Relational Research between China’s Marine S&T and Economy Based on RPGRA Model

To make up the defect of the existing model, an improved grey relational model based on radian perspective (RPGRA) is put forward. According to the similarity of the relative change trend of time series translating traditional grey relational degree into radian algorithm within different piecewise functions, it greatly improves the accuracy and validity of the research results by making full use of the poor information in time series. Meanwhile, the properties of the RPGRA were discussed. The relationship between China’s marine S&T and marine economy is researched using the new model, so the validity and creditability of RPGRA are illustrated. The empirical results show that marine scientific and technological research projects, marine scientific and technological patents granted, and research funds receipts of the marine scientific research institutions have greater relationship with GOP, which indicates that they have more impact on China’s marine economy

Synthesis of 4-thio-5-(2′′-thienyl)uridine and cytotoxicity activity against colon cancer cells <i>in vitro</i>

A novel anti-tumor agent 4-thio-5-(2′′-thienyl)uridine (6) was synthesized and the in vitro cytotoxicity activity against mice colon cancer cells (MC-38) and human colon cancer cells (HT-29) was evaluated by MTT assay. The results showed that the novel compound had antiproliferative activity toward MC-38 and HT-29 cells in a dose-dependent manner. The cell cycle analysis by flow cytometry indicated that compound 6 exerted in tumor cell proliferation inhibition by arresting HT-29 cells in the G2/M phase. In addition, cell death detected by propidium iodide staining showed that compound 6 efficiently induced cell apoptosis in a concentration-dependent manner. Moreover, the sensitivity of human fibroblast cells to compound 6 was far lower than that of tumor cells, suggesting the specific anti-tumor effect of 4-thio-5-(2′′-thienyl)uridine. Taken together, novel compound 6 effectively inhibits colon cancer cell proliferation, and hence would have potential value in clinical application as an antitumor agent

What Makes a Helpful Online Review When Information Overload Exists?

With the increasing of online reviews, information overload has become a major problem in online community. What makes a helpful online review when information overload exists? In this study, the research model is developed to examine the helpfulness of online consumer reviews when information overload exists. Information quality is measured by review length and pictures in the model. The result is showed the relationship between review length and review helpfulness is usually described as an inverted U curve. The impact of review length and picture review on helpfulness is stronger when information overload exists. The impact of is also stronger with negative reviews than without negative reviews. As a result, our findings help extend the literature on information diagnosticity within the context of information overload

Molecular characterization and expression of DgZFP1, a gene encoding a single zinc finger protein in chrysanthemum

A single zinc finger protein gene was isolated from chrysanthemum by rapid amplification of cDNA ends (RACE) approach and was designated as DgZFP1. The DgZFP1 encodes a protein of 168 amino acids residues with a calculated molecular mass of 18.1 kDa and theoretical isoelectric point is 4.71. DgZFP1 contains one single zinc finger motif and one ethylene-responsive element-binding factor (ERF)-associated amphiphilic repression (EAR) domain. The transcripts of DgZFP1 was enriched in nodes and ray petal than in disc petal, disc stamen, disc pistil and ray pistil, but not detected in other tissues. Subcellular localization revealed that DgZFP1 was preferentially distributed to nucleus. We argued that DgZFP1 is a new member of the single zinc finger protein genes and it may be the ortholog of LIF

The Synthesis of (E)-4-Thio-5-(2-Bromovinyl)Uridine/Deoxyuridine and Its Characterization and Cytotoxicity

(E)-4-Thio-5-(2-brominevinyl)uridine/2'-deoxyuridine(8a/8b) were efficiently and in an environmental friendly way synthesized from uridine/2'-deoxyuridine (1a/1b) that were first transformed to (E)-(2-brominevinyl) uridine / 2'-deoxyuridine(5a/5b) via iodination, selective oxidation, Heck reaction steps. The resulting products (5a/5b) were then converted to the targets (8a/8b) through esterification, thio-reaction of carbonyl, hydrolysis steps. Two new compounds (8a/8b) and three new intermediates (7a 7b 10) were obtained, and their structures have been fully characterized by 1H NMR, 13C NMR, IR, UV, HR-MS, X-Ray. The study of 8a and their derivatives regarding cytotoxicity was carried out by using MTT experiment method, and the initial findings suggest (E)-4-Thio-5-(2-brominevinyl) uridine/ 2'-deoxyuridine (8a / 8b) would be potential antitumor drugs

Isolation and molecular characterization of RcSERK1: A Rosa canina gene transcriptionally induced during initiation of protocorm-like bodies

A somatic embryogensis receptor-like kinase (SERK) gene was isolated from protocorm-like bodies (PLBs) of Rosa canina by a rapid amplification of cDNA ends (RACE) approach and was designated as RcSERK1. The RcSERK1 encodes a protein of 626 amino acid residues with a calculated molecular mass of 68.79 kDa and theoretical isoelectric point of 5.65. The amino acid sequence of RcSERK1 shares all the characteristic features of a SERK protein, including the signal peptide (SP), the leucine zipper (LZ), the five leucine-rich repeats (LRRs), the pro-rich domain containing the so-called Ser-Pro- Pro (SPP) motif, the transmembrane domain (TM), the kinase domain and the C-terminal domain. The transcripts of RcSERK1 were more enriched in PLBs than in rhizoids and callus, but not detected in leaflets (incubated under dark and before producing callus) and the regenerated shoots. Subcellular localization indicated that the fluorescence of RcSERK1-GFP was recorded in the plasma membrane. We argue that RcSERK1 is a Leu-rich repeat receptor-like kinase (LRR-RLK) and plasma  membrane localization protein.Keywords: somatic embryogensis receptor-like kinase (SERK)1, protocorm-like bodies (PLBs), Rosa canina, RACE, RcSERK1

Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning

The increasing prevalence of nanoplastics in the environment underscores the need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification of nanoplastics is challenging due to their small size and complex composition. In this work, we combined highly reflective substrates and machine learning to accurately identify nanoplastics using Raman spectroscopy. Our approach established Raman spectroscopy data sets of nanoplastics, incorporated peak extraction and retention data processing, and constructed a random forest model that achieved an average accuracy of 98.8% in identifying nanoplastics. We validated our method with tap water spiked samples, achieving over 97% identification accuracy, and demonstrated the applicability of our algorithm to real-world environmental samples through experiments on rainwater, detecting nanoscale polystyrene (PS) and polyvinyl chloride (PVC). Despite the challenges of processing low-quality nanoplastic Raman spectra and complex environmental samples, our study demonstrated the potential of using random forests to identify and distinguish nanoplastics from other environmental particles. Our results suggest that the combination of Raman spectroscopy and machine learning holds promise for developing effective nanoplastic particle detection and monitoring strategies.</p

Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning

The increasing prevalence of nanoplastics in the environment underscores the need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification of nanoplastics is challenging due to their small size and complex composition. In this work, we combined highly reflective substrates and machine learning to accurately identify nanoplastics using Raman spectroscopy. Our approach established Raman spectroscopy data sets of nanoplastics, incorporated peak extraction and retention data processing, and constructed a random forest model that achieved an average accuracy of 98.8% in identifying nanoplastics. We validated our method with tap water spiked samples, achieving over 97% identification accuracy, and demonstrated the applicability of our algorithm to real-world environmental samples through experiments on rainwater, detecting nanoscale polystyrene (PS) and polyvinyl chloride (PVC). Despite the challenges of processing low-quality nanoplastic Raman spectra and complex environmental samples, our study demonstrated the potential of using random forests to identify and distinguish nanoplastics from other environmental particles. Our results suggest that the combination of Raman spectroscopy and machine learning holds promise for developing effective nanoplastic particle detection and monitoring strategies.</p