Composition analysis and antioxidant activity evaluation of a high purity oligomeric procyanidin prepared from sea buckthorn by a green method

Procyanidin is an important polyphenol for its health-promoting properties, however, the study of procyanidin in sea buckthorn was limited. In this paper, sea buckthorn procyanidin (SBP) was obtained through a green isolation and enrichment technique with an extraction rate and purity of 9.1% and 91.5%. The structure of SBP was analyzed using Ultraviolet–visible spectroscopy (UV–vis), Fourier-transform infrared spectroscopy (FT-IR), and liquid chromatography-mass spectrometry (LC-MS/MS). The results show that SBP is an oligomeric procyanidin, mainly composed of ( )-epicatechin gallate, procyanidin B, (+)-gallocatechin-(+)-catechin, and (+)-gallocatechin dimer. SBP showed superior scavenging capacity on free radicals. Furthermore, the cleaning rate of the ABTS radical was 4.8 times higher than vitamin C at the same concentration. Moreover, SBP combined with vitamin C presented potent synergistic antioxidants with combined index values below 0.3 with concentration rates from 5:5 to 2:8. SBP also provided significant protection against oxidative stress caused by hydrogen peroxide (H2O2) on RAW264.7 cells. These findings prove the potential of SBP as a natural antioxidant in food additives and support the in-depth development of sea buckthorn resources

The mob as tumor suppressor (mats1) gene is required for growth control in developing zebrafish embryos

The mob as tumor suppressor (mats) family genes are highly conserved in evolution. The Drosophila mats gene functions in the Hippo signaling pathway to control tissue growth by regulating cell proliferation and apoptosis. However, nothing is known about whether matsfamily genes are required for the normal development of vertebrates. Here we report that zebrafish has three mats family genes. Expression of mats1 is maternally activated and continues during embryogenesis. Through a morpholino-based knockdown approach, we found that mats1 is required for normal embryonic development. Reduction of mats 1 function caused developmental delay, a phenotype similar to that of Drosophila mats homozygous mutants. Both cell proliferation and apoptosis were defective in mats1 morphant embryos. Moreover, mats1 morphant cells exhibited a growth advantage in chimeric embryos, similar to mats mutant cells in mosaic tissues in Drosophila. Therefore mats1 plays a critical role in regulating cell proliferation and apoptosis during early development in zebrafish, and the role of matsfamily genes in growth regulation is conserved in both invertebrates and vertebrates. This work shows that zebrafish can be a good model organism for further analysis of Hippo signaling pathway.Developmental BiologySCI(E)PubMed2ARTICLE4525-5335

Anthropogenic influence on 2018 summer persistent heavy and daily extreme rainfall in central western China

Anthropogenic forcing has reduced the probability of summer persistent heavy rainfall in central western China similar to 2018 by ~47%, but increased that of daily extremes by ~1.5 times, based on HadGEM3-GA6 ensembles

Detection of intergenic non-coding RNAs expressed in the main developmental stages in Drosophila melanogaster

How many intergenically encoded non-coding RNAs (ncRNAs) are expressed during various developmental stages in Drosophila? Previous analyses in one or a few developmental stages indicated abundant expression of intergenic ncRNAs. However, some reported that ncRNAs have been recently falsified, and, in general, the false positive rate for ncRNA detection is unknown. In this report, we used reverse transcription-PCR (RT-PCR), a more robust method, to detect ncRNAs from the intergenic regions that are expressed in four major developmental stages (6–8 h embryo, 20–22 h embryo, larvae and adult). We tested 1027 regions, ∼10% of all intergenic regions, and detected transcription by RT–PCR. The results from 18 342 RT–PCR experiments revealed evidence for transcription in 72.7% of intergenic regions in the developmental process. The early developmental stage appears to be associated with more abundant ncRNAs than later developmental stages. In the early stage, we detected 43.6% of intergenic regions that encode transcripts in the triplicate RT–PCR experiments, yielding an estimate of 5006 intergenic regions in the entire genome likely encoding ncRNAs. We compared the RT–PCR-related approach with previous tiling array-based approach and observed that the latter method is insensitive to short ncRNAs, especially the molecules less than 120 bp. We measured false positive rates for the analyzed genomic approaches including the RT–PCR and tiling array method

A novel function of the mitochondrial transcription factor Mtf1 in fission yeast; Mtf1 regulates the nuclear transcription of srk1

In eukaryotic cells, Mtf1 and its homologues function as mitochondrial transcription factors for the mitochondrial RNA polymerase in the mitochondrion. Here we show that in fission yeast Mtf1 exerts a non-mitochondrial function as a nuclear factor that regulates transcription of srk1, which is a kinase involved in the stress response and cell cycle progression. We first found Mtf1 expression in the nucleus. A ChIP-chip approach identified srk1 as a putative Mtf1 target gene. Over expression of Mtf1 induced transcription of the srk1 gene and Mtf1 deletion led to a reduction in transcription of the srk1 gene in vivo. Mtf1 overexpression causes cell elongation in a srk1 dependent manner. Mtf1 overexpression can cause cytoplasmic accumulation of Cdc25. We also provide biochemical evidence that Mtf1 binds to the upstream sequence of srk1. This is the first evidence that a mitochondrial transcription factor Mtf1 can regulate a nuclear gene. Mtf1 may also have a role in cell cycle progression

RESEARCH Open Access

metastasis and poor survival of lung adenocarcinoma patient

Differential Evolution for Lifetime Maximization of Heterogeneous Wireless Sensor Networks

Maximizing the lifetime of wireless sensor networks (WSNs) is a hot and significant issue. However, using differential evolution (DE) to research this problem has not appeared so far. This paper proposes a DE-based approach that can maximize the lifetime of WSN through finding the largest number of disjoint sets of sensors, with every set being able to completely cover the target. Different from other methods in the literature, firstly we introduce a common method to generate test data set and then propose an algorithm using differential evolution to solve disjoint set covers (DEDSC) problems. The proposed algorithm includes a recombining operation, which performs after initialization and guarantees at least one critical target’s sensor is divided into different disjoint sets. Moreover, the fitness computation in DEDSC contains both the number of complete cover subsets and the coverage percent of incomplete cover subsets. Applications for sensing a number of target points, named point-coverage, have been used for evaluating the effectiveness of algorithm. Results show that the proposed algorithm DEDSC is promising and simple; its performance outperforms or is similar to other existing excellent approaches in both optimization speed and solution quality

A Step Length Estimation Model of Coefficient Self-Determined Based on Peak-Valley Detection

Without any preinstalled infrastructure, pedestrian dead reckoning (PDR) is a promising indoor positioning technology for pedestrians carrying portable devices to navigate. Step detection and step length estimation (SLE) are two essential components for the pedestrian navigation based on PDR. To solve the overcounting problem, this study proposes a peak-valley detection method, which can remove the abnormal values effectively. The current step length models mostly depend on individual parameters that need to be predetermined for different users. Based on fuzzy logic (FL), we establish a rule base that can adjust the coefficient in the Weinberg model adaptively for every detected step of various human shapes walking. Specifically, to determine the FL rule base, we collect user acceleration data from 10 volunteers walking under the combination of diverse step length and stride frequency, and each one walks 49 times at all. The experimental results demonstrate that our proposed method adapts to different kinds of persons walking at various step velocities. Peak-valley detection can achieve an average accuracy of 99.77% during 500 steps of free walking. Besides, the average errors of 5 testers are all less than 4 m per 100 m and the smallest one is 1.74 m per 100 m using our coefficient self-determined step length estimation model