Cellular receptor binding and entry of human papillomavirus

Human papillomaviruses (HPVs), recognized as the etiological agents for the skin, plantar, genital, and laryngopharyngeal wart, have been previously in numerous studies demonstrated to present a close link between HPV infection and certain human cancers, some putative candidates of HPV cell receptor and possible pathways of cell entry proposed. This review was to highlight the investigations and remaining questions regarding the binding and entry process

Polypharmacy and Multimorbidity Among Medicaid Enrollees: A Multistate Analysis

The purpose of this study is to explore the associations between polypharmacy and multimorbidity using conventional and novel measures of polypharmacy. In this cross-sectional study, data on fee-for-service (FFS) Medicaid enrollees with at least 1 chronic condition and aged 18–64 years (N = 38,329) were derived from the 2010 Medicaid Analytic eXtract (MAX) files of Maryland and West Virginia. Polypharmacy, by the authors\u27 novel definition, was determined as simultaneous use of ≥5 drugs for a consecutive period of 60 days. Multimorbidity was defined as having ≥2 chronic conditions based on the US Department of Health and Human Services framework. The association between multimorbidity and polypharmacy was examined with chi-square tests and logistic regression. Polypharmacy prevalence was estimated at 50.9% using the novel definition, as compared to 16.7% and 64.9% for the 2 commonly used conventional measures, respectively. For all 3 definitions, individuals with multimorbidity were more likely to have polypharmacy than those without multimorbidity (P \u3c 0.001). The authors also consistently found, using all definitions, that those who were older, female, white, and eligible for Medicaid because of cash assistance were more likely to have polypharmacy (all P \u3c 0.001). Polypharmacy was highly prevalent and significantly associated with multimorbidity among Medicaid FFS enrollees irrespective of the definitions used. The new measure may provide a more comprehensive and accurate estimation of polypharmacy than the conventional measures. These findings suggest the need for a paradigm shift from disease-specific care to patient-centered collaborative care to manage patients with multimorbidity and polypharmacy

DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typing

In the field of representation learning on knowledge graphs (KGs), a hyper-relational fact consists of a main triple and several auxiliary attribute-value descriptions, which is considered more comprehensive and specific than a triple-based fact. However, currently available hyper-relational KG embedding methods in a single view are limited in application because they weaken the hierarchical structure that represents the affiliation between entities. To overcome this limitation, we propose a dual-view hyper-relational KG structure (DH-KG) that contains a hyper-relational instance view for entities and a hyper-relational ontology view for concepts that are abstracted hierarchically from the entities. This paper defines link prediction and entity typing tasks on DH-KG for the first time and constructs two DH-KG datasets, JW44K-6K, extracted from Wikidata, and HTDM based on medical data. Furthermore, we propose DHGE, a DH-KG embedding model based on GRAN encoders, HGNNs, and joint learning. DHGE outperforms baseline models on DH-KG, according to experimental results. Finally, we provide an example of how this technology can be used to treat hypertension. Our model and new datasets are publicly available.Comment: Accepted by AAAI 202

DisAsymNet:Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms Using Self-adversarial Learning

Asymmetry is a crucial characteristic of bilateral mammograms (Bi-MG) when abnormalities are developing. It is widely utilized by radiologists for diagnosis. The question of “what the symmetrical Bi-MG would look like when the asymmetrical abnormalities have been removed ?” has not yet received strong attention in the development of algorithms on mammograms. Addressing this question could provide valuable insights into mammographic anatomy and aid in diagnostic interpretation. Hence, we propose a novel framework, DisAsymNet, which utilizes asymmetrical abnormality transformer guided self-adversarial learning for disentangling abnormalities and symmetric Bi-MG. At the same time, our proposed method is partially guided by randomly synthesized abnormalities. We conduct experiments on three public and one in-house dataset, and demonstrate that our method outperforms existing methods in abnormality classification, segmentation, and localization tasks. Additionally, reconstructed normal mammograms can provide insights toward better interpretable visual cues for clinical diagnosis. The code will be accessible to the public.</p

DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms using Self-adversarial Learning

Asymmetry is a crucial characteristic of bilateral mammograms (Bi-MG) when abnormalities are developing. It is widely utilized by radiologists for diagnosis. The question of 'what the symmetrical Bi-MG would look like when the asymmetrical abnormalities have been removed ?' has not yet received strong attention in the development of algorithms on mammograms. Addressing this question could provide valuable insights into mammographic anatomy and aid in diagnostic interpretation. Hence, we propose a novel framework, DisAsymNet, which utilizes asymmetrical abnormality transformer guided self-adversarial learning for disentangling abnormalities and symmetric Bi-MG. At the same time, our proposed method is partially guided by randomly synthesized abnormalities. We conduct experiments on three public and one in-house dataset, and demonstrate that our method outperforms existing methods in abnormality classification, segmentation, and localization tasks. Additionally, reconstructed normal mammograms can provide insights toward better interpretable visual cues for clinical diagnosis. The code will be accessible to the public

Memories are One-to-Many Mapping Alleviators in Talking Face Generation

Talking face generation aims at generating photo-realistic video portraits of a target person driven by input audio. Due to its nature of one-to-many mapping from the input audio to the output video (e.g., one speech content may have multiple feasible visual appearances), learning a deterministic mapping like previous works brings ambiguity during training, and thus causes inferior visual results. Although this one-to-many mapping could be alleviated in part by a two-stage framework (i.e., an audio-to-expression model followed by a neural-rendering model), it is still insufficient since the prediction is produced without enough information (e.g., emotions, wrinkles, etc.). In this paper, we propose MemFace to complement the missing information with an implicit memory and an explicit memory that follow the sense of the two stages respectively. More specifically, the implicit memory is employed in the audio-to-expression model to capture high-level semantics in the audio-expression shared space, while the explicit memory is employed in the neural-rendering model to help synthesize pixel-level details. Our experimental results show that our proposed MemFace surpasses all the state-of-the-art results across multiple scenarios consistently and significantly.Comment: Project page: see https://memoryface.github.i

MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models

AI-empowered music processing is a diverse field that encompasses dozens of tasks, ranging from generation tasks (e.g., timbre synthesis) to comprehension tasks (e.g., music classification). For developers and amateurs, it is very difficult to grasp all of these task to satisfy their requirements in music processing, especially considering the huge differences in the representations of music data and the model applicability across platforms among various tasks. Consequently, it is necessary to build a system to organize and integrate these tasks, and thus help practitioners to automatically analyze their demand and call suitable tools as solutions to fulfill their requirements. Inspired by the recent success of large language models (LLMs) in task automation, we develop a system, named MusicAgent, which integrates numerous music-related tools and an autonomous workflow to address user requirements. More specifically, we build 1) toolset that collects tools from diverse sources, including Hugging Face, GitHub, and Web API, etc. 2) an autonomous workflow empowered by LLMs (e.g., ChatGPT) to organize these tools and automatically decompose user requests into multiple sub-tasks and invoke corresponding music tools. The primary goal of this system is to free users from the intricacies of AI-music tools, enabling them to concentrate on the creative aspect. By granting users the freedom to effortlessly combine tools, the system offers a seamless and enriching music experience

Optimal Online Service Strategy and Price Decision in Omnichannel Retail

As a new emerging sales promotion tool, various types of online services are increasingly adopted by firms to improve consumers' satisfaction and then increase profit. This paper simulates a two-echelon supply chain where a supplier sells the product through an offline or online retailer. Online channel is characterised by direct selling and reselling. We consider three online service strategies: no online service, pre-emptive and reactive online service. Several results are obtained. We find that investing in online services can benefit all players in most scenarios more than no service scenario. In the direct selling case, the offline retailer benefits the most from the reactive service strategy due to the webrooming effect, whereas the supplier performance best in the pre-emptive service strategy. However, in the reselling case, we find that the supplier, the online and offline retailers benefit the most from reactive service strategy. Furthermore, we compare the prices and service levels of the three strategies and find that the webrooming effect coefficient can affect the optimal wholesale price, retail prices and online service level. Finally, the findings indicate that the supplier's choice of the two cases depends on the fixed cost of the online channel