采食后奶牛瘤胃短链脂肪酸及其吸收相关蛋白表达的研究

本试验旨在研究奶牛采食前后瘤胃中短链脂肪酸(SCFA)浓度的变化及其吸收相关蛋白表达量的差异。试验选用3头体重(720±30)kg且装有瘘管的健康荷斯坦牛(动物伦理审查编号为SXAU-EAW-2019-C002013),采食精粗比为40:60的日粮(10kg),试验预试期10d,于第11天饲喂前开始取样,采用气相色谱法检测奶牛采食前(0 h)和采食后(1、2、3、4、5、6、7、8 h)瘤胃液中SCFA浓度;并采用荧光定量PCR方法检测瘤胃上皮组织中与SCFA吸收相关的蛋白表达量。结果表明:在采食后1 h奶牛瘤胃中SCFA浓度最高(P<0.05);在采食后一段时间内(2~5h)与SCFA吸收相关蛋白表达量上调(P<0.05),AE2、MCT1基因表达量均在5 h最高,PAT1、NHE3基因表达量均在4 h最高,MCT4基因表达量在4、5、6h均较高,NHE1基因表达量在2h达到最高;AE2、MCT1、MCT4、NHE1基因表达量与SCFA浓度负相关或正相关(P<0.05),AE2、MCT1、MCT4基因表达量与瘤胃内pH正相关(P<0.05)。以上结果初步揭示,在采食后一定时间内,瘤胃中与SCFA吸收相关蛋白表达受SCFA浓度和pH的调节。     

Soil water response to precipitation in different microtopographies on the semi-arid Loess Plateau,China

Soil water is an important factor restricting afforestation on the semi-arid Loess Plateau.The microtopography of the loess slope has changed the distribution pattern of soil water on the slope.To improve water utilization efficiency and optimize afforestation configuration patterns,the relationship between soil water and precipitation at micro-topographic scale must be studied.We used time series analysis to study the temporal variation of soil water and its response to precipitation in four kinds of micro-topographies and undisturbed slope on loess slopes.Micro-topographies significantly influenced soil water distribution and dynamics on the slopes.Soil water stored in the platform,sinkhole,and ephemeral gully influenced subsequent soil water for 4 weeks,whereas soil water stored in the scarp and undisturbed slope could influence soil water for 2 weeks.It took 12 weeks,10 weeks,18 weeks,6 weeks,and 12 weeks for precipitation to reach the deeper soil layer in the platform,sinkhole,scarp,ephemeral gully,and undisturbed slope,respectively.These soil water characteristics in different micro-topographies are vital factors that should be taken into consideration when undertaking afforestation on the Loess Plateau.     

Simulation of future dissolved oxygen distribution in pond culture based on sliding window-temporal convolutional network and trend surface analysis

Dissolved oxygen (DO) is a key ecological factor to measure the quality of water in the aquaculture. As the pond water body is affected by the breeding environment, the spatial distribution of DO shows a certain law in the entire pond. Therefore, to simulate the distribution of DO in aquaculture waters and grasp the temporal and spatial variation of DO is the key to achieving precise regulation of DO. For this purpose, this paper proposed a method for simulating the temporal and spatial distribution of DO in pond culture based on a sliding window-temporal convolutional network together with trend surface analysis (SW-TCN-TSA). This paper first utilized SW to construct DO data sets with different prediction durations, and then used the improved TCN model to realize one-dimensional time series prediction for DO at single monitoring point. Based on the prediction results of DO, a TSA method was performed on the predicted values of DO at the extreme moments of all discrete monitoring points, so as to realize the simulation of the temporal and spatial distribution of DO in the pond. Experimental results show that the SW-TCN model has better prediction performance for one-dimensional time series prediction of DO. Compared with traditional deep networks, such as CNN, GRU, LSTM, CNN-GRU and CNN-LSTM, the values of evaluation indicators (MSE, MAE and RMSE) have been greatly improved. In the process of trend surface fitting, all fitting R² of DO at different water depths are higher than 0.9, indicating that the TSA can accurately reflect the temporal and spatial distribution of DO. This method can provide a basis for the prediction and early warning of DO in the three-dimensional space of the pond and has high practicability in aquaculture.     

Evaluation of optical sensor measurements of canopy reflectance and of leaf flavonols and chlorophyll contents to assess crop nitrogen status of muskmelon

Nitrogen losses from intensive vegetal production systems are commonly associated with contamination of water bodies. Sustainable and optimal economic N management requires correct and timely on-farm assessment of crop N status to detect N deficiency or excess. Optical sensors are promising tools for the assessment of crop N status throughout a crop or at critical times. We evaluated optical sensor measurement of canopy reflectance and of leaf flavonols and chlorophyll contents to assess crop N status weekly throughout a muskmelon crop. The Crop Circle ACS 470 was used for reflectance measurement, the SPAD 502 for leaf chlorophyll, and the DUALEX 4 Scientific for leaf chlorophyll and flavonols. Four indices of canopy reflectance (NDVI, GNDVI, RVI, GVI), leaf flavonols and chlorophyll contents and the nitrogen balance index (NBI), the ratio of chlorophyll to flavonols contents, were linearly related to crop N content and to crop Nitrogen Nutrition Index (NNI) throughout most of the crop. NBI most accurately predicted crop N status; in five consecutive weekly measurements, R² values were 0.80–0.95. For NDVI during the same period, R² values were 0.76–0.87 in the first three measurements but R² values in the last two measurements were 0.39–0.45. Similar relationships were found with the three other reflectance indices. Generally, the relationships with NNI were equal to or slightly better than those with crop N content. These optical sensor measurements provided (i) estimation of crop N content in the range 1.5–4.5%, and (ii) an assessment of whether crop N content was sufficient or excessive for optimal crop growth for NNI ranges of 0.8–2.0. Composite equations that integrated the relationships between successive measurements with the optical sensors and crop N content or NNI for periods of ≥2 weeks (often 2–3 weeks) were derived for most indices/parameters. Overall, these results demonstrated the potential for the use of these optical sensor measurements for on-farm monitoring of crop N status in muskmelon.     

Sound Pressure Levels in 2 Veterinary Intensive Care Units

Background

Intensive care units (ICUs) in human hospitals are consistently noisy environments with sound levels sufficient to substantially decrease sleep quality. Sound levels in veterinary ICUs have not been studied previously, but environmental sound has been shown to alter activity in healthy dogs.

Hypothesis

Veterinary ICUs, like those in human medicine, will exceed international guidelines for hospital noise.

Animals

NA.

Methods

Prospective, observational study performed consecutively and simultaneously over 4 weeks in 2 veterinary ICUs. Conventional A‐weighted sound pressure levels (equivalent continuous level [a reflection of average sound], the sound level that is exceeded 90% of the recording period time [reflective of background noise], and maximum sound levels) were continuously recorded and the number of spikes in sound >80 dBA were manually counted.

Results

Noise levels were comparable to ICUs in human hospitals. The equivalent continuous sound level was higher in ICU1 than in ICU2 at every time point compared, with greatest differences observed on week day (ICU1, 60.1 ± 3.7 dBA; ICU2, 55.9 ± 2.5 dBA, P < .001) and weekend nights (ICU1, 59.9 ± 2.4 dBA; ICU2, 53.4 ± 1.7 dBA, P < .0001) reflecting a 50% difference in loudness. Similar patterns were observed for the maximum and background noise levels. The number of sound spikes was up to 4 times higher in ICU1 (162.3 ± 84.9 spikes) than in ICU2 (40.4 ± 12.2 spikes, P = .001).

Conclusions and Clinical Importance

These findings show that sound in veterinary ICUs is loud enough to potentially disrupt sleep in critically ill veterinary patients.     

白藤花粉采集时间及萌发条件的初步研究

对白藤开花习性、花粉采集时间及萌发条件进行了初步研究,结果表明：在白藤开花盛期,日开花高峰期集中在早晨4：00～8：00,其中6：00开放的花朵占当日开花总数的62．36％,其他时段开花量极少;上午8：30～11：30收集的花粉萌发力最强;在25％蔗糖液体培养基中和35℃下培养6h,花粉萌发与生长效果最佳。     

不同取样时期对杜仲、黄柏、厚朴三种皮类药材质量影响的初步研究

对贵州省林业科学研究院树木园内的杜仲林、黄皮树林、凹叶厚朴林随机分别选取6株样树，从2001年9月至2002年8月每月定日取样，用烘干法测定其含水率，用醇溶性浸出物测定法测定其萃取率。结果表明，不同取样时期杜仲、黄柏、厚朴的含水率差异显著，三种药材含水率变化相对一致，在4、5、6、7月含水率较高，9、10月含水率较低。杜仲、黄柏的最佳取皮期为6月，其萃取率分别为7．92％、6．77％；厚朴的最佳取皮期为7月，萃取率为8．93％。     

基于时延神经网络的木材干燥模型辨识的研究

木材干燥是一个复杂、多变的过程，传统的系统辨识方法很难建立一个理想的木材干燥模型。利用时延神经网络的特点，本文提出了基于时延神经网络的木材干燥模型辨识方法，并给出了辨识网络和结构。对辨识得到模型的仿真结果表明，利用时延神经网络所建立的模型是可靠且有效的。