An investigation study on the mental disorder related topics in the subject directory of MedlinePlus portal

We examined a subject directory system related to Mental Disorder on the MedlinePlus portal. According to the comparison between the link connection network and the semantic connection network among the 99 collected health topics, 55 bi-directional as well as 23 unidirectional connections were identified and proposed to be added to the corresponding health topic pages. In addition, Mental Disorder related topics were found to be linked to Youth & Child related topics and Daily Health related topics in the subject directory. A mixed research method combining social network analysis and inferential analysis was applied. The recommended connections were evaluated by domain ex- perts and visualized from various perspectives. Suggestions for optimizing and enhancing the current link network among Mental Disorder and related groups of health topics were provided. The findings in this study offered insights to public health portal creators for designing subject directory-based navigation system

Tensor Neural Network and Its Numerical Integration

In this paper, we introduce a type of tensor neural network. For the first time, we propose its numerical integration scheme and prove the computational complexity to be the polynomial scale of the dimension. Based on the tensor product structure, we develop an efficient numerical integration method by using fixed quadrature points for the functions of the tensor neural network. The corresponding machine learning method is also introduced for solving high-dimensional problems. Some numerical examples are also provided to validate the theoretical results and the numerical algorithm.Comment: 27 pages, 30 figure

Influencing factors of resident satisfaction in smart community services： An empirical study in Chengdu

Smart communities have shown great advantages in China\u27s pandemic control, but also exposed the shortcomings that some smart community services (SCS) are out of touch with residents\u27 needs in the post-pandemic era. Therefore, This study aims to explore those SCSs were needed to promote the sustainable development of smart communities. Based on the expectation disconfirmation theory and the modified ASCI model, this study establishes a smart community service resident satisfaction model and analyzes it with Amos structural equation model. The study results are as follows: (1) SCS outcome, ICT infrastructure, and SCS delivery all have a positive influence on resident satisfaction and their performances decrease in turn. (2) some of the factors that drive resident satisfaction most, such as Smart Property Service and Public Facility, have a lower rating. (3) residents are more concerned about the cost (including financial and emotional costs) than the quality of the SCSs. (4) Most residents\u27 expectations of SCS are irrational and that’s why it does not have a significant impact on satisfaction. (5) Resident Satisfaction is an important factor in enhancing Resident Confidence in SCS and promoting Resident Participation in improving SCS. This enlightens us that improving resident satisfaction is one of the effective ways to promote the sustainable development of Smart Community and continuously enhance the emergency response capabilities of grassroots communities in the post-pandemic era

KV Inversion: KV Embeddings Learning for Text-Conditioned Real Image Action Editing

Text-conditioned image editing is a recently emerged and highly practical task, and its potential is immeasurable. However, most of the concurrent methods are unable to perform action editing, i.e. they can not produce results that conform to the action semantics of the editing prompt and preserve the content of the original image. To solve the problem of action editing, we propose KV Inversion, a method that can achieve satisfactory reconstruction performance and action editing, which can solve two major problems: 1) the edited result can match the corresponding action, and 2) the edited object can retain the texture and identity of the original real image. In addition, our method does not require training the Stable Diffusion model itself, nor does it require scanning a large-scale dataset to perform time-consuming training

GeneGPT: Teaching Large Language Models to Use NCBI Web APIs

In this paper, we present GeneGPT, a novel method for teaching large language models (LLMs) to use the Web Application Programming Interfaces (APIs) of the National Center for Biotechnology Information (NCBI) and answer genomics questions. Specifically, we prompt Codex (code-davinci-002) to solve the GeneTuring tests with few-shot URL requests of NCBI API calls as demonstrations for in-context learning. During inference, we stop the decoding once a call request is detected and make the API call with the generated URL. We then append the raw execution results returned by NCBI APIs to the generated texts and continue the generation until the answer is found or another API call is detected. Our preliminary results show that GeneGPT achieves state-of-the-art results on three out of four one-shot tasks and four out of five zero-shot tasks in the GeneTuring dataset. Overall, GeneGPT achieves a macro-average score of 0.76, which is much higher than retrieval-augmented LLMs such as the New Bing (0.44), biomedical LLMs such as BioMedLM (0.08) and BioGPT (0.04), as well as other LLMs such as GPT-3 (0.16) and ChatGPT (0.12).Comment: Work in progres

Cryo-EM Studies of TMEM16F Calcium-Activated Ion Channel Suggest Features Important for Lipid Scrambling.

As a Ca2+-activated lipid scramblase and ion channel that mediates Ca2+ influx, TMEM16F relies on both functions to facilitate extracellular vesicle generation, blood coagulation, and bone formation. How a bona fide ion channel scrambles lipids remains elusive. Our structural analyses revealed the coexistence of an intact channel pore and PIP2-dependent protein conformation changes leading to membrane distortion. Correlated to the extent of membrane distortion, many tightly bound lipids are slanted. Structure-based mutagenesis studies further reveal that neutralization of some lipid-binding residues or those near membrane distortion specifically alters the onset of lipid scrambling, but not Ca2+ influx, thus identifying features outside of channel pore that are important for lipid scrambling. Together, our studies demonstrate that membrane distortion does not require open hydrophilic grooves facing the membrane interior and provide further evidence to suggest separate pathways for lipid scrambling and ion permeation