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Background: The interaction between RNA and protein plays an important role in life activities. Long ncRNAs (lncRNAs) are large non-coding RNAs, and have received extensive attention in recent years. Because the interaction between RNA and protein is tissue-specific and condition-specific, it is time-consuming and expensive to predict the interaction between lncRNA and protein based on biological wet experiments. Objective: The contribution of this paper is to propose a method for prediction based on the local structural similarity of lncRNA-protein interaction (LPI) network. Methods: The method computes the local structure similarity of network space, and maps it to LPI space, and uses an innovative algorithm that combined Resource Allocation and improved Collaborative Filtering algorithm to calculate the potential LPI. Conclusion: AUPR and AUC are significantly better than the five popular baseline methods. In addition, the case study shows that some results of LPLSG prediction on the actual data set have been verified by NPInterV4.0 database and some literatures.

We makes three kinds of important features from Arabidopsis thaliana: protein secondary structure based on the Chou-Fasman parameter, amino acids hydrophobicity and polarity information, and analyze their properties. Ubiquitination modification is an important post-translational modification of proteins, which participates in the regulation of many important life activities in cells. At present, ubiquitination proteomics research is mostly concentrated in animals and yeasts, while relatively few studies have been carried out in plants. It can be said that the calculation and prediction of Arabidopsis thaliana ubiquitination sites is still in its infancy. Based on this, we describe a calculation method, PseAraUbi (Prediction of Arabidopsis thaliana ubiquitination sites using pseudo amino acid composition), that can effectively detect ubiquitination sites on Arabidopsis thaliana using support vector machine learning classifiers. Based on protein sequence information, extract features from the Chou-Fasman parameter, amino acids hydrophobicity features, polarity information and selected for classification with the Boruta algorithm. PseAraUbi achieves promising performances with an AUC score of 0.953 with fivefold cross-validation on the training dataset, which are significantly better than that of the pioneer Arabidopsis thaliana ubiquitination sites method. We also proved the ability of our proposed method on independent test sets, thus gaining a competitive advantage. In addition, we also in-depth analyzed the physicochemical properties of amino acids in the region adjacent to the ubiquitination site. To facilitate the community, the source code, optimal feature subset, ubiquitination sites dataset in the Arbidopsis proteome are available at GitHub ( https://github.com/HNUBioinformatics/PseAraUbi.git ) for interest users.

Identification of drug–target interactions (DTIs) has great practical importance in the drug discovery process for known diseases. However, only a small proportion of DTIs in these databases has been verified experimentally, and the computational methods for predicting the interactions remain challenging. As a result, some effective computational models have become increasingly popular for predicting DTIs. In this work, the authors predict potential DTIs from the local structure of drug–target associations' network, which is different from the traditional global network similarity methods based on structure and ligand. A novel method called PPDTS is proposed to predict DTIs. First, according to the DTIs’ network local structure, the known DTIs are converted into a binary network. Second, the Resource Allocation algorithm is used to obtain a drug–drug similarity network and a target–target similarity network. Third, a Collaborative Filtering algorithm is used with the known drug–target topology information to obtain similarity scores. Fourth, the linear combination of drug–target similarity model and the target–drug similarity model are innovatively proposed to obtain the final prediction results. Finally, the experimental performance of PPDTS has proved to be higher than that of the previously mentioned four popular network‐based similarity methods, which is validated in different experimental datasets. Some of the predicted results can be supported in UniProt and DrugBank databases.

We present the case of a 71-year-old man admitted because of chest tightness, palpitations, and progressive shortness of breath. The diagnosis of severe aortic stenosis, coarctation, and aneurysm was established, as well as severely depressed left ventricular ejection fraction. Three consecutive transcatheter procedures were successfully performed in a single session. (Level of Difficulty: Advanced.), Central Illustration

The flexibility of protein structure is related to various biological processes, such as molecular recognition, allosteric regulation, catalytic activity, and protein stability. At the molecular level, protein dynamics and flexibility are important factors to understand protein function. DNA-binding proteins and Coronavirus proteins are of great concern and relatively unique proteins. However, exploring the flexibility of DNA-binding proteins and Coronavirus proteins through experiments or calculations is a difficult process. Since protein dihedral rotational motion can be used to predict protein structural changes, it provides key information about protein local conformation. Therefore, this paper introduces a method to improve the accuracy of protein flexibility prediction, DihProFle (Prediction of DNA-binding proteins and Coronavirus proteins flexibility introduces the calculated dihedral Angle information). Based on protein dihedral Angle information, protein evolution information, and amino acid physical and chemical properties, DihProFle realizes the prediction of protein flexibility in two cases on DNA-binding proteins and Coronavirus proteins, and assigns flexibility class to each protein sequence position. In this study, compared with the flexible prediction using sequence evolution information, and physicochemical properties of amino acids, the flexible prediction accuracy based on protein dihedral Angle information, sequence evolution information and physicochemical properties of amino acids improved by 2.2% and 3.1% in the nonstrict and strict conditions, respectively. And DihProFle achieves better performance than previous methods for protein flexibility analysis. In addition, we further analyzed the correlation of amino acid properties and protein dihedral angles with residues flexibility. The results show that the charged hydrophilic residues have higher proportion in the flexible region, and the rigid region tends to be in the angular range of the protein dihedral angle (such as the ψ angle of amino acid residues is more flexible than rigid in the range of 91°-120°). Therefore, the results indicate that hydrophilic residues and protein dihedral angle information play an important role in protein flexibility.

Based on 467 pairs of δ18O and δ13C records and 8 230Th dates from a stalagmite (BF4) from Xiniu Cave, central China, we present a reconstruction of ～9-yr resolution monsoon rainfall record for the past 4700 years. Our δ18O record shows good coherence with East Asian summer monsoon (EASM) rainfall proxies from adjacent regions during the overlapping intervals, suggesting that δ18O signal in BF4 can be interpreted as a monsoon rainfall proxy. The δ13C variations are related to changes in local processes at the cave site, and regional rainfall and temperature changes. Based on the δ18O record, a series of dry periods can be identified at 4500–4200, 3500–3200, 2800–2500, 1900–1600, 1400–1200, 700–500, and 400–200 yr BP, while a series of wet periods can be identified at 4200–3600, 2400–2200, 3200–2800, 1100–900, 600–400, and 200–100 yr BP. Power spectrum analysis on our δ18O record reveals significant cycles at ～470 and ～80 yr, coinciding with the typical solar periodic variations. This result suggests that changes in solar activity play a dominant role in driving centennial–decadal monsoon rainfall variation in central China. Due to minor changes in solar irradiance (less than 1.5 W m−2) over the past 4700 years, our record was further compared to the El Nino–Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) proxies, confirming that solar forcing on monsoon rainfall changes might be amplified by the ENSO and PDO variations. From 600 to 150 yr BP (the Little Ice Age, LIA), a positive shift of ～2‰ can be revealed in both the δ18O and δ13C records, indicating a cold/dry climatic pattern. By comparing our δ18O and δ13C records with historical documents, we suggest that the climatic deteriorations between 450 and 250 yr BP may have caused serious social unrest at the end of the Ming Dynasty.

Determining the interaction of drug and target plays a key role in the process of drug development and discovery. The calculation methods can predict new interactions and speed up the process of drug development. In recent studies, the network-based approaches have been proposed to predict drug-target interactions. However, these methods cannot fully utilize the node information from heterogeneous networks. Therefore, we propose a method based on heterogeneous graph convolutional neural network for drug-target interaction prediction, GCHN-DTI (Predicting drug-target interactions by graph convolution on heterogeneous net-works), to predict potential DTIs. GCHN-DTI integrates network information from drug-target interactions, drug-drug interactions, drug-similarities, target-target interactions, and target-similarities. Then, the graph convolution operation is used in the heterogeneous network to obtain the node embedding of the drugs and the targets. Furthermore, we incorporate an attention mechanism between graph convolutional layers to combine node embedding from each layer. Finally, the drug-target interaction score is predicted based on the node embedding of the drugs and the targets. Our model uses fewer network types and achieves higher prediction performance. In addition, the prediction performance of the model will be significantly improved on the dataset with a higher proportion of positive samples. The experimental evaluations show that GCHN-DTI outperforms several state-of-the-art prediction methods.

Cross-modal similarity query has become a highlighted research topic for managing multimodal datasets such as images and texts. Existing researches generally focus on query accuracy by designing complex deep neural network models and hardly consider query efficiency and interpretability simultaneously, which are vital properties of cross-modal semantic query processing system on large-scale datasets. In this work, we investigate multi-grained common semantic embedding representations of images and texts and integrate interpretable query index into the deep neural network by developing a novel Multi-grained Cross-modal Query with Interpretability (MCQI) framework. The main contributions are as follows: (1) By integrating coarse-grained and fine-grained semantic learning models, a multi-grained cross-modal query processing architecture is proposed to ensure the adaptability and generality of query processing. (2) In order to capture the latent semantic relation between images and texts, the framework combines LSTM and attention mode, which enhances query accuracy for the cross-modal query and constructs the foundation for interpretable query processing. (3) Index structure and corresponding nearest neighbor query algorithm are proposed to boost the efficiency of interpretable queries. (4) A distributed query algorithm is proposed to improve the scalability of our framework. Comparing with state-of-the-art methods on widely used cross-modal datasets, the experimental results show the effectiveness of our MCQI approach.

The catalytic efficiency of the enzyme is thousands of times higher than that of ordinary catalysts. Thus, they are widely used in industrial and medical fields. However, enzymes with protein structure can be destroyed and inactivated in high temperature, over acid or over alkali environment. It is well known that most of enzymes work well in an environment with pH of 6-8, while some special enzymes remain active only in an alkaline environment with pH > 8 or an acidic environment with pH < 6. Therefore, the identification of acidic and alkaline enzymes has become a key task for industrial production. Because of the wide varieties of enzymes, it is hard work to determine the acidity and alkalinity of the enzyme by experimental methods, and even this task cannot be achieved. Converting protein sequences into digital features and building computational models can efficiently and accurately identify the acidity and alkalinity of enzymes. This review summarized the progress of the digital features to express proteins and computational methods to identify acidic and alkaline enzymes. We hope that this paper will provide more convenience, ideas, and guides for computationally classifying acid and alkaline enzymes.

The use of artificial intelligence technologies in biology, pharmacy, and medicine has brought about a dramatic change in these industries. Drug repositioning is a method of drug development in the process of applying existing therapeutic agents to new diseases. This paper first outlines the use of artificial intelligence technology in the field of drug repositioning, then reviews a variety of application methods of artificial intelligence in the realm of drug repositioning, and finally summarizes the advantages and disadvantages of these methods, and proposes the difficulties faced by artificial intelligence in drug repositioning in the future and the corresponding suggestions to achieve the goal of helping researchers to develop more effective methods of drug repositioning.

OBJECTIVES: Sleep quality has been associated with a number of health conditions such as cognitive impairment and cardiovascular disease. 5-Hydroxytryptophan (5-HTP) is known as a precursor of the sleep regulating serotonin and melatonin. However, 5-HTP's efficacy for improving sleep in older adults is unclear. The aim of this study is to assess the impact of 5-HTP on sleep quality in older Singaporean adults. METHODS: This study is a single-blinded, 12-week parallel randomized controlled trial, where 20 older adults (67 ± 4 years, mean ± SD; 8 M, 12 F) were randomly assigned to either consume or not consume 100 mg 5-HTP daily. Sleep quality data were collected every 4 weeks via both subjective and objective measures such as Pittsburg Sleep Quality Index (PSQI) questionnaire and actigraphy watch. A global sleep score (GSS) was obtained from the PSQI, where a GSS > 5 has been validated to be associated with poor sleep quality. In addition, other sleep quality components that were also extracted include total sleep time (TST), sleep latency (SL) and sleep efficiency (SE). The main effect and interaction of 5-HTP on sleep were evaluated by repeated-measure analysis of variance (ANOVA). RESULTS: At baseline, subjects had an overall GSS of 5.9 ± 3.1 a.u. (5-HTP group: 6.3 ± 3.8 a.u.; Control group: 5.5 ± 2.3 a.u.) and this indicated poor sleep quality. Consumption of 5-HTP after 4 and 8 weeks showed a favorable decrease in their objective SL (SL(Obj)) from their baseline (week 4: −18.2 ± 21.1 min; week 8: −17.8 ± 19.2 min). On the other hand, the control group did not display desirable change in SL(Obj) (week 4: 2.4 ± 21.6 min; week 8: 1.5 ± 21.4 min). Repeated-measures ANOVA revealed a significant treatment-by-time effect between 5-HTP consumption and SL(Obj) at week 4 (P = 0.045) and week 8 (P = 0.033), but not at week 12 (P = 0.135). A significant treatment-by-time decrease in subjective TST (−0.1 ± 0.4 hrs, P = 0.027) observed at week 12. No changes in GSS and other subjective and objective measures of sleep quality were detected. CONCLUSIONS: Older adults had poor sleep quality and 5-HTP supplementation helped reduce sleep latency for up to 8 weeks. Prolonged effects were not observed, and this may be due to acclimation of the body to longer-term 5-HTP supplementation. FUNDING SOURCES: NUS iHealthtech Microbiome in Health, Disease and Ageing.

OBJECTIVES: Brewer's spent grains (BSG), the key food processing by-product of beer manufacturing, is rich in dietary fiber and protein. However, BSG has limited use in food production due to its poor digestibility and texture. Therefore, this study aimed to investigate the effects of Rhizopus oligosporus (RO) solid-state fermentation (SSF) on the nutritional and physical properties of BSG containing food. METHODS: Autoclaved BSG (AUBSG) was obtained via sterilization of raw BSG (UBSG). SSF with RO inoculation at 2% (w/w) on AUBSG was conducted to produce 2% ROUBSG. AUBSG and 2%ROUBSG substituted biscotti (AUB and ROUB respectively) were developed by substituting 30% of wheat flour while control biscotti (C) was made with wheat flour only. Crude protein, dietary fiber (total – TDF, insoluble – IDF, soluble – SDF), fat, ash, available carbohydrates (ACHO), phytic acid and anti-oxidant capacity were analyzed for UBSG, AUBSG and 2%ROUBSG. Nutritional properties, in vitro starch digestibility and physical properties, including loaf height, hardness and fracturability, were also analyzed for AUB, ROUB and C. Statistical significant difference (p ≤ 0.05) was determined via one-way analysis of variance, followed by Tukey post hoc test. RESULTS: Compared to AUBSG and UBSG, 2%ROUBSG showed nutritional improvement with significantly higher crude protein, SDF, anti-oxidant capacity and lower phytic acid and ACHO. When compared to C, both ROUB and AUB had significantly higher crude protein, TDF, IDF, anti-oxidant capacity as well as significantly lower ACHO and slower rate of in vitro starch digestion. Further improvement of crude protein, anti-oxidant capacity and phytic acid was also observed in ROUB as compared to AUB. For physical properties, ROUB showed no significant difference in hardness and fracturability from C and significantly higher loaf height than AUB. CONCLUSIONS: Findings from this study reveal the beneficial effect of SSF in improving nutritional properties of BSG along with enhancement of nutritional and physical properties of BSG incorporated food. This study may suggest the viability of such fermentation technology in upcycling food by-product into nutritious food ingredients. FUNDING SOURCES: Singapore Ministry of Education, National University of Singapore.

The catalytic activity of the enzyme is different from that of the inorganic catalyst. In a high-temperature, over-acid or over-alkaline environment, the structure of the enzyme is destroyed and then loses its activity. Although the biochemistry experiments can measure the optimal PH environment of the enzyme, these methods are inefficient and costly. In order to solve these problems, computational model could be established to determine the optimal acidic or alkaline environment of the enzyme. Firstly, in this paper, we introduced a new feature called dual g-gap dipeptide composition to formulate enzyme samples. Subsequently, the best feature was selected by using the F value calculated from analysis of variance. Finally, support vector machine was utilized to build prediction model for distinguishing acidic from alkaline enzyme. The overall accuracy of 95.9% was achieved with Jackknife cross-validation, which indicates that our method is professional and efficient in terms of acid and alkaline enzyme predictions. The feature proposed in this paper could also be applied in other fields of bioinformatics.

Left ventricular free wall rupture (LVFWR) is a rare but lethal complication of acute myocardial infarction (AMI). Urgent surgery is essential but associated with high postoperative mortality. Even worse, LVFWR patients may experience sudden death without a chance for surgery. In this article, we report our successful transcatheter closure of a patient with the most extensive pseudoventricular aneurysm after AMI reported thus far. Cardiac magnetic resonance imaging (MRI) revealed a giant pseudoventricular aneurysm located in the inferior and lateral walls of the left ventricle; the rupture diameter was 28 mm, and the maximum tumor diameter was 90.2 mm. We used transcarotid approach (TCA) and atrial septal defect closure umbrella to complete the operation, which solved the lack of special interventional instruments to treat pseudoventricular aneurysm after AMI. In addition, we still needed to treat liver and kidney failure caused by hemolysis after operation, and undergone strict follow-up. In conclusion, transcatheter closure is practical and feasible for the treatment of pseudoventricular aneurysm after AMI, although hemolysis and decline of cardiac pumping function after the successful interventional treatment deserve special attention. Future multicenter studies are required to identify patients best suited for interventional treatment timing. And further developments in devices and delivery techniques are required in order to optimize interventional outcomes.

The purpose of Text-to-SQL is to obtain the correct answer for a textual question from the database, which can take advantage of advanced database system to provide reliable and efficient response. Existing Text-to-SQL methods generally focus on accuracy by designing complex deep neural network models, and hardly consider interpretability, which is very important for serious applications. To address this, in this paper we propose a novel framework for Interpretable Text-to-SQL Generation (ITSG) with joint optimization, which achieves state-of-the-art accuracy and possesses two-level interpretability at the same time. The framework mainly consists of three layers: a sequence encoder which encodes questions, table headers and significant table contents, an attention-based LSTM layer which generates SQL queries and a reinforcement learning layer which boosts the execution accuracy. Comparing with state-of-the-art methods on benchmark datasets, the experimental results show the effectiveness and interpretability of our ITSG framework.

Multi-receiver authentication plays an important role in network security. Many researchers have studied the constructions and the properties of the multi-receiver authentication scheme. However, most of these schemes treat the capability of all the receivers equally. In practice, receivers may have different other than equal powers in many cases. The authors consider the new scenario in the multi-receiver authentication. The authors propose a multi-receiver authentication scheme with hierarchical structure among the receivers. The authors construct an unconditionally secure multi-receiver authentication code by using the Birkhoff interpolation. The authentication scheme is also able to send multiple messages.

Linear codes have been an interesting topic in both theory and practice for many years. In this paper, two classes of linear codes over the finite field $${\mathrm {GF}}(p)$$ are presented and their weight distributions are also determined, where p is an odd prime. Some of the linear codes obtained are optimal or almost optimal in the sense that their parameters meet certain bound on linear codes.

In this paper, we devise ideal and probabilistic secret sharing schemes for two kinds of compartmented access structures. The first one is a compartmented access structures with hierarchical compartments. The second one is the compartmented access structures with strictly lower bounds. We propose ideal and probabilistic schemes for these two compartmented access structures by using the idea of bivariate interpolation.

In this paper, we give the complete weight enumerators of two classes of linear codes over the finite field Fp$\mathbb {F}_{p}$, where p is a prime. These linear codes are the torsion codes of MacDonald codes over the finite non-chain ring Fp+vFp$\mathbb {F}_{p}+v\mathbb {F}_{p}$, where v2 = v. We also employ these linear codes to construct systematic authentication codes with new parameters.

Fault diagnosis is becoming an important issue in biochemical process, and a novel online fault detection and diagnosis approach is designed by combining fuzzy c-means (FCM) and support vector machine (SVM). The samples are preprocessed via FCM algorithm to enhance the ability of classification firstly. Then, those samples are input to the SVM classifier to realize the biochemical process fault diagnosis. In this study, a glutamic acid fermentation process is chosen as an example to diagnose the fault by this method, the result shows that the diagnosis time is largely shortened, and the accuracy is extremely improved by comparing to a single SVM method.

The use of the Lunderquist exchange guide wire via the retrograde approach of the right femoral vein-inferior vena cava-right atrium-right ventricle-ventricular septal perforation-left ventricle-descending aorta can maintain guide wire tension and significantly reduce the operative time. The patient was admitted due to chest pain for 3 hours. The diagnosis was acute anterior septal myocardial infarction with ventricular septal perforation. One week after admission, a drug-eluting stent was implanted in the left anterior descending branch. Repeated echocardiography revealed that the diameter of the ventricular septal perforation had increased from 6 to 12 mm. During this period, the patient suffered from repeated episodes of shortness of breath that were progressively exacerbated. The patient was transferred to the intensive care unit (ICU) and underwent intra-aortic balloon pump (IABP) implantation. Twenty days after admission, the Lunderquist exchange guide wire was used via the retrograde approach of the right femoral vein-inferior vena cava-right atrium-right ventricle-ventricular septal perforation-left ventricle-descending aorta. A 26-mm occluder was released for transcatheter closure of the ventricular septal perforation. Shortness of breath was immediately relieved. The patient was discharged 3 days later. Retrograde transcatheter closure of ventricular septal perforation can effectively reduce operative time and is conducive to quick and stable improvement of the patient’s condition.

The heteroassociative morphological memories (HMMs) are one kind of morphological associative memories (MAMs). Although they have many advantages, there is a serious shortcoming: HMMs are incomplete, that is, they cannot give a guarantee of perfect recall memories, even though without any input noises. This problem has become a bottleneck in the development and applications of HMMs, and also one of difficult subjects in this field. Aiming at the problem, this paper presents a new method of HMMs, called four-dimensional stored HMMs (FDSHMMs). It includes three work stages: pretreatment, memory, and recall. Both computational method and experimental method show that FDSHMMs can significantly improve the HMMs. They can guarantee perfect recall memories for perfect inputs or within a certain noise range, just like the auto-associative morphological memories (AMMs) can do. FDSHMMs remove the bottleneck of HMMs and bring us a bright prospect of applications.

In this paper, we consider the problem of distributed power control over ad hoc wireless networks. We formulate the power control problem as a convex optimization problem that minimizes the summation of packets completion time at each node. We then explore the problem structure and propose a distributed method to solve the problem where each node only need information exchange with its neighbors. Finally, we illustrate the algorithm and the associated implementation issues with a simulation example.