Insulin signaling establishes a developmental trajectory of adipose regulatory T cells.

Systematic characterizations of adipose regulatory T (Treg) cell subsets and their phenotypes remain uncommon. Using single-cell ATAC-sequencing and paired single-cell RNA and T cell receptor (TCR) sequencing to map mouse adipose Treg cells, we identified CD73hiST2lo and CD73loST2hi subsets with distinct clonal expansion patterns. Analysis of TCR-sharing data implied a state transition between CD73hiST2lo and CD73loST2hi subsets. Mechanistically, we revealed that insulin signaling occurs through a HIF-1α-Med23-PPAR-γ axis to drive the transition of CD73hiST2lo into a CD73loST2hi adipose Treg cell subset. Treg cells deficient in insulin receptor, HIF-1α or Med23 have decreased PPAR-γ expression that in turn promotes accumulation of CD73hiST2lo adipose Treg cells and physiological adenosine production to activate beige fat biogenesis. We therefore unveiled a developmental trajectory of adipose Treg cells and its dependence on insulin signaling. Our findings have implications for understanding the dynamics of adipose Treg cell subsets in aged and obese contexts.

Companion cell mediates wound-stimulated leaf-to-leaf electrical signaling.

Leaf wounding triggers rapid long-range electrical signaling that initiates systemic defense responses to protect the plants from further attack. In Arabidopsis, this process largely depends on clade three GLUTAMATE RECEPTOR-LIKE (GLR) genes GLR3.3 and GLR3.6. In the cellular context, phloem sieve elements and xylem contact cells where GLRs were mostly present are implicated in the signaling events. In spite of that, the spatial requirements of different leaf cell types for leaf-to-leaf signaling remain poorly investigated. In this study, we dissected cell-type-specific long-distance wound signaling mediated by GLR3s and showed that phloem companion cells are critical in shaping the functions of GLR3.3 and GLR3.6 in the signaling pathway. GLR3.3-mediated response is phloem-specific, during which, GLR3.3 has to be renewed from companion cells to allow its function in sieve elements. GLR3.6 functions dually in ectopic phloem companion cells, in addition to xylem contact cells. Furthermore, the action of GLR3.6 in phloem is independent of its paralog GLR3.3 and probably requires synthesis of GLR3.6 from xylem contact cells. Overall, our work highlights that the phloem companion cell is crucial for both GLRs in controlling leaf-to-leaf electrical signaling.

Senolytic and senomorphic agent procyanidin C1 alleviates structural and functional decline in the aged retina.

Increased cellular senescence burden contributes in part to age-related organ dysfunction and pathologies. In our study, using mouse models of natural aging, we observed structural and functional decline in the aged retina, which was accompanied by the accumulation of senescent cells and senescence-associated secretory phenotype factors. We further validated the senolytic and senomorphic properties of procyanidin C1 (PCC1) both in vitro and in vivo, the long-term treatment of which ameliorated age-related retinal impairment. Through high-throughput single-cell RNA sequencing (scRNA-seq), we comprehensively characterized the retinal landscape after PCC1 administration and deciphered the molecular basis underlying the senescence burden increment and elimination. By exploring the scRNA-seq database of age-related retinal disorders, we revealed the role of cellular senescence and the therapeutic potential of PCC1 in these pathologies. Overall, these results indicate the therapeutic effects of PCC1 on the aged retina and its potential use for treating age-related retinal disorders.

A new paradigm for applying deep learning to protein-ligand interaction prediction.

Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein-ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein-ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.

Transcription factor OsNF-YC1 regulates grain size by coordinating the transcriptional activation of OsMADS1 in Oryza sativa L.

Grain weight, grain number per panicle, and the number of panicles are the three factors that determine rice (Oryza sativa L.) yield. Of these, grain weight, which not only directly determines rice yield but also influences appearance and quality, is often considered the most important for rice production. Here, we describe OsNF-YC1, a member of the NF-Y transcription factor family that regulates rice grain size. OsNF-YC1 knockout plants (osnf-yc1), obtained using CRISPR-Cas9 technology, showed reduced grain weight due to reduced width and thickness, with no change in grain length, leading to a slenderer grain shape. Downregulation of OsNF-YC1 using RNA interference resulted in similar grain phenotypes as osnf-yc1. OsNF-YC1 affects grain formation by regulating both cell proliferation and cell expansion. OsNF-YC1 localizes in both the nucleus and cytoplasm, has transcriptional activation activity at both the N-terminus and C-terminus, and is highly expressed in young panicles. OsNF-YC1 interacts with OsMADS1 both in vivo and in vitro. Further analysis showed that the histone-like structural CBFD-NFYB-HMF domain of OsNF-YC1 conserved in the OsNF-YC transcription factor family can directly interact with the MADS-box domain of OsMADS1 to enhance its transcriptional activation activity. This interaction positively regulates the expression of OsMADS55, the direct downstream target of OsMADS1. Therefore, this paper reveals a potential grain size regulation pathway controlled by an OsNF-YC1-OsMADS1-OsMADS55 module in rice.

PxBLAT: an efficient python binding library for BLAT.

BACKGROUND: With the surge in genomic data driven by advancements in sequencing technologies, the demand for efficient bioinformatics tools for sequence analysis has become paramount. BLAST-like alignment tool (BLAT), a sequence alignment tool, faces limitations in performance efficiency and integration with modern programming environments, particularly Python. This study introduces PxBLAT, a Python-based framework designed to enhance the capabilities of BLAT, focusing on usability, computational efficiency, and seamless integration within the Python ecosystem. RESULTS: PxBLAT demonstrates significant improvements over BLAT in execution speed and data handling, as evidenced by comprehensive benchmarks conducted across various sample groups ranging from 50 to 600 samples. These experiments highlight a notable speedup, reducing execution time compared to BLAT. The framework also introduces user-friendly features such as improved server management, data conversion utilities, and shell completion, enhancing the overall user experience. Additionally, the provision of extensive documentation and comprehensive testing supports community engagement and facilitates the adoption of PxBLAT. CONCLUSIONS: PxBLAT stands out as a robust alternative to BLAT, offering performance and user interaction enhancements. Its development underscores the potential for modern programming languages to improve bioinformatics tools, aligning with the needs of contemporary genomic research. By providing a more efficient, user-friendly tool, PxBLAT has the potential to impact genomic data analysis workflows, supporting faster and more accurate sequence analysis in a Python environment.

Enantioselective Synthesis of ß-Aminoboronic Acids via Borylalkylation of Enamides.

Aminoboronic acids represent a class of significant compounds that have attracted significant attention in the fields of drug discovery and organic synthesis. Despite notable progress in their synthesis, the efficient construction of chiral ß-aminoboronic acids with alkyl side chains remains a challenging endeavor. Here, we introduce an unprecedented nickel-catalyzed asymmetric borylalkylation of enamides, employing a simple chiral diamine ligand, readily available B2pin2, and alkyl halides as coupling partners. This reaction serves as an efficient platform for assembling a diverse range of ß-aminoboronic acid derivatives with flexible alkyl side chains, displaying exceptional regio-, stereo-, and enantioselectivities. Moreover, this transformation exhibits a broad substrate scope and remarkable tolerance toward various functional groups. Theoretical calculations demonstrate that the benzyl group on the ligand is the key to the high enantiocontrol in this transformation. Additionally, we exemplify the practical application of this strategy through the concise synthesis of complex bioactive molecules.

Pleiotropic Effects of miR5504 Underlying Plant Height, Grain Yield and Quality in Rice.

MicroRNAs (miRNAs) are known to play critical roles in regulating rice agronomic traits through mRNA cleavage or translational repression. Our previous study indicated that miR5504 regulates plant height by affecting cell proliferation and expansion. Here, the two independent homozygous mir5504 mutants (CR1 and CR2) and overexpression lines (OE1 and OE2) were further used to investigate the functions of miR5504. The panicle length, 1000-grain weight and grain yield per plant of miR5504-OE lines were identical to those of Nipponbare (NIP), but the 1000-grain weight of mir5504 mutants was reduced by about 10% and 9%, respectively. Meanwhile, the grain width and thickness of mir5504 mutants decreased significantly by approximately 10% and 11%, respectively. Moreover, the cytological results revealed a significant decrease in cell number along grain width direction and cell width in spikelet in mir5504, compared with those in NIP. In addition, several major storage substances of the rice seeds were measured. Compared to NIP, the amylose content of the mir5504 seeds was noticeably decreased, leading to an increase of nearly 10 mm in gel consistency (GC) in mir5504 lines. Further investigation confirmed that LOC_Os08g16914 was the genuine target of miR5504: LOC_Os08g16914 over-expression plants phenocopied the mir5504 mutants. This study provides insights into the role of miR5504 in rice seed development.

Overexpression of SESN1 improves mitochondrial damage and mitophagy, a potential therapeutic strategy for cognitive dysfunction after anaesthesia.

Postoperative cognitive dysfunction (POCD) is a prevalent central nervous system complication predominantly observed in elderly patients. Sevoflurane, a general anaesthetic agent, has been implicated in the development of POCD, yet the underlying regulatory mechanisms potentially involving Sestrin1 (SESN1), a stress-responsive protein that plays a critical role in cellular homeostasis and protection against stress-induced damage, including oxidative stress and DNA damage, remain elusive. This study endeavoured to elucidate the impact of SESN1 on sevoflurane-induced cognitive impairment in rats. Employing a model in which SESN1 was transfected into SD male rats and cognitive dysfunction was induced by sevoflurane. The Morris Water Maze test was used for behavioural evaluation, Enzyme-Linked Immunosorbent Assay, Western blotting and immunofluorescence were applied to assess the influence of SESN1 on the inflammatory response and mitophagy in the rat hippocampus. The study further aimed to uncover the putative mechanism by which SESN1, through SIRT1, might modulate cognitive function. Concurrently, levels of malondialdehyde, superoxide dismutase and mitochondrially produced ATP within the rat hippocampus were quantified. Experimental outcomes suggested that SESN1 overexpression significantly mitigated the deleterious effects of sevoflurane anaesthesia, ameliorated neuroinflammation and inflammasome activation, modified mitochondrial function and facilitated mitophagy. Additionally, SESN1, via the activation of SIRT1, may suppress inflammasome activation and mitochondrial dysfunction. Collectively, these findings underscore SESN1's integral role in counteracting sevoflurane-induced cognitive impairment, impeding inflammasome activation, enhancing mitochondrial function and fostering mitophagy, which appear to be intricately linked to SESN1-mediated SIRT1 activation. SESN1 is a novel therapeutic target for POCD, potentially advancing neuroprotective strategies in clinical settings.

Fine mapping and identification of two NtTOM2A homeologs responsible for tobacco mosaic virus replication in tobacco (Nicotiana tabacum L.).

BACKGROUND: Tobacco mosaic virus (TMV) is a widely distributed viral disease that threatens many vegetables and horticultural species. Using the resistance gene N which induces a hypersensitivity reaction, is a common strategy for controlling this disease in tobacco (Nicotiana tabacum L.). However, N gene-mediated resistance has its limitations, consequently, identifying resistance genes from resistant germplasms and developing resistant cultivars is an ideal strategy for controlling the damage caused by TMV. RESULTS: Here, we identified highly TMV-resistant tobacco germplasm, JT88, with markedly reduced viral accumulation following TMV infection. We mapped and cloned two tobamovirus multiplication protein 2A (TOM2A) homeologs responsible for TMV replication using an F2 population derived from a cross between the TMV-susceptible cultivar K326 and the TMV-resistant cultivar JT88. Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated loss-of-function mutations of two NtTOM2A homeologs almost completely suppressed TMV replication; however, the single gene mutants showed symptoms similar to those of the wild type. Moreover, NtTOM2A natural mutations were rarely detected in 577 tobacco germplasms, and CRISPR/Cas9-mediated variation of NtTOM2A led to shortened plant height, these results indicating that the natural variations in NtTOM2A were rarely applied in tobacco breeding and the NtTOM2A maybe has an impact on growth and development. CONCLUSIONS: The two NtTOM2A homeologs are functionally redundant and negatively regulate TMV resistance. These results deepen our understanding of the molecular mechanisms underlying TMV resistance in tobacco and provide important information for the potential application of NtTOM2A in TMV resistance breeding.

GWAS combined with QTL mapping reveals the genetic loci of leaf morphological characters in Nicotiana tabacum.

BACKGROUND: Leaf morphology plays a crucial role in photosynthetic efficiency and yield potential in crops. Cigar tobacco plants, which are derived from common tobacco (Nicotiana tabacum L.), possess special leaf characteristics including thin and delicate leaves with few visible veins, making it a good system for studying the genetic basis of leaf morphological characters. RESULTS: In this study, GWAS and QTL mapping were simultaneously performed using a natural population containing 185 accessions collected worldwide and an F2 population consisting of 240 individuals, respectively. A total of 26 QTLs related to leaf morphological traits were mapped in the F2 population at three different developmental stages, and some QTL intervals were repeatedly detected for different traits and at different developmental stages. Among the 206 significant SNPs identified in the natural population using GWAS, several associated with the leaf thickness phenotype were co-mapped via QTL mapping. By analyzing linkage disequilibrium and transcriptome data from different tissues combined with gene functional annotations, 7 candidate genes from the co-mapped region were identified as the potential causative genes associated with leaf thickness. CONCLUSIONS: These results presented a valuable cigar tobacco resource showing the genetic diversity regarding its leaf morphological traits at different developmental stages. It also provides valuable information for novel genes and molecular markers that will be useful for further functional verification and for molecular breeding of leaf morphological traits in crops in the future.

Spin Effect to Regulate the Electronic Structure of Ir─Fe Aerogels for Efficient Acidic Water Oxidation.

"Spin" has been recently reported as an important degree of electronic freedom to promote catalysis, yet how it influences electronic structure remains unexplored. This work reports the spin-induced orbital hybridization in Ir─Fe bimetallic aerogels, where the electronic structure of Ir sites is effectively regulated by tuning the spin property of Fe atoms. The spin-optimized electronic structure boosts oxygen evolution reaction (OER) electrocatalysis in acidic media, resulting in a largely improved catalytic performance with an overpotential of as low as 236 mV at 10 mA cm-2. Furthermore, the gelation kinetics for the aerogel synthesis is improved by an order of magnitude based on the introduction of a magnetic field. Density functional theory calculation reveals that the increased magnetic moment of Fe (3d orbital) changes the d-band structure (i.e., the d-band center and bandwidth) of Ir (5d orbital) via orbital hybridization, resulting in optimized binding of reaction intermediates. This strategy builds the bridge between the electron spin theory with the d-band theory and provides a new way for the design of high-performance electrocatalysts by using spin-induced orbital interaction.

Engineered Microrobots for Targeted Delivery of Bacterial Outer Membrane Vesicles (OMV) in Thrombus Therapy.

In the realm of thrombosis treatment, bioengineered outer membrane vesicles (OMVs) offer a novel and promising approach, as they have rich content of bacterial-derived components. This study centers on OMVs derived from Escherichia coli BL21 cells, innovatively engineered to encapsulate the staphylokinase-hirudin fusion protein (SFH). SFH synergizes the properties of staphylokinase (SAK) and hirudin (HV) to enhance thrombolytic efficiency while reducing the risks associated with re-embolization and bleeding. Building on this foundation, this study introduces two cutting-edge microrobotic platforms: SFH-OMV@H for venous thromboembolism (VTE) treatment, and SFH-OMV@MΦ, designed specifically for cerebral venous sinus thrombosis (CVST) therapy. These platforms have demonstrated significant efficacy in dissolving thrombi, with SFH-OMV@H showcasing precise vascular navigation and SFH-OMV@MΦ effectively targeting cerebral thrombi. The study shows that the integration of these bioengineered OMVs and microrobotic systems marks a significant advancement in thrombosis treatment, underlining their potential to revolutionize personalized medical approaches to complex health conditions.

Wafer Scale Gallium Nitride Integrated Electrode Toward Robust High Temperature Energy Storage.

Gallium Nitride (GaN), as the representative of wide bandgap semiconductors, has great prospects in accomplishing rapid charge delivery under high-temperature environments thanks to excellent structural stability and electron mobility. However, there is still a gap in wafer-scale GaN single-crystal integrated electrodes applied in the energy storage field. Herein, Si-doped GaN nanochannel with gallium oxynitride (GaON) layer on a centimeter scale (denoted by GaN NC) is reported. The Si atoms modulate electronic redistribution to improve conductivity and drive nanochannel formation. Apart from that, the distinctive nanochannel configuration with a GaON layer provides adequate active sites and extraordinary structural stability. The GaN-based supercapacitors are assembled and deliver outstanding charge storage capabilities at 140 °C. Surprisingly, 90% retention is maintained after 50 000 cycles. This study opens the pathway toward wafer-scale GaN single-crystal integrated electrodes with self-powered characteristics that are compatible with various (opto)-electronic devices.

The characteristics of BCR-CDR3 repertoire in COVID-19 patients and SARS-CoV-2 vaccinated volunteers.

The global COVID-19 pandemic has caused more than 1 billion infections, and numerous SARS-CoV-2 vaccines developed rapidly have been administered over 10 billion doses. The world is continuously concerned about the cytokine storms induced by the interaction between SARS-CoV-2 and host, long COVID, breakthrough infections postvaccination, and the impact of SARS-CoV-2 variants. BCR-CDR3 repertoire serves as a molecular target for monitoring the antiviral response "trace" of B cells, evaluating the effects, mechanisms, and memory abilities of individual responses to B cells, and has been successfully applied in analyzing the infection mechanisms, vaccine improvement, and neutralizing antibodies preparation of influenza virus, HIV, MERS, and Ebola virus. Based on research on BCR-CDR3 repertoire of COVID-19 patients and volunteers who received different SARS-CoV-2 vaccines in multiple laboratories worldwide, we focus on analyzing the characteristics and changes of BCR-CDR3 repertoire, such as diversity, clonality, V&J genes usage and pairing, SHM, CSR, shared CDR3 clones, as well as the summary on BCR sequences targeting virus-specific epitopes in the preparation and application research of SARS-CoV-2 potential therapeutic monoclonal antibodies. This review provides comparative data and new research schemes for studying the possible mechanisms of differences in B cell response between SARS-CoV-2 infection or vaccination, and supplies a foundation for improving vaccines after SARS-CoV-2 mutations and potential antibody therapy for infected individuals.

Nickel Chain-Walking Catalysis: A Journey to Migratory Carboboration of Alkenes.

ConspectusChain-walking offers extensive opportunities for innovating synthetic methods that involve constructing chemical bonds at unconventional sites. This approach provides previously inaccessible retrosynthetic disconnections in organic synthesis. Through chain-walking, transition metal-catalyzed alkene difunctionalization reactions can take place in a 1,n-addition (n ≠ 2) mode. Unlike classical 1,2-regioselective difunctionalization reactions, there remains a scarcity of reports regarding migratory patterns. Moreover, the range of olefins utilized in these studies is quite limited.About five years ago, our research group embarked on a project aimed at developing valuable migratory difunctionalization reactions of alkenes through chain-walking. Our focus was on carboboration of alkenes utilizing nickel catalysis. The reaction commences with the migratory insertion of an olefin into a Ni-Bpin species. Subsequently, a thermodynamically stable alkyl nickel complex is generated through a chain-walking process. This complex then couples with a carbon-based electrophile, leading to the formation of an alkylboron compound. It is worth highlighting that the success of these transformations relies significantly on the utilization of a bisnitrogen-based ligand and LiOMe as a B2pin2 activator. Synthetically, these migratory carboboration reactions establish a robust platform for the rapid and efficient synthesis of a wide range of structurally diverse organoboron compounds, which are not facially accessed by conventional methods. The incorporation of a versatile boron group introduces a wealth of possibilities for subsequent diversifications, significantly enhancing the value of the resulting products and allowing for the creation of a broader range of valuable derivatives and applications.This Account provides a comprehensive overview of our research efforts and advancements in the field of migratory carboboration of unactivated alkenes using nickel catalysis. We begin by outlining the development of a series of 1,1-regioselective carboboration reactions of terminal alkenes. A significant focus is placed on the initial integration of boronate, which not only triggers the formation of thermodynamically stable metal species but also exerts control over remote stereochemistry in reactions involving substituted methylenecyclohexenes. Continuing our exploration, remarkable success is achieved in 1,3-regio- and cis-stereoselectivity when dealing with cyclic alkenes. Remarkably, nickel chain-walking catalysis enables heterocyclic alkenes to be viable coupling partners within our transformations. Moreover, it grants us the ability to achieve regioselectivity for cyclohexenes that was previously unattainable, thus expanding the horizons of regiochemical control in these reactions. Lastly, we present the evolution of ligand-modulated regiodivergent carboboration of allylarenes. By gaining insights into the underlying mechanisms driving regiodivergence, we lay a strong foundation for tackling challenges related to selecting specific sites in chain-walking reactions, especially when dealing with multiple stable factors. We anticipate that our findings, coupled with the mechanistic insights we've gained, will not only advance the realm of nickel chain-walking catalysis but also contribute to the broader understanding of selectivity control in reactions of this nature. This advancement will also catalyze the synthesis of intricate functional molecules, contributing to the creation of complex and valuable compounds in the realm of organic chemistry.

Quantitative characterization of micro-scanning imaging aliasing and optical parameter optimization.

Imaging aliasing is a common problem in the imaging domain. The aliasing of micro-scanning imaging is difficult to characterize accurately, and the matching relationship between the optical system and micro-scanning sampling is unclear. In this paper, a micro-scanning aliasing analysis model is proposed based on the property of sampling squeeze, in which the transfer functions of the optical system, detector, and digital filter are coupled with the micro-scanning sampling process. First, the imaging aliasing under different micro-scanning sampling modes is evaluated based on the constraint relationship of the transfer functions for each part. The stretch factor of the transfer function under micro-scanning sampling is calculated by utilizing the amount of aliasing. Second, the micro-scanning imaging transfer function under different optical parameters is predicted by the stretch factor, and the results indicate the existence of an optimal F-number that maximizes the micro-scanning performance improvement. Furthermore, the optimal micro-scanning imaging F-numbers for different fill factors are given, and the matching relationship between optical parameters, fill factors and micro-scanning mode is analyzed. Finally, a micro-scanning imaging simulation is performed based on the actual imaging transfer and micro-scanning sampling process. The simulation experiment verifies the accuracy of the micro-scanning aliasing model and gives the consistent test results of the optimal F-number. This paper can provide theoretical support for the matching relationship among micro-scanning imaging parameters, which is of great significance for the refined optimal design of micro-scanning imaging systems.

Sub-pixel target fine spatial feature extraction method based on aperture coding and micro-scanning imaging mechanism.

The small imaging size of targets over long distances results in the loss of geometry and spatial features. Current methods are subject to sampling limitations and cannot accurately capture the spatial features of sub-pixel targets. This paper proposes a method to accurately locate and extract the fine spatial features of sub-pixel targets through aperture coding and micro-scanning imaging. First, the formation mechanism of imaging features for sub-pixel targets is analyzed. Second, the optical aperture is anisotropically coded in different directions to modulate the spreading spots of the target. The primary spreading direction and the center of the anisotropic spreading spots are extracted. The contour and the location of the target are determined from the spreading length and the intersections of the primary spreading directions. Then, the target is sampled by different detector units through various micro-scanning offsets. The pixel units containing different sub-pixel components of the target after offset are determined based on the location results. The fine spatial distribution of the sub-pixel target is reconstructed based on the intensity variations in the pixel units containing the target. Finally, the accuracy of the sub-pixel target fine spatial feature extraction method is validated. The results show a sub-pixel localization error of less than 0.02 and an effective improvement of the sub-pixel target spatial resolution. This paper provides significant potential for improving the ability to capture spatial features of targets over long distances.

Coulomb-induced emission time shifts in high-order harmonic generation from H2.

Accurate emission times of high-order harmonic generation (HHG) are vital for high-precision ultrafast detection in attosecond science, but a quantitative analysis of Coulomb effects on this time is absent in the molecular HHG. Here, we investigate the Coulomb-induced emission-time shift in HHG of H2+ with two different internuclear distances R, where the times obtained via the Gabor transform of numerical data from solving the time-dependent Schrödinger equation are used as simulation experiment results. Based on the molecular strong-field approximation, we develop a trajectory-resolved classical model that takes into account the molecular two-center structure. By selecting appropriate electron trajectories and including Coulomb interactions, the classical trajectory method can reproduce Gabor emission times well. This consistence reveals that Coulomb tails cause an emission-time shift of ∼35 as at the R = 2.0 a.u. case and of ∼40-60 as at the R = 2.6 a.u. case under the present laser parameters when compared to the Coulomb-free quantum-orbit model. Our results are of significance to probe the attosecond dynamics via two-center interference.

Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning.

Metabolic dysfunction-associated fatty liver disease (MAFLD) has reached epidemic proportions worldwide and is the most frequent cause of chronic liver disease in developed countries. Within the spectrum of liver disease in MAFLD, steatohepatitis is a progressive form of liver disease and hepatocyte ballooning (HB) is a cardinal pathological feature of steatohepatitis. The accurate and reproducible diagnosis of HB is therefore critical for the early detection and treatment of steatohepatitis. Currently, a diagnosis of HB relies on pathological examination by expert pathologists, which may be a time-consuming and subjective process. Hence, there has been interest in developing automated methods for diagnosing HB. This narrative review briefly discusses the development of artificial intelligence (AI) technology for diagnosing fatty liver disease pathology over the last 30 years and provides an overview of the current research status of AI algorithms for the identification of HB, including published articles on traditional machine learning algorithms and deep learning algorithms. This narrative review also provides a summary of object detection algorithms, including the principles, historical developments, and applications in the medical image analysis. The potential benefits of object detection algorithms for HB diagnosis (specifically those combined with a transformer architecture) are discussed, along with the future directions of object detection algorithms in HB diagnosis and the potential applications of generative AI on transformer architecture in this field. In conclusion, object detection algorithms have huge potential for the identification of HB and could make the diagnosis of MAFLD more accurate and efficient in the near future.