Probability Of Pairs Research Articles

Deuterium-depletion has no significant impact on the mutation rate of Escherichia coli, deuterium abundance therefore has a probabilistic, not deterministic effect on spontaneous mutagenesis.

Deuterium (D), the second most abundant isotope of hydrogen is present in natural waters at an approximate concentration of 145-155 ppm (ca. 1.5E-4 atom/atom). D is known to influence various biological processes due to its physical and chemical properties, which significantly differ from those of hydrogen. For example, increasing D-concentration to >1000-fold above its natural abundance has been shown to increase the frequency of genetic mutations in several species. An interesting deterministic hypothesis, formulated with the intent of explaining the mechanism of D-mutagenicity is based on the calculation that the theoretical probability of base pairs to comprise two adjacent D-bridges instead of H-bridges is 2.3E-8, which is equal to the mutation rate of certain species. To experimentally challenge this hypothesis, and to infer the mutagenicity of D present at natural concentrations, we investigated the effect of a nearly 100-fold reduction of D concentration on the bacterial mutation rate. Using fluctuation tests, we measured the mutation rate of three Escherichia coli genes (cycA, ackA and galK) in media containing D at either <2 ppm or 150 ppm concentrations. Out of 15 pair-wise fluctuation analyses, nine indicated a significant decrease, while three marked the significant increase of the mutation/culture value upon D-depletion. Overall, growth in D-depleted minimal medium led to a geometric mean of 0.663-fold (95% confidence interval: 0.483-0.911) change in the mutation rate. This falls nowhere near the expected 10,000-fold reduction, indicating that in our bacterial systems, the effect of D abundance on the formation of point mutations is not deterministic. In addition, the combined results did not display a statistically significant change in the mutation/culture value, the mutation rate or the mutant frequency upon D-depletion. The potential mutagenic effect of D present at natural concentrations on E. coli is therefore below the limit of detection using the indicated methods.

Full-Duplex MAC Protocol Based on CSMA/CA for Switching Transmission Mode

Full-duplex (FD) radios can transmit and receive packets simultaneously in the same frequency band. Although this potentially doubles the spectral efficiency, it also causes inter-node interference, which was not an issue for half-duplex (HD) radios and can significantly reduce FD radio throughput. This paper proposes a hybrid transmission switching algorithm to reduce performance degradation due to inter-node interference. In contrast to previous studies that only considered channel capacity, we determine the transmission mode by considering the modulation and coding scheme (MCS). The access point compares the required transmission time for HD, FD, and hybrid mode and selects the mode with the best network throughput. We also designed an analytical model to evaluate network throughput for the proposed algorithm. In particular, we consider FD pair probability, which has not been addressed previously. Analytical and simulation results verify that the proposed algorithm improves network performance.

Preparation of Novel Ag–Si–TiO2 Composite and Research on Its Photocatalytic Degradation of Formaldehyde

A novel Ag–Si–TiO2 composite was prepared via sol–gel method for removing residual formaldehyde in shiitake mushroom. The structure of Ag–Si–TiO2 composite was characterized by scanning electron microscope (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) analyses. Ultraviolet-visible absorption spectroscopy (UV-Vis) and N2 adsorption-desorption tests showed that Ag and Si co-doped decreased the band gap, the Brunauer-Emmett-Teller (BET) specific surface area of the samples increased and the recombination probability of electron-hole pairs (e--h+) reduced. Effect on removal rate of formaldehyde with different Ag-Si co-doped content, formaldehyde concentration and solution pH were investigated, and the results showed that 6.0 wt%Ag-3.0 wt%Si-TiO2 samples had an optimum catalytic performance, and the degradation efficiency reached 96.6% after 40 W 365 nm UV lamp irradiation for 360 min. The kinetics of formaldehyde degradation by Ag–Si–TiO2 composite photocatalyst could be described by Langmuir-Hinshelwood first-order kinetic model.

Deep Learning Method for RNA Secondary Structure Prediction with Pseudoknots Based on Large-Scale Data

Traditional machine learning methods are widely used in the field of RNA secondary structure prediction and have achieved good results. However, with the emergence of large-scale data, deep learning methods have more advantages than traditional machine learning methods. As the number of network layers increases in deep learning, there will often be problems such as increased parameters and overfitting. We used two deep learning models, GoogLeNet and TCN, to predict RNA secondary results. And from the perspective of the depth and width of the network, improvements are made based on the neural network model, which can effectively improve the computational efficiency while extracting more feature information. We process the existing real RNA data through experiments, use deep learning models to extract useful features from a large amount of RNA sequence data and structure data, and then predict the extracted features to obtain each base’s pairing probability. The characteristics of RNA secondary structure and dynamic programming methods are used to process the base prediction results, and the structure with the largest sum of the probability of each base pairing is obtained, and this structure will be used as the optimal RNA secondary structure. We, respectively, evaluated GoogLeNet and TCN models based on 5sRNA, tRNA data, and tmRNA data, and compared them with other standard prediction algorithms. The sensitivity and specificity of the GoogLeNet model on the 5sRNA and tRNA data sets are about 16% higher than the best prediction results in other algorithms. The sensitivity and specificity of the GoogLeNet model on the tmRNA dataset are about 9% higher than the best prediction results in other algorithms. As deep learning algorithms’ performance is related to the size of the data set, as the scale of RNA data continues to expand, the prediction accuracy of deep learning methods for RNA secondary structure will continue to improve.

Influence of Sr/Nd partial replacement on fundamental properties of Bi-2223 superconducting system

This comprehensive work aims to examine the change in flux pinning mechanism, physical, mechanical, and structural characteristics of pure and Sr-site Nd-substituted Bi1.8Pb0.35Sr1.9−yNdyCa2.2Cu3Ox (Bi-2223) systems. The magnetoresistivity performances for all the samples are carried out by magnetotransport experiments in the existence of external magnetic field strength intervals 0–7 T. It is found that the increment of Nd/Sr substitution amount in bulk Bi-2223 system retrogrades the pinning capability of thermal flux motions for interlayer Josephson junction between the isolated grains. Similarly, the coupling probabilities of copper pairs and potential energy barriers are significantly diminished by increasing Nd impurity. This is in association with the enhancement of permanent structural problems in the crystal structure. Therefore, the excessive Nd inclusions improve the reattached linear/split pancake-like nature. In this regard, the best magnetic performance quantities are obtained for the pure sample. Besides, the SEM images show that the grain connectivity and surface morphology damage significantly with the Nd impurity. Additionally, the experimental microhardness findings conducted at various external loads (0.245–2.940 N) display that the Nd purity in the superconducting system degrades dramatically the key design mechanical features. Besides, we analyze the mechanical characteristic properties founded on the theoretical approaches with the proportional sample resistance, elastic/plastic deformation, and Hays–Kendall methods. The results obtained show that the Nd purity causes the indentation size effect behavior to decrease dramatically for all the samples. Furthermore, the findings of Hays–Kendall method are noticed to much more agree with the real hardness parameters. Thus, the Hays–Kendall model is the best methods to find the load-independent Vickers hardness values for the Sr-site Nd-substituted Bi1.8Pb0.35Sr1.9−yNdyCa2.2Cu3Ox (Bi-2223) systems. Moreover, in the dynamic microhardness measurements, the contact depth (hc), elastic modulus (Er), and load (Pmax) of all the samples are experimentally recorded for the first time. The results reveal that the mechanical properties depend strongly on the load and Nd impurity level.

Enhancing Physical Layer Security With Artificial Noise in Large-Scale NOMA Networks

This paper investigates the secrecy outage performance for a large-scale downlink system with full-duplex (FD) non-orthogonal multiple access (NOMA) transmission aided by artificial noise (AN). In the system, legitimate users (LUs) are randomly distributed according to homogeneous Poisson point processes whereas a passive eavesdropper (Eve) is certainly located. We introduce a secure cooperative communication scheme, in which nearby NOMA users operating in FD mode act as jammers and generate AN to enhance the physical layer security (PLS) of the legitimate transmission. Closed-form expressions in terms of the secrecy outage probability (SOP) for a user pair are acquired. Following the mathematical results, secrecy diversity order analysis is further carried out for two scenarios, i.e., 1) fixed transmit signal-to-noise ratio (SNR) at the Eve, and 2) transmit SNR at the Eve being proportional to that of LUs. Closed-form expressions of asymptotic SOP for the two scenarios are also obtained. Monte Carlo simulations are performed to certify the veracity of the theoretic analysis and illustrate that the proposed scheme employing both AN and FD-NOMA is superior in the secrecy outage to the NOMA scheme without AN and the orthogonal multiple access scheme with AN. Furthermore, the SOP performance enhancement through proper secrecy rate target selection and power allocation optimization is also evidenced by the simulations.

Accelerating computational modeling and design of high-entropy alloys.

High-entropy alloys, with N elements and compositions {cν = 1,N} in competing crystal structures, have large design spaces for unique chemical and mechanical properties. Here, to enable computational design, we use a metaheuristic hybrid Cuckoo search (CS) to construct alloy configurational models on the fly that have targeted atomic site and pair probabilities on arbitrary crystal lattices, given by supercell random approximates (SCRAPs) with S sites. Our Hybrid CS permits efficient global solutions for large, discrete combinatorial optimization that scale linearly in a number of parallel processors, and linearly in sites S for SCRAPs. For example, a four-element, 128-site SCRAP is found in seconds-a more than 13,000-fold reduction over current strategies. Our method thus enables computational alloy design that is currently impractical. We qualify the models and showcase application to real alloys with targeted atomic short-range order. Being problem-agnostic, our Hybrid CS offers potential applications in diverse fields.

The Pairing Probability of Massive Black Holes in Merger Galaxies in the Presence of Radiative Feedback

Abstract Dynamical friction (DF) against stars and gas is thought to be an important mechanism for orbital evolution of massive black holes (MBHs) in merger remnant galaxies. Recent theoretical investigations, however, show that DF does not always lead to MBH inspiral. For MBHs evolving in gas-rich backgrounds, the ionizing radiation that emerges from the innermost parts of their accretion flow can affect the surrounding gas in such a way to cause the MBHs to accelerate and gain orbital energy. This effect was dubbed “negative DF.” We use a semianalytic model to study the impact of negative DF on pairs of MBHs in merger remnant galaxies evolving under the combined influence of stellar and gaseous DF. Our results show that for a wide range of merger galaxy and MBH properties negative DF reduces the MBH pairing probability by ∼46%. The suppression of MBH pairing is most severe in galaxies with one or more of these properties: (1) a gas fraction of f g ≥ 0.1; (2) a galactic gas disk rotating close to the circular velocity; (3) MBH pairs in prograde, low eccentricity orbits; and (4) MBH pairs with mass &lt;108 M ⊙. The last point is of importance because MBH pairs in this mass range are direct progenitors of merging binaries targeted by the future space-based gravitational wave observatory the Laser Interferometer Space Antenna (LISA).

Fast and accurate structure probability estimation for simultaneous alignment and folding of RNAs with Markov chains

MotivationSimultaneous alignment and folding (SA&F) of RNAs is the indispensable gold standard for inferring the structure of non-coding RNAs and their general analysis. The original algorithm, proposed by Sankoff, solves the theoretical problem exactly with a complexity of O(n^6) in the full energy model. Over the last two decades, several variants and improvements of the Sankoff algorithm have been proposed to reduce its extreme complexity by proposing simplified energy models or imposing restrictions on the predicted alignments.ResultsHere, we introduce a novel variant of Sankoff’s algorithm that reconciles the simplifications of PMcomp, namely moving from the full energy model to a simpler base pair-based model, with the accuracy of the loop-based full energy model. Instead of estimating pseudo-energies from unconditional base pair probabilities, our model calculates energies from conditional base pair probabilities that allow to accurately capture structure probabilities, which obey a conditional dependency. This model gives rise to the fast and highly accurate novel algorithm Pankov (Probabilistic Sankoff-like simultaneous alignment and folding of RNAs inspired by Markov chains).ConclusionsPankov benefits from the speed-up of excluding unreliable base-pairing without compromising the loop-based free energy model of the Sankoff’s algorithm. We show that Pankov outperforms its predecessors LocARNA and SPARSE in folding quality and is faster than LocARNA.

Effects of pairing correlation on the quasiparticle resonance in neutron-rich Ca isotopes

We investigated the effects of the pairing correlation on the quasiparticle resonant energy and width in the neutron-rich Ca isotopes by using the self-consistent continuum Skyrme Hartree-Fock-Bogoliubov theory with Green's function method. The resonant energy and width can be read from three kinds of quasiparticle spectra, including the quasiparticle-state probability density, the occupation probability density, and the pair probability density. The results read from different quasiparticle spectra calculated with the same potential are exactly the same for the narrow resonances, while for the broad resonances, the difference among the results are relatively larger. From the comparison between the quasiparticle-state probability density calculated with and without the pairing field, we could separate the effect only from the pairing correlation on the quasiparticle resonances. We found that the pairing correlation can increase the resonant energy and width for both the hole-like and particle-like quasiparticle resonances. And this increase is larger when the state is closer to the Fermi energy, especially for the particle-like quasiparticle resonance originated from the unbound single-particle states with smaller angular momentum $l$ in weakly bound nucleus.

P.714 Brain correlates of speech perception in schizophrenia patients with and without auditory verbal hallucinations

We demonstrate a general strategy to design step by step the Ag nanoparticle-functionalized ZnO micro-flowers (Ag/ZnO composites). XRD patterns confirmed the presence of Ag nanoparticles in ZnO/Ag composites, and the SEM and TEM results further demonstrated that Ag nanoparticles were highly dispersed and anchored onto the surface of each ZnO nanosheets. By using the ZnO/Ag composites, the photodegradation of two herbicide derivatives, metamitron and metribuzin, were studied. The enhanced photocatalytic performance was ascribed to the fact that the Ag deposition could reduce the recombination probability of electron-hole pairs, and the photocatalytic mechanism were also investigated in this paper.

RNMFMDA: A Microbe-Disease Association Identification Method Based on Reliable Negative Sample Selection and Logistic Matrix Factorization With Neighborhood Regularization.

Microbes with abnormal levels have important impacts on the formation and development of various complex diseases. Identifying possible Microbe-Disease Associations (MDAs) helps to understand the mechanisms of complex diseases. However, experimental methods for MDA identification are costly and time-consuming. In this study, a new computational model, RNMFMDA, was developed to find possible MDAs. RNMFMDA contains two main processes. First, Reliable Negative MDA samples were selected based on Positive-Unlabeled (PU) learning and random walk with restart on the heterogeneous microbe-disease network. Second, Logistic Matrix Factorization with Neighborhood Regularization (LMFNR) was developed to compute the association probabilities for all microbe-disease pairs. To evaluate the performance of the proposed RNMFMDA method, we compared RNMFMDA with five state-of-the-art MDA prediction methods based on five-fold cross-validations on microbes, diseases, and MDAs. As a result, RNMFMDA obtained the best AUCs of 0.6332, 0.8669, and 0.9081, respectively for the three five-fold cross validations, significantly outperforming other models. The promising prediction performance may be attributed to the following three features: highly quality negative MDA sample selection, LMFNR-based MDA prediction model, and various biological information integration. In addition, a few predicted microbe-disease pairs with high association scores are worthy of further experimental validation.

Efficient hydrogen production at a rationally designed MoSe2@Co3O4 p-n heterojunction

During the past several years, transition metal compounds have shown high activity in the field of photocatalysis. Therefore, the MoSe2@Co3O4 with excellent photocatalytic properties through simple hydrothermal and physical mixing methods was prepared. This composite material was composed of n-type semiconductor MoSe2 and p-type semiconductor Co3O4. After optimizing the loading of Co3O4, the optimal hydrogen production can reached 7029.2 μmol g-1h−1, which was 2.34 times that of single MoSe2. In addition, some characterization methods were used to explore the hydrogen production performance of the composite catalyst under EY sensitized conditions. Among them, the UV–vis diffuse reflectance spectra suggests that MoSe2@Co3O4 exhibits stronger visible light absorption performance than the single material. Fluorescence performance and photoelectrochemical characterization experiments further prove that, the special structure formed by MoSe2 and Co3O4 and the existence of p-n heterojunction effectively accelerate the separation and transfer of carriers meanwhile inhibit the recombination probability of electron-hole pairs. Combined with other characterizations such as XRD, XPS, SEM and BET, the possible hydrogen production mechanism was proposed.

PrGCN: Probability prediction with graph convolutional network for person re-identification

Robust similarity measurement is an important issue for person re-identification (ReID). Most existing ReID models estimate the similarity between query and gallery images by computing their Euclidean distances while ignoring the rich context information contained in the image space. In this paper, we propose a graph convolutional network (GCN) based method to improve the similarity measurement in ReID, which regards the ReID task as a prediction problem of the link probability between node pairs. Our method is named as PrGCN (Probability GCN), in which each person is regarded as an instance node. Firstly, an Instance Centered Sub-graphs (ICS) is constructed for each instance node to depict its rich local context information. Secondly, the constructed ICS is input to a GCN to infer and predict the link probability of node pairs, followed by a similarity ranking between the query and gallery images according to the predicted probabilities. Extensive experiments show that the proposed method improves the mAP and Top-1 accuracy of ReID significantly, yielding better or comparable results to the state-of-the-art methods on various benchmarks (Market1501, DukeMTMC-ReID and CUHK03). In addition, we validate that the proposed PrGCN can be easily embedded into other deep learning architectures to replace Euclidean distance metric and achieve significant performance improvements.

Longitudinal species turnover rates are predictable and should guide location of sampling sites for South African river surveys to assess aquatic biodiversity

Representative samples of biota collected relative to environmental gradients are important for measuring present distributions and predicting shifts in distribution of taxa in response to climate change or reduced river connectivity. Based on river ecology theory and established measures of species diversity, we present a method to identify suitable river segments for sampling and monitoring changes in taxon diversity. Alpha and beta diversities of selected aquatic macroinvertebrates were assessed in seven South African rivers. Data were drawn from historical and field samples and represented longitudinal species patterns down longitudinal river axes. Representative sampling sites were identified using a logistic regression model to predict the probability of site pairs that were more than 50% similar as a function of up-/downstream distance. Alpha diversities peaked in the upper third of river lengths; beta diversities showed predictable exponential decay rates down river axes up to and excluding the start of estuarine conditions. Application of the model to a 370-km long river indicated that 14 sites should be selected for sampling to capture overall biodiversity patterns. Additional factors, such as confluences tributaries, which influence alpha diversity at sites, are identified and incorporated into site selection.

Fuzzy logic programming and adaptable design of medical products for the COVID-19 anti-epidemic normalization

BackgroundThe COVID-19 prevention and control constantly affects lives worldwide. In this paper, household medical products were analyzed using fuzzy logic. Considering the household anti-epidemic status, economic and environmental benefits, the adaptable design method of anti-epidemic products in the vestibule was proposed. The measure of adaptable design method still have shortcomings. Therefore, an improved method that is based on fuzzy logic programming is required. MethodFirstly, common medical product types used in vestibules and household anti-epidemic products were identified and summarized into product sets. Then matching degree matrix was obtained by functional configuration decomposition and matching calculations. Secondly, experts were invited to evaluate the paired comparative probability matrices and linguistic variables, and the evaluation data were converted by trapezoidal membership functions, fuzzy numbers and the defuzzification method to obtain the usage probability values (PR) for product functions. Finally, the matching degree value (P) and the product function (PF) were calculated by adaptability measure formula, and product function, the adaptability factor and the adaptability (A) were obtained. Results and DiscussionOur results show that the degree of adaptability of each product function in the product set from PF1 to PF10can be evaluated. Based on the principles of sorting of values from high to low, the top five PF (n = 10) for P value is PF10, PF5, PF6, PF8 and PF1; The top five PF for P value is PF2, PF1, PF3, PF7 and PF8; The top five PF for A value is PF2(0.242), PF1(0.232), PF5(0.225), PF8(0.222) and PF3(0.221). These values allow us to summarize and draw visual charts according to the above data sorting mode. The higher the value of the product function, the more it can be prioritized for design development with functional cost savings, simplification or clustering. ConclusionThis study proposes an adaptable design method based on fuzzy logic programming. The data results in this study can guide the development and programming of the vestibule anti-epidemic products. The higher adaptability value of a product function indicates that it is more capable of being simplified, clustered, and adapting to changes in the product set.

Contention-Based Radio Resource Management for URLLC-Oriented D2D Communications

Device-to-device (D2D) communications are becoming a promising technology to support ultra-reliable low-latency communications (URLLC). This paper incorporates contention-based access scheme into radio resource management (RRM), which can not only deal with the sporadic packet arrivals for URLLC-oriented D2D communications, but also avoid wasting time on sending requests and waiting for feedback. However, it cannot be ignored that the collisions may occur to yield transmission failure, especially when two or more users access the same resource block (RB). Thus, we should pay more attention to optimizing users’ RB selection to foster strengths and offset weaknesses of introducing contention-based access scheme, and thereby improve the transmission reliability within the latency budget. Specifically, we formulate the contention-based RRM as an optimization problem which aims to maximize the sum successful transmission probability (STP) of all D2D pairs under the strict latency constraint. Due to its intractability, this optimization problem is transformed into a mixed strategy non-cooperative game, which is proved to be an exact potential game with the sum STP as its potential function. Accordingly, the existence of pure strategy Nash equilibrium (NE) can be guaranteed, and importantly, the optimal integral solution to the optimization problem constitutes a pure strategy NE of the game. Moreover, we propose a distributed RB selection algorithm based on sequential spatial adaptive play (S-SAP), which can converge to the optimal pure strategy NE of the proposed game. The numerical results verify the convergence and optimality of the proposed algorithm.

Adaptive differential evolution with a new joint parameter adaptation method

Differential evolution (DE) is a population-based metaheuristic algorithm that has been proved powerful in solving a wide range of real-parameter optimization tasks. However, the selection of the mutation strategy and control parameters in DE is problem dependent, and inappropriate specification of them will lead to poor performance of the algorithm such as slow convergence and early stagnation in a local optimum. This paper proposes a new method termed as Joint Adaptation of Parameters in DE (JAPDE). The key idea lies in dynamically updating the selection probabilities for a complete set of pairs of parameter generating functions based on feedback information acquired during the search by DE. Further, for mutation strategy adaptation, the Rank-Based Adaptation (RAM) method is utilized to facilitate the learning of multiple probability distributions, each of which corresponds to an interval of fitness ranks of individuals in the population. The coupling of RAM with JAPDE results in the new RAM-JAPDE algorithm that enables simultaneous adaptation of the selection probabilities for pairs of control parameters and mutation strategies in DE. The merit of RAM-JAPDE has been evaluated on the benchmark test suit proposed in CEC2014 in comparison to many well-known DE algorithms. The results of experiments demonstrate that the proposed RAM-JAPDE algorithm outperforms or is competitive to the other related DE variants that perform mutation strategy and control parameter adaptation, respectively.

Atomistic modeling of hardening in spinodally-decomposed Fe–Cr binary alloys

Spinodal decomposition in thermally aged Fe–Cr alloys leads to significant hardening, which is the direct cause of the so-called 475∘C-embrittlement. To illustrate how spinodal decomposition induces hardening by atomistic interactions, we conducted a series of numerical simulations as well as reference experiments. The numerical results indicated that the hardness scales linearly with the short-range order (SRO) parameter, while the experimental result reproduced this relationship within statistical error. Both seemingly suggest that neighboring Cr–Cr atomic pairs essentially cause hardening, because SRO is by definition uniquely dependent on the appearance probability of such pairs. A further numerical investigation supported this notion, as it suggests that the dominant cause of hardening is the pinning effect of dislocations passing over such Cr–Cr pairs. Moreover, this finding has a practical merit, that is, the SRO parameter can serve as a good index of hardening of steels, which is critical for evaluating their lifetime.

LinearPartition: linear-time approximation of RNA folding partition function and base-pairing probabilities.

MotivationRNA secondary structure prediction is widely used to understand RNA function. Recently, there has been a shift away from the classical minimum free energy methods to partition function-based methods that account for folding ensembles and can therefore estimate structure and base pair probabilities. However, the classical partition function algorithm scales cubically with sequence length, and is therefore prohibitively slow for long sequences. This slowness is even more severe than cubic-time free energy minimization due to a substantially larger constant factor in runtime.ResultsInspired by the success of our recent LinearFold algorithm that predicts the approximate minimum free energy structure in linear time, we design a similar linear-time heuristic algorithm, LinearPartition, to approximate the partition function and base-pairing probabilities, which is shown to be orders of magnitude faster than Vienna RNAfold and CONTRAfold (e.g. 2.5 days versus 1.3 min on a sequence with length 32 753 nt). More interestingly, the resulting base-pairing probabilities are even better correlated with the ground-truth structures. LinearPartition also leads to a small accuracy improvement when used for downstream structure prediction on families with the longest length sequences (16S and 23S rRNAs), as well as a substantial improvement on long-distance base pairs (500+ nt apart).Availability and implementationCode: http://github.com/LinearFold/LinearPartition; Server: http://linearfold.org/partition.Supplementary information Supplementary data are available at Bioinformatics online.