117 research outputs found
A Review of Forest Resources and Forest Biodiversity Evaluation System in China
China is a country rich in diverse forest ecosystems due to the large span of the country, complex topography, and multiple climate regimes. In this paper, the basic information of forest resources in China was briefly introduced and the current state in the measurements of forest biodiversity and the establishment of forest biodiversity index systems in related studies were reviewed. The results showed that a lot of studies on forest biodiversity have been conducted mostly at landscape or stand level in China and the commonly used biodiversity indicators were identified and compared. Several comprehensive forest biodiversity index systems were proposed. However, there are still some problems during the construction of forest biodiversity assessment system. Due to the late establishment of biodiversity monitoring system in China, the availability of data that could be included in a forest biodiversity index system is limited, which hurdles the precise assessment of forest biodiversity. It is suggested to develop long-term monitoring stations and keep data recording consistently. Concerns should also be given to the construction of the framework of the forest biodiversity index system and the determination of the indicators’ weight. The results will provide reference for the establishment of national or regional forest biodiversity evaluation indicator systems in China
Effects of dietary oxidized fish oil on the growth performance, intestinal health, and antioxidant capacity of zebrafish
This study aimed to investigate the effects of oxidized fish oil (OFO) on growth performance, intestinal health, and antioxidant function and to determine the minimum concentration of oxidized fish oil to cause irreversible damage to the intestinal tissue structure of zebrafish. A 30-day feeding trial on zebrafish (average weight 0.054 g) was conducted in triplicate groups of fish fed four test diets containing different concentrations of OFO: 0% OFO (OFF, blank control), 2% OFO (OF1), 4% OFO (OF2), and 6% OFO (OF3). The body weight gain (WG), specific growth rates (SGR), feed conversion ratio (FCR), survival rate (SR), and antioxidant function {glutathione peroxidase (GSH-PX), total superoxide dismutase (T-SOD), catalase (CAT), and malondialdehyde (MDA)} were recorded. The intestinal structure was observed at the end of the trial. After the 14-day experimental period, Final body weight (FBW), WG, and SGR decreased significantly with the increase in the concentration of feed OFO (P < 0.05), while FCR showed a downward trend. The activity of T-SOD decreased significantly, the activities of GSH-PX and CAT, and the MDA content increased significantly with the increase in the concentration of feed OFO (P < 0.05). The intestinal morphological damage score showed an upward trend with the increase in the concentration of OFO, and it was significantly higher in group OF2 and OF3 than in group OF1 (P < 0.05). After the 28-day test period, the experimental indexes and intestinal antioxidant function trends were the same as those on 14 days. The increased OFO concentration significantly increased the intestinal morphological injury score (P < 0.05). These results demonstrated that adding 4% OFO to the feed for 14 days could induce irreversible damage to the intestinal tissue structure, weaken the antioxidant function, and decrease the growth performance of zebrafish
Small RNA zippers lock miRNA molecules and block miRNA function in mammalian cells.
MicroRNAs (miRNAs) loss-of-function phenotypes are mainly induced by chemically modified antisense oligonucleotides. Here we develop an alternative inhibitor for miRNAs, termed \u27small RNA zipper\u27. It is designed to connect miRNA molecules end to end, forming a DNA-RNA duplex through a complementary interaction with high affinity, high specificity and high stability. Two miRNAs, miR-221 and miR-17, are tested in human breast cancer cell lines, demonstrating the 70∼90% knockdown of miRNA levels by 30-50 nM small RNA zippers. The miR-221 zipper shows capability in rescuing the expression of target genes of miR-221 and reversing the oncogenic function of miR-221 in breast cancer cells. In addition, we demonstrate that the miR-221 zipper attenuates doxorubicin resistance with higher efficiency than anti-miR-221 in human breast cancer cells. Taken together, small RNA zippers are a miRNA inhibitor, which can be used to induce miRNA loss-of-function phenotypes and validate miRNA target genes
Unsupervised Segmentation Method for Diseases of Soybean Color Image Based on Fuzzy Clustering
The method of color image segmentation based on Fuzzy C-Means (FCM) clustering is simple, intuitive and is to be implemented. However, the clustering performance is affected by the center point of initialization and high computation and other issues. In this research, we propose a new color image unsupervised segmentation method based on fuzzy clustering. This method combines advantages of the fuzzy C-means algorithm and unsupervised clustering algorithm. Firstly, by gradually changing clusters c, and according to validity measurement, it can unsupervised search for optimal clusters c; then in order to achieve higher accuracy of clustering effect, the distance measurement scale was improved. In our experiments, this method was applied to color image segmentation for three kinds of soybean diseases. The results show that this method can more accurately segment the lesion area from the color image, and the segmentation processing of soybean disease is ideal, robustness, and have a high accuracy
MUC1 Contributes to BPDE-Induced Human Bronchial Epithelial Cell Transformation through Facilitating EGFR Activation
Although it is well known that epidermal growth factor receptor (EGFR) is involved in lung cancer progression, whether EGFR contributes to lung epithelial cell transformation is less clear. Mucin 1 (MUC1 in human and Muc1 in animals), a glycoprotein component of airway mucus, is overexpressed in lung tumors; however, its role and underlying mechanisms in early stage lung carcinogenesis is still elusive. This study provides strong evidence demonstrating that EGFR and MUC1 are involved in bronchial epithelial cell transformation. Knockdown of MUC1 expression significantly reduced transformation of immortalized human bronchial epithelial cells induced by benzo[a]pyrene diol epoxide (BPDE), the active form of the cigarette smoke (CS) carcinogen benzo(a)pyrene (BaP)s. BPDE exposure robustly activated a pathway consisting of EGFR, Akt and ERK, and blocking this pathway significantly increased BPDE-induced cell death and inhibited cell transformation. Suppression of MUC1 expression resulted in EGFR destabilization and inhibition of the BPDE-induced activation of Akt and ERK and increase of cytotoxicity. These results strongly suggest an important role for EGFR in BPDE-induced transformation, and substantiate that MUC1 is involved in lung cancer development, at least partly through mediating carcinogen-induced activation of the EGFR-mediated cell survival pathway that facilitates cell transformation
THE CORRELATION BETWEEN PERIPARTUM CARDIOMYOPATHY AND AUTOANTIBODIES AGAINST CARDIOVASCULAR RECEPTORS
Analysis of Polyphenol Profiles in Fractional Extracts of Passion Fruit Peels and Screening of Their Antioxidant Active Substances
To investigate the extraction efficiency of polyphenols from passion fruit (Passiflora edulis Sims) peel by using different solvents and to identify the characteristic polyphenols with high contribution to antioxidant activity, ethanolic crude extract (CE) was sequentially extracted with petroleum ether (PE), ethyl acetate (EE), n-butanol (BE) and water (WE). The total content of phenolics and flavonoids was then measured by using spectrophotometry. Phenolic compounds in the extracts were profiled by using ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS), and differential metabolites were screened and quantified by using an untargeted metabolomics approach. Antioxidant activities in vitro were assessed through DPPH free radical scavenging, ABTS+ free radical scavenging and FRAP methods. Furthermore, the phenolic markers of antioxidant activity in the peels were explored through pearson correlation analysis. The results indicated obvious variations in the total phenolic and flavonoid content among the extracts (PBE>CE>WE>PE. A total of 33 phenolic compounds were identified from both CE and each fractional extract, while CE, EE, and BE exhibited a greater variety and quantity of phenolic compounds. Principal component analysis (PCA) distinguished the metabolites of extracts, while orthogonal partial least squares discriminant analysis (OPLS-DA) identified p-hydroxybenzoic acid, protocatechuic acid, isoquercitrin and iso-orientin as differential compounds. EE exhibited the highest levels of p-hydroxybenzoic acid (653.44 μg/g), isoquercitrin (2420.64 μg/g) and iso-orientin (113.23 μg/g), while CE showed the highest content of proto-catechuic acid (152.40 μg/g). The in vitro antioxidant activity of passion fruit peel varied significantly among extracts, with EE showing the strongest capacity, suggesting its potential as a premium antioxidant agent. The correlation analysis revealed that the total phenols and flavonoids formed the basis of antioxidant active substances, while iso-orientin and isoquercitrin were the antioxidant active phenolic markers. The finding provided fundamental data for establishing quality control standards of antioxidant products derived from passion fruit peels, as well as for the precise development and utilization of phenolic compounds in the peels
Sensor Fault Tolerant Control of a Fast Steering Mirror System Using Adaptive PI-Based Sliding Mode Observer and Hardware Redundancy
The aim of this paper is to present a sensor fault-tolerant control (FTC) scheme for a two-axis fast steering mirror (FSM) system with minimum power consumption and without changing the controller structure. In this paper, an adaptive PI-based sliding mode observer (APISMO) is adopted firstly to estimate the fault signal, which does not require any prior knowledge of the fault. The estimation is then used by the fault isolation logic to identify the fault. The redundant sensor would be powered up to replace the faulty one when faults occur. During the backup sensor booting up, for maintaining the normal performance of the closed-loop system approximately, a fault-free estimation of the position provided by the APISMO is used as feedback signal. Experimental studies on a prototype system show that the proposed APISMO can effectively reconstruct the fault signals even when the two primary position sensors are faulty simultaneously. Meanwhile, the effectiveness and performance of the proposed scheme have been verified
Classification of Handheld Laser Scanning Tree Point Cloud Based on Different KNN Algorithms and Random Forest Algorithm
Handheld mobile laser scanning (HMLS) can quickly acquire point cloud data, and has the potential to conduct forest inventory at the plot scale. Considering the problems associated with HMLS data such as large discreteness and difficulty in classification, different classification models were compared in order to realize efficient separation of stem, branch and leaf points from HMLS data. First, the HMLS point cloud was normalized and ground points were removed, then the neighboring points were identified according to three KNN algorithms and eight geometric features were constructed. On this basis, the random forest classifier was used to calculate feature importance and perform dataset training. Finally, the classification accuracy of different KNN algorithms-based models was evaluated. Results showed that the training sample classification accuracy based on the adaptive radius KNN algorithm was the highest (0.9659) among the three KNN algorithms, but its feature calculation time was also longer; The validation accuracy of two test sets was 0.9596 and 0.9201, respectively, which is acceptable, and the misclassification mainly occurred in the branch junction of the canopy. Therefore, the optimal classification model can effectively achieve the classification of stem, branch and leaf points from HMLS point cloud under the premise of comprehensive training
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