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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]

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]

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]

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]

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]

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]

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]

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]

This paper is concerned with the first-order and second-order necessary optimality conditions for discrete-time stochastic systems with delay under weak assumptions. Based on a characteristic method and a discrete-time backward stochastic equation, we establish the linearized discrete-time stochastic maximum principle and Euler-type necessary optimality condition. Moreover, by a new discrete-time backward stochastic matrix equation, we also obtain the necessary optimality condition of quasi-singular control and the pointwise second-order optimality condition. [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]

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]

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]

The moods, feelings, and attitudes represented in a novel will resonate in the reader by activating similar sentiments. It is generally accepted that sentiment analysis can capture aspects of such moods, feelings, and attitudes and can be used to summarize a novel's plot in a story arc. With the availability of a number of algorithms to automatically extract sentiment-based story arcs, new approaches for their utilization becomes pertinent. We propose to use nonlinear adaptive filtering and fractal analysis in order to analyze the narrative coherence and dynamic evolution of a novel. Using Never Let Me Go by Kazuo Ishiguro, the winner of the 2017 Nobel Prize for Literature as an illustrative example, we show that: (1) nonlinear adaptive filtering can extract a story arc that reflects the tragic trend of the novel; (2) the story arc displays persistent dynamics as measured by the Hurst exponent at short and medium timescales; (3) the plot's dynamic evolution is reflected in the time-varying Hurst exponent. We argue that these findings are indicative of the potential that multifractal theory has for computational narratology and large-scale literary analysis. Specifically that the global Hurst exponent of a story arc is an index of narrative coherence that can identify bland, incoherent, and coherent narratives on a continuous scale. And, further, that the local time-varying Hurst exponent captures variation of a novel's plot such that the extrema have specific narratological interpretations. [ABSTRACT FROM AUTHOR]

As one of the most important tasks in protein structure prediction, protein fold recognition has attracted more and more attention. In this regard, some computational predictors have been proposed with the development of machine learning and artificial intelligence techniques. However, these existing computational methods are still suffering from some disadvantages. In this regard, we propose a new network-based predictor called ProtFold-DFG for protein fold recognition. We propose the Directed Fusion Graph (DFG) to fuse the ranking lists generated by different methods, which employs the transitive closure to incorporate more relationships among proteins and uses the KL divergence to calculate the relationship between two proteins so as to improve its generalization ability. Finally, the PageRank algorithm is performed on the DFG to accurately recognize the protein folds by considering the global interactions among proteins in the DFG. Tested on a widely used and rigorous benchmark data set, LINDAHL dataset, experimental results show that the ProtFold-DFG outperforms the other 35 competing methods, indicating that ProtFold-DFG will be a useful method for protein fold recognition. The source code and data of ProtFold-DFG can be downloaded from http://bliulab.net/ProtFold-DFG/download [ABSTRACT FROM AUTHOR]

We present a novel intelligent quasar continuum neural network (iQNet), predicting the intrinsic continuum of any quasar in the rest-frame wavelength range of |$1020 \, {\mathring{\rm A}}\le \lambda _{\text{rest}} \le 1600 \, {\mathring{\rm A}}$|⁠. We train this network using high-resolution Hubble Space Telescope /Cosmic Origin Spectrograph ultraviolet quasar spectra at low redshift (z ∼ 0.2) from the Hubble Spectroscopic Legacy Archive (HSLA), and apply it to predict quasar continua in different astronomical surveys. We utilize the HSLA quasar spectra that are well defined in the rest-frame wavelength range of [1020, 1600] Å with an overall median signal-to-noise ratio of at least 5. The iQNet model achieves a median absolute fractional flux error of 2.24 per cent on the training quasar spectra, and 4.17 per cent on the testing quasar spectra. We apply iQNet and predict the continua of ∼3200 Sloan Digital Sky Survey Data Release 16 quasar spectra at higher redshift (2 < z ≤ 5) and measure the redshift evolution of mean transmitted flux (〈 F 〉) in the Ly α forest region. We measure a gradual evolution of 〈 F 〉 with redshift, which we characterize as a power-law fit to the effective optical depth of the Ly α forest. Our measurements are broadly consistent with other estimates of 〈 F 〉 in the literature but provide a more accurate measurement as we are directly measuring the quasar continuum where there is minimum contamination from the Ly α forest. This work proves that the deep learning iQNet model can predict the quasar continuum with high accuracy and shows the viability of such methods for quasar continuum prediction. [ABSTRACT FROM AUTHOR]

The merging black hole (BH) binaries GW190412, GW190814, and GW190521 from the third LIGO/VIRGO observing run exhibit some extraordinary properties, including highly asymmetric masses, significant spin, and component mass in the 'mass gap'. These features can be explained if one or both components of the binary are the remnants of previous mergers. In this paper, we explore hierarchical mergers in multiple stellar systems, taking into account the natal kick and mass-loss due to the supernova explosion (SN) on each component, as well as the merger kick received by the merger remnant. The binaries that have survived the SNe and kicks generally have too wide orbital separations to merge by themselves, but can merge with the aid of an external companion that gives rise to Lidov–Kozai oscillations. The BH binaries that consist of second-generation BHs can also be assembled in dense star clusters through binary interactions. We characterize the parameter space of these BH binaries by merger fractions in an analytical approach. Combining the distributions of the survived binaries, we further constrain the parameters of the external companion, using the analytically formulated tertiary perturbation strength. We find that to produce the three LIGO/VIRGO O3 events, the external companions must be at least a few hundreds M⊙, and fall in the intermediate-mass BH and supermassive BH range. We suggest that GW190412, GW190814, and GW190521 could all be produced via hierarchical mergers in multiples, likely in a nuclear star cluster, with the final merger induced by a massive BH. [ABSTRACT FROM AUTHOR]

We investigate the possible origin of the transiting giant planet WD 1856+534 b, the first strong exoplanet candidate orbiting a white dwarf, through high-eccentricity migration (HEM) driven by the Lidov–Kozai (LK) effect. The host system's overall architecture is a hierarchical quadruple in the '2 + 2' configuration, owing to the presence of a tertiary companion system of two M-dwarfs. We show that a secular inclination resonance in 2 + 2 systems can significantly broaden the LK window for extreme eccentricity excitation (e  ≳ 0.999), allowing the giant planet to migrate for a wide range of initial orbital inclinations. Octupole effects can also contribute to the broadening of this 'extreme' LK window. By requiring that perturbations from the companion stars be able to overcome short-range forces and excite the planet's eccentricity to e  ≃ 1, we obtain an absolute limit of |$a_{1} \gtrsim 8 \, \mathrm{au}\, (a_{3} / 1500 \, \mathrm{au})^{6/7}$| for the planet's semimajor axis just before migration (where a 3 is the semimajor axis of the 'outer' orbit). We suggest that, to achieve a wide LK window through the 2 + 2 resonance, WD 1856 b likely migrated from |$30 \, \mathrm{au}\lesssim a_{1} \lesssim 60 \, \mathrm{au}$|⁠ , corresponding to ∼10– |$20 \, \mathrm{au}$| during the host's main-sequence phase. We discuss possible difficulties of all flavours of HEM affecting the occurrence rate of short-period giant planets around white dwarfs. [ABSTRACT FROM AUTHOR]

Protein fold recognition is one of the most critical tasks to explore the structures and functions of the proteins based on their primary sequence information. The existing protein fold recognition approaches rely on features reflecting the characteristics of protein folds. However, the feature extraction methods are still the bottleneck of the performance improvement of these methods. In this paper, we proposed two new feature extraction methods called MotifCNN and MotifDCNN to extract more discriminative fold-specific features based on structural motif kernels to construct the motif-based convolutional neural networks (CNNs). The pairwise sequence similarity scores calculated based on fold-specific features are then fed into support vector machines to construct the predictor for fold recognition, and a predictor called MotifCNN-fold has been proposed. Experimental results on the benchmark dataset showed that MotifCNN-fold obviously outperformed all the other competing methods. In particular, the fold-specific features extracted by MotifCNN and MotifDCNN are more discriminative than the fold-specific features extracted by other deep learning techniques, indicating that incorporating the structural motifs into the CNN is able to capture the characteristics of protein folds. [ABSTRACT FROM AUTHOR]

As an important task in protein structure and function studies, protein fold recognition has attracted more and more attention. The existing computational predictors in this field treat this task as a multi-classification problem, ignoring the relationship among proteins in the dataset. However, previous studies showed that their relationship is critical for protein homology analysis. In this study, the protein fold recognition is treated as an information retrieval task. The Learning to Rank model (LTR) was employed to retrieve the query protein against the template proteins to find the template proteins in the same fold with the query protein in a supervised manner. The triadic closure principle (TCP) was performed on the ranking list generated by the LTR to improve its accuracy by considering the relationship among the query protein and the template proteins in the ranking list. Finally, a predictor called Fold-LTR-TCP was proposed. The rigorous test on the LE benchmark dataset showed that the Fold-LTR-TCP predictor achieved an accuracy of 73.2%, outperforming all the other competing methods. [ABSTRACT FROM AUTHOR]

Escherichia coli includes clonal groups of both commensal and pathogenic strains, with some of the latter causing serious infectious diseases. O antigen variation is current standard in defining strains for taxonomy and epidemiology, providing the basis for many serotyping schemes for Gram-negative bacteria. This review covers the diversity in E. coli O antigen structures and gene clusters, and the genetic basis for the structural diversity. Of the 187 formally defined O antigens, six (O31, O47, O67, O72, O94 and O122) have since been removed and three (O34, O89 and O144) strains do not produce any O antigen. Therefore, structures are presented for 176 of the 181 E. coli O antigens, some of which include subgroups. Most (93%) of these O antigens are synthesized via the Wzx/Wzy pathway, 11 via the ABC transporter pathway, with O20, O57 and O60 still uncharacterized due to failure to find their O antigen gene clusters. Biosynthetic pathways are given for 38 of the 49 sugars found in E. coli O antigens, and several pairs or groups of the E. coli antigens that have related structures show close relationships of the O antigen gene clusters within clades, thereby highlighting the genetic basis of the evolution of diversity. [ABSTRACT FROM AUTHOR]

With the continuous development of space and sensor technologies during the last 40 years, ocean remote sensing has entered into the big-data era with typical five-V (volume, variety, value, velocity and veracity) characteristics. Ocean remote-sensing data archives reach several tens of petabytes and massive satellite data are acquired worldwide daily. To precisely, efficiently and intelligently mine the useful information submerged in such ocean remote-sensing data sets is a big challenge. Deep learning—a powerful technology recently emerging in the machine-learning field—has demonstrated its more significant superiority over traditional physical- or statistical-based algorithms for image-information extraction in many industrial-field applications and starts to draw interest in ocean remote-sensing applications. In this review paper, we first systematically reviewed two deep-learning frameworks that carry out ocean remote-sensing-image classifications and then presented eight typical applications in ocean internal-wave/eddy/oil-spill/coastal-inundation/sea-ice/green-algae/ship/coral-reef mapping from different types of ocean remote-sensing imagery to show how effective these deep-learning frameworks are. Researchers can also readily modify these existing frameworks for information mining of other kinds of remote-sensing imagery. [ABSTRACT FROM AUTHOR]

Protein fold recognition is critical for studying the structures and functions of proteins. The existing protein fold recognition approaches failed to efficiently calculate the pairwise sequence similarity scores of the proteins in the same fold sharing low sequence similarities. Furthermore, the existing feature vectorization strategies are not able to measure the global relationships among proteins from different protein folds. In this article, we proposed a new computational predictor called DeepSVM-fold for protein fold recognition by introducing a new feature vector based on the pairwise sequence similarity scores calculated from the fold-specific features extracted by deep learning networks. The feature vectors are then fed into a support vector machine to construct the predictor. Experimental results on the benchmark dataset (LE) show that DeepSVM-fold obviously outperforms all the other competing methods. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) are a group of novel discovered non-coding RNAs with closed-loop structure, which play critical roles in various biological processes. Identifying associations between circRNAs and diseases is critical for exploring the complex disease mechanism and facilitating disease-targeted therapy. Although several computational predictors have been proposed, their performance is still limited. In this study, a novel computational method called iCircDA-MF is proposed. Because the circRNA-disease associations with experimental validation are very limited, the potential circRNA-disease associations are calculated based on the circRNA similarity and disease similarity extracted from the disease semantic information and the known associations of circRNA-gene, gene-disease and circRNA-disease. The circRNA-disease interaction profiles are then updated by the neighbour interaction profiles so as to correct the false negative associations. Finally, the matrix factorization is performed on the updated circRNA-disease interaction profiles to predict the circRNA-disease associations. The experimental results on a widely used benchmark dataset showed that iCircDA-MF outperforms other state-of-the-art predictors and can identify new circRNA-disease associations effectively. [ABSTRACT FROM AUTHOR]

The human intestinal pathogen enterohemorrhagic Escherichia coli (EHEC) O157:H7 causes bloody diarrhea, hemorrhagic colitis, and fatal hemolytic uremic syndrome. Its genome contains 177 unique O islands (OIs), which contribute largely to the high virulence and pathogenicity although most OI genes remain uncharacterized. In the current study, we demonstrated that OI-19 is required for EHEC O157:H7 adherence to host cells. Z0442 (OI-encoded virulence regulator A [OvrA]) encoded in OI-19 positively regulated bacterial adherence by activating locus of enterocyte effacement (LEE) gene expression through direct OvrA binding to the gene promoter region of the LEE gene master regulator Ler. Mouse colonization experiments revealed that OvrA promotes EHEC O157:H7 adherence in mouse intestine, preferentially the colon. Finally, OvrA also regulated virulence in other non-O157 pathogenic E. coli, including EHEC strains O145:H28 and O157:H16 and enteropathogenic E. coli strain O55:H7. Our work markedly enriches the understanding of bacterial adherence control and provides another example of laterally acquired regulators that mediate LEE gene expression. [ABSTRACT FROM AUTHOR]

As one of the most important fundamental problems in protein sequence analysis, protein remote homology detection is critical for both theoretical research (protein structure and function studies) and real world applications (drug design). Although several computational predictors have been proposed, their detection performance is still limited. In this study, we treat protein remote homology detection as a document retrieval task, where the proteins are considered as documents and its aim is to find the highly related documents with the query documents in a database. A protein similarity network was constructed based on the true labels of proteins in the database, and the query proteins were then connected into the network based on the similarity scores calculated by three ranking methods, including PSI-BLAST, Hmmer and HHblits. The PageRank algorithm and Hyperlink-Induced Topic Search (HITS) algorithm were respectively performed on this network to move the homologous proteins of query proteins to the neighbors of the query proteins in the network. Finally, PageRank and HITS algorithms were combined, and a predictor called HITS-PR-HHblits was proposed to further improve the predictive performance. Tested on the SCOP and SCOPe benchmark datasets, the experimental results showed that the proposed protocols outperformed other state-of-the-art methods. For the convenience of the most experimental scientists, a web server for HITS-PR-HHblits was established at http://bioinformatics.hitsz.edu.cn/HITS-PR-HHblits , by which the users can easily get the results without the need to go through the mathematical details. The HITS-PR-HHblits predictor is a protocol for protein remote homology detection using different sets of programs, which will become a very useful computational tool for proteome analysis. [ABSTRACT FROM AUTHOR]

It has been shown that time delay of strong gravitational lensing is not only an effective cosmological probe to constrain the Hubble constant, the matter density, and the curvature of the universe but also useful for breaking the degeneracy of the dark energy equation of state and thus provide complementarity with other popular probes, such as Type Ia supernovae, baryon acoustic oscillations, and cosmic microwave background radiation. Interestingly, compared to traditional strong lensing systems where quasars act as sources, strongly lensed gravitational waves (GWs) from compact binary coalescence and their electromagnetic (EM) counterpart systems have been recently proposed to be more powerful for studying cosmology since GWs and their EM counterparts are transients. Short durations of GWs and their EM counterparts are very advantageous to time delay measurement and lens profile modelling. Here, in the framework of Chevalier–Polarski–Linder parametrization, we investigate improvement of constraining power on the dark energy equation of state due to including time delay measurements of strong lensed GW systems. It is suggested that, on the basis of the third generation ground-based detector, e.g. the Einstein Telescope, adding time delay of only 30 strong lensed GW systems to Type Ia supernovae and cosmic microwave background radiation can improve the dark energy figure of merit by a factor of 2. For the traditional standard siren method where the uncertainties of luminosity distances of GWs are |${\sim }10{{\ \rm per\ cent}}$|⁠ , a few × 104 events are expected to present similar constraints. In the precision cosmology era, this progress is of great significance for studying the nature of dark energy. [ABSTRACT FROM AUTHOR]

With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems is how to computationally analyze their structures and functions. Machine learning techniques are playing key roles in this field. Typically, predictors based on machine learning techniques contain three main steps: feature extraction, predictor construction and performance evaluation. Although several Web servers and stand-alone tools have been developed to facilitate the biological sequence analysis, they only focus on individual step. In this regard, in this study a powerful Web server called BioSeq-Analysis (http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/) has been proposed to automatically complete the three main steps for constructing a predictor. The user only needs to upload the benchmark data set. BioSeq-Analysis can generate the optimized predictor based on the benchmark data set, and the performance measures can be reported as well. Furthermore, to maximize user's convenience, its stand-alone program was also released, which can be downloaded from http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/download/ , and can be directly run on Windows, Linux and UNIX. Applied to three sequence analysis tasks, experimental results showed that the predictors generated by BioSeq-Analysis even outperformed some state-of-the-art methods. It is anticipated that BioSeq-Analysis will become a useful tool for biological sequence analysis. [ABSTRACT FROM AUTHOR]

We study the orbital evolution of black hole (BH) binaries in quadruple systems, where the tertiary binary excites large eccentricity in the BH binary through Lidov–Kozai (LK) oscillations, causing the binary BHs to merge via gravitational radiation. For typical BH binaries with masses |$m_{1,2}\simeq 20\!-\!30\, \mathrm{M}_\odot$| and initial semimajor axis a 0 ∼ 100 au (such that the binaries have no chance of merging by themselves within ∼1010 yr), we show that binary–binary interactions can significantly increase the LK window for mergers (the range of companion inclinations that allows the BH binary to merge within 10 Gyr). This increase arises from a secular resonance between the LK oscillation of the BH binary and the nodal precession of the outer (binary–binary) orbit driven by the tertiary binary. Therefore, in the presence of tertiary binary, the BH merger fraction is increased to |$10\!-\!30{{\ \rm per\ cent}}$|⁠, an order of magnitude larger than the merger fraction found in similar triple systems. Our analysis (with appropriate scalings) can be easily adapted to other configurations of systems, such as relatively compact BH binaries and moderately hierarchical triples, which may generate even higher merger fractions. Since the occurrence rate of stellar quadruples in the galactic fields is not much smaller than that of stellar triples, our result suggests that dynamically induced BH mergers in quadruple systems may be an important channel of producing BH mergers observed by LIGO/VIRGO. [ABSTRACT FROM AUTHOR]

Motivation Identification of enhancers and their strength is important because they play a critical role in controlling gene expression. Although some bioinformatics tools were developed, they are limited in discriminating enhancers from non-enhancers only. Recently, a two-layer predictor called 'iEnhancer-2L' was developed that can be used to predict the enhancer's strength as well. However, its prediction quality needs further improvement to enhance the practical application value. Results A new predictor called 'iEnhancer-EL' was proposed that contains two layer predictors: the first one (for identifying enhancers) is formed by fusing an array of six key individual classifiers, and the second one (for their strength) formed by fusing an array of ten key individual classifiers. All these key classifiers were selected from 171 elementary classifiers formed by SVM (Support Vector Machine) based on kmer, subsequence profile and PseKNC (Pseudo K-tuple Nucleotide Composition), respectively. Rigorous cross-validations have indicated that the proposed predictor is remarkably superior to the existing state-of-the-art one in this area. Availability and implementation A web server for the iEnhancer-EL has been established at http://bioinformatics.hitsz.edu.cn/iEnhancer-EL/, by which users can easily get their desired results without the need to go through the mathematical details. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Methyl salicylate (MeSA) is a long-distance signal transduction chemical that plays an important role in plant responses to abiotic stress and herbivore and pathogen attacks. However, it is unclear how photosynthesis and elicitation of plant volatile organic compounds (VOC) from different metabolic pathways respond to the dose of MeSA. We applied different MeSA concentrations (0–50 mM) to study how exogenous MeSA alters VOC profiles of silver birch (Betula pendula Roth) leaves from application through recovery (0.5–23 h). Methyl salicylate application significantly reduced net assimilation rate in 10 mM and 20 mM MeSA-treated plants. No significant effects of MeSA were observed on the stomatal conductance at any MeSA concentration. Methyl salicylate elicited emissions of benzenoids (BZ), monoterpenes (MT) and fatty acid derived compounds (LOX products). Emission rates of BZ were positively, but emission rates of MT were negatively correlated with MeSA concentration. Total emission of LOX products was not influenced by MeSA concentration. Emission rate of MT was negatively correlated with BZ and the share of MT in the total emission blend decreased and the share of BZ increased with increasing MeSA concentration. Although the share of LOX products was similar across MeSA treatments, some LOX products responded differently to MeSA concentration, ultimately resulting in unique VOC blends. Overall, this study demonstrates inverse responses of MT and BZ to different MeSA doses such that plant defense mechanisms induced by lower MeSA doses mainly lead to enhanced MT synthesis, whereas greater MeSA doses trigger BZ-related defense mechanisms. Our results will contribute to improving the understanding of birch defenses induced upon regular herbivore attacks and pathogen infections in boreal forests. [ABSTRACT FROM AUTHOR]

Motivation DNA replication is the key of the genetic information transmission, and it is initiated from the replication origins. Identifying the replication origins is crucial for understanding the mechanism of DNA replication. Although several discriminative computational predictors were proposed to identify DNA replication origins of yeast species, they could only be used to identify very tiny parts (250 or 300 bp) of the replication origins. Besides, none of the existing predictors could successfully capture the ‘GC asymmetry bias’ of yeast species reported by experimental observations. Hence it would not be surprising why their power is so limited. To grasp the CG asymmetry feature and make the prediction able to cover the entire replication regions of yeast species, we develop a new predictor called ‘iRO-3wPseKNC’. Results Rigorous cross validations on the benchmark datasets from four yeast species (Saccharomyces cerevisiae, Schizosaccharomyces pombe, Kluyveromyces lactis and Pichia pastoris) have indicated that the proposed predictor is really very powerful for predicting the entire DNA duplication origins. Availability and implementation The web-server for the iRO-3wPseKNC predictor is available at http://bioinformatics.hitsz.edu.cn/iRO-3wPseKNC/, by which users can easily get their desired results without the need to go through the mathematical details. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Spatially correlated common mode error (CME) always exists in regional, or-larger, global positioning system (GPS) networks. We applied independent component analysis (ICA) to GPS vertical coordinate time-series in Antarctica from 2010 to 2014 and made a comparison with the principal component analysis (PCA). Using PCA/ICA, the time-series can be decomposed into a set of temporal components and their spatial responses. We assume the components with common spatial responses are CME. An average reduction of ∼40 per cent about the root-mean-square (RMS) values was achieved in both PCA and ICA filtering. However, the common mode components obtained from the two approaches have different spatial and temporal features. ICA time-series present interesting correlations with modelled atmospheric and non-tidal ocean loading displacements. A white noise plus power law (PL) noise model was adopted in the GPS velocity estimation using maximum likelihood estimation analysis, with ∼55 per cent reduction of the velocity uncertainties after filtering using ICA. Meanwhile, spatiotemporal filtering reduces the amplitude of PL and periodic terms in the GPS time-series. Finally, we compare the GPS uplift velocities, after correction for elastic effects, with recent models of glacial isostatic adjustment (GIA). The agreements of the GPS observed velocities and four GIA models are generally improved after the spatiotemporal filtering, with a mean reduction of ∼0.9 mm yr−1 of the weighted RMS values, possibly allowing for more confident separation of various GIA model predictions. [ABSTRACT FROM AUTHOR]

Motivation: Being responsible for initiating transaction of a particular gene in genome, promoter is a short region of DNA. Promoters have various types with different functions. Owing to their importance in biological process, it is highly desired to develop computational tools for timely identifying promoters and their types. Such a challenge has become particularly critical and urgent in facing the avalanche of DNA sequences discovered in the postgenomic age. Although some prediction methods were developed, they can only be used to discriminate a specific type of promoters from non-promoters. None of them has the ability to identify the types of promoters. This is due to the facts that different types of promoters may share quite similar consensus sequence pattern, and that the promoters of same type may have considerably different consensus sequences. Results: To overcome such difficulty, using the multi-window-based PseKNC (pseudo K-tuple nucleotide composition) approach to incorporate the short-, middle-, and long-range sequence information, we have developed a two-layer seamless predictor named as 'iPromoter-2 L'. The first layer serves to identify a query DNA sequence as a promoter or non-promoter, and the second layer to predict which of the following six types the identified promoter belongs to: σ24, σ28, σ32, σ38, σ54 and σ70. [ABSTRACT FROM AUTHOR]

Oxidative stress-induced myocardial apoptosis and necrosis are involved in ischemia/reperfusion (I/R) injury. This study was performed to investigate microRNA (miR)-210’s role in oxidative stress-related myocardial damage. The expression of miR-210 was upregulated in myocardial tissues of I/R rats, while that of Bcl-2 adenovirus E1B 19kDa-interacting protein 3 (BNIP3) was downregulated. To simulatein vivooxidative stress, H9c2 cells were treated with H2O2for 48 h. MiR-210 level was increased upon H2O2stimulation, peaked at 8 h, and then decreased. An opposite expression pattern of BNIP3 was observed. BNIP3 was demonstrated as a direct target of miR-210 via luciferase reporter assay. H2O2-induced cell apoptosis was attenuated by miR-210 mimics, whereas aggravated by miR-210 inhibitor. MiR-210 knockdown-induced cell apoptosis in presence of H2O2was attenuated by BNIP3 siRNA. Our work demonstrates that miR-210 plays a protective role in H2O2-induced cardiomyocyte apoptosis at least by regulating the pro-apoptotic BNIP3. MiR-210 targeted 3’-UTR and CDS of BNIP3 mRNA in H9c2 cells (A). The miR-210 knockdown-induced apoptosis was attenuated by BNIP3 siRNA (B). [ABSTRACT FROM PUBLISHER]

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]

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]

Changes in allele frequencies and the fixation of beneficial mutations are central to evolution. The precise relationship between mutational and phenotypic sweeps is poorly described however, especially when multiple alleles are involved. Here, we investigate these relationships in a bacterial population over 60 days in a glucose-limited chemostat in a large population. High coverage metagenomic analysis revealed a disconnection between smooth phenotypic sweeps and the complexity of genetic changes in the population. Phenotypic adaptation was due to convergent evolution and involved soft sweeps by 7–26 highly represented alleles of several genes in different combinations. Allele combinations spread from undetectably low baselines, indicating that minor subpopulations provide the basis of most innovations. A hard sweep was also observed, involving a single combination of rpoS, mglD, malE, sdhC, and malT mutations sweeping to greater than 95% of the population. Other mutant genes persisted but at lower abundance, including hfq, consistent with its demonstrated frequency-dependent fitness under glucose limitation. Other persistent, newly identified low-frequency mutations were in the aceF, galF, ribD and asm genes, in noncoding regulatory regions, three large indels and a tandem duplication; these were less affected by fluctuations involving more dominant mutations indicating separate evolutionary paths. Our results indicate a dynamic subpopulation structure with a minimum of 42 detectable mutations maintained over 60 days. We also conclude that the massive population-level mutation supply in combination with clonal interference leads to the soft sweeps observed, but not to the exclusion of an occasional hard sweep. [ABSTRACT FROM AUTHOR]

Motivation: Owing to its importance in both basic research (such as molecular evolution and protein attribute prediction) and practical application (such as timely modeling the 3D structures of proteins targeted for drug development), protein remote homology detection has attracted a great deal of interest. It is intriguing to note that the profile-based approach is promising and holds high potential in this regard. To further improve protein remote homology detection, a key step is how to find an optimal means to extract the evolutionary information into the profiles.Results: Here, we propose a novel approach, the so-called profile-based protein representation, to extract the evolutionary information via the frequency profiles. The latter can be calculated from the multiple sequence alignments generated by PSI-BLAST. Three top performing sequence-based kernels (SVM-Ngram, SVM-pairwise and SVM-LA) were combined with the profile-based protein representation. Various tests were conducted on a SCOP benchmark dataset that contains 54 families and 23 superfamilies. The results showed that the new approach is promising, and can obviously improve the performance of the three kernels. Furthermore, our approach can also provide useful insights for studying the features of proteins in various families. It has not escaped our notice that the current approach can be easily combined with the existing sequence-based methods so as to improve their performance as well.Availability and implementation: For users’ convenience, the source code of generating the profile-based proteins and the multiple kernel learning was also provided athttp://bioinformatics.hitsz.edu.cn/main/∼binliu/remote/Contact: bliu@insun.hit.edu.cn or bliu@gordonlifescience.orgSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

This review covers the structures and genetics of the 46 O antigens of Salmonella, a major pathogen of humans and domestic animals. The variation in structures underpins the serological specificity of the 46 recognized serogroups. The O antigen is important for the full function and virulence of many bacteria, and the considerable diversity of O antigens can confer selective advantage. Salmonella O antigens can be divided into two major groups: those which have N-acetylglucosamine ( Glc NAc) or N-acetylgalactosamine ( Gal NAc) and those which have galactose ( Gal) as the first sugar in the O unit. In recent years, we have determined 21 chemical structures and sequenced 28 gene clusters for Glc NAc-/ Gal NAc-initiated O antigens, thus completing the structure and DNA sequence data for the 46 Salmonella O antigens. The structures and gene clusters of the Glc NAc-/ Gal NAc-initiated O antigens were found to be highly diverse, and 24 of them were found to be identical or closely related to Escherichia coli O antigens. Sequence comparisons indicate that all or most of the shared gene clusters were probably present in the common ancestor, although alternative explanations are also possible. In contrast, the better-known eight Gal-initiated O antigens are closely related both in structures and gene cluster sequences. [ABSTRACT FROM AUTHOR]

Aims Recent studies suggest that proarrhythmic effects of cardiac glycosides (CGs) on cardiomyocyte Ca2+ handling involve generation of reactive oxygen species (ROS). However, the specific pathway(s) of ROS production and the subsequent downstream molecular events that mediate CG-dependent arrhythmogenesis remain to be defined. Methods and results We examined the effects of digitoxin (DGT) on Ca2+ handling and ROS production in cardiomyocytes using a combination of pharmacological approaches and genetic mouse models. Myocytes isolated from mice deficient in NADPH oxidase type 2 (NOX2KO) and mice transgenically overexpressing mitochondrial superoxide dismutase displayed markedly increased tolerance to the proarrhythmic action of DGT as manifested by the inhibition of DGT-dependent ROS and spontaneous Ca2+ waves (SCW). Additionally, DGT-induced mitochondrial membrane potential depolarization was abolished in NOX2KO cells. DGT-dependent ROS was suppressed by the inhibition of PI3K, PKC, and the mitochondrial KATP channel, suggesting roles for these proteins, respectively, in activation of NOX2 and in mitochondrial ROS generation. Western blot analysis revealed increased levels of oxidized CaMKII in WT but not in NOX2KO hearts treated with DGT. The DGT-induced increase in SCW frequency was abolished in myocytes isolated from mice in which the Ser 2814 CaMKII phosphorylation site on RyR2 is constitutively inactivated. Conclusion These results suggest that the arrhythmogenic adverse effects of CGs on Ca2+ handling involve PI3K- and PKC-mediated stimulation of NOX2 and subsequent NOX2-dependent ROS release from the mitochondria; mitochondria-derived ROS then activate CaMKII with consequent phosphorylation of RyR2 at Ser 2814. [ABSTRACT FROM AUTHOR]

Mitogen-activated protein kinase-activated protein kinase 2 (MAPKAPK2) is recognized as oncogenic and simulative role on tumorigenesis by virtue of abnormal expression in cancer including nasopharyngeal carcinoma (NPC). We hypothesized that the copy number variation (CNV)-30450, which duplicates the MAPKAPK2 promoter, may affect MAPKAPK2 expression and be associated with NPC risk. In two independent case–control panels of southern and eastern Chinese with a total of 1590 NPC patients and 1979 cancer-free controls, we investigated the association between CNV-30450 and NPC risk by genotyping the CNV-30450 with the TaqMan assay, and tested its biological effect. Consistent findings were observed in the two populations, that the increased copy number of CNV-30450 was associated with increased risk of NPC (3/4-copy versus 2-copy: odds ratio = 1.28, 95% confidence interval = 1.10–1.49), in which lies a biological mechanism that the adverse genotypes enhanced the promoter activity of MAPKAPK2 and elevated MAPKAPK2 expression. Moreover, the CNV-30450 adverse genotypes significantly interacted with Epstein–Barr virus (EBV) infection on increasing NPC risk (P = 0.035), and the genotype–phenotype correlation was only significant in EBV-positive cases (P = 0.037) but not in EBV-negative ones (P = 0.366). These data suggest that the functional CNV-30450 in the MAPKAPK2 promoter elevates the NPC risk with a modulation by EBV infection, which may be an indicator of susceptibility to NPC.Summary: This case–control study suggests that the functional CNV-30450 in the MAPKAPK2 promoter elevates the NPC risk with a modulation by EBV infection, which may be an indicator of susceptibility to NPC. [ABSTRACT FROM AUTHOR]

Floral organ shedding is a cell separation event preceded by cell-wall loosening and generally accompanied by cell expansion. Mutations in NEVERSHED (NEV) or INFLORESCENCE DEFICIENT IN ABSCISSION (IDA) block floral organ abscission in Arabidopsis thaliana. NEV encodes an ADP-ribosylation factor GTPase-activating protein, and cells of nev mutant flowers display membrane-trafficking defects. IDA encodes a secreted peptide that signals through the receptor-like kinases HAESA (HAE) and HAESA-LIKE2 (HSL2). Analyses of single and double mutants revealed unique features of the nev and ida phenotypes. Cell-wall loosening was delayed in ida flowers. In contrast, nev and nev ida mutants displayed ectopic enlargement of abscission zone (AZ) cells, indicating that cell expansion alone is not sufficient to trigger organ loss. These results suggest that NEV initially prevents precocious cell expansion but is later integral for cell separation. IDA is involved primarily in the final cell separation step. A mutation in KNOTTED-LIKE FROM ARABIDOPSIS THALIANA1 (KNAT1), a suppressor of the ida mutant, could not rescue the abscission defects of nev mutant flowers, indicating that NEV-dependent activity downstream of KNAT1 is required. Transcriptional profiling of mutant AZs identified gene clusters regulated by IDA-HAE/HSL2. Several genes were more strongly downregulated in nev-7 compared with ida and hae hsl2 mutants, consistent with the rapid inhibition of organ loosening in nev mutants, and the overlapping roles of NEV and IDA in cell separation. A model of the crosstalk between the IDA signalling pathway and NEV-mediated membrane traffic during floral organ abscission is presented. [ABSTRACT FROM AUTHOR]

A number of cis-regulatory elements (CREs) conserved during evolution have been found to be responsible for phenotypic novelty and variation. Cucurbit crops such as cucumber (Cucumis sativus), watermelon (Citrullus lanatus), melon (Cucumis melo), and squash (Cucurbita maxima) develop fruits from an inferior ovary and share some similar biological processes during fruit development. Whether conserved regulatory sequences play critical roles in fruit development of cucurbit crops remains to be explored. In six well-studied cucurbit species, we identified 392,438 conserved noncoding sequences (CNSs), including 82,756 that are specific to cucurbits, by comparative genomics. Genome-wide profiling of accessible chromatin regions (ACRs) and gene expression patterns mapped 20,865 to 43,204 ACRs and their potential target genes for two fruit tissues at two key developmental stages in six cucurbits. Integrated analysis of CNSs and ACRs revealed 4,431 syntenic orthologous CNSs, including 1,687 cucurbit-specific CNSs that overlap with ACRs that are present in all six cucurbit crops and that may regulate the expression of 757 adjacent orthologous genes. CRISPR mutations targeting two CNSs present in the 1,687 cucurbit-specific sequences resulted in substantially altered fruit shape and gene expression patterns of adjacent NAC1 (NAM, ATAF1/2, and CUC2) and EXT-like (EXTENSIN-like) genes, validating the regulatory roles of these CNSs in fruit development. These results not only provide a number of target CREs for cucurbit crop improvement, but also provide insight into the roles of CREs in plant biology and during evolution. Integrative analyses of conserved noncoding sequences and accessible chromatin regions provide insights into cis-regulatory elements and target sequences for fruit development in cucurbits. [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 O-polysaccharide chain of the lipopolysaccharide ( O-antigen) on the bacterial cell surface is one of the most structurally variable cell components and serves as a basis for serotyping of Gram-negative bacteria, including human opportunistic pathogens of the genus Providencia. In this work, the O-antigen of Providencia alcalifaciens O40 was obtained by mild acid degradation of the isolated lipopolysaccharide and studied by chemical methods and high-resolution NMR spectroscopy. The following structure of the O-polysaccharide was established: →4)-β- d- Qui p3 NFo-(1→3)-α- d- Gal p-(1→3)-β- d- Glc pA-(1→3)-β- d- Gal p NAc-(1→, where Glc A stands for glucuronic acid and Qui3 NFo for 3,6-dideoxy-3-formamidoglucose. The O40-antigen was found to be structurally and serologically related to the O-antigens of P. alcalifaciens O5 and Providencia stuartii O18. The O40-antigen gene cluster between cpxA and yibK was sequenced, and the gene functions were predicted in silico. In agreement with the O-polysaccharide structure established, the genes for the synthesis of d TDP- d- Qui3 NFo, UDP- d- Gal, UDP- d- Glc A, and UDP- d- Gal NAc as well as those encoding three glycosyltransferases, flippase ( Wzx), and O-antigen polymerase ( Wzy) were recognized. In addition, homologues of wza, wzb, and wzc genes, which are required for the surface expression of capsular polysaccharides, were found within the gene cluster, suggesting that the O-polysaccharide studied is a part of the capsule-related form of the lipopolysaccharide called KLPS. [ABSTRACT FROM AUTHOR]