Visulization of peroxynitrite variation for accurate diagnosis and assessing treatment response of hepatic fibrosis using a Golgi-targetable ratiometric fluorescent probe.

Hepatic fibrosis (HF) is caused by persistent inflammation, which is closely associated with hepatic oxidative stress. Peroxynitrite (ONOO-) is significantly elevated in HF, which would be regarded as a potential biomarker for the diagnosis of HF. Research has shown that ONOO- in the Golgi apparatus can be overproduced in HF, and it can induce hepatocyte injury by triggering Golgi oxidative stress. Meanwhile, the ONOO- inhibitors could effectively relieve HF by inhibiting Golgi ONOO-, but as yet, no Golgi-targetable fluorescent probe available for diagnosis and assessing treatment response of HF through sensing Golgi ONOO-. To this end, we reported a ratiometric fluorescent probe, Golgi-PER, for diagnosis and assessing treatment response of HF through monitoring the Golgi ONOO-. Golgi-PER displayed satisfactory sensitivity, low detection limit, and exceptional selectivity to ONOO-. Combined with excellent biocompatibility and good Golgi-targeting ability, Golgi-PER was further used for ratiometric monitoring the Golgi ONOO- fluctuations and screening of ONOO- inhibitors from polyphenols in living cells. Meanwhile, using Golgi-PER as a probe, the overexpression of Golgi ONOO- in HF and the treatment response of HF to the screened rosmarinic acid were precisely visualized for the first time. Furthermore, the screened RosA has a remarkable therapeutic effect on HF, which may be a new strategy for HF treatment. These results demonstrated the practicability of Golgi-PER for monitoring the occurrence, development, and personalized treatment response of HF.

Coactivation of Hydrogen Peroxide Using Pyrogenic Carbon and Magnetite for Sustainable Oxidation of Organic Pollutants.

Pyrogenic carbon and magnetite (Fe3O4) were mixed together for the activation of hydrogen peroxide (H2O2), aiming to enhance the oxidation of refractory pollutants in a sustainable way. The experimental results indicated that the straw-derived carbon obtained by pyrolysis at 500-800 °C was efficient on coactivation of H2O2, and the most efficient one was that prepared at 700 °C (C700) featured with abundant defects. Specifically, the reaction rate constant (kobs) for removal of an antibiotic ciprofloxacin in the coactivation system (C700/Fe3O4/H2O2) is 12.5 times that in the magnetite-catalyzed system (Fe3O4/H2O2). The faster pollutant oxidation is attributed to the sustainable production of •OH in the coactivation process, in which the carbon facilitated decomposition of H2O2 and regeneration of Fe(II). Besides the enhanced H2O2 utilization in the coactivation process, the leaching of iron was controlled within the concentration limit in drinking water (0.3 mg·L-1) set by the World Health Organization.

A survey of deep learning applications in cryptocurrency.

This study aims to comprehensively review a recently emerging multidisciplinary area related to the application of deep learning methods in cryptocurrency research. We first review popular deep learning models employed in multiple financial application scenarios, including convolutional neural networks, recurrent neural networks, deep belief networks, and deep reinforcement learning. We also give an overview of cryptocurrencies by outlining the cryptocurrency history and discussing primary representative currencies. Based on the reviewed deep learning methods and cryptocurrencies, we conduct a literature review on deep learning methods in cryptocurrency research across various modeling tasks, including price prediction, portfolio construction, bubble analysis, abnormal trading, trading regulations and initial coin offering in cryptocurrency. Moreover, we discuss and evaluate the reviewed studies from perspectives of modeling approaches, empirical data, experiment results and specific innovations. Finally, we conclude this literature review by informing future research directions and foci for deep learning in cryptocurrency.

China's GDP forecasting using Long Short Term Memory Recurrent Neural Network and Hidden Markov Model.

This paper presents a Long Short Term Memory Recurrent Neural Network and Hidden Markov Model (LSTM-HMM) to predict China's Gross Domestic Product (GDP) fluctuation state within a rolling time window. We compare the predictive power of LSTM-HMM with other dynamic forecast systems within different time windows, which involves the Hidden Markov Model (HMM), Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) and LSTM-HMM with an input of monthly Consumer Price Index (CPI) or quarterly CPI within 4-year, 6-year, 8-year and 10-year time window. These forecasting models employed in our empirical analysis share the basic HMM structure but differ in the generation of observable CPI fluctuation states. Our forecasting results suggest that (1) among all the models, LSTM-HMM generally performs better than the other models; (2) the model performance can be improved when model input transforms from quarterly to monthly; (3) among all the time windows, models within 10-year time window have better overall performance; (4) within 10-year time window, the LSTM-HMM, with either quarterly or monthly input, has the best accuracy and consistency.

The apricot (Prunus armeniaca L.) genome elucidates Rosaceae evolution and beta-carotenoid synthesis.

Apricots, scientifically known as Prunus armeniaca L, are drupes that resemble and are closely related to peaches or plums. As one of the top consumed fruits, apricots are widely grown worldwide except in Antarctica. A high-quality reference genome for apricot is still unavailable, which has become a handicap that has dramatically limited the elucidation of the associations of phenotypes with the genetic background, evolutionary diversity, and population diversity in apricot. DNA from P. armeniaca was used to generate a standard, size-selected library with an average DNA fragment size of ~20 kb. The library was run on Sequel SMRT Cells, generating a total of 16.54 Gb of PacBio subreads (N50 = 13.55 kb). The high-quality P. armeniaca reference genome presented here was assembled using long-read single-molecule sequencing at approximately 70× coverage and 171× Illumina reads (40.46 Gb), combined with a genetic map for chromosome scaffolding. The assembled genome size was 221.9 Mb, with a contig NG50 size of 1.02 Mb. Scaffolds covering 92.88% of the assembled genome were anchored on eight chromosomes. Benchmarking Universal Single-Copy Orthologs analysis showed 98.0% complete genes. We predicted 30,436 protein-coding genes, and 38.28% of the genome was predicted to be repetitive. We found 981 contracted gene families, 1324 expanded gene families and 2300 apricot-specific genes. The differentially expressed gene (DEG) analysis indicated that a change in the expression of the 9-cis-epoxycarotenoid dioxygenase (NCED) gene but not lycopene beta-cyclase (LcyB) gene results in a low ß-carotenoid content in the white cultivar "Dabaixing". This complete and highly contiguous P. armeniaca reference genome will be of help for future studies of resistance to plum pox virus (PPV) and the identification and characterization of important agronomic genes and breeding strategies in apricot.

Association between Chloroplast and Mitochondrial DNA sequences in Chinese Prunus genotypes (Prunus persica, Prunus domestica, and Prunus avium).

BACKGROUND: The nuclear DNA is conventionally used to assess the diversity and relatedness among different species, but variations at the DNA genome level has also been used to study the relationship among different organisms. In most species, mitochondrial and chloroplast genomes are inherited maternally; therefore it is anticipated that organelle DNA remains completely associated. Many research studies were conducted simultaneously on organelle genome. The objectives of this study was to analyze the genetic relationship between chloroplast and mitochondrial DNA in three Chinese Prunus genotypes viz., Prunus persica, Prunus domestica, and Prunus avium. RESULTS: We investigated the genetic diversity of Prunus genotypes using simple sequence repeat (SSR) markers relevant to the chloroplast and mitochondria. Most of the genotypes were genetically similar as revealed by phylogenetic analysis. The Y2 Wu Xing (Cherry) and L2 Hong Xin Li (Plum) genotypes have a high similarity index (0.89), followed by Zi Ye Li (0.85), whereas; L1 Tai Yang Li (plum) has the lowest genetic similarity (0.35). In case of cpSSR, Hong Tao (Peach) and L1 Tai Yang Li (Plum) genotypes demonstrated similarity index of 0.85 and Huang Tao has the lowest similarity index of 0.50. The mtSSR nucleotide sequence analysis revealed that each genotype has similar amplicon length (509 bp) except M5Y1 i.e., 505 bp with CCB256 primer; while in case of NAD6 primer, all genotypes showed different sizes. The MEHO (Peach), MEY1 (Cherry), MEL2 (Plum) and MEL1 (Plum) have 586 bps; while MEY2 (Cherry), MEZI (Plum) and MEHU (Peach) have 585, 584 and 566 bp, respectively. The CCB256 primer showed highly conserved sequences and minute single polymorphic nucleotides with no deletion or mutation. The cpSSR (ARCP511) microsatellites showed the harmonious amplicon length. The CZI (Plum), CHO (Peach) and CL1 (Plum) showed 182 bp; whileCHU (Peach), CY2 (Cherry), CL2 (Plum) and CY1 (Cherry) showed 181 bp amplicon lengths. CONCLUSIONS: These results demonstrated high conservation in chloroplast and mitochondrial genome among Prunus species during the evolutionary process. These findings are valuable to study the organelle DNA diversity in different species and genotypes of Prunus to provide in depth insight in to the mitochondrial and chloroplast genomes.