Probability Of Pairs Research Articles

Modelling the impacts of en-route ride-pooling service in a mixed pooling and non-pooling market

En-route ride-pooling services, e.g. Didi Pinche, UberPool, have been considered a promising way to alleviate traffic congestion during rush hours. Yet in reality, its market share is still small compared to non-pooling service. For a ride-hailing platform that provides both en-route pooling and non-pooling services, this paper endeavors to answer under what circumstance would it have incentive to encourage ride-pooling, and whether drivers, riders, the platform and the society can simultaneously benefit from the introduction of en-route ride-pooling service. We propose an aggregate model to characterize the equilibrium of a ride-hailing market where a platform operates a fleet of vehicles to provide en-route pooling and non-pooling services. The model captures the complex interactions among riders’ mode choices between pooling and non-pooling, the waiting times of pooling and non-pooling users, the pairing probability and the expected detour time of pooling users, the matching rates of cruising and halfly occupied vehicles with different types of riders, and the numbers of cruising, halfly occupied and fully occupied vehicles at each instant. Based on the model, we theoretically reveal the impacts of the total ride-hailing demand, vehicle fleet size, and the platform’s pricing and vehicle dispatching strategies on the market equilibrium performance through partial derivative-based sensitivity analysis, and establish conditions under which the introduction of en-route ride-pooling service improves platform profit, driver income, rider utility and social welfare.

Validation of the Neogen® Molecular Detection Assay 2 - Salmonella Enteritidis/Salmonella Typhimurium Method for Specific Detection of Salmonella enterica Ser. Enteritidis and Salmonella enterica Ser. Typhimurium in Chicken Carcass Rinse and Raw Ground Chicken AOAC Performance Tested MethodSM 122302.

The Neogen® Molecular Detection Assay 2-Salmonella Enteritidis/Salmonella Typhimurium (MDA2-SE/ST) method is a rapid, nucleic acid amplification-based test for specific detection of Salmonella enterica ser. Enteritidis (SE) and Salmonella enterica ser. Typhimurium (ST), including the ST monophasic variant Salmonella enterica 1,4 , [5] , 12: i:-, in select poultry samples. Results for SE and ST are generated separately. The objective of the study was to validate the MDA2-SE/ST method for detection of SE and ST in chicken carcass rinse and raw ground chicken (325 g) for Performance Tested Methods SM (PTM) certification. The study consisted of inclusivity/exclusivity testing and independent laboratory testing of chicken carcass rinse and raw ground chicken using inoculated matrixes. Data were analyzed using a paired probability of detection model to determine if differences in the number of positive results obtained with the MDA2-SE/ST and reference methods were significant. In inclusivity testing, all 50 SE strains produced positive results in the SE assay and negative results in the ST assay. All 53 ST strains (including the monophasic variant) produced positive results in the ST assay and negative results in the SE assay. All 35 exclusivity strains produced negative results in both assays. The exclusivity panel included multiple non-SE group D1 Salmonella serovars, multiple non-ST group B serovars, Salmonella spp. from other somatic groups, and other Enterobacteriaceae. In matrix testing, results for the MDA2-SE/ST and reference methods were in complete agreement for both matrixes. Results of the validation study showed that the MDA2-SE/ST method is an accurate, specific method for detection of SE and ST in select poultry matrixes. The data were reviewed by the AOAC PTM Program and approval was granted for certification of the Molecular Detection Assay 2-Salmonella Enteritidis/Salmonella Typhimurium method as PTM 122302.

Using the RNAstructure Software Package to Predict Conserved RNA Structures.

The structures of many non-coding RNAs (ncRNA) are conserved by evolution to a greater extent than their sequences. By predicting the conserved structure of two or more homologous sequences, the accuracy of secondary structure prediction can be improved as compared to structure prediction for a single sequence. Here, we provide protocols for the use of four programs in the RNAstructure suite topredict conserved structures: Multilign, TurboFold, Dynalign, and PARTS. TurboFold iteratively aligns multiple homologous sequences and estimates the pairing probabilities for the conserved structure. Dynalign, PARTS, and Multilign are dynamic programming algorithms that simultaneously align sequences and identify the common secondary structure. Dynalign uses a pair of homologs and finds the lowest free energy common structure. PARTS uses a pair of homologs and estimates pairing probabilities from the base pairing probabilities estimated for each sequence. Multilign uses two or morehomologs and finds the lowest free energy common structure using multiple pairwise calculations with Dynalign. It scales linearly with the number of sequences. We outline the strengths of each program. These programs can be run through web servers, on the command line, or with graphical user interfaces. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Predicting a structure conserved in three or more sequences with the RNAstructure web server Basic Protocol 2: Predicting a structure conserved in two sequences with the RNAstructure web server Alternative Protocol 1: Predicting a structure conserved in multiple sequences in the RNAstructure graphical user interface Alternative Protocol 2: Predicting a structure conserved in two sequences with Dynalign in the RNAstructure graphical user interface Alternative Protocol 3: Running TurboFold on the command line.

Prediction of the effects of the top 10 synonymous mutations from 26645 SARS-CoV-2 genomes of early pandemic phase

Background The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had led to a global pandemic since December 2019. SARS-CoV-2 is a single-stranded RNA virus, which mutates at a higher rate. Multiple works had been done to study nonsynonymous mutations, which change protein sequences. However, there is little study on the effects of SARS-CoV-2 synonymous mutations, which may affect viral fitness. This study aims to predict the effect of synonymous mutations on the SARS-CoV-2 genome. Methods A total of 26645 SARS-CoV-2 genomic sequences retrieved from Global Initiative on Sharing all Influenza Data (GISAID) database were aligned using MAFFT. Then, the mutations and their respective frequency were identified. Multiple RNA secondary structures prediction tools, namely RNAfold, IPknot++ and MXfold2 were applied to predict the effect of the mutations on RNA secondary structure and their base pair probabilities was estimated using MutaRNA. Relative synonymous codon usage (RSCU) analysis was also performed to measure the codon usage bias (CUB) of SARS-CoV-2. Results A total of 150 synonymous mutations were identified. The synonymous mutation identified with the highest frequency is C3037U mutation in the nsp3 of ORF1a. Of these top 10 highest frequency synonymous mutations, C913U, C3037U, U16176C and C18877U mutants show pronounced changes between wild type and mutant in all 3 RNA secondary structure prediction tools, suggesting these mutations may have some biological impact on viral fitness. These four mutations show changes in base pair probabilities. All mutations except U16176C change the codon to a more preferred codon, which may result in higher translation efficiency. Conclusion Synonymous mutations in SARS-CoV-2 genome may affect RNA secondary structure, changing base pair probabilities and possibly resulting in a higher translation rate. However, lab experiments are required to validate the results obtained from prediction analysis.

Enhancing integration of administrative databases in South Africa's HIV program: Validation of record linkage using non-representative gold standard

IntroductionLinked administrative data are widely used in epidemiology to capture patient data across multiple databases. Linkage error rates, critical to measure linkage performance, are rarely reported due to difficulty in obtaining representative gold standard. We propose a training and validation approach for linkage procedures that yield unbiased performance estimates even with a non-representative gold standard. MethodsWe linked patient records from two non-deduplicated databases for HIV monitoring in South Africa, TIER.Net and NHLS laboratory database, using a network-based probabilistic linkage and deduplication approach. National IDs (gold standard) were available for a non-representative minority of records (10%). We calculated sensitivity (Sen, share of true matches identified by the algorithm) and positive predictive value (PPV, share of algorithm-identified matches that were true matches). We adjusted for bias due to informative missingness in National IDs using inverse probability weights to break the link between missingness and match probability. Results111,755 record pairs were considered. National IDs were not missing completely at random. Match probabilities for National ID record pairs exhibited substantially less uncertainty (mid-range match probabilities), inflating Sen and PPV. Before bias correction, Sen and PPV were estimated at 97.0% and 97.8% respectively. After bias correction for missing National IDs, Sen and PPV were estimated at 95.7% and 96.6%. Failure to address this bias understated the overlinkage rate (100% - PPV) by 35% and the underlinkage rate (100% - Sen) by 30%. ConclusionFailure to adjust for informative missingness in the gold standard may lead to biased validation metrics and over/underconfidence in linked data.

Evaluating energy harvesting UAV-NOMA network with random user pairing in the finite blocklength regime

This study proposes an integrated system that combines energy harvesting (EH) enabled unmanned aerial vehicles (UAVs) with non-orthogonal multiple access (NOMA) to enhance communication system performance within a cellular network. Addressing the limitations of existing analyses that often assume an infinite blocklength scenario, we explore EH-enabled UAV-NOMA systems within a cellular framework under a finite blocklength (FBL) scenario. The study investigates the complex interactions and advantages resulting from the integration of EH, NOMA, and UAV technologies, aiming to assess whether EH can sustain communication within this framework. The network model considers base stations (BSs), UAVs, and terrestrial devices distributed with independent Poisson point processes (PPPs) over a large area. In this network, BSs employ NOMA to serve cell center devices directly, while cell edge devices, which are nor in direct contact with BS, are served via simultaneous wireless information and power transfer (SWIPT) enabled UAVs. The study derives metrics including joint harvesting and decoding probability for a randomly selected UAV, coverage probability (CP) for cell devices, and end-to-end block error rate (BLER) probabilities for typical device pairs. The findings demonstrate that the proposed scheme effectively supplies all the necessary transmit power for communication purposes through EH, achieving reasonable reliability. Additionally, the study highlights the importance of considering a combination of blocklengths from different phases to achieve optimal performance, rather than solely relying on an increment in blocklength. Finally, the effects of parameter variations on network performance are examined.

Detecting relativistic Doppler in galaxy clustering with tailored galaxy samples

We present a method to obtain a high-significance detection of relativistic effects on cosmological scales. Measurements of such effects would be instrumental for our understanding of the Universe, as they would provide a further confirmation of the validity of general relativity as the correct description of the gravitational interaction, in a regime very far from that of strong gravity, where it has been tested to exquisite accuracy. Despite its relevance, the detection of relativistic effects has hitherto eluded us, mainly because they are stronger on the largest cosmic scales, plagued by cosmic variance. Our work focuses on the cosmological probe of galaxy clustering, describing the excess probability of finding pairs of galaxies at a given separation due to them being part of the same underlying cosmic large-scale structure. We focus on the two-point correlation function of the distribution of galaxies in Fourier space — the power spectrum — where relativistic effects appear as an imaginary contribution to the real power spectrum. By carefully tailoring cuts in magnitude/luminosity, we are able to obtain two samples (bright and faint) of the same galaxy population, whose cross-correlation power spectrum allows for a detection of the relativistic contribution. In particular, we optimise the definition of the samples to maximise the detection significance of the relativistic Doppler term for both a low-z Bright Galaxy Sample and a high-z Hα emission line galaxy population.

Boosted photocatalytic and electrochemical activity of hydrothermally synthesized WO3 nanoparticles co‑doped with transition elements (Mn, Co)

In this paper, undoped WO3 and (Mn, Co) co-doped WO3 nanoparticles were successfully prepared using a facile and viable hydrothermal technique and then characterized them using different complimentary techniques such as X-ray diffraction, scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, UV–visible absorption spectroscopy, photoluminescence spectroscopy, photocatalytic and cyclic voltammetry measurements. Upon (Mn, Co) co-doping, the absorption edge shifted towards a higher wavelength with respect to that of WO3 nanoparticles, and the band gap as calculated from Tauc relation was reduced from 3.30 to 2.70 eV. The reduced intensity of photoluminescence spectra indicated that the incorporation of Mn and Co ions in WO3 nanoparticles suppressed the recombination of electron-hole pairs. The photocatalytic activity of produced samples/catalysts was estimated for the removal of Rhodamine B dye under visible light irradiation. The (Mn 5 %, Co 3 %) co-doped WO3 nanoparticles showed superior photocatalytic performance compared to undoped WO3 nanoparticles, which could be due to reduction in band gap as well as reduction in recombination rate probability of photo-generated electron-hole pairs. The electrochemical performance of (Mn, Co) co-doped WO3 nanoparticles was found to be superior than that of undoped WO3 nanoparticles. The results demonstrated that (Mn 5 %, Co 3 %) co-doped WO3 nanoparticles is a promising potential candidate for practical applications in environmental remediation (dye degradation) and energy storage devices (supercapacitors).

Fine-mapping causal tissues and genes at disease-associated loci.

Heritable diseases often manifest in a highly tissue-specific manner, with different disease loci mediated by genes in distinct tissues or cell types. We propose Tissue-Gene Fine-Mapping (TGFM), a fine-mapping method that infers the posterior probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing GWAS summary statistics (and in-sample LD) and leveraging eQTL data from diverse tissues to build cis-predicted expression models; TGFM also assigns PIPs to causal variants that are not mediated by gene expression in assayed genes and tissues. TGFM accounts for both co-regulation across genes and tissues and LD between SNPs (generalizing existing fine-mapping methods), and incorporates genome-wide estimates of each tissue's contribution to disease as tissue-level priors. TGFM was well-calibrated and moderately well-powered in simulations; unlike previous methods, TGFM was able to attain correct calibration by modeling uncertainty in cis-predicted expression models. We applied TGFM to 45 UK Biobank diseases/traits (average N = 316K) using eQTL data from 38 GTEx tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease/trait, of which 11% were gene-tissue pairs. Implicated gene-tissue pairs were concentrated in known disease-critical tissues, and causal genes were strongly enriched in disease-relevant gene sets. Causal gene-tissue pairs identified by TGFM recapitulated known biology (e.g., TPO-thyroid for Hypothyroidism), but also included biologically plausible novel findings (e.g., SLC20A2-artery aorta for Diastolic blood pressure). Further application of TGFM to single-cell eQTL data from 9 cell types in peripheral blood mononuclear cells (PBMC), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs at PIP > 0.5-primarily for autoimmune disease and blood cell traits, including the biologically plausible example of CD52 in classical monocyte cells for Monocyte count. In conclusion, TGFM is a robust and powerful method for fine-mapping causal tissues and genes at disease-associated loci.

Dynamics of spike transmission and suppression between principal cells and interneurons in the hippocampus and entorhinal cortex.

Synaptic excitation and inhibition are essential for neuronal communication. However, the variables that regulate synaptic excitation and inhibition in the intact brain remain largely unknown. Here, we examined how spike transmission and suppression between principal cells (PCs) and interneurons (INTs) are modulated by activity history, brain state, cell type, and somatic distance between presynaptic and postsynaptic neurons by applying cross-correlogram analyses to datasets recorded from the dorsal hippocampus and medial entorhinal cortex (MEC) of 11 male behaving and sleeping Long Evans rats. The strength, temporal delay, and brain-state dependency of the spike transmission and suppression depended on the subregions/layers. The spike transmission probability of PC-INT excitatory pairs that showed short-term depression versus short-term facilitation was higher in CA1 and lower in CA3. Likewise, the intersomatic distance affected the proportion of PC-INT excitatory pairs that showed short-term depression and facilitation in the opposite manner in CA1 compared with CA3. The time constant of depression was longer, while that of facilitation was shorter in MEC than in CA1 and CA3. During sharp-wave ripples, spike transmission showed a larger gain in the MEC than in CA1 and CA3. The intersomatic distance affected the spike transmission gain during sharp-wave ripples differently in CA1 versus CA3. A subgroup of MEC layer 3 (EC3) INTs preferentially received excitatory inputs from and inhibited MEC layer 2 (EC2) PCs. The EC2 PC-EC3 INT excitatory pairs, most of which showed short-term depression, exhibited higher spike transmission probabilities than the EC2 PC-EC2 INT and EC3 PC-EC3 INT excitatory pairs. EC2 putative stellate cells exhibited stronger spike transmission to and received weaker spike suppression from EC3 INTs than EC2 putative pyramidal cells. This study provides detailed comparisons of monosynaptic interaction dynamics in the hippocampal-entorhinal loop, which may help to elucidate circuit operations.

Prefrontal cortical network dysfunction from acute neurotoxicant exposure.

Prefrontal cortical (PFC) dysfunction has been linked to disorders exhibiting deficits in cognitive performance, attention, motivation, and impulse control. Neurons of the PFC are susceptible to glutamatergic excitotoxicity, an effect associated with cortical degeneration in frontotemporal disorders (FTDs). PFC susceptibility to environmental toxicant exposure, one possible contributor to sporadic FTD, has not been systematically studied. Here, we tested the ability of a well-known environmental neurotoxicant, methylmercury (MeHg), to induce hyperexcitability in medial prefrontal cortex (mPFC) excitatory pyramidal neurons, using whole cell patch-clamp recording. Acute MeHg exposure (20 μM) produced significant mPFC dysfunction, with a shift in the excitatory to inhibitory (E-I) balance toward increased excitability. Both excitatory postsynaptic current (EPSC) and inhibitory postsynaptic current (IPSC) charges were significantly increased after MeHg exposure. MeHg increased EPSC frequency, but there was no observable effect on IPSC frequency, EPSC amplitude or IPSC amplitude. Neither evoked AMPA receptor- nor NMDA receptor-mediated EPSC amplitudes were affected by MeHg. However, excitatory synapses experienced a significant reduction in paired-pulse depression and probability of release. In addition, MeHg induced temporal synchrony in spontaneous IPSCs, reflecting mPFC inhibitory network dysfunction. MeHg exposure also produced increased intrinsic excitability in mPFC neurons, with an increase in action potential firing rate. The observed effects of MeHg on mPFC reflect key potential mechanisms for neuropsychological symptoms from MeHg poisoning. Therefore, MeHg has a significant effect on mPFC circuits known to contribute to cognitive and emotional function and might contribute to etiology of neurodegenerative diseases, such as FTD.NEW & NOTEWORTHY Prefrontal cortical neurons are highly susceptible to glutamatergic excitotoxicity associated with neuronal degeneration in frontal dementia and to environmental toxicant exposure, one potential contributor to FTD. However, this has not been systematically studied. Our results demonstrate that methylmercury exposure leads to hyperexcitability of prefrontal cortical neurons by shifting excitatory to inhibitory (E-I) balance and raising sensitivity for spiking. Our results provide a mechanism by which environmental neurotoxicants may contribute to pathogenesis of diseases such as FTD.

Efficient DNA Coding Algorithm for Polymerase Chain Reaction Amplification Information Retrieval

Polymerase Chain Reaction (PCR) amplification is widely used for retrieving information from DNA storage. During the PCR amplification process, nonspecific pairing between the 3’ end of the primer and the DNA sequence can cause cross-talk in the amplification reaction, leading to the generation of interfering sequences and reduced amplification accuracy. To address this issue, we propose an efficient coding algorithm for PCR amplification information retrieval (ECA-PCRAIR). This algorithm employs variable-length scanning and pruning optimization to construct a codebook that maximizes storage density while satisfying traditional biological constraints. Subsequently, a codeword search tree is constructed based on the primer library to optimize the codebook, and a variable-length interleaver is used for constraint detection and correction, thereby minimizing the likelihood of nonspecific pairing. Experimental results demonstrate that ECA-PCRAIR can reduce the probability of nonspecific pairing between the 3’ end of the primer and the DNA sequence to 2–25%, enhancing the robustness of the DNA sequences. Additionally, ECA-PCRAIR achieves a storage density of 2.14–3.67 bits per nucleotide (bits/nt), significantly improving storage capacity.

Probabilistic microkinetic modeling: Species balance equations for a catalyst surface containing multiple short-range order parameters to capture spatial correlations.

Adsorbed molecules on a catalyst almost always arrange themselves in a manner that is far from perfectly random, which gives rise to spatial correlations. These correlations are a result of the interactions between the adsorbed species (adspecies) as well as elementary processes such as diffusion and reaction events that shape the adspecies arrangements. Despite their importance, spatial correlations are usually ignored while writing species balance equations for the modeling of heterogeneous catalytic systems. Recently, we have introduced a probabilistic microkinetic modeling (p-MKM) framework that aims at incorporating spatial correlations in the form of a short-ranged order (SRO) parameter into species balance equations. Here, we extend the approach to catalytic systems of higher complexity, namely, longer interactions and multiple species. This is made possible by including multiple pair probabilities in the p-MKM model for the first time. The interplay between different SRO parameters is probed. An important consideration is how many pair probabilities should be included to capture the underlying complexity with sufficient accuracy.

Prediction of Paratope-Epitope Pairs Using Convolutional Neural Networks.

Antibodies play a central role in the adaptive immune response of vertebrates through the specific recognition of exogenous or endogenous antigens. The rational design of antibodies has a wide range of biotechnological and medical applications, such as in disease diagnosis and treatment. However, there are currently no reliable methods for predicting the antibodies that recognize a specific antigen region (or epitope) and, conversely, epitopes that recognize the binding region of a given antibody (or paratope). To fill this gap, we developed ImaPEp, a machine learning-based tool for predicting the binding probability of paratope-epitope pairs, where the epitope and paratope patches were simplified into interacting two-dimensional patches, which were colored according to the values of selected features, and pixelated. The specific recognition of an epitope image by a paratope image was achieved by using a convolutional neural network-based model, which was trained on a set of two-dimensional paratope-epitope images derived from experimental structures of antibody-antigen complexes. Our method achieves good performances in terms of cross-validation with a balanced accuracy of 0.8. Finally, we showcase examples of application of ImaPep, including extensive screening of large libraries to identify paratope candidates that bind to a selected epitope, and rescoring and refining antibody-antigen docking poses.

Joint Failure Probability of Dams Based on Probabilistic Flood Hazard Analysis

Probabilistic risk methods are becoming increasingly accepted as a means of carrying out risk-informed decision making regarding the design and operation policy of structures such as dams. Probabilistic risk calculations require the quantification of epistemic and aleatory uncertainties not investigated through deterministic methodologies. In this hydrological study, a stochastic sampling methodology is employed to investigate the joint failure probability of three dams in adjacent similarly sized watersheds within the same hydrologic unit code (HUC) 6 basin. A probabilistic flood hazard analysis (PFHA) framework is used to simulate the hydrologic loading of a range of extreme precipitation events across the combined watershed area of the three studied dams. Precipitation events are characterized by three distinct storm types influential in the Tennessee Valley region with implications for weather variability and climate change. The stochastic framework allows for the simulation of hundreds of thousands of spillway outflows that are used to produce empirical bivariate exceedance probabilities for spillway discharge pairs at selected dams. System response curves that indicate the probability of failure given spillway discharge are referenced for each dam and applied to generate empirical bivariate failure probability (joint failure probability) estimates. The stochastic simulation results indicate the range of spillway discharges for each pair of dams that pose the greatest risk of joint failure. The estimate of joint failure considering the dependence of spillway discharges between dams is shown to be three to four orders of magnitude more likely (7.42 × 102 to 5.68 × 103) than estimates that assume coincident failures are the result of independent hydrologic events.

EKGDR: An End-to-End Knowledge Graph-Based Method for Computational Drug Repurposing.

The lengthy and expensive process of developing new drugs from scratch, coupled with a high failure rate, has prompted the emergence of drug repurposing/repositioning as a more efficient and cost-effective approach. This approach involves identifying new therapeutic applications for existing approved drugs, leveraging the extensive drug-related data already gathered. However, the diversity and heterogeneity of data, along with the limited availability of known drug-disease interactions, pose significant challenges to computational drug design. To address these challenges, this study introduces EKGDR, an end-to-end knowledge graph-based approach for computational drug repurposing. EKGDR utilizes the power of a drug knowledge graph, a comprehensive repository of drug-related information that encompasses known drug interactions and various categorization information, as well as structural molecular descriptors of drugs. EKGDR employs graph neural networks, a cutting-edge graph representation learning technique, to embed the drug knowledge graph (nodes and relations) in an end-to-end manner. By doing so, EKGDR can effectively learn the underlying causes (intents) behind drug-disease interactions and recursively aggregate and combine relational messages between nodes along different multihop neighborhood paths (relational paths). This process generates representations of disease and drug nodes, enabling EKGDR to predict the interaction probability for each drug-disease pair in an end-to-end manner. The obtained results demonstrate that EKGDR outperforms previous models in all three evaluation metrics: area under the receiver operating characteristic curve (AUROC = 0.9475), area under the precision-recall curve (AUPRC = 0.9490), and recall at the top-200 recommendations (Recall@200 = 0.8315). To further validate EKGDR's effectiveness, we evaluated the top-20 candidate drugs suggested for each of Alzheimer's and Parkinson's diseases.

User Identification Across Multiple Social Networks Based on Naive Bayes Model.

Recently, the problem of user identification across multiple social networks (UIAMSNs) has attracted considerable attention since it is a prerequisite for many downstream tasks and applications. Although substantial network feature-based approaches have been proposed to solve the UIAMSNs' problem, the matching degree in most of the current works is given by experience, which lacks a solid theoretical basis. To alleviate the above predicament, we propose a user identification algorithm based on naive Bayes model (UI-NBM) within the network feature-based framework. First, a matching degree index is designed based on the naive Bayes model, which can accurately measure the contributions of different common matched node pairs (MNPs) to the connection probability of unmatched node pairs (UMNPs). Second, the matching degrees of all UMNPs are formulated as the product of matrices, giving rise to the great reduction of the time complexity and the compact expression; Finally, with the idea of recursion process, more UMNPs can be iteratively predicted even when only a small amount of prior information (i.e., a few number of MNPs) is known. The experimental results on the synthetic and real cross platforms demonstrate that the method outperforms the baseline methods within the feature-based framework.

Heterogeneous Feature Selection Based on Neighborhood Combination Entropy.

Feature selection aims to remove irrelevant or redundant features and thereby remain relevant or informative features so that it is often preferred for alleviating the dimensionality curse, enhancing learning performance, providing better readability and interpretability, and so on. Data that contain numerical and categorical representations are called heterogeneous data, and they exist widely in many real-world applications. Neighborhood rough set (NRS) can effectively deal with heterogeneous data by using neighborhood binary relation, which has been successfully applied to heterogeneous feature selection. In this article, the NRS model as a unified framework is used to design a feature selection method to handle categorical, numerical, and heterogeneous data. First, the concept of neighborhood combination entropy (NCE) is presented. It can reflect the probability of pairs of the neighborhood granules that are probably distinguishable from each other. Then, the conditional neighborhood combination entropy (cNCE) based on NCE is proposed under the condition of considering decision attributes. Moreover, some properties and relationships between cNCE and NCE are derived. Finally, the functions of inner and outer significances are constructed to design a feature selection algorithm based on cNCE (FScNCE). The experimental results show the effectiveness and superiority of the proposed algorithm.

Prediction of the effects of the top 10 synonymous mutations from 26645 SARS-CoV-2 genomes of early pandemic phase.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had led to a global pandemic since December 2019. SARS-CoV-2 is a single-stranded RNA virus, which mutates at a higher rate. Multiple works had been done to study nonsynonymous mutations, which change protein sequences. However, there is little study on the effects of SARS-CoV-2 synonymous mutations, which may affect viral fitness. This study aims to predict the effect of synonymous mutations on the SARS-CoV-2 genome. A total of 26645 SARS-CoV-2 genomic sequences retrieved from Global Initiative on Sharing all Influenza Data (GISAID) database were aligned using MAFFT. Then, the mutations and their respective frequency were identified. Multiple RNA secondary structures prediction tools, namely RNAfold, IPknot++ and MXfold2 were applied to predict the effect of the mutations on RNA secondary structure and their base pair probabilities was estimated using MutaRNA. Relative synonymous codon usage (RSCU) analysis was also performed to measure the codon usage bias (CUB) of SARS-CoV-2. A total of 150 synonymous mutations were identified. The synonymous mutation identified with the highest frequency is C3037U mutation in the nsp3 of ORF1a. Of these top 10 highest frequency synonymous mutations, C913U, C3037U, U16176C and C18877U mutants show pronounced changes between wild type and mutant in all 3 RNA secondary structure prediction tools, suggesting these mutations may have some biological impact on viral fitness. These four mutations show changes in base pair probabilities. All mutations except U16176C change the codon to a more preferred codon, which may result in higher translation efficiency. Synonymous mutations in SARS-CoV-2 genome may affect RNA secondary structure, changing base pair probabilities and possibly resulting in a higher translation rate. However, lab experiments are required to validate the results obtained from prediction analysis.

Concurrent prediction of RNA secondary structures with pseudoknots and local 3D motifs in an integer programming framework.

The prediction of RNA structure canonical base pairs from a single sequence, especially pseudoknotted ones, remains challenging in a thermodynamic models that approximates the energy of the local 3D motifs joining canonical stems. It has become more and more apparent in recent years that the structural motifs in the loops, composed of noncanonical interactions, are essential for the final shape of the molecule enabling its multiple functions. Our capacity to predict accurate 3D structures is also limited when it comes to the organization of the large intricate network of interactions that form inside those loops. We previously developed the integer programming framework RNA Motifs over Integer Programming (RNAMoIP) to reconcile RNA secondary structure and local 3D motif information available in databases. We further develop our model to now simultaneously predict the canonical base pairs (with pseudoknots) from base pair probability matrices with or without alignment. We benchmarked our new method over the all nonredundant RNAs below 150 nucleotides. We show that the joined prediction of canonical base pairs structure and local conserved motifs (i) improves the ratio of well-predicted interactions in the secondary structure, (ii) predicts well canonical and Wobble pairs at the location where motifs are inserted, (iii) is greatly improved with evolutionary information, and (iv) noncanonical motifs at kink-turn locations. The source code of the framework is available at https://gitlab.info.uqam.ca/cbe/RNAMoIP and an interactive web server at https://rnamoip.cbe.uqam.ca/.