X-ray activated near-infrared persistent luminescence nanoparticles for trimodality in vivo imaging.

As promising luminescence nanoparticles, near-infrared (NIR) persistent luminescence nanoparticles (PLNPs) have received extensive attention in the field of high-sensitivity bioimaging in recent years. However, NIR PLNPs face problems such as short excitation wavelengths and single imaging modes, which limit their applications in in vivo reactivated imaging and multimodal imaging. Here, we report for the first time novel Gd2GaTaO7:Cr3+,Yb3+ (GGTO) NIR PLNPs that integrate X-ray activated NIR persistent luminescence (PersL), high X-ray attenuation and excellent magnetic properties into a single nanoparticle (NP). In this case, Cr3+ is used as the luminescence center. The co-doped Yb3+ and coating effectively enhance the X-ray activated NIR PersL. At the same time, the presence of the high-Z element Ta also makes the GGTO NPs exhibit high X-ray attenuation performance, which can be used as a CT contrast agent to achieve in vivo CT imaging. In addition, since the matrix contains a large amount of Gd, the GGTO NPs show remarkable magnetic properties, which can realize in vivo MR imaging. GGTO NPs combine the trimodal benefits of X-ray reactivated PersL, CT and MR imaging and are suitable for single or combined applications that require high sensitivity and spatial resolution imaging.

Functional Green-Emitting Mn2+-doped Zinc Germanate Persistent Luminescent Nanoparticles for Dual-Mode Immunochromatographic Detection.

Immunochromatography is a commonly used immediate detection technique, using signal labels to generate detection signals for rapid medical diagnosis. However, its detection sensitivity is affected by background fluorescence caused by the excitation light source. We have developed an immunochromatographic test strip using Zn2GeO4:Mn2+ (ZGM) persistent luminescent nanoparticles (PLNPs) for immediate fluorescence detection and highly sensitive persistent luminescence (PersL) detection without background fluorescence interference. ZGM emits a strong green light when exposed to ultraviolet (UV) excitation, and its green PersL can persist for over 30 min after the excitation light is turned off. We modified the surface of ZGM with heparin-binding protein (HBP) antibodies to create immunochromatographic test strips for the detection of HBP as the target analyte. Under UV excitation, the chromatography test paper can be visually observed at concentrations as low as 25 ng/mL. After the excitation light source is switched off, PersL can achieve a detection limit of 4.7 ng/mL without background interference. This dual-mode immunochromatographic detection, based on ZGM, shows great potential for in vitro diagnostic applications.

Near-Infrared Persistent Luminescence Nanoprobe for Early Detection of Atherosclerotic Plaque.

Atherosclerosis (AS) is a crucial contributor to various cardiovascular diseases (CVDs), which seriously threaten human life and health. Early and accurate recognition of AS plaques is essential for the prevention and treatment of CVD. Herein, we introduce an AS-targeting nanoprobe based on near-infrared (NIR) persistent luminescence nanoparticles (PLNPs), developing a highly sensitive NIR persistent luminescence (PersL) AS plaque imaging technique and successfully realizing early AS plaque detection. The nanoprobe exhibits good monodispersity and regular spherical morphology and also owns exceptional NIR PersL performance upon repetitive irradiation by biological window light. The surface-conjugated antibody (anti-osteopontin) endowed nanoprobe excellent targeting ability to foam cells within plaques. After intravenously injected nanoprobe into AS model mice, the highly sensitive PersL imaging technique can accurately detect AS plaques prior to ultrasonography (US) and magnetic resonance imaging (MRI). Specifically, the NIR PersL imaging reveals AS plaques at the earliest within 2 weeks, with higher signal-to-background ratio (SBR) up to 5.72. Based on this technique, the nanoprobe has great potential for applications in the prevention and treatment of CVD, the study of AS pathogenesis, and the screening of anti-AS drugs.

HEMU: An integrated comparative genomics database and analysis platform for Andropogoneae grasses.

This study reports an online database and analysis platform HEMU, which integrates 75 genome assemblies from 20 unique species, large amounts of multi-omics data, and six sophisticated analysis toolkits. HEMU will facilitate comparative genomics analysis within the tribe Andropogoneae.

Resolve integer ambiguity based on the global deep grid-based algorithms.

Grid theory is rather commonly-used through out the research of integer ambiguity. In order to promote the efficiency of computation, it is of great necessity to reduce the correlations of the grid basis through the reduction. The classical reduction algorithm is known as the LLL (Lenstra-Lenstra-Lovász) algorithm. So as to further enhance the reduction effect, the deep-insertion LLL algorithm can be utilized as an alternative to the basis vector exchange algorithm. In practice, the deep-insertion LLL algorithm can achieve a better reduction effect, but it requires more time for reduction. The PotLLL algorithm replaces the basis vector exchange condition of deep-insertion LLL with an improving in the basis quality, and it can run in polynomial time, but with certain limitations. Therefore, this article proposes a global deep-insertion PLLL algorithm (GS-PLLL) to address the issue of integer ambiguity. GS-PLLL adopts a global strategy for deep-insertion processing, and introduces a rotation sorting method for preconditioning the grid basis. Comparative evaluations were conducted using simulation experiments and real-world measurements on the LLL, DeepLLL, PotLLL, and GS-PLLL algorithms. The experimental results indicate that the GS-PLLL algorithm achieves a better reduction effect than the PotLLL algorithm while improving the efficiency of reduction.

A complete telomere-to-telomere assembly of the maize genome.

A complete telomere-to-telomere (T2T) finished genome has been the long pursuit of genomic research. Through generating deep coverage ultralong Oxford Nanopore Technology (ONT) and PacBio HiFi reads, we report here a complete genome assembly of maize with each chromosome entirely traversed in a single contig. The 2,178.6 Mb T2T Mo17 genome with a base accuracy of over 99.99% unveiled the structural features of all repetitive regions of the genome. There were several super-long simple-sequence-repeat arrays having consecutive thymine-adenine-guanine (TAG) tri-nucleotide repeats up to 235 kb. The assembly of the entire nucleolar organizer region of the 26.8 Mb array with 2,974 45S rDNA copies revealed the enormously complex patterns of rDNA duplications and transposon insertions. Additionally, complete assemblies of all ten centromeres enabled us to precisely dissect the repeat compositions of both CentC-rich and CentC-poor centromeres. The complete Mo17 genome represents a major step forward in understanding the complexity of the highly recalcitrant repetitive regions of higher plant genomes.

A graph-based genome and pan-genome variation of the model plant Setaria.

Setaria italica (foxtail millet), a founder crop of East Asian agriculture, is a model plant for C4 photosynthesis and developing approaches to adaptive breeding across multiple climates. Here we established the Setaria pan-genome by assembling 110 representative genomes from a worldwide collection. The pan-genome is composed of 73,528 gene families, of which 23.8%, 42.9%, 29.4% and 3.9% are core, soft core, dispensable and private genes, respectively; 202,884 nonredundant structural variants were also detected. The characterization of pan-genomic variants suggests their importance during foxtail millet domestication and improvement, as exemplified by the identification of the yield gene SiGW3, where a 366-bp presence/absence promoter variant accompanies gene expression variation. We developed a graph-based genome and performed large-scale genetic studies for 68 traits across 13 environments, identifying potential genes for millet improvement at different geographic sites. These can be used in marker-assisted breeding, genomic selection and genome editing to accelerate crop improvement under different climatic conditions.

Persistent luminescent nanoparticles for plant imaging: toward exploring the distribution of nanoparticles in plants.

Nanotoxicology on plants has raised great concerns about the prevalence of nanoparticles (NPs) in ecosystems, and the most fundamental aspect is to assess the fate of NPs in plants. Nonetheless, the lack of high-sensitivity tracking techniques in vivo constraints intensive research on NP distribution in plants. Herein, we have overcome this limitation by first introducing persistent luminescent nanoparticles (PLNPs) as an imaging probe, which can detect the distribution of NPs in whole plants with high sensitivity by completely eliminating interference from autofluorescence. We synthesized two differently surface-charged PLNPs with excellent biosafety and then exposed these NPs to plants in the hydroponic medium. Persistent luminescence (PersL) images visually showed the disparate accumulation of PLNPs in plants. PersL signals of positively charged PLNPs were observed in the whole exposed portion of the plant roots, while negatively charged PLNPs were mainly in the root collars instead of the exposed portion. With prolonged exposure, the presence of PersL signals in leaves indicated the long-distance translocation of differently charged PLNPs from roots to leaves via hypocotyls. For further confirmation of imaging results, the distribution of NPs in plants was investigated using electron microscopy. Based on their unique optical properties, PLNPs provide a promising strategy for tracking NP's fate in plants.

Near-Infrared Persistent Luminescence Nanoprobe for Ultrasensitive Image-Guided Tumor Resection.

Near-infrared (NIR) fluorescence imaging poses significant superiority over traditional medical imaging for tumor resection, thus having attracted widely attention. However, for tiny tumor residues, it requires relative high sensitivity to determine. Here, based on persistent luminescence nanoparticles (PLNPs), an ultrasensitive nanoprobe with extraordinary tumor imaging result is developed to guide surgical removal. Persistent luminescence (PersL) is quenched in normal tissue by the outer layer of MnO2 , and is recovered due to the degradation of MnO2 in tumor microenvironment, significantly improving the sensitivity of tumor imaging. Combined with the absence of background fluorescence in imaging of PLNPs, ultrahigh sensitivity is achieved. In orthotopic breast cancer model, the intraoperative tumor-to-normal tissue (T/NT) signal ratio of the nanoprobe is 58.8, about 9 times that of downconversion nanoparticles. The T/NT ratio of residual tumor (<2 mm) remains 12.4, considerably high to distinguish tumor tissue from normal tissue. Besides, multiple-microtumor, 4T1 liver-implanted tumor and lung metastasis models are built to prove that this ultrasensitive nanoprobe is feasible to recognize tumor residues. Notably, PersL imaging takes only 1.5 min, appropriate to be applied for intraoperative imaging. Overall, an ultrasensitive and convenient imaging for recognizing residual tumor tissue is introduced, holding promise for complete surgical removal.

De novo genome assembly and analyses of 12 founder inbred lines provide insights into maize heterosis.

Hybrid maize displays superior heterosis and contributes over 30% of total worldwide cereal production. However, the molecular mechanisms of heterosis remain obscure. Here we show that structural variants (SVs) between the parental lines have a predominant role underpinning maize heterosis. De novo assembly and analyses of 12 maize founder inbred lines (FILs) reveal abundant genetic variations among these FILs and, through expression quantitative trait loci and association analyses, we identify several SVs contributing to genomic and phenotypic differentiations of various heterotic groups. Using a set of 91 diallel-cross F1 hybrids, we found strong positive correlations between better-parent heterosis of the F1 hybrids and the numbers of SVs between the parental lines, providing concrete genomic support for a prevalent role of genetic complementation underlying heterosis. Further, we document evidence that SVs in both ZAR1 and ZmACO2 contribute to yield heterosis in an overdominance fashion. Our results should promote genomics-based breeding of hybrid maize.

Novel Self-Assembled Multifunctional Nanoprobes for Second-Near-Infrared-Fluorescence-Image-Guided Breast Cancer Surgery and Enhanced Radiotherapy Efficacy.

Breast-conserving surgery (BCS) is the predominant treatment approach for initial breast cancer. However, due to a lack of effective methods evaluating BCS margins, local recurrence caused by positive margins remains an issue. Accordingly, radiation therapy (RT) is a common modality in patients with advanced breast cancer. However, while RT also protects normal tissue and enhances tumor bed doses to improve therapeutic effects, current radiosensitizers cannot meet these urgent clinical needs. To address this, a novel self-assembled multifunctional nanoprobe (NP) gadolinium (Gd)-diethylenetriaminepentaacetic acid-human serum albumin (HSA)@indocyanine green-Bevacizumab (NPs-Bev) is synthesized to improve the efficacy of fluorescence-image-guided BCS and RT. Fluorescence image guidance of the second near infrared NP improves complete resection in tumor-bearing mice and accurately discriminates between benign and malignant mammary tissue in transgenic mice. Moreover, targeting tumors with NPs induces more reactive oxygen species under X-ray radiation therapy, which not only increases RT sensitivity, but also reduces tumor progression in mice. Interestingly, self-assembled NPs-Bev using HSA, the magnetic resonance contrast agent and Bevacizumab-targeting vascular growth factor A, which are clinically safe reagents, are safe in vitro and in vivo. Therefore, the novel self-assembled NPs provide a solid precision therapy platform to treat breast cancer.

Dual-Triggered Near-Infrared Persistent Luminescence Nanoprobe for Autofluorescence-Free Imaging-Guided Precise Therapy of Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a common, chronic, and highly disabling autoimmune disease characterized by difficult treatment, long disease duration, and easy recurrence. The development and application of high-sensitivity theranostic probes for RA that will facilitate precise monitoring of disease progression and enable effective treatment are currently hotspots in the field of RA theranostics. In this study, mZMI@HA, a dual-triggered theranostics nanoprobe, is constructed based on near-infrared persistent luminescence nanoparticles (NIR-PLNPs) for precise RA treatment and therapeutic evaluation. This is the first reported use of high-sensitivity autofluorescence-free imaging based on NIR-PLNPs for precise RA treatment and therapeutic evaluation. Compared with the NIR fluorescence imaging probe-indocyanine green, the signal-to-background ratio of persistent luminescence (PersL) imaging is improved nearly 14-fold. Using PersL imaging to guide photothermal therapy and controllable drug release through NIR/pH-responsiveness, the progress of collagen-induced RA is relieved. Additionally, the therapeutic evaluation of RA by PersL imaging is consistent with clinical micro-computed tomography and histological analyses. This study demonstrates the potential of NIR-PLNPs for high-sensitivity imaging-guided RA treatment, providing a new strategy for RA precise theranostics.

Plant pan-genomics and its applications.

Plant genomes are so highly diverse that a substantial proportion of genomic sequences are not shared among individuals. The variable DNA sequences, along with the conserved core sequences, compose the more sophisticated pan-genome that represents the collection of all non-redundant DNA in a species. With rapid progress in genome sequencing technologies, pan-genome research in plants is now accelerating. Here we review recent advances in plant pan-genomics, including major driving forces of structural variations that constitute the variable sequences, methodological innovations for representing the pan-genome, and major successes in constructing plant pan-genomes. We also summarize recent efforts toward decoding the remaining dark matter in telomere-to-telomere or gapless plant genomes. These new genome resources, which have remarkable advantages over numerous previously assembled less-than-perfect genomes, are expected to become new references for genetic studies and plant breeding.

Inhibition of Pro-Survival Autophagy Induced by Rare-Earth Nanocomposites for Promoting Photothermal Therapy of Visualized Tumors.

Manipulation of autophagic processes has emerged as a promising strategy for synergizing nanoagent-mediated photothermal therapy (PTT). Most of the current studies focus on improving PTT efficacy by inhibiting pro-survival autophagy induced by the heat generated from the photothermal process. However, autophagy induced by the nanoagents is usually ignored, which may weaken the effect of autophagy-mediated efficacy improvement in PTT if induced autophagy is pro-death. Therefore, this work aims at developing a nanoagent that is able to induce heat-synergetic pro-survival autophagy to optimize the efficacy of PTT. An approach is developed to coat carbon layer, polyethylenimine (PEI), and folic acid (FA) on NaYF4 :Er,Yb,Nd@NaNdF4 (DCNPs@C@PEI@FA, DCPF) nanoparticles successively, giving access to the nanoagent to induce pro-survival autophagy. The synthetic imaging-guided photothermal nanoagent displays outstanding targeting ability and biocompatibility based on the surface modification of PEI and FA. By using an autophagy inhibitor chloroquine, a conspicuously synergistic effect on DCPF-mediated PTT in vitro and in vivo tumor models (HeLa) is achieved. A promising strategy is presented here to enhance the efficacy of imaging-guided PTT by modulating the autophagy induced by the nanoagent.

A dual-functional nanoplatform based on NIR and green dual-emissive persistent luminescence nanoparticles for X-ray excited persistent luminescence imaging and photodynamic therapy.

Persistent luminescence nanoparticles (PLNPs) possess advantages for high-sensitivity bioimaging and continuous photodynamic therapy (PDT) because they can emit persistent luminescence (PerL) after excitation ceases. However, PLNPs are limited to single-wavelength emission, which can only efficiently realize one of the functions of bioimaging or PDT. In addition, most PLNPs are excited by shallow tissue penetrating excitation light, which makes it difficult to achieve repeatable in vivo applications with high efficiency. Herein, X-ray-excited PLNPs (Zn3Ga2Ge2O10:Cr3+,Mn2+, ZGGCM) with dual emission for in vivo X-rays repeatedly activated PerL imaging and tumor PDT are reported for the first time. ZGGCM exhibits dual-emission peaks after X-ray excitation/re-excitation, located at 698 nm and 532 nm, respectively. Additionally, ZGGCM is modified with the photosensitizer rose bengal (RB) to construct a dual-functional nanoplatform based on PerL imaging and PDT. The results indicate that the PerL emission peak (698 nm) of Cr3+ ions in ZGGCM possesses excellent near-infrared (NIR) PerL imaging performance, and the green PerL emission peak (532 nm) of Mn2+ ions can activate RB effectively and generate reactive oxygen species (ROS), thereby causing a significant antitumor effect. This unique dual-functional nanoplatform is expected to further promote the application of PLNPs in the integration of efficient tumor diagnosis and treatment.

Designing X-ray-Excited UVC Persistent Luminescent Material via Band Gap Engineering and Its Application to Anti-Counterfeiting and Information Encryption.

Persistent luminescent materials (PLMs) are promising candidates for the anti-counterfeiting and information encryption field. However, ultraviolet (UV) excitation and visible emission are partially responsible for enabling information that has been encrypted to combat counterfeiting to be accessed by trial and error, resulting in imitation and information leakage. Here, we propose the possibility of controlling the persistent luminescent (PersL) emission spectra and its excitation light source with the use of band gap engineering, while obtaining X-ray exciting, not UV exciting UV PLM for advanced anti-counterfeiting and encryption application. Cationic substitution was used to adjust the width of the band gap of Lu(X)O4 (X = V, Nb, Ta, and P) from ∼4 to 9 eV. In addition, Bi3+ was introduced into the host as an emitter, which enabled the PersL emission spectra to be modulated from ∼550 to 230 nm. Among these PLMs, LuPO4:Bi3+ has unique optical properties. Under UV excitation, LuPO4:Bi3+ exhibits weak, inconspicuous visible down-conversion luminescence (DCL), without PersL ceasing once excitation is discontinued. Interestingly, LuPO4:Bi3+ displays UV PersL after X-ray excitation, and human eyes are insensitive to UV PersL, which requires specialized optical equipment to detect. A proof-of-concept assessment of LuPO4:Bi3+ for anti-counterfeiting and information encryption applications demonstrated its suitability in this regard.

Deep Neural Network for 3D Shape Classification Based on Mesh Feature.

Virtual reality, driverless cars, and robotics all make extensive use of 3D shape classification. One of the most popular ways to represent 3D data is with polygonal meshes. In particular, triangular mesh is frequently employed. A triangular mesh has more features than 3D data formats such as voxels, multi-views, and point clouds. The current challenge is to fully utilize and extract useful information from mesh data. In this paper, a 3D shape classification network based on triangular mesh and graph convolutional neural networks was suggested. The triangular face of this model was viewed as a unit. By obtaining an adjacency matrix from mesh data, graph convolutional neural networks can be utilized to process mesh data. The studies were performed on the ModelNet40 dataset with an accuracy of 91.0%, demonstrating that the classification network in this research may produce effective results.

DOA Estimation in B5G/6G: Trends and Challenges.

Direction-of-arrival (DOA) estimation is the preliminary stage of communication, localization, and sensing. Hence, it is a canonical task for next-generation wireless communications, namely beyond 5G (B5G) or 6G communication networks. Both massive multiple-input multiple-output (MIMO) and millimeter wave (mmW) bands are emerging technologies that can be implemented to increase the spectral efficiency of an area, and a number of expectations have been placed on them for future-generation wireless communications. Meanwhile, they also create new challenges for DOA estimation, for instance, through extremely large-scale array data, the coexistence of far-field and near-field sources, mutual coupling effects, and complicated spatial-temporal signal sampling. This article discusses various open issues related to DOA estimation for B5G/6G communication networks. Moreover, some insights on current advances, including arrays, models, sampling, and algorithms, are provided. Finally, directions for future work on the development of DOA estimation are addressed.