Optimization of terrestrial ecosystem model parameters using atmospheric CO2 concentration data with the Global Carbon Assimilation System (GCAS)

Author Posting. © American Geophysical Union, 2017. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Biogeosciences 122 (2017): 3218–3237, doi:10.1002/2016JG003716.The Global Carbon Assimilation System that assimilates ground-based atmospheric CO2 data is used to estimate several key parameters in a terrestrial ecosystem model for the purpose of improving carbon cycle simulation. The optimized parameters are the leaf maximum carboxylation rate at 25°C (V25 max), the temperature sensitivity of ecosystem respiration (Q10), and the soil carbon pool size. The optimization is performed at the global scale at 1° resolution for the period from 2002 to 2008. The results indicate that vegetation from tropical zones has lower V25 max values than vegetation in temperate regions. Relatively high values of Q10 are derived over high/midlatitude regions. Both V25 max and Q10 exhibit pronounced seasonal variations at middle-high latitudes. The maxima in V25 max occur during growing seasons, while the minima appear during nongrowing seasons. Q10 values decrease with increasing temperature. The seasonal variabilities of V25 max and Q10 are larger at higher latitudes. Optimized V25 max and Q10 show little seasonal variabilities at tropical regions. The seasonal variabilities of V25 max are consistent with the variabilities of LAI for evergreen conifers and broadleaf evergreen forests. Variations in leaf nitrogen and leaf chlorophyll contents may partly explain the variations in V25 max. The spatial distribution of the total soil carbon pool size after optimization is compared favorably with the gridded Global Soil Data Set for Earth System. The results also suggest that atmospheric CO2 data are a source of information that can be tapped to gain spatially and temporally meaningful information for key ecosystem parameters that are representative at the regional and global scales.National Key R&D Program of China Grant Number: 2016YFA0600204; National Natural Science Foundation of China Grant Number: 415713382018-06-2

Polydopamine-Coated Manganese Carbonate Nanoparticles for Amplified Magnetic Resonance Imaging-Guided Photothermal Therapy

【Abstract】This study reports a multifunctional nanoparticle (NP) with function of amplified magnetic resonance image (MRI)-guided photothermal therapy (PTT) by the surface coating of polydopamine (PDA) shell. Importantly, by means of introducing the surface coating of PDA，it helps entrap large quantities of water around NPs and allow more efficient water exchange， leading to greatly improved MR contrast signals compared with the one without PDA coating. Besides，the distinct photothermal effect can be obtained arising from the strong absorption of PDA in the near-infrared (NIR) region. By synthesizing the multifunctional MnCOs@PDA NPs as example，we found that the longitudinal relaxivity (ri) of MnCOs NPs might improve from 5.7 to 8.3 mM-is-i. Subsequently，In vitro MRI and PTT results verified that MnCOs@PDA could serve well as an excellent MRI/PTT theranostic agent. Furthermore， the MnCO3@PDA nanoparticles were applied as MRI/PTT theranostic agent for in vivo MRI-guided photothermal ablation of tumors by intratumorally injection in 4T1 tumor bearing-mice. The MR imaging result shows a significantly bright MR image in the tumor site. The MnCO3@PDA-mediated PTT result shows high therapy efficiency as a result of their high photothermal conversion efficiency. The present strategy of amplified MRI-guided PTT based on PDA coating on NPs will be widely applicable to other multifunctional nanoparticles.This study was financially supported by the National Natural Science Foundation of China (31271071，31371012，andU1505228)

A scalable algorithm for structure identification of complex gene regulatory network from temporal expression data.

BACKGROUND: Gene regulatory interactions are of fundamental importance to various biological functions and processes. However, only a few previous computational studies have claimed success in revealing genome-wide regulatory landscapes from temporal gene expression data, especially for complex eukaryotes like human. Moreover, recent work suggests that these methods still suffer from the curse of dimensionality if a network size increases to 100 or higher. RESULTS: Here we present a novel scalable algorithm for identifying genome-wide gene regulatory network (GRN) structures, and we have verified the algorithm performances by extensive simulation studies based on the DREAM challenge benchmark data. The highlight of our method is that its superior performance does not degenerate even for a network size on the order of 104, and is thus readily applicable to large-scale complex networks. Such a breakthrough is achieved by considering both prior biological knowledge and multiple topological properties (i.e., sparsity and hub gene structure) of complex networks in the regularized formulation. We also validate and illustrate the application of our algorithm in practice using the time-course gene expression data from a study on human respiratory epithelial cells in response to influenza A virus (IAV) infection, as well as the CHIP-seq data from ENCODE on transcription factor (TF) and target gene interactions. An interesting finding, owing to the proposed algorithm, is that the biggest hub structures (e.g., top ten) in the GRN all center at some transcription factors in the context of epithelial cell infection by IAV. CONCLUSIONS: The proposed algorithm is the first scalable method for large complex network structure identification. The GRN structure identified by our algorithm could reveal possible biological links and help researchers to choose which gene functions to investigate in a biological event. The algorithm described in this article is implemented in MATLAB Ⓡ , and the source code is freely available from https://github.com/Hongyu-Miao/DMI.git

Origins and stepwise expansion of R2R3-MYB transcription factors for the terrestrial adaptation of plants

The R2R3-MYB transcription factors play critical roles in various processes in embryophytes (land plants). Here, we identified genes encoding R2R3-MYB proteins from rhodophytes, glaucophytes, Chromista, chlorophytes, charophytes, and embryophytes. We classified the R2R3-MYB genes into three subgroups (I, II, and III) based on their evolutionary history and gene structure. The subgroup I is the most ancient group that includes members from all plant lineages. The subgroup II was formed before the divergence of charophytes and embryophytes. The subgroup III genes form a monophyletic group and only comprise members from land plants with conserved exon–intron structure. Each subgroup was further divided into multiple clades. The subgroup I can be divided into I-A, I-B, I-C, and I-D. The I-A, I-B, and I-C are the most basal clades that have originated before the divergence of Archaeplastida. The I-D with the II and III subgroups form a monophyletic group, containing only green plants. The II and III subgroups form another monophyletic group with Streptophyta only. Once on land, the subgroup III genes have experienced two rounds of major expansions. The first round occurred before the origin of land plants, and the second round occurred after the divergence of land plants. Due to significant gene expansion, the subgroup III genes have become the predominant group of R2R3-MYBs in land plants. The highly unbalanced pattern of birth and death evolution of R2R3-MYB genes indicates their important roles in the successful adaptation and massive radiation of land plants to occupy a multitude of terrestrial environments

Carbon-coated magnetic particles increase tissue temperatures after laser irradiation

Purpose: This work focused on the investigation the hyperthermia performance of the carbon-coated magnetic particles (CCMPs) in laser-induced hyperthermia. Materials and methods: We prepared CCMPs using the organic carbonization method, and then characterized them with transmission electron microscopy (TEM), ultraviolet-visible (UV-Vis) spectrophotometry, vibrating sample magnetometer (VSM) and X-ray diffraction (XRD). In order to evaluate their performance in hyperthermia, the CCMPs were tested in laser-induced thermal therapy (LITT) experiments, in which we employed a fully distributed fiber Bragg grating (FBG) sensor to profile the tissue's dynamic temperature change under laser irradiation in real time. Results: The sizes of prepared CCMPs were about several micrometers, and the LITT results show that the tissue injected with the CCMPs absorbed more laser energy, and its temperature increased faster than the contrast tissue without CCMPs. Conclusions: The CCMPs may be of great help in hyperthermia applications

Incubating Advances in Integrated Photonics with Emerging Sensing and Computational Capabilities

As photonic technologies continue to grow in multidimensional aspects, integrated photonics holds a unique position and continuously presents enormous possibilities to research communities. Applications span across data centers, environmental monitoring, medical diagnosis, and highly compact communication components, with further possibilities growing endlessly. Here, we provide a review of state of the art integrated photonic sensors operating in near and mid infrared wavelength regions on various material platforms. Among different materials, architectures, and technologies leading the way for on chip sensors, we discuss optical sensing principles commonly applied to biochemical and gas sensing. Our focus is particularly on passive and active optical waveguides, including dispersion engineered metamaterial based structures an essential approach for enhancing the interaction between light and analytes in chip scale sensors. We harness a diverse array of cutting edge sensing technologies, heralding a revolutionary on chip sensing paradigm. Our arsenal includes refractive index based sensing, plasmonic, and spectroscopy, forging an unparalleled foundation for innovation and precision. Furthermore, we include a brief discussion of recent trends and computational concepts incorporating Artificial Intelligence & Machine Learning (AI/ML) and deep learning approaches over the past few years to improve the qualitative and quantitative analysis of sensor measurements

Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey

Transformer-based Large Language Models (LLMs) have been applied in diverse areas such as knowledge bases, human interfaces, and dynamic agents, and marking a stride towards achieving Artificial General Intelligence (AGI). However, current LLMs are predominantly pretrained on short text snippets, which compromises their effectiveness in processing the long-context prompts that are frequently encountered in practical scenarios. This article offers a comprehensive survey of the recent advancement in Transformer-based LLM architectures aimed at enhancing the long-context capabilities of LLMs throughout the entire model lifecycle, from pre-training through to inference. We first delineate and analyze the problems of handling long-context input and output with the current Transformer-based models. We then provide a taxonomy and the landscape of upgrades on Transformer architecture to solve these problems. Afterwards, we provide an investigation on wildly used evaluation necessities tailored for long-context LLMs, including datasets, metrics, and baseline models, as well as optimization toolkits such as libraries, frameworks, and compilers to boost the efficacy of LLMs across different stages in runtime. Finally, we discuss the challenges and potential avenues for future research. A curated repository of relevant literature, continuously updated, is available at https://github.com/Strivin0311/long-llms-learning.Comment: 40 pages, 3 figures, 4 table

Driving innovations in cancer research through spatial metabolomics: a bibliometric review of trends and hotspot

BackgroundSpatial metabolomics has revolutionized cancer research by offering unprecedented insights into the metabolic heterogeneity of the tumor microenvironment (TME). Unlike conventional metabolomics, which lacks spatial resolution, spatial metabolomics enables the visualization of metabolic interactions among cancer cells, stromal components, and immune cells within their native tissue context. Despite its growing significance, a systematic and visualized analysis of spatial metabolomics in cancer research remains lacking, particularly in the integration of multi-omics data and the standardization of methodologies for comprehensive tumor metabolic mapping.ObjectivesThis study aims to conduct a bibliometric analysis to systematically evaluate the development trends, key contributors, research hotspots, and future directions of spatial metabolomics in cancer research.MethodsA bibliometric approach was employed using data retrieved from the Web of Science Core Collection. Analytical tools such as VOSviewer and CiteSpace were utilized to visualize and assess co-citation networks, keyword co-occurrence, and institutional collaborations. Key metrics, including publication trends, authorship influence, country contributions, and journal impact, were analyzed to map the research landscape in this domain.ResultsA total of 182 publications on spatial metabolomics in cancer research were identified over the past two decades, with a notable surge in research output beginning in 2018. The field has experienced accelerated growth, with an annual average of 40 publications since 2021, reflecting its increasing relevance in cancer studies. Among 28 contributing countries, China (n=53), the United States (n=35), Germany (n=18), and the United Kingdom (n=13) have been the most active contributors. China leads in publication volume, while the United States exhibits the highest citation impact, indicating significant research influence. International collaboration networks are particularly strong among the United States, Germany, and China, underscoring the global interest in this emerging field. Analysis of key authors and institutions identifies He Jiuming as the most prolific author and Song Xiaowei as the researcher with the highest average citations. Other influential authors include Abliz Zeper and Sun Chenglong. Leading research institutions driving advancements in this field include the Chinese Academy of Medical Sciences, Peking Union Medical College, Harvard Medical School, and Stanford University. Regarding journal impact, Nature Communications (n=11), Journal of Pharmaceutical Analysis (n=9), and Nature Methods (n=8) are the most active publishing platforms in this domain. Citation analysis reveals that Cell, BioEssays, and Genome Medicine are among the most highly cited journals, reflecting the interdisciplinary nature of spatial metabolomics research