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The Xishuangbanna (XIS) cucumber (Cucumis sativus var. xishuangbannanesis) is a semiwild variety that has many distinct agronomic traits. Here, long reads generated by Nanopore sequencing technology helped assembling a high-quality genome (contig N50 = 8.7 Mb) of landrace XIS49. A total of 10,036 structural/sequence variations (SVs) were identified when comparing with Chinese Long (CL), and known SVs controlling spines, tubercles, and carpel number were confirmed in XIS49 genome. Two QTLs of hypocotyl elongation under low light, SH3.1 and SH6.1, were fine-mapped using introgression lines (donor parent, XIS49; recurrent parent, CL). SH3.1 encodes a red-light receptor Phytochrome B (PhyB, CsaV3_3G015190). A ∼4 kb region with large deletion and highly divergent regions (HDRs) were identified in the promoter of the PhyB gene in XIS49. Loss of function of this PhyB caused a super-long hypocotyl phenotype. SH6.1 encodes a CCCH-type zinc finger protein FRIGIDA-ESSENTIAL LIKE (FEL, CsaV3_6G050300). FEL negatively regulated hypocotyl elongation but it was transcriptionally suppressed by long terminal repeats retrotransposon insertion in CL cucumber. Mechanistically, FEL physically binds to the promoter of CONSTITUTIVE PHOTOMORPHOGENIC 1a (COP1a), regulating the expression of COP1a and the downstream hypocotyl elongation. These above results demonstrate the genetic mechanism of cucumber hypocotyl elongation under low light. Structural variation in quantitative trait loci affects hypocotyl elongation in Xishuangbanna cucumber. [ABSTRACT FROM AUTHOR]

Aims Potential loss-of-function variants of ATP13A3 , the gene encoding a P5B-type transport ATPase of undefined function, were recently identified in patients with pulmonary arterial hypertension (PAH). ATP13A3 is implicated in polyamine transport but its function has not been fully elucidated. In this study, we sought to determine the biological function of ATP13A3 in vascular endothelial cells (ECs) and how PAH-associated variants may contribute to disease pathogenesis. Methods and results We studied the impact of ATP13A3 deficiency and overexpression in EC models [human pulmonary ECs, blood outgrowth ECs (BOECs), and human microvascular EC 1], including a PAH patient–derived BOEC line harbouring an ATP13A3 variant (LK726X). We also generated mice harbouring an Atp13a3 variant analogous to a human disease–associated variant to establish whether these mice develop PAH. ATP13A3 localized to the recycling endosomes of human ECs. Knockdown of ATP13A3 in ECs generally reduced the basal polyamine content and altered the expression of enzymes involved in polyamine metabolism. Conversely, overexpression of wild-type ATP13A3 increased polyamine uptake. Functionally, loss of ATP13A3 was associated with reduced EC proliferation, increased apoptosis in serum starvation, and increased monolayer permeability to thrombin. The assessment of five PAH-associated missense ATP13A3 variants (L675V, M850I, V855M, R858H, and L956P) confirmed loss-of-function phenotypes represented by impaired polyamine transport and dysregulated EC function. Furthermore, mice carrying a heterozygous germline Atp13a3 frameshift variant representing a human variant spontaneously developed a PAH phenotype, with increased pulmonary pressures, right ventricular remodelling, and muscularization of pulmonary vessels. Conclusion We identify ATP13A3 as a polyamine transporter controlling polyamine homeostasis in ECs, a deficiency of which leads to EC dysfunction and predisposes to PAH. This suggests a need for targeted therapies to alleviate the imbalances in polyamine homeostasis and EC dysfunction in PAH. [ABSTRACT FROM AUTHOR]

Motivation Exploring potential associations between diseases can help in understanding pathological mechanisms of diseases and facilitating the discovery of candidate biomarkers and drug targets, thereby promoting disease diagnosis and treatment. Some computational methods have been proposed for measuring disease similarity. However, these methods describe diseases without considering their latent multi-molecule regulation and valuable supervision signal, resulting in limited biological interpretability and efficiency to capture association patterns. Results In this study, we propose a new computational method named DiSMVC. Different from existing predictors, DiSMVC designs a supervised graph collaborative framework to measure disease similarity. Multiple bio-entity associations related to genes and miRNAs are integrated via cross-view graph contrastive learning to extract informative disease representation, and then association pattern joint learning is implemented to compute disease similarity by incorporating phenotype-annotated disease associations. The experimental results show that DiSMVC can draw discriminative characteristics for disease pairs, and outperform other state-of-the-art methods. As a result, DiSMVC is a promising method for predicting disease associations with molecular interpretability. Availability and implementation Datasets and source codes are available at https://github.com/Biohang/DiSMVC. [ABSTRACT FROM AUTHOR]

The detection of a secular post-merger gravitational wave (GW) signal in a binary neutron star (BNS) merger serves as strong evidence for the formation of a long-lived post-merger neutron star (NS), which can help constrain the maximum mass of NSs and differentiate NS equations of state. We specifically focus on the detection of GW emissions from rigidly rotating NSs formed through BNS mergers, using several kilohertz GW detectors that have been designed. We simulate the BNS mergers within the detecting limit of LIGO-Virgo-KARGA O4 and attempt to find out on what fraction the simulated sources may have a detectable secular post-merger GW signal. For kilohertz detectors designed in the same configuration of LIGO A+, we find that the design with peak sensitivity at approximately 2 kHz is most appropriate for such signals. The fraction of sources that have a detectable secular post-merger GW signal would be approximately |$0.94{\!-\!}11~{{ \rm per\ cent}}$| when the spin-downs of the post-merger rigidly rotating NSs are dominated by GW radiation, while it would be approximately |$0.46{\!-\!}1.6~{{ \rm per\ cent}}$| when the contribution of electromagnetic (EM) radiation to the spin-down processes is non-negligible. We also estimate this fraction based on other well-known proposed kilohertz GW detectors and find that, with advanced design, it can reach approximately |$12{\!-\!}45~{{ \rm per\ cent}}$| for the GW-dominated spin-down case and |$4.7{\!-\!}16~{{\rm per\ cent}}$| when both the GW and EM radiations are considered. [ABSTRACT FROM AUTHOR]

This paper investigates a two-species chemotaxis-fluid system with indirect pursuit–evasion interaction in a bounded domain with smooth boundary. Under suitably regular initial data and no-flux/no-flux/no-flux/no-flux/Dirichlet boundary conditions, we prove that the system possesses a global bounded classical solution in the two-dimensional and three-dimensional cases. Our results extend the result obtained in previously known ones and partly result is new. [ABSTRACT FROM AUTHOR]

Objectives: Ferroptosis, a new regulated cell death pathway, plays a crucial part in the development of cardiovascular disease. However, the precise underlying mechanism remains unclear. Therefore, this study aimed to elucidate this. Methods: Herein, an erastin-induced H9C2 cell ferroptosis in vitro model and a myocardial infarction murine model, which was created by ligating the left anterior descending coronary artery, were established. Ferroptosis-related indicators, myocardial injury-related indicators, and Nrf2 signaling-related proteins expression were analyzed to explore the potential mechanism underlying cardiomyocyte ferroptosis-mediated cardiovascular disease development. Results: We demonstrated that Nrf2 downregulation in myocardial tissue, accompanied by ferroptotic events and changes in xCT and GPX4 expressions, induced cardiomyocyte ferroptosis and myocardial injury after myocardial infarction. These events, including ferroptosis and changes in Nrf2, xCT, and GPX4 expressions, were improved by ferrostatin-1 in vivo and in vitro. Besides, Nrf2 deficiency or inhibition aggravated myocardial infarction-induced cardiomyocyte ferroptosis by decreasing xCT and GPX4 expressions in vivo and in vitro. Moreover, ferrostatin-1 directly targeted Nrf2, as evidenced by surface plasmon resonance analysis. Conclusions: These results indicated that myocardial infarction is accompanied by cardiomyocyte ferroptosis and that Nrf2 signaling plays a crucial part in regulating cardiomyocyte ferroptosis after myocardial infarction. Graphical Abstract [ABSTRACT FROM AUTHOR]

Protein remote homology detection is essential for structure prediction, function prediction, disease mechanism understanding, etc. The remote homology relationship depends on multiple protein properties, such as structural information and local sequence patterns. Previous studies have shown the challenges for predicting remote homology relationship by protein features at sequence level (e.g. position-specific score matrix). Protein motifs have been used in structure and function analysis due to their unique sequence patterns and implied structural information. Therefore, designing a usable architecture to fuse multiple protein properties based on motifs is urgently needed to improve protein remote homology detection performance. To make full use of the characteristics of motifs, we employed the language model called the protein cubic language model (PCLM). It combines multiple properties by constructing a motif-based neural network. Based on the PCLM, we proposed a predictor called PreHom-PCLM by extracting and fusing multiple motif features for protein remote homology detection. PreHom-PCLM outperforms the other state-of-the-art methods on the test set and independent test set. Experimental results further prove the effectiveness of multiple features fused by PreHom-PCLM for remote homology detection. Furthermore, the protein features derived from the PreHom-PCLM show strong discriminative power for proteins from different structural classes in the high-dimensional space. Availability and Implementation: http://bliulab.net/PreHom-PCLM. [ABSTRACT FROM AUTHOR]

Subcellular localizations of ncRNAs are associated with specific functions. Currently, an increasing number of biological researchers are focusing on computational approaches to identify subcellular localizations of ncRNAs. However, the performance of the existing computational methods is low and needs to be further studied. First, most prediction models are trained with outdated databases. Second, only a few predictors can identify multiple subcellular localizations simultaneously. In this work, we establish three human ncRNA subcellular datasets based on the latest RNALocate, including lncRNA, miRNA and snoRNA, and then we propose a novel multi-label classification model based on ensemble learning called ncRNALocate-EL to identify multi-label subcellular localizations of three ncRNAs. The results show that the ncRNALocate-EL outperforms previous methods. Our method achieved an average precision of 0.709,0.977 and 0.730 on three human ncRNA datasets. The web server of ncRNALocate-EL has been established, which can be accessed at https://bliulab.net/ncRNALocate-EL. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are human post-transcriptional regulators in humans, which are involved in regulating various physiological processes by regulating the gene expression. The subcellular localization of miRNAs plays a crucial role in the discovery of their biological functions. Although several computational methods based on miRNA functional similarity networks have been presented to identify the subcellular localization of miRNAs, it remains difficult for these approaches to effectively extract well-referenced miRNA functional representations due to insufficient miRNA–disease association representation and disease semantic representation. Currently, there has been a significant amount of research on miRNA–disease associations, making it possible to address the issue of insufficient miRNA functional representation. In this work, a novel model is established, named DAmiRLocGNet, based on graph convolutional network (GCN) and autoencoder (AE) for identifying the subcellular localizations of miRNA. The DAmiRLocGNet constructs the features based on miRNA sequence information, miRNA–disease association information and disease semantic information. GCN is utilized to gather the information of neighboring nodes and capture the implicit information of network structures from miRNA–disease association information and disease semantic information. AE is employed to capture sequence semantics from sequence similarity networks. The evaluation demonstrates that the performance of DAmiRLocGNet is superior to other competing computational approaches, benefiting from implicit features captured by using GCNs. The DAmiRLocGNet has the potential to be applied to the identification of subcellular localization of other non-coding RNAs. Moreover, it can facilitate further investigation into the functional mechanisms underlying miRNA localization. The source code and datasets are accessed at http://bliulab.net/DAmiRLocGNet. [ABSTRACT FROM AUTHOR]

Glucose-6-phosphate dehydrogenase (G6PD) is involved in the catalytic pentose phosphate pathway (PPP), which is closely related to energy metabolism. G6PD plays a crucial role in many types of cancer, but the specific molecular mechanisms of G6PD in cancer remain unclear. Therefore, we investigated the potential oncogenic role of G6PD in various tumors based on The Cancer Genome Atlas (TCGA), the cBioPortal datasets, the University of California Santa Cruz (UCSC) Xena browser, and the UALCAN-based online tool. G6PD was highly expressed in several cancer tissues (hepatocellular carcinoma, glioma, and breast cancer) compared with normal tissues and was significantly associated with poor prognosis of hepatocellular carcinoma, clear cell renal cell carcinoma, and breast cancer. Promoter methylation levels of G6PD were lower in Bladder Urothelial Carcinoma (BLCA) (P = 2.77e−02), breast invasive carcinoma (BRCA) (P = 1.62e−12), kidney renal clear cell carcinoma (KIRC) (P = 4.23e−02), kidney renal papillary cell carcinoma (KIRP) (P = 2.64e−03), liver hepatocellular carcinoma (LIHC) (P = 1.76e−02), stomach adenocarcinoma (STAD) (P = 3.50e−02), testicular germ cell tumors (TGCT) (P = 1.62e−12), higher in prostate adenocarcinoma (PRAD) (P = 1.81e−09), and uterine corpus endometrial carcinoma (UCEC) (P = 2.96e−04) compared with corresponding normal tissue samples. G6PD expression was positively correlated with the infiltration level of immune cells in most tumors, suggesting that G6PD may be involved in tumor immune infiltration. In addition, the functional mechanism of G6PD also involves 'Carbon metabolism', 'Glycolysis/Gluconeogenesis', 'Pentose phosphate pathway', and 'Central carbon pathway metabolism in cancer signaling pathway'. This pan-cancer study provides a relatively broad understanding of the oncogenic role of G6PD in various tumors and presents a theoretical basis for the development of G6PD inhibitors as therapeutic drugs for multiple cancers. [ABSTRACT FROM AUTHOR]

Motivation: Therapeutic peptides play an important role in immune regulation. Recently various therapeutic peptides have been used in the field of medical research, and have great potential in the design of therapeutic schedules. Therefore, it is essential to utilize the computational methods to predict the therapeutic peptides. However, the therapeutic peptides cannot be accurately predicted by the existing predictors. Furthermore, chaotic datasets are also an important obstacle of the development of this important field. Therefore, it is still challenging to develop a multi-classification model for identification of therapeutic peptides and their types. Results: In this work, we constructed a general therapeutic peptide dataset. An ensemble-learning method named PreTP-2L was developed for predicting various therapeutic peptide types. PreTP-2L consists of two layers. The first layer predicts whether a peptide sequence belongs to therapeutic peptide, and the second layer predicts if a therapeutic peptide belongs to a particular species. Availability and implementation: A user-friendly webserver PreTP-2L can be accessed at http://bliulab.net/PreTP-2L. [ABSTRACT FROM AUTHOR]

The discovery of drug–target interactions (DTIs) is a pivotal process in pharmaceutical development. Computational approaches are a promising and efficient alternative to tedious and costly wet-lab experiments for predicting novel DTIs from numerous candidates. Recently, with the availability of abundant heterogeneous biological information from diverse data sources, computational methods have been able to leverage multiple drug and target similarities to boost the performance of DTI prediction. Similarity integration is an effective and flexible strategy to extract crucial information across complementary similarity views, providing a compressed input for any similarity-based DTI prediction model. However, existing similarity integration methods filter and fuse similarities from a global perspective, neglecting the utility of similarity views for each drug and target. In this study, we propose a Fine-Grained Selective similarity integration approach, called FGS, which employs a local interaction consistency-based weight matrix to capture and exploit the importance of similarities at a finer granularity in both similarity selection and combination steps. We evaluate FGS on five DTI prediction datasets under various prediction settings. Experimental results show that our method not only outperforms similarity integration competitors with comparable computational costs, but also achieves better prediction performance than state-of-the-art DTI prediction approaches by collaborating with conventional base models. Furthermore, case studies on the analysis of similarity weights and on the verification of novel predictions confirm the practical ability of FGS. [ABSTRACT FROM AUTHOR]

During tunnel construction, accurately ascertaining the adverse geological condition in front of the tunnel face is essential to ensure construction safety and decrease economic loss. As an accurate wave velocity modelling method, seismic full-waveform inversion (FWI) still faces many difficulties when applied in a tunnel observation environment because of fewer observation data and smaller offset than ground seismic detection. This paper analysed the FWI for tunnel active seismic forward prospecting with a single shot. We adopted the N -order time integral wavefield and normalized integration objective function method to improve the stability and reduce the dependence of FWI on the initial model. Based on two assumptions on the wave velocity distribution in the tunnel, we proposed a 1-D velocity structure correction method to reduce the multiplicity of inversion. Then the improved tunnel FWI method based on these two inversion strategies was applied to a synthetic model with a rectangular anomaly and a lithology interface, verifying the method's effectiveness. Based on typical adverse geological bodies during tunnelling, three additional adverse geological models were built and verified the reliability of the methods in tunnel detection environments. There are still some false anomalies interference in the inversion results of the synthetic models from the inversion results. However, these problems are acceptable in limited tunnel observation space. Then the method was applied in a field example in the Yinsong water supply project in Jilin Province, China, and verified the effect on field data. Finally, the influence of the shot number on the inversion effect and the method's robustness are discussed. [ABSTRACT FROM AUTHOR]

Motivation Protein function annotation is fundamental to understanding biological mechanisms. The abundant genome-scale protein–protein interaction (PPI) networks, together with other protein biological attributes, provide rich information for annotating protein functions. As PPI networks and biological attributes describe protein functions from different perspectives, it is highly challenging to cross-fuse them for protein function prediction. Recently, several methods combine the PPI networks and protein attributes via the graph neural networks (GNNs). However, GNNs may inherit or even magnify the bias caused by noisy edges in PPI networks. Besides, GNNs with stacking of many layers may cause the over-smoothing problem of node representations. Results We develop a novel protein function prediction method, CFAGO, to integrate single-species PPI networks and protein biological attributes via a multi-head attention mechanism. CFAGO is first pre-trained with an encoder–decoder architecture to capture the universal protein representation of the two sources. It is then fine-tuned to learn more effective protein representations for protein function prediction. Benchmark experiments on human and mouse datasets show CFAGO outperforms state-of-the-art single-species network-based methods by at least 7.59%, 6.90%, 11.68% in terms of m-AUPR, M-AUPR, and Fmax, respectively, demonstrating cross-fusion by multi-head attention mechanism can greatly improve the protein function prediction. We further evaluate the quality of captured protein representations in terms of Davies Bouldin Score, whose results show that cross-fused protein representations by multi-head attention mechanism are at least 2.7% better than that of original and concatenated representations. We believe CFAGO is an effective tool for protein function prediction. Availability and implementation The source code of CFAGO and experiments data are available at: http://bliulab.net/CFAGO/. [ABSTRACT FROM AUTHOR]

The 'plainification of materials' has been conceptualized to promote the sustainable development of materials. This perspective, for the first time in the field of biomaterials, proposes and defines 'plain metallic biomaterials (PMBs)' with demonstrated research and application case studies of pure titanium with high strength and toughness, and biodegradable, fine-grained and high-purity magnesium. Then, after discussing the features, benefits and opportunities of PMBs, the challenges are analyzed from both technical and regulatory aspects. Regulatory perspectives on PMB-based medical devices are also provided for the benefit of future research, development and commercialization. [ABSTRACT FROM AUTHOR]

Rodent models of lipopolysaccharide (LPS)–induced pulmonary inflammation are used for anti-inflammatory drug testing. We aimed to characterize mice responses to aerosolized LPS alone or with intraperitoneal (i.p.) delivery of alpha1-antitrypsin (AAT). Balb/c mice were exposed to clean air or aerosolized LPS (0.21 mg/mL) for 10 min per day, for 3 d. One hour after each challenge, animals were treated i.p. with saline or with (4 mg/kg body weight) one of the AAT preparations: native (AAT), oxidized (oxAAT), recombinant (recAAT), or peptide of AAT (C-36). Experiments were terminated 6 h after the last dose of AATs. Transcriptome data of mice lungs exposed to clean air versus LPS revealed 656 differentially expressed genes and 155 significant gene ontology terms, including neutrophil migration and toll-like receptor signaling pathways. Concordantly, mice inhaling LPS showed higher bronchoalveolar lavage fluid neutrophil counts and levels of myeloperoxidase, inducible nitric oxide synthase, IL-1β, TNFα, KC, IL-6, and granulocyte-macrophage colony-stimulating factor (GM-CSF). Plasma inflammatory markers did not increase. After i.p. application of AATs, about 1% to 2% of proteins reached the lungs but, except for GM-CSF, none of the proteins significantly influenced inflammatory markers. All AATs and C-36 significantly inhibited LPS-induced GM-CSF release. Surprisingly, only oxAAT decreased the expression of several LPS-induced inflammatory genes, such as Cxcl3 , Cd14 , Il1b , Nfkb1 , and Nfkb2 , in lung tissues. According to lung transcriptome data, oxAAT mostly affected genes related to transcriptional regulation while native AAT or recAAT affected genes of inflammatory pathways. Hence, we present a feasible mice model of local lung inflammation induced via aerosolized LPS that can be useful for systemic drug testing. [ABSTRACT FROM AUTHOR]

Discovering the relationships between long non-coding RNAs (lncRNAs) and diseases is significant in the treatment, diagnosis and prevention of diseases. However, current identified lncRNA-disease associations are not enough because of the expensive and heavy workload of wet laboratory experiments. Therefore, it is greatly important to develop an efficient computational method for predicting potential lncRNA-disease associations. Previous methods showed that combining the prediction results of the lncRNA-disease associations predicted by different classification methods via Learning to Rank (LTR) algorithm can be effective for predicting potential lncRNA-disease associations. However, when the classification results are incorrect, the ranking results will inevitably be affected. We propose the GraLTR-LDA predictor based on biological knowledge graphs and ranking framework for predicting potential lncRNA-disease associations. Firstly, homogeneous graph and heterogeneous graph are constructed by integrating multi-source biological information. Then, GraLTR-LDA integrates graph auto-encoder and attention mechanism to extract embedded features from the constructed graphs. Finally, GraLTR-LDA incorporates the embedded features into the LTR via feature crossing statistical strategies to predict priority order of diseases associated with query lncRNAs. Experimental results demonstrate that GraLTR-LDA outperforms the other state-of-the-art predictors and can effectively detect potential lncRNA-disease associations. Availability and implementation: Datasets and source codes are available at http://bliulab.net/GraLTR-LDA. [ABSTRACT FROM AUTHOR]

Motivation Antimicrobial peptides (AMPs) are essential components of therapeutic peptides for innate immunity. Researchers have developed several computational methods to predict the potential AMPs from many candidate peptides. With the development of artificial intelligent techniques, the protein structures can be accurately predicted, which are useful for protein sequence and function analysis. Unfortunately, the predicted peptide structure information has not been applied to the field of AMP prediction so as to improve the predictive performance. Results In this study, we proposed a computational predictor called sAMPpred-GAT for AMP identification. To the best of our knowledge, sAMPpred-GAT is the first approach based on the predicted peptide structures for AMP prediction. The sAMPpred-GAT predictor constructs the graphs based on the predicted peptide structures, sequence information and evolutionary information. The Graph Attention Network (GAT) is then performed on the graphs to learn the discriminative features. Finally, the full connection networks are utilized as the output module to predict whether the peptides are AMP or not. Experimental results show that sAMPpred-GAT outperforms the other state-of-the-art methods in terms of AUC, and achieves better or highly comparable performance in terms of the other metrics on the eight independent test datasets, demonstrating that the predicted peptide structure information is important for AMP prediction. Availability and implementation A user-friendly webserver of sAMPpred-GAT can be accessed at http://bliulab.net/sAMPpred-GAT and the source code is available at https://github.com/HongWuL/sAMPpred-GAT/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Observation of Interplanetary Scintillation (IPS) provides an important and effective way to study the solar wind and the space weather. A series of IPS observations were conducted by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The extraordinary sensitivity and the wide frequency coverage make FAST an ideal platform for IPS studies. In this paper, we present some first scientific results from FAST observations of IPS with the L -band receiver. Based on the solar wind velocity fitting values of FAST observations on 2020 September 26–28, we found that the velocity decreases with increasing frequency linearly, which has not yet been reported in literature. And we have also detected a variation of solar wind velocity on a time-scale of 3–5 min, which imply the slow change of the background solar wind, a co-existence of high- and low-speed streams, or a reflect of the quasi-periodic electron-density fluctuations. [ABSTRACT FROM AUTHOR]

The discovery of drug–target interactions (DTIs) is a very promising area of research with great potential. The accurate identification of reliable interactions among drugs and proteins via computational methods, which typically leverage heterogeneous information retrieved from diverse data sources, can boost the development of effective pharmaceuticals. Although random walk and matrix factorization techniques are widely used in DTI prediction, they have several limitations. Random walk-based embedding generation is usually conducted in an unsupervised manner, while the linear similarity combination in matrix factorization distorts individual insights offered by different views. To tackle these issues, we take a multi-layered network approach to handle diverse drug and target similarities, and propose a novel optimization framework, called Multiple similarity DeepWalk-based Matrix Factorization (MDMF), for DTI prediction. The framework unifies embedding generation and interaction prediction, learning vector representations of drugs and targets that not only retain higher order proximity across all hyper-layers and layer-specific local invariance, but also approximate the interactions with their inner product. Furthermore, we develop an ensemble method (MDMF2A) that integrates two instantiations of the MDMF model, optimizing the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC), respectively. The empirical study on real-world DTI datasets shows that our method achieves statistically significant improvement over current state-of-the-art approaches in four different settings. Moreover, the validation of highly ranked non-interacting pairs also demonstrates the potential of MDMF2A to discover novel DTIs. [ABSTRACT FROM AUTHOR]

TIC 470710327, a massive compact hierarchical triple-star system, was recently identified by NASA's Transiting Exoplanet Survey Satellite. TIC 470710327 is comprised of a compact (1.10 d) circular eclipsing binary, with total mass |$\approx 10.9\!-\!13.2\, \rm {M_{\odot }}$|⁠ , and a more massive |$\approx 14\!-\!17\, \rm {M_{\odot }}$| eccentric non-eclipsing tertiary in a 52.04 d orbit. Here, we present a progenitor scenario for TIC 470710327 in which '2 + 2' quadruple dynamics result in Zeipel–Lidov–Kozai oscillations that lead to a contact phase of the more massive binary. In this scenario, the two binary systems should form in a very similar manner, and dynamics trigger the merger of the more massive binary either during late phases of star formation or several Myr after the zero-age main sequence, when the stars begin to expand. Any evidence that the tertiary is a highly magnetized (∼1–10 kG), slowly rotating blue main-sequence star would hint towards a quadruple origin. Finally, our scenario suggests that the population of inclined compact multiple-stellar systems is reduced into coplanar systems, via mergers, late during star formation or early in the main sequence. The elucidation of the origin of TIC 470710327 is crucial in our understanding of multiple massive star formation and evolution. [ABSTRACT FROM AUTHOR]

Aims Diastolic Ca release (DCR) from sarcoplasmic reticulum (SR) Ca release channel ryanodine receptor (RyR2) has been linked to multiple cardiac pathologies, but its exact role in shaping divergent cardiac pathologies remains unclear. We hypothesize that the SR-mitochondria interplay contributes to disease phenotypes by shaping Ca signalling. Methods and results A genetic model of catecholaminergic polymorphic ventricular tachycardia (CPVT2 model of CASQ2 knockout) and a pre-diabetic cardiomyopathy model of fructose-fed mice (FFD), both marked by DCR, are employed in this study. Mitochondria Ca (mCa) is modulated by pharmacologically targeting mitochondria Ca uniporter (MCU) or permeability transition pore (mPTP), mCa uptake, and extrusion mechanisms, respectively. An MCU activator abolished Ca waves in CPVT2 but exacerbated waves in FFD cells. Mechanistically this is ascribed to mitochondria's function as a Ca buffer or source of reactive oxygen species (mtROS) to exacerbate RyR2 functionality, respectively. Enhancing mCa uptake reduced and elevated mtROS production in CPVT2 and FFD, respectively. In CPVT2, mitochondria took up more Ca in permeabilized cells, and had higher level of mCa content in intact cells vs. FFD. Conditional ablation of MCU in the CPVT2 model caused lethality and cardiac remodelling, but reduced arrhythmias in the FFD model. In parallel, CPVT2 mitochondria also employ up-regulated mPTP-mediated Ca efflux to avoid mCa overload, as seen by elevated incidence of MitoWinks (an indicator of mPTP-mediated Ca efflux) vs. FFD. Both pharmacological and genetic inhibition of mPTP promoted mtROS production and exacerbation of myocyte Ca handling in CPVT2. Further, genetic inhibition of mPTP exacerbated arrhythmias in CPVT2. Conclusion In contrast to FFD, which is more susceptible to mtROS-dependent RyR2 leak, in CPVT2 mitochondria buffer SR-derived DCR to mitigate Ca-dependent pathological remodelling and rely on mPTP-mediated Ca efflux to avoid mCa overload. SR-mitochondria interplay contributes to the divergent pathologies by disparately shaping intracellular Ca signalling. [ABSTRACT FROM AUTHOR]

We study the long-term orbital evolution of stars around a merging massive or supermassive black hole binary (BHB), taking into account the general relativistic effect induced by the black hole (BH) spin. When the BH spin is significant compared to and misaligned with the binary orbital angular momentum, the orbital axis (⁠|$\hat{\boldsymbol {l}}$|⁠) of the circumbinary star can undergo significant evolution during the binary orbital decay driven by gravitational radiation. Including the spin effect of the primary (more massive) BH, we find that starting from nearly coplanar orbital orientations, the orbital axes |$\hat{\boldsymbol {l}}$| of circumbinary stars preferentially evolve towards the spin direction after the merger of the BHB, regardless of the initial BH spin orientation. Such alignment phenomenon, i.e. small final misalignment angle between |$\hat{\boldsymbol {l}}$| and the spin axis of the remnant BH |$\hat{\boldsymbol {S}}$|⁠ , can be understood analytically using the principle of adiabatic invariance. For the BHBs with extremely mass ratio (m 2/ m 1 ≲ 0.01), |$\hat{\boldsymbol {l}}$| may experience more complicated evolution as adiabatic invariance breaks down, but the trend of alignment still works reasonably well when the initial binary spin–orbit angle is relatively small. Our result suggests that the correlation between the orientations of stellar orbits and the spin axis of the central BH could provide a potential signature of the merger history of the massive BH. [ABSTRACT FROM AUTHOR]

Protein-DNA and protein-RNA interactions are involved in many biological activities. In the post-genome era, accurate identification of DNA- and RNA-binding residues in protein sequences is of great significance for studying protein functions and promoting new drug design and development. Therefore, some sequence-based computational methods have been proposed for identifying DNA- and RNA-binding residues. However, they failed to fully utilize the functional properties of residues, leading to limited prediction performance. In this paper, a sequence-based method iDRNA-ITF was proposed to incorporate the functional properties in residue representation by using an induction and transfer framework. The properties of nucleic acid-binding residues were induced by the nucleic acid-binding residue feature extraction network, and then transferred into the feature integration modules of the DNA-binding residue prediction network and the RNA-binding residue prediction network for the final prediction. Experimental results on four test sets demonstrate that iDRNA-ITF achieves the state-of-the-art performance, outperforming the other existing sequence-based methods. The webserver of iDRNA-ITF is freely available at http://bliulab.net/iDRNA-ITF. [ABSTRACT FROM AUTHOR]

Identifying miRNA-disease associations is an important task for revealing pathogenic mechanism of complicated diseases. Different computational methods have been proposed. Although these methods obtained encouraging performance for detecting missing associations between known miRNAs and diseases, how to accurately predict associated diseases for new miRNAs is still a difficult task. In this regard, a ranking framework named idenMD-NRF is proposed for miRNA-disease association identification. idenMD-NRF treats the miRNA-disease association identification as an information retrieval task. Given a novel query miRNA, idenMD-NRF employs Learning to Rank algorithm to rank associated diseases based on high-level association features and various predictors. The experimental results on two independent test datasets indicate that idenMD-NRF is superior to other compared predictors. A user-friendly web server of idenMD-NRF predictor is freely available at http://bliulab.net/idenMD-NRF/. [ABSTRACT FROM AUTHOR]

Motivation Therapeutic peptide prediction is important for the discovery of efficient therapeutic peptides and drug development. Researchers have developed several computational methods to identify different therapeutic peptide types. However, these computational methods focus on identifying some specific types of therapeutic peptides, failing to predict the comprehensive types of therapeutic peptides. Moreover, it is still challenging to utilize different properties to predict the therapeutic peptides. Results In this study, an adaptive multi-view based on the tensor learning framework TPpred-ATMV is proposed for predicting different types of therapeutic peptides. TPpred-ATMV constructs the class and probability information based on various sequence features. We constructed the latent subspace among the multi-view features and constructed an auto-weighted multi-view tensor learning model to utilize the high correlation based on the multi-view features. Experimental results showed that the TPpred-ATMV is better than or highly comparable with the other state-of-the-art methods for predicting eight types of therapeutic peptides. Availability and implementation The code of TPpred-ATMV is accessed at: https://github.com/cokeyk/TPpred-ATMV. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Deep learning image reconstruction (DLIR) is a new reconstruction method for maintaining image quality at reduced radiation dose. The purpose of this study was to compare image quality of reduced-dose DLIR images with the standard-dose adaptive statistical iterative reconstruction (ASIR-V) images in chest CT. Our prospective study included 48 adult patients (30 women and 18 men, mean age ±SD, 49.8 ± 14.3 years) who underwent both the standard-dose CT (SDCT) and low-dose CT (LDCT) on a GE Revolution CT scanner. All patients gave written informed consent. All scans were reconstructed with ASIR-V40%. Additionally, LDCT scans were reconstructed with DLIR with high-setting (DLIR-H) and medium-setting (DLIR-M). Image noise and contrast-noise-ratio (CNR) of thoracic aorta with different reconstruction modes were measured and compared. LDCT reduced radiation dose by 96% compared with SDCT (CTDIvol: 0.54mGy vs 12.46mGy). In LDCT, DLIR significantly reduced image noise compared with the state-of-the-art ASIR-V40% with DLIR-H provided the lowest image noise and highest image quality score. In addition, the image noise, CNR of aorta and overall image quality of the low-dose DLIR-H images did not have significant difference compared with the SDCT ASIR-V40% images (all p > 0.05). DLIR significantly reduces image noise in LDCT chest scans and provides similar image quality as the SDCT ASIR-V images at 4% of the radiation dose. DLIR uses high-quality FBP data to train deep neural networks to learn how to distinguish between signal and noise, and effectively suppresses noise without affecting anatomical and pathological structures. It opens a new era of CT image reconstruction. DLIR significantly reduces image noise and improves image quality compared with ASIR-V40% under same radiation dose condition. DLIR-H achieves similar image quality at 4% radiation dose as ASIR-V40% at standard-dose level in non-contrast chest CT. [ABSTRACT FROM AUTHOR]

The Square Kilometre Array (SKA) , as an eminent radio telescope of the next generation, will observe a huge number of objects with complex morphologies and sizes. An efficient method for locating and classifying radio sources becomes a requirement for scientific exploration. The SKA Science Data Challenge 1 (SDC1) is focused on the source detection, characterization, and classification for the SKA mid-frequency dish array of simulated continuum data. Three frequencies are covered (560, 1400, and 9200 MHz) to three depths (8, 100, and 1000 h). In this paper, we present an efficient deep learning framework, which is an entirely parallel, Python-based method for confronting the data challenge. The method can exceptionally achieve the source finding and categorizing simultaneously for both point and extended sources. In addition, the proposed denoising model can be a good noise estimator as a plugin for other similar applications. Compared with the published best, our score has improved by at least 22 per cent and up to 125 per cent in nine images of SDC1. [ABSTRACT FROM AUTHOR]

Motivation RNA-binding proteins (RBPs) play crucial roles in post-transcriptional regulation. Accurate identification of RBPs helps to understand gene expression, regulation, etc. In recent years, some computational methods were proposed to identify RBPs. However, these methods fail to accurately identify RBPs from some specific species with limited data, such as bacteria. Results In this study, we introduce a computational method called PreRBP-TL for identifying species-specific RBPs based on transfer learning. The weights of the prediction model were initialized by pretraining with the large general RBP dataset and then fine-tuned with the small species-specific RPB dataset by using transfer learning. The experimental results show that the PreRBP-TL achieves better performance for identifying the species-specific RBPs from Human , Arabidopsis , Escherichia coli and Salmonella , outperforming eight state-of-the-art computational methods. It is anticipated PreRBP-TL will become a useful method for identifying RBPs. Availability and implementation For the convenience of researchers to identify RBPs, the web server of PreRBP-TL was established, freely available at http://bliulab.net/PreRBP-TL. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 B.1.617.2 (Delta) variant flared up in late May in Guangzhou, China. Transmission characteristics of Delta variant were analysed for 153 confirmed cases and two complete transmission chains with seven generations were fully presented. A rapid transmission occurred in five generations within 10 days. The basic reproduction number (R0) was 3.60 (95% confidence interval: 2.50–5.30). After redefining the concept of close contact, the proportion of confirmed cases discovered from close contacts increased from 43% to 100%. With the usage of a yellow health code, the potential exposed individuals were self-motivated to take a nucleic acid test and regained public access with a negative testing result. Facing the massive requirement of screening, novel facilities like makeshift inflatable laboratories were promptly set up as a vital supplement and 17 cases were found, with 1 pre-symptomatic. The dynamic adjustment of these three interventions resulted in the decline of Rt from 5.00 to 1.00 within 9 days. By breaking the transmission chain and eliminating the transmission source through extending the scope of the close-contact tracing, health-code usage and mass testing, the Guangzhou Delta epidemic was effectively contained. [ABSTRACT FROM AUTHOR]

Protein remote homology detection is one of the most fundamental research tool for protein structure and function prediction. Most search methods for protein remote homology detection are evaluated based on the Structural Classification of Proteins-extended (SCOPe) benchmark, but the diverse hierarchical structure relationships between the query protein and candidate proteins are ignored by these methods. In order to further improve the predictive performance for protein remote homology detection, a search framework based on the predicted protein hierarchical relationships (PHR-search) is proposed. In the PHR-search framework, the superfamily level prediction information is obtained by extracting the local and global features of the Hidden Markov Model (HMM) profile through a convolution neural network and it is converted to the fold level and class level prediction information according to the hierarchical relationships of SCOPe. Based on these predicted protein hierarchical relationships, filtering strategy and re-ranking strategy are used to construct the two-level search of PHR-search. Experimental results show that the PHR-search framework achieves the state-of-the-art performance by employing five basic search methods, including HHblits, JackHMMER, PSI-BLAST, DELTA-BLAST and PSI-BLASTexB. Furthermore, the web server of PHR-search is established, which can be accessed at http://bliulab.net/PHR-search. [ABSTRACT FROM AUTHOR]

Patients receiving pelvic or abdominal radiotherapy may experience acute and/or chronic side effects due to gastrointestinal changes. However, effective medicine for treating radiation enteritis has not been found yet. Sargentodoxa cuneata is a famous Chinese medicine used to treat intestinal inflammation, and our research team has found the main biologically active compound through its extraction, which is Liriodendrin. In this study, we found that Liriodendrin can reduce the expression of Cer, Cer1P and S1P in the sphingolipid pathway, thereby reducing the histological damage to the intestinal tract of mice and inhibiting the apoptosis of intestinal tissue cells. In addition, Liriodendrin can reduce the levels of pro-inflammatory cytokines (IL-6 and TNF-α), and it is suggested through flow cytometry that the proportion of neutrophils in the intestinal tissue can decrease due to the existence of Liriodendrin. At the same time, the western blot evaluation revealed that Liriodendrin significantly inhibited the activation of Bcl-2/Bax/Caspase-3 and NF-κB signaling pathways. The results show that Liriodendrin can inhibit intestinal inflammation and intestinal cell apoptosis through the sphingolipid pathway. Therefore, the aforementioned results demonstrated that Liriodendrin may be a promising drug for the treatment of radiation enteritis. [ABSTRACT FROM AUTHOR]

Motivation Intrinsically disordered regions (IDRs) are widely distributed in proteins. Accurate prediction of IDRs is critical for the protein structure and function analysis. The IDRs are divided into long disordered regions (LDRs) and short disordered regions (SDRs) according to their lengths. Previous studies have shown that LDRs and SDRs have different proprieties. However, the existing computational methods fail to extract different features for LDRs and SDRs separately. As a result, they achieve unstable performance on datasets with different ratios of LDRs and SDRs. Results In this study, a two-layer predictor was proposed called DeepIDP-2L. In the first layer, two kinds of attention-based models are used to extract different features for LDRs and SDRs, respectively. The hierarchical attention network is used to capture the distribution pattern features of LDRs, and convolutional attention network is used to capture the local correlation features of SDRs. The second layer of DeepIDP-2L maps the feature extracted in the first layer into a new feature space. Convolutional network and bidirectional long short term memory are used to capture the local and long-range information for predicting both SDRs and LDRs. Experimental results show that DeepIDP-2L can achieve more stable performance than other exiting predictors on independent test sets with different ratios of SDRs and LDRs. Availability and implementation For the convenience of most experimental scientists, a user-friendly and publicly accessible web-server for the new predictor has been established at http://bliulab.net/DeepIDP-2L/. It is anticipated that DeepIDP-2L will become a very useful tool for identification of intrinsically disordered regions. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Outbreaks of pine wood nematode (PWN; Bursaphelenchus xylophilus) represent a severe biotic epidemic for the Pinus massoniana in China. When invaded by the PWN, the resistant P. massoniana might secret abundant oleoresin terpenoid to form certain defensive fronts for survival. However, the regulatory mechanisms of this process remain unclear. Here, the geranyl diphosphate synthase (PmGPPS1) gene was identified from resistant P. massoniana. Tissue-specific expression patterns of PmGPPS1 at transcript and protein level in resistant P. massoniana were determined by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry. Functional characteristics analysis of PmGPPS1 was performed on transgenic Nicotiana benthamiana by overexpression, as genetic transformation of P. massoniana is, so far, not possible. In summary, we identified and functionally characterized PmGPPS1 from the resistant P. massoniana following PWN inoculation. Tissue-specific expression patterns and localization of PmGPPS1 indicated that it may play a positive role involved in the metabolic and defensive processes of oleoresin terpenes production in response to PWN attack. Furthermore, overexpression of PmGPPS1 may enhance the production of monoterpene, among which limonene reduced the survival of PWN in vitro. In addition, PmGPPS1 upregulated the expression level of key genes involved in mevalonic acid (MVA) pathway, the methylerythritol phosphate (MEP) pathway and gibberellins (GAs) biosynthesis to boost the growth and development of tobacco through a feedback regulation mechanism. Our results offered new insights into the pivotal role of the PmGPPS1 involved in terpene-based defense mechanisms responding to the PWN invasion in resistant P. massoniana and provided a new metabolic engineering scenario to improve monoterpene production in tobacco. [ABSTRACT FROM AUTHOR]

Forty-five years after the Apollo and Luna missions returned lunar samples, China's Chang'E-5 (CE-5) mission collected new samples from the mid-latitude region in the northeastern Oceanus Procellarum of the Moon. Our study shows that 95% of CE-5 lunar soil sizes are found to be within the range of 1.40–9.35 μm, while 95% of the soils by mass are within the size range of 4.84–432.27 μm. The bulk density, true density and specific surface area of CE-5 soils are 1.2387 g/cm3, 3.1952 g/cm3 and 0.56 m2/g, respectively. Fragments from the CE-5 regolith are classified into igneous clasts (mostly basalt), agglutinate and glass. A few breccias were also found. The minerals and compositions of CE-5 soils are consistent with mare basalts and can be classified as low-Ti/low-Al/low-K type with lower rare-earth-element contents than materials rich in potassium, rare earth element and phosphorus. CE-5 soils have high FeO and low Mg index, which could represent a new class of basalt. [ABSTRACT FROM AUTHOR]

Dendroctonus armandi (Tsai and Li) (Coleoptera: Curculionidae: Scolytinae) is considered to be the most destructive forest pest in the Qinling and Bashan Mountains of China. Low winter temperatures limit insect's populations, distribution, activity, and development. Insects have developed different strategies such as freeze-tolerance and freeze-avoidance to survive in low temperature conditions. In the present study, we used gene cloning, real-time polymerase chain reaction (PCR), RNA interference (RNAi), and heterologous expression to study the function of the D. armandi antifreeze protein gene (DaAFP). We cloned the 800 bp full-length cDNA encoding 228 amino acids of DaAFP and analyzed its structure using bioinformatics analysis. The DaAFP amino acid sequence exhibited 24–86% similarity with other insect species. The expression of DaAFP was high in January and in the larvae, head, and midgut of D. armandi. In addition, the expression of DaAFP increased with decreasing temperature and increasing exposure time. RNAi analysis also demonstrated that AFP plays an important role in the cold tolerance of overwintering larvae. The thermal hysteresis and antifreeze activity assay of DaAFP and its mutants indicated that the more regular the DaAFP threonine-cystine-threonine (TXT) motif, the stronger the antifreeze activity. These results suggest that DaAFP plays an essential role as a biological cryoprotectant in overwintering D. armandi larvae and provides a theoretical basis for new pest control methods. [ABSTRACT FROM AUTHOR]

In vivo testing is crucial for the evaluation of orthopedic implant efficacy and safety. However, the translation and reproducibility of preclinical animal experiments are not always satisfactory, and reporting quality is among the essential factors that ensure appropriate delivery of information. In this study, we assessed the reporting quality of in vivo investigations that examined the use of degradable metal materials in fracture or bone defect repair. We employed scientific databases, such as PubMed, EMBASE, Web of Science, Cochrane Library, CNKI, WanFang, VIP and Sinomed to screen for in vivo investigations on fracture or bone defect repair using degradable metal materials, and extracted both epidemiological and main characteristics of eligible studies, and assessed their reporting quality using the ARRIVE guidelines 2.0. Overall, 263 publications were selected, including 275 animal experiments. The overall coincidence rate of Essential 10 (22 sub-items) and Recommended Set (16 sub-items) were 42.0% and 41.5%, respectively. Based on our analysis, the reporting quality of the published in vivo investigations examining fracture/bone defect repair with degradable metal materials was low, and there was a lack of transparent, accurate and comprehensive reporting on key elements of the experimental design and other elements that are meant to avoid bias. [ABSTRACT FROM AUTHOR]

Motivation Protein remote homology detection is a challenging task for the studies of protein evolutionary relationships. PSI-BLAST is an important and fundamental search method for detecting homology proteins. Although many improved versions of PSI-BLAST have been proposed, their performance is limited by the search processes of PSI-BLAST. Results For further improving the performance of PSI-BLAST for protein remote homology detection, a supervised two-layer search framework based on PSI-BLAST (S2L-PSIBLAST) is proposed. S2L-PSIBLAST consists of a two-level search: the first-level search provides high-quality search results by using SMI-BLAST framework and double-link strategy to filter the non-homology protein sequences, the second-level search detects more homology proteins by profile-link similarity, and more accurate ranking lists for those detected protein sequences are obtained by learning to rank strategy. Experimental results on the updated version of Structural Classification of Proteins-extended benchmark dataset show that S2L-PSIBLAST not only obviously improves the performance of PSI-BLAST, but also achieves better performance on two improved versions of PSI-BLAST: DELTA-BLAST and PSI-BLASTexB. Availability and implementation http://bliulab.net/S2L-PSIBLAST. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Context: The risk of persistent and recurrent disease in patients with differentiated thyroid cancer (DTC) is a continuum that ranges from very low to very high, even within the 3 primary risk categories. It is important to identify independent clinicopathological parameters to accurately predict clinical outcomes.Objective: To examine the association between pre-ablation stimulated thyroglobulin (ps-Tg) and persistent and recurrent disease in DTC patients and investigate whether incorporation of ps-Tg could provide a more individualized estimate of clinical outcomes.Design, Setting, and Participants: Medical records of 2524 DTC patients who underwent total thyroidectomy and radioiodine ablation between 2006 and 2018 were retrospectively reviewed.Main Outcome Measure: Ps-Tg was measured under thyroid hormone withdrawal before remnant ablation. Association of ps-Tg and clinical outcomes.Results: In multivariate analysis, age, American Thyroid Association (ATA) risk stratification, distant metastasis, ps-Tg, and cumulative administered activities were the independent predictive factors for persistent/recurrent disease. Receiver operating characteristic analysis identified ps-Tg cutoff (≤10.1 ng/mL) to predict disease-free status with a negative predictive value of 95%, and validated for all ATA categories. Integration of ps-Tg into ATA risk categories indicated that the presence of ps-Tg ≤ 10.1 ng/mL was associated with a significantly decreased chance of having persistent/recurrent disease in intermediate- and high-risk patients (9.9% to 4.1% in intermediate-risk patients, and 33.1% to 8.5% in high-risk patients).Conclusion: The ps-Tg (≤10.1 ng/mL) was a key predictor of clinical outcomes in DTC patients. Its incorporation as a variable in the ATA risk stratification system could more accurately predict clinical outcomes. [ABSTRACT FROM AUTHOR]

Motivation Due to the inherent stability and close relationship with the progression of diseases, circRNAs are serving as important biomarkers and drug targets. Efficient predictors for identifying circRNA–disease associations are highly required. The existing predictors consider circRNA–disease association prediction as a classification task or a recommendation problem, failing to capture the ranking information among the associations and detect the diseases associated with new circRNAs. However, more and more circRNAs are discovered. Identification of the diseases associated with these new circRNAs remains a challenging task. Results In this study, we proposed a new predictor called iCricDA-LTR for circRNA–disease association prediction. Different from any existing predictor, iCricDA-LTR employed a ranking framework to model the global ranking associations among the query circRNAs and the diseases. The Learning to Rank (LTR) algorithm was employed to rank the associations based on various predictors and features in a supervised manner. The experimental results on two independent test datasets showed that iCircDA-LTR outperformed the other competing methods, especially for predicting the diseases associated with new circRNAs. As a result, iCircDA-LTR is more suitable for the real-world applications. Availability and implementation For the convenience of researchers to detect new circRNA–disease associations. The web server of iCircDA-LTR was established and freely available at http://bliulab.net/iCircDA-LTR/. [ABSTRACT FROM AUTHOR]

Study Question: Is the sperm acrosome membrane-associated protein 1 (SPACA1) gene critical to human globozoospermia?Summary Answer: The biallelic loss-of-function (variant of SPACA1) causes globozoospermia as a result of acrosome-acroplaxome complex damage.What Is Known Already: SPACA1 expression decreases in patients with globozoospermia. Spaca1 gene-disrupted mice have abnormally shaped sperm heads that resemble those of human globozoospermia.Study Design, Size, Duration: We recruited a consanguineous family with two brothers affected by infertility as a consequence of globozoospermia. The semen analysis data and ART outcomes were collected. Exome sequencing (ES) was used to identify potential pathogenic variants. Protein-protein interaction (PPI) technologies and proteomic analysis were utilized to explore the pathogenic mechanism.Participants/materials, Setting, Methods: Two globozoospermic brothers and their consanguineous parents were recruited to identify the potential pathogenic variant through ES. A homozygous nonsense variant in the SPACA1 gene in both brothers inherited from the heterozygous parents was identified. Twenty normal fertile males were recruited as controls. Sperm ultrastructure was observed with transmission electron microscopy. Western blotting was performed to measure SPACA1 expression level in the sperm from the patients. Mass spectrometry (MS) analyses were used to identify differentially expressed proteins and to investigate proteins that interact with SPACA1. Co-immunoprecipitation (co-IP), yeast two-hybrid (Y2H) and immunofluorescence colocalization assays were used to confirm the PPI.Main Results and the Role Of Chance: A nonsense variant (NM_030960.2: c.53G>A; p. Trp18*) in the SPACA1 gene was identified as the pathogenic variant in a family with globozoospermia. Patient IV:1 and Patient IV:2 had a phenotype very similar to that of Spaca1 gene-disrupted mice. The nonsense variant in SPACA1 led to premature transcriptional termination in the signal peptide, which was confirmed by western blotting. MS-based proteomics analysis showed that eight interactors of SPACA1 were differentially expressed in the patients' sperm, including actin-like Protein 7A (ACTL7A), an important component of the acrosome-acroplaxome complex. The PPI of SPACA1 and ACTL7A was confirmed via co-IP and Y2H assays. Immunofluorescence showed that SPACA1 and ACTL7A colocalized in mature sperm, revealing that these proteins were coexpressed spatially.Limitations, Reasons For Caution: Given the rarity of globozoospermia, only two patients from one family harbouring the SPACA1 variant were found. Future studies should evaluate SPACA1 variants in larger cohorts to corroborate this finding.Wider Implications Of the Findings: This study revealed that the SPACA1 gene was critical for globozoospermia, which expanded the spectrum of causative genes for globozoospermia. This study also provided evidence for ICSI clinical outcomes for patients with SPACA1-deficient globozoospermia, which may guide clinical treatment strategies. Furthermore, this study explored the pathogenesis of globozoospermia caused by SPACA1 deficiency.Study Funding/competing Interest(s): This work was funded by the Precision Medical Research of National Key Research and Development Program (2018YFC1002400), National Natural Science Foundation of China (81873724), and Natural Science Foundation of Shanghai (20ZR1472700). The authors have no conflicts of interest to disclose.Trial Registration Number: N/A. [ABSTRACT FROM AUTHOR]

The interactions between proteins and nucleic acid sequences play many important roles in gene expression and some cellular activities. Accurate prediction of the nucleic acid binding residues in proteins will facilitate the research of the protein functions, gene expression, drug design, etc. In this regard, several computational methods have been proposed to predict the nucleic acid binding residues in proteins. However, these methods cannot satisfactorily measure the global interactions among the residues along protein. Furthermore, these methods are suffering cross-prediction problem, new strategies should be explored to solve this problem. In this study, a new computational method called NCBRPred was proposed to predict the nucleic acid binding residues based on the multilabel sequence labeling model. NCBRPred used the bidirectional Gated Recurrent Units (BiGRUs) to capture the global interactions among the residues, and treats this task as a multilabel learning task. Experimental results on three widely used benchmark datasets and an independent dataset showed that NCBRPred achieved higher predictive results with lower cross-prediction, outperforming 10 existing state-of-the-art predictors. The web-server and a stand-alone package of NCBRPred are freely available at http://bliulab.net/NCBRPred. It is anticipated that NCBRPred will become a very useful tool for identifying nucleic acid binding residues. [ABSTRACT FROM AUTHOR]

Many proposed scenarios for black hole (BH) mergers involve a tertiary companion that induces von Zeipel–Lidov–Kozai (ZLK) eccentricity cycles in the inner binary. An attractive feature of such mechanisms is the enhanced merger probability when the octupole-order effects, also known as the eccentric Kozai mechanism, are important. This can be the case when the tertiary is of comparable mass to the binary components. Since the octupole strength [∝(1 − q)/(1 + q)] increases with decreasing binary mass ratio q , such ZLK-induced mergers favour binaries with smaller mass ratios. We use a combination of numerical and analytical approaches to fully characterize the octupole-enhanced binary BH mergers and provide semi-analytical criteria for efficiently calculating the strength of this enhancement. We show that for hierarchical triples with semimajor axial ratio a / a out ≳ 0.01–0.02, the binary merger fraction can increase by a large factor (up to ∼20) as q decreases from unity to 0.2. The resulting mass-ratio distribution for merging binary BHs produced in this scenario is in tension with the observed distribution obtained by the LIGO/VIRGO collaboration, although significant uncertainties remain about the initial distribution of binary BH masses and mass ratios. [ABSTRACT FROM AUTHOR]