Search Results (79)

This study introduces a multi-task prediction model, MT-MBLAE, designed to use DNA methylation data from blood to predict the advancement of Alzheimer’s disease. By integrating various modules, including bi-directional long short-term memory (BiLSTM), long short-term memory (LSTM), and RepeatVector, among others, the model encodes DNA methylation profile data, capturing temporal and spatial information from instantaneous DNA methylation spectra data. Leveraging the network properties of BiLSTM and LSTM enables the consideration of both preceding and subsequent information in sequences, facilitating the extraction of richer features and enhancing the model’s comprehension of sequential data. Moreover, the model employs LSTM, time distributed to reconstruct time series DNA methylation profiles. The time-distributed layer applies identical layers at each time step of the sequence, sharing weights and biases uniformly across all time steps. This approach achieves parameter sharing, reduces the model’s parameter count, and ensures consistency in handling time series data. Experimental findings show the excellent performance of the MT-MBLAE model in predicting cognitively normal (CN) to mild cognitive impairment (MCI), and mild cognitive impairment (MCI) to Alzheimer’s disease (AD). Full article

Red lead (Pb₃O₄) has been extensively utilized as a red pigment for centuries. However, the discoloration and blackening of red lead in historical paintings have significantly compromised the aesthetic value of mural artworks. Investigating the mechanisms behind the blackening of Pb₃O₄ is of paramount importance. This study examined the effects of four kinds of reactive oxygen species (ROS) on the aging process of Pb₃O₄ in an alkaline environment. Specifically, singlet oxygen (¹O₂), superoxide radical (O₂⁻·), hydrogen peroxide (H₂O₂), or peroxynitrite (ONOO⁻) was individually reacted with Pb₃O₄. The resulting products were analyzed qualitatively and quantitatively using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy–energy-dispersive spectroscopy (SEM-EDS), Raman spectroscopy, inductively coupled plasma mass spectrometry (ICP-MS), and UV-Vis spectroscopy. The findings indicate that singlet oxygen (¹O₂) and superoxide radicals (O₂⁻·) effectively induce the aging of Pb₃O₄, whereas hydrogen peroxide (H₂O₂) and peroxynitrite (ONOO⁻) exhibit little impact on its aging. This research elucidates the aging mechanisms of Pb₃O₄ in alkaline environments and provides valuable insights for the preservation and restoration of mural paintings. Full article

Background: Smilax glabra (S. glabra) and Smilax zeylanica (S. zeylanica), two medicinally important species within the genus Smilax, have been widely used in Traditional Chinese Medicine (TCM) for the treatment of rheumatism, traumatic injuries, and related ailments. Despite their medicinal significance, research on the mitochondrial DNA (mtDNA) of Smilax species remains limited. Methods: We utilized NovaSeq 6000 and PromethION sequencing platforms to assemble the complete mitochondrial genomes of Smilax glabra and Smilax zeylanica, and conducted in-depth comparative genomic and evolutionary analyses. Results: The complete mitochondrial genomes of S. glabra and S. zeylanica were assembled and annotated, with total lengths of 535,215 bp and 471,049 bp, respectively. Both genomes encode 40 unique protein-coding genes (PCGs), composed of 24 core and 16 non-core genes, alongside multiple tRNA and rRNA genes. Repetitive element analysis identified 158 and 403 dispersed repeats in S. glabra and S. zeylanica, respectively, as well as 123 and 139 simple sequence repeats (SSRs). RNA editing site predictions revealed C-to-U conversions in both species. Additionally, chloroplast-to-mitochondrial DNA migration analysis detected 34 homologous fragments in S. glabra and 28 homologous fragments in S. zeylanica. Phylogenetically, S. glabra and S. zeylanica cluster within the Liliales order and Smilacaceae family, closely related to Lilium species. Collinearity analysis indicated numerous syntenic blocks between Smilax and three other Liliopsida species, though gene order was not conserved. Conclusions: This study presents high-quality mitochondrial genome assemblies for S. glabra and S. zeylanica, providing valuable insights into molecular identification and conservation efforts of these traditional medicinal plants. Full article

Nitrogen (N), as a macro-element, plays a vital role in plant growth and development. N deficiency affects plant productivity by decreasing the photosynthesis, leaf area, and longevity of green leaf. The experimental design was a randomized complete block design with four replicates: N0 (0 kg N ha⁻¹), N90 (90 kg N ha⁻¹), N180 (180 kg N ha⁻¹), and N210 (210 kg N ha⁻¹), respectively, i.e., the effects of different N application levels on photosynthetic physiology, leaf characteristics, yield, and production. The findings of the present study underscore the importance of optimizing nitrogen application to maximize light capture, photosynthetic efficiency, and crop productivity. Under N-treated groups (N90, N180, and N210), the average photosynthetically active radiation (PAR) of panicle leaves at all levels, N210, was determined to be higher than that of other treated groups, as well as the N0 level and the upper, middle, and lower regions of N0, N90, and N180 plants under the same leaf area index (LAI), and it was noted to be higher under N210, respectively. Dry matter accumulation under N180, and N210 increased, respectively, and under N210, the dry matter accumulation of the population was significantly higher than that under N180, respectively. The nitrogen use efficiency (NUE), nitrogen recovery efficiency (NRE), nitrogen internal efficiency (NIE), and partial factor productivity of nitrogen (PFPN) under different nitrogen (N) application rates were significantly higher than N0, where the NIE of N180 was significantly higher than that of N210, the NUE and NRE of N180 and N210 were higher than those of N0, and the difference from PFPN was not significant, respectively. Full article

The Al-Ti-C alloy is the most widely used grain refiner for Al-7Si alloys. However, TiC particles in Al-7Si alloys tend to aggregate and settle, thereby reducing their refinement efficiency. In the present paper, a novel Al-3Ti-4.35La master alloy was developed, and its influence on the stability of TiC particles in Al-7Si alloys was investigated by XRD, SEM, and TEM. The results show that when the Al-TiC alloy is added to the Al-7Si alloy, TiC will accumulate and settle obviously after holding the alloy for a certain time (15 min, 30 min, and 60 min). When the Al-TiC alloy and the Al-3Ti-4.35La master alloy are added to the Al-7Si alloy, the aggregation and settlement of TiC are weakened under the same holding time. Additionally, the refinement effect of TiC is enhanced. The Ti and La elements dissolved by Ti₂Al₂₀La in the Al-3Ti-4.35La master alloy are absorbed on the surface of the TiC particles, which improves the wettability between the TiC particles and the aluminum melt, reduces the agglomeration and sedimentation of TiC particles in the aluminum melt, and improves its stability. Full article

Bryophytes, including liverworts, mosses, and hornworts, play an irreplaceable role in soil moisture retention, erosion prevention, and pollution monitoring. The precise identification of bryophyte species enhances our understanding and utilization of their ecological functions. However, their complex morphology and structural symmetry make identification difficult. Although deep learning improves classification efficiency, challenges remain due to limited datasets and the inadequate adaptation of existing methods to multi-scale features, causing poor performance in fine-grained multi-classification. Thus, we propose MOSSNet, a lightweight neural network for bryophyte feature detection. It has a four-stage architecture that efficiently extracts multi-scale features using a modular design with symmetry consideration in feature representation. At the input stage, the Convolutional Patch Embedding (CPE) module captures representative features through a two-layer convolutional structure. In each subsequent stage, Dual-Branch Multi-scale (DBMS) modules are employed, with one branch utilizing convolutional operations and the other utilizing the Dilated Convolution Enhanced Attention (DCEA) module for multi-scale feature fusion. The DBMS module extracts fine-grained and coarse-grained features by a weighted fusion of the outputs from two branches. Evaluating MOSSNet on the self-constructed dataset BryophyteFine reveals a Top-1 accuracy of 99.02% in classifying 26 bryophyte species, 7.13% higher than the best existing model, while using only 1.58 M parameters, 0.07 G FLOPs. Full article

With the increasing energy demands and concern for environmental protection, researching and optimizing multi-energy systems have become prominent issues in the energy field. To improve the overall performance of multi-energy systems, there are two main difficulties that must be overcome: the first is the issue regarding the coupling relationships between energy sources, and the second is the uncertainties related to multiple types of energy and loads. Particularly with regard to the second difficulty, it is necessary to generate a large number of effective scenarios, as many multi-energy systems have only been built recently, and operational data exhibit uncertainties. However, at the same time, the introduction of a large number of random scenarios can lead to computational difficulties, making it impossible for a model to solve this problem. To this end, in this paper, we propose an improved scheduling approach for multi-energy microgrids, balancing scenario insufficiency and computational complexity. Latin Hypercube sampling is creatively used to generate enough uncertain scenarios, and hierarchical clustering is employed to create representative scenarios to reduce the computational complexity. Then, based on these effective clustered scenarios, a multi-energy collaborative optimization method considering the coupling relationship between energy sources is proposed. The effectiveness of this method is verified through numerical tests and sensitivity analysis. The results show that the economic cost of this method is only 0.305% higher than that of the deterministic method and that it has a certain degree of robustness and a good economic performance, but it is limited by its computational efficiency. In summary, this study provides an effective solution for collaboratively optimizing the operation of multi-energy systems, aiming to provide valuable insights for research in the energy field. Full article

Cinnabar has been used as a red pigment for centuries, but its degradation significantly impacts the aesthetic quality of historical paintings, particularly murals. Therefore, investigating the preparation method and transformation process of HgS is highly significant for mural research. In this study, we compared different sulfur sources for HgS synthesis and precisely synthesized α-HgS and β-HgS by adjusting the S/HgCl₂ ratio. SEM and XRD analyses under optimal conditions demonstrated that spherical β-HgS-1.2 exhibited significant morphological differences in comparison with α-HgS-1.0 and α-HgS-1.5. Elemental analysis of HgS was conducted using XPS and ICP-MS for qualitative and quantitative insights. Based on the potential mechanism of cinnabar discoloration, two strategies for converting black β-HgS to α-HgS were proposed and successfully implemented by adding sulfur or HgCl₂. Full article

In 2017, Nikiforov defined the $A_{\alpha }$-matrix of the graph G as $A_{\alpha }\left(G\right)=\alpha D\left(G\right)+(1-\alpha )A\left(G\right),0\le \alpha \le 1,$ which merges the diagonal degree matrix $D\left(G\right)$ and the adjacency matrix $A\left(G\right)$. In this paper, we characterize the graphs which attain the maximum $A_{\alpha }$-index among triangle-free non-bipartite graphs of order n for $1/2<\alpha <1$. Full article

Indoor heating methods may influence the prevalence of respiratory and allergic diseases among preschool children. However, limited research has explored the relationship between indoor heating methods and childhood illnesses over time or on a large urban scale, and particularly the relationship between heating methods and asthma or allergic rhinitis among preschoolers from 2010 to 2019. This study conducted cross-sectional investigations in two northern cities (Taiyuan and Urumqi) and two southern cities (Chongqing and Changsha) in China during two periods: Period I (2010) and Period II (2019). Using Pearson’s chi-squared tests, we analyzed the associations between four indoor heating methods—convective heating (CH), convective and radiant heating (CH&RH), radiant heating (RH), and polluting heating (PH)—and nine respiratory and allergic diseases. Logistic regression models were employed to explore the relationships between heating methods and disease prevalence. The results revealed substantial differences in heating method choices between northern and southern Chinese cities (p < 0.001). These differences were significantly associated with the prevalence of respiratory and allergic diseases in preschoolers. Heating behaviors may have contributed to a decrease in the lifetime prevalence of asthma, pneumonia, rhinitis, and the 12-month prevalence of eczema in preschool children. In southern households, CH was linked to a lower risk of lifetime asthma (AOR: 0.63) and 12-month wheezing (AOR: 0.53). However, RH in southern households increased disease risks (AOR: 0.53). This study provides insights into the associations between heating methods and the prevalence of diseases among preschoolers across two periods in China. The findings offer new perspectives and guidance for families in selecting appropriate heating methods. Full article

Cadmium (Cd), as one of the most toxic nonessential elements, severely prohibits plant growth and development. Hydrogen sulfide (H₂S) and methyl jasmonate (MeJA) play essential roles in plant response to abiotic stress. However, the potential mechanism of H₂S and MeJA in alleviating Cd stress in plants remains unclear. In the current study, the importance and crosstalk of H₂S and MeJA in the Cd tolerance of cucumber seedlings have been investigated. Our results revealed that Cd stress obviously prohibited the growth of cucumber seedlings. Optimal concentrations of H₂S donor sodium hydrosulfide (NaHS) or MeJA treatment, respectively, or in combination, significantly enhanced seedling growth under Cd stress. However, the positive effects of H₂S during seedling growth were obviously reversed by the application of MeJA biosynthesis inhibitors, which implied that MeJA might be involved in the H₂S-improved growth of cucumber seedlings under Cd stress. Moreover, Cd stress resulted in the increase in hydrogen peroxide (H₂O₂), superoxide radical (O₂^·−) accumulation, and impaired the functioning of the ascorbate–glutathione cycle. Both H₂S and MeJA decreased the reactive oxygen species (ROS) level and ameliorated the negative effects of Cd stress through significantly increasing the ratio of ascorbate (AsA)/dehydroascorbic acid (DHA) and reduced glutathione (GSH)/oxidized glutathione (GSSG). Besides that, the expression level of ROS scavenge genes was significantly upregulated by the application of exogenous H₂S or MeJA treatment. Moreover, H₂S and MeJA significantly enhanced the chlorophyll concentration and inhibited chlorophyll degradation through decreasing the expression levels of chlorophyll catabolic enzymes. Additionally, exogenous H₂S and MeJA obviously enhanced the chlorophyll fluorescence. However, MeJA biosynthesis inhibitors significantly suppressed the positive role of H₂S. The above results suggested MeJA is involved in H₂S-induced Cd stress alleviation in cucumber seedlings through enhancing ROS-scavenge capacity and improving the photosynthesis system. Full article

Lonicera caerulea L. has high nutritional and health value, and it is an important emerging small berry tree species. In this study, the morphology and nutrient composition of 60 cultivars were used to analyze and evaluate the diversity of the genus. Morphological analysis showed that the phenotypic traits of different cultivars had significant differences (p < 0.01). The phenotypic coefficient of variation (PCV) of each trait was 12.42%~84.06%, and the coefficient of genetic variation (GCV) was between 7.07%~71.72%. The analysis of nutrient content showed significant differences among the cultivars (p < 0.01). The PCV of each trait was 3.95%~96.10%, and the GCV was 0.13%~32.83%. Based on breeding objectives, cultivars with excellent growth and leaf quantitative traits, fruit quantitative traits and nutrient contents were selected through the method of comprehensive analysis of multiple characters. Traits of the selected varieties were all above average, and specific genetic gain was higher. At the same time, the selection of varieties was carried out according to flowering and fruiting phenology, which provided an indication for the breeding of improved varieties. In this study, growth, leaf and fruit quantitative traits, phenological period and nutrient components of different cultivars provided valuable information for the breeding of improved varieties. Full article

The growth stage of wheat is key information for critical decision-making related to cultivar screening of wheat and farming activities. In order to solve the problem that it is difficult to determine the growth stages of a large number of wheat breeding materials grown in an artificial climate room accurately and quickly, the first attempt was made to determine the growth stages of wheat using image sequences of growth and development. A hybrid model (DenseNet–BiLSTM) based on the DenseNet and Bidirectional Long Short-Term Memory was proposed for determining the growth stage of wheat. The spatiotemporal characteristics of wheat growth and development were modeled by DenseNet–BiLSTM synthetically to classify the growth stage of each wheat image in the sequence. The determination accuracy of the growth stages obtained by the proposed DenseNet–BiLSTM model was 98.43%. Of these, the determination precisions of the tillering, re-greening, jointing, booting, and heading period were 100%, 97.80%, 97.80%, 85.71%, and 95.65%, respectively. In addition, the accurate determination of the growth stages and further analysis of its relationship with meteorological conditions will help biologists, geneticists, and breeders to breed, screen, and evaluate wheat varieties with ecological adaptability. Full article

Background: Hearing loss (HL) is the most common disorder in newborns with a highly heterogeneous genetic background. Despite significant progress in screening and identifying genes related to congenital hearing loss, there are still candidate genes implicated in HL that remain undiscovered. Methods: We investigated HL in 43 Chinese families by segregating bilateral sensorineural HL via whole-exome sequencing (WES) and Sanger sequencing. Results: Variants were found in 10 known non-syndromic hearing loss (NSHL) genes, 5 known syndromic hearing loss (SHL) genes, and 1 candidate HL gene, ATP7B. RNA sequencing revealed ATP7B mRNA expression in developing and adult cochleae. The immunohistochemistry of the adult mouse cochlear tissue revealed the prominent expression of ATP7B in the organ of Corti and the spiral ganglion neuron. Overall, we propose a new candidate gene, ATP7B, for congenital hearing loss and novel variants in known HL genes, which expands our understanding of the etiology of HL. Conclusions: The next-generation sequencing could effectively improve the etiological diagnosis rate of congenital hearing loss in children. Full article

The conventional Ant Colony Optimization (ACO) algorithm, applied to logistics robot path planning in a two-dimensional grid environment, encounters several challenges: slow convergence rate, susceptibility to local optima, and an excessive number of turning points in the planned paths. To address these limitations, an improved ant colony algorithm has been developed. First, the heuristic function is enhanced by incorporating artificial potential field (APF) attraction, which introduces the influence of the target point’s attraction on the heuristic function. This modification accelerates convergence and improves the optimization performance of the algorithm. Second, an additional pheromone increment, calculated from the difference in pheromone levels between the best and worst paths of the previous generation, is introduced during the pheromone update process. This adjustment adaptively enhances the path length optimality. Lastly, a triangle pruning method is applied to eliminate unnecessary turning points, reducing the number of turns the logistics robot must execute and ensuring a more direct and efficient path. To validate the effectiveness of the improved algorithm, extensive simulation experiments were conducted in two grid-based environments of varying complexity. Several performance indicators were utilized to compare the conventional ACO algorithm, a previously improved version, and the newly proposed algorithm. MATLAB simulation results demonstrated that the improved ant colony algorithm significantly outperforms the other methods in terms of path length, number of iterations, and the reduction of inflection points, confirming its superiority in logistics robot path planning. Full article