Characterization and Comparative Analysis of Small RNAs in Three Small RNA Libraries of the Brown Planthopper (Nilaparvata lugens)

BACKGROUND: The brown planthopper (BPH), Nilaparvata lugens (Stå;l), which belongs to Homopteran, Delphacidae, is one of the most serious and destructive pests of rice. Feeding BPH with homologous dsRNA in vitro can lead to the death of BPH, which gives a valuable clue to the prevention and control of this pest, however, we know little about its small RNA world. METHODOLOGY/PRINCIPAL FINDINGS: Small RNA libraries for three developmental stages of BPH (CX-male adult, CC-female adult, CY-last instar female nymph) had been constructed and sequenced. It revealed a prolific small RNA world of BPH. We obtained a final list of 452 (CX), 430 (CC), and 381 (CY) conserved microRNAs (miRNAs), respectively, as well as a total of 71 new miRNAs in the three libraries. All the miRNAs had their own expression profiles in the three libraries. The phylogenic evolution of the miRNA families in BPH was consistent with other species. The new miRNA sequences demonstrated some base biases. CONCLUSION: Our study discovered a large number of small RNAs through deep sequencing of three small RNA libraries of BPH. Many animal-conserved miRNA families as well as some novel miRNAs have been detected in our libraries. This is the first achievement to discover the small RNA world of BPH. A lot of new valuable information about BPH small RNAs has been revealed which was helpful for studying insect molecular biology and insect resistant research

Recent Ogallala Aquifer Region Drought Conditions as Observed by Terrestrial Water Storage Anomalies from GRACE

Recent severe drought events have occurred over the Ogallala Aquifer region (OAR) during the period 2011–2015, creating significant impacts on water resources and their use in regional environmental and economic systems. The changes in terrestrial water storage (TWS), as indicated by the Gravity Recovery and Climate Experiment (GRACE), reveals a detailed picture of the temporal and spatial evolution of drought events. The observations by GRACE indicate the worst drought conditions occurred in September 2012, with an average TWS deficit of ~8 cm in the northern OAR and ~11 cm in the southern OAR, consistent with precipitation data from the Global Precipitation Climatology Project. Comparing changes in TWS with precipitation shows the TWS changes can be predominantly attributable to variations in precipitation. Power spectrum and squared wavelet coherence analysis indicate a significant correlation between TWS change and the El Nino- Southern Oscillation, and the influence of equatorial Pacific sea surface temperatures on TWS change is much stronger in the southern OAR than the northern OAR. The results of this study illustrate the value of GRACE in not just the diagnosis of significant drought events, but also in possibly improving the predictive power of remote signals that are impacted by nonregional climatic events (El Nino), ultimately leading to improved water resource management applications on a regional scale. Editor’s note: This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series

Transcriptome And Expression Profiling Analysis Link Patterns Of Gene Expression To Antennal Responses In Spodoptera Litura

Background: The study of olfaction is key to understanding the interaction of insects with their environment and provides opportunities to develop novel tactics for control of pest species. Recent developments in transcriptomic approaches enable the molecular basis of olfaction to be studied even in species with limited genomic information. Here we use transcriptome and expression profiling analysis to characterize the antennal transcriptome of the noctuid moth and polyphagous pest Spodoptera litura. Results: We identify 74 candidate genes involved in odor detection and recognition, encoding 26 ORs, 21 OBPs, 18 CSPs and 9 IRs. We examine their expression levels in both sexes and seek evidence for their function by relating their expression with levels of EAG response in male and female antennae to 58 host and non-host plant volatiles and sex pheromone components. The majority of olfactory genes showed sex-biased expression, usually male-biased in ORs. A link between OR gene expression and antennal responses to odors was evident, a third of the compounds tested evoking a sex-biased response, in every case also male-biased. Two candidate pheromone receptors, OR14 and OR23 were especially strongly expressed and male-biased and we suggest that these may respond to the two female sex pheromone components of S. litura, Z9E11-14:OAc and Z9E12-14:OAc, which evoked strongly male-biased EAG responses. Conclusions: Our results provide the molecular basis for elucidating the olfactory profile of moths and the sexual divergence of their behavior and could enable the targeting of particular genes, and behaviors for pest management

Mutagenetic analysis of the biosynthetic pathway of tetramate bripiodionen bearing 3-(2H-pyran-2-ylidene)pyrrolidine-2,4-dione skeleton

Peer reviewe

Magnetic Borophenes from an Evolutionary Search

A computational methodology based on ab initio evolutionary algorithms and spin-polarized density functional theory was developed to predict two-dimensional magnetic materials. Its application to a model system borophene reveals an unexpected rich magnetism and polymorphism. A metastable borophene with nonzero thickness is an antiferromagnetic semiconductor from first-principles calculations, and can be further tuned into a half-metal by finite electron doping. In this borophene, the buckling and coupling among three atomic layers are not only responsible for magnetism, but also result in an out-of-plane negative Poisson\u27s ratio under uniaxial tension, making it the first elemental material possessing auxetic and magnetic properties simultaneously

ID-centric Pre-training for Recommendation

Classical sequential recommendation models generally adopt ID embeddings to store knowledge learned from user historical behaviors and represent items. However, these unique IDs are challenging to be transferred to new domains. With the thriving of pre-trained language model (PLM), some pioneer works adopt PLM for pre-trained recommendation, where modality information (e.g., text) is considered universal across domains via PLM. Unfortunately, the behavioral information in ID embeddings is still verified to be dominating in PLM-based recommendation models compared to modality information and thus limits these models' performance. In this work, we propose a novel ID-centric recommendation pre-training paradigm (IDP), which directly transfers informative ID embeddings learned in pre-training domains to item representations in new domains. Specifically, in pre-training stage, besides the ID-based sequential model for recommendation, we also build a Cross-domain ID-matcher (CDIM) learned by both behavioral and modality information. In the tuning stage, modality information of new domain items is regarded as a cross-domain bridge built by CDIM. We first leverage the textual information of downstream domain items to retrieve behaviorally and semantically similar items from pre-training domains using CDIM. Next, these retrieved pre-trained ID embeddings, rather than certain textual embeddings, are directly adopted to generate downstream new items' embeddings. Through extensive experiments on real-world datasets, both in cold and warm settings, we demonstrate that our proposed model significantly outperforms all baselines. Codes will be released upon acceptance

UNeR3D: Versatile and Scalable 3D RGB Point Cloud Generation from 2D Images in Unsupervised Reconstruction

In the realm of 3D reconstruction from 2D images, a persisting challenge is to achieve high-precision reconstructions devoid of 3D Ground Truth data reliance. We present UNeR3D, a pioneering unsupervised methodology that sets a new standard for generating detailed 3D reconstructions solely from 2D views. Our model significantly cuts down the training costs tied to supervised approaches and introduces RGB coloration to 3D point clouds, enriching the visual experience. Employing an inverse distance weighting technique for color rendering, UNeR3D ensures seamless color transitions, enhancing visual fidelity. Our model's flexible architecture supports training with any number of views, and uniquely, it is not constrained by the number of views used during training when performing reconstructions. It can infer with an arbitrary count of views during inference, offering unparalleled versatility. Additionally, the model's continuous spatial input domain allows the generation of point clouds at any desired resolution, empowering the creation of high-resolution 3D RGB point clouds. We solidify the reconstruction process with a novel multi-view geometric loss and color loss, demonstrating that our model excels with single-view inputs and beyond, thus reshaping the paradigm of unsupervised learning in 3D vision. Our contributions signal a substantial leap forward in 3D vision, offering new horizons for content creation across diverse applications. Code is available at https://github.com/HongbinLin3589/UNeR3D.Comment: 17 page