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Abstract Influence maximization aims to select a subset of top- k influential nodes to maximize the influence propagation, and it remains an open research topic of viral marketing and social network analysis. Submodularity-based methods including greedy algorithm can provide solutions with performance guarantee, but the time complexity is unbearable especially in large-scale networks. Meanwhile, conventional centrality-based measures cannot provide steady performance for multiple influential nodes identification. In this paper, we propose an improved discrete particle swarm optimization with an enhanced network topology-based strategy for influence maximization. According to the strategy, the k influential nodes in a temporary optimal seed set are recombined firstly in ascending order by degree metric to let the nodes with lower degree centrality exploit more influential neighbors preferentially. Secondly, a local greedy strategy is applied to replace the current node with the most influential node from the direct neighbor set of each node from the temporary seed set. The experimental results conducted in six social networks under independent cascade model show that the proposed algorithm outperforms typical centrality-based heuristics, and achieves comparable results to greedy algorithm but with less time complexity. Highlights • We make further exploration on discrete PSO for influence maximization problem. • We design a network topology-based local search to enhance the exploitation of DPSO. • Our algorithm achieves comparable results to greedy algorithm and the CELF algorithm. • The proposed algorithm provides steady performance on identifying influential nodes. [ABSTRACT FROM AUTHOR]

Traditional therapeutics have encountered a bottleneck caused by diagnosis delay and subjective and unreliable assessment. Biomarkers can overcome this bottleneck and guide us toward personalized precision medicine for oral squamous cell carcinoma. To achieve this, it is important to efficiently and accurately screen out specific biomarkers from among the huge number of molecules. Progress in omics-based high-throughput technology has laid a solid foundation for biomarker discovery. With credible and systemic biomarker models, more precise and personalized diagnosis and assessment would be achieved and patients would be more likely to be cured and have a higher quality of life. However, this is not straightforward owing to the complexity of molecules involved in tumorigenesis. In this context, there is a need to focus on tumor heterogeneity and homogeneity, which are discussed in detail. In this review, we aim to provide an understanding of biomarker discovery and application for precision medicine of oral squamous cell carcinoma, and have a strong belief that biomarker will pave the road toward future precision medicine. [ABSTRACT FROM AUTHOR]

Simplified Molecular-Input Line-Entry System (SMILES) is one of a widely used molecular representation methods for molecular property prediction. We conjecture that all the characters in the SMILES string of a molecule are essential for making up the molecules, but most of them make little contribution to determining a particular property of the molecule. Therefore, we verified the conjecture in the pre-experiment. Motivated by the result, we propose to inject proper noisy information into the SMILES to augment the training data by increasing the diversity of the labeled molecules. To this end, we explore injecting perturbing noise into the original labeled SMILES strings to construct augmented data for alleviating the limitation of the labeled compound data and enhancing the model to extract more useful molecular representation for molecular property prediction. Specifically, we directly adopt mask, swap, deletion, and fusion operations on SMILES strings to randomly mask, swap, and delete atoms in SMILES strings. Then, the augmented data is used by two strategies: each epoch alternately feeds the original and perturbing noisy molecules, or each batch alternately feeds the original and perturbing noisy molecules. We conduct experiments on both Transformer and BiGRU models to validate the effectiveness by adopting widely used datasets from MoleculeNet and ZINC. Experimental results demonstrate that the proposed method outperforms strong baselines on all the datasets. NoiseMol obtains the best performance on BBBP and FDA when compared with state-of-the-art methods. Besides, NoiseMol achieves the best accuracy on LogP. Therefore, injecting perturbing noise into the labeled SMILES strings is an effective and efficient method, which improves the prediction performance, generalization, and robustness of the deep learning models. [Display omitted] • Propose the NoiseMol, a data augmentation method that injects perturbing noise into the original SMILES strings and is conveniently used for molecular representation based on atom-level and substring-level. • Propose four types of injecting perturbing noise, including mask, swap, deletion, and fusion. NoiseMol increases the diversity of SMILES strings and improves the prediction performance, as well as the generalization and robustness of the model. • Conduct experiments on BiGRU and Transformer models to evaluate the performance of the proposed data augmentation method, respectively. Compared with conventional and state-of-the-art baselines, NoiseMol demonstrates comparable or superior performance. [ABSTRACT FROM AUTHOR]

Real-world networks are usually sparse and high-dimensional representations that occupy considerable storage space and increase the computational complexity when performing network tasks. Recently, various methods of network representation learning that can effectively learn dense and low-dimensional network representations have been proposed. However, while these methods consider either the global or local structure of the network, the structures are not considered comprehensively. To address this problem, we propose a novel network representation learning method based on random walk with restart (NRL-RWR), which effectively captures the global and local structures of a network by means of an improved random walk with restart strategy. First, the random walk with restart is improved by an attention mechanism that assigns weights to edges by measuring the similarity between nodes. Second, the improved random walk with restart strategy has the ability to obtain the probability distribution of nodes, and always returns to the starting position with a certain probability in the process of walking, so that more global and local structures of the network can be retained. Finally, we construct an objective function based on Kullback–Leibler divergence, and we adopt negative sampling and edge sampling to optimize the objective function and reduce the computational complexity of network representation learning. Experimental results based on six real-world networks show that NRL-RWR outperforms state-of-the-art network representation learning methods in obtaining low-dimensional vector representations of networks. • NRL-RWR is network representation learning method based on random walk with restart. • NRL-RWR can preserve both the global and local structures of a network. • NRL-RWR improves random walk with restart by attention mechanism. • NRL-RWR is optimized by edge sampling and negative sampling. • NRL-RWR has better node classification and visualization abilities than SOTA methods. [ABSTRACT FROM AUTHOR]

Predicting molecular properties and compound-protein interactions (CPIs) are two important areas of drug design and discovery. They are also an essential way to discover lead compounds in virtual screening. Recently, in silico methods based on deep learning have demonstrated excellent performance in various challenges. It is imperative to develop efficient computational methods to predict accurately both molecular properties and CPIs in drug research using deep learning techniques. In this paper, we propose a deep learning method applicable to both molecular property prediction and CPI prediction based on the idea that both are generally influenced by chemical structure and sequence information of compounds and proteins. Molecular properties are inferred by integrating the molecular structure and sequence information of compounds, and CPIs are predicted by integrating protein sequence and compound structure. The method combines topological structure and sequence fingerprint information of molecules, extracts adequately raw data features, and generates highly representative features for prediction. Molecular property prediction experiments were conducted on BACE, P53 and hERG datasets, and CPI prediction experiments were conducted on Human, C. elegans and KIBA datasets. MG-S achieves outperformance in molecular property prediction on P53, the differences in AUC, Precision and MCC are 0.030, 0.050 and 0.100, respectively, over the suboptimal baseline model, and provides consistently good results on BACE and hERG.The model also achieves impressive performance in CPI prediction, the differences in AUC, Precision and MCC on KIBA are 0.141, 0.138, 0.090 and 0.082, respectively, compared with the state-of-the-art models. The comprehensive results show that the MG-S model has higher performance, better classification ability, and faster convergence. MG-S will serve as a useful method to predict compound properties and CPIs in the early stages of drug design and discovery.Our code and datasets are available at: https://github.com/happay-ending/cpi_cpp. [Display omitted] [ABSTRACT FROM AUTHOR]

Smoking carcinogen N-nitrosamines such as 4-methylnitrosamino-l-3-pyridyl-butanone (NNK) require metabolic activation to exert their genotoxicity. The first activation step is mainly catalyzed by cytochrome P450 (CYP) family. Estrogen receptor α (ERα) plays a role in lung pathology. The association between them is unknown. In this study, we explored the relationship and function of CYP1B1 and ERα in NNK-induced lung tumorigenesis. CYP1B1 and ERα expression was analyzed in human lung cancer tissues and NNK-induced lung tumor of A/J mice. Cell lines NCI-H23 and NCI-H460 were employed to further study the responsible mechanisms using various cellular and molecular approaches. Our in vivo experiments demonstrated that CYP1B1 and ERα were over-expressed at the early stage of NNK-induced lung tumorigenesis. Microarray analysis found that ERα was involved in the extracellular-signal-regulated kinase (ERK)/MAPK pathway. NNK activated RAS/ERK/AP1 as it remarkably increased the levels of p-ERK, c-Fos, and c-Jun but inhibited multiple negative regulators of Ras/ERK/AP1, Pdcd4, Spry1, Spry2, and Btg2 through up-regulating miR-21. Both CYP1B1 siRNA and ERK-specific inhibitor U0126 suppressed NNK-mediated ERα up-regulation, suggesting that ERα was downstream of CYP1B1 and ERK. ERK inactivation led to the accumulation of CYP1B1, indicating that CYP1B1 was upstream of ERK activation. Inhibition of ERK or ERα decreased NNK-induced cell proliferation. Blockage of CYP1B1 or ERα induced apoptosis of lung cancer cells. Collectively, NNK-mediated ERα induction is via CYP1B1 and ERK and contributes to the lung carcinogenesis. The inhibition of CYP1B1, ERK, or ERα may arrest the lung cancer cell growth, implicating a pivotal strategy for the treatment of lung cancer. Key messages: [ABSTRACT FROM AUTHOR]

Five 19(4 → 3)- abeo -abietane diterpenoids, scrodentoids A–E ( 1 – 5 ), were isolated from the whole plant of Scrophularia dentata . Planar structures of scrodentoids A–E were elucidated mainly by using 1D, 2D NMR and MS data. The absolute configurations of compounds 1 and 2 were established using X-ray crystallographic analysis. The absolute configurations of other compounds were confirmed using HPLC-UV/CD detection. The immunosuppressive effects of compounds 1 – 5 were studied using a ConA-induced splenocyte proliferation model. These compounds significantly inhibited ConA-induced splenocyte proliferation, with IC 50 values in the range of 3.49–133.86 μM. Compounds 1 – 5 (IC 50 > 10 μM) showed no discernible cytotoxic activity against B16 or MCF-7 cells. [ABSTRACT FROM AUTHOR]

Highlights: [•] First phytochemical investigation of Scrophularia dentata Royle ex Benth. [•] Two new phenylpropanoid glycosides along with seven known ones were isolated. [•] Compounds 1 and 2 failed to be separated, the interesterification mechanism was presumed and discussed firstly. [•] The quantum chemical calculation was applied to elucidate the interesterification mechanism. [ABSTRACT FROM AUTHOR]

Abstract: Optimization of Radix Rehmanniae polysaccharides (RRPs) was done using response surface methodology (RSM). Optimum extraction conditions for maximizing the determination of Radix Rehmanniae polysaccharides were: extraction temperature 100°C, extraction time 2h and ratio of liquid to solid 6. The model had a satisfactory coefficient of R 2 (=0.9815) and was verified experimentally. The results suggested that the conditions were mild and useful for extraction yield of Radix Rehmanniae polysaccharides. Pharmacological experiment showed that Radix Rehmanniae polysaccharides could enhance serum interleukin-2 (IL-2), interleukin-4 (IL-4) and interleukin-10 (IL-10) levels, skin glutathione (GSH), superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GSH-Px) activities, and decrease skin malondialdehyde (MDA) level in ultraviolet B (UVB) ray treated mice, this study suggests that Radix Rehmanniae polysaccharides extract may prove to be a useful therapeutic option in the reversal of skin diseases. [Copyright &y& Elsevier]

Spatial variations in lake sediments are a product of their depositional heterogeneities. To better correlate synchronous strata in Huangqihai Lake, two sections were excavated from the lake-center and the lake-shore. The study involved detailed lithology, mineralogy, and rock magnetic analysis. The results show that the frequency-dependent susceptibility, which is related to the content of super-paramagnetic minerals, is relatively easy to contrast between the two sections. However, mass susceptibility and saturation isothermal remanence, which are related to the content of total magnetic minerals, are difficult to contrast. A possible reason for this difference is that small-particle super-paramagnetic minerals are formed mainly during pedogenic processes, which correlates well with climate change and avoids the effects of local factors when corrections are made at the whole-lake scale. Both sections can be divided into three stages, which appear to be well correlated with previous results obtained through other environmental proxies. [ABSTRACT FROM AUTHOR]

Abstract: A polysaccharide was isolated from the Herba Cistanches. This polysaccharides contained glucose, galactose, rhamnose, arabinose and fructose. The native polysaccharide could be fractionated by size exclusion chromatography into one major fraction. The apparent average molecular weight of the purified polysaccharide was estimated to be approximately 385kDa by size-exclusion HPLC. Furthermore, this polysaccharides still exhibited strong antioxidant activities in aged rats’ skin. Namely, Herba Cistanches polysaccharides inhibited the oxidative modification of lipids, thus protecting cells from injury by the oxidized molecules in aged rats’ skin. The present results were in accordance with previous studies on the mode of the protective effect of the plant extract which showed positively that the protection may possibly be attributed to the combination of more than one biological activities and that the use of antioxidants might be an useful approach in the treatment of some skin aging diseases. [Copyright &y& Elsevier]

: BackgroundThe polymerase chain reaction products of 8400 human genes were spotted on a chip in array. The DNAs were then fixed on the glass plate by a series of treatments. Total RNAs were isolated from freshly excised human keloids and normal skins and then were purified to mRNAs by Oligotex. Both the mRNAs from keloids and normal skins were reversely transcribed to cDNAs with the incorporation of fluorescent dUTP for preparing the hybridization probes. The mixed probes were then hybridized to the cDNA microarray. After highly stringent washing, the cDNA microarray was scanned for the fluorescent signals to display the differences between two kinds of tissues.: ResultsAmong 8400 human genes, there were 402 genes (4.79%) with different expression levels between the keloids and normal skins in all cases, 250 genes, including TGF-β1 and NGF, were upregulated (2.98%) and 152 downregulated (1.81%). Analyses of collagen, fibronectin, proteoglycan, growth factors, and apoptosis-related molecule gene expression confirmed that our molecular data obtained by cDNA microarray were consistent with the published biochemical and clinical observations of keloids. Higher expression of TGF-β1 and NGF in keloids versus normal skins was also testified with reverse transcription polymerase chain reaction method.: ConclusionsDNA microarray technology is an effective technique in screening for differences in gene expression between keloid and normal skin. Many genes are involved in the formation of keloids. Further analysis of the obtained genes will help to understand the molecular mechanism of keloid formation. [Copyright &y& Elsevier]

Prostate cancer (PC) is the most diagnosed type of malignancy in men and the major frequently cause of cancer-related death worldwide. The androgen receptor (AR) has become a promising drug target for the treatment of PC. Here, we reported the design, optimization and evaluation of pyridine tetrahydroisoquinoline thiohydantoin derivatives with improved activity and safety as potent AR antagonists. The most promising compound 42f exhibited potent inhibitory activity on AR and strongly blocked AR nuclear translocation. Moreover, 42f displayed promising in vitro antitumor activity toward AR-dependent prostate cancer cell lines (LNCaP) and also demonstrated therapeutic effects in LNCaP xenograft tumor model in mice (TGI: 79%) with no apparent toxicity observed in vivo. More importantly, 42f showed negligible penetration of the brain-blood barrier (BBB) compared with enzalutamide. These results provide a foundation for the development of a new class of androgen receptor antagonists for potential therapeutics against PC with lower seizurogenic risk for patients. Image 1 • A series of pyridine tetrahydroisoquinoline thiohydantoin derivatives with improved activity and safety as AR antagonists were reported. • The most promising compound 42f exhibited potent therapeutic effects in LNCaP xenograft tumor model (TGI: 79%) with no apparent toxicity. • 42f showed negligible penetration of the BBB compared with enzalutamide. • These results provide a foundation for the development of a new class of androgen receptor antagonists with low seizurogenic risk. [ABSTRACT FROM AUTHOR]

Gentiana straminea Maxim. is a perennial herb and mainly distributed in the Qinghai-Tibetan Plateau. To adapt to the extreme environment, it has developed particular morphological, physiological, and genetic structures. Also, rich in iridoids, it is one of the original plants of traditional Chinese herb 'Qinjiao'. Herein, we present its first chromosome-level genome sequence assembly and compare it with the genomes of other Gentiana species to facilitate the analysis of genomic characteristics. The assembled genome size of G. straminea was 1.25 Gb, with a contig N50 of 7.5 Mb. A total of 96.08% of the genome sequences was anchored on 13 pseudochromosomes, with a scaffold N50 of 92.70 Mb. A total of 54,310 protein-coding genes were predicted, 80.25% of which were functionally annotated. Comparative genomic analyses indicated that G. straminea experienced two whole-genome duplication events after the γ whole-genome triplication with other eudicots, and it diverged from other Gentiana species at ~3.2 Mya. A total of 142 enzyme-coding genes related to iridoid biosynthesis were identified in its genome. Additionally, we identified differences in the number and expression patterns of iridoid biosynthetic pathway genes in G. straminea compared with two other Gentiana species by integrating whole-genome sequence and transcriptomic analyses., (© The Author(s) 2024. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.)

Background: Gentiana crassicaulis Duthie ex Burk., a key species used in traditional Chinese medicine for treating rheumatic pain and stroke, contains iridoids as its primary active component. However, the biosynthetic mechanisms underlying iridoid production are not fully understood. Methods: This study focused on iridoid biosynthesis during the germination of G. crassicaulis seeds, integrating metabolomic and transcriptomic analyses to uncover the underlying pathways and key candidate genes. Results: 196,132 unigenes and 10 iridoid compounds were identified through RNA-seq and ultra performance liquid chromatography-quadrupole time of flight-mass spectrometer (UPLC-Q-TOF-MS), respectively. The intersection of results from Pearson correlation analysis and weighted gene co-expression network analysis (WGCNA) revealed a significant correlation between 26 genes and iridoid levels, suggesting their potential role in the iridoid metabolism. Notably, six highly expressed candidate genes ( DL7H , SLS , CYP76 , CYP72A2 , CYP84A1 , and 13-LOX3 ) and five iridoids (loganic acid, sweroside, swertiamarin, gentiopicroside, and 6'-O-β-D-glucosyl-gentiopicroside) responded to methyl jasmonate stimulation in G. crassicaulis seedlings. Conclusions: by combining the known functions of candidate gene families, It is hypothesized that the CYP716 and LOX families exert indirect influences on iridoid metabolism, while the CYP71 , CYP81 , CYP72 , CYP76 , CYP710 families, 2OG-FeII family, and the glucosyltransferase family are likely to play direct roles in the biosynthetic transformations of the five iridoids. This study provides a theoretical basis for further functional gene validation and metabolic engineering aimed at enhancing iridoid production. The insights gained could lead to improved iridoid production efficiency in medicinal plants, ultimately benefiting the quality and efficacy of medicinal materials.

Introduction: Proton pump inhibitors (PPIs) and histamine type-2 receptor antagonists (H2RAs) are generally effective in preventing delayed bleeding and healing artificial wounds after endoscopic mucosal resection (EMR) or endoscopic submucosal dissection (ESD). This study aimed to review the therapeutic effects of PPIs and H2RAs on damage caused by EMR and ESD., Material and Methods: Thirteen articles were collected between 2002 and 2022 by searching Medlib, ScienceDirect, PubMed, International Scientific Indexing (ISI), Embase, and Scopus databases using valid keywords. The main inclusion criteria were delayed wound healing, bleeding, epigastric pain, intraoperative bleeding, and perforation. The odds ratio (OR) and 95% confidence interval (95% CI) were evaluated using a random or fixed effects model. Data analysis was performed using Stata v. 14.2., Results: A total of 13 articles including 1,483 patients were analyzed. The results showed that delayed bleeding was significantly less frequent in the PPI group than in the H2RA group (OR = 0.6; 95% CI: 0.39-0.92). Subgroup analysis showed that PPI was more effective in preventing delayed bleeding than H2RA for ESD wounds (OR = 0.65; 95% CI: 0.44-1.08). There was no statistically significant difference between both groups regarding the incidence of epigastric pain, intraoperative bleeding, wound healing, and perforation after endoscopic treatments., Conclusion: The meta-analysis results reveal that PPI is more effective than H2RA in preventing delayed bleeding after endoscopic treatment, particularly in patients treated with ESD. However, there was no significant difference between PPI and H2RA in terms of intraoperative bleeding, epigastric pain, wound healing, and perforation from endoscopic therapy.

Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable prediction approaches. In this study, we propose GraphsformerCPI, an end-to-end deep learning framework that improves prediction performance and interpretability. GraphsformerCPI treats compounds and proteins as sequences of nodes with spatial structures, and leverages novel structure-enhanced self-attention mechanisms to integrate semantic and graph structural features within molecules for deep molecule representations. To capture the vital association between compound atoms and protein residues, we devise a dual-attention mechanism to effectively extract relational features through .cross-mapping. By extending the powerful learning capabilities of Transformers to spatial structures and extensively utilizing attention mechanisms, our model offers strong interpretability, a significant advantage over most black-box deep learning methods. To evaluate GraphsformerCPI, extensive experiments were conducted on benchmark datasets including human, C. elegans, Davis and KIBA datasets. We explored the impact of model depth and dropout rate on performance and compared our model against state-of-the-art baseline models. Our results demonstrate that GraphsformerCPI outperforms baseline models in classification datasets and achieves competitive performance in regression datasets. Specifically, on the human dataset, GraphsformerCPI achieves an average improvement of 1.6% in AUC, 0.5% in precision, and 5.3% in recall. On the KIBA dataset, the average improvement in Concordance index (CI) and mean squared error (MSE) is 3.3% and 7.2%, respectively. Molecular docking shows that our model provides novel insights into the intrinsic interactions and binding mechanisms. Our research holds practical significance in effectively predicting CPIs and binding affinities, identifying key atoms and residues, enhancing model interpretability., (© 2024. International Association of Scientists in the Interdisciplinary Areas.)

To compare the effects of Ivor-Lewis esophagectomy and McKeown esophagectomy on perioperative anxiety and depression in patients with esophageal cancer. Sixty-three patients with stage I-III middle and lower esophageal carcinoma from June 2021 to December 2022 were randomly divided into observation group (n = 32) treated with laparoscopic Ivor-Lewis esophagectomy and control group (n = 31) treated with laparoscopic McKeown esophagectomy. Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) were measured on the second day of admission and the fifth day after surgery to assess the presence of depression and anxiety. The preoperative and postoperative clinical data of both groups were compared, and multivariate analysis was used to identify risk factors associated with depression and anxiety in patients with esophageal cancer. There was no significant difference in SDS and SAS standard scores between the observation group and the control group ( P  > 0.05). The postoperative SDS and SAS scores in the control group were significantly higher than those before and after operation in the observation group ( P  < 0.01). According to univariate analysis, patients with TNM stage III, tumor diameter greater than 3 cm, postoperative complications, radical McKeown esophagectomy, and C-reactive protein levels above 10 mg/L had a higher incidence of depression and anxiety ( P  < 0.05). Multivariate logistic analysis showed that TNM stage III (depression: OR 1.683, 95 CI 1.429-1.861; Anxiety: OR 1.739, 95 CI 1.516-1.902), postoperative complications (depression: OR 2.345, 95 CI 1.435-3.891; Anxiety: OR 1.872, 95 CI 1.372-3.471), surgical approach (depression: OR 1.609, 95 CI 1.502-3.193; Anxiety: OR 1.658, 95 CI 1.469-2.059), and C-reactive protein (depression: OR 2.260, 95 CI 1.157-4.059; Anxiety: OR 0.373, 95 CI 0.253-0.976) were all independent factors for depression and anxiety in patients after esophageal cancer surgery ( P  < 0.05). The Ivor-Lewis esophagectomy has the advantages of fewer complications and low inflammatory response, which can help alleviate anxiety and depression and improve patients' quality of life and prognosis., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

Gentiana crassicaulis and G. straminea are alpine plants of Gentiana with important medicinal value and complex genetic backgrounds. In this study, the mitochondrial genomes (mtDNAs) of these two species were sequenced. The mtDNAs of G. crassicaulis and G. straminea are 368,808 and 410,086 bp long, respectively, 52 and 49 unique genes are annotated in the two species, and the gene arrangement varies widely. Compared to G. crassicaulis, G. straminea loses three effective genes, namely atp6, trnG-GCC and trnV-GAC. As a pseudogene, the atp6 gene of G. straminea is incomplete, which is rare in higher plants. We detected 1696 and 1858 pairs of long repeats and 213 SSRs and 250 SSs in the mtDNAs of G. crassicaulis and G. straminea, respectively. There are 392 SNPs and 18 InDels between the two genomes, and syntenic sequence and structural variation analysis show low collinearity between the two genomes. Chloroplast DNA transferring to mtDNA is observed in both species, and 46,511 and 55,043 bp transferred segments containing three tRNA genes are identified, respectively. Comparative analysis of mtDNAs of G. crassicaulis, G. straminea and four species of Gentianales determined 18 core genes, and there is no specific gene in G. crassicaulis and G. straminea. The phylogenetic tree based on mtDNAs places Gentianaceae in a branch of Gentianales. This study is the first to analyze the mtDNAs of Gentianaceae, which could provide information for analysis of the structure of mtDNAs of higher plants and phylogenetic research of Gentianaceae and Gentianales., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Ala et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Molecular property prediction is an essential but challenging task in drug discovery. The recurrent neural network (RNN) and Transformer are the mainstream methods for sequence modeling, and both have been successfully applied independently for molecular property prediction. As the local information and global information of molecules are very important for molecular properties, we aim to integrate the bi-directional gated recurrent unit (BiGRU) into the original Transformer encoder, together with self-attention to better capture local and global molecular information simultaneously. To this end, we propose the TranGRU approach, which encodes the local and global information of molecules by using the BiGRU and self-attention, respectively. Then, we use a gate mechanism to reasonably fuse the two molecular representations. In this way, we enhance the ability of the proposed model to encode both local and global molecular information. Compared to the baselines and state-of-the-art methods when treating each task as a single-task classification on Tox21, the proposed approach outperforms the baselines on 9 out of 12 tasks and state-of-the-art methods on 5 out of 12 tasks. TranGRU also obtains the best ROC-AUC scores on BBBP, FDA, LogP, and Tox21 (multitask classification) and has a comparable performance on ToxCast, BACE, and ecoli. On the whole, TranGRU achieves better performance for molecular property prediction. The source code is available in GitHub: https://github.com/Jiangjing0122/TranGRU., Competing Interests: Conflict of InterestsThe authors declare that they have no conflicts of interest., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Nanostructured physical antibacterial surfaces are of great interest due to the increasing antibiotic resistance. In this work, the titania nanotube (TNT) array, a potential physical antibacterial surface, was used for antimicrobial evaluation. The early antibacterial properties of TNTs were assessed based on three growth phases of Staphylococcus aureus ( S. aureus ), and the physical factors influencing the antibacterial properties were comprehensively discussed. The results show apparent early antibacterial effects of TNTs, including the anti-initial attachment during the lag phase, the inhibition of proliferation and bactericidal effect during the logarithmic phase, and the inhibition of biofilm formation during the stationary phase. These antimicrobial effects are closely related to the combined influence of various physical properties of TNTs, such as diameter, hydrophilicity, roughness, and charge. The present work suggests that the evaluation of the early antimicrobial behavior of biomaterials should pay more attention on the biological characteristics of bacteria.

Enzalutamide (ENZ) is a second-generation androgen receptor (AR) antagonist used for the treatment of castration-resistant prostate cancer (CRPC) and reportedly prolongs survival time within a year of starting therapy. However, CRPC patients can develop ENZ resistance (ENZR), mainly driven by abnormal reactivation of AR signaling, involving increased expression of the full-length AR (ARfl) or dominantly active androgen receptor splice variant 7 (ARv7) and ARfl/ARv7 heterodimers. There is currently no efficient treatment for ENZR in CRPC. Herein, a small molecule LLU-206 was rationally designed based on the ENZ structure and exhibited potent inhibition of both ARfl and constitutively active ARv7 to inhibit PCa proliferation and suppress ENZR in CRPC. Mechanically, LLU-206 promoted ARfl/ARv7 protein degradation and decreased ARfl/ARv7 heterodimers through mouse double minute 2-mediated ubiquitination. Finally, LLU-206 exhibited favorable pharmacokinetic properties with poor permeability across the blood-brain barrier, leading to a lower prevalence of adverse effects, including seizure and neurotoxicity, than ENZ-based therapies. In a nutshell, our findings demonstrated that LLU-206 could effectively inhibit ARfl/ARv7-driven CRPC by dual-targeting of ARfl/ARv7 heterodimers and protein degradation, providing new insights for the design of new-generation AR inhibitors to overcome ARfl/ARv7-driven CRPC., Competing Interests: The authors declare no conflicts of interest., (© 2022 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences. Production and hosting by Elsevier B.V.)

Motivation: Extracting useful molecular features is essential for molecular property prediction. Atom-level representation is a common representation of molecules, ignoring the sub-structure or branch information of molecules to some extent; however, it is vice versa for the substring-level representation. Both atom-level and substring-level representations may lose the neighborhood or spatial information of molecules. While molecular graph representation aggregating the neighborhood information of a molecule has a weak ability in expressing the chiral molecules or symmetrical structure. In this article, we aim to make use of the advantages of representations in different granularities simultaneously for molecular property prediction. To this end, we propose a fusion model named MultiGran-SMILES, which integrates the molecular features of atoms, sub-structures and graphs from the input. Compared with the single granularity representation of molecules, our method leverages the advantages of various granularity representations simultaneously and adjusts the contribution of each type of representation adaptively for molecular property prediction., Results: The experimental results show that our MultiGran-SMILES method achieves state-of-the-art performance on BBBP, LogP, HIV and ClinTox datasets. For the BACE, FDA and Tox21 datasets, the results are comparable with the state-of-the-art models. Moreover, the experimental results show that the gains of our proposed method are bigger for the molecules with obvious functional groups or branches., Availability and Implementation: The code and data underlying this work are available on GitHub at https://github. com/Jiangjing0122/MultiGran., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In nature, some plant species produce seedpods with spines, which is an adaptive biological trait for protecting the seed and helping seed dispersal. However, the molecular mechanism of spine formation is still unclear. While conducting routine tissue culture and transformation in the model legume Medicago truncatula, we identified a smooth seedpod (ssp1) mutant with a suite of other phenotypic changes. Preliminary analysis showed that the mutation was derived from the tissue culture process. Genetic segregation analysis suggested that ssp1 is a recessive mutant. By combining whole-genome sequencing and bioinformatics analysis, we found that the mutant phenotype was caused by a single nucleotide polymorphism and a 30 bp deletion in the gene locus Medtr4g039430, named SSP1. Complementation of the M. truncatula ssp1 and Arabidopsis twd1 mutants showed complete restoration, indicating that SSP1 is an ortholog of Arabidopsis TWD1 which encodes an immunophilin-like FK506-binding protein 42. The formation of spines on seedpods is associated with auxin transport. The method used in this study offers an effective way for detecting genes responsible for somaclonal variations. The results demonstrate, for the first time, that SSP1 plays a crucial role in the determination of spine formation on seedpods., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Purpose: Microvascular free tissue transfer has become a mainstay for head and neck reconstruction, and vascular anastomosis is 1 of the most challenging aspects. This study aimed to explore a safe, convenient, and efficient technique for end-to-side venous anastomosis using the coupling device., Materials and Methods: We evaluated a novel surgical strategy, double thread suspension (DTS), by reviewing data collected in a consecutive series of 60 patients who underwent free tissue grafting after surgical resection of head and neck tumors. Patients who underwent end-to-side microvascular anastomosis using the microvascular coupler device were randomly divided into the DTS group (n = 30) and the traditional procedure group (TRA, n = 30), the demographic data, coupler size, anastomosis time, microvascular complications, and flap survival were determined., Results: The optional size range of coupler devices in the DTS group is better than that of the conventional group, ranging from 1.5 to 4.0 mm compared with 2.0 to 4.0 mm. The average anastomosis time was 4.68 ± 0.43 minutes in the DTS group which was significantly lower than the conventional group that was 9.24 ± 1.46 minutes (P < .0001) . There was no statistically significant difference between the 2 groups in coupler related complications or flap survival., Conclusion: The novel DTS technique provides the advantages of lowering procedure complexity, reducing operative time, and preventing tearing of vessel wall, therefore making DTS-guided end-to-side anastomosis a safe, relatively straightforward, and reliable technique., (Copyright © 2021 The American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.)

The legume plant alfalfa ( Medicago sativa L.) is a widely cultivated perennial forage due to its high protein content, palatability, and strong adaptability to diverse agro-ecological zones. Alfalfa is a self-incompatible cross-pollinated autotetraploid species with tetrasomic inheritance. Therefore, maintaining excellent traits through seed reproduction is a prime challenge in alfalfa. However, the cutting propagation technology could enable consistent multiplication of quality plants that are genetically identical to the parent plant. The current study aimed to develop a simple, cost-effective, reproducible, and efficient hydroponic cutting method to preserve alfalfa plants and for molecular research. In this study, alfalfa landrace 'Wudi' was grown in hydroponics for 30 days and used as source material for cuttings. The top, middle and bottom sections of its stem were used as cuttings. The rooting rate, root length, and stem height of the different stem sections were compared to determine the best segment for alfalfa propagation in four nutrient treatments (H M , H M  + 1/500H, H M  + 1/1000H and d H M  + 1/2000H). After 21 days of culture, the rooting rates of all the three stem types under four cutting nutrient solutions were above 78%. The rooting rate of the middle and bottom parts in H M  + 1/1000 H and H M  + 1/2000 H nutrient solutions reached more than 93%, with a higher health survey score (> 4.70). In conclusion, this study developed a de novo cutting propagation method that can be used to conserve and propagate germplasm in breeding programs and research. This method is a new report on the cutting propagation of alfalfa by hydroponics, which could supplement the existing cutting propagation methods., Competing Interests: Conflict of interestThe authors declare that they have no conflicts of interest., (© The Author(s) 2021.)

Purpose: The aim of this study was to evaluate the effect of preoperative oral carbohydrate administration on patients undergoing Cesarean section with epidural anesthesia., Design: Randomized controlled clinical study., Methods: A total of 75 patients undergoing Cesarean section (American Society of Anesthesiologists physical status grade I-II) were randomized to preparation with a carbohydrate drink (CHO group), flavored water (placebo group), or to the fasting group. The CHO and placebo groups were double-blinded and given 300 mL of the drink 2 hours before surgery. Visual analog scores of the patient were assessed to evaluate thirst, hunger, and anxiety level, and the gastric antral cross-sectional areas were recorded by ultrasonography during the operative period. Insulin resistance was calculated on the basis of the blood glucose and insulin levels assessed before administration and after surgery., Findings: The CHO and placebo groups did not show an increase in gastric fluid volumes in terms of gastric antral cross-sectional area, and there were no adverse events. The visual analog scale scores at preoperative baseline were not different between groups . During the preoperative waiting period, preparation with CHO reduced not only thirst and anxiety more efficiently than water (placebo) but also hunger (P < .05), whereas water did not. No difference was observed in insulin resistance between groups before intake of the drink. Compared with the preoperative levels, insulin resistance showed a statistically significant increase in all groups (P < .05); however, the increase was significantly higher in the fasting and placebo groups than in the CHO group (P < .05)., Conclusions: Preoperative administration of CHO decreases postoperative insulin resistance and enhances pregnant women's comfort, leading to a reduced sense of thirst, hunger, and anxiety during the preoperative period for Cesarean section., (Copyright © 2020 American Society of PeriAnesthesia Nurses. Published by Elsevier Inc. All rights reserved.)

Quantum dots (QDs) have received considerable attention in information displays owing to their high quantum yield, high colour purity and low-cost fabrication. However, light emission for ultra-thin QD films with low mass percentage of QDs still need to be improved because the blue light can directly transmit the films, leading to insufficient energy to excite the QDs. In this study, we report QD films based on a poly(zinc methacrylate) coating with alloyed green-emitting CdZnSeS/ZnS quantum dots (QDs@PZnMA) together with high refractive-index BaTiO 3 nanoparticles to enhance the scattering coefficient of the QD films. Results demonstrate a 7.5-fold increase in the absorption coefficient, 11.3-fold increase in the scattering coefficient, 8.5-fold increase in the optical density (OD) and 8.6-fold increase in the green-light emission of QD films, compared with films that have the same mass percentage of pristine QDs. This approach provides a promising strategy for developing QD optical films with high scattering and enhanced light emission for flexible displays., Competing Interests: The authors declare no conflict of interest., (This journal is © The Royal Society of Chemistry.)

The computational drug discovery methods can find potential drug-target interactions more efficiently and have been widely studied over past few decades. Such methods explore the relationship between the structural properties of compounds and their biological activity with the assumption that similar compounds tend to share similar biological targets and vice versa. However, traditional Quantitative Structure - Activity Relationship (QSAR) methods often do not have desired accuracy due to insufficient data of compound activity. In this paper, we focus on building Multi-Task Learning (MTL)-based QSAR models by considering multiple similar biological targets together and make shared information transfer across from one task to another, thereby improving not only the learning efficiency, but also the prediction accuracy. This paper selects 6 assay groups with similar biological targets from PubChem and builds their QSAR models with MTL simultaneously. According to the experiment results, our MTL-based QSAR models have better performance over traditional prominent machine learning algorithms and the improvements are even more obvious when other baseline models have low accuracy. The superiority of our models is also proved by Student's t-test with level of significance 5%. Moreover, this paper also explores three different assumptions on the underlying pattern in the dataset and finds that the joint feature MTL models further improve the performance of the QSAR models and are more suitable for building QSAR models for multiple similar biological targets., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Data-driven statistical air quality prediction methods usually build models fast with moderate accuracy and have been studied a lot in recent years. However, due to the complexity of air quality prediction which usually involves multiple factors, such as meteorological, spatial, and temporal properties, it is still a challenge to propose a model with required accuracy. In this paper, we propose a hybrid ensemble model CERL to exploit the merits of both forward neural networks and recurrent neural networks that are designed for handling time serial data to predict air quality hourly. Measured air pollutant factors including Air Quality Index (AQI), PM 2.5 , PM 10 , CO, SO 2 , NO 2 , and O 3 are used as input to predict air quality from 1 to 8 h ahead. Based on the air quality prediction evaluation in Lanzhou and Xi'an, which are two important provincial capitals in Northwest China, CERL provides better performance over other baseline models. Moreover, as the step length increases, CERL has more obvious improvement. For example, the improvements of CERL in the 1-step, 3-step, 5-step, and 8-step prediction for PM 2.5 in Lanzhou are 1.82%, 8.01%, 9.98%, and 20.03%, respectively. The superiority of CERL is also proved by a hypothesis Diebold Mariano test with level of significance 5%.

The genus Gentiana is the largest in the Gentianaceae family with ca. 400 species. However, with most species growing on the Qinghai-Tibet plateau, the processes of adaptive evolution and speciation within the genus is not clear. Also, the genomic analyses could provide important information. So far, the complete chloroplast (cp) genome data of the genus are still deficient. As the second and third sequenced members within Gentianaceae, we report the construction of complete cp sequences of Gentiana robusta King ex Hook. f. and Gentiana crassicaulis Duthie ex Burk., and describe a comparative study of three Gentiana cp genomes, including the cp genome of Gentiana straminea Maxim. published previously. These cp genomes are highly conserved in gene size, gene content, and gene order and the rps16 pseudogene with exon2 missing was found common. Three repeat types and five SSR types were investigated, and the number and distribution are similar among the three genomes. Sixteen genome divergent hotspot regions were identified across these cp genomes that could provide potential molecular markers for further phylogenetic studies in Gentiana. The IR/SC boundary organizations in Gentianales cp genomes were compared and three different types of boundaries were observed. Six data partitions of cp genomes in Gentianales were used for phylogenetic analyses and different data partitions were largely congruent with each other. The ML phylogenetic tree was constructed based on the fragments in cp genomes commonly available in 33 species from Lamiids, including 12 species in Gentianales, 1 in Boraginaceae, 10 in Solanales, and 10 in Lamiales. The result strongly supports the position of Boraginaceae (Ehretia acuminata) as the sister of Solanales, with the bootstrap values of 97 %. This study provides a platform for further research into the molecular phylogenetics of species in the order Gentianales (family Gentianaceae) notably in respect of speciation and species identification.

Compound selectivity prediction plays an important role in identifying potential compounds that bind to the target of interest with high affinity. However, there is still short of efficient and accurate computational approaches to analyze and predict compound selectivity. In this paper, we propose two methods to improve the compound selectivity prediction. We employ an improved multitask learning method in Neural Networks (NNs), which not only incorporates both activity and selectivity for other targets, but also uses a probabilistic classifier with a logistic regression. We further improve the compound selectivity prediction by using the multitask learning method in Deep Belief Networks (DBNs) which can build a distributed representation model and improve the generalization of the shared tasks. In addition, we assign different weights to the auxiliary tasks that are related to the primary selectivity prediction task. In contrast to other related work, our methods greatly improve the accuracy of the compound selectivity prediction, in particular, using the multitask learning in DBNs with modified weights obtains the best performance.

Objective: To study seed biological characteristics of Gentiana crassicaulis., Method: The samples were collected from five localities, and the morphological observation, germination test and viability TTC test were carried out., Result: The seed morphological characters of all samples were similar to each other. The viability of all samples was similar to each other, but the germination rate of wild seed was the highest in all samples after stored for 8 months. Also, the seed energy of wild seed and sample 2008XR02 was higher than other three samples. There was no obvious correlation between thousand seed weight and germination rate., Conclusion: The quality of wild seed and sample 2008XR02 was superior than that of the other three samples. The results can be used for basic data of in situ conservation and germplasm breeding of G. crassicaulis.

Objective: To explore the regulatory mechanisms of transforming growth factor-beta1 (TGF-beta1) and two transcriptional factors Smad 2, 3 on hypertrophic scar formation and fetal scarless healing., Methods: Thirty-two cases were detected to compare the gene expression of TGF-beta1, Smad 2 and Smad 3 with RT-PCR. Among those cases, there were 8 cases of hypertrophic scars, 8 cases of control skins, 8 cases of fetal skins and 8 cases of adult skins., Results: TGF-beta1, Smad 2 and Smad 3 gene expression could all be detected in hypertrophic scars, fetal and adult skins. Among 8 groups examinated in this experiment (each group comprised a hypertrophic scar and its corresponding normal skin), there were 5, 8 and 5 groups in which TGF-beta1, Smad 2 and Smad 3 gene expression were higher in hypertrophic scars than in normal skins respectively. The fetal skins showed significantly lower level of TGF-beta1 and Smad 3 gene expression compared with adult skins (t = 2.204, P < 0.05 and t = 4.269, P < 0.01 respectively), while mRNA contents of Smad 2 were obviously higher in fetal skins than in adult skins (t = 6.685, P < 0.01)., Conclusion: TGF-beta1 and its downstream signal molecules Smad 2, Smad 3 might be involved in hypertrophic scar formation. Higher gene expression of TGF-beta1, Smad 2 and Smad 3 in hypertrophic scars might lead to stimulating extracellular matrix deposition, inducing fibroblast proliferation and accelerating fibrogenesis. Lower mRNA contents of TGF-beta1 and Smad 3 in fetal skins compared with adult skins might be associated with fetal scarless healing.