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.

Motion primitives play an important role in motion planning for autonomous vehicles, as they effectively address the sampling challenges inherent in nonholonomic motion planning. Employing motion primitives (MPs) is a widely accepted approach in nonholonomic motion planning based on sampling. This study specifically addresses the problem of learning from human-driving data to create human-like trajectories from predefined start-to-end states, which then serve as MP within the sampling-based nonholonomic motion planning framework. In this paper, we propose a deep learning-based method for generating MP that capture human-driving trajectory data features. By processing human-driving trajectory data, we create a Motion Primitive dataset that uniformly covers typical urban driving scenarios. Based on this dataset, a vehicle model long short-term memory neural network model is constructed to learn the features of the human-driving trajectory data. Finally, a framework for the generation of MP for practical applications is given based on this neural network. Our experiments, which focus on the dataset, the MMP generation network, and the generation process, demonstrate that our method significantly improves the training efficacy of the MP generation network. Additionally, the MP generated by our method exhibit higher accuracy compared to traditional methods.

Nontuberculous mycobacteria (NTM) is a large group of mycobacteria other than the Mycobacterium tuberculosis complex and Mycobacterium leprae. Epidemiological investigations have found that the incidence of NTM infections is increasing in China, and it is naturally resistant to many antibiotics. Therefore, studies of NTM species in clinical isolates are useful for understanding the epidemiology of NTM infections. The present study aimed to investigate the incidence of NTM infections and types of NTM species. Of the 420 samples collected, 285 were positive for M. tuberculosis, 62 samples were negative, and the remaining 73 samples contained NTM, including 35 (8.3%) only NTM and 38 (9%) mixed (M. tuberculosis and NTM). The most prevalent NTM species were Mycobacterium intracellulare (30.1%), followed by Mycobacterium abscessus (15%) and M. triviale (12%). M. gordonae infection was detected in 9.5% of total NTM-positive cases. Moreover, this study reports the presence of Mycobacterium nonchromogenicum infection and a high prevalence of M. triviale for the first time in Henan. M. intracellulare is the most prevalent, accompanied by some emerging NTM species, including M. nonchromogenicum and a high prevalence of M. triviale in Henan Province. Monitoring NTM transmission and epidemiology could enhance mycobacteriosis management in future.

Seven accelerator mass spectrometry radiocarbon (AMS 14C) dates (7260±106∼7607±95 BP averaged 7444±103 BP) on a giant oyster shell, collected from an ancient shore of the Taipei Basin, are similar to the LSC (liquid scintillation counting) 14C age (7260±46 BP) of a grass sample inside the shell. The calibrated 14C ages of the C. gigas by Marine20 are 7490±240∼7805±230 cal BP (average 7660±96 cal BP), generally agreed with the calibrated LSC 14C ages of the grass and the oyster shell. Combined with other 14C ages of shoreline samples in the Taipei Basin, it is evident that sea level rose from 8600 to 7600 cal BP and reached a stand higher than modern sea level. During this marine transgression, the sedimentation rate along the shoreline was very high because 14C dating was not able to detect age differences for 4–5 m thick sediment sequences. Sixty-nine analyses of δ18O and δ13C from the oldest part of the shell exhibit clear seasonal cycles, with a 4-year period of growth in the 5.5-cm section. According to the δ18O values, the ancient oyster grew in a warmer-than-present shoreline environment, suggesting that the current absence of the giant oyster in Taiwan is not due to warming conditions.

The characterization of energetic protons generated in the ShenGuang-II UP petawatt laser interactions with foil targets has been systematically studied. The proton energy spectra and angular distributions are measured with a radiochromic film stack. It shows that the proton energy spectra have a Boltzmann distribution with temperature of about 2.8 MeV and cutoff energy of about 20 MeV. The divergence angles of protons vary from 10° to 60°, dependent on the proton energy. The proton source size and location are investigated via the proton point-projection mesh imaging. The proton virtual sources are found to locate tens to hundreds of microns in front of the foil target, depending on the proton energies. A Monte Carlo simulation estimates the diameter of the virtual proton source to be about 12 μm for the protons with energy of 16.8 MeV, which is much smaller than the laser focus size of about 50 μm. The spatial resolution of the 16.8 MeV proton imaging is quantified with the point spread function to be about 15 μm, which is consistent with the proton virtual source size. These results will be important for the users conducting experiments with the protons as a backlighting source on the ShenGuang-II UP petawatt laser.

Galls function as provide shelter for gall inducers, guarding them against their natural enemies. Previous research has illuminated the interactions between galls, gall inducers, and their corresponding parasitoids within various caltrop plants. However, less is known about these relationships within Nitraria sibirica, particularly regarding the efficacy of parasitism. Therefore, this study aimed to identify the morphometric relationships among the swollen galls, gall inducers, and their parasitoids. Two species of gall inducers and three species of parasitoids were obtained from the swollen galls of N. sibirica. The correlations of the parasitization indexes, the lifespan of gall inhabitants, and temperature and the morphometric relationships between the galls and their inhabitants were analyzed. The dominant gall inducer identified was Contarinia sp. (Diptera: Cecidomyiidae). Furthermore, it was observed that three solitary parasitoids attacked Contarinia sp. in the swollen galls, with only Eupelmus gelechiphagus acting as an idiobiont ectoparasitoid. The dominant parasitoids were Platygaster sp. and Cheiloneurus elegans at sites 1 and 2, respectively, with Platygaster sp. displaying greater abundance than C. elegans in the swollen galls. The lifespan of the gall inhabitants shortened gradually as the temperature increased. Moreover, the optimal number of gall chambers ranged from two to four per swollen gall with maximized fitness, which can be considered the optimal population density for the gall inducer Contarinia sp. Morphometric analysis exhibited a strong linear correlation between gall size and chamber number or the number of gall inhabitants, as well as a weak correlation between gall size and body size of the primary inhabitants of swollen galls. Our results highlight the importance of the biological investigation of parasitoids and gall inducers living in closed galls with multiple chambers and may pave the way for potential application in biological control.

The advent of time-domain sky surveys has generated a vast amount of light variation data, enabling astronomers to investigate variable stars with large-scale samples. However, this also poses new opportunities and challenges for the time-domain research. In this paper, we focus on the classification of variable stars from the Catalina Surveys Data Release 2 and propose an imbalanced learning classifier based on Self-paced Ensemble (SPE) method. Compared with the work of Hosenie et al. (2020), our approach significantly enhances the classification Recall of Blazhko RR Lyrae stars from 12% to 85%, mixed-mode RR Lyrae variables from 29% to 64%, detached binaries from 68% to 97%, and LPV from 87% to 99%. SPE demonstrates a rather good performance on most of the variable classes except RRab, RRc, and contact and semi-detached binary. Moreover, the results suggest that SPE tends to target the minority classes of objects, while Random Forest is more effective in finding the majority classes. To balance the overall classification accuracy, we construct a Voting Classifier that combines the strengths of SPE and Random Forest. The results show that the Voting Classifier can achieve a balanced performance across all classes with minimal loss of accuracy. In summary, the SPE algorithm and Voting Classifier are superior to traditional machine learning methods and can be well applied to classify the periodic variable stars. This paper contributes to the current research on imbalanced learning in astronomy and can also be extended to the time-domain data of other larger sky survey projects (LSST, etc.).

A two-dimensional (2-D) double-distribution lattice Boltzmann method (LBM) is implemented to study isothermal drying of a colloidal suspension considering local nanoparticle effects. The two LBMs solve isothermal two-phase flow and nanoparticle transport, respectively. The three local nanoparticle effects on the fluid dynamics considered in this paper are viscosity increase, surface tension drop and local drying rate reduction. The proposed model is first validated by the study of the drying of a 2-D suspended colloidal droplet for two different Péclet numbers, where the evolution of the diameter squared agrees well with experimental results. The model is further validated looking at drying of a colloid in a 2-D capillary tube with two open ends. Compared with experimental results, the best agreement in terms of deposition profile and drying time is obtained when considering all three nanoparticle effects. Afterwards, we apply the model to investigate the complicated drying of a colloidal suspension in a 2-D porous asphalt, considering all three local nanoparticle effects. The drying dynamics, resultant nanoparticle transport, accumulation and deposition are first analysed for a base case. Then a parametric study is conducted varying the initial nanoparticle concentration, porous medium contact angle, nanoparticle contact angle and nanoparticle diffusion coefficient. The influence of these parameters on drying dynamics, drying rate, deposition process and final deposition configurations is analysed in detail, together with the mutual influence of local nanoparticle behaviour. Finally, a unified relation between the average drying rate and the studied parameters is proposed and verified, covering the full parameter ranges of simulations.

Nitrogen fertilizer and water are two major nutrients required for the optimal production of rice worldwide. The utilization of different irrigation techniques to save water and fertigation to maximize rice production has been the main focus. A field experiment was conducted to explore the responses of 16 rice varieties to different irrigation and nitrogen fertilizer regimes. Two nitrogen treatments, 270 kg ha−1 and 225 kg ha−1 (urea N ≥ 46.4%), and two irrigation regimes, 8.7 t ha−1 and 5.22 t ha−1, were applied three times. Plant height and the soil and plant analyser development (SPAD) values were measured throughout the growth period. The total yield and quality characteristics of the rice varieties were also determined. Based on the yield, the 16 rice varieties were divided into three groups: high yield (I), middle yield (II) and low yield (III) using cluster analysis. A positive correlation was found between the growth period and yield of these 16 rice varieties. In the water-deficient regime, the growth period of the 16 varieties was reduced by 1.68–2.93%. Furthermore, nitrogen- and water-deficient regimes had significant effects on the polishing rate, protein content and taste values of all varieties. At maturity stage under these regimes, plant height and chlorophyll SPAD values were decreased by 1.25–6.05% and 1.60–31.48%, respectively. Deficient nitrogen fertilization, along with appropriate irrigation, is an effective method for the efficient utilization of irrigation and fertilizer resources in rice-growing areas.

The study was to evaluate the reproducibility and validity of the FFQ for residents of northeast China. A total of 131 participants completed two FFQ (FFQ1 and FFQ2) within a 3-month period, 125 participants completed 8-d weighed diet records (WDR) and 112 participants completed blood biomarker testing. Reproducibility was measured by comparing nutrient and food intake between FFQ1 and FFQ2. The validity of the FFQ was assessed by WDR and the triad method. The Spearman correlation coefficients (SCC) and intraclass correlation coefficients (ICC) for reproducibility ranged from 0·41 to 0·69 (median = 0·53) and from 0·18 to 0·68 (median = 0·53) for energy and nutrients and from 0·37 to 0·73 (median = 0·59) and from 0·33 to 0·86 (median = 0·60) for food groups, respectively. The classifications of same or adjacent quartiles ranged from 73·64 to 93·80 % for both FFQ. The crude SCC between the FFQ and WDR ranged from 0·27 to 0·55 (median = 0·46) for the energy and nutrients and from 0·26 to 0·70 (median = 0·52) for food groups, and classifications of the same or adjacent quartiles ranged from 65·32 to 86·29 %. The triad method indicated that validation coefficients for the FFQ were above 0·3 for most nutrients, which indicated a moderate or high level of validity. The FFQ that was developed for residents of northeast China for the Northeast Cohort Study of China is reliable and valid for assessing the intake of most foods and nutrients.

The aim of our study was to determine the distribution of hepatitis B virus (HBV) genotypes and subgenotypes in ethnic minorities in Yunnan province to provide evidence supporting the theoretical basis for hepatitis B prevention and control. We obtained serum samples and demographic data from 765 individuals reported by Yunnan province who had either acute or chronic HBV infection and were from one of 20 ethnic minority populations: Achang, Bai, Brown, Tibetan, Dai, Deang, Dulong, Hani, Hui, Jingpo, Lahu, Yi, Lisu Miao, Naxi, Nu, Pumi, Wa, Yao, or Zhuang people. We sequenced the HBV DNA and determined the genotypes and subgenotypes of the isolated HBVs. We mapped the genotype and subgenotype distribution by ethnic minority population and conducted descriptive analyses. There were four genotypes among the 20 ethnic groups: genotype B (21.3% of samples), C (76.6%), D (1.8%) and I (0.3%). The most common subgenotype was C1. There were no genotype differences by gender (P = 0.954) or age (P = 0.274), but there were differences by region (P < 0.001). There were differences in genotype distribution (P < 0.001) and subgenotype distribution (P = 0.011) by ethnic group. Genotype D was most prominent in Tibet and most HBV isolates were C/D recombinant viruses. The only two genotype I virus isolates were in Zhuang people. Susceptibility and geographic patterns may influence HBV prevalence in different ethnic populations, but additional research is needed for such a determination.

Natural enemies that impact pest populations must be understood in order to build integrated pest control strategies and to understand the most important aspects affecting pest dynamics. Haloxylon ammodendron (C. A. Mey.) Bunge is an important perennial plant species extensively used in sand stabilization and wind prevention in arid areas. This study aimed to determine the main defoliators that damage H. ammodendron and the parasitoid complex associated with them. Twelve species of defoliators were found in Northern Xinjiang, and Teia dubia (Tauscher) (Lepidoptera: Lymantriidae), Scrobipalpa sp. (Lepidoptera: Gelechiidae), and Eucharia festiva Hüfnagel (Lepidoptera: Arctiidae) were the dominant pests. T. dubia is the predominant defoliator with three generations a year. Northwest China, Central Asia, and the Mediterranean region are potentially suitable habitats for T. dubia in the world, while Xinjiang is the primary distribution area in China. Parasitoids belonging to seven species and four families were reared from the larvae of T. dubia, they were all endoparasitoids and koinobiont. Cotesia sp. (Hymenoptera: Braconidae) is the dominant parasitoid and prefer to parasitic in the 3rd–5th instar larvae. The present study provides the basis for understanding the species composition and natural enemies of lepidopteran defoliators. It will be an effective tool for the integrated pest management programs of H. ammodendron forest.

Comprehensive screening of rice (Oryza sativa L. subsp. japonica Kato) germplasm resources with different nitrogen (N) efficiency levels is effective for improving N use efficiency (NUE) while reducing pollution and providing high quality, yield, and efficiency agriculture. We investigated 14 indices of 38 varieties under three N application levels to assess differences among genotypes. Rice varieties were classified for screening and identifying N efficient. Descriptive statistical analysis results indicated significant differences in relative yield, and also in NUE indices (agronomic utilization rate and partial productivity of N fertilizer). The genotype main effects and genotype–environment interaction effects (GGE) biplot analysis was used to evaluate suitable varieties, compare the stable and high yield capabilities of different varieties, find the ideal variety, and describe the correlation, discrimination and representativeness of the indices under different N application levels. Descriptive statistical, discrimitiveness and representativeness and factor analysis were used to select indices, in which the panicle number per plant and soil and plant analyzer development (SPAD) value were the key indices for evaluation and identification. Heatmap and hierarchical cluster analysis based on the average value of evaluation indices, and scatter plot based on the comprehensive value of N efficiency (P) according to formula showed that all varieties could be divided into five types under different N treatments. Our findings work toward developing N efficient rice varieties to improve NUE, reduce N fertilizer application and thus N waste, consequently mitigating the effects of rice production on the environment to ensure food security and sustainable agricultural development.

P values and confidence intervals (CIs) are the most widely used statistical indices in scientific literature. Several surveys have revealed that these two indices are generally misunderstood. However, existing surveys on this subject fall under psychology and biomedical research, and data from other disciplines are rare. Moreover, the confidence of researchers when constructing judgments remains unclear. To fill this research gap, we surveyed 1,479 researchers and students from different fields in China. Results reveal that for significant (i.e., p < .05, CI does not include zero) and non-significant (i.e., p > .05, CI includes zero) conditions, most respondents, regardless of academic degrees, research fields and stages of career, could not interpret p values and CIs accurately. Moreover, the majority were confident about their (inaccurate) judgements (see osf.io/mcu9q/ for raw data, materials, and supplementary analyses). Therefore, as misinterpretations of p values and CIs prevail in the whole scientific community, there is a need for better statistical training in science.

Flaxseed oil is rich in α-linolenic acid (ALA), which is the metabolic precursor of EPA and DHA. The present study investigated the effect of flaxseed oil supplementation on lipopolysaccharide (LPS)-induced muscle atrophy and carbohydrate oxidation impairment in a piglet model. Twenty-four weaned pigs were used in a 2 × 2 factorial experiment including dietary treatment (5 % maize oil v. 5 % flaxseed oil) and LPS challenge (saline v. LPS). On day 21 of treatment, the pigs were injected intraperitoneally with 100 μg/kg body weight LPS or sterile saline. At 4 h after injection, blood, gastrocnemius muscle and longissimus dorsi muscle were collected. Flaxseed oil supplementation increased ALA, EPA, total n-3 PUFA contents, protein:DNA ratio and pyruvate dehydrogenase complex quantity in muscles (P < 0·05). In addition, flaxseed oil reduced mRNA expression of toll-like receptor (TLR) 4 and nucleotide-binding oligomerisation domain protein (NOD) 2 and their downstream signalling molecules in muscles and decreased plasma concentrations of TNF-α, IL-6 and IL-8, and mRNA expression of TNF-α, IL-1β and IL-6 (P < 0·05). Moreover, flaxseed oil inclusion increased the ratios of phosphorylated protein kinase B (Akt) 1:total Akt1 and phosphorylated Forkhead box O (FOXO) 1:total FOXO1 and reduced mRNA expression of FOXO1, muscle RING finger (MuRF) 1 and pyruvate dehydrogenase kinase 4 in muscles (P < 0·05). These results suggest that flaxseed oil might have a positive effect on alleviating muscle protein loss and carbohydrates oxidation impairment induced by LPS challenge through regulation of the TLR4/NOD and Akt/FOXO signalling pathways.