Dense object detection methods in RAW UAV imagery based on YOLOv8.

Accurate, fast and lightweight dense target detection methods are highly important for precision agriculture. To detect dense apricot flowers using drones, we propose an improved dense target detection method based on YOLOv8, named D-YOLOv8. First, we introduce the Dense Feature Pyramid Networks (D-FPN) to enhance the model's ability to extract dense features and Dense Attention Layer (DAL) to focus on dense target areas, which enhances the feature extraction ability of dense areas, suppresses features in irrelevant areas, and improves dense target detection accuracy. Finally, RAW data are used to enhance the dataset, which introduces additional original data into RAW images, further enriching the feature input of dense objects. We perform validation on the CARPK challenge dataset and constructed a dataset. The experimental results show that our proposed D-YOLOv8m achieved 98.37% AP, while the model parameters were only 13.2 million. The improved network can effectively support any task of dense target detection.

Enhancing interfacial electron transfer and photoelectrochemical kinetics for efficient water-treatment strategy through N-doped carbon dots modified PhC2Cu.

To improve the water environment quality, the development of an effective photocatalyst for pollutant removal was considered a promising strategy. The aim of the development of a novel photocatalyst PNC is pursued by modifying copper-phenylacetylide (PhC2Cu) with nitrogen-doped carbon quantum dots (N-CDs). Leading to a remarkable improvement in its light absorption capability, electron transfer efficiency and photoelectrochemical properties. Importantly, PNC possesses the characteristic of straightforward synthesis and demonstrates remarkable performance in the photodegradation of 99.87% sulfamethoxazole (SMX) within just 15 min, with a 3.95-fold increase in the photocatalytic rate. Analysis of the active substances revealed that 1O2, O2·-, and h+ are the generated active species by PNC. Active sites and degradation pathways of SMX were explored through density functional theory (DFT) calculations and intermediate analysis. Key evidence regarding the direction of electron transfer within the system was obtained through in-situ irradiated X-ray (ISI-XPS) techniques. This study deepened our understanding of the electron transfer characteristics of phenylacetylene copper and provided new insights for the modification of photocatalysts.

Vision-Based Estimation of Force Balance of Near-Suspended Melt Pool for Drooping and Collapsing Prediction.

Wire-arc additive manufacturing (WAAM) is favored by the industry for its high material utilization rate and low cost. However, wire-arc additive manufacturing of lattice structures faces problems with forming accuracy such as broken rod and surface morphology defects, which cannot meet the industrial demand. This article innovatively combines the melt pool stress theory with visual perception algorithms to visually study the force balance of the near-suspended melt pool to predict the state of the melt pool. First, the method for melt pool segmentation was studied. The results show that the optimized U-net achieved high accuracy in melt pool segmentation tasks, with accuracies of 98.18%, MIOU 96.64%, and Recall 98.34%. In addition, a method for estimating melt pool force balance and predicting normal, sagging, and collapsing states of the melt pool is proposed. By combining experimental testing with computer vision technology, an analysis of the force balance of the melt pool during the inclined rod forming process was conducted, showing a prediction rate as high as 90% for the testing set. By using this method, monitoring and predicting the state of the melt pool is achieved, preemptively avoiding issues of broken rods during the printing process. This approach can effectively assist in adjusting process parameters and improving welding quality. The application of this method will further promote the development of intelligent unmanned WAAM and provide some references for the development of artificial intelligence monitoring systems in the manufacturing field.

Discovery and Biosynthesis of Streptolateritic Acids A-D: Acyclic Pentacarboxylic Acids from Streptomyces sp. FXJ1.172 with Promising Activity against Potato Common Scab.

Potato common scab (PCS) is a widespread plant disease that lacks effective control measures. Using a small molecule elicitor, we activate the production of a novel class of polyketide antibiotics, streptolateritic acids A-D, in Streptomyces sp. FXJ1.172. These compounds show a promising control efficacy against PCS and an unusual acyclic pentacarboxylic acid structure. A gene cluster encoding a type I modular polyketide synthase is identified to be responsible for the biosynthesis of these metabolites. A cytochrome P450 (CYP) and an aldehyde dehydrogenase (ADH) encoded by two genes in the cluster are proposed to catalyze iterative oxidation of the starter-unit-derived methyl group and three of six branching methyl groups to carboxylic acids during chain assembly. Our findings highlight how activation of silent biosynthetic gene clusters can be employed to discover completely new natural product classes able to combat PCS and new types of modular polyketide synthase-based biosynthetic machinery.

VmmScore: An umami peptide prediction and receptor matching program based on a deep learning approach.

Peptides, with recognized physiological and medical implications, such as the ability to lower blood pressure and lipid levels, are central to our research on umami taste perception. This study introduces a computational strategy to tackle the challenge of identifying optimal umami receptors for these peptides. Our VmmScore algorithm includes two integral components: Mlp4Umami, a predictive module that evaluates the umami taste potential of peptides, and mm-Score, which enhances the receptor matching process through a machine learning-optimized molecular docking and scoring system. This system encompasses the optimization of docking structures, clustering of umami peptides, and a comparative analysis of docking energies across peptide clusters, streamlining the receptor identification process. Employing machine learning, our method offers a strategic approach to the intricate task of umami receptor determination. We undertook virtual screening of peptides derived from Lateolabrax japonicus, experimentally verifying the umami taste of three identified peptides and determining their corresponding receptors. This work not only advances our understanding of the mechanisms behind umami taste perception but also provides a rapid and cost-effective method for peptide screening. The source code is publicly accessible at https://github.com/heyigacu/mlp4umami/, encouraging further scientific exploration and collaborative efforts within the research community.

The F-box protein OsFBX156 positively regulates rice defence against the blast fungus Magnaporthe oryzae by mediating ubiquitination-dependent degradation of OsHSP71.1.

F-box protein is a subunit of the SCF (SKP1-CUL1-F-box protein) E3 ubiquitin ligase complex, which plays a critical role in regulating different pathways in plant immunity. In this study, we identified the rice (Oryza sativa) F-box protein OsFBX156, which targets the heat shock protein 70 (OsHSP71.1) to regulate resistance to the rice blast fungus Magnaporthe oryzae. Overexpression of OsFBX156 or knockout of OsHSP71.1 in rice resulted in the elevation of pathogenesis-related (PR) genes and an induction burst of reactive oxygen species (ROS) after flg22 and chitin treatments, thereby enhancing resistance to M. oryzae. Furthermore, OsFBX156 can promote the degradation of OsHSP71.1 through the 26S proteasome pathway. This study sheds lights on a novel mechanism wherein the F-box protein OsFBX156 targets OsHSP71.1 for degradation to promote ROS production and PR gene expression, thereby positively regulating rice innate immunity.

Highly Permeable and Regenerative Microhoneycomb Filters.

Allergic reactions can profoundly influence the quality of life. To address the health risks posed by allergens and overcome the permeability limitations of the current filter materials, this work introduces a novel microhoneycomb (MH) material for practical filter applications such as masks. Through a synthesis process integrating ice-templating and a gas-phase post-treatment with silane, MH achieves unprecedented levels of moisture resistance and mechanical stability while preserving the highly permeable microchannels. Notably, MH is extremely elastic, with a 92% recovery rate after being compressed to 80% deformation. The filtration efficiency surpasses 98.1% against pollutant particles that simulate airborne pollens, outperforming commercial counterparts with fifth-fold greater air permeability while ensuring unparalleled user comfort. Moreover, MH offers a sustainable solution, being easily regenerated through back-flow blowing, distinguishing it from conventional nonwoven fabrics. Finally, a prototype mask incorporating MH is presented, demonstrating its immense potential as a high-performance filtration material, effectively addressing health risks posed by allergens and other harmful particles.

Enantioselective Nickel-Electrocatalyzed Reductive Propargylic Carboxylation with CO2.

The exploitation of carbon dioxide (CO2) as a sustainable, plentiful, and harmless C1 source for the catalytic synthesis of enantioenriched carboxylic acids has long been acknowledged as a pivotal task in synthetic chemistry. Herein, we present a current-driven nickel-catalyzed reductive carboxylation reaction with CO2 fixation, facilitating the formation of C(sp3)-C(sp2) bonds by circumventing the handling of moisture-sensitive organometallic reagents. This electroreductive protocol serves as a practical platform, paving the way for the synthesis of enantioenriched propargylic carboxylic acids (up to 98% enantiomeric excess) from racemic propargylic carbonates and CO2. The efficacy of this transformation is exemplified by its successful utilization in the asymmetric total synthesis of (S)-arundic acid, (R)-PIA, (S)-chizhine D, (S)-cochlearin G, and (S,S)-alexidine, thereby underscoring the potential of asymmetric electrosynthesis to achieve complex molecular architectures sustainably.

Electron wave and quantum optics in graphene.

In the last decade, graphene has become an exciting platform for electron optical experiments, in some aspects superior to conventional two-dimensional electron gases (2DEGs). A major advantage, besides the ultra-large mobilities, is the fine control over the electrostatics, which gives the possibility of realising gap-less and compact p-n interfaces with high precision. The latter host non-trivial states,e.g., snake states in moderate magnetic fields, and serve as building blocks of complex electron interferometers. Thanks to the Dirac spectrum and its non-trivial Berry phase, the internal (valley and sublattice) degrees of freedom, and the possibility to tailor the band structure using proximity effects, such interferometers open up a completely new playground based on novel device architectures. In this review, we introduce the theoretical background of graphene electron optics, fabrication methods used to realise electron-optical devices, and techniques for corresponding numerical simulations. Based on this, we give a comprehensive review of ballistic transport experiments and simple building blocks of electron optical devices both in single and bilayer graphene, highlighting the novel physics that is brought in compared to conventional 2DEGs. After describing the different magnetic field regimes in graphene p-n junctions and nanostructures, we conclude by discussing the state of the art in graphene-based Mach-Zender and Fabry-Perot interferometers.

Cross-Modal Graph Semantic Communication Assisted by Generative AI in the Metaverse for 6G.

Recently, the development of the Metaverse has become a frontier spotlight, which is an important demonstration of the integration innovation of advanced technologies in the Internet. Moreover, artificial intelligence (AI) and 6G communications will be widely used in our daily lives. However, the effective interactions with the representations of multimodal data among users via 6G communications is the main challenge in the Metaverse. In this work, we introduce an intelligent cross-modal graph semantic communication approach based on generative AI and 3-dimensional (3D) point clouds to improve the diversity of multimodal representations in the Metaverse. Using a graph neural network, multimodal data can be recorded by key semantic features related to the real scenarios. Then, we compress the semantic features using a graph transformer encoder at the transmitter, which can extract the semantic representations through the cross-modal attention mechanisms. Next, we leverage a graph semantic validation mechanism to guarantee the exactness of the overall data at the receiver. Furthermore, we adopt generative AI to regenerate multimodal data in virtual scenarios. Simultaneously, a novel 3D generative reconstruction network is constructed from the 3D point clouds, which can transfer the data from images to 3D models, and we infer the multimodal data into the 3D models to increase realism in virtual scenarios. Finally, the experiment results demonstrate that cross-modal graph semantic communication, assisted by generative AI, has substantial potential for enhancing user interactions in the 6G communications and Metaverse.

Gender-affirming hormone therapy and cardiovascular health in transgender adults.

A growing number of people identify as transgender and gender non-binary in the USA and worldwide. Concomitantly, an increasing number of patients are receiving gender-affirming hormone therapy (GAHT) to achieve gender congruence. GAHT has far-ranging effects on clinical and subclinical markers of cardiovascular risk. Transgender patients also appear to be at higher risk for cardiovascular diseases compared to their cisgender peers and the impact of gender-affirming therapy on cardiovascular health is unclear. Studies on the effect of GAHT on cardiovascular outcomes are confounded by differences in GAHT regimens and methodological challenges in a diverse and historically hard-to-reach population. Current cardiovascular guidelines do not incorporate gender identity and hormone status into risk stratification and clinical decision-making. In this review, we provide an overview on the cardiometabolic impact and clinical considerations of GAHT for cardiovascular risk in transgender patients.

Ionic Covalent Organic Frameworks in Adsorption and Catalysis.

The ion extraction and electro/photo catalysis are promising methods to address environmental and energy issues. Covalent organic frameworks (COFs) are a class of promising template to construct absorbents and catalysts because of their stable frameworks, high surface areas, controllable pore environments, and well-defined catalytic sites. Among them, ionic COFs as unique class of crystalline porous materials, with charges in the frameworks or along the pore walls, have shown different properties and resulting performance in these applications with those from charge-neutral COFs. In this review, current research progress based on the ionic COFs for ion extraction and energy conversion, including cationic/anionic materials and electro/photo catalysis is reviewed in terms of the synthesis strategy, modification methods, mechanisms of adsorption and catalysis, as well as applications. Finally, we demonstrated the current challenges and future development of ionic COFs in design strategies and applications.

Effect of epidural dexmedetomidine in single-dose combined with ropivacaine for cesarean section.

BACKGROUND: Dexmedetomidine has arousal sedation and analgesic effects. We hypothesize that epidural dexmedetomidine in single-dose combined with ropivacaine improves the experience of parturient undergoing cesarean section under epidural anesthesia. This study is to investigate the effect of 0.5 µg/kg epidural dexmedetomidine combined with epidural anesthesia (EA) in parturients undergoing cesarean section. METHODS: A total of 92 parturients were randomly divided into Group R (receiveing epidural ropivacaine alone) Group RD (receiveing epidural ropivacaine with 0.5 µg/kg dexmedetomidine). The primary outcome and second outcome will be intraoperative NRS pain scores and Ramsay Sedation Scale. RESULTS: All 92 parturients were included in the analysis. The NRS were significantly lower in Group RD compared to Group R at all observation timepoint (P > 0.05). Higher Ramsay Sedation Scale was found in Group RD compared to Group R (P < 0.001). No parturient has experienced sedation score of 4 and above. No significant difference regarding the incidence of hypotension, bradycardia and nausea or vomiting, Apgar scores and the overall satisfaction with anesthesia was found between Group R and Group RD (P > 0.05). CONCLUSION: Epidural dexmedetomidine of 0.5 µg/kg added slightly extra analgesic effect to ropivacaine in EA for cesarean section. The sedation of 0.5 µg/kg epidural dexmedetomidine did not cause mother-baby bonding deficit. Satisfaction with anesthesia wasn't significantly improved by epidural dexmedetomidine of 0.5 µg/kg. No additional side effect allows larger dose of epidural dexmedetomidine attempt. TRIAL REGISTRATION: This study was registered at www.chictr.org.cn (ChiCTR2000038853).

The burden and management competency of cardiomyopathies in China: a nationwide survey study.

Background: The public health burden of cardiomyopathies and competency in their management by health agencies in China are not well understood. Methods: This study adopted a multi-stage sampling method for hospital selection. In the first stage, nationwide tertiary hospital recruitment was performed. As a result, 88 hospitals with the consent of the director of cardiology and access to an established electronic medical records system, were recruited. In the second stage, we sampled 66 hospitals within each geographic-economic stratification through a random sampling process. Data on (1) the outpatient and inpatient visits for cardiomyopathies between 2017 and 2021 and (2) the competency in the management of patients with cardiomyopathies, were collected. The competency of a hospital to provide cardiomyopathy care was evaluated using a specifically devised scale. Findings: The outpatient and inpatient visits for cardiomyopathies increased between 2017 and 2021 by 38.6% and 33.0%, respectively. Most hospitals had basic facilities for cardiomyopathy assessment. However, access to more complex procedures was limited, and the integrated management pathway needs improvement. Only 4 (6.1%) of the 66 participating hospitals met the criteria for being designated as a comprehensive cardiomyopathy center, and only 29 (43.9%) could be classified as a primary cardiomyopathy center. There were significant variations in competency between hospitals with different administrative and economic levels. Interpretation: The health burden of cardiomyopathies has increased significantly between 2017 and 2021 in China. Although most tertiary hospitals in China can offer basic cardiomyopathy care, more advanced facilities are not yet universally available. Moreover, inconsistencies in the management of cardiomyopathies across hospitals due to differing administrative and economic levels warrants a review of the nation allocation of medical resources. Funding: This work was supported by the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (2023-I2M-1-001) and the National High Level Hospital Clinical Research Funding (2022-GSP-GG-17).

The Molecular Mechanism of the Response of Rice to Arsenic Stress and Effective Strategies to Reduce the Accumulation of Arsenic in Grain.

Rice (Oryza sativa L.) is the staple food for more than 50% of the world's population. Owing to its growth characteristics, rice has more than 10-fold the ability to enrich the carcinogen arsenic (As) than other crops, which seriously affects world food security. The consumption of rice is one of the primary ways for humans to intake As, and it endangers human health. Effective measures to control As pollution need to be studied and promoted. Currently, there have been many studies on reducing the accumulation of As in rice. They are generally divided into agronomic practices and biotechnological approaches, but simultaneously, the problem of using the same measures to obtain the opposite results may be due to the different species of As or soil environments. There is a lack of systematic discussion on measures to reduce As in rice based on its mechanism of action. Therefore, an in-depth understanding of the molecular mechanism of the accumulation of As in rice could result in accurate measures to reduce the content of As based on local conditions. Different species of As have different toxicity and metabolic pathways. This review comprehensively summarizes and reviews the molecular mechanisms of toxicity, absorption, transport and redistribution of different species of As in rice in recent years, and the agronomic measures to effectively reduce the accumulation of As in rice and the genetic resources that can be used to breed for rice that only accumulates low levels of As. The goal of this review is to provide theoretical support for the prevention and control of As pollution in rice, facilitate the creation of new types of germplasm aiming to develop without arsenic accumulation or within an acceptable limit to prevent the health consequences associated with heavy metal As as described here.

Mlp4green: A Binary Classification Approach Specifically for Green Odor.

Fresh green leaves give off a smell known as "green odor." It has antibacterial qualities and can be used to attract or repel insects. However, a common method for evaluating green odor molecules has never existed. Machine learning techniques are widely used in research to forecast molecular attributes for binary classification. In this work, the green odor molecules were first trained and learned using machine learning methods, and then clustering analysis and molecular docking were performed to further explore their molecular characteristics and mechanisms of action. For comparison, four algorithmic models were employed, MLP performed the best in all metrics, including Accuracy, Precision, Average Precision, Matthews coefficient, and Area under curve. We determined by difference analysis that, in comparison to non-green odor molecules, green odor molecules have a lower molecular mass and fewer electrons. Based on the MLP algorithm, we constructed a binary classification prediction website for green odors. The first application of deep learning techniques to the study of green odor molecules can be seen as a signal of a new era in which green odor research has advanced into intelligence and standardization.

Elaborate Modulating Binding Strength of Intermediates via Three-component Covalent Organic Frameworks for CO2 Reduction Reaction.

The catalytic performance for electrocatalytic CO2 reduction reaction (CO2RR) depends on the binding strength of the reactants and intermediates. Covalent organic frameworks (COFs) have been adopted to catalyze CO2RR, and their binding abilities are tuned via constructing donor-acceptor (DA) systems. However, most DA COFs have single donor and acceptor units, which caused wide-range but lacking accuracy in modulating the binding strength of intermediates. More elaborate regulation of the interactions with intermediates are necessary and challenge to construct high-efficiency catalysts. Herein, the three-component COF with D-A-A units was first constructed by introducing electron-rich diarylamine unit, electron-deficient benzothiazole and Co-porphyrin units. Compared with two-component COFs, the designed COF exhibit elevated electronic conductivity, enhanced reducibility, high efficiency charge transfer, further improving the electrocatalytic CO2RR performance with the faradic efficiency of 97.2 % at -0.8 V and high activity with the partial current density of 27.85 mA cm-2 at -1.0 V which exceed other two-component COFs. Theoretical calculations demonstrate that catalytic sites in three-component COF have suitable binding ability of the intermediates, which are benefit for formation of *COOH and desorption of *CO. This work offers valuable insights for the advancement of multi-component COFs, enabling modulated charge transfer to improve the CO2RR activity.

3D Crown Ether Covalent Organic Framework as Interphase Layer toward High-Performance Lithium Metal Batteries.

The practical application of lithium (Li) metal batteries is inhibited by accumulative Li dendrites and continuous active Li consumption during cycling, which results in a low Coulombic efficiency and short lifetime. Constructing artificial solid-electrolyte interphase (SEI) layer in Li anode, such as 2D covalent organic frameworks (COFs), is an effective strategy to restrain the formation of Li dendrites and improve cycling performance. However, the exploration of 3D COFs as protecting layers is rarely reported, because of the preconception that the interconnect pores in 3D COFs eventually cause Li dendrites in disordered direction. 3D crown ether-based COF with ffc topology as interphase layer, in which the crown ether units are arranged in parallel and vertical orientation along the electrode, is demonstrated. The strong coupling effect between the crown ether and Li+ accelerates Li+ diffusion kinetics and enables homogeneous Li+ flux, resulting in a high Li+ transference number of 0.85 and smooth Li deposition in 3D direction. Li/COF-Cu cells display a lower Li-nucleation overpotential (17.4 mV) and high average Coulombic efficiency of ≈98.6% during 340 cycles with COF incorporation. This work gives a new insight into designing COFs for energy storage systems.

Solvent Effects on Metal-free Covalent Organic Frameworks in Oxygen Reduction Reaction.

Binding water molecules to polar sites in covalent organic frameworks (COFs) is inevitable, but the corresponding solvent effects in electrocatalytic process have been largely overlooked. Herein, we investigate the solvent effects on COFs for catalyzing the oxygen reduction reaction (ORR). Our designed COFs incorporated different kinds of nitrogen atoms (imine N, pyridine N, and phenazine N), enabling tunable interactions with water molecules. These interactions play a crucial role in modulating electronic states and altering the catalytic centers within the COFs. Among the synthesized COFs, the one with pyridine N atoms exhibits the highest activity, with characterized by a half-wave potential of 0.78 V and a mass activity of 0.32 A mg-1, which surpass those from other metal-free COFs. Theoretical calculations further reveal that the enhanced activity can be attributed to the stronger binding ability of *OOH intermediates to the carbon atoms adjacent to the pyridine N sites. This work sheds light on the significance of considering solvent effects on COFs in electrocatalytic systems, providing valuable insights into their design and optimization for improved performance.