Kinetic Monte Carlo Simulations

Kinetic Monte Carlo (kMC) is a set of scientific libraries designed to deploy kMC simulations intended to simulate the time evolution of some processes occurring in nature. kMC is currently allows the user to intuitively generate single component crystal lattices to simulate, post process, and visualize the kinetic Monte Carlo-based atomistic evolution of materials. kMC provides an interface to the Stochastic Parallel PARticle Kinetic Simulator (SPPARKS) [1] and is specifically designed to simulate individual atomic deposition (condensation) and dissolution (evaporation) events, while simultaneously tracking the surface and bulk crystallographic anisotropic diffusion. The main goal of this project is to create Graphical User Interfaces for WulffShape and Physical Vapor Deposition (PVD) examples. The Wulff shape is the shape that possesses the lowest surface energy for a fixed volume and Physical Vapor Deposition is a collective set of processes used to deposit thin layers of material. We are trying to offer the user an option to choose a material, specify the material and change environmental parameters. kMC could generate crystal lattices, simulate, and render images according to the user\u27s setting. Moreover, there is an option for users to see three-dimensional structured atoms created by visIt. In conclusion, this application is going to simulate the time evolution of Wulff Shape and PVD

Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs), which rely on natural language conversations to satisfy user needs. In this paper, we embark on an investigation into the utilization of ChatGPT for conversational recommendation, revealing the inadequacy of the existing evaluation protocol. It might over-emphasize the matching with the ground-truth items or utterances generated by human annotators, while neglecting the interactive nature of being a capable CRS. To overcome the limitation, we further propose an interactive Evaluation approach based on LLMs named iEvaLM that harnesses LLM-based user simulators. Our evaluation approach can simulate various interaction scenarios between users and systems. Through the experiments on two publicly available CRS datasets, we demonstrate notable improvements compared to the prevailing evaluation protocol. Furthermore, we emphasize the evaluation of explainability, and ChatGPT showcases persuasive explanation generation for its recommendations. Our study contributes to a deeper comprehension of the untapped potential of LLMs for CRSs and provides a more flexible and easy-to-use evaluation framework for future research endeavors. The codes and data are publicly available at https://github.com/RUCAIBox/iEvaLM-CRS.Comment: Accepted by EMNLP 202

Distance measurements via the morphogen gradient of Bicoid in Drosophila embryos

<p>Abstract</p> <p>Background</p> <p>Patterning along the anterior-posterior (A-P) axis in <it>Drosophila </it>embryos is instructed by the morphogen gradient of Bicoid (Bcd). Despite extensive studies of this morphogen, how embryo geometry may affect gradient formation and target responses has not been investigated experimentally.</p> <p>Results</p> <p>In this report, we systematically compare the Bcd gradient profiles and its target expression patterns on the dorsal and ventral sides of the embryo. Our results support a hypothesis that proper distance measurement and the encoded positional information of the Bcd gradient are along the perimeter of the embryo. Our results also reveal that the dorsal and ventral sides of the embryo have a fundamentally similar relationship between Bcd and its target Hunchback (Hb), suggesting that Hb expression properties on the two sides of the embryo can be directly traced to Bcd gradient properties. Our 3-D simulation studies show that a curvature difference between the two sides of an embryo is sufficient to generate Bcd gradient properties that are consistent with experimental observations.</p> <p>Conclusions</p> <p>The findings described in this report provide a first quantitative, experimental evaluation of embryo geometry on Bcd gradient formation and target responses. They demonstrate that the physical features of an embryo, such as its shape, are integral to how pattern is formed.</p

Recent advances in non-ionic surfactant vesicles (niosomes): fabrication, characterization, pharmaceutical and cosmetic applications

Development of nanocarriers for drug delivery has received considerable attention due to their potential in achieving targeted delivery to the diseased site while sparing the surrounding healthy tissue. Safe and efficient drug delivery has always been a challenge in medicine. During the last decade, a large amount of interest has been drawn on the fabrication of surfactant-based vesicles to improve drug delivery. Niosomes are self-assembled vesicular nano-carriers formed by hydration of non-ionic surfactant, cholesterol or other amphiphilic molecules that serve as a versatile drug delivery system with a variety of applications ranging from dermal delivery to brain-targeted delivery. A large number of research articles have been published reporting their fabrication methods and applications in pharmaceutical and cosmetic fields. Niosomes have the same advantages as liposomes, such as the ability to incorporate both hydrophilic and lipophilic compounds. Besides, niosomes can be fabricated with simple methods, require less production cost and are stable over an extended period, thus overcoming the major drawbacks of liposomes. This review provides a comprehensive summary of niosomal research to date, it provides a detailed overview of the formulation components, types of niosomes, effects of components on the formation of niosomes, fabrication and purification methods, physical characterization techniques of niosomes, recent applications in pharmaceutical field such as in oral, ocular, topical, pulmonary, parental and transmucosal drug delivery, and cosmetic applications. Finally, limitations and the future outlook for this delivery system have also been discussed

Towards artificial general intelligence via a multimodal foundation model

The fundamental goal of artificial intelligence (AI) is to mimic the core cognitive activities of human. Despite tremendous success in the AI research, most of existing methods have only single-cognitive ability. To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks. To achieve this goal, we propose to pre-train our foundation model by self-supervised learning with weak semantic correlation data crawled from the Internet and show that promising results can be obtained on a wide range of downstream tasks. Particularly, with the developed model-interpretability tools, we demonstrate that strong imagination ability is now possessed by our foundation model. We believe that our work makes a transformative stride towards AGI, from our common practice of "weak or narrow AI" to that of "strong or generalized AI".Comment: Published by Nature Communications, see https://www.nature.com/articles/s41467-022-30761-

TikTalk: A Video-Based Dialogue Dataset for Multi-Modal Chitchat in Real World

To facilitate the research on intelligent and human-like chatbots with multi-modal context, we introduce a new video-based multi-modal dialogue dataset, called TikTalk. We collect 38K videos from a popular video-sharing platform, along with 367K conversations posted by users beneath them. Users engage in spontaneous conversations based on their multi-modal experiences from watching videos, which helps recreate real-world chitchat context. Compared to previous multi-modal dialogue datasets, the richer context types in TikTalk lead to more diverse conversations, but also increase the difficulty in capturing human interests from intricate multi-modal information to generate personalized responses. Moreover, external knowledge is more frequently evoked in our dataset. These facts reveal new challenges for multi-modal dialogue models. We quantitatively demonstrate the characteristics of TikTalk, propose a video-based multi-modal chitchat task, and evaluate several dialogue baselines. Experimental results indicate that the models incorporating large language models (LLM) can generate more diverse responses, while the model utilizing knowledge graphs to introduce external knowledge performs the best overall. Furthermore, no existing model can solve all the above challenges well. There is still a large room for future improvements, even for LLM with visual extensions. Our dataset is available at \url{https://ruc-aimind.github.io/projects/TikTalk/}.Comment: Accepted to ACM Multimedia 202

New risk score for predicting progression of membranous nephropathy

Abstract Background Patients with Idiopathic membranous nephropathy (IMN) have various outcomes. The aim of this study is to construct a tool for clinicians to precisely predict outcome of IMN. Methods IMN patients diagnosed by renal biopsy from Shanghai Ruijin Hospital from 2009.01 to 2013.12 were enrolled in this study. Primary outcome was defined as a combination of renal function progression [defined as a reduction of estimated glomerular filtration rate (eGFR) equal to or over 30% comparing to baseline], ESRD or death. Risk models were established by Cox proportional hazard regression analysis and validated by bootstrap resampling analysis. ROC curve was applied to test the performance of risk score. Results Totally 439 patients were recruited in this study. The median follow-up time was 38.73 ± 19.35 months. The enrolled patients were 56 (15–83) years old with a male predominance (sex ratio: male vs female, 1:0.91). The median baseline serum albumin, eGFR-EPI and proteinuria were 23(8–43) g/l, 100.31(12.81–155.98) ml/min/1.73 m2 and 3.98(1.50–22.98) g/24 h, respectively. In total, there were 36 primary outcomes occurred. By Cox regression analysis, the best risk model included age [HR: 1.04(1.003–1.08), 95% CI from bootstrapping: 1.01–1.08), eGFR [HR: 0.97 (0.96–0.99), 95% CI from bootstrapping: 0.96–0.99) and proteinuria [HR: 1.09 (1.01–1.18), 95% CI from bootstrapping: 1.02–1.16). One unit increasing of the risk score based on the best model was associated with 2.57 (1.97–3.36) fold increased risk of combined outcome. The discrimination of this risk score was excellent in predicting combined outcome [C statistics: 0.83, 95% CI 0.76–0.90]. Conclusions Our study indicated that older IMN patients with lower eGFR and heavier proteinuria at the time of renal biopsy were at a higher risk for adverse outcomes. A risk score based on these three variables provides clinicians with an effective tool for risk stratification.https://deepblue.lib.umich.edu/bitstream/2027.42/147736/1/12967_2019_Article_1792.pd

Micro RNA expression profile and functional analysis reveal that mi R ‐382 is a critical novel gene of alcohol addiction

Alcohol addiction is a major social and health concern. Here, we determined the expression profile of microRNAs (miRNAs) in the nucleus accumbens (NAc) of rats treated with alcohol. The results suggest that multiple miRNAs were aberrantly expressed in rat NAc after alcohol injection. Among them, miR‐382 was down‐regulated in alcohol‐treated rats. In both cultured neuronal cells in vitro and in the NAc in vivo , we identified that the dopamine receptor D1 ( Drd1 ) is a direct target gene of miR‐382. Via this target gene, miR‐382 strongly modulated the expression of DeltaFosB. Moreover, overexpression of miR‐382 significantly attenuated alcohol‐induced up‐regulation of DRD1 and DeltaFosB, decreased voluntary intake of and preference for alcohol and inhibited the DRD1‐induced action potential responses. The results indicated that miRNAs are involved in and may represent novel therapeutic targets for alcoholism. The underlying molecular causes of alcohol addiction remain unclear. Many miRNAs are found modulated in the nucleus accumbens of rats chronically treated with alcohol. Specifically, miR‐382 is shown to regulate alcohol intake via DRD1 and DeltaFosB.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/99635/1/emmm201201900-sm-0001-Review_Process_File.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/99635/2/emmm201201900.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/99635/3/emmm201202100-sm-0006-SourceData-S5.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/99635/4/emmm201201900-sm-0002-SuppData-S1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/99635/5/emmm201202100-sm-0005-SourceData-S4.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/99635/6/emmm201202100-sm-0004-SourceData-S3.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/99635/7/emmm201202100-sm-0003-SourceData-S2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/99635/8/emmm201202100-sm-0007-SourceData-S6.pd