Major Innovations Research Articles

Afila, the origin and nature of a major innovation in the history of pea breeding.

The afila (af) mutation causes the replacement of leaflets by a branched mass of tendrils in the compound leaves of pea - Pisum sativum L. This mutation was first described in 1953, and several reports of spontaneous af mutations and induced mutants with a similar phenotype exist. Despite widespread introgression into breeding material, the nature of af and the origin of the alleles used remain unknown. Here, we combine comparative genomics with reverse genetic approaches to elucidate the genetic determinants of af. We also investigate haplotype diversity using a set of AfAf and afaf cultivars and breeding lines and molecular markers linked to seven consecutive genes. Our results show that deletion of two tandemly arranged genes encoding Q-type Cys(2)His(2) zinc finger transcription factors, PsPALM1a and PsPALM1b, is responsible for the af phenotype in pea. Eight haplotypes were identified in the af-harbouring genomic region on chromosome 2. These haplotypes differ in the size of the deletion, covering more or less genes. Diversity at the af locus is valuable for crop improvement and sheds light on the history of pea breeding for improved standing ability. The results will be used to understand the function of PsPALM1a/b and to transfer the knowledge for innovation in related crops.

Vascular regional analysis unveils differential responses to anti-angiogenic therapy in pancreatic xenografts through macroscopic photoacoustic imaging.

Pancreatic cancer (PC) is a highly lethal malignancy and the third leading cause of cancer deaths in the U.S. Despite major innovations in imaging technologies, there are limited surrogate radiographic indicators to aid in therapy planning and monitoring. Amongst the various imaging techniques Ultrasound-guided photoacoustic imaging (US-PAI) is a promising modality based on endogenous blood (hemoglobin) and blood oxygen saturation (StO 2 ) contrast to monitor response to anti-angiogenic therapies. Adaptation of US-PAI to the clinical realm requires macroscopic configurations for adequate depth visualization, illuminating the need for surrogate radiographic markers, including the tumoral microvessel density (MVD). In this work, subcutaneous xenografts with PC cell lines AsPC-1 and MIA-PaCa-2 were used to investigate the effects of receptor tyrosine kinase inhibitor (sunitinib) treatment on MVD and StO 2 . Through histological correlation, we have shown that regions of high and low vascular density (HVD and LVD) can be identified through frequency domain filtering of macroscopic PA images which could not be garnered from purely global analysis. We utilized vascular regional analysis (VRA) of treatment-induced StO 2 and total hemoglobin (HbT) changes. VRA as a tool to monitor treatment response allowed us to identify potential timepoints of vascular remodeling, highlighting its ability to provide insights into the TME not only for sunitinib treatment but also other anti-angiogenic therapies.

Making a green transition at Christchurch International Airport

Commitments to rapidly reduce greenhouse gas (GHG) emissions from aviation are growing stronger, stimulating efforts to meet net zero targets. As critical infrastructure for air travel, airports obviously have an important role to play in making the transition away from fossil fuels. This requires major organisational change, operational innovation and future planning for low carbon and zero emission travel. New Zealand’s Christchurch International Airport has taken a pioneering lead, developing a transformational agenda, achieving global recognition for its emissions reductions and mobilising to build renewable energy infrastructure. This paper reports on research commissioned to assess the airport’s journey toward a green transition. It discusses how the airport made sustainability integral to its business purpose and organisational culture, how it developed a Green Transition Plan, and how it has raised sustainability on the agenda, locally, nationally and internationally. It also reports the airport’s development of a renewable energy precinct and its partnerships for the future implementation of low emission travel powered by renewable electricity and green hydrogen.

Innovation and Accountability in Teacher Education: Setting Directions for New Cultures in Teacher Education. By Claire Wyatt Smith and Lenore Adie (Editors), 2018. 340 pp.

Wyatt-Smith and Adie’s book introduction makes a strong case that teacher quality is key to better results. Three major teacher education innovations are described in this book. Many international reviews of teacher education and reform attempts have been published in the last decade. This textbook encourages governments to evaluate schools and beginning teacher education applicants (in Australia, both at intake and in national assessment before completion). Thus, this book focuses on curriculum orientation consistency since teacher opinions on curriculum orientation affect curriculum decision-making, teaching methodologies, and strategies (Cheung & Ng, 2000). Teachers are crucial to encourage and help students through varied learning and teaching methods. In a fun learning environment, teachers can help kids acquire values like acceptance and respect (Mak et al., 2018). Wyatt-Smith and Adie emphasize ‘the complex ecologies of teacher education’ (p. 13) using chapter contributors from Scotland, Norway, South Africa, Hong Kong, Singapore, Canada, the USA, New Zealand, and Australia. Traditions and culture mediate global imperatives in teacher education. The book’s worldwide perspectives are motivated by the need to create a scholarly platform for critical teacher education concerns in the 21st century and stimulate fresh, evidence-based thinking.

How does green credit guidelines affect environmentally friendly enterprises' ESG? A quasi-natural experiment from China.

Following decades of extensive economic development, promoting the transition to greening and decarbonization in economic development have become inevitable choices for controlling environmental pollution and achieving high-quality development in China. Green Credit Guidelines (NIGCG) is a major policy innovation to promote green credit and further improve sustainable economic development. The influence of these guidelines on environmentally friendly enterprises' sustainable development capacity, proxied by environmental, social, and corporate governance (ESG), has not yet been discussed. Therefore, this study takes the NIGCG issued in 2012 as a quasi-natural experiment, and adopts a propensity score matching-difference-in-differences (PSM-DID) model to test whether the NIGCG has affected ESG in environmentally friendly enterprises from 2009 to 2022. Our results indicate that the NIGCG significantly boosts environmentally friendly enterprises' ESG, and this finding remains robust to a series of tests. In addition, a mediating effect analysis reveals that the NIGCG affects enterprises' ESG through research and development (R&D) investment, verifying the Porter hypothesis in China. Finally, we determine that the role of NIGCG in promoting ESG is significantly reflected in the non-politically connected enterprises and enterprises in the eastern region. The empirical results suggest that the authorities should stimulate enterprises' R&D investments through supporting policies, such as tax reimbursement and government subsidies, and formulate differentiated policies according to the characteristics of enterprises and their regions, so as to improve the effect of NIGCG.

New Era Business Administration Major Student Innovation Ability Training Model Exploration and Practice

This article explores and practices the cultivation model of innovation ability for business administration students in the new era. It first analyzes the development trends of business administration major in the new era, the importance of innovation ability in the major, and the basic composition of the cultivation model of innovation ability. It points out the existing problems in current business administration education, such as the disconnection between curriculum setting and actual needs, the lack of practical teaching links, and the mismatch between individual differences of students and the cultivation model. In response to these issues, this article proposes optimization strategies, including the construction of an innovation ability-oriented curriculum system, the strengthening of practical teaching, and the focus on the individual development of students. It summarizes the research findings and provides insights for the cultivation of innovative business administration professionals in the future.

Social and biological innovations are essential to deliver transformative forest biotechnologies.

Forests make immense contributions to societies in the form of ecological services and sustainable industrial products. However, they face major challenges to their viability and economic use due to climate change and growing biotic and economic threats, for which recombinant DNA (rDNA) technology can sometimes provide solutions. But the application of rDNA technologies to forest trees faces major social and biological obstacles that make its societal acceptance a 'wicked' problem without straightforward solutions. We discuss the nature of these problems, and the social and biological innovations that we consider essential for progress. As case studies of biological challenges, we focus on studies of modifications in wood chemistry and transformation efficiency. We call for major innovations in regulations, and the dissolution of method-based market barriers, that together could lead to greater research investments, enable wide use of field studies, and facilitate the integration of rDNA-modified trees into conventional breeding programs. Without near-term adoption of such innovations, rDNA-based solutions will be largely unavailable to help forests adapt to the growing stresses from climate change and the proliferation of forest pests, nor will they be available to provide economic and environmental benefits from expanded use of wood and related bioproducts as part of an expanding bioeconomy.

Enhanced 3D printing and crack control in melt-grown eutectic ceramic composites with high-entropy alloy doping

As a 3D printing method, laser powder bed fusion (LPBF) technology has been extensively proven to offer significant advantages in fabricating complex structured specimens, achieving ultra-fine microstructures, and enhancing performances. In the domain of manufacturing melt-grown oxide ceramics, it encounters substantial challenges in suppressing crack defects during the rapid solidification process. The strategic integration of high entropy alloys (HEA), leveraging the significant ductility and toughness into ceramic powders represents a major innovation in overcoming the obstacles. The ingenious doping of HEA particles preserves the eutectic microstructures of the Al2O3/GdAlO3(GAP)/ZrO2 ceramic composite. The high damage tolerance of the HEA alloy under high strain rates enables the absorption of crack energy and alleviation of internal stresses during LPBF, effectively reducing crack initiation and growth. Due to increased curvature forces and intense Marangoni convection at the top of the molt pool, particle collision intensifies, leading to the tendency of HEA particles to agglomerate at the upper part of the molt pool. However, this phenomenon can be effectively alleviated in the remelting process of subsequent layer deposition. Furthermore, a portion of the HEA particles partially dissolves and sinks into the molten pool, acting as heterogeneous nucleation particles, inducing the formation of equiaxed eutectic and leading primary phase nucleation. Some HEA particles diffuse into the lamellar ternary eutectic structures, further promoting the refinement of eutectic microstructures due to increased undercooling. The innovative doping of HEA particles has effectively facilitated the fabrication of turbine-structured, conical, and cylindrical ternary eutectic ceramic composite specimens with diameters of about 70 mm, demonstrating significant developmental potential in the field of ceramic composite manufacturing.

Phylogeny and historical biogeography of the swallow family (Hirundinidae) inferred from comparisons of thousands of UCE loci

Swallows (Hirundinidae) are a globally distributed family of passerine birds that exhibit remarkable similarity in body shape but tremendous variation in plumage, sociality, nesting behavior, and migratory strategies. As a result, swallow species have become models for empirical behavioral ecology and evolutionary studies, and variation across the Hirundinidae presents an excellent opportunity for comparative analyses of trait evolution. Exploiting this potential requires a comprehensive and well-resolved phylogenetic tree of the family. To address this need, we estimated swallow phylogeny using genetic data from thousands of ultraconserved element (UCE) loci sampled from nearly all recognized swallow species. Maximum likelihood, coalescent-based, and Bayesian approaches yielded a well-resolved phylogenetic tree to the generic level, with minor disagreement among inferences at the species level, which likely reflect ongoing population genetic processes. The UCE data were particularly useful in helping to resolve deep nodes, which previously confounded phylogenetic reconstruction efforts. Divergence time estimates from the improved swallow tree support a Miocene origin of the family, roughly 13 million years ago, with subsequent diversification of major groups in the late Miocene and Pliocene. Our estimates of historical biogeography support the hypothesis that swallows originated in the Afrotropics and have subsequently expanded across the globe, with major in situ diversification in Africa and a secondary major radiation following colonization of the Neotropics. Initial examination of nesting and sociality indicates that the origin of mud nesting – a relatively rare nest construction phenotype in birds – was a major innovation coincident with the origin of a clade giving rise to over 40% of extant swallow diversity. In contrast, transitions between social and solitary nesting appear less important for explaining patterns of diversification among swallows.

The Role of Artificial Intelligence in Personalizing Learning for Each Student

Artificial Intelligence (AI) has become one of the major technological innovations in modern education. This article explores the role of AI in personalizing each student's learning. Individual learning is a learning method that adapts each student's learning experience to their needs, abilities and interests. AI enables this personalization of learning to be more efficient and productive. AI plays an important role in personalizing each student's learning. With the ability to analyze data, deliver personalized content, provide better feedback, and predict student progress, AI creates a more adaptive and effective learning environment. However, it should be remembered that the role of the teacher as a learning facilitator is still very important in the use of AI, because human interaction is still an important factor in an effective learning process. The method used in this research is a quantitative method. Researchers conducted a survey using a Google Form which consisted of 15 sentences related to the search title. The study found that applying artificial intelligence (AI) to personalize each student's learning produces significant tracking effects in educational settings. Overall, the use of AI in personalized learning has a significant positive impact on education. However, this requires careful oversight and regulation to ensure that this technology is used properly and provides maximum benefit to all students. The limitation of this research is that the researcher only conducted research in schools, and the researcher did not conduct research directly in schools, but distributed a survey link on a Google form which contained statements about the role of artificial intelligence (AI) in individualization. learning of each student.

Current perspectives of viral hepatitis.

Viral hepatitis represents a major danger to public health, and is a globally leading cause of death. The five liver-specific viruses: Hepatitis A virus, hepatitis B virus, hepatitis C virus, hepatitis D virus, and hepatitis E virus, each have their own unique epidemiology, structural biology, transmission, endemic patterns, risk of liver complications, and response to antiviral therapies. There remain few options for treatment, in spite of the increasing prevalence of viral-hepatitis-caused liver disease. Furthermore, chronic viral hepatitis is a leading worldwide cause of both liver-related morbidity and mortality, even though effective treatments are available that could reduce or prevent most patients' complications. In 2016, the World Health Organization released its plan to eliminate viral hepatitis as a public health threat by the year 2030, along with a discussion of current gaps and prospects for both regional and global eradication of viral hepatitis. Today, treatment is sufficiently able to prevent the disease from reaching advanced phases. However, future therapies must be extremely safe, and should ideally limit the period of treatment necessary. A better understanding of pathogenesis will prove beneficial in the development of potential treatment strategies targeting infections by viral hepatitis. This review aims to summarize the current state of knowledge on each type of viral hepatitis, together with major innovations.

FPGA-enhanced IoT methods for disease pre-screening and prediction: An energy optimization approach

The research paper has introduced a system based on the Internet of Things (IoT) that has the potential to contribute to the creation of a Consumer Electronics (CE) product. In the event that any health-related measure deviates from the normal range, this system, which is based on IoT technology, will send a notification to the user. The data collected by this system is sent to an analysis system based on a Field Programmable Gate Array (FPGA) through a mobile application, and then transferred to the cloud for storage. To display this raw data, a wearable IoT device can be utilized, and the system will process the data accordingly. The major innovation of this proposed project lies in the development of a fuzzy classifier that can accurately predict the pathological condition of an illness. By implementing a fuzzy classifier using FPGA, the execution time is greatly reduced i.e. 57.7μs compared to other algorithms. Ans also the accuracy of the proposed model is 98.8 %, which higher then other machine learning model such as 97 %, 96.5 %, 93.2 % respectively for SVM, KNN and DT models.previous models like KNN, DT, SVM, and NB (Naive Bayes).

A study of oil–water two-phase flow pattern recognition based on thermal sensors

To understand the flow characteristics and kinetic modelling of oil–water two-phase flow(OWTPF), this paper employs thermal array sensors to identify the flow pattern (FP) in OWTPF horizontal pipelines. Firstly, the thermal array sensor installation was constructed. Secondly, the time-varying characteristics of multi-feature signals were qualitatively analyzed using the adaptive optimal kernel time–frequency representation (AOK-TFR) method and unthresholded recurrence plot (URP) method. Finally, the sparrow search algorithm-support vector machine (SSA-SVM) machine learning algorithm is used for quantitative prediction of multi-feature classification, which leads to high prediction accuracy. The experiment proves that the correct rate of the thermal method in recognizing six types of OWTPF is above 91%, the correct rate of five types of flow is above 97.33%. This paper adopts the thermal method to identify the multiphase FP, which is a major innovation in this paper and also lays the foundation for recognizing the flow characteristics of multiphase flow.

Evaluating the Usability of a Gamified Virtual Course Designed to Teach Cultural Heritage

Virtual courses represent a major innovation in online teaching and learning, offering diverse training options tailored to individual needs and interests. Proposals have been developed in the field of didactics to provide heritage-related instruction using methodologies and strategies supported by digital sources and offered online. However, there is no comprehensive data compilation available on the evaluation of the usability of digital resources developed for imparting learning about cultural heritage. Hence, this study aims primarily to present the instructional design and usability evaluation of an expert-validated gamified virtual course titled “Cultural Heritage Guardians.” The study results show high usability scores for the designed course, as measured by the system usability scale. This study significantly contributes to aspects related to user evaluations of the usability experiences of virtual courses focused on pedagogy related to cultural heritage.

Le principe de précaution en médecine : vingt ans après

ObjectivesThe precautionary principle (PP) is a risk management method in situations of scientific uncertainty. Its lack of precise definition in official texts led to numerous interpretations in the past. The PP was initially limited to the environment. The extension to human health raised fears of an increase of proceedings and an interference with innovative research. MethodTwenty-four people involved in the precautionary principle have been interviewed and a literature review has been carried out. ResultsA renewed analysis of the PP has emphasized several points: the PP is a complex process that includes a spirit of caution, rigorous reasoning, and evaluation of hypotheses and, lastly, precautionary measures. The major part of confusion raises when the PP is in danger of drifting into inappropriate precautionary measures; individual care medicine is well controlled by the “Code de déontologie médicale” (Code of Medical Ethics); biomedical research has scientific constraints and its own rules. In these two medical fields, PP is not applicable unless a major innovation raises an ethical problem; on the other hand, public health may be submitted to the PP. ConclusionConsequently, mastery of the PP implies to promote training actions devoted to medical doctors, administrative and political decision makers, educational establishments and the media.

Deep mining of the Sequence Read Archive reveals major genetic innovations in coronaviruses and other nidoviruses of aquatic vertebrates.

Virus discovery by genomics and metagenomics empowered studies of viromes, facilitated characterization of pathogen epidemiology, and redefined our understanding of the natural genetic diversity of viruses with profound functional and structural implications. Here we employed a data-driven virus discovery approach that directly queries unprocessed sequencing data in a highly parallelized way and involves a targeted viral genome assembly strategy in a wide range of sequence similarity. By screening more than 269,000 datasets of numerous authors from the Sequence Read Archive and using two metrics that quantitatively assess assembly quality, we discovered 40 nidoviruses from six virus families whose members infect vertebrate hosts. They form 13 and 32 putative viral subfamilies and genera, respectively, and include 11 coronaviruses with bisegmented genomes from fishes and amphibians, a giant 36.1 kilobase coronavirus genome with a duplicated spike glycoprotein (S) gene, 11 tobaniviruses and 17 additional corona-, arteri-, cremega-, nanhypo- and nangoshaviruses. Genome segmentation emerged in a single evolutionary event in the monophyletic lineage encompassing the subfamily Pitovirinae. We recovered the bisegmented genome sequences of two coronaviruses from RNA samples of 69 infected fishes and validated the presence of poly(A) tails at both segments using 3'RACE PCR and subsequent Sanger sequencing. We report a genetic linkage between accessory and structural proteins whose phylogenetic relationships and evolutionary distances are incongruent with the phylogeny of replicase proteins. We rationalize these observations in a model of inter-family S recombination involving at least five ancestral corona- and tobaniviruses of aquatic hosts. In support of this model, we describe an individual fish co-infected with members from the families Coronaviridae and Tobaniviridae. Our results expand the scale of the known extraordinary evolutionary plasticity in nidoviral genome architecture and call for revisiting fundamentals of genome expression, virus particle biology, host range and ecology of vertebrate nidoviruses.

Regulation 2017/745 on medical devices, two major innovations: 1) the physiological action of devices consisting of natural materials such as vegetal matrices; 2) the chemical-physical-mechanical action of devices made of “substances”, which as such are artificial derivatives

The Medical Device Regulation 2017/745 (MDR) enables the development of a wide range of innovative products. With respect to Directive 93/42, the MDR explicitly identifies the so-called “medical devices made of substances” (MDMS) through specific requirements. In addition, the MDR expands the definition of medical device (MD) by including the “modification of a physiological or pathological state” as a medical purpose specific to devices. This clarifies that materials interacting with the human body in such a way as to modify its “state” are medical devices. Natural materials, such as vegetal matrices, are characterized by the presence of both functional and structural interactions between their components; they can thus be described as “network/s" and interact with the human body in a coordinated, complex way. Since the “state” of the human body is a network of biological functions, the “network/s over a network” interaction between the natural material and the human body is likely to modify the “state” of the human body. Thus, therapeutic products consisting of natural materials, such as vegetal matrices, seem to fit perfectly into the definition of a medical device. Here we analyze the main characteristics of medicinal products, of medical devices made of substances and of medical devices consisting of natural materials. We see that medicinal products and medical devices made of substances have the common characteristic of being based on substances, either synthetic or derivatives of natural materials, but differ in their mechanism of action. On the other hand, medical devices constituted of natural materials relate to the general category of medical devices and cannot be characterized by any single component, identified as an active component. We also discuss how these characteristics relate to the mechanism of action of each type of product. This analysis should allow to identify the most appropriate path for each product, a necessary step to promote research and development of innovative therapies for a large number of unmet medical needs.

Deep learning based automatic segmentation of the Internal Pudendal Artery in definitive radiotherapy treatment planning of localized prostate cancer

Background and purposeRadiation-induced erectile dysfunction (RiED) commonly affects prostate cancer patients, prompting clinical trials across institutions to explore dose-sparing to internal-pudendal-arteries (IPA) for preserving sexual potency. IPA, challenging to segment, isn't conventionally considered an organ-at-risk (OAR). This study proposes a deep learning (DL) auto-segmentation model for IPA, using Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) or CT alone to accommodate varied clinical practices. Materials and methodsA total of 86 patients with CT and MRI images and noisy IPA labels were recruited in this study. We split the data into 42/14/30 for model training, testing, and a clinical observer study, respectively. There were three major innovations in this model: 1) we designed an architecture with squeeze-and-excite blocks and modality attention for effective feature extraction and production of accurate segmentation, 2) a novel loss function was used for training the model effectively with noisy labels, and 3) modality dropout strategy was used for making the model capable of segmentation in the absence of MRI. ResultsTest dataset metrics were DSC 61.71 ± 7.7 %, ASD 2.5 ± .87 mm, and HD95 7.0 ± 2.3 mm. AI segmented contours showed dosimetric similarity to expert physician’s contours. Observer study indicated higher scores for AI contours (mean = 3.7) compared to inexperienced physicians’ contours (mean = 3.1). Inexperienced physicians improved scores to 3.7 when starting with AI contours. ConclusionThe proposed model achieved good quality IPA contours to improve uniformity of segmentation and to facilitate introduction of standardized IPA segmentation into clinical trials and practice.

PURSES OF MITHRIDATES’ MERCENARIES FROM THE CIMMERIAN BOSPORUS

This article examines the numismatic sources from the Cimmerian Bosporus: hoards representing soldiers’ purses, cases of cash payments for rations, as well as the coinages minted to make these payments during the Mithridatic Wars. Several hoards of a military context came from this time, consisting of coins, which were precisely used for the allowances of soldiers. On the one hand, these hoards provide evidence of the payment made to Mithridates’ mercenaries who served in garrisons in cities and in their chora to protect the grain-producing areas that supplied the king’s army with bread. Such hoards consist of silver or bronze coinages specially minted for military payments in Bosporus. However, except for two cases of wage payment made to the garrison in silver (CH XI, 137, 138), using bronze coins for military payments is characteristic of the Bosporus in the Late Hellenistic period. On the other hand, at least five garrisons in Bosporus, whose existence has been attested due to finds of soldiers’ purses, are in Tyritace (IGCH 1145 = СΗ ΧΙ, 140), Myrmecium (CH XI, 116, 141), Patraeus (CH XI, 142), Phanagoria (CH XI, 145A-B) and the Vyshesteblievskaya 3 settlement, located in the grain-producing area subordinate to Phanagoria. These purses show that during the Mithridatic Wars, mercenaries were paid in Bosporan and Pontic bronze coins of significant denominations, which apparently amounted to less than an obol per day. The purses of mercenaries containing the same amount indicate the approximate level of their pay, equal to 22(?) obols per month. The case of Bosporus demonstrates the success of one of the major innovations of the Hellenistic period – the systematic use of bronze for military payment.

Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs.

As the long non-coding RNAs (lncRNAs) play important roles during the incurrence and development of various human diseases, identifying disease-related lncRNAs can contribute to clarifying the pathogenesis of diseases. Most of the recent lncRNA-disease association prediction methods utilized the multi-source data about the lncRNAs and diseases. A single lncRNA may participate in multiple disease processes, and multiple lncRNAs usually are involved in the same disease process synergistically. However, the previous methods did not completely exploit the biological characteristics to construct the informative prediction models. We construct a prediction model based on adaptive hypergraph and gated convolution for lncRNA-disease association prediction (AGLDA), to embed and encode the biological characteristics about lncRNA-disease associations, the topological features from the entire heterogeneous graph perspective, and the gated enhanced pairwise features. First, the strategy for constructing hyperedges is designed to reflect the biological characteristic that multiple lncRNAs are involved in multiple disease processes. Furthermore, each hyperedge has its own biological perspective, and multiple hyperedges are beneficial for revealing the diverse relationships among multiple lncRNAs and diseases. Second, we encode the biological features of each lncRNA (disease) node using a strategy based on dynamic hypergraph convolutional networks. The strategy may adaptively learn the features of the hyperedges and formulate the dynamically evolved hypergraph topological structure. Third, a group convolutional network is established to integrate the entire heterogeneous topological structure and multiple types of node attributes within an lncRNA-disease-miRNA graph. Finally, a gated convolutional strategy is proposed to enhance the informative features of the lncRNA-disease node pairs. The comparison experiments indicate that AGLDA outperforms seven advanced prediction methods. The ablation studies confirm the effectiveness of major innovations, and the case studies validate AGLDA's ability in application for discovering potential disease-related lncRNA candidates.