CD8+ Trms against malaria liver-stage: prospects and challenges

Attenuated sporozoites provide a valuable model for exploring protective immunity against the malarial liver stage, guiding the design of highly efficient vaccines to prevent malaria infection. Liver tissue-resident CD8+ T cells (CD8+ Trm cells) are considered the host front-line defense against malaria and are crucial to developing prime-trap/target strategies for pre-erythrocytic stage vaccine immunization. However, the spatiotemporal regulatory mechanism of the generation of liver CD8+ Trm cells and their responses to sporozoite challenge, as well as the protective antigens they recognize remain largely unknown. Here, we discuss the knowledge gap regarding liver CD8+ Trm cell formation and the potential strategies to identify predominant protective antigens expressed in the exoerythrocytic stage, which is essential for high-efficacy malaria subunit pre-erythrocytic vaccine designation

OpenAGI: When LLM Meets Domain Experts

Human intelligence has the remarkable ability to assemble basic skills into complex ones so as to solve complex tasks. This ability is equally important for Artificial Intelligence (AI), and thus, we assert that in addition to the development of large, comprehensive intelligent models, it is equally crucial to equip such models with the capability to harness various domain-specific expert models for complex task-solving in the pursuit of Artificial General Intelligence (AGI). Recent developments in Large Language Models (LLMs) have demonstrated remarkable learning and reasoning abilities, making them promising as a controller to select, synthesize, and execute external models to solve complex tasks. In this project, we develop OpenAGI, an open-source AGI research platform, specifically designed to offer complex, multi-step tasks and accompanied by task-specific datasets, evaluation metrics, and a diverse range of extensible models. OpenAGI formulates complex tasks as natural language queries, serving as input to the LLM. The LLM subsequently selects, synthesizes, and executes models provided by OpenAGI to address the task. Furthermore, we propose a Reinforcement Learning from Task Feedback (RLTF) mechanism, which uses the task-solving result as feedback to improve the LLM's task-solving ability. Thus, the LLM is responsible for synthesizing various external models for solving complex tasks, while RLTF provides feedback to improve its task-solving ability, enabling a feedback loop for self-improving AI. We believe that the paradigm of LLMs operating various expert models for complex task-solving is a promising approach towards AGI. To facilitate the community's long-term improvement and evaluation of AGI's ability, we open-source the code, benchmark, and evaluation methods of the OpenAGI project at https://github.com/agiresearch/OpenAGI.Comment: 18 pages, 6 figures, 7 table

Semi-supervised Road Updating Network (SRUNet): A Deep Learning Method for Road Updating from Remote Sensing Imagery and Historical Vector Maps

A road is the skeleton of a city and is a fundamental and important geographical component. Currently, many countries have built geo-information databases and gathered large amounts of geographic data. However, with the extensive construction of infrastructure and rapid expansion of cities, automatic updating of road data is imperative to maintain the high quality of current basic geographic information. However, obtaining bi-phase images for the same area is difficult, and complex post-processing methods are required to update the existing databases.To solve these problems, we proposed a road detection method based on semi-supervised learning (SRUNet) specifically for road-updating applications; in this approach, historical road information was fused with the latest images to directly obtain the latest state of the road.Considering that the texture of a road is complex, a multi-branch network, named the Map Encoding Branch (MEB) was proposed for representation learning, where the Boundary Enhancement Module (BEM) was used to improve the accuracy of boundary prediction, and the Residual Refinement Module (RRM) was used to optimize the prediction results. Further, to fully utilize the limited amount of label information and to enhance the prediction accuracy on unlabeled images, we utilized the mean teacher framework as the basic semi-supervised learning framework and introduced Regional Contrast (ReCo) in our work to improve the model capacity for distinguishing between the characteristics of roads and background elements.We applied our method to two datasets. Our model can effectively improve the performance of a model with fewer labels. Overall, the proposed SRUNet can provide stable, up-to-date, and reliable prediction results for a wide range of road renewal tasks.Comment: 22 pages, 8 figure

GenRec: Large Language Model for Generative Recommendation

In recent years, large language models (LLM) have emerged as powerful tools for diverse natural language processing tasks. However, their potential for recommender systems under the generative recommendation paradigm remains relatively unexplored. This paper presents an innovative approach to recommendation systems using large language models (LLMs) based on text data. In this paper, we present a novel LLM for generative recommendation (GenRec) that utilized the expressive power of LLM to directly generate the target item to recommend, rather than calculating ranking score for each candidate item one by one as in traditional discriminative recommendation. GenRec uses LLM's understanding ability to interpret context, learn user preferences, and generate relevant recommendation. Our proposed approach leverages the vast knowledge encoded in large language models to accomplish recommendation tasks. We first we formulate specialized prompts to enhance the ability of LLM to comprehend recommendation tasks. Subsequently, we use these prompts to fine-tune the LLaMA backbone LLM on a dataset of user-item interactions, represented by textual data, to capture user preferences and item characteristics. Our research underscores the potential of LLM-based generative recommendation in revolutionizing the domain of recommendation systems and offers a foundational framework for future explorations in this field. We conduct extensive experiments on benchmark datasets, and the experiments shows that our GenRec has significant better results on large dataset

Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure

Urban public transport and air quality: Empirical study of China cities

Abstract(#br)To analyze the impact of the increase of public transport on the urban air quality will contribute to the sustainable development of urbanization. But many existing studies have not paid attention to the potential endogeneity of estimation, which comes from the fact that the deterioration of air quality would in turn affect the policies of public transport investment. This paper attempts to control this endogeneity by introducing an instrument variable of the urban built-up area into the empirical models. Using city-level data from China, our study adopts 2SLS method and conducts a series of robustness tests to ensure the estimation results more convincing and robust. The results show that the urban air quality could be improved if the city provides more buses for public transport. Moreover, after controlling the endogeneity, the marginal improving effect of increasing the public transport on urban air quality could be larger from 0.082 to 0.678. This finding indicates that the endogeneity bias is likely to cause the underestimation of the improving effect, and may result in some errors of the policy decisions of urban investment

Rational Design of Electrochemiluminescent Devices

ConspectusElectrochemiluminescence (ECL) is a light-emitting process which combines the intriguing merits of both electrochemical and chemiluminescent methods. It is an extensively used method especially in clinical analysis and biological research due to its high sensitivity, wide dynamic range, and good reliability. ECL devices are critical for the development and applications of ECL. Much effort has been expended to improve the sensitivity, portability, affordability, and throughput of new ECL devices, which allow ECL to adapt broad usage scenarios.In this Account, we summarize our efforts on the recent development of ECL devices including new electrodes, ECL devices based on a wireless power transfer (WPT) technique, and novel bipolar electrochemistry. As the essential components in the ECL devices, electrodes play an important role in ECL detection. We have significantly improved the sensitivity of luminol ECL detection of H2O2 by using a stainless steel electrode. By using semiconductor materials (e.g., silicon and BiVO4), we have exploited photoinduced ECL to generate intense emission at much lower potentials upon illumination. For convenience, portability, and disposability, ECL devices based on cheap WPT devices have been designed. A small diode has been employed to rectify alternating current into direct current to dramatically enhance ECL intensity, enabling sensitive ECL detection using a smart phone as a detector. Finally, we have developed several ECL devices based on bipolar electrochemistry in view of the convenience of multiplex ECL sensing using a bipolar electrode (BPE). On the basis of the wireless feature of BPE, we have employed movable BPEs (e.g., BPE swimmers and magnetic rotating BPE) for deep exploration of the motional and ECL properties of dynamic BPE systems. To make full use of the ECL solution, we have dispersed numerous micro-/nano-BPEs in solution to produce intense 3D ECL in the entire solution, instead of 2D ECL in conventional ECL devices. In addition, the interference of ECL noise from driving electrodes was minimized by introducing the stainless steel with a passivation layer as the driving electrode. To eliminate the need for the fabrication of electrode arrays and the interference from the driving electrode and to decrease the applied voltage, we develop a new-type BPE device consisting of a single-electrode electrochemical system (SEES) based on a resistance-induced potential difference. The SEES is fabricated easily by attaching a multiperforated plate to a single film electrode. It enables the simultaneous detection of many samples and analytes using only a single film electrode (e.g., screen-printed electrode) instead of electrode arrays. It is of great potential in clinical analysis especially for multiple-biomarker detection, drug screening, and biological studies. Looking forward, we believe that more ECL devices and related ECL materials and detection methods will be developed for a wide range of applications, such as in vitro diagnosis, point-of-care testing, high-throughput analysis, drug screening, biological study, and mechanism investigation.Conversion lumineuse par électrochimiluminescence photoinduit