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Chengying Hong,1 Yihan Xiong,2 Jinquan Xia,3 Wei Huang,4 Andi Xia,1 Shunyao Xu,1 Yuting Chen,1 Zhikun Xu,1 Huaisheng Chen,1 Zhongwei Zhang5 1Department of Critical Care Medicine, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, 518020, People’s Republic of China; 2Neurology Department, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, 518020, People’s Republic of China; 3Department of Clinical Medical Research Center, The Second Clinical Medical College, Jinan University (Shenzhen People’s Hospital), The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, 518020, People’s Republic of China; 4Department of Clinical Microbiology, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, 518020, People’s Republic of China; 5Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of ChinaCorrespondence: Huaisheng Chen, Department of Critical Care Medicine, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, 518020, People’s Republic of China, Email sunshinic@hotmail.com Zhongwei Zhang, Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China, Email 716461751@qq.comObjective: The objective of this study was to utilize LASSO regression (Least Absolute Shrinkage and Selection Operator Regression) to identify key variables in septic patients and develop a predictive model f

How to better evaluate the capabilities of Large Language Models (LLMs) is the focal point and hot topic in current LLMs research. Previous work has noted that due to the extremely high cost of iterative updates of LLMs, they are often unable to answer the latest dynamic questions well. To promote the improvement of Chinese LLMs' ability to answer dynamic questions, in this paper, we introduce CDQA, a Chinese Dynamic QA benchmark containing question-answer pairs related to the latest news on the Chinese Internet. We obtain high-quality data through a pipeline that combines humans and models, and carefully classify the samples according to the frequency of answer changes to facilitate a more fine-grained observation of LLMs' capabilities. We have also evaluated and analyzed mainstream and advanced Chinese LLMs on CDQA. Extensive experiments and valuable insights suggest that our proposed CDQA is challenging and worthy of more further study. We believe that the benchmark we provide will become one of the key data resources for improving LLMs' Chinese question-answering ability in the future., Comment: Work in progress!

The Open-Domain Question Answering (ODQA) task involves retrieving and subsequently generating answers from fine-grained relevant passages within a database. Current systems leverage Pretrained Language Models (PLMs) to model the relationship between questions and passages. However, the diversity in surface form expressions can hinder the model's ability to capture accurate correlations, especially within complex contexts. Therefore, we utilize Abstract Meaning Representation (AMR) graphs to assist the model in understanding complex semantic information. We introduce a method known as Graph-as-Token (GST) to incorporate AMRs into PLMs. Results from Natural Questions (NQ) and TriviaQA (TQ) demonstrate that our GST method can significantly improve performance, resulting in up to 2.44/3.17 Exact Match score improvements on NQ/TQ respectively. Furthermore, our method enhances robustness and outperforms alternative Graph Neural Network (GNN) methods for integrating AMRs. To the best of our knowledge, we are the first to employ semantic graphs in ODQA., Comment: Accepted by ACL2023 findings, reviewer scores: 4 4 4

This study focuses on the evaluation of the Open Question Answering (Open-QA) task, which can directly estimate the factuality of large language models (LLMs). Current automatic evaluation methods have shown limitations, indicating that human evaluation still remains the most reliable approach. We introduce a new task, Evaluating QA Evaluation (QA-Eval) and the corresponding dataset EVOUNA, designed to assess the accuracy of AI-generated answers in relation to standard answers within Open-QA. Our evaluation of these methods utilizes human-annotated results to measure their performance. Specifically, the work investigates methods that show high correlation with human evaluations, deeming them more reliable. We also discuss the pitfalls of current methods and methods to improve LLM-based evaluators. We believe this new QA-Eval task and corresponding dataset EVOUNA will facilitate the development of more effective automatic evaluation tools and prove valuable for future research in this area. All resources are available at \url{https://github.com/wangcunxiang/QA-Eval} and it is under the Apache-2.0 License., Comment: Accepted by Neurips-2023 Datasets and Benchmarks track; 28 pages

The increasing incidence of diseases affecting the central nervous system (CNS) demands the urgent development of efficient drugs. While many of these medicines are already available, the Blood Brain Barrier and to a lesser extent, the Blood Spinal Cord Barrier pose physical and biological limitations to their diffusion to reach target tissues. Therefore, efforts are needed not only to address drug development but specially to design suitable vehicles for delivery into the CNS through systemic administration. In the context of the functional and structural versatility of proteins, recent advances in their biological fabrication and a better comprehension of the physiology of the CNS offer a plethora of opportunities for the construction and tailoring of plain nanoconjugates and of more complex nanosized vehicles able to cross these barriers. We revise here how the engineering of functional proteins offers drug delivery tools for specific CNS diseases and more transversally, how proteins can be engineered into smart nanoparticles or 'artificial viruses' to afford therapeutic requirements through alternative administration routes.

The authors acknowledge the financial support granted to E.V. (PI12/00327) and R.M. (PI12/01861) from FIS, to E.V. (TV32013-133930) and to R.M. and A.V. (TV32013-132031) from La Marató de TV3 (416/C/2013), to A.V. from MINECO (Grant BIO2013-41019-P) and from the Centro de Investigación Biomédica en Red (CIBER) de Bioingeniería, Biomateriales y Nanomedicina (NANOPROTHER and NANOCOMETS projects). We are grateful to the Protein Production Platform (CIBER-BBN-UAB) for protein production and purification services (http://www.ciber-bbn.es/en/programas/89-plataforma-de-produccion-de-proteinas-ppp), to the Servei de Cultius Cel·lulars, Producció d׳Anticossos i Citometria (SCAC), to the Servei de Microscòpia, both at the UAB and to the Soft Materials Service (ICMAB-CSIC/CIBER-BBN). Z.X. received a fellowship grant from China Scholarship Council (Grant no. 2011630065) and U.U. from ISCIII. AV received an ICREA ACADEMIA award., Homing peptides are exploited in nanomedicine to functionalize either free drugs or nanostructured materials used as drug carriers. However, the influence of multivalent versus monovalent peptide presentation on the interaction with the receptor and on the consequent intracellular delivery of the associated cargo remains poorly explored. By using a tumor-homing peptide (T22) with regulatable self-assembling properties we have investigated here if its display in a either a monomeric form or as multimeric, self-assembled protein nanoparticles might determine the efficacy of receptor-mediated penetrability into target cells. This has been monitored by using a fluorescent cargo protein (iRFP), which when fused to the homing peptide acts as convenient reporter. The results indicate that the nanoparticulate protein versions are significantly more efficient in mediating receptor-dependent uptake than their unassembled counterparts. These finding stresses an additional benefit of nanostructured materials based on repetitive building blocks, regarding the multivalent presentation of cell ligands that facilitate cell penetration in drug delivery applications.

The increasing incidence of diseases affecting the central nervous system (CNS) demands the urgent development of efficient drugs. While many of these medicines are already available, the Blood Brain Barrier and to a lesser extent, the Blood Spinal Cord Barrier pose physical and biological limitations to their diffusion to reach target tissues. Therefore, efforts are needed not only to address drug development but specially to design suitable vehicles for delivery into the CNS through systemic administration. In the context of the functional and structural versatility of proteins, recent advances in their biological fabrication and a better comprehension of the physiology of the CNS offer a plethora of opportunities for the construction and tailoring of plain nanoconjugates and of more complex nanosized vehicles able to cross these barriers. We revise here how the engineering of functional proteins offers drug delivery tools for specific CNS diseases and more transversally, how proteins can be engineered into smart nanoparticles or ‘artificial viruses’ to afford therapeutic requirements through alternative administration routes.

Mobile traffic is projected to increase 1000 times from 2010 to 2020. This poses significant challenges on the 5th generation (5G) wireless communication system design, including network structure, air interface, key transmission schemes, multiple access, and duplexing schemes. In this paper, full duplex networking issues are discussed, aiming to provide some insights on the design and possible future deployment for 5G. Particularly, the interference scenarios in full duplex are analyzed, followed by discussions on several candidate interference mitigation approaches, interference proof frame structures, transceiver structures for channel reciprocity recovery, and super full duplex base station where each sector operates in time division duplex (TDD) mode. The extension of TDD and frequency division duplex (FDD) to full duplex is also examined. It is anticipated that with future standardization and deployment of full duplex systems, TDD and FDD will be harmoniously integrated, supporting all the existing half duplex mobile phones efficiently, and leading to a substantially enhanced 5G system performance., Comment: This paper has been withdrawn by the author due to there are some errors in this paper