OceanGPT: A Large Language Model for Ocean Science Tasks

Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface. Recently, advances in Large Language Models (LLMs) have transformed the paradigm in science. Despite the success in other domains, current LLMs often fall short in catering to the needs of domain experts like oceanographers, and the potential of LLMs for ocean science is under-explored. The intrinsic reason may be the immense and intricate nature of ocean data as well as the necessity for higher granularity and richness in knowledge. To alleviate these issues, we introduce OceanGPT, the first-ever LLM in the ocean domain, which is expert in various ocean science tasks. We propose DoInstruct, a novel framework to automatically obtain a large volume of ocean domain instruction data, which generates instructions based on multi-agent collaboration. Additionally, we construct the first oceanography benchmark, OceanBench, to evaluate the capabilities of LLMs in the ocean domain. Though comprehensive experiments, OceanGPT not only shows a higher level of knowledge expertise for oceans science tasks but also gains preliminary embodied intelligence capabilities in ocean technology. Codes, data and checkpoints will soon be available at https://github.com/zjunlp/KnowLM.Comment: Work in progress. Project Website: https://zjunlp.github.io/project/OceanGPT

Strategic Planning of Large-scale, Multimodal and Time-definite Networks for Overnight Express Delivery Services

The rising demand of express delivery service (EDS) and fierce market competition motivate EDS providers to improve service quality by modifying current networks. This project-based dissertation focuses on strategic planning of a large-scale, multi-modal and time-definite EDS network for a top nationwide EDS provider in China, based on its current network. An air-ground Hub-and-Spoke (H/S) network with a fully interconnected/star shaped structure was established to provide trans-city overnight EDS among relatively developed cities in China. The corresponding models are a combination of the hub location problem with fixed cost and the hub set covering problem. The objective function is to minimize the sum of the hub-location fixed cost and transportation cost under the constraints that all demand nodes are covered by their "home" hub. First, the basic model with linear air cost was proposed. Next, the basic model was extended to include air service selection decisions (or aircraft fleet owner-ship decisions) under the consideration of a cost select function for the backbone air service. Finally, two ex-tension models were studied, one to obtain the optimal aircraft fleet composition (Ext.1) and the other under the constraints of current aircraft fleet composition (Ext.2). Due to the large scale of project instances, hybrid genetic algorithms (GAs) were applied to get desirable solutions in an acceptable time period, but without the guarantee of finding optimal solutions. In particular, the overall problem includes three kinds of decisions: 1) hub location decisions, 2) demand allocation decisions and 3) air service selection decisions. A specific algorithm was proposed for each kind of decision, namely, GAs,local search heuristics and integer programming, respectively. These three algorithms were invoked hierarchically and iteratively to solve the original problem. 5 improvement techniques were proposed to different procedures of the original algorithms in order to improve the performance of the algorithms. Computational tests were conducted to evaluate the performance of the proposed algorithms in terms of computational time and solution quality. Tests under small-scale instances with CAB data sets were conducted to evaluate the overall performance of the proposed algorithm by comparing the solutions with the optimal solutions generated by CPLEX. Tests under large-scale instances with AP data sets and project data sets were conducted to evaluate the performance of the proposed improvement techniques. Since neither the optimal solutions nor solutions by other algorithms under large-scale instances were available to serve as benchmarks,the performance of the tailored algorithms and that of the un-tailored simple GAs was compared. Information about the stability of the algorithms with values of the coefficient of variation (CV) and the reliability of the results with T-tests was also provided. The models and the tailored GAs were applied to real-life instances of the project. This study introduces how the input data were collected and modified and how to deal with pertinent problems. By analyzing and com-paring the basic solutions of Ext.1 and Ext.2, the study not only reveals some important features of the net-work, but also arrives at some general conclusions and provided a dynamic aircraft fleet update strategy to guide the implementation of the project. Finally, scenario planning was executed to help decision-makers balance between costs and corresponding decision risks by identifying critical uncontrollable and controllable factors

Role of exosome-mediated molecules SNORD91A and SLC40A1 in M2 macrophage polarization and prognosis of ESCC

Abstract Background Exosome-mediated interaction serves as a significant regulatory factor for M2 macrophage polarization in cancer. Methods All accessible data were acquired from The Cancer Genome Atlas (TCGA) database and analyzed using R software. Molecules implicated in exocrine secretion were amassed from the ExoCarta database. Our research initially quantified the immune microenvironment in Esophageal Squamous Cell Carcinoma (ESCC) patients based on the expression profile sourced from the TCGA database. Additionally, we delved into the biological role of M2 macrophages in ESCC via Gene Set Enrichment Analysis (GSEA). Results We observed that patients with high M2 macrophage infiltration typically have a poorer prognosis. Subsequently, a total of 1457 molecules were identified, with 103 of these molecules believed to function through exocrine mechanisms, as supported by data from the ExoCarta database. SNORD91A and SLC40A1 were ultimately pinpointed due to their correlation with patient prognosis. Moreover, we investigated their potential roles in ESCC, including biological enrichment, immune infiltration, and genomic instability analysis. Conclusions Our study identified exosome-associated molecules, namely SNORD91A and SLC40A1, which notably impact ESCC prognosis and local M2 macrophage recruitment, thereby presenting potential therapeutic targets for ESCC

Metabolomics Reveals Process of Allergic Rhinitis Patients with Single- and Double-Species Mite Subcutaneous Immunotherapy

Allergen immunotherapy (AIT) is the only treatment that can change the course of allergic diseases. However, there has not been any research on metabolic reactions in relation to AIT with single or mixed allergens. In this study, patients with allergic rhinitis caused by Dermatophagoides pteronyssinus (Der p) and Dermatophagoides farinae (Der f) were treated with single-mite (Der p) and double-mite (Der p:Der f = 1:1) subcutaneous immunotherapy (SCIT), respectively. To compare the efficacy and the dynamic changes of inflammation-related single- and double-species mite subcutaneous immunotherapy (SM-SCIT and DM-SCIT), we performed visual analogue scale (VAS) score, rhinoconjunctivitis quality of life questionnaire (RQLQ) score and serum metabolomics in allergic rhinitis patients during SCIT. VAS and RQLQ score showed no significant difference in efficacy between the two treatments. A total of 57 metabolites were identified, among which downstream metabolites (5(S)-HETE (Hydroxyeicosatetraenoic acid), 8(S)-HETE, 11(S)-HETE, 15(S)-HETE and 11-hydro TXB2) in the ω-6-related arachidonic acid and linoleic acid pathway showed significant differences after approximately one year of treatment in SM-SCIT or DM-SCIT, and the changes of the above serum metabolic components were correlated with the magnitude of RQLQ improvement, respectively. Notably, 11(S)-HETE decreased more with SM-SCIT, and thus it could be used as a potential biomarker to distinguish the two treatment schemes. Both SM-SCIT and DM-SCIT have therapeutic effects on patients with allergic rhinitis, but there is no significant difference in efficacy between them. The reduction of inflammation-related metabolites proved the therapeutic effect, and potential biomarkers (arachidonic acid and its downstream metabolites) may distinguish the options of SCIT

Ellagic acid ameliorates cisplatin-induced acute kidney injury by regulating inflammation and SIRT6/TNF-α signaling

Despite cisplatin has been widely used in the treatment of various cancers, the noteworthy nephrotoxicity greatly constrained its clinical value. For this reason, finding novel targeted therapies to attenuate the nephrotoxicity of cisplatin should be pretty significant. Our previous study found that histone deacetylase sirtuin 6 (SIRT6) could be an ideal target for the treatment of cisplatin-induced acute kidney injury. In this study, we explored the protective effects of ellagic acid, a natural polyphenol compound that activates SIRT6, on cisplatin-induced nephrotoxicity. Pre-treatment of ellagic acid attenuated cytotoxicity of cisplatin in primary renal cells and TCMK-1 cells. Moreover, ellagic acid ameliorated renal dysfunction, apoptosis and fibrosis induced by cisplatin in mice. Furthermore, ellagic acid reduced nephrotoxicity-associated inflammatory factor interleukin (IL)-1β and IL-6 expression both in vitro and in vivo. Mechanistically, ellagic acid reversed cisplatin-reduced SIRT6 expression and diminished cisplatin-induced tumor necrosis factor (TNF)-α expression. And SIRT6 knockdown abrogated the protective effects of ellagic acid on cisplatin-induced cell apoptosis, indicating the renal-protective effects of ellagic acid are mainly dependent on ellagic acid-mediated SIRT6 activation. Our results provide preclinical rationale for using ellagic acid as a feasible and promising agent to ameliorate cisplatin-induced acute kidney injury, and support ellagic acid as a potential adjunctive therapy for future cancer treatment

Ternary Heterostructural Pt/CNx/Ni as a Supercatalyst for Oxygen Reduction

Summary: We report here a supercatalyst for oxygen reduction of Pt/CNx/Ni in a unique ternary heterostructure, in which the Pt and the underlying Ni nanoparticles are separated by two to three layers of nitrogen-doped carbon (CNx), which mediates the transfer of electrons from the inner Ni to the outer Pt and protects the Ni against corrosion at the same time. The well-engineered low-Pt catalyst shows ∼780% enhanced specific mass activity or 490% enhanced specific surface activity compared with a commercial Pt/C catalyst toward oxygen reduction. More importantly, the exceptionally strong tune on the Pt by the unique structure makes the catalyst superbly stable, and its mass activity of 0.72 A/mgPt at 0.90 V (well above the US Department of Energy's 2020 target of 0.44 A/mgPt at 0.90 V) after 50,000 cyclic voltammetry cycles under acidic conditions is still better than that of the fresh commercial catalyst. : Catalysis; Electrochemical Energy Conversion; Energy Materials Subject Areas: Catalysis, Electrochemical Energy Conversion, Energy Material

Development Status and Prospects of Artificial Intelligence in the Field of Energy Conversion Materials

With the characteristics of high-speed calculation and high-accuracy prediction, artificial intelligence (AI) which also known as machine intelligence, including deep learning, machine learning, etc., have shown great advantages in cross-field applications. In material science field, AI can be used to discover new materials and predict corresponding critical properties. At present, AI has been used in the exploitation of energy conversion materials and other energy-related materials. In this review, we summary the current achievements of AI applications in energy conversions, analyze the advantages and disadvantages of AI techniques in material researches and point out future development prospects