Learning Sequential and Structural Dependencies Between Nucleotides for RNA N6-Methyladenosine Site Identification

N6-methyladenosine(m6A)is an important RNA methylation modification involved in regulating diverse biological processes across multiple species.Hence,the identification of m6A modification sites provides valuable insight into the biological mechanisms of complex diseases at the post-transcriptional level.Although a variety of identification algorithms have been proposed recently,most of them capture the features of m6A modification sites by focusing on the sequential dependencies of nucleotides at different positions in RNA sequences,while ignoring the structural dependencies of nucleotides in their threedimensional structures.To overcome this issue,we propose a cross-species end-to-end deep learning model,namely CR-NSSD,which conduct a cross-domain representation learning process integrating nucleotide structural and sequential dependencies for RNA m6A site identification.Specifically,CR-NSSD first obtains the pre-coded representations of RNA sequences by incorporating the position information into single-nucleotide states with chaos game representation theory.It then constructs a crossdomain reconstruction encoder to learn the sequential and structural dependencies between nucleotides.By minimizing the reconstruction and binary cross-entropy losses,CR-NSSD is trained to complete the task of m6A site identification.Extensive experiments have demonstrated the promising performance of CR-NSSD by comparing it with several state-of-the-art m6A identification algorithms.Moreover,the results of cross-species prediction indicate that the integration of sequential and structural dependencies allows CR-NSSD to capture general features of m6A modification sites among different species,thus improving the accuracy of cross-species identification.

Sequential Pattern Technology for Visual Fire Detection

Visual fire detection technologies can detect fire and alarm warnings earlier than conventional fire detectors. This study proposes an effective visual fire detection method that combines the statistical fire color model and sequential pattern mining technology to detect fire in an image. Furthermore, the proposed method also supports real-time fire detection by integrating adaptive background subtraction technologies. Experimental results show that the proposed method can effectively detect fire in test images and videos. The detection accuracy of the proposed hybrid method is better than that of Celik＇s method.

A Fast Interactive Sequential Pattern Mining Algorithm

In order to reduce the computational and spatial complexity in rerunning algorithm of sequential patterns query, this paper proposes sequential patterns based and projection database based algorithm for fast interactive sequential patterns mining algorithm （FISP）, in which the number of frequent items of the projection databases constructed by the correct mining which based on the previously mined sequences has been reduced. Furthermore, the algorithm＇s iterative running times are reduced greatly by using global-threshold. The results of experiments testify that FISP outperforms PrefixSpan in interactive mining

Identifying Cancer Disease through Deoxyribonucleic Acid (DNA) Sequential Pattern Mining

This paper aims to propose the sequential pattern discovery method of Deoxyribonucleic Acid (DNA) sequence database in order to identify cancer disease. The DNA which is composed of amino acids of gene P53 is mutated. It effects to change of P53 formation. Sequential pattern discovery is a process of extracting data to generate knowledge about the series of events that has the sequences in a certain frequency so that it creates a pattern. PrefixSpan is to propose method to find a pattern of DNA sequence database. As a result, there are various selected patterns of DNA sequence. The pattem which has high similarity is used as biomarker to identify the breast cancer disease. The performance measure of support value average is 0.8. It means that the frequent sequence pattern is high. Another measure is confidence. All of the confidence values are 1. Then, the last performance measure is lift ratio at average more than 1. It means that the composed sequence items in the pattern has high dependency and relatedness. Futhermore, the selected patterns are applied as biomarker with accuracy as 100%.

Spatial pattern formation of a ratio-dependent predator-prey model

This paper presents a theoretical analysis of evolutionary process that involves organisms distribution and their interaction of spatially distributed population with diffusion in a Holling-III ratio-dependent predator-prey model, the sufficient conditions for diffusion-driven instability with Neumann boundary conditions are obtained. Furthermore, it presents novel numerical evidence of time evolution of patterns controlled by diffusion in the model, and finds that the model dynamics exhibits complex pattern replication, and the pattern formation depends on the choice of the initial conditions. The ideas in this paper may provide a better understanding of the pattern formation in ecosystems.

Pattern dependence in synergistic effects of total dose on single-event upset hardness

The pattern dependence in synergistic effects was studied in a 0.18 μm static random access memory（SRAM） circuit.Experiments were performed under two SEU test environments：3 Me V protons and heavy ions.Measured results show different trends.In heavy ion SEU test,the degradation in the peripheral circuitry also existed because the measured SEU cross section decreased regardless of the patterns written to the SRAM array.TCAD simulation was performed.TIDinduced degradation in n MOSFETs mainly induced the imprint effect in the SRAM cell,which is consistent with the measured results under the proton environment,but cannot explain the phenomena observed under heavy ion environment.A possible explanation could be the contribution from the radiation-induced GIDL in pMOSFETs.

Spatial Distribution Pattern of Overwintering Larvae of Dendrlimus punctatus

Based on the data of mean population density of overwintering larvae of Dendrolimus punctatus in Shatang forest farm in Guangxi Province, the spatial pattern of overwintering larva of D. punctatus were analyzed by the distribution index and regression model method. The results showed that the spatial pattern of overwintering larvae of D. punctatus assumed the aggregation pattern, the basic component of distribution was individual group. The optimal sampling number of forest survey and the sequential sampling analysis were presented, and the upper and low bound index for controlling D. punctatus were put forward to provide certain theoretical basis for integrated pest management.

Multifactorial study of mobile phone dependence in medical students: Relationship to health-related lifestyle, Type A behavior, and depressive state

We investigated factors contributing to mobile phone dependence. To 139 medical students, we administered a self-reporting questionnaire designed to evaluate mobile phone dependence, health-related lifestyle, patterns of behavior, and depressive state. Multivariate logistic regression analysis revealed that scores for poor health-related lifestyle, Type A behavior pattern, and presence of depression are independently associated with degree of mobile phone dependency. These findings suggest that persons with an unhealthy lifestyle, Type A behavior traits, or depression might benefit from mobile phone use guidance.

New multi-pattern matching algorithm

The traditional multiple pattern matching algorithm, deterministic finite state automata, is implemented by tree structure. A new algorithm is proposed by substituting sequential binary tree for traditional tree. It is proved by experiment that the algorithm has three features, its construction process is quick, its cost of memory is small. At the same time, its searching process is as quick as the traditional algorithm. The algorithm is suitable for the application which requires preprocessing the patterns dynamically.

The Quantitative Evaluation of Output Efficiency in Different Cropping Patterns

Quantitative indexes such as land equivalent ratio, yield equivalent and value of output equivalent were used to evaluate output efficiencies of different cropping patterns, i.e., sequential cropping, intercrops and crop rotation. Compared to single cropping, land use efficiencies under sequential cropping, intercrops and crop rotation were raised by 62, 38 and 21%, respectively. The unit area yield under sequential cropping, intercrops and crop rotation were raised by 63, 29 and 16%, respectively. The unit area value was also enhanced under sequential cropping and intercrops, 76 and 35% higher than that under single cropping. The paper provides a useful tool for comparing farm output efficiency and build up a theoretical basis for further research on output efficiency of various cropping patterns in the future.

Multilevel Pattern Mining Architecture for Automatic Network Monitoring in Heterogeneous Wireless Communication Networks

The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks.This paper presents a multilevel pattern mining architecture to support automatic network management by discovering interesting patterns from telecom network monitoring data.This architecture leverages and combines existing frequent itemset discovery over data streams,association rule deduction,frequent sequential pattern mining,and frequent temporal pattern mining techniques while also making use of distributed processing platforms to achieve high-volume throughput.

On Spatial Distribution Pattern of Broccoli Clubroot Disease and and the Sampling Technique

[Objective] To further improve the prediction and forecast and continuous control ability of broccoli clubroot disease. [Methods] The spatial distribution pattern of diseased or infected plants was analyzed using the least square method, fre- quency distribution, aggregation index, m＊-m regression analysis and Taylor＇s pow- er law model. [Result] The field distribution of broccoli plants with clubroot disease tended to be aggregated distribution, m＇-m regression analysis showed that the el- ementary composition of the spatial distribution of diseased or infected plants was individual colony, the individuals attracted each other; the disease had obvious dis- ease focus in the field, and the individual colony showed uniform distribution pattern in the field. Taylor＇s power law showed that the spatial pattern of individual dis- eased or infected plant with clubroot disease tended to be uniform distribution with the increase of the density. On the basis of this, Iwao optimal theoretical sampling model and sequential sampling model were established, namely N =273.954 1/m- 59.698 5, To （N）=0.368 4N±1.926 8√N, respectively, it meant that when surveying N plants, if the accumulative incidence rate exceeded upper bound, the field can be set as control object; if the accumulative incidence rate didn＇t reach lower bound, it can be set as uncontrol field; if the accumulative incidence rate was between upper bound and lower bound, it should be surveyed continuously until the maximum sample size （mo=0.368 4） appeared, that was, the disease incidence was 15%, so the sampling number should be 684 plants. [Conclusion] The research results had very important instructive meaning for disease control.

Plasticity-induced characteristic changes of pattern dynamics and the related phase transitions in small-world neuronal networks

Phase transitions widely exist in nature and occur when some control parameters are changed. In neural systems, their macroscopic states are represented by the activity states of neuron populations, and phase transitions between different activity states are closely related to corresponding functions in the brain. In particular, phase transitions to some rhythmic synchronous firing states play significant roles on diverse brain functions and disfunctions, such as encoding rhythmical external stimuli, epileptic seizure, etc. However, in previous studies, phase transitions in neuronal networks are almost driven by network parameters （e.g., external stimuli）, and there has been no investigation about the transitions between typical activity states of neuronal networks in a self-organized way by applying plastic connection weights. In this paper, we discuss phase transitions in electrically coupled and lattice-based small-world neuronal networks （LBSW networks） under spike-timing-dependent plasticity （STDP）. By applying STDP on all electrical synapses, various known and novel phase transitions could emerge in LBSW networks, particularly, the phenomenon of self-organized phase transitions （SOPTs）： repeated transitions between synchronous and asynchronous firing states. We further explore the mechanics generating SOPTs on the basis of synaptic weight dynamics.

High Utility Periodic Frequent Pattern Mining in Multiple Sequences

Periodic patternmining has become a popular research subject in recent years;this approach involves the discoveryof frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic patternmining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodicpatterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequencesis more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences isimportant. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To addressexisting problems, three new measures are defined: the utility, high support, and high-utility period sequenceratios. Further, a new upper bound, upSeqRa, and two new pruning properties were proposed. MHUPFPS usesa newly defined HUPFPS-list structure to significantly accelerate the reduction of the search space and improvethe overall performance of the algorithm. Furthermore, the proposed algorithmis evaluated using several datasets.The experimental results indicate that the algorithm is accurate and effective in filtering several non-high-utilityperiodic frequent patterns.

An efficient embedding tree matching algorithm based on metaphoric dependency syntax tree

To find out all dependency relationships in which metaphors probably exist between syntax constituents in a given sentence,a dependency tree matching algorithm oriented to Chinese metaphor processing is proposed based on a research of unordered tree inclusion matching.In this algorithm,the pattern library is composed of formalization dependency syntax trees that are derived from large-scale metaphor sentences.These kinds of metaphor sentences are saved in the pattern library in advance.The main process of this algorithm is up-down searching and bottom-up backtracking revising.The algorithm discovers potential metaphoric structures in Chinese sentences from metaphoric dependency pattern library.Finally,the feasibility and efficiency of the new matching algorithm are further testified by the results of a series of experiments on dependency pattern library.Hence,accurate dependency relationships can be achieved through this algorithm.

Trees species’ dispersal mode and habitat heterogeneity shape negative density dependence in a temperate forest

Conspecific negative density dependence(CNDD)is a potentially important mechanism in maintaining species diversity.While previous evidence showed habitat heterogeneity and species’dispersal modes affect the strength of CNDD at early life stages of trees(e.g.,seedlings),it remains unclear how they affect the strength of CNDD at later life stages.We examined the degree of spatial aggregation between saplings and trees for species dispersed by wind and gravity in four topographic habitats within a 25-ha temperate forest dynamic plot in the Qinling Mountains of central China.We used the replicated spatial point pattern(RSPP)analysis and bivariate paircorrelation function(PCF)to detect the spatial distribution of saplings around trees at two scales,15 and 50 m,respectively.Although the signal was not apparent across the whole study region(or 25-ha),it is distinct on isolated areas with specific characteristics,suggesting that these characteristics could be important factors in CNDD.Further,we found that the gravity-dispersed tree species experienced CNDD across habitats,while for wind-dispersed species CNDD was found in gully,terrace and low-ridge habitats.Our study suggests that neglecting the habitat heterogeneity and dispersal mode can distort the signal of CNDD and community assembly in temperate forests.

基于情境感知和序列模式挖掘的气象学习资源推荐算法

随着互联网的快速发展,气象部门职工作为学习者可以获得的学习资源得到极大丰富。信息超载导致检索合适的在线学习资源时遇到了困难;学习者在不同学习环境和序列访问模式上也有不同的学习需求。但是,现有的推荐系统,如基于内容的推荐和协同过滤,没有结合学习者的情境和序列访问模式,推荐结果准确度不高。本文提出了一种结合情境感知、序列模式挖掘和协同过滤算法的混合推荐算法来为学习者推荐学习资源。混合推荐算法中,情境感知被用来整合学习者的情境信息,如知识水平和学习目标;序列模式挖掘被用来对网络日志进行挖掘,发现学习者的序列访问模式;协同过滤被用来根据学习者的情境数据和序列访问模式为目标学习者计算预测并生成建议。实验和应用效果表明,该混合推荐算法推荐的质量和准确性方面优于其他推荐算法。

Rhombic Analysis Extension of a Plant-Surface Water Interaction-Diffusion Model for Hexagonal Pattern Formation in an Arid Flat Environment

An existing weakly nonlinear diffusive instability hexagonal planform analysis for an interaction-diffusion plant-surface water model system in an arid flat environment [11] is extended by performing a rhombic planform analysis as well. In addition a threshold-dependent paradigm that differs from the usually employed implicit zero-threshold methodology is introduced to interpret stable rhombic patterns. The results of that analysis are synthesized with those of the existing hexagonal planform analysis. In particular these synthesized results can be represented by closed-form plots in the rate of precipitation versus the specific rate of plant density loss parameter space. From those plots, regions corresponding to bare ground and vegetative Turing patterns consisting of tiger bush (parallel stripes and labyrinthine mazes), pearled bush (hexagonal gaps and rhombic pseudo-gaps), and homogeneous distributions of vegetation, respectively, may be identified in this parameter space. Then that predicted sequence of stable states along a rainfall gradient is both compared with observational evidence and used to motivate an aridity classification scheme. Finally this system is shown to be isomorphic to the chemical reaction-diffusion Gray-Scott model and that isomorphism is employed to draw some conclusions about sideband instabilities as applied to vegetative patterning.

Vegetative Rhombic Pattern Formation Driven by Root Suction for an Interaction-Diffusion Plant-Ground Water Model System in an Arid Flat Environment

A rhombic planform nonlinear cross-diffusive instability analysis is applied to a particular interaction-diffusion plant-ground water model system in an arid flat environment. This model contains a plant root suction effect as a cross-diffusion term in the ground water equation. In addition a threshold-dependent paradigm that differs from the usually employed implicit zero-threshold methodology is introduced to interpret stable rhombic patterns. These patterns are driven by root suction since the plant equation does not yield the required positive feedback necessary for the generation of standard Turing-type self-diffusive instabilities. The results of that analysis can be represented by plots in a root suction coefficient versus rainfall rate dimensionless parameter space. From those plots regions corresponding to bare ground and vegetative patterns consisting of isolated patches, rhombic arrays of pseudo spots or gaps separated by an intermediate rectangular state, and homogeneous distributions from low to high density may be identified in this parameter space. Then, a morphological sequence of stable vegetative states is produced upon traversing an experimentally-determined root suction characteristic curve as a function of rainfall through these regions. Finally, that predicted sequence along a rainfall gradient is compared with observational evidence relevant to the occurrence of leopard bush, pearled bush, or labyrinthine tiger bush vegetative patterns, used to motivate an aridity classification scheme, and placed in the context of some recent biological nonlinear pattern formation studies.

一种有效的周期高效用序列模式增量挖掘算法

周期高效用序列模式挖掘(PHUSPM)因其能够发现时间序列中更具实际价值的规律性模式而备受关注,但现有的PHUSPM算法难以有效地处理数据集的增量更新,且未考虑大规模数据下算法的向下闭包性和复杂性。针对该问题,提出了IncPUS-Miner算法,有效地实现了周期高效用序列模式(PHUSPs)的增量挖掘。IncPUS-Miner引入了一种名为pu-tree的新型数据结构,每个树节点对应一个更新效用列表(UUL)用于存储相应序列的辅助信息,当有增量数据加入时,该结构使得项目信息能够灵活更新,从而增强了算法的动态适应性和可扩展性。此外,还提出了两种新的序列效用上界PUB和EUB,以及两种相应的剪枝策略,有效地减少了计算负担。实验结果表明,在真实数据集上,IncPUS-Miner算法可以有效地增量挖掘PHUSPs,与其他算法相比,在运行效率和内存消耗上展现出了优越的性能。