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.

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.

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。

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。

雨生红球藻钙依赖蛋白激酶(CDPK)家族鉴定与表达分析

【目的】钙依赖蛋白激酶(CDPK或CPK)广泛参与植物生长发育和环境胁迫响应。为探究CDPK基因在雨生红球藻(Haematococcus pluvialis)生长发育及虾青素积累中的功能,对HpCDPK基因家族进行鉴定和表达分析。【方法】利用生物信息学方法鉴定雨生红球藻HpCDPK基因家族成员,分析其编码蛋白的理化特性、系统进化、保守基序和功能结构域,以及缺氮等胁迫条件下HpCDPK成员基因的表达谱。【结果】结果表明,共鉴定7个HpCDPK基因家族成员,所有HpCDPK蛋白都含有典型EF-hand基序和蛋白激酶结构域。系统发育分析表明,这些HpCDPK与来自莱茵衣藻的CrCDPK聚为一类,与高等植物拟南芥的AtCDPK分开,暗示在进化过程中发生了种属特异性的基因复制事件。转录组数据分析表明,HpCDPK基因表达受到多种胁迫诱导,其中HpCDPK7基因转录水平在缺氮条件下上调最为明显。此外,HpCDPK与类胡萝卜素合成相关基因的表达相关性分析结果显示,HpCDPK与多个类胡萝卜素合成相关基因表达密切关联,特别是HpCDPK2与虾青素合成关键基因(BKT和BCH)的表达显著正相关(P<0.05)。【结论】本研究鉴定了7个HpCDPK基因,HpCDPK7基因在缺氮条件下的表达上调最为明显,HpCDPK2可能在类胡萝卜素及虾青素合成积累中发挥重要调控作用。研究结果为后续深入解析HpCDPK介导雨生红球藻胁迫响应和类胡萝卜素合成积累的功能及分子机制提供参考依据。

一次性条件下的三支序列模式挖掘

一次性条件下的序列模式挖掘旨在从序列数据中挖掘出带有间隙约束的重复序列模式。然而,现有方法不考虑用户的兴趣度,将序列中的每个字符视作同等重要,导致许多用户不感兴趣的冗余模式被发现。为了解决这个问题,将三支决策思想引入序列模式挖掘领域,提出了一次性条件下的三支序列模式挖掘问题及其求解算法。在支持度计算方面,该算法基于深度优先搜索和回溯的策略,结合三支模式的特点以高效求解模式支持度。在候选模式生成方面,该算法采用模式连接策略缩减候选模式数量。此外,该算法还采用了并行化方案充分利用现代处理器的多核性能,提高算法的挖掘效率。最后,实验结果验证了研究一次性条件下的三支序列模式挖掘问题的意义和算法的高效性。

一种对比序列模式挖掘算法

对比序列模式挖掘作为序列模式挖掘领域的一个重要分支,可以有效识别不同类别间差异显著的模式,并被广泛应用在序列分类、特征提取等场景中.但传统的对比序列模式挖掘仅考虑了模式在序列中是否出现,忽略了模式在序列中的重复性;并且需要用户预先设置间隙约束值,导致算法的灵活性较差.为了解决上述问题,本文提出一次性条件下自适应对比序列模式挖掘算法OSCP,该算法采用逆向填充策略计算模式支持度,不仅关注了模式在序列中的具体出现情况,还提高了算法的计算效率;同时采用模式连接策略以减少候选模式数量.此外,本文采用自适应间隙,无需用户预先设置间隙约束,可基于序列的实际特征计算模式的支持度.实验结果表明,OSCP算法的挖掘性能和分类效果均优于其他对比算法.

一种共生保序模式挖掘算法

作为数据挖掘的一个新兴方向,研究人员在时间序列领域提出了用于挖掘相对次序相同的保序模式.尽管现有的保序模式挖掘算法可以有效地找出全部的频繁模式,但在当用户仅对某个特定的模式及其为前缀的模式较为感兴趣时,现有的挖掘算法效率过于低下.为了解决上述问题,本文提出了一种共生保序模式挖掘算法,用于挖掘出以给定模式为前缀的共生保序模式.该算法包括融合准备和计算超模式的支持度两个主要部分,其中,融合准备分为4个步骤:获取模式p的后缀保序模式,计算后缀保序模式的出现,前向验证模式p的出现,后向查找所有可融合模式的出现;在计算超模式的支持度时,提出一种剪枝策略,使得候选模式的个数进一步减少.在真实数据集上,实验结果验证了本文算法的高效性.

IL-10、TGF-β1在小鼠深静脉血栓中的时序性变化

目的检测小鼠深静脉血栓发展过程中白细胞介素-10(interleukin-10,IL-10)和转化生长因子-β1(transforming growth factor-β1,TGF-β1)的表达变化,探讨其在血栓形成时间推断中的应用价值。方法对实验组小鼠建立下腔静脉结扎模型,分别于术后1 d、3 d、5 d、7 d、10 d、14 d、21 d过量麻醉处死小鼠,在结扎点的下方提取形成血栓的下腔静脉段。对照组小鼠不进行结扎,提取与实验组相同部位的正常下腔静脉段作为对照样本。应用免疫组织化学染色、蛋白质印迹法和real-time qPCR检测IL-10和TGF-β1在血栓形成期间的表达变化。结果免疫组织化学染色结果显示,IL-10主要表达于血栓中的单核细胞,TGF-β1主要表达于血栓中的单核细胞和成纤维样细胞。蛋白质印迹法以及real-time qPCR检测结果显示,各实验组IL-10和TGF-β1的表达水平均高于对照组。IL-10的mRNA及蛋白水平分别于术后7 d和10 d达到峰值,术后7 d的mRNA表达量是对照组的(4.72±0.15)倍,术后10 d的蛋白表达量是对照组的(7.15±0.28)倍。TGF-β1的mRNA及蛋白水平分别于术后10 d和14 d达到峰值,术后10 d的mRNA表达量是对照组的(2.58±0.14)倍,术后14 d的蛋白表达量是对照组的(4.34±0.19)倍。结论深静脉血栓演变期间IL-10和TGF-β1呈现时序性表达变化,IL-10和TGF-β1的表达水平可以为血栓形成时间推断提供参考依据。

一次性条件下top-k高平均效用序列模式挖掘算法

针对传统序列模式挖掘(SPM)不考虑模式重复性且忽略各项的效用(单价或利润)与模式长度对用户兴趣度影响的问题,提出一次性条件下top-k高平均效用序列模式挖掘(TOUP)算法。TOUP算法主要包括两个核心步骤:平均效用计算和候选模式生成。首先,提出基于各项出现位置与项重复关系数组的CSP(Calculation Support of Pattern)算法计算模式支持度,从而实现模式平均效用的快速计算;其次,采用项集扩展和序列扩展生成候选模式,并提出了最大平均效用上界,基于该上界实现对候选模式的有效剪枝。在5个真实数据集和1个合成数据集上的实验结果表明,相较于TOUP-dfs和HAOP-ms算法,TOUP算法的候选模式数分别降低了38.5%~99.8%和0.9%~77.6%;运行时间分别降低了33.6%~97.1%和57.9%~97.2%。TOUP的算法性能更优,能更高效地挖掘用户感兴趣的模式。

高效的一次性弱间隙序列模式挖掘算法

间隙约束序列模式挖掘作为序列模式挖掘的一个重要分支,可以发现模式在序列中的重复出现。然而,当前研究主要针对单项序列进行挖掘,并且序列中每一项都被认为具有相同意义。为解决该问题,提出一次性弱间隙序列模式挖掘(OWP)算法,该算法由准备阶段、支持度计算和候选模式生成3个步骤组成。在准备阶段,建立倒排索引,并对不频繁的项进行剪枝;在支持度计算方面,利用倒排索引结构记录出现位置,避免对原始数据集的重复扫描;在候选模式生成方面,采用模式连接策略,减少冗余候选模式的生成。在项集序列和单项序列共6个真实数据集上的实验结果表明,OWP算法相比OWP-p、Ows-OWP和OWP-e算法在运行时间上分别提升了2.653、1.348、3.592倍,在内存消耗上分别减少了3.51%、0.07%、5%,说明OWP算法可以更高效地挖掘出用户感兴趣的模式。此外,OWP算法在以D1数据集为基础的6倍大小的数据集上的运行时间比D1数据集增长了3.763倍,内存消耗增长了2.310倍,运行时间和内存消耗的增加倍数均小于数据集大小的增加倍数,说明OWP算法具有良好的可扩展性。

基于用户行为和上下文语义的分层ST-LSTM位置预测

当前的位置预测方法大多没有考虑到用户行为信息,由于用户的访问时间、行为模式等能够在很大程度上反映所处位置,因此在对位置潜在向量进行预训练时有必要使用该信息。进行位置预测时,采样粒度较细的序列长度较长,难以捕获长距离依赖。针对这2个问题,提出了基于用户行为和上下文语义的分层时空长短期记忆网络(Hierarchical Spatiotemporal Long Short-Term Memory Based on User Behavior and Contextual Semantics,CHST-LSTM)模型。该模型通过Transformer编码层处理轨迹数据,将用户相关行为信息考虑在内,融合位置的上下文语义信息,通过预训练得到位置的嵌入表征。根据用户的行为状态分割轨迹阶段,采用编码器-解码器方式对ST-LSTM进行分段分层扩展,利用BiLSTM对全局信息建模,同时处理轨迹的长短期变化,解决长序列的长距离依赖问题。对外卖员用户群体的真实移动轨迹数据进行分析和实验,通过聚类发现其特有的工作模式,在预训练时加入工作模式信息与到访时间信息,得到位置的特征向量并用于预测模型。结果表明CHST-LSTM模型在预测用户下一位置时精度更高。

基于滞后序列分析的学生合作学习行为模式研究

采用滞后序列分析法对合作学习过程中学生的学习行为模式进行量化分析,结果发现,学生在合作学习的过程中,小组成员之间产生了9条具有显著意义的学习行为序列,其中代表学习者正在进行高阶思维以及知识建构的行为序列占70%;在合作解决问题过程中,产生了3条具有显著价值的行为序列路径,解释了小组成员是如何互相支持、围绕着共同的目标解决问题的。合作学习有助于学生理解和建构知识,形成问题解决等高阶思维,进而提高课堂参与度。分析学生在合作学习情境中的学习行为,能够帮助教师把握学生的学习行为模式,诊断影响学习效果的潜在问题,从而调整教学策略以促进其获得更好的学习结果。

全油气系统理论基本原理

通过阐释全油气系统基本原理,并阐明全油气系统的结构,进而揭示常规油气—致密油气—页岩油气序列成藏规律以及全油气系统成藏模式与成藏机理;此外,阐述了页岩油气-致密油气储层地质模型、流动模型与开发生产机理,并给出了进一步的研究方向。研究表明:(1)全油气系统的主要结构包括3类流体动力场、3种油气藏与油气资源,以及两种成藏作用。常规油气—致密油气—页岩油气具有形成时间和空间分布的有序性、基于成因机理的序列合理性,表现出“序列成藏”的地质规律。(2)全油气系统成藏模式可以分为“碎屑岩盆地成藏模式”与“碳酸盐岩盆地(层系)成藏模式”两类。非常规油气的聚集成藏是一种“自封闭成藏”,油气自封闭作用的微观来源是分子间作用力(范德华力)。(3)非常规油气生产实践证实,页岩油气-致密油气储层地质模型、流动模型与开发生产机理是一个全新的领域,极为复杂,有待进一步研究。页岩油气一定是中国油气资源中最重要的资源接替。(4)进一步研究方向包括:碳酸盐岩盆地全油气系统特征及复合盆地演化源储耦合规律;页岩油气与致密油气运移、成藏与开发生产的流动机理;深层超深层页岩油气、致密油气和煤层气油气地质特征与富集规律;全油气系统油气资源评价与新一代盆地模拟技术;地球系统—地球有机岩与化石能源系统—全油气系统研究。