Surgical repair of a peritoneopericardial diaphragmatic hernia in a pregnant dog

Objective: To describe the surgical repair and pre‐ and postoperative management of a peritoneopericardial diaphragmatic hernia (PPDH) in a pregnant dog. Case summary: A pregnant dog was presented for vomiting, lethargy, and pale mucous membranes. Pulsus paradoxus was noted on physical examination. The dog was diagnosed with a PPDH via thoracic radiographs, abdominal ultrasound, and an echocardiogram. The hernia was surgically repaired and the dog received supportive medical care until the puppies were old enough to be delivered via cesarean section. The mother and all puppies survived. New or unique information provided: This is the first report that describes the surgical repair and postoperative management of a PPDH in a pregnant dog.     

多措并举保障北京市昌平区设施草莓优质绿色生产

为了保障草莓的安全生产,打造昌平草莓产业的良性健康发展模式,主要从技术支撑、政府扶持及宣传3个方面阐述了其重要举措。具体措施包括通过注重提升田园清理、土壤消毒、田间管理等一系列技术措施,确保草莓安全生产;通过政府政策、资金支持及专业技术指导,打造强有力的后盾保障;建立绿色安全生产宣传模式,扩大品牌影响力,如多渠道、多方面进行宣传;组织培训,增强种植水平;充分利用互联网新媒体等。     

鲁南砂姜黑土及其有机无机复合体中无机磷的组成和分布

采用石灰性土壤无机磷分级法研究了不同肥力砂姜黑土无机磷的组成与分布。结果表明，砂姜黑土及其有机无机复合体中无机磷以磷酸＝钙盐和闭蓄态磷酸盐为主，分别占无机磷总量的２７％－４８％和２６％－３７％。土壤无机磷中的Ｃ２－Ｐ，Ｃａ８－Ｐ，Ａｌ－Ｐ，Ｆｅ－Ｐ均与速效磷存在着显著的正相关，并且随着土壤肥力水平的提高，其含量亦随之增加。土壤无机磷主要集中分布于〈５μｍ粒级的有机无机复合体中，各种形态无机磷的含量     

基于Web的造林专家咨询系统研究

利用新的信息技术,研究造林专家的造林决策方法,提出造林专家决策咨询系统的目的和功能,给出系统环境和结构、知识库和推理机的设计,建立一个基于网络的、适于不同地区的造林专家决策咨询系统,解决困扰着传统森林培育中的适地适树和造林设计问题.     

基于Internet的林业资源环境信息服务系统(FINFOSYS)研究

运用面向对象的思想和方法,设计并实现了基于Internet的林业资源环境信息服务系统——FINFOSYS,为林业资源和环境信息提供信息管理、信息共享、电子商务和分析决策等综合服务：在参照现有国家、行业相关标准的基础上,建立林业资源和环境信息共享标准和共享规范,在此基础上建立林业资源环境分布式网络数据库及共享网络系统;设计林业资源环境数据仓库,开展多维数据的在线分析、数据挖掘并建立网络运行的统计和分析模型,实现林业资源环境在线决策支持的网络系统;根据林业资源环境有偿信息的特点,设计电子商务系统,实现林业信息产品的网上交易和交流。     

Renibacterium salmoninarum bar forms: characterization, occurrence, and evidence of a host response to a R. salmoninarum infection

Unique-staining Renibacterium salmoninarum (Rs) cells, termed bar forms, first observed in a coho salmon, Oncorhynchus kisutch (Walbaum), in 1983, could not be cultured, making their characterization difficult and significance obscure. They can be detected only by the fluorescent-antibody technique (FAT) and their numbers estimated only by a quantitative FAT (QFAT). Data collected over a 10-year period showed that bar forms were observed only in vivo and appeared associated with a host response. Bar forms were observed in 10 salmonid species from five countries and in fish from < 1 g to spawning adults. They were observed in 50.1% of kidney smears prepared from 10,061 Rs positive chinook, O. tshawytscha (Walbaum), coho, and Atlantic salmon, Salmo salar L. Bar forms were shown to be Rs cells based on absorption studies, their reaction with an Rs-specific FAT and enzyme-linked immunosorbent assay, and a transition from 'typical' Rs cells to bar forms in naturally and experimentally infected fish. Bar forms were determined to be non-virulent, damaged or dead Rs cells, based on fluorescence and electron microscopy observations, the inability to culture them, and mortality data. Bar forms appeared to represent visual markers of recovery from an Rs infection.     

牧草产业是我国家兔产业可持续发展的重要支撑

针对我国家兔规模化养殖的快速发展,牧草作为家兔生产主要饲料原料来源,供需矛盾日益突出。本文重点从家兔消化生理特点、兔产品安全生产、农民增收、需求现状与问题等方面进行了论述,并提出了相应对策。     

猪群抗应激的营养解决方案综述

应激是动物机体在多种环境、生理和心理等因素的刺激下,出现的生理与心理等全身性的非特异性的适应性反应.随着我国生猪养殖业规模化和集约化程度的提高,养猪生产者都追求以有限的养殖空间来生产更多的猪只,这导致了养殖密度的增加、猪群应激发生的概率不断上升.应激导致的危害日益突出,成为了阻碍猪只健康生长的重要的因素之一.本文回顾了...     

拉曼光谱结合支持向量机对猪胞质内单精子显微注射胚胎性别的鉴定分析

【目的】 探求一种无损的、非侵入性的、快速的胚胎性别鉴定方法。【方法】 以207个猪卵胞质内单精子显微注射(intracytoplasmic sperm injection,ICSI)胚胎及其培养液为研究对象,单个胚胎经巢式PCR扩增鉴定其性别后随机分成训练集和测试集,同时利用拉曼光谱技术获取单个ICSI胚胎培养液的拉曼光谱。原始光谱经过处理后,采用支持向量机(support vector machine,SVM)算法构建分类模型,先用训练样本进行训练和建模,然后预测测试样本的性别,结合PCR性别鉴定的结果,计算分类准确率。【结果】 通过巢式PCR对207个胚胎进行性别鉴定,鉴定出71个雄性胚胎、128个雌性胚胎,8个样品扩增失败。猪ICSI雄性胚胎培养液在1 082和1 360 cm^-1拉曼位移处特征峰强度明显高于雌性胚胎,构建的胚胎性别鉴定模型的分类准确率为81.5%,雌性和雄性胚胎的分类准确率分别为81.3%和81.8%。【结论】 本研究将拉曼光谱技术与SVM法相结合构建了胚胎性别鉴定模型,分类准确率达到81.5%,提供了一种新的、无损的胚胎性别鉴定方法。     

The use of infrared spectroscopy and machine learning tools for detection of Meloidogyne infestations

Plant parasitic nematodes are generally soilborne pathogens that attack plants and cause economic losses in many crops. The infested plants show nonspecific symptoms or, often, are symptomless; therefore, diagnosis is performed by taking soil and root tissue samples. Here, we show that a combination of different infrared spectra analysis and machine learning algorithms can be used to detect plant parasitic nematode infestations before symptoms become visible, using leaves instead of roots and soil as samples. We found that tomato and guava plants infested with Meloidogyne enterorlobii produced different spectral patterns compared to uninfested plants. Using partial spectra from 1,450 to 900/cm as the "fingerprint region", principal component analyses indicated that after 5 (tomatoes) or 8 weeks (guava), plants with no visible symptoms of infestations were positively diagnosed. To improve the early detection response, we used machine learning modelling. A support vector machine (SVM) was used to obtain more robust, accurate models. The SVM model contained 34 support vectors, 17 for each level. The overall performance of the model was >97% and the total accuracy was significantly higher, demonstrating the absence of chance prediction. The best prediction of infestation was obtained at the second and fourth weeks for tomatoes and guavas, respectively, reducing the diagnostic time by half. The combined application of these techniques reduces the processing time from field to laboratory and shows enormous advantages by avoiding root and soil sampling.