieve better performance than their spatial-domain counterparts.

Gene function verification is a crucial step in studying the molecular mechanisms regulating various plant life activities. However, a stable and efficient homologous genetic transgenic system for herbaceous peonies has not been established. In this study, using virus-induced gene silencing technology (VIGS), a highly efficient homologous transient verification system with distinctive advantages was proposed, which not only achieves true “intact-plant” infiltration but also minimizes the operation. One-year-old roots of the representative species, Paeonia lactiflora Pall., were used as the materials; prechilling (4 °C) treatment for 3–5 weeks was applied as a critical precondition for P. lactiflora to acquire a certain chilling accumulation. A dormancy-related gene named HOMEOBOX PROTEIN 31 (PlHB31), believed to negatively regulate bud endodormancy release (BER), was chosen as the target gene in this study. GFP fluorescence was detected in directly infiltrated and newly developed roots and buds; the transgenic plantlets exhibited remarkably earlier budbreak, and PlHB31 was significantly downregulated in silenced plantlets. This study established a homologous transient silencing system featuring intact-plant infiltration and minimized manipulation for gene function research, and also offers technical support and serves as a theoretical basis for gene function discovery in numerous other geophytes.

A novel reinforcement deep learning deterministic policy gradient agent-based sliding mode control (DDPG-SMC) approach is proposed to suppress the chattering phenomenon in attitude control for quadrotors, in the presence of external disturbances. First, the attitude dynamics model of the quadrotor under study is derived, and the attitude control problem is described using formulas. Second, a sliding mode controller, including its sliding mode surface and reaching law, is chosen for the nonlinear dynamic system. The stability of the designed SMC system is validated through the Lyapunov stability theorem. Third, a reinforcement learning (RL) agent based on deep deterministic policy gradient (DDPG) is trained to adaptively adjust the switching control gain. During the training process, the input signals for the agent are the actual and desired attitude angles, while the output action is the time-varying control gain. Finally, the trained agent mentioned above is utilized in the SMC as a parameter regulator to facilitate the adaptive adjustment of the switching control gain associated with the reaching law. The simulation results validate the robustness and effectiveness of the proposed DDPG-SMC method.

Summary: Lysine lactylation (Kla) is a novel histone post-translational modification discovered in late 2019. Later, HDAC1-3, were identified as the robust Kla erasers. While the Sirtuin family proteins showed weak eraser activities toward Kla, as reported. However, the catalytic mechanisms and physiological functions of HDACs and Sirtuins are not identical. In this study, we observed that SIRT3 exhibits a higher eraser activity against the H4K16la site than the other human Sirtuins. Crystal structures revealed the detailed binding mechanisms between lactyl-lysine peptides and SIRT3. Furthermore, a chemical probe, p-H4K16laAlk, was developed to capture potential Kla erasers from cell lysates. SIRT3 was captured by this probe and detected via proteomic analysis. And another chemical probe, p-H4K16la-NBD, was developed to detect the eraser-Kla delactylation processes directly via fluorescence indication. Our findings and chemical probes provide new directions for further investigating Kla and its roles in gene transcription regulation.

Three-dimensional (3D) printing allows for the creation of complex, layered structures with precise micro and macro architectures that are not achievable through traditional methods. By designing 3D structures with geometric precision, it is possible to achieve selective regulation of mechanical properties, enabling efficient dissipation of mechanical energy. In this study, a series of modular samples inspired by the Bouligand structure were designed and produced using a direct ink writing system, along with a classical printable polydimethylsiloxane ink. By altering the angles of filaments in adjacent layers (from 30° to 90°) and the filament spacing during printing (from 0.8 mm to 2.4 mm), the mechanical properties of these modular samples can be adjusted. Compression mechanical testing revealed that the 3D printed modular Bouligand structures exhibit stress-strain responses that enable multiple adjustments of the elastic modulus from 0.06 MPa to over 0.8 MPa. The mechanical properties were adjusted more than 10 times in printed samples prepared using uniform materials. The gradient control mechanism of mechanical properties during this process was analyzed using finite element analysis. Finally, 3D printed customized modular Bouligand structures can be assembled to create an array with Bouligand structures displaying various orientations and interlayer details tailored to specific requirements. By decomposing the original Bouligand structure and then assembling the modular samples into a specialized array, this research aims to provide parameters for achieving gradient energy absorption structures through modular 3D printing.

Digital entrepreneurial narratives compensate for the limited interactivity of traditional entrepreneurship narratives. This is achieved by incorporating elements of social entrepreneurship into tangible, real or fictional scenarios, thereby providing immersive experiences for the audience. In this sense, social entrepreneurs construct shared meanings and form socially identifiable values through the joint participation, communication, and value creation of entrepreneurs and audiences. We conceptualized the digital entrepreneurial narrative and proposed documentary and fictional narratives as its most salient features. Then, we explored the effect of these narratives on attentional resources in a digital entrepreneurial narrative from the perspective of sensemaking theory. Based on novel methods of natural language processing and machine learning, the empirical results from an examination of 304 social entrepreneurs who participated in the short-video platforms indicated that both documentary and fictional narratives have a positive impact on the acquisition of attentional resources for social entrepreneurship. Additionally, we found the documentary aspect of digital entrepreneurial narratives attracts attentional resources by fostering a shared sense of meaningfulness. However, the mediating effect is less effective with fictional narratives. Our results broaden the study of entrepreneurial narratives, and also expand the content of social entrepreneurial legitimacy and sensemaking perspectives.

Abstract Hybrid entrepreneurship research is to probe into the increasingly widespread phenomenon of “employment + entrepreneurship” in real life, and to break the traditional practice of dichotomizing self‐employment and employment. Hybrid entrepreneurship provides entrepreneurs with another employment alternative by combining entrepreneurship and employment. By analyzing data collected mainly from prospective entrepreneurs in China, this paper aims to explore the main influencing factors of hybrid entrepreneurial intentions, in particular how gender moderates the relationship between entrepreneurial motives and hybrid entrepreneurial intentions. Empirical findings reveal that gender differences are evident in prospective entrepreneurs who are motivated to be self‐employed by necessity‐driven factors and those who aim to test the waters of entrepreneurship via the hybrid path.

Intersubgeneric hybrids of Paeonia lactiflora (Paeonia lactiflora pall., P. lactiflora.) cover a huge variety of systems in the genus Paeonia. In recent years, many studies have confirmed that the intersubgeneric hybrids of P. lactiflora. are rich in paeoniflorin and other medicinal ingredients, however, it has always proved difficult to clarify the medicinal value of the hybrids and whether they can be used for medicinal purposes. In this study, the consistency of the plant population was evaluated through DUS evaluation, in order to clarify whether the selected research materials had stability and consistency within the population and specificity between populations. The differences between the paeoniflorin contents in the roots of the nine intersubgeneric hybrids of the P. lactiflora. varieties and two medicinal varieties were critically compared. The differences in the chemical components of the roots of nine intersubgeneric hybrids of P. lactiflora. and reference medicine substances of P. lactiflora. and Paeonia anomala subsp. veitchii (Lynch) D. Y. Hong and K. Y. Pan (Paeonia veitchii Lynch., P. veitchii.) were explored via stoichiometric and chemical fingerprint high performance liquid chromatography analyses. The results showed that there were significant differences in the chemical compositions between the intersubgeneric hybrids of P. lactiflora. and the medicinal reference materials, and the contents of paeoniflorin were elevated such that the hybrids could be used as the raw material for extraction of paeoniflorin, thus providing an opportunity to explore the medicinal value of the hybrids. This study explored the key differential components among the varieties and provides a reference and basis for the study of the medicinal value and the identification of the intersubgeneric hybrids of the P. lactiflora. varieties.

Accurate phenotypic identification is the basis of research for resistance genetics and rice breeding for resistance to RBSDV disease. Obtaining rice black-streaked dwarf virus (RBSDV) viruliferous small brown planthoppers (SBPHs) with high transmission efficiency is an essential part of accurate phenotypic identification. Here, through quantifying number of RBSDV copies in infected rice plants, optimizing times of SBPHs fed on RBSDV-infected rice plants and leaf stage of rice seedlings, a method to acquire an RBSDV-carrying SBPH population more efficiently was improved. The results showed that rate of viruliferous SBPHs was significantly higher when fed on RBSDV-infected rice plants that had the copy numbers of RBSDV S10 of 3.0*104 and 1.1*104 than 8.3*102. Therefore, it is more efficient for SBPHs to acquire the virus when fed on RBSDV-infected rice plants that have copy numbers of RBSDV S10 above 1.1*104. The rate of viruliferous SBPHs were 50% and 54%, respectively, after the insects fed on RBSDV-infected rice plants for 7 and 9 days and being transferred to healthy rice seedlings for 5 and 3 days, which was significantly higher than those at other feeding times. The optimal inoculation leaf stage of rice seedlings was the 2-3-leaf stage (3 effective SBPHs per seedling for 72 h), but a high rate of viruliferous SBPHs may be suggested for inoculation of older rice seedlings.

A scalable segmented-based proton-exchange membrane fuel cell (PEMFC) hybrid power system model is developed in this paper. The fuel cell (FC) is developed as a dynamic lumped parameter model to predict the current distributions during dynamic load scenarios. The fuel cell is segmented into 3×3 segments connected with several physical ports and with the variables balanced automatically. Based on the proposed model, a real-time energy management framework is designed to distribute the load current demand during dynamic operations. Simulation results show that the proposed strategy has good performance on both single/segmented fuel cell–battery hybrid systems and the low battery state of charge (SOC) situation. This paper proposes an approach that uses an interconnected ordinary differential equations (ODEs) system model in control problems, which makes the control algorithms readily applicable.

Asphalt mixtures are multi-phase composites composed of aggregates, bitumen, mineral powders, and voids, and various structures are intertwined during the compaction process. Most of the traditional research focuses on the macro-scale domain, and it is difficult to obtain the internal structure of asphalt mixture in different compaction processes. With the continuous development of digital image technology, the influence of the meso-structure of the asphalt mixture on the compaction quality of the asphalt mixture has become a new means to evaluate the performance of the asphalt mixture. In this paper, different numbers of compactions are selected to represent different stages in the compaction process, the digital images of specimens in different compaction stages are obtained by industrial CT scanning technology. Then, the images are processed and reconstructed in three dimensions using improved image segmentation methods, and the position characteristics and geometric information of coarse aggregate are obtained by combining the Oriented Bounding Box (OBB). The meso-response characteristics of the skeleton structure of the asphalt mixture during compaction were studied. The influence of the internal structure of the mixture on the compaction quality of the mixture was obtained, which is of great significance for the study of improving the durability of the pavement. The results show that the “effective coordination number” (the number of aggregate particles that can transmit force in the skeleton structure) is greatly related to the aggregate size. With the compaction process, the centroid of coarse aggregate in the upper layer of the specimen reflects the overall downward movement trend. The inclination angle of the aggregate spindle tends to be in the range of 80°~100°; the anisotropic amplitude of the xy plane increases, and the direction of the aggregate spindle becomes more and more consistent. With the increase in the number of rotational compactions, these four parameters showed obvious rules, indicating that this meso-characteristic index could well characterize the compaction quality of the asphalt mixture in the compaction process.

The quality of the acoustic environments at Xi’an Jiatong-Liverpool University (XJTLU) and Soochow University (Dushuhu Campus, SUDC) in Suzhou City were investigated in the present work through real-time noise level measurements and questionnaire surveys. Before commencing the measurements and surveys, these two campuses’ sound sources were summarized and classified into four categories through on-site observation: human-made, machinery, living creatures, and natural physical sounds. For the zones near the main traffic road, with a high volume of crowds and surrounded by a park, sound from road vehicles, humans talking, and birds/insects were selected by the interviewees as the major sound sources, respectively. Only zone 3 (near to a park) at XJTLU could be classified as A zone (noise level < 55 dBA) with an excellent quality acoustical environment. All other zones had either good or average quality acoustical environments, except zone 1 (near to main traffic road) at XJTLU, with a fair-quality acoustical environment.

This work presents an LC resonant passive wireless gas sensor with a novel structure designed to mitigate the negative impact of substrate. The LC sensor antenna in the new structure, and the reader antenna, were designed and optimized utilizing HFSS software to improve the transfer efficiency. The superiority of the designed structure compared with general examples is highlighted and verified. The change in the substrate capacitance essentially makes no interference with the parameters of the LC sensor to be measured. The sensor for the new structure was prepared by combining etching and sputtering methods. The ZnO nanowires (NWs) were characterized to confirm their high purity and wurtzite crystal structure. The LC gas sensors demonstrated excellent wireless sensing performance, including a low detection limit of 0.5 ppm NO2, high response of 1.051 and outstanding stability at 180 °C. The newly developed sensor structure not only prevented interference from the substrate during gas sensing testing, but also expanded the choice of sensor substrates, playing a critical role in the development of sensors based on the LC resonance principle.

The ultra-reliable and low-latency communication (URLLC) systems are expected to support the stringent quality of service (QoS) demands in the Internet of Things (IoT) networks. In order to support the strict latency and reliability constraints, it is preferable to deploy a reconfigurable intelligent surface (RIS) in the URLLC systems to improve the link quality. In this paper, we focus on the uplink of an RIS-assisted URLLC system, and we propose to minimize the transmission latency under the reliability constraints. To solve the non-convex problem, a low-complexity algorithm is proposed by using the Alternating Direction Method of Multipliers (ADMM) technique. The RIS phase shifts optimization, which is typically non-convex, is efficiently solved by formulating as a Quadratically Constrained Quadratic Programming (QCQP) problem. Simulation results verify that our proposed ADMM-based method is able to achieve better performance than the conventional semi-definite relaxation (SDR)-based method with lower computational complexity. Our proposed RIS-assisted URLLC system is able to significantly reduce the transmission latency, which highlights the great potential in deploying RIS in the IoT networks with strict reliability requirements.

Patients infected with SARS-CoV-2 at various severities have different clinical manifestations and treatments. Mild or moderate patients usually recover with conventional medical treatment, but severe patients require prompt professional treatment. Thus, stratifying infected patients for targeted treatment is meaningful. A computational workflow was designed in this study to identify key blood methylation features and rules that can distinguish the severity of SARS-CoV-2 infection. First, the methylation features in the expression profile were deeply analyzed by a Monte Carlo feature selection method. A feature list was generated. Next, this ranked feature list was fed into the incremental feature selection method to determine the optimal features for different classification algorithms, thereby further building optimal classifiers. These selected key features were analyzed by functional enrichment to detect their biofunctional information. Furthermore, a set of rules were set up by a white-box algorithm, decision tree, to uncover different methylation patterns on various severity of SARS-CoV-2 infection. Some genes (PARP9, MX1, IRF7), corresponding to essential methylation sites, and rules were validated by published academic literature. Overall, this study contributes to revealing potential expression features and provides a reference for patient stratification. The physicians can prioritize and allocate health and medical resources for COVID-19 patients based on their predicted severe clinical outcomes.

Fowl adenovirus serotype 4 (FAdV-4) is recognized as a pathogen that causes hydropericardium syndrome. Irrespective of the pathway used by the virus to invade the chicken, the pathological characteristics of the disease include degeneration and necrosis of hepatocytes, formation of intranuclear inclusions, as well as inflammatory cell infiltration. Liver dysfunction constitutes one of the critical factors leading to death. Therefore, it is vital to investigate the virus-mediated severe pathological liver damage to further understand the pathogenesis of FAdV-4. Here, proteomics, a tandem mass tag (TMT)-based approach to directly analyze protein expression, was used to determine the protein expression during FAdV-4 proliferation in leghorn male hepatoma (LMH) cells. We identified 177 differentially expressed proteins associated with various biological processes and pathways. The functional enrichment analysis revealed that FAdV-4 could downregulate some signaling pathways in LMH cells, including NOD-like receptor signaling, RIG-I-like receptor signaling, NF-κB signaling, TNF signaling pathway, and Notch signaling, FoxO signaling, PI3K-Akt signaling, and autophagy. The results of proteomics screening suggested an association between FAdV-4 infection and Notch signaling in LMH in vitro, indicating that Notch signaling regulated the expression of inflammatory cytokines and interferons but not viral replication in LMH cells. These data contributed to the understanding of the immunopathogenesis and inflammopathogenesis of FAdV-4 infection and also provided valuable information for the further analysis of the molecular mechanisms underlying viral pathogenesis.

The abnormal iron deposition of the deep gray matter nuclei is related to many neurological diseases. With the quantitative susceptibility mapping (QSM) technique, it is possible to quantitatively measure the brain iron content in vivo. To assess the magnetic susceptibility of the deep gray matter nuclei in the QSM, it is mandatory to segment the nuclei of interest first, and many automatic methods have been proposed in the literature. This study proposed a contrast attention U-Net for nuclei segmentation and evaluated its performance on two datasets acquired using different sequences with different parameters from different MRI devices. Experimental results revealed that our proposed method was superior on both datasets over other commonly adopted network structures. The impacts of training and inference strategies were also discussed, which showed that adopting test time augmentation during the inference stage can impose an obvious improvement. At the training stage, our results indicated that sufficient data augmentation, deep supervision, and nonuniform patch sampling contributed significantly to improving the segmentation accuracy, which indicated that appropriate choices of training and inference strategies were at least as important as designing more advanced network structures.

According to the literature, there is no reliable and quantitative method available for the diagnosis and prognosis of early or potential temporomandibular joint (TMJ) condylar resorption (CR) thus far. The purpose of this study was to raise a new noninvasive method to quantitatively evaluate condylar quality using the signal intensity ratio (SIR) on magnetic resonance imaging (MRI) in order to assist in the diagnosis of TMJ CR. A retrospective exploratory study was performed to compare the condyle-to-cerebral cortex signal intensity ratios (SIR) on MRI among young female patients. We included 60 patients, and they were divided into three groups: the bilateral normal TMJ group (group 1), the bilateral TMJ anterior disc displacement (ADD) but without CR group (group 2), and the bilateral TMJ anterior disc displacement (ADD) with CR group (group 3). The SIR difference between the three groups was analyzed by the Kruskal–Wallis test (K-W test). The sensitivity, specificity, accuracy, and area under curve (AUC) were calculated by the receiver operating characteristic (ROC) curves. There was high consistency between the surgeon and the radiologist in the evaluation of the magnetic signal intensity with intraclass correlation coefficients of 0.939–0.999. The average SIR was 1.07 in the bilateral normal TMJ group (group 1), 1.03 in the ADD without CR group (group 2), and 0.78 in the ADD with CR group (group 3). It could be found by the K-W test that group 3 was significantly different from group 1 and group 2 (p < 0.05), while there was no significant difference between group 1 and group 2. The optimal critical SIR value was 0.96 for the diagnosis of CR according to the ROC curves and Youden index (p < 0.001, AUC = 0.9). The condyle-to-cerebral cortex SIR can be used as a noninvasive diagnostic tool for the quantitative evaluation of condylar quality and diagnosis and prognosis of CR. SIR ≥ 0.96 indicates a healthy condyle, while SIR < 0.96 is considered the optimal critical value for the diagnosis of CR. These findings are important for personalized and accurate treatment and prognosis prediction.

Energetic electron measurements and spacecraft charging are of great significance for theoretical research in space physics and space weather applications. In this paper, the energetic electron detection package (EEDP) deployed on three Chinese navigation satellites in medium Earth orbit (MEO) is reviewed. The instrument was developed by the space science payload team led by Peking University. The EEDP includes a pinhole medium-energy electron spectrometer (MES), a high-energy electron detector (HED) based on ΔE-E telescope technology, and a deep dielectric charging monitor (DDCM). The MES measures the energy spectra of 50−600 keV electrons from nine directions with a 180°×30° field of view (FOV). The HED measures the energy spectrum of 0.5−3.0 MeV electrons from one direction with a 30° cone-angle FOV. The ground test and calibration results indicate that these three sensors exhibit excellent performance. Preliminary observations show that the electron spectra measured by the MES and HED are in good agreement with the results from the magnetic electron-ion spectrometer (MagEIS) of the Van Allen Probes spacecraft, with an average relative deviation of 27.3% for the energy spectra. The charging currents and voltages measured by the DDCM during storms are consistent with the high-energy electron observations of the HED, demonstrating the effectiveness of the DDCM. The observations of the EEDP on board the three MEO satellites can provide important support for theoretical research on the radiation belts and the applications related to space weather.

Object location is a fundamental yet challenging problem in object detection. In the remote sensing image, different imaging projection directions make the same object have various rotation angles, and in some scenes, the object distribution is relatively dense. Most of the existing deep learning-based object detection algorithms utilize horizontal bounding box to locate objects, which causes inaccurate location of the objects with dense distribution or arbitrary direction, thus leading to the detection misses. In this paper, we propose an arbitrary-angle bounding box based object location and embed it into the Faster R-CNN, developing a new framework called Rotated Faster R-CNN (R-FRCNN) for object detection in remote sensing image. In R-FRCNN, we specially improve anchor ratios to adapt to the objects like ship with large aspect ratio and increase the weights of the horizontal bounding box regression to reduce the interference of the arbitrary-angle bounding box on the horizontal bounding box prediction. Comprehensive experiments on a public dataset and a self-assembled dataset (which we make publically available) show the superior performance of our method compared to standalone state-of-the-art object detectors.

To improve the image quality of the deblurring results restored by existing blind deblurring method, an effective image blind deblurring method based on a single channel and L1-norm is proposed for the blurry images. Firstly, the blur influence on different channels in common colorspaces are analyzed and concluded to verify that the transformed channel for deblurring is reasonable. Then a more suitable fidelity term is proposed for increasing image details and restraining ringing effects. Finally, the deblurring model in a single luminance channel is generated and solved by semi-quadratic regularization method. The comprehensive experiments on several representative blind deblurring methods to evaluate the performances in terms of objective evaluation, subjective vision and running time. The results have demonstrated that the proposed method can ensure the deblurring performance and meanwhile decrease the running time. The deblurring performance of many different images reveals that our method is practical in image deblurring.

Slope failures lead to large casualties and catastrophic societal and economic consequences, thus potentially threatening access to sustainable development. Slope stability assessment, offering potential long-term benefits for sustainable development, remains a challenge for the practitioner and researcher. In this study, for the first time, an automated machine learning (AutoML) approach was proposed for model development and slope stability assessments of circular mode failure. An updated database with 627 cases consisting of the unit weight, cohesion, and friction angle of the slope materials; slope angle and height; pore pressure ratio; and corresponding stability status has been established. The stacked ensemble of the best 1000 models was automatically selected as the top model from 8208 trained models using the H2O-AutoML platform, which requires little expert knowledge or manual tuning. The top-performing model outperformed the traditional manually tuned and metaheuristic-optimized models, with an area under the receiver operating characteristic curve (AUC) of 0.970 and accuracy (ACC) of 0.904 based on the testing dataset and achieving a maximum lift of 2.1. The results clearly indicate that AutoML can provide an effective automated solution for machine learning (ML) model development and slope stability classification of circular mode failure based on extensive combinations of algorithm selection and hyperparameter tuning (CASHs), thereby reducing human efforts in model development. The proposed AutoML approach has the potential for short-term severity mitigation of geohazard and achieving long-term sustainable development goals.

Cu nanowires and a nanoporous Ag matrix were fabricated through directional solidification and selective dissolution of Ag–Cu eutectic alloys. Ag-39.9at.%Cu eutectic alloys were directionally solidified at growth rates of 14, 25, and 34 μm/s at a temperature gradient of 10 K/cm. The Cu phase in the Ag matrix gradually changed from lamellar to fibrous with an increase in the growth rate. The Ag matrix phase was selectively dissolved, and Cu nanowires of 300–600 nm in diameter and tens of microns in length were prepared in 0.1 M borate buffer with a pH of 9.18 at a constant potential of 0.7 V (vs. SCE). The nanoporous Ag matrix was fabricated through selective dissolution of Cu fiber phase in 0.1 M acetate buffer with a pH of 6.0 at a constant potential of 0.5 V (vs. SCE). The diameter of Ag pores decreased with increasing growth rate. The diameter and depth of Ag pores increased when corrosion time was extended. The depth of the pores was 30 μm after 12 h.

Phorate is a systemic, broad-spectrum organophosphorus insecticide. Although it is commonly used worldwide, phorate, like other pesticides, not only causes environmental pollution but also poses serious threats to human and animal health. Herein, we measured the blood glucose concentrations of high-fat-diet-fed mice exposed to various concentrations of phorate (0, 0.005, 0.05, or 0.5 mg/kg); we also assessed the blood glucose concentrations of high-fat-diet-fed mice exposed to phorate; we also assessed the distribution characteristics of the resistance genes in the intestinal microbiota of these mice. We found that 0.005 and 0.5 mg/kg of phorate induced obvious hyperglycaemia in the high-fat-diet-fed mice. Exposure to phorate markedly reduced the abundance of Akkermansia muciniphila in the mouse intestine. The resistance genes vanRG, tetW/N/W, acrD, and evgS were significantly upregulated in the test group compared with the control group. Efflux pumping was the primary mechanism of drug resistance in the Firmicutes, Proteobacteria, Bacteroidetes, Verrucomicrobia, Synergistetes, Spirochaetes, and Actinobacteria found in the mouse intestine. Our findings indicate that changes in the abundance of the intestinal microbiota are closely related to the presence of antibiotic-resistant bacteria in the intestinal tract and the metabolic health of the host.

The stratospheric satellite is regarded as an ideal stratosphere flight platform and is able to accomplish various missions such as surveillance, earth observation, and remote sensing, which requires a robust and effective trajectory tracking control method to support these tasks. A novel observer-based robust finite-time control scheme is proposed to address the trajectory tracking control problem dedicated to a stratospheric satellite in the presence of external disturbance and actuator saturation. Firstly, an extended state observer (ESO) is adopted to observe the unavailable velocity states and unknown disturbances simultaneously, and the estimated data are utilized in the robust control law design. Then, an auxiliary system based on anti-windup compensator is developed to directly compensate for the actuator saturation difference. After that, a backstepping nonsingular fast terminal sliding mode control (BNFTSMC) strategy is designed to track the desired trajectory with high accuracy, fast convergence rate, and finite-time convergence. Then, a stability analysis using Lyapunov-based theory is performed, in which the stabilization of the stratospheric satellite system and finite-time convergence are proven. Furthermore, a number of simulations are conducted further to verify the excellent performance of the designed control strategy.

Abstract Background Rice black-streaked dwarf virus (RBSDV) causes one of the most important rice virus diseases of plants in East Asia. However, molecular mechanism(s)controlling rice resistance to infection is largely unknown. Results In this paper, we showed that RBSDV infection in rice significantly induced nitric oxide (NO) production. This finding was further validated through a genetic approach using a RBSDV susceptible (Nipponbare) and a RBSDV resistant (15HPO187) cultivar. The production of endogenous NO was muchhigher in the 15HPO187 plants, leading to a much lower RBSDV disease incidence. Pharmacological studies showed that the applications of NO-releasingcompounds (i.e., sodium nitroprusside [SNP] and nitrosoglutathione [GSNO]) to rice plants reduced RBSDV disease incidence. After RBSDV infection, the levels of OsICS1, OsPR1b and OsWRKY 45 transcripts were significantly up-regulated by NO in Nipponbare. The increased salicylic acid contents were also observed. After the SNP treatment, protein S-nitrosylation in rice plants was also increased, suggesting that the NO-triggered resistance to RBSDV infection was partially mediated at the post-translational level. Although Osnia2 mutant rice produced less endogenous NO after RBSDV inoculation and showed a higher RBSDV disease incidence, its RBSDV susceptibility could be reduced by SNP treatment. Conclusions Collectively, our genetic and molecular evidence revealed that endogenous NO was a vital signal responsible for rice resistance to RBSDV infection.

The neural successive cancellation (NSC) decoder with tanh-based modified log-likelihood ratio (LLR) is proposed for reducing the decoding latency of polar codes over free space optical (FSO) turbulence channel. The conventional successive cancellation (SC) decoder is partitioned into multiple sub-blocks, which are replaced by multiple sub neural network (NN) decoders with tanh-based modified LLR. The recursive characteristic of the polar sequences reliability ranking given in 5G standard enables the sub-NN decoder to be uniquely determined by code length and the number of information bits. Confirmed by the simulation, the bit error rate (BER) performance of NSC decoder with tanh-based modified LLR is close to the conventional SC decoder over turbulence channel for the practical-length polar codes. Regarding turbulence-stability, the NSC decoder trained in moderate and strong turbulence conditions have stable performance in a wide range of turbulence conditions. Moreover, in comparison of decoding latency, the NSC decoder with tanh-based modified LLR takes less than 25% time steps of SC decoder in the same code length.

Abstract Rice stripe virus (RSV) causes one of the most important rice virus diseases of plants in East Asia. However, the molecular mechanisms controlling rice resistance to RSV infection are largely unknown. Recently, several studies presented a novel model that melatonin (MT) and nitric oxide (NO) participate in the plant-pathogen interaction in a synergetic manner. In this study, there was a difference in MT content between two rice varieties that correlated with one being susceptible and one being resistant to RSV, which suggested that MT is related to RSV resistance. In addition, a test with two NO biosynthesis inhibitors revealed that NO inhibitor were able to increase the disease incidence of RSV. A pharmacological experiment with exogenous MT and NO showed that increased MT and NO in the MT-pretreated plants led to lower disease incidences; however, only NO increased in a NO-releasing reagent [sodium nitroprusside (SNP)] pretreated plants. The expressions level of OsPR1b and OsWRKY 45 were significantly induced by MT and NO. These results suggest that rice resistance to RSV can be improved by increased MT through a NO-dependent pathway.

The bacterial invasions and inflammatory responses after implant placement often affect osseointegration; the increased secretion of pro-inflammatory cytokines can lead to poor formation of bone and bone absorption. Previous research has shown that the antimicrobial peptide 6K-F17 has antibacterial and immunomodulatory properties. The objective of this study was to optimize KR−1 and KR−2, based on 6K-F17, to apply to the tissue around the oral implant. Our first objective is to study its antibacterial properties, and then we intend to further study its osteogenic ability to osteoblasts by modulating the immune response of macrophages. In this research, KR−1 and KR−2 can inhibit the formation of bacterial biofilm, and further kill bacteria S. gordonii and F. nucleatum by destroying the cell wall and cell membrane of bacteria. The novel peptides restrained the activation of the NF-κB signaling pathway by reducing the phosphorylation levels of IκBα and p65, inhibiting the degradation of IκBα and the nuclear translocation of p65, and increasing the percentage of M2 phenotype in macrophages. This suppressed the inflammatory response induced by lipopolysaccharides and enhanced the osteogenic activity of osteoblasts; this, in turn, promoted osteogenesis. The antimicrobial peptide KR−1 showed better performance. Our results demonstrate that KR−1 and KR−2 have antibacterial and bone immunomodulatory effects, and further promote osteogenesis by modulating the immune microenvironment, which provides the possibility for the adjuvant treatment of peri-implant diseases.

Peri-implantitis, an infectious disease originating from dental biofilm that forms around dental implants, which causes the loss of both osseointegration and bone tissue. KN-17, a truncated cecropin B peptide, demonstrated efficacy against certain bacterial strains associated with peri-implantitis. This study aimed to assess the antibacterial and anti-inflammatory properties and mechanisms of KN-17. The effects of KN-17 on oral pathogenic bacteria were assessed by measuring its minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC). Moreover, the cytotoxicity and anti-inflammatory effects of KN-17 were evaluated. KN-17 inhibited the growth of Streptococcus gordonii and Fusobacterium nucleatum during in vitro biofilm formation and possessed low toxicity to hBMSCs cells. KN-17 also caused RAW264.7 macrophages to transform from M1 to M2 by downregulating pro-inflammatory and upregulating anti-inflammatory factors. It inhibited the NF-κB signaling pathway by reducing IκBα and P65 protein phosphorylation while promoting IκBα degradation and nuclear P65 translocation. KN-17 might be an efficacious prophylaxis against peri-implant inflammation.

Geohazard prevention and mitigation are highly complex and remain challenges for researchers and practitioners. Artificial intelligence (AI) has become an effective tool for addressing these challenges. Therefore, for decades, an increasing number of researchers have begun to conduct AI research in the field of geohazards leading to rapid growth in the number of related papers. This has made it difficult for researchers and practitioners to grasp information on cutting-edge developments in the field, thus necessitating a comprehensive review and analysis of the current state of development in the field. In this study, a comprehensive scientometric analysis appraising the state-of-the-art research for geohazard was performed based on 9226 scientometric records from the Web of Science core collection database. Multiple types of scientometric techniques, including coauthor analysis, co-citation analysis, and cluster analysis were employed to identify the most productive researchers, institutions, and hot research topics. The results show that research related to the application of AI in the field of geohazards experienced a period of rapid growth after 2000, with major developments in the field occurring in China, the United States, and Italy. The hot research topics in this field are ground motion, deep learning (DL), and landslides. The commonly used AI algorithms include DL, support vector machine (SVM), and decision tree (DT). The obtained visualization on research networks offers valuable insights and an in-depth understanding of the key researchers, institutions, fundamental articles, and salient topics through animated maps. We believe that this scientometric review offers useful reference points for early-stage researchers and provides valuable in-depth information to experienced researchers and practitioners in the field of geohazard research. This scientometric analysis and visualization are promising for reflecting the global picture of AI-based geohazard research comprehensively and possess potential for the visualization of the emerging trends in other research fields.

Ischemic stroke has been a common disease in the elderly population, which can cause long-term disability and even death. However, the time window for treatment of ischemic stroke in its acute stage is very short. To fast localize and quantitively evaluate the acute ischemic stroke (AIS) lesions, many deep-learning-based lesion segmentation methods have been proposed in the literature, where a deep convolutional neural network (CNN) was trained on hundreds of fully-labeled subjects with accurate annotations of AIS lesions. Such methods, however, require a large number of subjects with pixel-by-pixel labels, making it very time-consuming in data collection and annotation. Therefore, in this paper, we propose to use a large number of weakly-labeled subjects with easy-obtained slice-level labels and a few fully-labeled ones with pixel-level annotations, and propose a semi-supervised learning method. In particular, a double-path classification network (DPC-Net) was proposed and trained using the weakly-labeled subjects to detect the suspicious AIS lesions. A K-means algorithm was used on the diffusion -weighted images (DWIs) to identify the potential AIS lesions due to the a priori knowledge that the AIS lesions appear as hyperintense. Finally, a region-growing algorithm combines the outputs of the DPC-Net and the K-means to obtain the precise lesion segmentation. By using 460 weakly-labeled subjects and 5 fully-labeled subjects to train and fine-tune the proposed method, our proposed method achieves a mean dice coefficient of 0.642, and a lesion-wise F1 score of 0.822 on a clinical dataset with 150 subjects.

Polymerase chain reaction (PCR) detection is a commonly used method for species identification of meat products. However, this method is not suitable for the analysis of meat products containing multiple mixtures. This study aimed to test whether next-generation sequencing (NGS) technology could be used as a method for the certification of mixed meat products. In this study, five kinds of common meat (pigs, cattle, sheep, chickens, and ducks) were mixed as samples with different proportions. The primers designed from mitochondrial 16S rRNA and nuclear genome gene (growth hormone receptor, GHR), respectively, were used to detect these meats. The sequencing results of NGS were analyzed using a self-designed bioinformatics program. The fragments with similar sequences were classified and compared with the database to determine their species. The results showed that all five kinds of meat components could be correctly identified using these two primers. The meat composition could be detected as low as 0.5% in the mixed samples using the NGS technology targeting GHR gene fragments, which was superior to those targeting mitochondrial 16S rRNA. However, the quantitative detection of species in the mixture was not likely to be quite accurate due to the amplification bias of PCR amplification. These results showed that the NGS technology could be applied to identify meat species in mixtures.

Abstract Intelligent stimulus–response (S/R) systems are the basis of natural process and machine control, which are intensively explored in biomimetic design and analytical/biological applications. However, nonmonotonic multi‐S/R systems are still rarely studied so far. In this work, a rational design strategy is proposed to achieve such a unique S/R system by integrating opposite luminescence behaviors in one molecule. When solvent polarity increases, many heterocyclic or carbonyl‐containing compounds often become more emissive due to the suppression of the proximity effect, whereas molecules with donor–acceptor (D–A) structures tend to be less emissive because of the twisted intramolecular charge transfer. Meanwhile, protonation on D/A moieties will weaken/strengthen the D–A interaction to result in blue/redshifted emissions. By combining a protonatable heterocyclic acceptor and a protonatable donor together in one molecule, nonmonotonic brightness responses to polarity stimuli and nonmonotonic color responses to pH stimuli can be achieved. The design strategy is successfully verified by a simple molecule named 4‐(dimethylamino)styryl)quinoxalin‐2(1H)‐one (ASQ). ASQ exhibits nonmonotonic responses to polarity and pH stimuli, and aggregation‐induced emission (AIE) with a nonmonotonic AIE curve. Meanwhile, ASQ can be adjusted to emit white light in an acidic environment, and it shows multivalent functionalities including albumin protein sensing, ratiometric pH sensing, and amine gas sensing.

The Surface plasmon resonance (SPR) of metal materials has been widely used in photocatalysis, light sensing, biomarkers, solar cells and other fields. In this study, the surface plasmon characteristics of tungsten (W) nanowires with different diameters and lengths are analyzed using the finite element method. The thermal effect in the gap of crossed nanowires induced by the plasma resonance is studied. Results show that the resonance peak shifts red, and the resonance intensity increases with the increase in diameter. The increasing diameter results in decreasing electric field intensity and heat in the gap of the crossed tungsten (W) nanowires. The frequency of resonance peaks almost remains unchanged with increasing length. The two to six wave belly plasma modes are visible with increasing nanowire length. With exposure to incident light, the SPR in the shortened spacing results in increasing electric field intensity and the generated heat of gap between the two crossed tungsten (W) nanowires. Once the two crossed nanowires are welded, the heat production in the crossed part decreases, which indicates the self-limitation of plasma welding.

Human immunodeficiency virus-1 (HIV-1) exhibits high diversity and complexity in China, challenging the disease surveillance and antiretroviral therapy. Between July 1, 2014 and January 30, 2017, we investigated the profiles of HIV-1 infection stages, genotype distribution and drug resistance mutations (DRMs) using plasma samples from HIV Western blot (WB) confirmed blood donors from five Chinese blood centers (Chongqing, Guangxi, Luoyang, Mianyang, and Urumqi). HIV pol regions consisted of whole protease and partial reverse transcriptase were genotyped and analyzed for DRMs. Lag-Avidity testing was performed to identify the infection stages. Of the 356 HIV-1 WB positive samples tested by Lag-avidity assay, 19.1% (68/356) were recent infections. Genotyping on 356 amplified sequences presented the subtype distributions as following: CRF07_BC (65.7%), CRF08_BC (7.3%), CRF01_AE (19.1%), B (4.2%), CRF55_01B (3.1%), CRF59_01B (0.3%) and CRF68_01B (0.3%). No significant difference in genotype distribution was observed between recent and long-term infections. 48 DRMs were identified from 43 samples, indicating a drug resistance prevalence of 12.1% (43/356), which include seven protease inhibitors (PIs) accessory DRMs (Q58E, L23I and I84M), two PIs major DRMs (M46I, M46L), seven nucleoside RT inhibitors DRMs (D67N, K70Q, K219R and M184L), and 32 non-nucleoside RT inhibitors DRMs (K103N, V179E, K238N, V179D, E138G, G190E, A98G, Y188D and E138A). In addition, we had also identified CRFs from the 01B subtype including CRF55_01B (3.1%), CRF59_01B (0.3%) and CRF68_01B (0.3%). As an important part of the continuous monitoring of HIV-1 circulating strains among blood donors, our findings were expected to contribute to the comprehensive AIDS control and development of proper diagnostics for HIV-1 in China.

The patient with ischemic stroke can benefit most from the earliest possible definitive diagnosis. While a quantitative evaluation of the stroke lesions on the magnetic resonance images (MRIs) is effective in clinical diagnosis, manually segmenting the stroke lesions is commonly used, which is, however, a tedious and time-consuming task. Therefore, how to segment the stroke lesions in a fully automated manner has recently extracted extensive attentions. Considering that the clinically acquired MRIs usually have thick slices, we propose a 2D-slice-based segmentation method. In particular, we use multi-spectral MRIs, i.e., diffusion weighted image, apparent diffusion coefficient, and T2-weighted image, as input, and propose a residual-structured fully convolutional network (Res-FCN). The proposed Res-FCN is trained and evaluated on a large data set with 212 clinically acquired MRIs, which achieves a mean dice coefficient of 0.645 with a mean number of false negative lesions of 1.515 per subject. The proposed Res-FCN is further evaluated on a public data set, i.e., ISLES2015-SISS, which presents a very competitive result among all 2D-slice-based segmentation methods.