Equivariant Neural Network Force Fields for Magnetic Materials

Neural network force fields have significantly advanced ab initio atomistic simulations across diverse fields. However, their application in the realm of magnetic materials is still in its early stage due to challenges posed by the subtle magnetic energy landscape and the difficulty of obtaining training data. Here we introduce a data-efficient neural network architecture to represent density functional theory total energy, atomic forces, and magnetic forces as functions of atomic and magnetic structures. Our approach incorporates the principle of equivariance under the three-dimensional Euclidean group into the neural network model. Through systematic experiments on various systems, including monolayer magnets, curved nanotube magnets, and moir\'e-twisted bilayer magnets of $\text{CrI}_{3}$, we showcase the method's high efficiency and accuracy, as well as exceptional generalization ability. The work creates opportunities for exploring magnetic phenomena in large-scale materials systems.Comment: 10 pages, 4 figure

Antibiotic Treatment Decrease the Fitness of Honeybee (Apis mellifera) Larvae

Symbiotic bacteria could increase the nutrient provision, regulate the physiological state, and promote immunity in their insect host. Honeybee larvae harbor plenty of bacteria in their gut, but their functions are not well studied. To determine their effect on honeybee larvae, the 1-day-old larvae were grafted on to 24-well plates from the comb and artificially reared in the lab. They were treated with penicillin–streptomycin to remove the gut symbiotic bacteria. Then, the 5-day-old larvae and the newly emerged adults were weighted. The developmental periods to pupae and eclosion were investigated, respectively. The bacterial amount, expression of developmental regulation genes (ecr and usp), nutrient metabolism genes (ilp1, ilp2, hex 70a, hex 70b, hex 70c, and hex 110), and immunity genes (apidaecin, abaecin, defensin-1, and hymenoptaecin) were determined by qRT-PCR. The result showed that the antibiotics-treated larvae have significantly lower body weights in the 5-day-old larvae and the emerged bees. The expression of ilp2 and hex 70c in 5-day-old larvae was down-regulated. The usp was down-regulated in 5-day-old larvae, but increased in 7-day-old larvae, which disturbed the normal developmental process and caused the extension of eclosion. Moreover, antibiotics treatment significantly decreased the expression of apidaecin and abaecin in 5-day-old larvae, and defensin-1 and hymenoptaecin in 7-day-old larvae, respectively. These results showed that antibiotics could weaken the nutrient metabolism, disturb the development process, and decrease the immune competence of honeybee larvae, indicating the vital roles of gut bacteria in bee larvae fitness, so the antibiotics should be avoided to control microbial disease in honeybee larvae

The first complete chloroplast genome of Briggsia chienii W. Y. Chun and its phylogenetic position within Gesneriaceae

Briggsia chienii W. Y. Chun 1946 is an endemic herbaceous perennial species distributed in southern China. In this study, we firstly characterized the complete chloroplast genome sequence of B. chienii and provided new molecular resources for promoting its conservation and taxonomic assignment. Its complete chloroplast genome is 154,082 bp in length and contains the typical quadripartite structure of angiosperm plastome, including two inverted repeat (IR) regions of 25,447 bp, a large single-copy (LSC) region of 85,035 bp, and a small single-copy (SSC) region of 18,153 bp. The plastome contains 114 genes, consisting of 80 protein-coding genes, 30 tRNA gene, and 4 rRNA genes. The overall GC content in the plastome of B. chienii is 37.4%, which is lower than lots of angiosperm plastome. The phylogenetic result indicated that B. chienii exhibited the closest relationship with Oreocharis cotinifolia W. T. Wang 1983, and provided new information for the phylogeny relationship of genus Briggsia

Metal Mesh and Narrow Band Gap Mn_0.5Cd_0.5S Photocatalyst Cooperation for Efficient Hydrogen Production

A novel co-catalyst system under visible-light irradiation was constructed using high-purity metal and alloy mesh and a Mn0.5Cd0.5S photocatalyst with a narrow band gap (1.91 eV) prepared by hydrothermal synthesis. The hydrogen production rate of Mn0.5Cd0.5S changed from 2.21 to 6.63 mmol·(g·h)−1 with the amount of thioacetamide, which was used as the sulphur source. The introduction of Ag, Mo, Ni, Cu, and Cu–Ni alloy meshes efficiently improved the H2 production rate of the co-catalyst system, especially for the Ni mesh. The improvement can reach an approximately six times greater production, with the highest H2 production rate being 37.65 mmol·(g·h)−1. The results showed that some bulk non-noble metal meshes can act as good or better than some noble metal nanoparticles deposited on the main photocatalyst for H2 evolution due to the promotion of photoinduced electron transfer, increase in redox reaction sites, and prevention of the recombination of carriers

Extremely Low-Frequency Electromagnetic Field Impairs the Development of Honeybee (Apis cerana)

Increasing ELF-EMF pollution in the surrounding environment could impair the cognition and learning ability of honeybees, posing a threat to the honeybee population and its pollination ability. In a social honeybee colony, the numbers of adult bees rely on the successful large-scale rearing of larvae and continuous eclosion of new adult bees. However, no studies exist on the influence of ELF-EMFs on honeybee larvae. Therefore, we investigated the survival rate, body weight, and developmental duration of first instar larvae continuously subjected to ELF-EMF exposure. Moreover, the transcriptome of fifth instar larvae were sequenced for analyzing the difference in expressed genes. The results showed that ELF-EMF exposure decreases the survival rate and body weight of both white-eye pupae and newly emerged adults, extends the duration of development time and seriously interferes with the process of metamorphosis and pupation. The transcriptome sequencing showed that ELF-EMF exposure decreases the nutrient and energy metabolism and impedes the degradation of larvae tissues and rebuilding of pupae tissues in the metamorphosis process. The results provide an experimental basis and a new perspective for the protection of honeybee populations from ELF-EMF pollution

<i>De novo</i> transcriptome analysis and microsatellite marker development for population genetic study of a serious insect pest, <i>Rhopalosiphum padi</i> (L.) (Hemiptera: Aphididae)

<div><p>The bird cherry-oat aphid, <i>Rhopalosiphum padi</i> (L.), is one of the most abundant aphid pests of cereals and has a global distribution. Next-generation sequencing (NGS) is a rapid and efficient method for developing molecular markers. However, transcriptomic and genomic resources of <i>R</i>. <i>padi</i> have not been investigated. In this study, we used transcriptome information obtained by RNA-Seq to develop polymorphic microsatellites for investigating population genetics in this species. The transcriptome of <i>R</i>. <i>padi</i> was sequenced on an Illumina HiSeq 2000 platform. A total of 114.4 million raw reads with a GC content of 40.03% was generated. The raw reads were cleaned and assembled into 29,467 unigenes with an N50 length of 1,580 bp. Using several public databases, 82.47% of these unigenes were annotated. Of the annotated unigenes, 8,022 were assigned to COG pathways, 9,895 were assigned to GO pathways, and 14,586 were mapped to 257 KEGG pathways. A total of 7,936 potential microsatellites were identified in 5,564 unigenes, 60 of which were selected randomly and amplified using specific primer pairs. Fourteen loci were found to be polymorphic in the four <i>R</i>. <i>padi</i> populations. The transcriptomic data presented herein will facilitate gene discovery, gene analyses, and development of molecular markers for future studies of <i>R</i>. <i>padi</i> and other closely related aphid species.</p></div

Equivariant neural network force fields for magnetic materials

Abstract Neural network force fields have significantly advanced ab initio atomistic simulations across diverse fields. However, their application in the realm of magnetic materials is still in its early stage due to challenges posed by the subtle magnetic energy landscape and the difficulty of obtaining training data. Here we introduce a data-efficient neural network architecture to represent density functional theory total energy, atomic forces, and magnetic forces as functions of atomic and magnetic structures. Our approach incorporates the principle of equivariance under the three-dimensional Euclidean group into the neural network model. Through systematic experiments on various systems, including monolayer magnets, curved nanotube magnets, and moiré-twisted bilayer magnets of CrI3, we showcase the method’s high efficiency and accuracy, as well as exceptional generalization ability. The work creates opportunities for exploring magnetic phenomena in large-scale materials systems

Total numbers of SSRs based on motif types in <i>R</i>. <i>padi</i>.

<p>Total numbers of SSRs based on motif types in <i>R</i>. <i>padi</i>.</p

Characteristics of 24 microsatellite loci developed for <i>R</i>. <i>padi</i>.

<p>Characteristics of 24 microsatellite loci developed for <i>R</i>. <i>padi</i>.</p