The extracellular matrices, such as the haemolymph, in insects are at the centre of most physiological processes and are protected from oxidative stress by the extracellular antioxidant enzymes. In this study, we identified two secreted superoxide dismutase genes (PxSOD3 and PxSOD5) and investigated the oxidative stress induced by chlorpyrifos (CPF) in the aquatic insect Protohermes xanthodes (Megaloptera: Corydalidae). PxSOD3 and PxSOD5 contain the signal peptides at the N-terminus. Structure analysis revealed that PxSOD3 and PxSOD5 contain the conserved CuZn-SOD domain, which is mainly composed of β-sheets and has conserved copper and zinc binding sites. Both PxSOD3 and PxSOD5 are predicted to be soluble proteins located in the extracellular space. After exposure to different concentrations of sublethal CPF, MDA content in P. xanthodes larvae were increased in a dose-dependent manner; SOD and CAT activities were also higher in CPF-treated groups than that in the no CPF control, indicating that sublethal CPF induces oxidative stress in P. xanthodes larvae. Furthermore, PxSOD3 and PxSOD5 expression levels and haemolymph SOD activity in the larvae were downregulated by sublethal CPF at different concentrations. Our results suggest that the PxSOD3 and PxSOD5 are putative extracellular antioxidant enzymes that may play a role in maintaining the oxidative balance in the extracellular space. Sublethal CPF may induce oxidative stress in the extracellular space of P. xanthodes by reducing the gene expression and catalytic activity of extracellular SODs.

Ultra-thin liquid sheets generated by impinging two liquid jets are crucial high-repetition-rate targets for laser ion acceleration and ultra-fast physics, and serve widely as barrier-free samples for structural biochemistry. The impact of liquid viscosity on sheet thickness should be comprehended fully to exploit its potential. Here, we demonstrate experimentally that viscosity significantly influences thickness distribution, while surface tension primarily governs shape. We propose a thickness model based on momentum exchange and mass transport within the radial flow, which agrees well with the experiments. These results provide deeper insights into the behaviour of liquid sheets and enable accurate thickness control for various applications, including atomization nozzles and laser-driven particle sources.

The whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) is economically one of the most threatening pests in tomato cultivation, which not only causes direct damage but also transmits many viruses. Breeding whitefly-resistant tomato varieties is a promising and environmentally friendly method to control whitefly populations in the field. Accumulating evidence from tomato and other model systems demonstrates that flavonoids contribute to plant resistance to herbivorous insects. Previously, we found that high flavonoid-producing tomato line deterred whitefly oviposition and settling behaviours, and was more resistant to whiteflies compared to the near-isogenic low flavonoid-producing tomato line. The objective of the current work is to describe in detail different aspects of the interaction between the whitefly and two tomato lines, including biochemical processes involved. Electrical penetration graph recordings showed that high flavonoid-producing tomato reduced whitefly probing and phloem-feeding efficiency. We also studied constitutive and induced plant defence responses and found that whitefly induced stronger reactive oxygen species accumulation through NADPH oxidase in high flavonoid-producing tomato than in low flavonoid-producing tomato. Moreover, whitefly feeding induced the expression of callose synthase genes and resulted in callose deposition in the sieve elements in high flavonoid-producing tomato but not in low flavonoid-producing tomato. As a consequence, whitefly feeding on high flavonoid-producing tomato significantly decreased uptake of phloem and reduced its performance when compared to low flavonoid-producing tomato. These results indicate that high flavonoid-producing tomato provides phloem-based resistance against whitefly infestation and that the breeding of such resistance in new varieties could enhance whitefly management.

Emission line galaxies (ELGs) are crucial for cosmological studies, particularly in understanding the large-scale structure of the Universe and the role of dark energy. ELGs form an essential component of the target catalogue for the Dark Energy Spectroscopic Instrument (DESI), a major astronomical survey. However, the accurate selection of ELGs for such surveys is challenging due to the inherent uncertainties in determining their redshifts with photometric data. In order to improve the accuracy of photometric redshift estimation for ELGs, we propose a novel approach CNN–MLP that combines convolutional neural networks (CNNs) with multilayer perceptrons (MLPs). This approach integrates both images and photometric data derived from the DESI Legacy Imaging Surveys Data Release 10. By leveraging the complementary strengths of CNNs (for image data processing) and MLPs (for photometric feature integration), the CNN–MLP model achieves a $\sigma_{\mathrm{NMAD}}$ (normalised median absolute deviation) of 0.0140 and an outlier fraction of 2.57%. Compared to other models, CNN–MLP demonstrates a significant improvement in the accuracy of ELG photometric redshift estimation, which directly benefits the target selection process for DESI. In addition, we explore the photometric redshifts of different galaxy types (Starforming, Starburst, AGN, and Broadline). Furthermore, this approach will contribute to more reliable photometric redshift estimation in ongoing and future large-scale sky surveys (e.g. LSST, CSST, and Euclid), enhancing the overall efficiency of cosmological research and galaxy surveys.

A theoretical framework has been established to investigate the modulational instability of electromagnetic waves in magnetized electron–positron plasmas. The framework is capable of analyzing electromagnetic waves of any intensity and plasmas at any temperature. A fully relativistic hydrodynamic model, incorporating relativistic velocities and thermal effects, is used to describe the relativistic dynamics of particles in plasmas. Under the weakly magnetized approximation, a modified nonlinear Schrödinger equation, governing the dynamics of the envelope of electromagnetic waves in plasmas, is obtained. The growth rate of the modulational instability is then given both theoretically and numerically. By analyzing the dependence of the growth rate on some key physical parameters, the coupled interplay of relativistic effects, ponderomotive forces, thermal effects and magnetic fields on electromagnetic waves can be clarified. The findings demonstrate that specific combinations of physical parameters can significantly enhance modulational instability, providing a theoretical basis for controlling the propagation of electromagnetic waves in plasmas. This framework has broad applicability to most current laser–plasma experiments and high-energy radiation phenomena from stellar surfaces.

The Rongjiang variety of Kam described in the present study is a southern Dong dialect (ISO 639-3: [kmc]), which belongs to the Tai-Kadai Languages (Edmondson & Luo 2008; Pittayaporn 2021).

This study delves into the intricate relationship between chief executive officers' (CEOs') experiences of poverty and the digital transformation of their firms. Employing comprehensive data collection on CEOs' birthplaces and leveraging advanced text analytics to quantify digitalization, our analysis encompasses a wide array of listed companies in China. The findings reveal that CEOs' impoverished experiences exert a detrimental influence on their firms' digital transformation efforts, primarily due to a lack of motivation and social resources necessary for such initiatives. However, this adverse effect can be ameliorated when CEOs gain access to substantial social resources in later life. Our conclusions are robust, supported by rigorous testing, and underscore not only the impact of CEOs' early-life poverty on corporate digitalization but also the potential for overcoming these challenges through the acquisition of external social resources and connections in adulthood. This study contributes significantly to existing literature and offers practical implications for enhancing corporate digital transformation strategies.

Brown dwarfs are failed stars with very low mass (13–75 Jupiter mass) and an effective temperature lower than 2 500 K. Their mass range is between Jupiter and red dwarfs. Thus, they play a key role in understanding the gap in the mass function between stars and planets. However, due to their faint nature, previous searches are inevitably limited to the solar neighbourhood (20 pc). To improve our knowledge of the low mass part of the initial stellar mass function and the star formation history of the Milky Way, it is crucial to find more distant brown dwarfs. Using James Webb Space Telescope (JWST) COSMOS-Web data, this study seeks to enhance our comprehension of the physical characteristics of brown dwarfs situated at a distance of kpc scale. The exceptional sensitivity of the JWST enables the detection of brown dwarfs that are up to 100 times more distant than those discovered in the earlier all-sky infrared surveys. The large area coverage of the JWST COSMOS-Web survey allows us to find more distant brown dwarfs than earlier JWST studies with smaller area coverages. To capture prominent water absorption features around 2.7 ${\unicode{x03BC}}$m, we apply two colour criteria, $\text{F115W}-\text{F277W}+1\lt\text{F277W}-\text{F444W}$ and $\text{F277W}-\text{F444W}\gt\,0.9$. We then select point sources by CLASS_STAR, FLUX_RADIUS, and SPREAD_MODEL criteria. Faint sources are visually checked to exclude possibly extended sources. We conduct SED fitting and MCMC simulations to determine their physical properties and associated uncertainties. Our search reveals 25 T-dwarf candidates and 2 Y-dwarf candidates, more than any previous JWST brown dwarf searches. They are located from 0.3 to 4 kpc away from the Earth. The spatial number density of 900–1 050 K dwarf is $(2.0\pm0.9) \times10^{-6}\text{ pc}^{-3}$, 1 050–1 200 K dwarf is $(1.2\pm0.7) \times10^{-6}\text{ pc}^{-3}$, and 1 200–1 350 K dwarf is $(4.4\pm1.3) \times10^{-6}\text{ pc}^{-3}$. The cumulative number count of our brown dwarf candidates is consistent with the prediction from a standard double exponential model. Three of our brown dwarf candidates were detected by HST, with transverse velocities $12\pm5$, $12\pm4$, and $17\pm6$ km s$^{-1}$. Along with earlier studies, the JWST has opened a new window of brown dwarf research in the Milky Way thick disk and halo.

In this paper, a polarization-reconfigurable antenna fed by a coplanar waveguide (CPW) using a stepped structure is presented. The main parts of the proposed antenna consist of a CPW-fed monopole and four branches. After studying and analyzing the structure and mechanism of the antenna, it was found that different polarizations can be generated by adjusting the antenna’s structure. Based on the mechanism, four PIN diodes are utilized and inserted between the four branches and the monopole for the switching. By controlling the ON/OFF states of the four PIN diodes, the antenna can switch among left-hand circular polarization (LHCP), right-hand circular polarization (RHCP), and linear polarization (LP). The optimized antenna has been fabricated and measured. Measured results indicate that the antenna’s −10-dB impedance bandwidth for LP is 15.93%, covering the frequency range from 2.3 to 2.71 GHz. The overlap bandwidth of −10-dB impedance and 3-dB axial ratio for the LHCP mode is 18.96%, covering frequencies from 2.1 to 2.54 GHz. For the RHCP mode, the overlap bandwidth is 20.5%, covering frequencies from 2 to 2.52 GHz. At all the three polarization modes, the antenna is capable of covering the 2.4-GHz WLAN band (2400–2480 MHz) as well as the LTE TD 2300 band (2300–2400 MHz).

This study examined global trends in influenza-associated lower respiratory infections (LRIs) deaths from 1990 to 2019 using data from the GBD 2019. The annual percentage change (APC) and average annual percentage change (AAPC) were used to analyze age-standardized death rates (ASDR). Globally, the ASDR of influenza-associated LRIs was 3.29/100,000 in 2019, which was higher in the African region (6.57/100,000) and among adults aged 70 years and older (29.88/100,000). The ASDR of influenza-associated LRIs decreased significantly from 1990 to 2019 (AAPC = −1.88%, P < 0.05). However, it was significantly increased among adults aged 70 years and older during 2017–2019 (APC = 2.31%, P < 0.05), especially in Western Pacific Region and South-East Asia Regions. The ratio of death rates between adults aged 70 years and older and children aged under 5 years increased globally from 1.63 in 1990 to 5.34 in 2019, and the Western Pacific Region experienced the most substantial increase, with the ratio soaring from 1.83 in 1990 to 12.98 in 2019. Despite a decline in the global ASDR of influenza-associated LRIs, it continues to impose a significant burden, particularly in the African, Western Pacific regions and among the elderly population.

Systematically monitoring the baseline sensitivity of troublesome weeds to herbicides is a crucial step in the early detection of their market lifespan. Florpyrauxifen-benzyl is one of the most important herbicides used in rice production throughout the world, and has been used for 5 yr in China. Barnyardgrass is one of the main targeted weed species of florpyrauxifen-benzyl. In total, 114 barnyardgrass populations were collected from rice fields in Jiangsu Province, China, and using whole-plant bioassays they were screened for susceptibility to florpyrauxifen-benzyl. The GR50 values (representing the dose that causes a 50% reduction in fresh weight of aboveground parts) of florpyrauxifen-benzyl for all populations ranged from 1.0 to 34.5 g ai ha−1, with an average of 6.8 g ai ha−1, a baseline sensitivity dose of 3.3 g ai ha−1, and a baseline sensitivity index of 34.5. Twenty-one days after treatment with florpyrauxifen-benzyl at the labeled dose (36 g ai ha−1), 90% of the barnyardgrass populations exhibited >95% reductions in fresh weight of aboveground parts. Compared with the baseline sensitivity dose, 63, 44, and 7 populations had, respectively, no resistance (55%), low resistance (39%), and moderate resistance (6%) to florpyrauxifen-benzyl. Furthermore, the GR50 distribution of barnyardgrass populations did not show a significant correlation with collection location, planting method (direct-seeding or transplanting), or rice species (Oryza sativa L. ssp. indica or ssp. japonica) at any of rice fields where seeds had been collected (P > 0.05). In conclusion, florpyrauxifen-benzyl remains effective for barnyardgrass control in rice fields despite serious resistance challenges.

The operational reliability of large mechanical equipment is typically influenced by the functional effectiveness of key components. Consequently, prompt repair before their failure is necessary to ensure the dependability of mechanical equipment. The prognostic and health management (PHM) technology could track the system’s health state and timely detect faults. Therefore, the remaining useful life (RUL) prediction as one of the key components of PHM is rather important. Accurate RUL prediction results could be the data support for condition-based equipment maintenance plans. Also, it could increase the dependability and safety of mechanical equipment while reducing the loss of human and financial resources and meet the requirements of sustainable manufacturing in the Industry 4.0 era. However, with the widespread use of deep learning in the field of intelligent manufacturing, there is a lack of review on RUL prediction based on deep learning. In this paper, different deep learning-based RUL prediction methods for mechanical components are summarized and classified, along with their pros and cons. Then, the case study on the C-MAPSS dataset is mainly conducted and different methods are compared. And finally, the difficulties and future directions of the RUL prediction in practical scenarios are discussed.