In recent decades, there has been an intensification of the socioeconomic and environmental drivers of pandemics, including ecosystem conversion, meat consumption, urbanization, and connectivity ...among cities and countries. This paper reviews how these four systemic drivers help explain the dynamics of the COVID-19 pandemic and other recent emerging infectious diseases, and the policies that can be adopted to mitigate their risks. Land-use change and meat consumption increase the likelihood of pathogen spillover from animals to people. The risk that such zoonotic outbreaks will then spread to become pandemics is magnified by growing urban populations and the networks of trade and travel within and among countries. Zoonotic spillover can be mitigated through habitat protection and restrictions on the wildlife trade. Containing infectious disease spread requires a high degree of coordination among institutions across geographic jurisdictions and economic sectors, all backed by international investment and cooperation.
Electrospinning is a versatile and viable technique for generating ultrathin fibers. Remarkable progress has been made with regard to the development of electrospinning methods and engineering of ...electrospun nanofibers to suit or enable various applications. We aim to provide a comprehensive overview of electrospinning, including the principle, methods, materials, and applications. We begin with a brief introduction to the early history of electrospinning, followed by discussion of its principle and typical apparatus. We then discuss its renaissance over the past two decades as a powerful technology for the production of nanofibers with diversified compositions, structures, and properties. Afterward, we discuss the applications of electrospun nanofibers, including their use as “smart” mats, filtration membranes, catalytic supports, energy harvesting/conversion/storage components, and photonic and electronic devices, as well as biomedical scaffolds. We highlight the most relevant and recent advances related to the applications of electrospun nanofibers by focusing on the most representative examples. We also offer perspectives on the challenges, opportunities, and new directions for future development. At the end, we discuss approaches to the scale-up production of electrospun nanofibers and briefly discuss various types of commercial products based on electrospun nanofibers that have found widespread use in our everyday life.
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IJS, KILJ, NUK, PNG, UL, UM
During virus infection, the adaptor proteins MAVS and STING transduce signals from the cytosolic nucleic acid sensors RIG-I and cGAS, respectively, to induce type I interferons (IFNs) and other ...antiviral molecules. Here we show that MAVS and STING harbor two conserved serine and threonine clusters that are phosphorylated by the kinases IKK and/or TBK1 in response to stimulation. Phosphorylated MAVS and STING then bind to a positively charged surface of interferon regulatory factor 3 (IRF3) and thereby recruit IRF3 for its phosphorylation and activation by TBK1. We further show that TRIF, an adaptor protein in Toll-like receptor signaling, activates IRF3 through a similar phosphorylation-dependent mechanism. These results reveal that phosphorylation of innate adaptor proteins is an essential and conserved mechanism that selectively recruits IRF3 to activate the type I IFN pathway.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Immunotherapy aiming at suppressing tumor development by relying on modifying or strengthening the immune system prevails among cancer treatments and points out a new direction for cancer therapy. B7 ...homolog 3 protein (B7-H3, also known as CD276), a newly identified immunoregulatory protein member of the B7 family, is an attractive and promising target for cancer immunotherapy because it is overexpressed in tumor tissues while showing limited expression in normal tissues and participating in tumor microenvironment (TME) shaping and development. Thus far, numerous B7-H3-based immunotherapy strategies have demonstrated potent antitumor activity and acceptable safety profiles in preclinical models. Herein, we present the expression and biological function of B7-H3 in distinct cancer and normal cells, as well as B7-H3-mediated signal pathways in cancer cells and B7-H3-based tumor immunotherapy strategies. This review provides a comprehensive overview that encompasses B7-H3's role in TME to its potential as a target in cancer immunotherapy.
Retroviruses, including HIV, can activate innate immune responses, but the host sensors for retroviruses are largely unknown. Here we show that HIV infection activates cyclic guanosine ...monophosphate—adenosine monophosphate (cGAMP) synthase (cGAS) to produce cGAMP, which binds to and activates the adaptor protein STING to induce type I interferons and other cytokines. Inhibitors of HIV reverse transcriptase, but not integrase, abrogated interferon-β induction by the virus, suggesting that the reverse-transcribed HIV DNA triggers the innate immune response. Knockout or knockdown of cGAS in mouse or human cell lines blocked cytokine induction by HIV, murine leukemia virus, and simian immunodeficiency virus. These results indicate that cGAS is an innate immune sensor of HIV and other retroviruses.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Crystal phase engineering is a powerful strategy for regulating the performance of electrocatalysts towards many electrocatalytic reactions, while its impact on the nitrogen electroreduction has been ...largely unexplored. Herein, we demonstrate that structurally ordered body‐centered cubic (BCC) PdCu nanoparticles can be adopted as active, selective, and stable electrocatalysts for ammonia synthesis. Specifically, the BCC PdCu exhibits excellent activity with a high NH3 yield of 35.7 μg h−1 mg−1cat, Faradaic efficiency of 11.5 %, and high selectivity (no N2H4 is detected) at −0.1 V versus reversible hydrogen electrode, outperforming its counterpart, face‐centered cubic (FCC) PdCu, and most reported nitrogen reduction reaction (NRR) electrocatalysts. It also exhibits durable stability for consecutive electrolysis for five cycles. Density functional theory calculation reveals that strong orbital interactions between Pd and neighboring Cu sites in BCC PdCu obtained by structure engineering induces an evident correlation effect for boosting up the Pd 4d electronic activities for efficient NRR catalysis. Our findings open up a new avenue for designing active and stable electrocatalysts towards NRR.
At face value: The body‐centered cubic (BCC) PdCu nanoparticles that were constructed and adopted as an active, selective, and stable electrocatalyst for the nitrogen reduction reaction display far better performance than the conventional face‐centered cubic (FCC) PdCu electrocatalyst.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
We propose a model‐based clustering method for high‐dimensional longitudinal data via regularization in this paper. This study was motivated by the Trial of Activity in Adolescent Girls (TAAG), which ...aimed to examine multilevel factors related to the change of physical activity by following up a cohort of 783 girls over 10 years from adolescence to early adulthood. Our goal is to identify the intrinsic grouping of subjects with similar patterns of physical activity trajectories and the most relevant predictors within each group. The previous analyses conducted clustering and variable selection in two steps, while our new method can perform the tasks simultaneously. Within each cluster, a linear mixed‐effects model (LMM) is fitted with a doubly penalized likelihood to induce sparsity for parameter estimation and effect selection. The large‐sample joint properties are established, allowing the dimensions of both fixed and random effects to increase at an exponential rate of the sample size, with a general class of penalty functions. Assuming subjects are drawn from a Gaussian mixture distribution, model effects and cluster labels are estimated via a coordinate descent algorithm nested inside the Expectation‐Maximization (EM) algorithm. Bayesian Information Criterion (BIC) is used to determine the optimal number of clusters and the values of tuning parameters. Our numerical studies show that the new method has satisfactory performance and is able to accommodate complex data with multilevel and/or longitudinal effects.
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BFBNIB, DOBA, FSPLJ, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
Abstract
Understanding changes in oral flora during pregnancy, its association to maternal health, and its implications to birth outcomes is essential. We searched PubMed, Embase, Web of Science, and ...Cochrane Library in May 2020 (updated search in April and June 2021), and conducted a systematic review and meta-analyses to assess the followings: (1) oral microflora changes throughout pregnancy, (2) association between oral microorganisms during pregnancy and maternal oral/systemic conditions, and (3) implications of oral microorganisms during pregnancy on birth outcomes. From 3983 records, 78 studies were included for qualitative assessment, and 13 studies were included in meta-analysis. The oral microflora remains relatively stable during pregnancy; however, pregnancy was associated with distinct composition/abundance of oral microorganisms when compared to postpartum/non-pregnant status. Oral microflora during pregnancy appears to be influenced by oral and systemic conditions (e.g. gestational diabetes mellitus, pre-eclampsia, etc.). Prenatal dental care reduced the carriage of oral pathogens (e.g.
Streptococcus mutans
). The
Porphyromonas gingivalis
in subgingival plaque was more abundant in women with preterm birth. Given the results from meta-analyses were inconclusive since limited studies reported outcomes on the same measuring scale, more future studies are needed to elucidate the association between pregnancy oral microbiota and maternal oral/systemic health and birth outcomes.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Imposition of a lasso penalty shrinks parameter estimates toward zero and performs continuous model selection. Lasso penalized regression is capable of handling linear regression problems where the ...number of predictors far exceeds the number of cases. This paper tests two exceptionally fast algorithms for estimating regression coefficients with a lasso penalty. The previously known ℓ₂ algorithm is based on cyclic coordinate descent. Our new ℓ₁ algorithm is based on greedy coordinate descent and Edgeworth's algorithm for ordinary ℓ₁ regression. Each algorithm relies on a tuning constant that can be chosen by cross-validation. In some regression problems it is natural to group parameters and penalize parameters group by group rather than separately. If the group penalty is proportional to the Euclidean norm of the parameters of the group, then it is possible to majorize the norm and reduce parameter estimation to ℓ₂ regression with a lasso penalty. Thus, the existing algorithm can be extended to novel settings. Each of the algorithms discussed is tested via either simulated or real data or both. The Appendix proves that a greedy form of the ℓ₂ algorithm converges to the minimum value of the objective function.
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BFBNIB, INZLJ, NMLJ, NUK, PNG, UL, UM, UPUK, ZRSKP
This paper underscores the significance of earth deformation observation in analyzing earth tide curves and predicting earthquakes, positioning it as a cornerstone of Earth observation technology. We ...delve into the critical task of detecting and diagnosing anomalies in geodetic data. Utilizing Python for data preprocessing, our approach identifies missing values, categorizes them by their spatial occurrence, and employs spline interpolation and autoregressive prediction methods for data imputation. This process ensures the integrity of the dataset for subsequent analysis and modeling, reinforcing the precision and reliability of geodetic data analysis in Earth science research.
To expand the data set, we propose three models.
Model I: Adding gaussian noise to the data.
Model II: Resample the data.
Model III: Using machine learning methods to learn the internal laws of the data and predict itself to generate new data. For each model, we discuss its advantages and disadvantages. Finally, we structurally fuse the three models to complete data enhancement.
To extract the noise, we use DB4 wavelet transform to denoise the data set and extract the noise. Then we make descriptive statistics on the noise distribution, and use Laplace distribution to fit the probability distribution of noise, and finally get the accurate noise distribution.
We start from the time domain and frequency domain to extract the features of the data. First, 17 features are extracted in the time domain, then the discrete fourier transform algorithm is used to transform the data into frequency domain data, and 13 are extracted. Therefore, we encode each data as a feature vector with a length of 30. We first use the decision tree as the baseline model to establish the recognition model to select the features. Logistic Regression, KNN, Naive Bayes and SVM are used to establish the recognition model. Finally, we use the Voting ensemble learning method to fuse the model, achieving an accuracy of 86% on the test set.
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