Patterns of nucleotides that flank substitutions in human orthologous genes

<p>Abstract</p> <p>Background</p> <p>Sequence context is an important aspect of base mutagenesis, and three-base periodicity is an intrinsic property of coding sequences. However, how three-base periodicity is influenced in the vicinity of substitutions is still unclear. The effect of context on mutagenesis should be revealed in the usage of nucleotides that flank substitutions. Relative entropy (also known as Kullback-Leibler divergence) is useful for finding unusual patterns in biological sequences.</p> <p>Results</p> <p>Using relative entropy, we visualized the periodic patterns in the context of substitutions in human orthologous genes. Neighbouring patterns differed both among substitution categories and within a category that occurred at three codon positions. Transition tended to occur in periodic sequences relative to transversion. Periodic signals were stronger in a set of flanking sequences of substitutions that occurred at the third-codon positions than in those that occurred at the first- or second-codon positions. To determine how the three-base periodicity was affected near the substitution sites, we fitted a sine model to the values of the relative entropy. A sine of period equal to 3 is a good approximation for the three-base periodicity at sites not in close vicinity to some substitutions. These periods were interrupted near the substitution site and then reappeared away from substitutions. A comparative analysis between the native and codon-shuffled datasets suggested that the codon usage frequency was not the sole origin of the three-base periodicity, implying that the native order of codons also played an important role in this periodicity. Synonymous codon shuffling revealed that synonymous codon usage bias was one of the factors responsible for the observed three-base periodicity.</p> <p>Conclusions</p> <p>Our results offer an efficient way to illustrate unusual periodic patterns in the context of substitutions and provide further insight into the origin of three-base periodicity. This periodicity is a result of the native codon order in the reading frame. The length of the period equal to 3 is caused by the usage bias of nucleotides in synonymous codons. The periodic features in nucleotides surrounding substitutions aid in further understanding genetic variation and nucleotide mutagenesis.</p

Impacts of mutation effects and population size on mutation rate in asexual populations: a simulation study

<p>Abstract</p> <p>Background</p> <p>In any natural population, mutation is the primary source of genetic variation required for evolutionary novelty and adaptation. Nevertheless, most mutations, especially those with phenotypic effects, are harmful and are consequently removed by natural selection. For this reason, under natural selection, an organism will evolve to a lower mutation rate. Overall, the action of natural selection on mutation rate is related to population size and mutation effects. Although theoretical work has intensively investigated the relationship between natural selection and mutation rate, most of these studies have focused on individual competition within a population, rather than on competition among populations. The aim of the present study was to use computer simulations to investigate how natural selection adjusts mutation rate among asexually reproducing subpopulations with different mutation rates.</p> <p>Results</p> <p>The competition results for the different subpopulations showed that a population could evolve to an "optimum" mutation rate during long-term evolution, and that this rate was modulated by both population size and mutation effects. A larger population could evolve to a higher optimum mutation rate than could a smaller population. The optimum mutation rate depended on both the fraction and the effects of beneficial mutations, rather than on the effects of deleterious ones. The optimum mutation rate increased with either the fraction or the effects of beneficial mutations. When strongly favored mutations appeared, the optimum mutation rate was elevated to a much higher level. The competition time among the subpopulations also substantially shortened.</p> <p>Conclusions</p> <p>Competition at the population level revealed that the evolution of the mutation rate in asexual populations was determined by both population size and mutation effects. The most striking finding was that beneficial mutations, rather than deleterious mutations, were the leading force that modulated the optimum mutation rate. The initial configuration of the population appeared to have no effect on these conclusions, confirming the robustness of the simulation method developed in the present study. These findings might further explain the lower mutation rates observed in most asexual organisms, as well as the higher mutation rates in some viruses.</p

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Traffic prediction plays an important role in the realization of traffic control and scheduling tasks in intelligent transportation systems. With the diversification of data sources, reasonably using rich traffic data to model the complex spatial-temporal dependence and nonlinear characteristics in traffic flow are the key challenge for intelligent transportation system. In addition, clearly evaluating the importance of spatial-temporal features extracted from different data becomes a challenge. A Double Layer - Spatial Temporal Feature Extraction and Evaluation (DL-STFEE) model is proposed. The lower layer of DL-STFEE is spatial-temporal feature extraction layer. The spatial and temporal features in traffic data are extracted by multi-graph graph convolution and attention mechanism, and different combinations of spatial and temporal features are generated. The upper layer of DL-STFEE is the spatial-temporal feature evaluation layer. Through the attention score matrix generated by the high-dimensional self-attention mechanism, the spatial-temporal features combinations are fused and evaluated, so as to get the impact of different combinations on prediction effect. Three sets of experiments are performed on actual traffic datasets to show that DL-STFEE can effectively capture the spatial-temporal features and evaluate the importance of different spatial-temporal feature combinations.Comment: 39 pages, 14 figures, 5 table

From Data Inferring to Physics Representing: A Novel Mobile MIMO Channel Prediction Scheme Based on Neural ODE

In this paper, we propose an innovative learning-based channel prediction scheme so as to achieve higher prediction accuracy and reduce the requirements of huge amount and strict sequential format of channel data. Inspired by the idea of the neural ordinary differential equation (Neural ODE), we first prove that the channel prediction problem can be modeled as an ODE problem with a known initial value through analyzing the physical process of electromagnetic wave propagation within a varying space. Then, we design a novel physics-inspired spatial channel gradient network (SCGNet), which represents the derivative process of channel varying as a special neural network and can obtain the gradients at any relative displacement needed for the ODE solving. With the SCGNet, the static channel at any location served by the base station is accurately inferred through consecutive propagation and integration. Finally, we design an efficient recurrent positioning algorithm based on some prior knowledge of user mobility to obtain the velocity vector, and propose an approximate Doppler compensation method to make up the instantaneous angular-delay domain channel. Only discrete historical channel data is needed for the training, whereas only a few fresh channel measurements is needed for the prediction, which ensures the scheme's practicability

A Transferable Intersection Reconstruction Network for Traffic Speed Prediction

Traffic speed prediction is the key to many valuable applications, and it is also a challenging task because of its various influencing factors. Recent work attempts to obtain more information through various hybrid models, thereby improving the prediction accuracy. However, the spatial information acquisition schemes of these methods have two-level differentiation problems. Either the modeling is simple but contains little spatial information, or the modeling is complete but lacks flexibility. In order to introduce more spatial information on the basis of ensuring flexibility, this paper proposes IRNet (Transferable Intersection Reconstruction Network). First, this paper reconstructs the intersection into a virtual intersection with the same structure, which simplifies the topology of the road network. Then, the spatial information is subdivided into intersection information and sequence information of traffic flow direction, and spatiotemporal features are obtained through various models. Third, a self-attention mechanism is used to fuse spatiotemporal features for prediction. In the comparison experiment with the baseline, not only the prediction effect, but also the transfer performance has obvious advantages.Comment: 14 pages, 12 figure

Development and validation of nomograms to predict the survival probability and occurrence of a second primary malignancy of male breast cancer patients: a population-based analysis

BackgroundMale breast cancer (MBC) is rare, which has restricted prospective research among MBC patients. With effective treatments, the prognosis of MBC patients has improved and developing a second primary malignancy (SPM) has become a life-threatening event for MBC survivors. However, few studies have focused on the prognosis of MBC patients and looked into the SPM issue in MBC survivors.MethodWe reviewed MBC patients diagnosed between 1990 and 2016 from the latest Surveillance, Epidemiology, and End Results (SEER) Plus database. Competing risk models and nomograms were conducted for predicting the risk of cancer-specific death and SPM occurrence. C-indexes, calibration curves, ROC curves, and decision curve analysis (DCA) curves were applied for validation.ResultA total of 1,843 MBC patients with complete information were finally enrolled and 60 (3.26%) had developed an SPM. Prostate cancer (40%) was the most common SPM. The median OS of all the enrolled patients was 102.41 months, while the median latency from the initial MBC diagnosis to the subsequent diagnosis of SPM was 67.2 months. The patients who suffered from an SPM shared a longer OS than those patients with only one MBC (p = 0.027). The patients were randomly divided into the development cohort and the validation cohort (at a ratio of 7:3). The Fine and Gray competing risk model was used to identify the risk factors. Two nomograms were constructed and validated to predict the 5-year, 8-year, and 10-year survival probability of MBC patients, both of which had good performance in the C-index, ROC curves, calibration plots, and DCA curves, showing the ideal discrimination capability and predictive value clinically. Furthermore, we, for the first time, constructed a nomogram based on the competing risk model to predict the 5-year, 8-year, and 10-year probability of developing an SPM in MBC survivors, which also showed good discrimination, calibration, and clinical effectiveness.ConclusionWe, for the first time, included treatment information and clinical parameters to construct a nomogram to predict not only the survival probability of MBC patients but also the probability of developing an SPM in MBC survivors, which were helpful in individual risk estimation, patient follow-up, and counseling in MBC patients

Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments

It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an unknown and stochastic environment under hard constraints that require the system state not to reach certain specified unsafe regions. Many popular safe RL methods such as those based on the Constrained Markov Decision Process (CMDP) paradigm formulate safety violations in a cost function and try to constrain the expectation of cumulative cost under a threshold. However, it is often difficult to effectively capture and enforce hard reachability-based safety constraints indirectly with such constraints on safety violation costs. In this work, we leverage the notion of barrier function to explicitly encode the hard safety constraints, and given that the environment is unknown, relax them to our design of \emph{generative-model-based soft barrier functions}. Based on such soft barriers, we propose a safe RL approach that can jointly learn the environment and optimize the control policy, while effectively avoiding unsafe regions with safety probability optimization. Experiments on a set of examples demonstrate that our approach can effectively enforce hard safety constraints and significantly outperform CMDP-based baseline methods in system safe rate measured via simulations.Comment: 13 pages, 7 figure

The preliminary evidence on the association of the gut microbiota with stroke risk stratification in South Chinese population

AimsThis study aimed to investigate the association between the gut microbiota and the risk of stroke.MethodsFaecal samples from 60 participants in South China, including 45 individuals with risk factors for stroke and 15 healthy controls, were collected and subjected to 16S rRNA sequencing. A bioinformatics analysis was performed to characterise the gut microbial diversity and taxonomic compositions at different risk levels (low, moderate, and high) of stroke. Functional prediction and correlation analyses between the microbiota and laboratory markers were performed to explore the potential mechanisms.ResultsA significant difference in beta diversity was observed between the participants from the stroke risk and healthy control groups. Linear discriminant effect size analysis revealed a large number of vascular beneficial bacteria enriched in the participants from the healthy control and low-risk groups, but a few vascular harmful bacteria were more abundant in the participants from the high-risk group than in those from the other groups. In addition, Anaerostipes, Clostridium_XlVb, and Flavonifractor, all of which belonged to the Firmicutes phylum, were enriched in the participants from the low-risk group, and their relative abundances gradually decreased as the stroke risk increased. Spearman’s analysis revealed that these outstanding microbiota correlated with the levels of triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, white blood cells, neutrophils, and carotid intima-media thickness.ConclusionThe preliminary evidence suggests that gut microbiota is associated with stroke risk. It potentially ameliorates atherosclerosis by targeting lipid metabolism and inflammation. This provides novel insights into the early screening of stroke risk and primary prevention

Large manipulative experiments revealed variations of insect abundance and trophic levels in response to the cumulative effects of sheep grazing

This study was supported by the National Natural Science Foundation of China, 31672485, the Earmarked Fund for China Agriculture Research System, CARS-34-07, and the Innovation Project of Chinese Academy of Agricultural Sciences.Livestock grazing can affect insects by altering habitat quality; however, the effects of grazing years and intensities on insect abundance and trophic level during manipulative sheep grazing are not well understood. Therefore, we investigated these effects in a large manipulative experiment from 2014 to 2016 in the eastern Eurasian steppe, China. Insect abundance decreased as sheep grazing intensities increased, with a significant cumulative effect occurring during grazing years. The largest families, Acrididae and Cicadellidae, were susceptible to sheep grazing, but Formicidae was tolerant. Trophic primary and secondary consumer insects were negatively impacted by increased grazing intensities, while secondary consumers were limited by the decreased primary consumers. Poor vegetation conditions caused by heavy sheep grazing were detrimental to the existence of Acrididae, Cicadellidae, primary and secondary consumer insects, but were beneficial to Formicidae. This study revealed variations in insect abundance and trophic level in response to continuous sheep grazing in steppe grasslands. Overall, our results indicate that continuous years of heavy- and over- sheep grazing should be eliminated. Moreover, our findings highlight the importance of more flexible sheep grazing management and will be useful for developing guidelines to optimize livestock production while maintaining species diversity and ecosystem health.Publisher PDFPeer reviewe