A Natural Isoquinoline Alkaloid With Antitumor Activity: Studies of the Biological Activities of Berberine

Coptis, a traditional medicinal plant, has been used widely in the field of traditional Chinese medicine for many years. More recently, the chemical composition and bioactivity of Coptis have been studied worldwide. Berberine is a main component of Rhizoma Coptidis. Modern medicine has confirmed that berberine has pharmacological activities, such as anti-inflammatory, analgesic, antimicrobial, hypolipidemic, and blood pressure-lowering effects. Importantly, the active ingredient of berberine has clear inhibitory effects on various cancers, including colorectal cancer, lung cancer, ovarian cancer, prostate cancer, liver cancer, and cervical cancer. Cancer, ranked as one of the world’s five major incurable diseases by WHO, is a serious threat to the quality of human life. Here, we try to outline how berberine exerts antitumor effects through the regulation of different molecular pathways. In addition, the berberine-mediated regulation of epigenetic mechanisms that may be associated with the prevention of malignant tumors is described. Thus, this review provides a theoretical basis for the biological functions of berberine and its further use in the clinical treatment of cancer

Templateâ based protein structure prediction in CASP11 and retrospect of Iâ TASSER in the last decade

We report the structure prediction results of a new composite pipeline for templateâ based modeling (TBM) in the 11th CASP experiment. Starting from multiple structure templates identified by LOMETS based metaâ threading programs, the QUARK ab initio folding program is extended to generate initial fullâ length models under strong constraints from template alignments. The final atomic models are then constructed by Iâ TASSER based fragment reassembly simulations, followed by the fragmentâ guided molecular dynamic simulation and the MQAPâ based model selection. It was found that the inclusion of QUARKâ TBM simulations as an intermediate modeling step could help improve the quality of the Iâ TASSER models for both Easy and Hard TBM targets. Overall, the average TMâ score of the first Iâ TASSER model is 12% higher than that of the best LOMETS templates, with the RMSD in the same threadingâ aligned regions reduced from 5.8 to 4.7 à . Nevertheless, there are nearly 18% of TBM domains with the templates deteriorated by the structure assembly pipeline, which may be attributed to the errors of secondary structure and domain orientation predictions that propagate through and degrade the procedures of template identification and final model selections. To examine the record of progress, we made a retrospective report of the Iâ TASSER pipeline in the last five CASP experiments (CASP7â 11). The data show no clear progress of the LOMETS threading programs over PSIâ BLAST; but obvious progress on structural improvement relative to threading templates was witnessed in recent CASP experiments, which is probably attributed to the integration of the extended ab initio folding simulation with the threading assembly pipeline and the introduction of atomicâ level structure refinements following the reduced modeling simulations. Proteins 2016; 84(Suppl 1):233â 246. © 2015 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134137/1/prot24918.pd

Fueling ab initio folding with marine metagenomics enables structure and function predictions of new protein families

Abstract Introduction The ocean microbiome represents one of the largest microbiomes and produces nearly half of the primary energy on the planet through photosynthesis or chemosynthesis. Using recent advances in marine genomics, we explore new applications of oceanic metagenomes for protein structure and function prediction. Results By processing 1.3 TB of high-quality reads from the Tara Oceans data, we obtain 97 million non-redundant genes. Of the 5721 Pfam families that lack experimental structures, 2801 have at least one member associated with the oceanic metagenomics dataset. We apply C-QUARK, a deep-learning contact-guided ab initio structure prediction pipeline, to model 27 families, where 20 are predicted to have a reliable fold with estimated template modeling score (TM-score) at least 0.5. Detailed analyses reveal that the abundance of microbial genera in the ocean is highly correlated to the frequency of occurrence in the modeled Pfam families, suggesting the significant role of the Tara Oceans genomes in the contact-map prediction and subsequent ab initio folding simulations. Of interesting note, PF15461, which has a majority of members coming from ocean-related bacteria, is identified as an important photosynthetic protein by structure-based function annotations. The pipeline is extended to a set of 417 Pfam families, built on the combination of Tara with other metagenomics datasets, which results in 235 families with an estimated TM-score over 0.5. Conclusions These results demonstrate a new avenue to improve the capacity of protein structure and function modeling through marine metagenomics, especially for difficult proteins with few homologous sequences.https://deepblue.lib.umich.edu/bitstream/2027.42/152239/1/13059_2019_Article_1823.pd

NF-kappaB P50/P65 hetero-dimer mediates differential regulation of CD166/ALCAM expression via interaction with micoRNA-9 after serum deprivation, providing evidence for a novel negative auto-regulatory loop

CD166/ALCAM plays an important role in tumor aggression and progression as well as protecting cancer cells against apoptosis and autophagy. However, the mechanism by which pro-cell death signals control CD166 expression remains unclear. Here we show that following serum deprivation (SD), upregulation of CD166 protein is shorter than that of CD166 mRNA. Molecular analysis revealed both CD166 and miR-9-1 as two novel NF-κB target genes in hepatoma cells. In vivo activation and translocation of the NF-κB P50/P65 hetero-dimer into the nucleus following the phosphorylation and accompanied degradation of its inhibitor, IκBα, contributes to efficient transcription of both genes following SD. We show that following serum starvation, delayed up-regulation of miR-9 represses translation of CD166 protein through its target sites in the 3′-UTR of CD166 mRNA. We also propose that miR-9 promotes cell migration largely due to inhibition of CD166. Collectively, the study elucidates a novel negative auto-regulatory loop in which NF-κB mediates differential regulation of CD166 after SD

Thermal Conductivity of Carbon Nanotubes and their Polymer Nanocomposites: A Review

Thermally conductive polymer composites offer new possibilities for replacing metal parts in several applications, including power electronics, electric motors and generators, heat exchangers, etc., thanks to the polymer advantages such as light weight, corrosion resistance and ease of processing. Current interest to improve the thermal conductivity of polymers is focused on the selective addition of nanofillers with high thermal conductivity. Unusually high thermal conductivity makes carbon nanotube (CNT) the best promising candidate material for thermally conductive composites. However, the thermal conductivities of polymer/CNT nanocomposites are relatively low compared with expectations from the intrinsic thermal conductivity of CNTs. The challenge primarily comes from the large interfacial thermal resistance between the CNT and the surrounding polymer matrix, which hinders the transfer of phonon dominating heat conduction in polymer and CNT. This article reviews the status of worldwide research in the thermal conductivity of CNTs and their polymer nanocomposites. The dependence of thermal conductivity of nanotubes on the atomic structure, the tube size, the morphology, the defect and the purification is reviewed. The roles of particle/polymer and particle/particle interfaces on the thermal conductivity of polymer/CNT nanocomposites are discussed in detail, as well as the relationship between the thermal conductivity and the micro- and nano-structure of the composite

The Trends in Research on the Effects of Biochar on Soil

The present study used bibliometric methods to analyze the literature regarding the biochar effects on soil that are included in the Web of Science Core Collection database and quantified the annual number of publications in the field and distribution of publications. Using CiteSpace as a visual analytic software for the literature, the distribution of the subject categories, author collaborations, institution collaborations, international (regional) collaborations, and cocitation and keyword clustering were analyzed. The results showed the basic characteristics of the literature related to the effects of biochar on soil. Furthermore, the main research powers in this field were identified. Then, we recognized the main intellectual base in the domain of biochar effects on soil. Meanwhile, this paper revealed the research hotspots and trends of this field. Furthermore, focuses of future research in this field are discussed. The present study quantitatively and objectively describes the research status and trends of biochar effects on soil from the bibliometric perspective to promote in-depth research in this field and provide reference information for scholars in the relevant fields to refine their research directions, address specific scientific issues, and help scholars to seek/establish relevant collaborations in their fields of interests

Effects of Biochar-Based Fertilizers on Energy Characteristics and Growth of Black Locust Seedlings

To understand ecological and energy problems in the karst area of Guizhou, China, the effects of using biochar-based fertilizers on the energy characteristics of different species of black locust were studied. To determine the most suitable species and the best rational application method of biochar, an outdoor pot experiment was performed using three species of black locust (White-flowered locust (W), Hong-sen locust (S), and Large-leaf fast-growing locust (L)). There were six treatments: control (CK), MF, RH2MF, RH4MF, W2MF, and W4MF (M—compost; F—NPK fertilizer; RH—rice husk biochar; and W—wood biochar), where the numbers represented the mass ratio of biochar to soil. Biochar-based fertilizers had significant effects on the total organic carbon (TOC), total nitrogen (TN), total potassium (TK), branch gross calorific values (GCV), and ash removal calorific values (AFCV) of seedlings. RH4MF had the best overall values. Different species had significant effects in all indicators (except for TN); the effect on S was better than that of W and L. Principal component analysis showed that RH4MF-S had the highest comprehensive scores. In summary, Hong-sen locust (S) was a high-quality energy species and RH4MF may be used as fertilization for energy forest development. This study provides a reference for future long-term energy forest research in this area

Improving ECG Classification Performance by Using an Optimized One-Dimensional Residual Network Model

Cardiovascular disease and its consequences on human health have never stopped and even show a trend of appearing in increasingly younger generations. The establishment of an excellent deep learning algorithm model to assist physicians in identifying and the early screening of ECG abnormalities can effectively improve the accuracy of diagnosis. Therefore, in this study, the deep residual network model is adapted for feature extraction and classification of ECG signals by pooling embedded into layers and double channel connection. At the same time, the wavelet adaptive threshold denoising algorithm is used to complete the high signal-to-noise filtering of ECG signals. Then, the alternate pooling residual network (APRN) is compared with the convolutional neural network (CNN), CNN with one residual unit (CNN-R), and the deep residual network (ResNet-18) using ECG datasets from the American MIT-BIH arrhythmia and ST segment abnormality database, European ST-T database, and sudden cardiac death ambulatory ECG database. The results are as follows: The average classification accuracy of the APRN on the four datasets is 97.89%, while the accuracies on CNN, CNN-R, and ResNet-18 are 97.17%, 97.53%, and 97.73%, respectively. In addition, compared with ResNet-18, the classification accuracy of our APRN on each class of data improves by 16.44% in total

Improving ECG Classification Performance by Using an Optimized One-Dimensional Residual Network Model

Cardiovascular disease and its consequences on human health have never stopped and even show a trend of appearing in increasingly younger generations. The establishment of an excellent deep learning algorithm model to assist physicians in identifying and the early screening of ECG abnormalities can effectively improve the accuracy of diagnosis. Therefore, in this study, the deep residual network model is adapted for feature extraction and classification of ECG signals by pooling embedded into layers and double channel connection. At the same time, the wavelet adaptive threshold denoising algorithm is used to complete the high signal-to-noise filtering of ECG signals. Then, the alternate pooling residual network (APRN) is compared with the convolutional neural network (CNN), CNN with one residual unit (CNN-R), and the deep residual network (ResNet-18) using ECG datasets from the American MIT-BIH arrhythmia and ST segment abnormality database, European ST-T database, and sudden cardiac death ambulatory ECG database. The results are as follows: The average classification accuracy of the APRN on the four datasets is 97.89%, while the accuracies on CNN, CNN-R, and ResNet-18 are 97.17%, 97.53%, and 97.73%, respectively. In addition, compared with ResNet-18, the classification accuracy of our APRN on each class of data improves by 16.44% in total