Chromosome-scale genome assembly of sweet cherry(Prunus avium L.)cv.Tieton obtained using long-read and Hi-C sequencing

Sweet cherry(Prunus avium)is an economically significant fruit species in the genus Prunus.However,in contrast to other important fruit trees in this genus,only one draft genome assembly is available for sweet cherry,which was assembled using only Illumina short-read sequences.The incompleteness and low quality of the current sweet cherry draft genome limit its use in genetic and genomic studies.A high-quality chromosome-scale sweet cherry reference genome assembly is therefore needed.A total of 65.05 Gb of Oxford Nanopore long reads and 46.24 Gb of Illumina short reads were generated,representing~190x and 136x coverage,respectively,of the sweet cherry genome.The final de novo assembly resulted in a phased haplotype assembly of 344.29 Mb with a contig N50 of 3.25 Mb.Hi-C scaffolding of the genome resulted in eight pseudochromosomes containing 99.59%of the bases in the assembled genome.Genome annotation revealed that more than half of the genome(59.40%)was composed of repetitive sequences,and 40,338 protein-coding genes were predicted,75.40%of which were functionally annotated.With the chromosomescale assembly,we revealed that gene duplication events contributed to the expansion of gene families for salicylic acid/jasmonic acid carboxyl methyltransferase and ankyrin repeat-containing proteins in the genome of sweet cherry.Four auxin-responsive genes(two GH3s and two SAURs)were induced in the late stage of fruit development,indicating that auxin is crucial for the sweet cherry ripening process.In addition,772 resistance genes were identified and functionally predicted in the sweet cherry genome.The high-quality genome assembly of sweet cherry obtained in this study will provide valuable genomic resources for sweet cherry improvement and molecular breeding.

MFCIS: an automatic leaf-based identification pipeline for plant cultivars using deep learning and persistent homology

Recognizing plant cultivars reliably and efficiently can benefit plant breeders in terms of property rights protection and innovation of germplasm resources.Although leaf image-based methods have been widely adopted in plant species identification,they seldom have been applied in cultivar identification due to the high similarity of leaves among cultivars.Here,we propose an automatic leaf image-based cultivar identification pipeline called MFCIS(Multi-feature Combined Cultivar Identification System),which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network(CNN).Persistent homology,a multiscale and robust method,was employed to extract the topological signatures of leaf shape,texture,and venation details.A CNN-based algorithm,the Xception network,was fine-tuned for extracting high-level leaf image features.For fruit species,we benchmarked the MFCIS pipeline on a sweet cherry(Prunus avium L.)leaf dataset with>5000 leaf images from 88 varieties or unreleased selections and achieved a mean accuracy of 83.52%.For annual crop species,we applied the MFCIS pipeline to a soybean(Glycine max L.Merr.)leaf dataset with 5000 leaf images of 100 cultivars or elite breeding lines collected at five growth periods.The identification models for each growth period were trained independently,and their results were combined using a score-level fusion strategy.The classification accuracy after score-level fusion was 91.4%,which is much higher than the accuracy when utilizing each growth period independently or mixing all growth periods.To facilitate the adoption of the proposed pipelines,we constructed a user-friendly web service,which is freely available at http://www.mfcis.online.

Thallium’s Threat to Aquatic Life:Stage-Specific Toxicity in Zebrafish Embryos and Larvae

The escalating frequency of thallium(Tl)contamination incidents amplifies its environmental risk.However,the potential risk of Tl to aquatic organisms,especially across varying developmental stages,remains poorly understood.In this study,we employed zebrafish as a representative model organism and exposed zebrafish embryos and larvae at distinct developmental periods(specifically,6 h postfertilization(hpf)and 72 hpf)to low concentrations of Tl(I)(0.25 and 0.50 mg/L).The exposure was performed for a short duration of 24 h,followed by a 96 h depuration period.Our results revealed that Tl(I)exerted disparate biological effects on zebrafish at different developmental stages.Embryos exhibited negligible uptake of Tl(I),whereas larvae showed a significant accumulation of Tl(I)and struggled with its rapid elimination.Notably,Tl(I)was able to permeate the blood-brain barrier,thereby posing a risk to the nervous system.Transcriptomic analysis indicated that Tl(I)triggered distinct toxicological pathways in embryos and larvae.It mainly interfered with metabolic processes in embryos,while in larvae,it mainly disrupted intracellular ion homeostasis,both consequently provoking neurotoxicity.This emphasizes that the multifaceted nature of Tl(I)toxicity depends on the developmental stages of the organism.This study clearly shows that the bioeffects of Tl are intricately related to the developmental stage of zebrafish,offering a valuable perspective for the pollutant toxicity assessment.

燃料棒接触问题的新型自适应预处理方法

通过分析接触前后雅可比矩阵的稀疏结构,构造基于接触压力判断准则的新型自适应预处理方法。在接触压力为零时使用完全的雅可比矩阵作为Preconditioned Jacobian-free Newton-Krylov(PJFNK)方法的预处理矩阵,在接触压力非零时使用雅可比矩阵的块对角作为预处理矩阵。数值试验表明:本文的自适应预处理方法能够有效提升模拟效率,求解总时间与Multiphysics Object-Oriented Simulation Environment(MOOSE)中现有方法相比减少了15.2%~33.1%。

Singlet oxygen-dominated peroxymonosulfate activation by layered crednerite for organic pollutants degradation in high salinity wastewater

Advanced oxidation processes have been widely studied for organic pollutants treatment in water,but the degradation performance of radical-dominated pathway was severely inhibited by the side reactions between the anions and radicals,especially in high salinity conditions.Here,a singlet oxygen(^(1)O_(2))-dominated non-radical process was developed for organic pollutants degradation in high salinity wastewater,with layered crednerite(CuMnO_(2))as catalysts and peroxymonosulfate(PMS)as oxidant.Based on the experiments and density functional theory calculations,^(1)O_(2)was the dominating reactive species and the constructed Cu-O-Mn with electron-deficient Mn captured electron from PMS promoting the generation of^(1)O_(2).The rapid degradation of bisphenol A(BPA)was achieved by CuMnO_(2)/PMS system,which was 5-fold and 21-fold higher than that in Mn_(2)O_(3)/PMS system and Cu_(2)O/PMS system.The CuMnO_(2)/PMS system shown prominent BPA removal performance under high salinity conditions,prominent PMS utilization efficiency,outstanding total organic carbon removal rate,wide range of applicable pH and good stability.This work unveiled that the^(1)O_(2)-dominated non-radical process of CuMnO_(2)/PMS system overcame the inhibitory effect of anions in high salinity conditions,which provided a promising technique to remove organic pollutants from high saline wastewater.

Polar solvent induced in-situ self-assembly and oxygen vacancies on Bi_(2)MoO_(6)for enhanced photocatalytic degradation of tetracycline

It has been proved to be an effective route to efficiently ameliorate photocatalytic performance of catalysts via designing three-dimensional(3D)hierarchical nanostructures and constructing oxygen vacancies(VOs).However,controlling the self-assembly of organization into 3D hierarchical nanostructures while introducing VOs in photocatalysts remains a challenge.Herein,we reported an ethylene glycol(EG)mediated approach to craft 3D hydrangea-structure Bi_(2)MoO_(6)with VOs for efficient photocatalytic degradation of tetracycline.Through manipulating the EG concentration during the fabrication process,the influence of EG concentration on the Bi_(2)MoO_(6)structure was systematically investigated.EG could promote the self-assembly of Bi_(2)MoO_(6)nanosheets to form a 3D hierarchical structure.Compared with 2D nanoplates,3D hierarchical architecture enhanced the surface area and the amount of active sites of Bi_(2)MoO_(6).In addition,the reduction effect of EG on metallic oxide enabled the generation of VOs in Bi_(2)MoO_(6).The VOs adjusted the electronic structure of Bi_(2)MoO_(6),which not only enhanced the light harvesting,but also facilitated the simultaneous utilization of photo-induced electrons and holes to form reactive oxygen species(·O2−and·OH)for the efficient tetracycline decomposition.3D Bi_(2)MoO_(6)hydrangea with VOs achieved a 79.4%removal efficiency of tetracycline after 75 min.This work provides a simple yet robust EG-mediated strategy,which not only promotes the self-assembly of nano-catalysts into 3D hierarchical architectures,but also crafts tunable VOs for highly efficient photocatalysis.

Recent progress in ZnO-based heterojunction ultraviolet light-emitting devices

Wide bandgap(3.37 eV)and high excitonbinding energy of ZnO(60 meV)make it a promising candidate for ultraviolet light-emitting diodes(LEDs)and low-threshold lasing diodes(LDs).However,the difficulty in producing stable and reproducible high-quality p-type ZnO has hindered the development of ZnO p–n homojunction LEDs.An alternative strategy for achieving ZnO electroluminescence is to fabricate heterojunction devices by employing other available p-type materials(such as p-GaN)or building new device structures.In this article,we will briefly review the recent progress in ZnO LEDs/LDs based on p–n heterostructures and metal–insulatorsemiconductor heterostructures.Some methods to improve device efficiency are also introduced in detail,including the introduction of Ag localized surface plasmons and single-crystalline nanowires into ZnO LEDs/LDs.

政府会计功能演进与地方政府债务风险治理

后疫情时代,国家制定了一系列针对地方政府举债的政策,助推经济恢复,增强发展后劲。数智化时代,如何将演进的政府会计契约和估值功能嵌入地方政府债务治理机制,提升地方政府债务治理绩效,是政府会计治理机制亟需解决的难题之一。文章分析了当前我国地方政府债务风险治理存在的问题,指出了政府会计契约和估值功能的演进方向,重点探讨了政府会计契约和估值功能如何提升地方政府债务风险的治理绩效。研究结论对于促进地方政府科学确立经济增长目标、防范与化解地方政府举债危机、提升地方政府债务风险治理绩效、优化地方政府财政生态环境、提升地方政府财政治理能力、助力经济与社会高质量发展具有重要启示。

Simultaneous removal of Cu(Ⅱ) and Cr(Ⅵ) by Mg–Al–Cl layered double hydroxide and mechanism insight

Mg-A1-C1 layered double hydroxide （C1-LDH） was prepared to simultaneously remove Cu（II） and Cr（VI） from aqueous solution. The coexisting Cu（II） （20 mg/L） and Cr（VI） （40 mg/L） were completely removed within 30 min by C1-LDH in a dosage of 2.0 g/L; the removal rate of Cu（II） was accelerated in the presence of cr（VI）. Moreover, compared with the adsorption of single Cu（II） or Cr（VI）, the adsorption capacities of C1-LDH for Cu（II） and Cr（VI） can be improved by 81.05% and 49.56%, respectively, in the case of coexisting Cu（II） （200 mg/L） and Cr（VI） （400 mg/L）. The affecting factors （such as solution initial pH, adsorbent dosage, and contact time） have been systematically investigated. Besides, the changes of pH values and the concentrations of Mg2＋ and A13＋ in relevant solutions were monitored. To get the underlying mechanism, the C1-LDH samples before and after adsorption were thoroughly characterized by X-ray powder diffraction, transmission electron microscopy, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy. On the basis of these analyses, a possible mechanism was proposed. The coadsorption process involves anion exchange of Cr（VI） with C1- in C1-LDH interlayer, isomorphic substitution of Mg2＋ with Cu2＋, formation of Cu2CI（OH）3 precipitation, and the adsorption of Cr（VI） by Cu2CI（OH）3. This work provides a new insight into simultaneous removal of heavy metal cations and anions from wastewater by CI-LDH.

Unraveling the synergetic mechanism of physisorption and chemisorption in laser-irradiated monolayer WS_(2)

To further improve the quantum efficiency of atomically thin transition metal dichalcogenides (TMDs) is crucial for the realization of high-performance optoelectronic applications. To this regard, a few chemical or physical approaches such as superacid treatment, electrical gating, dielectric screening, and laser irradiation have been developed. In particular, the laser irradiation appears to be a more efficient way with good processability and spatial selectivity. However, the underlying mechanism especially about whether chemisorption or physisorption plays a more important role is still debatable. Here, we unravel the mystery of laser irradiation induced photoluminescence enhancement in monolayer WS_(2) by precisely controlling irradiation time and environment. It is found that the synergetic effect of physisorption and chemisorption is responsible for the photoluminescence enhancement, where the physisorption dominates with more than 74% contribution. The comprehensive understanding of the adsorption mechanism in laser-irradiated TMDs may trigger the potential applications for patterned light source, effective photosensor and ultrathin optical memory.

Self-powered and broadband opto-sensor with bionic visual adaptation function based on multilayer γ-InSe flakes

Visual adaptation that can autonomously adjust the response to light stimuli is a basic function of artificial visual systems for intelligent bionic robots.To improve efficiency and reduce complexity,artificial visual systems with integrated visual adaptation functions based on a single device should be developed to replace traditional approaches that require complex circuitry and algorithms.Here,we have developed a single two-terminal opto-sensor based on multilayer γ-InSe flakes,which successfully emulated the visual adaptation behaviors with a new working mechanism combining the photo-pyroelectric and photo-thermoelectric effect.The device can operate in self-powered mode and exhibit good human-eye-like adaptation behaviors,which include broadband light-sensing image adaptation(from ultraviolet to near-infrared),near-complete photosensitivity recovery(99.6%),and synergetic visual adaptation,encouraging the advancement of intelligent opto-sensors and machine vision systems.

StripeRust-Pocket:A Mobile-Based Deep Learning Application for Efficient Disease Severity Assessment of Wheat Stripe Rust

Wheat stripe rust poses a marked threat to global wheat production.Accurate and effective disease severity assessments are crucial for disease resistance breeding and timely management of field diseases.In this study,we propose a practical solution using mobile-based deep learning and model-assisted labeling.StripeRust-Pocket,a user-friendly mobile application developed based on deep learning models,accurately quantifies disease severity in wheat stripe rust leaf images,even under complex backgrounds.Additionally,StripeRust-Pocket facilitates image acquisition,result storage,organization,and sharing.The underlying model employed by StripeRust-Pocket,called StripeRustNet,is a balanced lightweight 2-stage model.The first stage utilizes MobileNetV2-DeepLabV3+for leaf segmentation,followed by ResNet50-DeepLabV3+in the second stage for lesion segmentation.Disease severity is estimated by calculating the ratio of the lesion pixel area to the leaf pixel area.StripeRustNet achieves 98.65%mean intersection over union(MIoU)for leaf segmentation and 86.08%MIoU for lesion segmentation.Validation using an additional 100 field images demonstrated a mean correlation of over 0.964 with 3 expert visual scores.To address the challenges in manual labeling,we introduce a 2-stage labeling pipeline that combines model-assisted labeling,manual correction,and spatial complementarity.We apply this pipeline to our self-collected dataset,reducing the annotation time from 20 min to 3 min per image.Our method provides an efficient and practical solution for wheat stripe rust severity assessments,empowering wheat breeders and pathologists to implement timely disease management.It also demonstrates how to address the"last mile"challenge of applying computer vision technology to plant phenomics.