Old trees bloom new flowers, lysosome targeted near-infrared fluorescent probe for ratiometric sensing of hypobromous acid in vitro and in vivo based on Nile red skeleton.

Hypobromous acid (HOBr), one of the significant reactive oxygen species (ROS) that acts as an important role in human immune system, however the increasing level of HOBr in human body can cause the disorder of eosinophils (EPO), leading to oxidative stress in organelles, and further causing a series of diseases. In this study, a ratiometric fluorescent probe DMBP based on Nile red skeleton was developed to detect HOBr specifically by the electrophilic substitution with HOBr. DMBP emits near-infrared (NIR) fluorescence at 653 nm, after reacting with HOBr, the emission wavelength of DMBP shifted blue and a new peak appeared at 520 nm, realizing a ratiometric examination of HOBr with a limit of detection of 89.00 nM. Based on its sensitive and specific response to HOBr, DMBP was applied in the visual imaging of HOBr in HepG2 cells and zebrafish. Foremost, probe DMBP has excellent lysosome targeting ability and NIR emission reduced the background interference of biological tissues, providing a potential analytical tool to further investigate the role of HOBr in lysosome.

INO80-Dependent Remodeling of Transcriptional Regulatory Network Underlies the Progression of Heart Failure.

BACKGROUND: Progressive remodeling of cardiac gene expression underlies decline in cardiac function, eventually leading to heart failure. However, the major determinants of transcriptional network switching from normal to failed hearts remain to be determined. METHODS: In this study, we integrated human samples, genetic mouse models, and genomic approaches, including bulk RNA sequencing, single-cell RNA sequencing, chromatin immunoprecipitation followed by high-throughput sequencing, and assay for transposase-accessible chromatin with high-throughput sequencing, to identify the role of chromatin remodeling complex INO80 in heart homeostasis and dysfunction. RESULTS: The INO80 chromatin remodeling complex was abundantly expressed in mature cardiomyocytes, and its expression further increased in mouse and human heart failure. Cardiomyocyte-specific overexpression of Ino80, its core catalytic subunit, induced heart failure within 4 days. Combining RNA sequencing, chromatin immunoprecipitation followed by high-throughput sequencing, and assay for transposase-accessible chromatin with high-throughput sequencing, we revealed INO80 overexpression-dependent reshaping of the nucleosomal landscape that remodeled a core set of transcription factors, most notably the MEF2 (Myocyte Enhancer Factor 2) family, whose target genes were closely associated with cardiac function. Conditional cardiomyocyte-specific deletion of Ino80 in an established mouse model of heart failure demonstrated remarkable preservation of cardiac function. CONCLUSIONS: In summary, our findings shed light on the INO80-dependent remodeling of the chromatin landscape and transcriptional networks as a major mechanism underlying cardiac dysfunction in heart failure, and suggest INO80 as a potential preventative or interventional target.

Self-recruited neutrophils trigger over-activated innate immune response and phenotypic change of cardiomyocytes in fulminant viral myocarditis.

Fulminant myocarditis (FM) is a life-threatening inflammatory disease. However, the mechanisms underlying its acute onset are unknown. By dynamic cardiac function measurement, we discovered that the initiation of sudden hemodynamic collapse was on day 4 in the mouse model of FM. Single-cell RNA-sequencing study revealed that healthy cardiomyocytes (CMs) lost their contractile and metabolic function and differentiated into pro-angiogenic and pro-inflammatory CMs. Meanwhile, neutrophils, the most expanded immune cells, exhibited a unique developmental trajectory only after migrating to the heart, where they continuously attracted peripheral neutrophils via Cxcl2/Cxcl3, resulting in the acute accumulation of neutrophils in the heart. Well-differentiated cardiac-infiltrating neutrophils, rather than viruses, induced phenotypic changes in CMs. Moreover, neutrophils could amplify cytokine storm by recruiting and activating pro-inflammatory monocytes. Blockade of the self-recruiting loop of neutrophils by targeting the Cxcl2/Cxcl3-Cxcr2 axis substantially alleviated FM in mice. Collectively, we provide a comprehensive single-cell atlas of immune cells and CMs in FM, elucidate the disease pathogenesis, and suggest potential therapeutic strategies.

Potential Target Discovery and Drug Repurposing for Coronaviruses: Study Involving a Knowledge Graph-Based Approach.

BACKGROUND: The global pandemics of severe acute respiratory syndrome, Middle East respiratory syndrome, and COVID-19 have caused unprecedented crises for public health. Coronaviruses are constantly evolving, and it is unknown which new coronavirus will emerge and when the next coronavirus will sweep across the world. Knowledge graphs are expected to help discover the pathogenicity and transmission mechanism of viruses. OBJECTIVE: The aim of this study was to discover potential targets and candidate drugs to repurpose for coronaviruses through a knowledge graph-based approach. METHODS: We propose a computational and evidence-based knowledge discovery approach to identify potential targets and candidate drugs for coronaviruses from biomedical literature and well-known knowledge bases. To organize the semantic triples extracted automatically from biomedical literature, a semantic conversion model was designed. The literature knowledge was associated and integrated with existing drug and gene knowledge through semantic mapping, and the coronavirus knowledge graph (CovKG) was constructed. We adopted both the knowledge graph embedding model and the semantic reasoning mechanism to discover unrecorded mechanisms of drug action as well as potential targets and drug candidates. Furthermore, we have provided evidence-based support with a scoring and backtracking mechanism. RESULTS: The constructed CovKG contains 17,369,620 triples, of which 641,195 were extracted from biomedical literature, covering 13,065 concept unique identifiers, 209 semantic types, and 97 semantic relations of the Unified Medical Language System. Through multi-source knowledge integration, 475 drugs and 262 targets were mapped to existing knowledge, and 41 new drug mechanisms of action were found by semantic reasoning, which were not recorded in the existing knowledge base. Among the knowledge graph embedding models, TransR outperformed others (mean reciprocal rank=0.2510, Hits@10=0.3505). A total of 33 potential targets and 18 drug candidates were identified for coronaviruses. Among them, 7 novel drugs (ie, quinine, nelfinavir, ivermectin, asunaprevir, tylophorine, Artemisia annua extract, and resveratrol) and 3 highly ranked targets (ie, angiotensin converting enzyme 2, transmembrane serine protease 2, and M protein) were further discussed. CONCLUSIONS: We showed the effectiveness of a knowledge graph-based approach in potential target discovery and drug repurposing for coronaviruses. Our approach can be extended to other viruses or diseases for biomedical knowledge discovery and relevant applications.

Comparative mitogenomic analysis of three bugs of the genus Hygia Uhler, 1861 (Hemiptera, Coreidae) and their phylogenetic position.

Hygia Uhler, 1861 is the largest genus in the bug family Coreidae. Even though many species of this genus are economically important, the complete mitogenomes of Hygia species have not yet been reported. Therefore, in the present study, the complete mitogenomes of three Hygia species, H.lativentris (Motschulsky, 1866), H.bidentata Ren, 1987, and H.opaca (Uhler, 1860), are sequenced and characterized, and submitted in a phylogenetic analysis of the Coreidae. The results show that mitogenomes of the three species are highly conserved, typically with 37 genes plus its control region. The lengths are 16,313 bp, 17,023 bp, and 17,022 bp, respectively. Most protein-coding genes (PCGs) in all species start with the standard codon ATN and terminate with one of three stop codons: TAA, TAG, or T. The tRNAs secondary structures of all species have a typical clover structure, except for the trnS1 (AGC) in H.bidentata, which lacks dihydrouridine (DHU) arm that forms a simple loop. Variation in the length of the control region led to differences in mitochondrial genome sizes. The maximum-likelihood (ML) and Bayesian-inference (BI) phylogenetic analyses strongly supported the monophyly of Hygia and its position within Coreidae, and the relationships are ((H.bidentata + (H.opaca + (H.lativentris + Hygia sp.))). The results provide further understanding for future phylogenetic studies of Coreidae.

Effects of microplastics and tetracycline on intestinal injury in mice.

Microplastics (MPs) and tetracycline are both emerging environmental pollutants that threaten human health. The toxic impacts of their single and coexposure on the intestine and gut microbiota have not been well studied in mammals. Given the spatial functional characteristics of the intestine, it is important to know whether the toxicities of MPs and tetracycline in different intestinal segments are distinct. This study investigated the pathological and functional injuries of different intestinal segments and the microbial disorder upon exposure to polystyrene microplastics (PS-MPs) and/or tetracycline hydrochloride (TCH). Both PS-MPs and TCH altered the intestinal morphology and induced functional impairment. However, the PS-MPs primarily damaged the colon, while TCH mainly damaged the small intestine, especially the jejunum. Combined treatment evoked ameliorative adverse effects on the intestinal segments except for the ileum. Gut microbiota analysis revealed that PS-MPs and/or TCH decreased gut microbiota diversity, especially PS-MPs. In addition, PS-MPs and TCH affected the microflora metabolic processes, especially protein absorption and digestion. Gut microbiota dysbiosis could partly lead to the physical and functional damage induced by PS-MPs and TCH. These findings enhance our knowledge regarding the hazards of coexisting microplastics and antibiotics for mammalian intestinal health.

Low-cost high-speed rapidly reconfigurable integrated half-duplex optical transceiver front-end.

We present and demonstrate an integrated approach to make low-cost high-speed half-duplex optical transceiver with rapid reconfigurability. A single Distributed-Feedback (DFB) diode is employed as the unified E/O-O/E device, which is dynamically biased as laser or photodetector in transmitter or receiver mode. The bias for the DFB and the transmit/receive (T/R) signal path are provided by a custom-designed reconfigurable CMOS chip, which contains the biasing circuitry, a high-speed T/R switch and a burst-mode transimpedance amplifier (BM-TIA). The transceiver front-end operates up to 5 Gb/s for both transmitter (Tx) and receiver (Rx) mode experimentally, while its reconfiguration time is less than 131 ns. This integrated approach not only halves the transceiver optics to facilitate low cost, but also enables high-speed signal transmission as well as rapid reconfiguration, which will be critical for the fiber-to-everything paradigm shift in the near future.

Guided Filter Network for Semantic Image Segmentation.

The existing publicly available datasets with pixel-level labels contain limited categories, and it is difficult to generalize to the real world containing thousands of categories. In this paper, we propose an approach to generate object masks with detailed pixel-level structures/boundaries automatically to enable semantic image segmentation of thousands of targets in the real world without manually labelling. A Guided Filter Network (GFN) is first developed to learn the segmentation knowledge from an existed dataset, and such GFN then transfers the learned segmentation knowledge to generate initial coarse object masks for the target images. These coarse object masks are treated as pseudo labels to self-optimize the GFN iteratively in the target images. Our experiments on six image sets have demonstrated that our proposed approach can generate object masks with detailed pixel-level structures/boundaries, whose quality is comparable to the manually-labelled ones. Our proposed approach also achieves better performance on semantic image segmentation than most existing weakly-supervised, semi-supervised, and domain adaptation approaches under the same experimental conditions.

Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records.

OBJECTIVE: Pituitary adenomas are the most common type of pituitary disorders, which usually occur in young adults and often affect the patient's physical development, labor capacity and fertility. Clinical free texts noted in electronic medical records (EMRs) of pituitary adenomas patients contain abundant diagnosis and treatment information. However, this information has not been well utilized because of the challenge to extract information from unstructured clinical texts. This study aims to enable machines to intelligently process clinical information, and automatically extract clinical named entity for pituitary adenomas from Chinese EMRs. METHODS: The clinical corpus used in this study was from one pituitary adenomas neurosurgery treatment center of a 3A hospital in China. Four types of fine-grained texts of clinical records were selected, which included notes from present illness, past medical history, case characteristics and family history of 500 pituitary adenoma inpatients. The dictionary-based matching, conditional random fields (CRF), bidirectional long short-term memory with CRF (BiLSTM-CRF), and bidirectional encoder representations from transformers with BiLSTM-CRF (BERT-BiLSTM-CRF) were used to extract clinical entities from a Chinese EMRs corpus. A comprehensive dictionary was constructed based on open source vocabularies and a domain dictionary for pituitary adenomas to conduct the dictionary-based matching method. We selected features such as part of speech, radical, document type, and the position of characters to train the CRF-based model. Random character embeddings and the character embeddings pretrained by BERT were used respectively as the input features for the BiLSTM-CRF model and the BERT-BiLSTM-CRF model. Both strict metric and relaxed metric were used to evaluate the performance of these methods. RESULTS: Experimental results demonstrated that the deep learning and other machine learning methods were able to automatically extract clinical named entities, including symptoms, body regions, diseases, family histories, surgeries, medications, and disease courses of pituitary adenomas from Chinese EMRs. With regard to overall performance, BERT-BiLSTM-CRF has the highest strict F1 value of 91.27% and the highest relaxed F1 value of 95.57% respectively. Additional evaluations showed that BERT-BiLSTM-CRF performed best in almost all entity recognition except surgery and disease course. BiLSTM-CRF performed best in disease course entity recognition, and performed as well as the CRF model for part of speech, radical and document type features, with both strict and relaxed F1 value reaching 96.48%. The CRF model with part of speech, radical and document type features performed best in surgery entity recognition with relaxed F1 value of 95.29%. CONCLUSIONS: In this study, we conducted four entity recognition methods for pituitary adenomas based on Chinese EMRs. It demonstrates that the deep learning methods can effectively extract various types of clinical entities with satisfying performance. This study contributed to the clinical named entity extraction from Chinese neurosurgical EMRs. The findings could also assist in information extraction in other Chinese medical texts.

TelecomNet: Tag-Based Weakly-Supervised Modally Cooperative Hashing Network for Image Retrieval.

We are concerned with using user-tagged images to learn proper hashing functions for image retrieval. The benefits are two-fold: (1) we could obtain abundant training data for deep hashing models; (2) tagging data possesses richer semantic information which could help better characterize similarity relationships between images. However, tagging data suffers from noises, vagueness and incompleteness. Different from previous unsupervised or supervised hashing learning, we propose a novel weakly-supervised deep hashing framework which consists of two stages: weakly-supervised pre-training and supervised fine-tuning. The second stage is as usual. In the first stage, we propose two formulations Tag-basEd weakLy-supErvised Modally COoperative hashing Network (TelecomNet) and Generalized TelecomNet (GTelecomNet). Rather than performing supervision on tags, TelecomNet first learns an observed semantic embedding vector for each image from attached tags and then uses it to guide hashing learning. GTelecomNet introduces a novel semantic network to exploit more precise semantic information. By carefully designing the optimization problem, they can well leverage tagging information and image content for hashing learning. The framework is general and does not depend on specific deep hashing methods. Empirical results on real world datasets show that they significantly increase the performance of state-of-the-art deep hashing methods.

Research progress in pharmacological activities and structure-activity relationships of tetralone scaffolds as pharmacophore and fluorescent skeleton.

The tetralone and tetralone derivatives, as crucial structural scaffolds of potential novel drugs targeted at multiple biological end-points, are normally found in several natural compounds and also, it can be used as parental scaffold and/or intermediate for the synthesis of a series of pharmacologically active compounds with a broad-spectrum of bioactivities including antibacterial, antitumor, CNS effect and so on. Meanwhile, SAR information of its analogues has drawn attentions among medicinal chemists, which could contribute to the further research related to tetralone derivatives aimed at multiple targets. This review encompasses pharmacological activities, SAR analysis and docking study of tetralone and its derivatives, expecting to provide a general retrospect and prospect on tetralone derivatives.

Characterization of the complete mitochondrial genome of Myrmuslateralis (Heteroptera, Rhopalidae) and its implication for phylogenetic analyses.

Mitochondrial genomes (mitogenomes) are widely used in research studies on phylogenetic relationships and evolutionary history. Here, we sequenced and analyzed the mitogenome of the scentless plant bug Myrmuslateralis Hsiao, 1964 (Heteroptera, Rhopalidae). The complete 17,309 bp genome encoded 37 genes, including 13 protein-coding genes (PCGs), 22 transfer RNA (tRNA) genes, two ribosomal RNA (rRNA) genes, and a control region. The mitogenome revealed a high A+T content (75.8%), a positive AT-skew (0.092), and a negative GC-skew (-0.165). All 13 PCGs were found to start with ATN codons, except for cox1, in which TTG was the start codon. The Ka/Ks ratios of 13 PCGs were all lower than 1, indicating that purifying selection evolved in these genes. All tRNAs could be folded into the typical cloverleaf secondary structure, except for trnS1 and trnV, which lack dihydrouridine arms. Phylogenetic trees were constructed and analyzed based on the PCG+rRNA from 38 mitogenomes, using maximum likelihood and Bayesian inference methods, showed that M.lateralis and Chorosomamacilentum Stål, 1858 grouped together in the tribe Chorosomatini. In addition, Coreoidea and Pyrrhocoroidea were sister groups among the superfamilies of Trichophora, and Rhopalidae was a sister group to Alydidae + Coreidae.

Deep Multiple Instance Hashing for Fast Multi-Object Image Search.

Multi-keyword query is widely supported in text search engines. However, an analogue in image retrieval systems, multi-object query, is rarely studied. Meanwhile, traditional object-based image retrieval methods often involve multiple steps separately. In this work, we propose a weakly-supervised Deep Multiple Instance Hashing (DMIH) approach for multi-object image retrieval. Our DMIH approach, which leverages a popular CNN model to build the end-to-end relation between a raw image and the binary hash codes of its multiple objects, can support multi-object queries effectively and integrate object detection with hashing learning seamlessly. We treat object detection as a binary multiple instance learning (MIL) problem and such instances are automatically extracted from multi-scale convolutional feature maps. We also design a conditional random field (CRF) module to capture both the semantic and spatial relations among different class labels. For hashing training, we sample image pairs to learn their semantic relationships in terms of hash codes of the most probable proposals for owned labels as guided by object predictors. The two objectives benefit each other in a multi-task learning scheme. Finally, a two-level inverted index method is proposed to further speed up the retrieval of multi-object queries. Our DMIH approach outperforms state-of-the-arts on public benchmarks for object-based image retrieval and achieves promising results for multi-object queries.

The complete mitochondrial genome of Pentatoma rufipes (Hemiptera, Pentatomidae) and its phylogenetic implications.

Pentatoma rufipes (Linnaeus, 1758) is an important agroforestry pest widely distributed in the Palaearctic region. In this study, we sequence and annotate the complete mitochondrial genome of P. rufipes and reconstruct the phylogenetic trees for Pentatomoidea using existing data for eight families published in the National Center for Biotechnology Information database. The mitogenome of P. rufipes is 15,887-bp-long, comprising 13 protein-coding genes, 22 transfer RNA genes, two ribosomal RNA genes, and a control region, with an A+T content of 77.7%. The genome structure, gene order, nucleotide composition, and codon usage of the mitogenome of P. rufipes were consistent with those of typical Hemiptera insects. Among the protein-coding genes of Pentatomoidea, the evolutionary rate of ATP8 was the fastest, and COX1 was found to be the most conservative gene in the superfamily. Substitution saturation assessment indicated that neither transition nor transversion substitutions were saturated in the analyzed datasets. Phylogenetic analysis using the Bayesian inference method showed that P. rufipes belonged to Pentatomidae. The node support values based on the dataset concatenated from protein-coding and RNA genes were the highest. Our results enrich the mitochondrial genome database of Pentatomoidea and provide a reference for further studies of phylogenetic systematics.

Head and Tail Entity Fusion Model in Medical Knowledge Graph Construction: Case Study for Pituitary Adenoma.

BACKGROUND: Pituitary adenoma is one of the most common central nervous system tumors. The diagnosis and treatment of pituitary adenoma remain very difficult. Misdiagnosis and recurrence often occur, and experienced neurosurgeons are in serious shortage. A knowledge graph can help interns quickly understand the medical knowledge related to pituitary tumor. OBJECTIVE: The aim of this study was to develop a data fusion method suitable for medical data using data of pituitary adenomas integrated from different sources. The overall goal was to construct a knowledge graph for pituitary adenoma (KGPA) to be used for knowledge discovery. METHODS: A complete framework suitable for the construction of a medical knowledge graph was developed, which was used to build the KGPA. The schema of the KGPA was manually constructed. Information of pituitary adenoma was automatically extracted from Chinese electronic medical records (CEMRs) and medical websites through a conditional random field model and newly designed web wrappers. An entity fusion method is proposed based on the head-and-tail entity fusion model to fuse the data from heterogeneous sources. RESULTS: Data were extracted from 300 CEMRs of pituitary adenoma and 4 health portals. Entity fusion was carried out using the proposed data fusion model. The F1 scores of the head and tail entity fusions were 97.32% and 98.57%, respectively. Triples from the constructed KGPA were selected for evaluation, demonstrating 95.4% accuracy. CONCLUSIONS: This paper introduces an approach to fuse triples extracted from heterogeneous data sources, which can be used to build a knowledge graph. The evaluation results showed that the data in the KGPA are of high quality. The constructed KGPA can help physicians in clinical practice.

Computational screening of potential regulators for mRNA-protein expression level discrepancy.

Complicated post-transcriptional and translational regulation processes contribute to the expression discrepancy between mRNA and protein in many tissues, but the underlying mechanisms have not been fully understood. In this study, we assessed to what extent and which RNA binding proteins (RBPs) contribute to mRNA-protein expression discrepancy. To this end, we exploited the RNA-seq transcriptome data and corresponding quantitative proteome data from the same set of human healthy tissues to estimate the mRNA-protein expression discrepancy, and observed that a considerable fraction of genes show obvious difference in expression rankings between transcriptome and proteome. We further assembled the latest CLIP-seq datasets from POSTAR2, ENCODE and GEO to map the binding profiles of known RBPs. A logistic regression model based on the RBP-binding features was established, which could predict the mRNA-protein expression discrepancy with acceptable performance. Finally, by applying two different feature selection methods on this logistic regression model, we identified a consensus set of known and putative translation regulators which may account for the expression level discrepancy, such as G3BP1, DGCR8, LARP4B, EIF4A3 and FXR2.

Comparative Mitogenomic Analysis of the Eurydema Genus in the Context of Representative Pentatomidae (Hemiptera: Heteroptera) Taxa.

The family Pentatomidae, the largest within the superfamily Pentatomoidae, comprises about 5,000 species; many of which are economically important pests. Although the phylogeny of Pentatomidae species has been studied using various molecular markers, their phylogenetic relationships remain controversial. Recently, mitochondrial genomes (mitogenomes) have been extensively employed to examine the phylogenetics and evolution of different insects, and in this study, we sequenced complete/near-complete mitochondrial genomes from five shield bug species of Eurydema to gain a better understanding of phylogenetic relationships in the Pentatomidae. The five mitogenomes ranged in length from 15,500 to 16,752 bp and comprised 13 protein-coding genes (PCGs), 22 transfer RNAs (tRNAs), 2 ribosomal RNAs (rRNAs), and a control region. We compared mitogenomic characteristics of the Pentatomidae and constructed phylogenetic trees using Bayesian inference and maximum likelihood methods. Our results showed that gene arrangements, base composition, start/stop codons, gene overlaps, and RNA structures were conserved within the Pentatomidae and that congeneric species shared more characteristics. Saturation and heterogeneity analyses revealed that our PCGs and PCGRNA datasets were valid for phylogenetic analysis. Phylogenetic analyses showed consistent topologies based on BI and ML methods. These analyses strongly supported that Eurydema species belong to the tribe Strachiini, and formed a sister group with Pentatomini. The relationships among Eurydema species were shown to be consistent with their morphological features. (Strachiini + Pentatomini) was found to be a stable sibling of the clade comprising Cappaeini, Graphosomini, and Carpocorini. Furthermore, our results indicated that Graphosoma rubrolineatum (Heteroptera: Pentatomidae) belongs to the Pentatominae and not the Podopinae.

PACES: prediction of N4-acetylcytidine (ac4C) modification sites in mRNA.

N4-acetylcytidine (ac4C) is a highly conserved RNA modification and is the first acetylation event described in mRNA. ac4C in mRNA has been demonstrated to be involved in the regulation of mRNA stability, processing and translation, but the exact means by which ac4C works remain unclear. In addition, ac4C is widely distributed within the human transcriptome at physiologically relevant levels and so far only a small fraction of modified sequences have been detected by experiments. In this study, we developed a predictor of ac4C sites in human mRNA named PACES to help mining possible modified motifs. PACES combines two random forest classifiers, position-specific dinucleotide sequence profile and K-nucleotide frequencies. With genomic sequences as input, PACES gives possible modified sequences based on the training model. PACES is freely available at http://www.rnanut.net/paces/.

Separation and enrichment of phenolics improved the antibiofilm and antibacterial activity of the fractions from Citrus medica L. var. sarcodactylis in vitro and in tofu.

Separation and enrichment of phenolic components from Citrus medica L. var. sarcodactylis were performed to improve the antibiofilm and antibacterial activity/efficiency of the fractions for the first time. Through separation of the crude extracts, the preparation of four fractions was done by chromatography on macroporous resin. It was found that the antibiofilm and antibacterial activity of the fractions were significantly enhanced. The obtained 30% ethanol eluted fraction (EEF) and 60% EEF both significantly inhibited biofilm formation. After the second separation by polyamide resin chromatography, the activity of the fractions was also improved. Complete inhibition (100%) on biofilm formation of S. aureus was achieved at 2.0 mg/mL. The MIC of the fractions on S. aureus was decreased to 2.0 mg/mL. There has been variation of 7.3-185.6 mg/g of phenolic content in the fractions, and a strong correlation between the anti-biofilm, the antibacterial activity and the phenolic content. Chemical composition analysis showed the EEF comprised 17 phenolic compounds. Moreover, the obtained EEF exhibited much higher antibacterial activity in tofu than crude extract. Therefore, chromatography separation significantly improved the antibacterial and antibiofilm activity/efficiency of the fractions both in vitro and in tofu.

Complete mitochondrial genome sequence and phylogenetic implications of Chorosoma macilentum (Heteroptera: Rhopalidae).

The complete mitogenome of Chorosoma macilentum Stål, 1858 was obtained using a next-generation sequencing approach, which is the first report in genus Chorosoma. The genome is 16,330 bp long with high A + T content. The phylogenetic analyses based on 13 protein-coding genes (PCGs) provide strong support for monophyly of each family within Pentatomomorpha. Chorosoma Macilentum was clustered together with other species within Rhopalidae, and grouped sibling species with Aeschyntelus notatus.