Association between porpoise presence and fish choruses: implications for feeding strategies and ecosystem-based conservation of the East Asian finless porpoise

The associations between feeding activities and environmental variables inform animal feeding tactics that maximize energetic gains by minimizing energy costs while maximizing feeding success. Relevant studies in aquatic animals, particularly marine mammals, are scarce due to difficulties in the observation of feeding behaviors in aquatic environments. This data scarcity concurrently hinders ecosystem-based fishery management in the context of small toothed-cetacean conservation. In the present study, a passive acoustic monitoring station was deployed in an East Asian finless porpoise habitat in Laizhou Bay to investigate potential relationships between East Asian finless porpoises and their prey. The data revealed that porpoises were acoustically present nearly every day during the survey period. Porpoise detection rates differed between spring and autumn in concert with activities of fish choruses. During spring, fish choruses were present throughout the afternoon, and this was the time when porpoise vocalizations were the most frequently detected. During autumn, when fish choruses were absent, porpoise detection rates decreased, and diurnal patterns were not detected. The close association between fish choruses and finless porpoise activities implies an “eavesdropping” feeding strategy to maximize energetic gains, similar to other toothed cetaceans that are known to engage similar feeding strategies. Underwater noise pollution, particularly those masking fish choruses, could interrupt finless porpoises’ feeding success. Fisheries competing soniferous fishes with finless porpoise could impact finless porpoise viability through ecosystem disruption, in addition to fishing gear entanglement.     

RFID技术在畜产品追踪中的应用

为使射频识别(RFID)技术在畜产品追踪中得到更好的应用,从RFID技术及应用原理构想了采用RFID技术对畜产品进行追踪的安全监控平台。     

采用改进CNN对生猪异常状态声音识别

猪只声音能够体现出其生长状态,该研究针对人工监测猪只声音造成的猪只疾病误判以及耗时耗力等问题,研究基于卷积神经网络（Convolutional Neural Network,CNN）的生猪异常状态声音识别方法。该研究首先设计猪只声音实时采集系统,并利用4G通讯技术将声音信息上传至云服务器,基于专业人员指导制作猪只异常声音（生病、打架、饥饿等）数据集,提取猪只异常声音的梅尔谱图特征信息;其次引入多种注意力机制对CNN进行改进,并对CBAM（Convolutional Block Attention Module）注意力机制进行优化,提出_CBAM-CNN网络模型;最后将_CBAM-CNN网络模型分别与引入SE_NET（Squeeze and Excitation Network）、ECA_NET（Efficient Channel Attention Networks）和CBAM注意力机制的CNN神经网络进行对比,试验结果表明该文提出的_CBAM-CNN网络模型在最优参数为128维梅尔频率、2 048点FFT（Fast Fourier Transform）点数、512点窗移下的梅尔谱图特征下相较于其他模型对猪只异常声音识别效果最佳,识别率达到94.46%,验证了算法的有效性。该研究有助于生猪养殖过程中对猪只异常行为的监测,并对智能化、现代化猪场的建设具有重要意义。     

人工合成抗菌肽D2A21基因的克隆和表达

为了获得高效表达的抗菌肽D2A21,本研究根据抗菌肽D2A21的氨基酸序列及大肠杆菌偏爱的密码子,设计并人工合成D2A21基因,并将该合成基因克隆至pProEXHTb载体中,构建成含有含6个组氨酸标签的重组质粒。重组质粒转化至大肠杆菌中,经摇瓶发酵,IPTG诱导后,发酵液用聚丙烯酰胺凝胶电泳分析。结果表明,成功合成D2A21基因,并构建了抗菌肽D2A21表达载体。通过镍柱亲和层析纯化,聚丙烯酰胺凝胶电泳分析,抗菌肽D2A21能够在大肠杆菌中稳定表达。本研究将为大规模表达纯化D2A21及其进一步的研究和利用提供一定基础。     

基于声学特性的西瓜糖度检测系统

该研究优化了由包裹橡皮的金属小球、压电式加速度传感器、电荷放大器、光电式触发电路、数据采集卡和计算机等组成的西瓜声学特性测试系统。由所获取的声波信号幅值谱计算出声透过率,采用TQ软件中SMLR(逐步多元线性回归)函数选取6个特征频率:752、869、1001、4556、322、3950Hz,由其对应的声透过率值建立了西瓜品质检测的多元线性回归模型。对47个西瓜样本的试验数据分析表明:将敲击点和接收点分别放置在西瓜自然生长状态的中部对侧可获得最佳的测定模型,模型的校正相关系数R、校正均方根误差RMSEC和预测均方根误差RMSEP分别是0.80753、0.646和0.655。实现了西瓜糖度检测目的,为声学无损检测西瓜成熟度提供了参考。     

Generation and analysis of expressed sequence tag sequences from a soft‐seeded pomegranate cDNA library

The cultivation of soft‐seeded pomegranate is an important direction in pomegranate breeding. To comprehensively understand the molecular mechanisms involved in the formation of soft‐seeded pomegranate (Punica granatum. var. Hongmanaozi), we established an expressed sequence tag (EST) resource. Two thousand valid sequences were generated, from which 907 unigenes were obtained after initial assembly using the clustalx program. Among these unigenes, 51 showed no similarity to any protein in the public databases, 433 matched with proteins of unknown function, and 423 matched with proteins of known or putative functions. The 423 unigenes were further classified into 13 categories. Among these categories, protein synthesis, cell structure, protein destination and storage, secondary metabolism, signal transduction and transporters accounted for 8%, 8%, 4%, 7%, 6% and 17%, respectively. We also successfully developed 10 highly polymorphic expressed sequence tag‐simple sequence repeat (EST‐SSR) markers for pomegranate. The results provide a new tool for future activities in pomegranate breeding.     

垂直轴风力机翼型涡流噪声的数值分析

为了了解垂直轴风力机翼型涡流噪声特性,以LUT翼型为研究对象,首先利用Fluent进行流场分析,流场计算选用DES湍流模型,再结合Lighthill声类比方法计算翼型周围声场,将数值模拟计算得到的气动特性相关数据与该翼型的风洞试验结果作对比分析,同时分析了不同攻角对该翼型气动噪声特性的影响,最后研究了在攻角为8°时不同雷诺数对该翼型的声压级指向性特征影响.结果表明:数值计算所得气动数据与风动试验数据拟合良好,建立的仿真模型、网格质量和边界条件合理有效;随攻角增加,翼型涡脱落从尾缘向前缘推进,同时涡流脱落强度增大,气动噪声增强;随着雷诺数的增加,翼型四周声压级先增加后减小;雷诺数与声压级关联较大,控制叶片雷诺数有助于降低叶片噪声,为该翼型适用于低噪声垂直轴叶片提供理论基础.     

单品种淡水活鱼数量的被动声学估计

针对单品种淡水活鱼数量估计问题,以鲫鱼和鳊鱼为研究对象,采用不同数量淡水鱼的被动水声信号作为样本,通过4~6层小波包分解算法,对比分析了样本的若干特征提取方案,明确了鲫鱼水声信号特征的提取方案为短时平均过零率+6层小波包分解频段能量,而鳊鱼只需提取6层小波包分解频段能量作为其信号特征。根据不同样本集划分方法比较结果,确定了鲫鱼样本集划分方法为Rank-SPXY(m=10),鳊鱼为Rank-SPXY(m=5)。利用竞争自适应重加权采样,筛选出样本的关键特征,并使用多元线性回归和偏最小二乘回归建立了若干淡水活鱼的数量估计模型,结果表明:多元线性回归模型的性能较好,鲫鱼和鳊鱼数量估计模型的复相关系数分别为0.835和0.893,相对分析误差分别为1.79和2.01。     

Isolation, Identification of Differentially Expressed Sequence Tags in the Backfat Tissue from Meishan, large White and Meishan×Large White Cross Pigs

In order to detect the molecular mechanism of heterosis in pigs, the mRNA differential display technique was performed to investigate the differences of gene expression in the backfat tissue from Meishan,Large White and Meishan×Large White cross pigs.Nine 3'-end anchored primers in combination with ten 5'-end arbitrary primers were used to perform the differential display PCR and nearly 3 000 reproducible bands were examined .Fifteen expressed sepuence tags that were differentially expressed were isolated and then isdentifide through semi-quantitative RT-PCR.BLAST analysis revealed that the fifteen expressed sequence tags (ESTs) were ntt homologous to any of the known porcine genes or ESTs. These novel ESTs were then submitted to GenBank.     

基于改进VMD的离心泵空化声发射信号特征提取

针对变分模态分解算法中分解层数和惩罚因子不易确定的问题,提出一种改进变分模态分解(improved variational mode decomposition,IVMD)算法,并将其应用于离心泵空化声发射信号特征提取.应用IVMD算法时,首先根据包络熵差异系数确定变分模态分解的分解层数;然后采用人工蜂群算法优化得出惩罚因子,并将其作为变分模态分解的最佳输入参数.利用IVMD算法对仿真信号进行分析,并与集合经验模态分解结果进行比较.以60%额定流量下采集到的离心泵进口处的声发射信号为例进行IVMD计算,分析携带原信号大量信息的信号分量的频域特征及其绝对能量随离心泵空化状态变化的关系.结果表明:IVMD算法能够择优确定分解层数和惩罚因子,实现非平稳信号的自适应分解.反映离心泵空化状态的声发射信号特征频率集中在50,100 kHz及其附近.随着离心泵空化从无到有、从弱到强的变化,这2个特征频率范围信号分量绝对能量值呈“基本保持不变-减小-增大”的变化规律.