Predictive Roles of Basal Metabolic Rate and Muscle Mass in Lung Function among Patients with Obese Asthma: A Prospective Cohort Study.

BACKGROUND: The metabolic-status-related mechanisms underlying the deterioration of the lung function in obese asthma have not been completely elucidated. OBJECTIVE: This study aimed to investigate the basal metabolic rate (BMR) in patients with obese asthma, its association with the lung function, and its mediating role in the impact of obesity on the lung function. METHODS: A 12-month prospective cohort study (n = 598) was conducted in a real-world setting, comparing clinical, body composition, BMR, and lung function data between patients with obese (n = 282) and non-obese (n = 316) asthma. Path model mediation analyses for the BMR and skeletal muscle mass (SMM) were conducted. We also explored the effects of the BMR on the long-term lung function in patients with asthma. RESULTS: Patients with obese asthma exhibited greater airway obstruction, with lower FEV1 (1.99 vs. 2.29 L), FVC (3.02 vs. 3.33 L), and FEV1/FVC (65.5 vs. 68.2%) values compared to patients with non-obese asthma. The patients with obese asthma also had higher BMRs (1284.27 vs. 1210.08 kcal/d) and SMM (23.53 vs. 22.10 kg). Both the BMR and SMM mediated the relationship between obesity and the lung function spirometers (FEV1, %FEV1, FVC, %FVC, and FEV1/FVC). A higher BMR or SMM was associated with better long-term lung function. CONCLUSIONS: Our study highlights the significance of the BMR and SMM in mediating the relationship between obesity and spirometry in patients with asthma, and in determining the long-term lung function. Interventions for obese asthma should focus not only on reducing adiposity but also on maintaining a high BMR.

Hi-C guided many-polymer model to decipher 3D genome organization.

We propose a Hi-C data-based many-polymer model which allows us to generate an ensemble of multi-scale genome structures. We demonstrate the efficacy of our model by validating the generated structures against experimental measurements and employ them to address key questions regarding genome organization. Our model first confirms a significant correlation between the chromosome size and the nuclear positioning. Specifically, smaller chromosomes are distributed at the core region, whereas the larger chromosomes are at the periphery interacting with the nuclear envelope. The spatial distribution of A- and B-type compartments, which is non-trivial to infer from the corresponding Hi-C maps alone, can also be elucidated using our model, accounting for an issue such as the effect of chromatin-lamina interaction on the compartmentalization of conventional and inverted nuclei. In accordance with imaging data, the overall shape of the 3D genome structures generated from our model displays significant variation. As a case study, we apply our method to the yellow fever mosquito genome, finding that the predicted morphology displays, on average, more globular shape than the previously suggested spindle-like organization, and that our prediction better aligns with the FISH data. Our model has a great potential to be extended to investigate many outstanding issues concerning the 3D genome organization.

[Research on portable airway impedance monitoring device based on expiratory oscillation].

Monitoring airway impedance has significant clinical value in accurately assessing and diagnosing pulmonary function diseases at an early stage. To address the issue of large oscillator size and high power consumption in current pulmonary function devices, this study adopts a new strategy of expiration-driven oscillation. A lightweight and low-power airway impedance monitoring system with integrated sensing, control circuitry, and dynamic feedback system, providing visual feedback on the system's status, was developed. The respiratory impedance measurement experiments and statistical comparisons indicated that the system could achieve stable measurement of airway impedance at 5 Hz. The frequency spectrum curves of respiratory impedance ( R and X) showed consistent trends with those obtained from the clinical pulmonary function instrument, specifically the impulse oscillometry system (IOS). The differences between them were all less than 1.1 cm H 2O·s/L. Additionally, there was a significant statistical difference in the respiratory impedance R5 between the exercise and rest groups, which suggests that the system can measure the variability of airway resistance parameters during exercise. Therefore, the impedance monitoring system developed in this study supports subjects in performing handheld, continuous measurements of dynamic changes in airway impedance over an extended period of time. This research provides a foundation for further developing low-power, portable, and even wearable devices for dynamic monitoring of pulmonary function.

A multifunctional collagen-base bilayer membrane integrated with a bimetallic/polydopamine network for enhanced guided bone regeneration.

The guided bone regeneration (GBR) technique is an effective treatment for small and medium-sized bone defects in the oral and maxillofacial region. However, currently available collagen membranes have limited functionality and are inadequate for clinical requirements. To address this challenge, this study pioneeringly developed a multifunctional bilayer membrane. Specifically, a bimetallic/polydopamine network (BPN), consisting of silver, magnesium, and dopamine, was successfully synthesized for the first time and integrated with collagen and hydroxyapatite. The resulting material was characterized, and its physicochemical properties, along with its barrier, osteogenic, angiogenic, antibacterial, hemostatic, and biosafety effects, were evaluated through both in vitro and in vivo studies. The results indicated that the BPN, composed of magnesium ions, silver nanoparticles (Ag NPs), and polydopamine (PDA), exhibited excellent thermal stability and slow release of silver and magnesium elements. The BPN/Col-HA membrane featured a bilayer structure with uniform distribution of silver and magnesium. It also demonstrated good hydrophilicity, suitable degradation and mechanical properties, as well as sustained release of silver and magnesium. In vitro experiments showed that the BPN/Col-HA membrane possessed desirable barrier, osteogenic, angiogenic, antibacterial, hemostatic, and biocompatible properties. In vivo results further confirmed its biosafety, hemostatic efficacy, and ability to effectively promote bone defect repair and angiogenesis. Thus, the BPN/Col-HA membrane emerges as a multifunctional GBR membrane with potential for clinical translation.

Evaluation of the clinical value of 10 estimating glomerular filtration rate equations and construction of a prediction model for kidney damage in adults from central China.

Objectives: This study aimed to evaluate 10 estimating glomerular filtration rate (eGFR) equations in central China population and construct a diagnostic prediction model for assessing the kidney damage severity. Methods: The concordance of 10 eGFR equations was investigated in healthy individuals from central China, and their clinical effectiveness in diagnosing kidney injury was evaluated. Subsequently, relevant clinical indicators were selected to develop a clinical prediction model for kidney damage. Results: The overall concordance between CKD-EPIASR-Scr and CKD-EPI2021-Scr was the highest (weightedκ = 0.964) in healthy population. The CG formula, CKD-EPIASR-Scr and CKD-EPI2021-Scr performed better than others in terms of concordance with referenced GFR (rGFR), but had poor ability to distinguish between rGFR < 90 or < 60 mL/min·1.73 m2. This finding was basically consistent across subgroups. Finally, two logistic regression prediction models were constructed based on rGFR < 90 or 60 mL/min·1.73 m2. The area under the curve of receiver operating characteristic values of two prediction models were 0.811 vs 0.846 in training set and 0.812 vs 0.800 in testing set. Conclusion: The concordance of CKD-EPIASR-Scr and CKD-EPI2021-Scr was the highest in the central China population. The Cockcroft-Gault formula, CKD-EPIASR-Scr, and CKD-EPI2021-Scr more accurately reflected true kidney function, while performed poorly in the staging diagnosis of CKD. The diagnostic prediction models showed the good clinical application performance in identifying mild or moderate kidney injury. These findings lay a solid foundation for future research on renal function assessment and predictive equations.

Catalytic Enantioselective Hydroxylation of Tertiary Propargylic C(sp3)-H Bonds in Acyclic Systems: a Kinetic Resolution Study.

Direct site-selective and enantioselective oxyfunctionalization of C(sp3)-H bonds to form alcohols with a general scope, with predictable selectivities, and in preparatively useful yields represents a paradigm shift in the standard logic of synthetic organic chemistry. However, the knowledge of either enzymatic or nonenzymatic asymmetric hydroxylation of tertiary C-H bonds for enantioenriched tertiary alcohol synthesis is sorely lacking. Here, we report a practical manganese-catalyzed enantio-differentiating hydroxylation of tertiary propargylic C-H bonds in acyclic systems, producing a wide range of structurally diverse enantioenriched tertiary propargyl alcohols in high efficiency with extremely efficient chemo- and enantio-discrimination. Other features include the use of C-H substrates as the limiting reagent, noteworthy functional group compatibility, great synthetic utilities, and scalability. The findings serve as a blueprint for the development of metal-catalyzed asymmetric oxidation of challenging substrates.

Impact of virtual monochromatic images of different low-energy levels in dual-energy CT on radiomics models for predicting muscle invasion in bladder cancer.

OBJECTIVE: The purpose of this study was to investigate the impact of different low-energy virtual monochromatic images (VMIs) in dual-energy CT on the performance of radiomics models for predicting muscle invasive status in bladder cancer (BCa). MATERIALS AND METHODS: A total of 127 patients with pathologically proven muscle-invasive BCa (n = 49) and non-muscle-invasive BCa (n = 78) were randomly allocated into the training and test cohorts at a ratio of 7:3. Feature extraction was performed on the venous phase images reconstructed at 40, 50, 60 and 70-keV (single-energy analysis) or in combination (multi-energy analysis). Recursive feature elimination (RFE) and the least absolute shrinkage and selection operator (LASSO) were employed to select the most relevant features associated with BCa. Models were built using a support vector machine (SVM) classifier. Diagnostic performance was assessed through receiver operating characteristic curves, evaluating sensitivity, specificity, accuracy, precision, and the area-under-the curve (AUC) values. RESULTS: In the test cohort, the multi-energy model achieved the best diagnostic performance with AUC, sensitivity, specificity, accuracy, and precision of 0.917, 0.800, 0.833, 0.821, and 0.750, respectively. Conversely, the single-energy model exhibited lower AUC and sensitivity in predicting the muscle invasion status. CONCLUSIONS: By combining information from VMIs of various energies, the multi-energy model displays superior performance in preoperatively predicting the muscle invasion status of bladder cancer.

Differential Antioxidant Enzyme Gene Expression and Functional Analysis of Pyridaben-Susceptible and -Resistant Strains of Tetranychus truncatus (Acari: Tetranychidae) under High Temperature Stress.

Tetranychus truncatus (Acari: Tetranychidae) has caused serious economic losses on some crops (soybean, corn, and cotton) in China, and has developed resistance to most acaricides. Our laboratory study found that T. truncatus was resistant to pyridaben and also adapted to high temperature (34-40 °C). High temperature stress may cause arthropods to produce a large amount of reactive oxygen species (ROS), causing oxidative damage. Antioxidant enzymes, as the main antioxidants, can reduce the damage caused by excessive ROS in arthropods. In order to study the adaptation mechanism of the pyridaben-resistant strain of T. truncatus to high temperature and the role of antioxidant enzyme genes under high temperature stress, four antioxidant enzyme genes, TtSOD, TtPOD3, TtPOD4, and TtGSTs2, were screened according to the transcriptome sequencing data of pyridaben-susceptible and -resistant strains in T. truncatus. Firstly, the phylogeny and structure analyses of these four genes were carried out. Then, real-time quantitative PCR (RT-qPCR) technology was used to analyze the gene expression patterns of antioxidant enzymes in two strains of T. truncatus at three different high temperature ranges (34 °C, 38 °C, and 42 °C). The results showed that the expression levels of four antioxidant enzyme genes of two strains of T. truncatus were induced by high temperature stress, and the expression levels of antioxidant enzyme genes were significantly different in each development state. The gene expression of antioxidant enzyme genes in resistant strains at the adult stage was significantly higher than that in susceptible strains. After the TtSOD and TtPOD4 genes of adult mites of the resistant strain were silenced by RNA interference (RNAi) technology, the mortality rate of mites with TtPOD4 gene silencing reached 41.11% after 96 h at 34 °C, which was significantly higher than that of the control and TtSOD gene silencing. It has been confirmed that the TtPOD4 gene plays a key role in the adaptation of pyridaben-resistant strain of T. truncatus to high temperature. It lays a theoretical foundation for revealing the thermal adaptation mechanism of T. truncatus.

Lipid Nanoparticles Outperform Electroporation in Delivering Therapeutic HPV DNA Vaccines.

Therapeutic HPV vaccines that induce potent HPV-specific cellular immunity and eliminate pre-existing infections remain elusive. Among various candidates under development, those based on DNA constructs are considered promising because of their safety profile, stability, and efficacy. However, the use of electroporation (EP) as a main delivery method for such vaccines is notorious for adverse effects like pain and potentially irreversible muscle damage. Moreover, the requirement for specialized equipment adds to the complexity and cost of clinical applications. As an alternative to EP, lipid nanoparticles (LNPs) that are already commercially available for delivering mRNA and siRNA vaccines are likely to be feasible. Here, we have compared three intramuscular delivery systems in a preclinical setting. In terms of HPV-specific cellular immune responses, mice receiving therapeutic HPV DNA vaccines encapsulated with LNP demonstrated superior outcomes when compared to EP administration, while the naked plasmid vaccine showed negligible responses, as expected. In addition, SM-102 LNP M exhibited the most promising results in delivering candidate DNA vaccines. Thus, LNP proves to be a feasible delivery method in vivo, offering improved immunogenicity over traditional approaches.

Differential network knockoff filter with application to brain connectivity analysis.

The brain functional connectivity can typically be represented as a brain functional network, where nodes represent regions of interest (ROIs) and edges symbolize their connections. Studying group differences in brain functional connectivity can help identify brain regions and recover the brain functional network linked to neurodegenerative diseases. This process, known as differential network analysis focuses on the differences between estimated precision matrices for two groups. Current methods struggle with individual heterogeneity in measuring the brain connectivity, false discovery rate (FDR) control, and accounting for confounding factors, resulting in biased estimates and diminished power. To address these issues, we present a two-stage FDR-controlled feature selection method for differential network analysis using functional magnetic resonance imaging (fMRI) data. First, we create individual brain connectivity measures using a high-dimensional precision matrix estimation technique. Next, we devise a penalized logistic regression model that employs individual brain connectivity data and integrates a new knockoff filter for FDR control when detecting significant differential edges. Through extensive simulations, we showcase the superiority of our approach compared to other methods. Additionally, we apply our technique to fMRI data to identify differential edges between Alzheimer's disease and control groups. Our results are consistent with prior experimental studies, emphasizing the practical applicability of our method.

Partially PEG-Grafted Poly(Terphenyl Piperidinium) Anion Exchange Membranes with Balanced Properties for Alkaline Fuel Cells.

Poly(ethylene glycol) (PEG) or oligo (ethylene glycol) (OEG) grafted anion exchange membranes (AEMs) exhibit improved ionic conductivity, high alkaline stability, and subsequent boosted AEM fuel cell performance, but too much PEG/OEG side chains may can result in a reduction in the ion exchange capacity (IEC), which can have adverse effects on ion transport. Here, a series of partially PEG-grafted poly(terphenyl piperidinium) with different side chain length were synthesized using simple post-polymerization modification to produce AEMs with balanced properties. The polar and flexible PEG side chains were responsible for the controlled water uptake and swelling, superior hydroxide conductivity (122 mS cm-1 at 80 °C with a IEC of 1.99 mmol g-1), and enhanced alkaline stability compared to the reference sample without PEG grafts (PTP). More importantly, the performance of AEM fuel cell (AEMFC) with the membrane containing partial PEG side chains surpassed that with PTP membrane, demonstrating a highest peak power density of 1110 mW cm-2 at 80 °C under optimized conditions. This work provided a novel approach to the fabrication of high-performance AEM materials with balanced properties for alkaline fuel cell application. This article is protected by copyright. All rights reserved.

Assessing the particulate matter emission reduction characteristics of small turbofan engine fueled with 100 % HEFA sustainable aviation fuel.

With the continuous increase in global air transportation, the impact of ultrafine particulate matter (PM) emissions from aviation on human health and environmental pollution is becoming increasingly severe. In addition to carbon reduction throughout the lifecycle, Sustainable Aviation Fuels (SAF) also represent a significant pathway for reducing PM emissions. However, due to issues such as airworthiness safety and adaptability, existing research has mostly focused on the emission performance of SAF when blended with traditional fuels at <50 %, leaving the emission characteristics of higher blending ratios to be explored. In this study, using measurement methods recommended by the International Civil Aviation Organization (ICAO), the PM emission reduction characteristics of small turbofan engines fueled with 100 % Hydroprocessed Esters and Fatty Acids (HEFA)-SAF were experimentally evaluated and compared with traditional fuels RP-3 and Diesel, while avoiding the interference of lubricant blending combustion. The results showed that the peak number concentration of particle size distribution (PSD), PM total number, as well as the number and mass concentration of non-volatile particulate matter (nvPM) decreased initially and then increased with rising thrust conditions. HEFA-SAF exhibits PSD with smaller diameters, and the Geometric Mean Diameter (GMD) ranges from 7.7 nm to 20.3 nm under all conditions. Both volatile particulates (vPM) and nvPM from HEFA-SAF are significantly reduced, with nvPM number emission index (EIn) being 92 % and 71 % lower than Diesel and RP-3, respectively. The nvPM mass emission index (EIm) also shows reductions of 96 % and 89 % compared to Diesel and RP-3. Microscopic characterization also indicated that using HEFA-SAF emitted fewer and smaller PMs. This study establishes a foundation for evaluating the effectiveness of 100 % SAF in reducing PM emissions within the aviation sector, and contributes to the airworthiness regulations development related to the use of SAF in a variety of application environments, alongside enhancing environmental protection measures.

Integrated Multi-Omics Analysis to Reveal the Molecular Mechanisms of Inflorescence Elongation in Medicago sativa.

The morphological architecture of inflorescence influences seed production. The regulatory mechanisms underlying alfalfa (Medicago sativa) inflorescence elongation remain unclear. Therefore, in this study, we conducted a comparative analysis of the transcriptome, proteome, and metabolome of two extreme materials at three developmental stages to explore the mechanisms underlying inflorescence elongation in alfalfa. We observed the developmental processes of long and short inflorescences and found that the elongation capacity of alfalfa with long inflorescence was stronger than that of alfalfa with short inflorescences. Furthermore, integrative analysis of the transcriptome and proteome indicated that the phenylpropanoid biosynthesis pathway was closely correlated with the structural formation of the inflorescence. Additionally, we identified key genes and proteins associated with lignin biosynthesis based on the differential expressed genes and proteins (DEGs and DEPs) involved in phenylpropanoid biosynthesis. Moreover, targeted hormone metabolome analysis revealed that IAA, GA, and CK play an important role in the peduncle elongation of alfalfa inflorescences. Based on omics analysis, we detected key genes and proteins related to plant hormone biosynthesis and signal transduction. From the WGCNA and WPCNA results, we furthermore screened 28 candidate genes and six key proteins that were correlated with lignin biosynthesis, plant hormone biosynthesis, and signaling pathways. In addition, 19 crucial transcription factors were discovered using correlation analysis that might play a role in regulating candidate genes. This study provides insight into the molecular mechanism of inflorescence elongation in alfalfa and establishes a theoretical foundation for improving alfalfa seed production.

Utilizing Geographical Distribution Statistical Data to Improve Zero-Shot Species Recognition.

Species recognition is a crucial part of understanding the abundance and distribution of various organisms and is important for biodiversity conservation and management. Traditional vision-based deep learning-driven species recognition requires large amounts of well-labeled, high-quality image data, the collection of which is challenging for rare and endangered species. In addition, recognition methods designed based on specific species have poor generalization ability and are difficult to adapt to new species recognition scenarios. To address these issues, zero-shot species recognition based on Contrastive Language-Image Pre-training (CLIP) has become a research hotspot. However, previous studies have primarily utilized visual descriptive information and taxonomic information of species to improve zero-shot recognition performance, and the use of geographic distribution characteristics of species to improve zero-shot recognition performance has not been explored. To fill this gap, we proposed a CLIP-driven zero-shot species recognition method that incorporates knowledge of the geographic distribution of species. First, we designed three prompts based on the species geographic distribution statistical data. Then, the latitude and longitude coordinate information attached to each image in the species dataset was converted into addresses, and they were integrated together to form the geographical distribution knowledge of each species. Finally, species recognition results were derived by calculating the similarity after acquiring features by the trained CLIP image encoder and text encoder. We conducted extensive experiments on multiple species datasets from the iNaturalist 2021 dataset, where the zero-shot recognition accuracies of mammals, mollusks, reptiles, amphibians, birds, and insects were 44.96%, 15.27%, 17.51%, 9.47%, 28.35%, and 7.03%, an improvement of 2.07%, 0.48%, 0.35%, 1.12%, 1.64%, and 0.61%, respectively, as compared to CLIP with default prompt. The experimental results show that the fusion of geographic distribution statistical data can effectively improve the performance of zero-shot species recognition, which provides a new way to utilize species domain knowledge.

Bibliometric analysis of the top 100 highly cited articles on sublobectomy for non-small cell lung cancer.

OBJECTIVES: The goal of this research is to pinpoint the top 100 most frequently referenced studies on sublobectomy for non-small cell lung cancer. METHODS: We identified the top 100 most frequently referenced studies on sublobectomy for non-small cell lung cancer by searching the Web of Science database. We extracted key information from the selected studies, including the author, journal, impact factor, type of article, year of publication, country, organization, and keyword. RESULTS: To the best of our understanding, this is the inaugural bibliometric study on sublobectomy for non-small cell lung cancer. The publication years of the top 100 most frequently referenced studies span from 1994 to 2022, with citation counts ranging from 51 to 795. The majority of the included studies are original (93/100) and primarily retrospective studies (82/93). The United States leads in terms of published articles and citations, with the Annals of Thoracic Surgery being the most frequently sourced journal (n = 27). High-density keywords primarily originate from limited resection, lobectomy, survival, carcinoma, recurrence, randomized trial, radiotherapy, lung cancer, outcome, 2 cm, as revealed by CiteSpace analysis. CONCLUSIONS: Our research compiles and analyzes the top 100 most frequently referenced studies in the field of sublobectomy for non-small cell lung cancer. The United States has the most published and cited works on this topic. Currently, the hot keywords for sublobectomy research are gradually shifting towards prognosis and obtaining better evidence-based medical evidence to demonstrate its value in the treatment of non-small cell lung cancer.

Transcriptomic Analyses Reveal the Effects of Walnut Kernel Cake on Adipose Deposition in Pigs.

With the rising cost of animal feed protein, finding affordable and effective substitutes is crucial. Walnut kernel cake, a polyphenol-, fiber-, protein- and fat-rich byproduct of walnut oil extraction, has been underexplored as a potential protein replacement in pig feed. In this study, we found that feeding large Diqing Tibetan pigs walnut kernel cake promoted adipose deposition and improved pork quality during pig growth. Transcriptome analysis revealed the upregulation of genes ANGPTL8, CCNP, ETV4, and TRIB3, associated with adipose deposition. Pathway analysis highlighted enrichment in adipose deposition-related pathways, including PPAR, insulin, PI3K-Akt, Wnt, and MAPK signaling. Further analysis identified DEGs (differentially expressed genes) positively correlated with adipose-related traits, such as PER2 and PTGES. Single-cell transcriptome data pointed to the specific expression of CD248 and PTGES in adipocyte progenitor/stem cells (APSCs), pivotal for adipocyte differentiation and adipose deposition regulation. This study demonstrates walnut kernel cake's potential to substitute soybean cake in pig feed, providing high-quality protein and promoting adipose deposition. It offers insights into feed protein replacement, human functional food, fat metabolism, and related diseases, with marker genes and pathways supporting pig breeding and pork quality improvement.

A Single-Frame Deflectometry Method for Online Inspection of Light-Transmitting Components.

Transparent materials are widely used in industrial applications, such as construction, transportation, and optics. However, the complex optical properties of these materials make it difficult to achieve precise surface form measurements, especially for bulk surface form inspection in industrial environments. Traditional structured light-based measurement methods often struggle with suboptimal signal-to-noise ratios, making them ineffective. Currently, there is a lack of efficient techniques for real-time inspection of such components. This paper proposes a single-frame measurement technique based on deflectometry for large-size transparent surfaces. It utilizes the reflective characteristics of the measured surface, making it independent of the surface's diffuse reflection properties. This fundamentally solves the issues associated with signal-to-noise ratios. By discretizing the phase map, it separates the multiple surface reflection characteristics of transparent devices, enabling transparent device measurement. To meet the requirements of industrial dynamic measurement, this technique only needs a simple and low-cost system structure, which contains just two cameras for image capture. It does not require phase shifting to complete the measurement, making it independent of the screen and having the potential for larger surface measurement. The proposed method was used to measure a 400mm aperture automobile glass, and the results showed that it is able to achieve a measurement accuracy on the order of 10 µ m. The method proposed in this paper overcomes the influence of surface reflection on transparent objects and significantly improves the efficiency and accuracy of large-sized transparent surface measurements by using a single-frame image measurement. Moreover, this method shows promise for broader applications, including measurements of lenses and HUD (Heads-Up Display) components, showcasing significant potential for industrial applications.

Deep integration of metabolome and transcriptome characterizes alkaloid metabolism in Houttuynia cordata.

Alkaloids are the main medicinal components in Houttuynia cordata. In this study, two accessions 6# and 7# of H. cordata underwent thorough metabolomic analyses to identify and quantify alkaloid phytometabolites. It turned out that the alkaloid types were largely similar between 6# and 7#, and the identified 81 alkaloids could be divided into nine structural classes. However, the content of alkaloids in the two accessions was quite different. According to transcriptome data, a total of 114 differentially expressed genes related to alkaloid metabolism were screened. The alkaloid synthesis pathway of the two varieties was mainly different in the isoquinoline alkaloid biosynthesis and indole alkaloid biosynthesis; four genes A22110063c_transcript_59323, A22110063c_transcript_60118, A22110063c_transcript_51672 and A22110063c_transcript_48784 were highly expressed in 7#, which could be key candidate genes of alkaloid metabolism and warrant further analysis. These results provide a reference for the medicinal application of H. cordata and breeding alkaloid rich varieties.

Cross-Modal Object Tracking via Modality-Aware Fusion Network and a Large-Scale Dataset.

Visual object tracking often faces challenges such as invalid targets and decreased performance in low-light conditions when relying solely on RGB image sequences. While incorporating additional modalities like depth and infrared data has proven effective, existing multimodal imaging platforms are complex and lack real-world applicability. In contrast, near-infrared (NIR) imaging, commonly used in surveillance cameras, can switch between RGB and NIR based on light intensity. However, tracking objects across these heterogeneous modalities poses significant challenges, particularly due to the absence of modality switch signals during tracking. To address these challenges, we propose an adaptive cross-modal object tracking algorithm called modality-aware fusion network (MAFNet). MAFNet efficiently integrates information from both RGB and NIR modalities using an adaptive weighting mechanism, effectively bridging the appearance gap and enabling a modality-aware target representation. It consists of two key components: an adaptive weighting module and a modality-specific representation module. The adaptive weighting module predicts fusion weights to dynamically adjust the contribution of each modality, while the modality-specific representation module captures discriminative features specific to RGB and NIR modalities. MAFNet offers great flexibility as it can effortlessly integrate into diverse tracking frameworks. With its simplicity, effectiveness, and efficiency, MAFNet outperforms state-of-the-art methods in cross-modal object tracking. To validate the effectiveness of our algorithm and overcome the scarcity of data in this field, we introduce CMOTB, a comprehensive and extensive benchmark dataset for cross-modal object tracking. CMOTB consists of 61 categories and 1000 video sequences, comprising a total of over 799K frames. We believe that our proposed method and dataset offer a strong foundation for advancing cross-modal object-tracking research. The dataset, toolkit, experimental data, and source code will be publicly available at: https://github.com/mmic-lcl/ Datasets-and-benchmark-code.