microRNA maintains nutrient homeostasis in the symbiont-host interaction.

Endosymbionts provide essential nutrients for hosts, promoting growth, development, and reproduction. However, the molecular regulation of nutrient transport from endosymbiont to host is not well understood. Here, we used bioinformatic analysis, RNA-Sequencing, luciferase assays, RNA immunoprecipitation, and in situ hybridization to show that a bacteriocyte-distributed MRP4 gene (multidrug resistance-associated protein 4) is negatively regulated by a host (aphid)-specific microRNA (miR-3024). Targeted metabolomics, microbiome analysis, vitamin B6 (VB6) supplements, 3D modeling/molecular docking, in vitro binding assays (voltage clamp recording and microscale thermophoresis), and functional complementation of Escherichia coli were jointly used to show that the miR-3024/MRP4 axis controls endosymbiont (Serratia)-produced VB6 transport to the host. The supplementation of miR-3024 increased the mortality of aphids, but partial rescue was achieved by providing an external source of VB6. The use of miR-3024 as part of a sustainable aphid pest-control strategy was evaluated by safety assessments in nontarget organisms (pollinators, predators, and entomopathogenic fungi) using virus-induced gene silencing assays and the expression of miR-3024 in transgenic tobacco. The supplementation of miR-3024 suppresses MRP4 expression, restricting the number of membrane channels, inhibiting VB6 transport, and ultimately killing the host. Under aphids facing stress conditions, the endosymbiont titer is decreased, and the VB6 production is also down-regulated, while the aphid's autonomous inhibition of miR-3024 enhances the expression of MRP4 and then increases the VB6 transport which finally ensures the VB6 homeostasis. The results confirm that miR-3024 regulates nutrient transport in the endosymbiont-host system and is a suitable target for sustainable pest control.

A two-task predictor for discovering phase separation proteins and their undergoing mechanism.

Liquid-liquid phase separation (LLPS) is one of the mechanisms mediating the compartmentalization of macromolecules (proteins and nucleic acids) in cells, forming biomolecular condensates or membraneless organelles. Consequently, the systematic identification of potential LLPS proteins is crucial for understanding the phase separation process and its biological mechanisms. A two-task predictor, Opt_PredLLPS, was developed to discover potential phase separation proteins and further evaluate their mechanism. The first task model of Opt_PredLLPS combines a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) through a fully connected layer, where the CNN utilizes evolutionary information features as input, and BiLSTM utilizes multimodal features as input. If a protein is predicted to be an LLPS protein, it is input into the second task model to predict whether this protein needs to interact with its partners to undergo LLPS. The second task model employs the XGBoost classification algorithm and 37 physicochemical properties following a three-step feature selection. The effectiveness of the model was validated on multiple benchmark datasets, and in silico saturation mutagenesis was used to identify regions that play a key role in phase separation. These findings may assist future research on the LLPS mechanism and the discovery of potential phase separation proteins.

MSlocPRED: deep transfer learning-based identification of multi-label mRNA subcellular localization.

Subcellular localization of messenger ribonucleic acid (mRNA) is a universal mechanism for precise and efficient control of the translation process. Although many computational methods have been constructed by researchers for predicting mRNA subcellular localization, very few of these computational methods have been designed to predict subcellular localization with multiple localization annotations, and their generalization performance could be improved. In this study, the prediction model MSlocPRED was constructed to identify multi-label mRNA subcellular localization. First, the preprocessed Dataset 1 and Dataset 2 are transformed into the form of images. The proposed MDNDO-SMDU resampling technique is then used to balance the number of samples in each category in the training dataset. Finally, deep transfer learning was used to construct the predictive model MSlocPRED to identify subcellular localization for 16 classes (Dataset 1) and 18 classes (Dataset 2). The results of comparative tests of different resampling techniques show that the resampling technique proposed in this study is more effective in preprocessing for subcellular localization. The prediction results of the datasets constructed by intercepting different NC end (Both the 5' and 3' untranslated regions that flank the protein-coding sequence and influence mRNA function without encoding proteins themselves.) lengths show that for Dataset 1 and Dataset 2, the prediction performance is best when the NC end is intercepted by 35 nucleotides, respectively. The results of both independent testing and five-fold cross-validation comparisons with established prediction tools show that MSlocPRED is significantly better than established tools for identifying multi-label mRNA subcellular localization. Additionally, to understand how the MSlocPRED model works during the prediction process, SHapley Additive exPlanations was used to explain it. The predictive model and associated datasets are available on the following github: https://github.com/ZBYnb1/MSlocPRED/tree/main.

scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks.

Single-cell ribonucleic acid sequencing (scRNA-seq) technology can be used to perform high-resolution analysis of the transcriptomes of individual cells. Therefore, its application has gained popularity for accurately analyzing the ever-increasing content of heterogeneous single-cell datasets. Central to interpreting scRNA-seq data is the clustering of cells to decipher transcriptomic diversity and infer cell behavior patterns. However, its complexity necessitates the application of advanced methodologies capable of resolving the inherent heterogeneity and limited gene expression characteristics of single-cell data. Herein, we introduce a novel deep learning-based algorithm for single-cell clustering, designated scDFN, which can significantly enhance the clustering of scRNA-seq data through a fusion network strategy. The scDFN algorithm applies a dual mechanism involving an autoencoder to extract attribute information and an improved graph autoencoder to capture topological nuances, integrated via a cross-network information fusion mechanism complemented by a triple self-supervision strategy. This fusion is optimized through a holistic consideration of four distinct loss functions. A comparative analysis with five leading scRNA-seq clustering methodologies across multiple datasets revealed the superiority of scDFN, as determined by better the Normalized Mutual Information (NMI) and the Adjusted Rand Index (ARI) metrics. Additionally, scDFN demonstrated robust multi-cluster dataset performance and exceptional resilience to batch effects. Ablation studies highlighted the key roles of the autoencoder and the improved graph autoencoder components, along with the critical contribution of the four joint loss functions to the overall efficacy of the algorithm. Through these advancements, scDFN set a new benchmark in single-cell clustering and can be used as an effective tool for the nuanced analysis of single-cell transcriptomics.

Advancing microRNA target site prediction with transformer and base-pairing patterns.

MicroRNAs (miRNAs) are short non-coding RNAs involved in various cellular processes, playing a crucial role in gene regulation. Identifying miRNA targets remains a central challenge and is pivotal for elucidating the complex gene regulatory networks. Traditional computational approaches have predominantly focused on identifying miRNA targets through perfect Watson-Crick base pairings within the seed region, referred to as canonical sites. However, emerging evidence suggests that perfect seed matches are not a prerequisite for miRNA-mediated regulation, underscoring the importance of also recognizing imperfect, or non-canonical, sites. To address this challenge, we propose Mimosa, a new computational approach that employs the Transformer framework to enhance the prediction of miRNA targets. Mimosa distinguishes itself by integrating contextual, positional and base-pairing information to capture in-depth attributes, thereby improving its predictive capabilities. Its unique ability to identify non-canonical base-pairing patterns makes Mimosa a standout model, reducing the reliance on pre-selecting candidate targets. Mimosa achieves superior performance in gene-level predictions and also shows impressive performance in site-level predictions across various non-human species through extensive benchmarking tests. To facilitate research efforts in miRNA targeting, we have developed an easy-to-use web server for comprehensive end-to-end predictions, which is publicly available at http://monash.bioweb.cloud.edu.au/Mimosa.

ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species.

A-to-I editing is the most prevalent RNA editing event, which refers to the change of adenosine (A) bases to inosine (I) bases in double-stranded RNAs. Several studies have revealed that A-to-I editing can regulate cellular processes and is associated with various human diseases. Therefore, accurate identification of A-to-I editing sites is crucial for understanding RNA-level (i.e. transcriptional) modifications and their potential roles in molecular functions. To date, various computational approaches for A-to-I editing site identification have been developed; however, their performance is still unsatisfactory and needs further improvement. In this study, we developed a novel stacked-ensemble learning model, ATTIC (A-To-I ediTing predICtor), to accurately identify A-to-I editing sites across three species, including Homo sapiens, Mus musculus and Drosophila melanogaster. We first comprehensively evaluated 37 RNA sequence-derived features combined with 14 popular machine learning algorithms. Then, we selected the optimal base models to build a series of stacked ensemble models. The final ATTIC framework was developed based on the optimal models improved by the feature selection strategy for specific species. Extensive cross-validation and independent tests illustrate that ATTIC outperforms state-of-the-art tools for predicting A-to-I editing sites. We also developed a web server for ATTIC, which is publicly available at http://web.unimelb-bioinfortools.cloud.edu.au/ATTIC/. We anticipate that ATTIC can be utilized as a useful tool to accelerate the identification of A-to-I RNA editing events and help characterize their roles in post-transcriptional regulation.

Advancing mRNA subcellular localization prediction with graph neural network and RNA structure.

MOTIVATION: The asymmetrical distribution of expressed mRNAs tightly controls the precise synthesis of proteins within human cells. This non-uniform distribution, a cornerstone of developmental biology, plays a pivotal role in numerous cellular processes. To advance our comprehension of gene regulatory networks, it is essential to develop computational tools for accurately identifying the subcellular localizations of mRNAs. However, considering multi-localization phenomena remains limited in existing approaches, with none considering the influence of RNA's secondary structure. RESULTS: In this study, we propose Allocator, a multi-view parallel deep learning framework that seamlessly integrates the RNA sequence-level and structure-level information, enhancing the prediction of mRNA multi-localization. The Allocator models equip four efficient feature extractors, each designed to handle different inputs. Two are tailored for sequence-based inputs, incorporating multilayer perceptron and multi-head self-attention mechanisms. The other two are specialized in processing structure-based inputs, employing graph neural networks. Benchmarking results underscore Allocator's superiority over state-of-the-art methods, showcasing its strength in revealing intricate localization associations. AVAILABILITY AND IMPLEMENTATION: The webserver of Allocator is available at http://Allocator.unimelb-biotools.cloud.edu.au; the source code and datasets are available on GitHub (https://github.com/lifuyi774/Allocator) and Zenodo (https://doi.org/10.5281/zenodo.13235798).

Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations.

Subcellular localization of messenger RNAs (mRNAs) plays a key role in the spatial regulation of gene activity. The functions of mRNAs have been shown to be closely linked with their localizations. As such, understanding of the subcellular localizations of mRNAs can help elucidate gene regulatory networks. Despite several computational methods that have been developed to predict mRNA localizations within cells, there is still much room for improvement in predictive performance, especially for the multiple-location prediction. In this study, we proposed a novel multi-label multi-class predictor, termed Clarion, for mRNA subcellular localization prediction. Clarion was developed based on a manually curated benchmark dataset and leveraged the weighted series method for multi-label transformation. Extensive benchmarking tests demonstrated Clarion achieved competitive predictive performance and the weighted series method plays a crucial role in securing superior performance of Clarion. In addition, the independent test results indicate that Clarion outperformed the state-of-the-art methods and can secure accuracy of 81.47, 91.29, 79.77, 92.10, 89.15, 83.74, 80.74, 79.23 and 84.74% for chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus and ribosome, respectively. The webserver and local stand-alone tool of Clarion is freely available at http://monash.bioweb.cloud.edu.au/Clarion/.

Targeting tumor heterogeneity: multiplex-detection-based multiple instance learning for whole slide image classification.

MOTIVATION: Multiple instance learning (MIL) is a powerful technique to classify whole slide images (WSIs) for diagnostic pathology. The key challenge of MIL on WSI classification is to discover the critical instances that trigger the bag label. However, tumor heterogeneity significantly hinders the algorithm's performance. RESULTS: Here, we propose a novel multiplex-detection-based multiple instance learning (MDMIL) which targets tumor heterogeneity by multiplex detection strategy and feature constraints among samples. Specifically, the internal query generated after the probability distribution analysis and the variational query optimized throughout the training process are utilized to detect potential instances in the form of internal and external assistance, respectively. The multiplex detection strategy significantly improves the instance-mining capacity of the deep neural network. Meanwhile, a memory-based contrastive loss is proposed to reach consistency on various phenotypes in the feature space. The novel network and loss function jointly achieve high robustness towards tumor heterogeneity. We conduct experiments on three computational pathology datasets, e.g. CAMELYON16, TCGA-NSCLC, and TCGA-RCC. Benchmarking experiments on the three datasets illustrate that our proposed MDMIL approach achieves superior performance over several existing state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: MDMIL is available for academic purposes at https://github.com/ZacharyWang-007/MDMIL.

PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships.

MOTIVATION: The rapid accumulation of high-throughput sequence data demands the development of effective and efficient data-driven computational methods to functionally annotate proteins. However, most current approaches used for functional annotation simply focus on the use of protein-level information but ignore inter-relationships among annotations. RESULTS: Here, we established PFresGO, an attention-based deep-learning approach that incorporates hierarchical structures in Gene Ontology (GO) graphs and advances in natural language processing algorithms for the functional annotation of proteins. PFresGO employs a self-attention operation to capture the inter-relationships of GO terms, updates its embedding accordingly and uses a cross-attention operation to project protein representations and GO embedding into a common latent space to identify global protein sequence patterns and local functional residues. We demonstrate that PFresGO consistently achieves superior performance across GO categories when compared with 'state-of-the-art' methods. Importantly, we show that PFresGO can identify functionally important residues in protein sequences by assessing the distribution of attention weightings. PFresGO should serve as an effective tool for the accurate functional annotation of proteins and functional domains within proteins. AVAILABILITY AND IMPLEMENTATION: PFresGO is available for academic purposes at https://github.com/BioColLab/PFresGO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Glypred: Lysine Glycation Site Prediction via CCU-LightGBM-BiLSTM Framework with Multi-Head Attention Mechanism.

Glycation, a type of posttranslational modification, preferentially occurs on lysine and arginine residues, impairing protein functionality and altering characteristics. This process is linked to diseases such as Alzheimer's, diabetes, and atherosclerosis. Traditional wet lab experiments are time-consuming, whereas machine learning has significantly streamlined the prediction of protein glycation sites. Despite promising results, challenges remain, including data imbalance, feature redundancy, and suboptimal classifier performance. This research introduces Glypred, a lysine glycation site prediction model combining ClusterCentroids Undersampling (CCU), LightGBM, and bidirectional long short-term memory network (BiLSTM) methodologies, with an additional multihead attention mechanism integrated into the BiLSTM. To achieve this, the study undertakes several key steps: selecting diverse feature types to capture comprehensive protein information, employing a cluster-based undersampling strategy to balance the data set, using LightGBM for feature selection to enhance model performance, and implementing a bidirectional LSTM network for accurate classification. Together, these approaches ensure that Glypred effectively identifies glycation sites with high accuracy and robustness. For feature encoding, five distinct feature types─AAC, KMER, DR, PWAA, and EBGW─were selected to capture a broad spectrum of protein sequence and biological information. These encoded features were integrated and validated to ensure comprehensive protein information acquisition. To address the issue of highly imbalanced positive and negative samples, various undersampling algorithms, including random undersampling, NearMiss, edited nearest neighbor rule, and CCU, were evaluated. CCU was ultimately chosen to remove redundant nonglycated training data, establishing a balanced data set that enhances the model's accuracy and robustness. For feature selection, the LightGBM ensemble learning algorithm was employed to reduce feature dimensionality by identifying the most significant features. This approach accelerates model training, enhances generalization capabilities, and ensures good transferability of the model. Finally, a bidirectional long short-term memory network was used as the classifier, with a network structure designed to capture glycation modification site features from both forward and backward directions. To prevent overfitting, appropriate regularization parameters and dropout rates were introduced, achieving efficient classification. Experimental results show that Glypred achieved optimal performance. This model provides new insights for bioinformatics and encourages the application of similar strategies in other fields. A lysine glycation site prediction software tool was also developed using the PyQt5 library, offering researchers an auxiliary screening tool to reduce workload and improve efficiency. The software and data sets are available on GitHub: https://github.com/ZBYnb/Glypred.

Catalysts of Healing: A Symphony of Synthesis and Clinical Artistry in Small-Molecule Agents for Breast Cancer Alleviation.

Breast cancer, characterized by its molecular intricacy, has witnessed a surge in targeted therapeutics owing to the rise of small-molecule drugs. These entities, derived from cutting-edge synthetic routes, often encompassing multistage reactions and chiral synthesis, target a spectrum of oncogenic pathways. Their mechanisms of action range from modulating hormone receptor signaling and inhibiting kinase activity, to impeding DNA damage repair mechanisms. Clinical applications of these drugs have resulted in enhanced patient survival rates, reduction in disease recurrence, and improved overall therapeutic indices. Notably, certain molecules have showcased efficacy in drug-resistant breast cancer phenotypes, highlighting their potential in addressing treatment challenges. The evolution and approval of small-molecule drugs have ushered in a new era for breast cancer therapeutics. Their tailored synthetic pathways and defined mechanisms of action have augmented the precision and efficacy of treatment regimens, paving the way for improved patient outcomes in the face of this pervasive malignancy. The present review embarks on a detailed exploration of small-molecule drugs that have secured regulatory approval for breast cancer treatment, emphasizing their clinical applications, synthetic pathways, and distinct mechanisms of action.

Widely Targeted Metabolomic Analysis Reveals the Improvement in Panax notoginseng Triterpenoids Triggered by Arbuscular Mycorrhizal Fungi via UPLC-ESI-MS/MS.

Panax notoginseng is a highly valued perennial medicinal herb in China and is widely used in clinical treatments. The main purpose of this study was to elucidate the changes in the composition of P. notoginseng saponins (PNSs), which are the main bioactive substances, triggered by arbuscular mycorrhizal fungi (AMF) via ultrahigh-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS). A total of 202 putative terpenoid metabolites were detected, of which 150 triterpene glycosides were identified, accounting for 74.26% of the total. Correlation analysis, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) of the metabolites revealed that the samples treated with AMF (group Ce) could be clearly separated from the CK samples. In total, 49 differential terpene metabolites were identified between the Ce and CK groups, of which 38 and 11 metabolites were upregulated and downregulated, respectively, and most of the upregulated differentially abundant metabolites were mainly triterpene glycosides. The relative abundances of the two major notoginsenosides (MNs), ginsenosides Rd and Re, and 13 rare notoginsenosides (RNs), significantly increased. The differential saponins, especially RNs, were more easily clustered into one branch and had a high positive correlation. It could be concluded that the biosynthesis and accumulation of some RNs share the same pathways as those triggered by AMF. This study provides a new way to obtain more notoginsenoside resources, particularly RNs, and sheds new light on the scientization and rationalization of the use of AMF agents in the ecological planting of medicinal plants.

Factors associated with eating rate: a systematic review and narrative synthesis informed by socio-ecological model.

Accumulating evidence shows associations between rapid eating and overweight. Modifying eating rate might be a potential weight management strategy without imposing additional dietary restrictions. A comprehensive understanding of factors associated with eating speed will help with designing effective interventions. The aim of this review was to synthesise the current state of knowledge on the factors associated with eating rate. The socio-ecological model (SEM) was utilised to scaffold the identified factors. A comprehensive literature search of eleven databases was conducted to identify factors associated with eating rate. The 104 studies that met the inclusion criteria were heterogeneous in design and methods of eating rate measurement. We identified thirty-nine factors that were independently linked to eating speed and mapped them onto the individual, social and environmental levels of the SEM. The majority of the reported factors pertained to the individual characteristics (n = 20) including demographics, cognitive/psychological factors and habitual food oral processing behaviours. Social factors (n = 11) included eating companions, social and cultural norms, and family structure. Environmental factors (n = 8) included food texture and presentation, methods of consumption or background sounds. Measures of body weight, food form and characteristics, food oral processing behaviours and gender, age and ethnicity were the most researched and consistent factors associated with eating rate. A number of other novel and underresearched factors emerged, but these require replication and further research. We highlight directions for further research in this space and potential evidence-based candidates for interventions targeting eating rate.

The miR-9b microRNA mediates dimorphism and development of wing in aphids.

Wing dimorphism is a phenomenon of phenotypic plasticity in aphid dispersal. However, the signal transduction for perceiving environmental cues (e.g., crowding) and the regulation mechanism remain elusive. Here, we found that aci-miR-9b was the only down-regulated microRNA (miRNA) in both crowding-induced wing dimorphism and during wing development in the brown citrus aphid Aphis citricidus We determined a targeted regulatory relationship between aci-miR-9b and an ABC transporter (AcABCG4). Inhibition of aci-miR-9b increased the proportion of winged offspring under normal conditions. Overexpression of aci-miR-9b resulted in decline of the proportion of winged offspring under crowding conditions. In addition, overexpression of aci-miR-9b also resulted in malformed wings during wing development. This role of aci-miR-9b mediating wing dimorphism and development was also confirmed in the pea aphid Acyrthosiphon pisum The downstream action of aci-miR-9b-AcABCG4 was based on the interaction with the insulin and insulin-like signaling pathway. A model for aphid wing dimorphism and development was demonstrated as the following: maternal aphids experience crowding, which results in the decrease of aci-miR-9b. This is followed by the increase of ABCG4, which then activates the insulin and insulin-like signaling pathway, thereby causing a high proportion of winged offspring. Later, the same cascade, "miR-9b-ABCG4-insulin signaling," is again involved in wing development. Taken together, our results reveal that a signal transduction cascade mediates both wing dimorphism and development in aphids via miRNA. These findings would be useful in developing potential strategies for blocking the aphid dispersal and reducing viral transmission.

Comprehensive Analysis of 34 Edible Flowers by the Determination of Nutritional Composition and Antioxidant Capacity Planted in Yunnan Province China.

The main purpose of this study was to reveal the nutritional value and antioxidant activity of 34 edible flowers that grew in Yunnan Province, China, through a comprehensive assessment of their nutritional composition and antioxidant indices. The results showed that sample A3 of Asteraceae flowers had the highest total flavonoid content, with a value of 8.53%, and the maximum contents of vitamin C and reducing sugars were from Rosaceae sample R1 and Gentianaceae sample G3, with values of 143.80 mg/100 g and 7.82%, respectively. Samples R2 and R3 of Rosaceae were the top two flowers in terms of comprehensive nutritional quality. In addition, the antioxidant capacity of Rosaceae samples was evidently better than that of three others, in which Sample R1 had the maximum values in hydroxyl radical (·OH) scavenging and superoxide anion radical (·O2-) scavenging rates, and samples R2 and R3 showed a high total antioxidant capacity and 2,2-diphenyl-1-pyridylhydrazine (DPPH) scavenging rate, respectively. Taken together, there were significant differences in the nutrient contents and antioxidant properties of these 34 flowers, and the comprehensive quality of Rosaceae samples was generally better than the other three families. This study provides references for 34 edible flowers to be used as dietary supplements and important sources of natural antioxidants.

PASSION: an ensemble neural network approach for identifying the binding sites of RBPs on circRNAs.

MOTIVATION: Different from traditional linear RNAs (containing 5' and 3' ends), circular RNAs (circRNAs) are a special type of RNAs that have a closed ring structure. Accumulating evidence has indicated that circRNAs can directly bind proteins and participate in a myriad of different biological processes. RESULTS: For identifying the interaction of circRNAs with 37 different types of circRNA-binding proteins (RBPs), we develop an ensemble neural network, termed PASSION, which is based on the concatenated artificial neural network (ANN) and hybrid deep neural network frameworks. Specifically, the input of the ANN is the optimal feature subset for each RBP, which has been selected from six types of feature encoding schemes through incremental feature selection and application of the XGBoost algorithm. In turn, the input of the hybrid deep neural network is a stacked codon-based scheme. Benchmarking experiments indicate that the ensemble neural network reaches the average best area under the curve (AUC) of 0.883 across the 37 circRNA datasets when compared with XGBoost, k-nearest neighbor, support vector machine, random forest, logistic regression and Naive Bayes. Moreover, each of the 37 RBP models is extensively tested by performing independent tests, with the varying sequence similarity thresholds of 0.8, 0.7, 0.6 and 0.5, respectively. The corresponding average AUC obtained are 0.883, 0.876, 0.868 and 0.883, respectively, highlighting the effectiveness and robustness of PASSION. Extensive benchmarking experiments demonstrate that PASSION achieves a competitive performance for identifying binding sites between circRNA and RBPs, when compared with several state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: A user-friendly web server of PASSION is publicly accessible at http://flagship.erc.monash.edu/PASSION/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Role of Reward System in Dishonest Behavior: A Functional Near-Infrared Spectroscopy Study.

Previous studies showed that the cortical reward system plays an important role in deceptive behavior. However, how the reward system activates during the whole course of dishonest behavior and how it affects dishonest decisions remain unclear. The current study investigated these questions. One hundred and two participants were included in the final analysis. They completed two tasks: monetary incentive delay (MID) task and an honesty task. The MID task served as the localizer task and the honesty task was used to measure participants' deceptive behaviors. Participants' spontaneous responses in the honesty task were categorized into three conditions: Correct-Truth condition (tell the truth after guessing correctly), Incorrect-Truth condition (tell the truth after guessing incorrectly), and Incorrect-Lie condition (tell lies after guessing incorrectly). To reduce contamination from neighboring functional regions as well as to increase sensitivity to small effects (Powell et al., Devel Sci 21:e12595, 2018), we adopted the individual functional channel of interest (fCOI) approach to analyze the data. Specially, we identified the channels of interest in the MID task in individual participants and then applied them to the honesty task. The result suggested that the reward system showed different activation patterns during different phases: In the pre-decision phase, the reward system was activated with the winning of the reward. During the decision and feedback phase, the reward system was activated when people made the decisions to be dishonest and when they evaluated the outcome of their decisions. Furthermore, the result showed that neural activity of the reward system toward the outcome of their decision was related to subsequent dishonest behaviors. Thus, the present study confirmed the important role of the reward system in deception. These results can also shed light on how one could use neuroimaging techniques to perform lie-detection.

Jaridon 6, a new diterpene from Rabdosia rubescens (Hemsl.) Hara, can display anti-gastric cancer resistance by inhibiting SIRT1 and inducing autophagy.

Tumor resistance is the main cause of treatment failure and is associated with many tumor factors. Jaridon 6, a new diterpene extracted from Rabdosia rubescens (Hemsl.) Hara, which has been previously extracted by our research team, has been tested having more obvious advantages in resistant tumor cells. However, its mechanism is unclear. In this study, we studied the effect and the specific mechanism of Jaridon 6 in resistant gastric cancer cells. Cytotoxicity test, colony test, western blotting, and nude test verified the anti-drug resistance ability of Jaridon 6 in the MGC803/PTX and MGC803/5-Fu cells. Jaridon 6 has shown obvious inhibitory effects in the sirtuin 1 (SIRT1) enzyme test. Transmission electron microscopy and immunofluorescence tests further proved the autophagic action of Jaridon 6. Jaridon 6 could inhibit the proliferation of the resistant gastric cancer cell in vivo and in vitro. Jaridon 6 inhibited SIRT1 enzyme and induced autophagy by inhibiting the phosphoinositide 3-kinase/protein kinase B (PI3K/AKT) pathway. Thus, it may be considered for treating gastric cancer resistance by individual or combined administration, as an SIRT1 inhibitor and autophagy inducer.