extfieldsofstudy:
Medicine
Computer Science

text:
Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks

The origin of tobacco is the most important factor in determining the style characteristics and intrinsic quality of tobacco. There are many applications for the identification of tobacco origin by near-infrared spectroscopy. In order to improve the accuracy of the tobacco origin classification, a near-infrared spectrum (NIRS) identification method based on multimodal convolutional neural networks (CNN) was proposed, taking advantage of the strong feature extraction ability of the CNN. Firstly, the one-dimensional convolutional neural network (1-D CNN) is used to extract and combine the pattern features of one-dimensional NIRS data, and then the extracted features are used for classification. Secondly, the one-dimensional NIRS data are converted into two-dimensional spectral images, and the structure features are extracted from two-dimensional spectral images by the two-dimensional convolutional neural network (2-D CNN) method. The classification is performed by the combination of global and local training features. Finally, the influences of different network structure parameters on model identification performance are studied, and the optimal CNN models are selected and compared. The multimodal NIR-CNN identification models of tobacco origin were established by using NIRS of 5,200 tobacco samples from 10 major tobacco producing provinces in China and 3 foreign countries. The classification accuracy of 1-D CNN and 2-D CNN models was 93.15% and 93.05%, respectively, which was better than the traditional PLS-DA method. The experimental results show that the application of 1-D CNN and 2-D CNN can accurately and reliably distinguish the NIRS data, and it can be developed into a new rapid identification method of tobacco origin, which has an important promotion value.

Introduction
e origin of tobacco directly determines the difference of the style characteristics and intrinsic quality of tobacco [1] and also serves as an important basis for highlighting the characteristics of different brands of cigarettes. At present, the identification of tobacco origin mainly relies on sensory assessment by experts, genetic detection, and chemical composition detection. However, due to the complicated process, long time period, and high cost, these methods cannot be widely applied and rapidly realize identification of tobacco origin. erefore, it is necessary to study a method that can identify the tobacco origin quickly, accurately, and conveniently.
Near-infrared spectroscopy has been widely used in the quantitative detection and qualitative analysis of tobacco fields because of its advantages of simplicity, rapidity, low cost, environmental protection, large amount of information, and simultaneous determination of multiple components [2]. Many researchers have attempted to identify tobacco origins by pattern recognition methods based on near-infrared spectrum (NIRS). For example, Shu et al. [3] used principal component analysis combined with support vector machine algorithm (PCA-SVM) to establish a NIRS origin identification model for flue-cured tobacco in six provinces of China. e partial least squares discriminant analysis algorithm (PLS-DA) was used to establish a tobacco NIRS origin identification model including four provinces in China [4]. e neural network method was used to identify the origin pattern of more than 1000 tobacco samples from the United States and abroad by near-infrared spectroscopy [5]. Most studies have shown that the direct construction of the identification model by full NIRS will not only increase the modeling complexity but also reduce the recognition performance and generalization ability, due to its high dimensionality, high-frequency noise, and redundant information.
e usual practice is to use data dimensionality reduction to compress high-dimensional spectral data into low-dimensional space and improve model recognition performance while preserving more feature information as much as possible. Most researchers use the PCA method to reduce the dimensionality of high-dimensional spectral data [6,7], but PCA is a linear algorithm, which cannot explain the complex polynomial relationship between features, and does not consider the category information of the data. Convolutional neural network (CNN) first achieved success in the field of image recognition and has been gradually widely used in other fields. Zhang et al. [8] proposed a fault diagnosis model based on deep one-dimensional convolutional neural network (1-D CNN), and a larger size convolution kernel and a neurons dropout method were used to realize accurate and stable diagnosis based on the original vibration signal. A fault diagnosis model based on LeNet-5 network was proposed, and the original time domain signal was converted into a two-dimensional gray image as the model input to realize fault diagnosis based on one-dimensional time domain signal [9]. It is concluded that the powerful feature extraction ability of the CNN is very suitable for the pattern feature extraction of NIRS, and the extracted features are used for classification. However, at present, there are few reports on the classification of NIRS based on CNN.
In view of the above problems, this paper presents the method of multimodal CNN to identify the NIRS of tobacco from different regions. e NIR-CNN identification model of tobacco origins is established by using NIRS of 5,200 tobacco samples from 10 major tobacco producing provinces in China and 3 foreign countries. With the excellent feature learning ability of CNN, the reconstructed feature data can better reflect the essential features of NIRS, which is helpful for the classification of spectral data. Firstly, simple patterns in one-dimensional near-infrared spectrum (1-D NIRS) are identified by the lower layer of 1-D CNN, then these simple patterns are combined into complex features in the upper layer, and then the extracted features are used for classification. Secondly, the 1-D NIRS are converted into two-dimensional spectral images, and the structure features are extracted from two-dimensional spectral images by the image convolution method. e global training features are extracted and classified by mining the spatial correlation in the data. Finally, the optimal CNN model is selected by adjusting the network structure parameters, and the identification performances of different spectral processing methods and different models are compared.

Sample Preparation.
Data balancing is very important in multiclassification problems. Generally, the data in the training set should be distributed as evenly as possible with respect to the category labels, that is to say, the data sets corresponding to each category label are basically equal in the training set, so as to avoid the classification model being too inclined to the characteristics of some certain categories. In this research work, a total of 5,200 tobacco samples were collected from 10 major tobacco planting provinces of China (Yunnan, Guizhou, Fujian, Jiangxi, Hunan, Sichuan, Chongqing, Henan, Shandong, and Jilin) and 3 major foreign countries (Brazil, Zimbabwe, and America). at means 400 tobacco samples were taken from each province or country. Among them, 320 samples were used for training and 80 samples were used for testing. e origins and quantities of all observations are summarized in Table 1. All origins of tobacco samples were identified by experts from State Tobacco Monopoly Administration of China.

2.2.
Near-Infrared Spectra. NIRS of tobacco samples were acquired by Antaris-II with diffuse reflection mode, which is a Fourier-transform near-infrared spectrometer produced by ermoFisher, America. e spectral scanning range was 4000 cm − 1 ∼10000 cm − 1 , the resolution was 8 cm − 1 , and the number of scans was 64. Each sample of tobacco was dried in an oven (60°C, 2 h) and grounded into powder (60 meshes). In order to ensure the consistency of spectral acquisition, each sample was repeatedly scanned for three times, and the mean spectrum was used as the final spectrum of the sample.

Spectral
Processing. Spectral data processing and algorithm calculation were based on MATLAB, version 2016b. Due to the problems, such as baseline drift, high-frequency noise, and mutual interference between components, in the raw NIRS, it is necessary to perform corresponding preprocessing before applying the spectra to improve the signalto-noise ratio and enhance the prediction performance of the model. In this work, the first-order derivative and Savitz-kyGolay (S-G) smoothing methods were adopted, the moving window width was 9, and the polynomial number was 3 [10]. Figure 1(a) shows the raw NIRS of the tobacco samples collected by the method mentioned above. Figure 1(b) shows the processed NIRS which are calculated after the 1st derivative and S-G smoothing. It can be seen from Figure 1 that the 1st derivative and smoothing processes effectively reduce the baseline drift and highlight the differences in the raw spectra of different samples, which is very important for the qualitative classification of the NIRS.

Convolutional Neural Network (CNN)
. CNN is a machine learning model under deep supervised learning, which requires very little data preprocessing and is highly adaptable [11]. By forward propagation through the filters of each layer in the network, the local features of the data can be better mined, and the global training features can be extracted and classified. Its weight-sharing structure network makes it more similar to biological neural networks and has achieved good results in various fields of pattern recognition. e basic structure of the CNN consists of an input layer, a convolution layer, a pooling layer, a fully connected layer, and an output layer [12]. Each feature map has a plurality of neurons and extracts a feature of the input through a convolution filter. e convolution layer and the pooling layer are the core modules for implementing the feature extraction function of the CNN. e lower hidden layer of the CNN is composed of the convolution layer and the pooling layer alternately. e upper layer is the fully connected layer corresponding to the hidden layer and regression classifier of the traditional multilayer perceptron (MLP) [13]. Various classifiers can be used to classify the feature data extracted from the lower layer. e network model minimizes the loss function by using the gradient descent method to reversely adjust the weight parameters in the network layer by layer [14] and improves the accuracy of the network through frequent iterative training.

Convolutional Layer Operation.
e convolutional layer convolves the input data using a plurality of convolutional kernels and outputs the convolved features [15], that is, the feature maps. Each convolutional kernel outputs a feature map corresponding to a type of extracted features. Since it is not affected by the input dimensions and is easy to train the depth structures, the convolution structure is an effective tool for feature extraction of complex and highdimensional inputs. e two-dimensional convolution operation process is shown in Figure 2.
e input data and the convolution kernel are both twodimensional tensors. e convolution calculation formula is as follows:

Journal of Analytical Methods in Chemistry
where i is the i-th convolution kernel; g(i) is the feature map of the i-th convolution kernel; a is the input data; b is the bias; x and y are the two dimensions of the input data; and f(·) is an activation function used to implement nonlinear transformations.
e ReLU activation function is the most widely used in CNN at the present stage [16], and its calculation process is as follows: (2)

Pooling Layer Operation.
e pooling layer downsamples the input feature vectors by the sampling kernels and further highlights the extracted features while realizing data dimensionality reduction [17]. e pooling operation mainly includes the maximum pooling and the average pooling [18], as shown in the following equation: where a l(i,t) is the t-th neuron of the i-th feature map in the lth layer, w is the width of the convolution kernel, and j is the j-th sampling kernel.

One-Dimensional Convolutional Neural Network (1-D CNN)
. 1-D CNN is a special CNN that can be well applied to one-dimensional time series analysis. It also can be used to analyze signal data with fixed length period or fixed position expression, such as NIRS data. e input of 1-D CNN is a one-dimensional vector, so the convolution kernel and feature map of the network are also one-dimensional. As with the two-dimensional CNN, the same pooling operation can be performed for the one-dimensional vectors, which is also used to extract the data features of the one-dimensional vectors. e one-dimensional convolution operation process is shown in Figure 3. e convolution calculation process is shown in where i is the i-th convolution kernel; g(i) is the feature map of the i-th convolution kernel; a is the input data, b is the bias; x is the one-dimensional input data; and f(·) is the ReLU activation function used to implement nonlinear transformations.

Softmax Function.
Softmax function is a gradient logarithmic normalization of a finite discrete probability distribution, which is commonly used in multiclassification processes [19]. It compresses a K-dimensional vector V with any real number to another K-dimensional real vector σ(V), so that every element is between 0 and 1, and the sum of all the elements is 1. e score value of each category is regarded as a probability to be understood, and the multiclassification is performed by selecting the node with the largest probability (that is, the node with the largest value) as the prediction target.
Suppose there is an array V, v i represents the i-th element in V, then the softmax value of this element is calculated as shown in where n is the number of elements in the array V. e softmax function is widely used in probability-based multiclassification problems. In the deep neural network, the input of softmax is the result obtained from K different neurons, and the probability that the sample vector x belongs to the j-th category is shown in It can be seen from the formula that the probability belonging to a certain class is a composite of K linear function softmax. ere is a significant correlation between the prediction results and the input features. e more the features of a certain class are included, the higher the probability of the output being of a certain class. When several features are often activated together, the training process will learn the larger joint distribution weights, making their joint probability closer to the real category.

Model Training Parameters Setting.
For the training of CNN, the learning rate is set to 0.01, the batch size is set to 40, and the epoch of training is set to 50. e model may appear overfitting during the training process, which can be dealt with by adjusting the model operation mode. erefore, this paper sets early termination for CNN training. In most cases, the model first learns the correct distribution of the data and then begins to overfit the data at some point in time. As the number of iterations increases, the training error will gradually decrease, while the testing error will decrease first and then increase. When the testing error increases, it indicates that the model is overfitting. erefore, by identifying when the testing error of the model has changed (turned up), the training process of the model can be terminated before the overfitting occurs.

Model Performance Evaluation Method.
e performance of the model is evaluated by the data set classification accuracy index. e classification accuracy rate P A is calculated as shown in where N C is the number of correctly classified samples in the data set and N T is the total number of samples in the data set.

Research on Dimensionality Reduction Classification of NIRS.
According to the 1st derivative and S-G smoothing preprocessed NIRS described in the experimental part, PCA and t-distributed stochastic neighbor embedding (t-SNE) [20] were used to reduce the dimension of the NIRS. And, the classification differentiation was preliminarily explored through the two-dimensional visual graphics. Figure 4(a) is the PC1 and PC2 score maps of PCA. As can be seen from the figure, in the PCA model constructed from the overall samples, the contribution of the first principal component (PC1) was 52.6%, and that of the second principal component (PC2) was 17.2%. However, tobacco samples from different regions exhibited high overlap due to the same compositional properties. erefore, the first two principal components, while characterizing the major part of the NIRS differences, are not sufficient to achieve the classification distinction. Figure 4(b) is the 2-dimensional feature distribution map of the t-SNE algorithm. In order to further demonstrate the difference of NIRS characteristics of different sample types, the t-SNE dimensionality reduction algorithm in manifold learning was introduced to visualize low-dimensional features. It can be seen from the figure that the distribution of the class features extracted by the t-SNE algorithm is slightly better than that of the PCA. However, due to the influences of spectral redundancy, noise, and other factors, different categories are still difficult to distinguish.
is result shows that it is very difficult to classify tobacco origins by full NIRS using conventional methods. It is necessary to use a deep feature extraction method to find local and global features with better classification discrimination from the NIRS.

Classification Performance of 1-D CNN.
In this paper, different parameters and depths of the network structure were designed for the application of 1-D CNN in the NIRS classification, and the 1-D CNN models with different network structures were applied to the tobacco origin classification. e dimension of 1-D NIRS data is 1609, so the input data value of 1-D CNN is 1 * 1609. e architecture of 1-D CNN is similar to that of 2-D CNN in the field of computer vision. It consists of an input layer, a fully connected layer, an output layer, and multiple convolutional and pooling layers stacked together. Generally speaking, the depth calculation of neural network does not include the input layer, only the hidden layer, and the output layer. For the 1D-CNN designed in this paper, its network depth is 2 fully connected layers (the first fully connected layer is the feature merge layer, which summarizes the local features of all feature maps after convolution and pooling), 1 output layer, and several convolutional and pooling layers added together. For example, a 1-D CNN with a depth of 5 includes 1 convolutional layer, 1 pooling layer, 2 fully connected layers, and and 1 output layer; a 1-D CNN with a depth of 7 includes 2 convolutional layers, 2 pooling layers, 2 fully connected layers, and 1 output layer. e rest of the depths are deduced by analogy. e basic structure of 1-D CNN is shown in Figure 5.     After feature extraction through the convolutional layers and pooling layers, all feature maps are stitched and merged by the first fully connected layer to summarize all local features. e number of neural nodes in the first fully connected layer changes with the convolution kernel size, the sampling kernel size, and the number of feature maps. e number of neural nodes in the second fully connected layer is the same as that in the first fully connected layer. e last output layer is classified by softmax, and the output category is 13 in the data set. e 2 fully connected layers and the softmax layer form a simple MLP structure. Each neuron in the fully connected layer is fully connected to all neurons in the previous layer. e excitation function of each neuron in the fully connected layer adopts the ReLU function, and the training method uses the BP algorithm. In the fully connected layer, a regularization method, dropout technology, is adopted to make the output value of the neurons in the fully connected layer become 0 with a probability of 0.5. By this method, parts of the neural nodes become invalid. ese nodes will neither participate in the forward propagation process of CNN nor will participate in the backpropagation process. At present, most researches on CNN use ReLU + dropout technology, which has achieved good classification performance.
Firstly, the effects of network depth on classification performance are studied by using 5 different depth network structures: (1) depth 5 (1 convolutional layer and 1 pooling layer); (2) depth 7 (2 convolutional layers and 2 pooling layers); (3) depth 9 (3 convolutional layers and 3 pooling layers); (4) depth 11 (4 convolutional layers and 4 pooling layers); (5) depth 13 (5 convolutional layers and 5 pooling layers). e simple structures of 1-D CNN at different depths are shown in Table 2, where C represents the convolutional layer and S represents the pooling layer. Stage represents the stacking stage of 1 convolutional layer and 1 pooling layer, and M represents the number of feature maps (i.e., the number of convolution kernels) of each stage. Table 3 shows the results of the NIRS classification by the 1D-CNN models with 5 different depths on the training set and test set. In the stacked structure of each convolutional layer and pooling layer, the convolution kernel size in the convolutional layer is 1 * 5, the sampling kernel size of the pooling layer is 1 * 2, and the number of feature maps in each stage is 12. As can be seen from Table 3, with the increase of neural network depth, the classification accuracy of training set increases continuously, and the classification accuracy of test set first rises and then declines [21]. is shows that, as the complexity of the network structure increases, the model overfitted the training set samples, resulting in a decline in generalization ability. erefore, the model with a depth of 11, the best comprehensive performance of training set and test set, is adopted in this paper for subsequent experiments.

Influence of Convolution Kernel Size on Model Classification Performance.
In order to test the influence of convolution kernel size on the performance of the model, under the condition of network depth 11, the convolution kernel size is gradually increased by Step 2 from 3, and the 1D-CNN models are established when other parameters are the same. e curve of classification accuracy of the model test set changing with the size of convolution kernel is shown in Figure 6. It is found through experiments that too large or too small convolution kernel is not conducive to model learning [22]. In this experiment, when the convolution kernel size is 15, the model can obtain better classification results.

Influence of Sampling Kernel Size on Model Classification Performance.
In order to test the influence of sampling kernel size on the performance of the model, under the condition of network depth 11, the sampling kernel size is changed and the 1-D CNN models are established when other parameters are the same. e pooling layer reduces the number of neurons through the downsampling operation, so the size of sampling kernel is generally required to be smaller than the convolution kernel and capable of divisible postconvolution sequence. In this experiment, when the convolution kernel size is 1 * 15, the results of classification accuracy for different sampling kernel sizes are shown in Table 4. It is found through experiments that, when the size of sampling kernel is 2, the model can obtain better classification results.

Influence of the Number of Feature Maps on Model
Classification Performance. In order to test the influence of  the number of feature maps (convolution kernels) on the performance of the model, under the condition of network depth 11, the number of feature maps is gradually increased by step 6 from 6 and the 1D-CNN models are established when other parameters are the same. e curve of classification accuracy of the model test set changing with the number of feature maps is shown in Figure 7. rough experiments, the classification accuracy increases first and then decreases. When the number of feature maps is too small, some features conducive to network learning are ignored, so its classification performance is poor [23]. However, when the number of feature maps is too large, not only the training time of the model is greatly increased but also the model is prone to overfitting. Considering comprehensively, the number of feature maps is set to 24. In order to explore the optimal combination of CNN depth, convolution kernel size, sampling kernel size, and the number of feature maps, more than 200 CNN models with different structures are designed in this paper. ese different CNN models all use the same training set and test set as above to carry out experiments.

Classification Performance of 2-D CNN.
In this paper, the LeNet-5 open source network [24], which is widely used in the field of two-dimensional image recognition and has a high classification accuracy, is adopted for training of twodimensional near-infrared spectral (2-D NIRS) images. Small random numbers are used to initialize the weights of LeNet-5 network, 2-D NIRS data of the training set are used for model training, and the weights of the network are updated through back propagation. And, the last fully connected layer is replaced to create output units of origin category in the data set. e retrained CNN is used to extract the features of 2-D NIRS images, and then the extracted features are used as input to train a brand new single-layer full-connected classifier to deal with the origin classification problem of NIRS.
In the case of a fixed CNN structure, the image structure of 2-D NIRS becomes an important factor affecting the classification accuracy. In order to test the influence of 2-D NIRS image structure on the classification performance of the model, the 2-D CNN models are established under the condition that other parameters are the same by changing the input structure of 2-D NIRS images combined with the extracted characteristic spectrum segments of the NIRS. e NIRS with different spectral lengths are transformed into 2-D NIRS images as follows.
1-D NIRS data are a series of digital vectors. When converting it into a 2-D image matrix, it is necessary to intercept subvectors of a fixed length from front to back and stack the subvectors in rows. Figure 8(a) illustrates a process of converting 1-D NIRS to 2-D NIRS by taking a one-dimensional near-infrared spectrum at 1600 points as an example. Since 1600 can be decomposed into 40 * 40, the converted 2-D NIRS image has 40 dimensions for both rows and columns. Take the values of points 1-40 in 1-D NIRS and put them into the first row of the two-dimensional matrix. Take the values of points 41-80 in 1-D NIRS and put them into the second row of the two-dimensional matrix. By     Full spectrum conversion is shown in Figure 9. 1-D NIRS data have 1609 detection points. After removing the last 9 points, it becomes 1 * 1600, which is converted into a twodimensional image matrix of 40 * 40.
Feature segment spectrum conversion is shown in Figure 10. For the spectrum range of "4000 cm − 1 ∼7740 cm − 1 " with high signal-to-noise ratio, there are 1024 detection points in 1-D NIRS, which is converted into a two-dimensional image matrix of 32 * 32. e NIRS is gradually intercepted from the right side by changing the width of the window, and the 2-D NIRS image size is compressed in a step of 2. Table 5 shows the classification accuracy of different 2-D spectral image sizes from 40 * 40 to 26 * 26 on the test set.
rough the experiments, the accuracy of model classification increases first and then decreases. When the 2-D NIRS image size is 32 * 32, the classification accuracy of the model reaches the highest level. is is because the effective signal is weak in the spectrum range of "7740 cm − 1 ∼10000 cm − 1 ", mainly the noise information. After filtering out this part of the data, it can effectively highlight the effective information of the spectrum. When the spectral compression range is large, for example, the spectrum range is "4000 cm − 1 ∼6400 cm − 1 ", the loss of effective spectral information leads to insufficient training of the model. erefore, it is necessary to extract a suitable feature segment for two-dimensional spectral image conversion.

Classification Performance Comparison of Different
Models.
e best performing classification models of 1-D CNN and 2-D CNN were selected by experiments, and the same training set and test set were used for modeling analysis. en, they were compared with the PLS-DA classification method. e classification performance of different models is shown in Table 6. e overall prediction performances of 1-D CNN and 2-D CNN are both better than those of PLS-DA.
In order to more clearly show the recognition results of different models for each category in the test set, the confusion matrix is introduced to analyze the experimental results in detail. e confusion matrixes of the three models are shown in  As can be seen from the comparison of the confusion matrix in Figures 11-13, the following can be observed. (1) e prediction accuracy of 1-D CNN and 2-D CNN models for each category is higher than that of the PLS-DA model, which can basically meet the requirements of practical application. (2) For some categories, the prediction accuracy of the three models is low (for example, C7), which may be caused by the lack of significant characteristics in the samples of this category. (3) e category distributions of 1-D CNN and 2-D CNN models are relatively concentrated, while the category distribution of PLS-DA model is relatively discrete.

Conclusions
In this paper, for the problem of identifying tobacco origin by NIRS, a multimodal CNN method is proposed to construct classification models for improving classification accuracy. With the current popular research method of deep CNN, NIRS data are directly input into the network. e CNN can directly extract feature information from the spectrum and conduct automatic learning, thus avoiding the accumulation of errors caused by manual feature extraction and finally achieving the accurate classification of tobacco origin. e 1-D CNN and 2-D CNN models have achieved good classification results in most tobacco origin categories, and both of the model performances are better than the traditional PLS-DA model. is method can be developed into an accurate and high performance method for identifying tobacco origin, which has important guiding significance and extensive popularization value for the scientific and rational utilization of tobacco raw materials of brand characteristic cigarettes.
In this paper, only some CNNs with specific structures are considered. How to adaptively select the network structures in the face of complex and variable input data remains to be further studied. In addition, when using CNN for NIRS classification, the input dimension should be consistent. erefore, the original data need to be intercepted to a fixed length. How to intercept the appropriate data so that CNN can maximize its advantages is a problem worthy of further study. Since recurrent neural network (RNN) can handle data of varying lengths [25], it is possible to consider combining RNN with CNN in the later stage.

Data Availability
e NIRS data of tobacco used to support the findings of this study are available from the corresponding author upon request because these data are obtained through relevant projects which can only be used for scientific research and cannot be used for any commercial purposes.


provenance: AgriculturalAndFoodSciences,2019-2020-Agricultural And Food Sciences-2020.jsonl.gz.gz

text:
Untraditional feeds influencing on poultry growth

Poultry farming is one of the most precocious and important branches of animal husbandry. It makes it possible to get food products such as meat and eggs in the shortest time. The main factor affecting the development of poultry farming is the feed base. For poultry, the lack of protein and minerals is very harmful. Far East has all the opportunities to realize its genetic potential. Primorye region makes it possible to widely use inexpensive seafood and waste products, as well as plant raw materials in animal and poultry feeding. Marine and fish products, as well as waste from their reprocessing, are rich in protein and minerals necessary to ensure the fullness of animal and poultry rations. Additions of marine origin have antioxidant, radioprotective activity, increase the body’s resistance to diseases, increase the immune system, and improve metabolism. In addition, the region has a fairly rich plant feed base. In particular, to improve metabolism, increase the body’s resistance, improve digestibility and digestibility of feed, we can use Galega orientalis. So that, the purpose of our research work was to study the possibility of including these additions in poultry rations.

Introduction
An effective way to compensate for protein, mineral and vitamin deficiencies is to introduce marine additions into the poultry rations. In Amur region, such seafood as cucumaria, kelp, bivalve mollusks are introduced into the compound feed. These additions in the complex have a positive effect on the growth and development of young chickens, improving egg productivity due to better absorption of nutrients. Marine hydrobionts have also been studied in feeding calves and pigs. Their role in feeding, their influence on the increase in live weight, and the reproductive qualities of experimental animals were studied. [1,2] In Primorye region, the using of Japanese Corbicula in the rations of pigs, boars, piglets was studied. It has been proved that the feed concentrate from Japanese Corbicula has a positive effect on growth and development, resistance, meat productinvost, environmental safety of the resulting products, digestibility of nutrients. Based on these studies, we set a goal to determine the effectiveness of including Corbicula flour in the diets of young poultry and determine the optimal doses of its inclusion. [3] The use of grass flour from the Eastern goat (Galega Orientalis) in the rations of young ducks in the amount of 3-6%, and the parent herd-10-15% of the mass of mixed feed instead of grass flour from alfalfa has a positive effect on the productive and reproductive indicators of poultry, contributes to AGRITECH-III-2020 IOP Conf. Series: Earth and Environmental Science 548 (2020) 072007 IOP Publishing doi:10.1088/1755-1315/548/7/072007 2 increased digestibility and use of feed nutrients. And so that, we decided to study effect of Gallega Orientalis and Corbicula Japonica on hens.
We conducted research experiments in Primorsky region on the hens of the Haysex White cross. The purpose of the research work is to study the possibility of including Corbicula flour and plant feed complex made from Galega in the diets of young hens. We had the following tasks: to determine the optimal doses of feeding these feed additions in the diets of hens, to study the growth when including feed additions.
The purpose of our research was to study the possibility of using a feed additions in the rations of young poultry, to determine the optimal feeding doses which provide maximum gain, growth and development index.

2.Methods and materials
We conducted two research experiments. In each scientific experiment, we formed 4 groups of chickens at the age of 5 weeks with 150 heads by means the method of pairs of analogs (control and three experimental ones) to study the effect of each feeding. The research scheme is shown in table 1. We entered the following doses of Corbicula feed flour in the diet of poultry: 2, 3 and 5 % in the ration and plant flour -1.5, 2, 3%. The control group was fed a diet adopted by the farm, the experimental groups received basic ration plus feed flours according the scheme. Young poultry were kept in cell batteries during the experiment. The scientific research lasted 90 days (12 weeks), according to the methodology.

3.Results
The using of additions had a positive effect on the growth and development of experimental poultry.
The results of the research are presented in table 2. The using of marine feed flour in the diets of chickens had a positive effect on growth. In the experimental groups, the absolute and average daily gain were maximal in the III experimental group. The absolute increase of gain was 824.74 grams, and the average daily gain was 9.2 grams. Similar results were obtained with the using of a plant flour from Eastern goat (Galega). The maximum gains were also obtained in the experimental groups and amounted to 813.7 grams and 9.04 grams.
We studied the development of an experimental poultry. We took measurements, in particular the length of the carcass in age of 120 days. The results are shown in table 3. The table shows that the highest index were in the experimental groups. The advantage of the experimental groups over the control when using Corbicula flour was 4.6-21 mm. Using Galega plant flour had positive effect and equal 6.3-21.7 mm. The superiority of experimental groups over control can be seen in more detail in figures 1 and 2.

Discussion
Research experiments proved our assumptions that this feed complex help increase growth and development index. Flour from Japanese Corbicula and plant flour have a positive effect on the growth and development of chickens. The research results are reliable and authenticity. During the experiment, all poultry were kept in the same conditions. By the end of research experiments poultry of experimental groups had the higher index than control group.
Using Corbicula flour absolute increase of gain was higher by 40.35-54.39 g than control and daily gain higher by 0.5-0.64 g than control. The highest length of carcass was in III experimental group (200.3g)/ Including feed flour of plant origin has also positive effect on gaining and length of carcass. Absolute increase of gain was the highest in the III experimental group (813.7 g), it was higher by control on38.6 g.
Safety of poultry population also was insignificant higher in experimental groups. It amount to 98.1-98.3% in comparison with control 98.0-98.1% As for optimal doses of plant additions, we confirmed the doses maximum 10%. Also growth of young poultry is to up 5% [4][5][6][7][8][9][10] Thus, using untraditional feed additions (marine feed and plant feed flours) in feeding of poultry Haysex White has a positive effect on growth, gaining, safety and length of carcass.


extfieldsofstudy: Biology Medicine

text:
Diversity of nutritional content in seeds of Brazilian common bean germplasm

Mineral deficiency is worldwide one of the major problems associated with human health, and biofortification through breeding is considered an important strategy to improve the nutritional content of staple food in countries that face this problem. The assessment of genetic variability for seed nutrient contents is a first step in the development of a biofortified crop. From the germplasm bank IDR–IAPAR–EMATER, a set of 1,512 common bean accessions, consisting of local and commercial varieties and improved lines, was analyzed. High variability among the accessions was observed for all evaluated nutrient contents (P, K, Ca, Mg, Cu, Zn, Mn, Fe and S and protein). In the mean, the contents of the carioca and black market groups (Mesoamerican gene pool), were around 7% higher for the minerals Ca, Cu, Mn and Fe and between 2–4% higher for P, K, Mg and Zn than in the other groups with Mesoamerican and Andean common bean. Few differences were observed among the Mesoamerican accessions that belong to the carioca and black commercial groups. Wide variability was observed among the evaluated genotypes, and the concentrations of the best accessions exceeded the overall mean by 14–28%. Due to the high variability in the evaluated accessions, these results may contribute to the selection of promising parents for the establishment of mating blocks. The nutritional contents of many of the improved lines evaluated in this study were higher than those of the commercial cultivars, indicating the possibility of developing new biofortified cultivars.

Introduction
Minerals are inorganic substances, present in all tissues and fluids of the human body, which play an important role in growth and regulation processes and repair functions of the body [1]. However, more than 2 billion people worldwide have one or more chronic micronutrient deficiencies, particularly of calcium (Ca), iodine (I), iron (Fe), selenium (Se), zinc (Zn) and vitamins [2]. Micronutrient deficiency can delay the growth and cognitive development, impair immune functions and increase the risk of cardiovascular and metabolic diseases [3].
Several strategies, e.g., of supplementation (e.g., in the form of pills), industrial fortification and biofortification, have been applied to combat micronutrient malnutrition. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 Supplementation and industrial fortification have some practical limitations, since the supplementation pills are not always taken regularly, while industrial food fortification requires a rigorous quality control, with specialized techniques and infrastructures, which are generally restricted in developing countries [4,5]. On the other hand, biofortification is considered a viable and economical means of providing populations who have limited access to dietary diversification and other micronutrient interventions with essential micronutrients [6,7].
Biofortification can be defined as the process of raising the concentration of vitamins and minerals in a crop by plant breeding (conventional methods or genetic engineering) or by agronomic practices (agronomic biofortification). Agronomic biofortification is the process of improving the micronutrient content in edible plant parts by applying fertilizers to the soil or leaves. However in the long term, genetic biofortification is more cost-effective [8].
Several studies have demonstrated the effectiveness of conventional breeding in developing biofortified crops, resulting in an improved micronutrient intake and minimizing deficiency among the target populations. Some examples are higher vitamin A contents in sweet potato [9,10] maize [11] and cassava [12], as well as increased Fe contents in common bean [13,14] and millet [15,16]. Altogether, more than 150 biofortified varieties of 10 different crops have been officially released for production in over 30 countries in Africa, Asia and Latin America and the Caribbean [7].
In conventional breeding, several steps are indispensable to establish biofortified cultivars. The first is to analyze the variability contained in germplasm banks in order to estimate the genetic potential of some accessions for future stages of the breeding program [17]. In an evaluation of the common bean (Phaseolus vulgaris L.) core collection of the International Center for Tropical Agriculture (CIAT), Islam et al. [18] found wide genetic variability for nutritional traits (mean Fe and Zn contents of 54.81 ppm (34.6-91.9 ppm) and 34.40 ppm (20.7-59.4 ppm) respectively). In a collection of 1,150 accessions, wide variability for mineral contents was also reported by Beebe, Gonzalez and Reginfo [19], who concluded that the Fe and Zn grain contents could be increased. The mean Fe and Zn values reported by these authors were 55 ppm (34-89 ppm) and 35 ppm (21 to 54 ppm), respectively. These studies showed evidences that the Mesoamerican gene pool have a tendency to have higher contents of calcium, phosphorus, sulfur and zinc than the Andean gene pool, but in the other hand, Andean accessions showed higher iron contents.
The common bean breeding program of the Rural Development Institute of Paraná -IAPAR-EMATER (IDR-IAPAR-EMATER) currently has a collection of 14,163 accessions deposited by different research institutions and universities, as well as lines developed by the institute itself [20]. These accessions are being characterized in detail and some of them extensively exploited in the breeding program for the development of new cultivars. To date, 39 common bean cultivars have been released, which are being widely cultivated by producers across Brazil. In this sense, the development of new cultivars that combine high yields with resistance or tolerance to biotic and abiotic stresses and high nutritional value becomes increasingly relevant. Therefore, this study evaluated the diversity of the mineral composition and protein content of common bean accessions of the IDR-IAPAR-EMATER germplasm bank, with a view to identifying accessions that can be used as parents in breeding programs to develop cultivars with seeds with higher nutritional value.

Plant material and experimental design
Of the germplasm bank of the IDR-IAPAR-EMATER, 1,512 accessions were evaluated. All information about seed coat color, genetic material and center of origin (1,473 and 39 belonging to Mesoamerican and Andean gene pool, respectively) of the accessions are described in the S1 Table. The experiments were carried out at different locations in the state of Paraná, Brazil, in two agricultural seasons, resulting in a total of four environments. In the rainy season of 2009, sowing was carried out in September in the counties of Pato Branco (lat. 260˚13'S; long. 5204 0'W 760 m asl,) and Lapa (lat. 250˚46 'S; long. 490˚42'W; 908 m), and in the dry season of 2010 in January, in Ponta Grossa (250˚05'S; long. 500˚09'W; 975 m asl) and Lapa.
Each experimental plot consisted of one 4-m row, at a spacing of 0.50 m between rows and a density of 12 plants per meter. Fertilization at sowing consisted of the application of 300 kg ha -1 of 4-30-10 (N, P 2 O 5 , K 2 O) and in growth stage V4, of 200 kg ha -1 ammonium sulfate. Pests, diseases and weeds were controlled according to the technical recommendations for the crop. At physiological maturity (R9), the plots were harvested and a 100-g sample of diseasefree seeds without physical or insect damage was collected and stored in a cold chamber (5.6˚C, humidity 33%) until laboratory analysis.

Analysis of mineral composition and protein content
The following minerals were determined: phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), copper (Cu), zinc (Zn), manganese (Mn), iron (Fe), sulfur (S) and nitrogen (N). Prior to laboratory analyses, the samples were washed in tap water, 0.01M HCl solution and distilled water to prevent contamination by soil particles that could be attached to the seeds. Then the seeds were oven-dried at 60˚C for 48h, ground and the flour stored in a glass recipient.
The mineral contents were determined by nitroperchloric digestion with HNO 3 :HClO 4 solution and by atomic emission spectrophotometry (ICP-AES) (Thermo Jarrell Ash ICAP 61E). The protein content was quantified by Kjeldahl's method and the samples were read on a UV-VIS spectrophotometer. Factor 6.25 was used to convert total seed nitrogen into crude protein [21]. The analysis was developed according to the methodology described by Miyazawa et al. [22].

Statistical analysis
The data were subjected to descriptive analysis (minimum, maximum and mean values; coefficient of variation; asymmetry and kurtosis), using software SAS [23]. Pearson's correlation analysis using the mean values for each accession was performed using the cor() function in the R package qgraph [24]. The accessions were separated based on two criteria: seed coat color (black, carioca and colored) and genetic material (cultivars, landraces and breeding lines). For these two criteria an analysis of variance (ANOVA) followed by a Tukey test (p < 0.05) were performed for each case to compare significantly differences between the formed groups. To visualize the distribution of the accessions grouped by each criteria a boxplot graph was made. The analyses were done using the R packages agricolae and ggplot2 [25][26][27].
Subsequently, multivariate analysis using the Ward-Modified Location Model (Ward-MLM) method was performed to group the accessions by the procedures CLUSTER and IML of software SAS [23]. For the Ward clustering method, the distance matrix was calculated by the Gower algorithm [28]. The ideal number of groups was defined according to the criteria pseudo-F and pseudo-T 2 , together with the likelihood profile associated with the likelihood ratio test. The data distribution in relation to the groups formed was visualized in a boxplot.
The differences between groups and correlation of the variables with the canonical variable were represented in a diagram using the SAS CANDISC procedure [23]. The distance proposed by Matusita [29], used by Krzanowski [30] and later by Franco et al. [31] for the distribution of variables, was applied to determine the dissimilarity between the groups.

Results
Wide variability for mineral and protein contents was detected in the accessions of the IDR-IAPAR-EMATER common bean germplasm bank ( Table 1). The coefficients of variation (CV) ranged from 5.66 (protein) to 13.53% (Ca), indicating good experimental precision. The kurtosis values, which indicate the degree of flatness of the data distribution, were close to 3.0 for protein, Zn and P, suggesting a mesokurtic distribution. The other nutrients were classified as leptokurtic, in which the data distribution curve is close to the central value that is greater than the mesokurtic distribution. The asymmetry was positive for Ca, Zn, Mn, Fe and protein, and negative for the other nutrients. Most nutrient distributions were not normal (all except P, Fe and protein) ( Table 1).
Positive correlations were observed among most nutrients. Protein content correlated only with P, while K correlated with P and Zn. In addition, two correlation groups (Fe-Zn-Cu-P and Ca-Mn-Mg) were formed, in which all nutrients involved are correlated with each other (Fig 1).
In the subdivision of accessions by seed coat color (Fig 2), the means of the colored group accessions, were lower than those of the carioca and black groups, except in the case of protein and S contents. The means of the colored group were around 7% lower for the minerals Ca, Cu, Mn and Fe and between 2-4% lower for P, K, Mg and Zn. Significant differences between the mean contents of the black and carioca groups were only observed for K, Mn and S; the black group had highest K and S and the carioca group highest Mn contents. However, some accessions of the black group also had high Fe and some accessions of the carioca group high Zn contents (Fig 2).
The variability in the breeding lines was higher than in the cultivars and landraces, aside from the higher mean contents for all nutrients except protein. The mean P, K and Zn concentrations of the breeding lines, cultivars and landraces were similar, while the breeding line means were significantly higher for Ca, Mg, Cu, Mn, Fe and S and lower for protein. For Fe content, the breeding line mean was 5% higher than that of the other groups (Fig 2).
By the logarithmic likelihood function, five groups were formed based on the criteria pseudo-t 2 and pseudo-F (S1 Fig and S1 Table). In the boxplot representing the groups, variability was observed between and within them (Fig 3 and Table 2).  Group 1 (G1) consisted of only 80 accessions, mostly with colored grain, and included most Andean accessions of this study, which accounted for approximately 31% of all accessions of the group. This group had the lowest contents for 8 of the 10 evaluated nutrients. Group 2 (G2) was the largest, with 52.2% of the studied accessions, with mainly black and carioca grain (71%). In general, the variability in G2 was the highest while the means were not notable for any of the nutrients. However, some accessions had high Ca, Mg and Fe contents. Group 3 (G3) comprised 202 accessions, of which 122 had black, 44 mulatto and the remaining accessions other seed coat colors. Group G3 stood out with the highest P, Cu, Zn and Fe, and some accessions had very high K contents.
Group 4 (G4) comprised mostly accessions of the commercial groups carioca and black, as well as brown, mulatto and others. Mainly the Mn content was very high in G4. Together with G3, the S content was also very high in G4. In the fifth group (G5), only carioca accessions were included, the S content was the lowest and the Mn and Fe contents were rather low, whereas the mean protein contents were the highest.
The first two canonical variables explained 67.64 and 19.44%, respectively, resulting in 87.07% of the total variation (Fig 4). The nutritional contents that contributed most to determine the genetic diversity among the accessions studied in the first canonical variable were S and Mn, while the most important for the second canonical variable were Zn, P and Cu ( Table 2). The genotype dispersion based on the first two canonical variables is shown in Fig 4. The distances between groups by the Ward-MLM strategy based on the distance proposed by Matusita [29] indicated that the shortest distance was between G2 and G3 (estimated value of 7.23). This indicates high similarity between the accessions of these groups. The highest dissimilarity (estimated at 64.28) was observed between G4 and G5 (Table 3).
In order to select promising parents to compose crossing panels in the breeding program with the aim of increase grain mineral content, the 20 best genotypes for each of the nine

PLOS ONE
Diversity of nutritional content in seeds of common bean Most accessions selected among the top 20 were breeding lines, and in relation to seed coat color, 61 were of the black, 57 of the carioca, 14 of the mulatto and 6 of the white group. In an individual analysis of each nutrient, the black group accessions had the best contents for most nutrients. Accessions of the white group had the highest Ca and protein and carioca group accessions the highest Mn contents. Two accessions of the white group simultaneously had Table 2

Nutrient
Groups CAN  highest Cu, Zn, S and protein contents, while two other accessions of the black group stood out with high Cu, Zn and P. For the Fe and Zn contents, which are the main targets of biofortification in common bean, only breeding lines were among the top 20, mostly of the carioca and black groups. One line of the carioca group simultaneously had high Fe as well as Zn contents.

Discussion
Common bean is a primary component of the diet of the population of countries in Latin America as well as in East and Southern Africa, which are regions of the world with high rates of chronic diseases associated with malnutrition [32]. Common bean is not only a crop that plays a key role in feeding populations of underdeveloped countries, but is also highly promising for breeding for biofortification. Previous studies suggest that the Fe content of common bean grain could be increased by 80% and the Zn content by up to 50% [19]. Thus, taking into account the amount of common bean consumed in these countries, the objective of biofortification of common bean for Fe would be to increase the content from 50 to 94 ppm, which would be enough to meet an adult's basic daily Fe requirement [7].
A wide variability for all mineral and protein contents was observed among accessions of the IDR-IAPAR-EMATER germplasm bank. These results indicate that it is possible to identify promising parents for exploitation in breeding programs with a view to increasing the grain nutritional contents. The existence of genetic variability and the possibility of raising the grain mineral content have been confirmed in several common bean germplasm banks in Brazil [33,34], Colombia [18,19], Portugal [35], as well as in the USA [32].
Positive correlations were observed among most nutrients, which may indicate co-segregation of the genetic factors for the different minerals [19]. This correlation is favorable for plant breeding for higher nutritional grain content, since selection for an increase in one nutrient may be favorable for other minerals simultaneously. As observed by Pinheiro et al. [35], two correlation sets (Fe-Zn-Cu-P and Ca-Mn-Mg) were formed. The correlation between Ca and Mn was also mentioned by other authors, who suggested that this correlation is due to the fact that the deposition mechanism in the grain at the uptake or transport level of the two elements is the same [32]. The correlation between P and protein can be explained by the fact that P can be accumulated by an association in the body of proteins, e.g., in the form of phytates [35]. Cosegregation evidences of Fe and Zn due to highly positive correlation was also observed in other crops, suggesting that common mechanisms regulate Fe and Zn accumulation [36].
The mean concentrations found in this study were similar to those reported by Silva et al. [33], who also evaluated mostly Brazilian genotypes of Mesoamerican origin. However, our results exceeded the contents determined in the Mesoamerican Diversity Panel studied by McClean et al. [32]. In an evaluation of common bean accessions of the Mesoamerican and Andean gene pools, Moraghan and Grafton [37] also found lower mean concentrations.
Genotypes of the Andean group as well as colored grains tend to have lower nutrient concentrations. Lower P contents in the Andean than the Mesoamerican group were described in Table 3. Distance between the groups formed by the Ward-MLM strategy based on the distance proposed by Matusita (1956 a study published by CIAT [18], and lower mean contents of K, Ca, Zn and Cu were observed by Ribeiro et al. [34] in an assessment of 32 common bean lines with special grain types. In this study, the mean mineral concentration of the colored group, which also includes Andean genotypes, was around 7-2% lower than that of the carioca and black genotypes. With regard to the mineral composition of accessions of the main commercial groups black and carioca consumed in Brazil, great variability was detected within each one. However, the differences between the groups are few. Most carioca and black genotypes were grouped in G2 and G4, which were the largest groups, with greater variability and amplitude for the studied contents, while G3 was predominantly formed by black accessions and G5 by carioca accessions. In black grain genotypes, Silva et al. [33] identified higher Fe, Zn and protein contents, and higher Mg and Mn contents in carioca grain. These results partly confirm the findings of our study. In the separation of genotypes by color, the black group stood out with high K, Fe and S and the carioca group with high Zn and Mn contents.
Commonly, the main objective of common bean breeding programs is to raise yields, although the improved lines evaluated in this study performed better for all nutrients except protein. Once again, these results refute arguments that claim that modern crop breeding techniques reduce the grain quality [32]. A gradual increase of the Fe and Mg contents over time was observed, i.e., the contents increased in response to conventional breeding, less in landraces and more in cultivars and improved lines.
Only the concentration of grain crude protein was lower in the breeding lines, which can be explained by the negative correlation between grain crude protein content and yield [38]. Despite this reduction, accessions were identified with a balance between the two characteristics, enabling a concomitant improvement of both.
Due to the large number of accessions evaluated, the best 20 with the highest contents for each mineral and protein were selected. The mean of the top 20 accessions in relation to the overall mean was between 22-28% higher for Ca, Mn and Cu and around 14-18% higher for P, K, S, Mg and protein. The mean Zn and Fe contents were, respectively, 18 and 22% higher than the overall mean. Common bean is known as an excellent source of Zn and Fe, since one cup of common bean can currently supply 15 and 25%, respectively, of the recommended daily allowance for these nutrients. Still, cultivars with higher nutritional Fe and Zn contents can potentially be developed [39].
Most of these top 20 accessions were breeding lines, many of which had been improved in the IDR-IAPAR-EMATER breeding program, indicating the potential for the release of new cultivars or even including them in cross panels. In addition, many of these accessions belong to the carioca and black groups, which are the most commonly consumed grain types in Brazil. Thus, new biofortified cultivars could easily be incorporated into the diet of the Brazilian population.
In general, the variability among the evaluated accessions was high, indicating a promising potential for the development of new common bean cultivars with higher grain nutrient contents. Although not addressed as a main target of crop improvement, the nutritional grain quality has been increased indirectly by human selection, evidenced by the fact that breeding lines showed higher contents for some nutrients compared to cultivars and landraces (Fig 2). Thus, if efforts are invested in the development of biofortified cultivars, it will be possible to help combat malnutrition in countries affected by this problem.


s2fieldsofstudy:
Agricultural And Food Sciences
Medicine

text:
In vitro anti-HIV activity of ethanol extract from gandarusa (Justicia gendarussa Burm. f) leaves

Anti retroviral drugs for HIV has problems with uncomfortable side effects and that endanger the lives of HIV sufferers. Several herbs have been empirically proven to have an effect on HIV eradication through inhibition of reverse transcriptase. One of such antiviral herbs is Justicia gendarussa (J. gendarussa). The aim of research is to evaluate anti-HIV activity of 70% fractionated-ethanol extract (with releasing alkaloids) and 70% ethanol extract (without releasing alkaloids) of J. gendarussa leaves on in vitro HIV-infected of MOLT-4 cells. The effect of the extracts in inhibiting viral replication and fusion process on acute HIV infection was identified through syncytia formation assay. Effect of the extracts on HIV p24 antigen was evaluated using HIV-1 p24 ELISA kit. It was found that 70% fractionated-ethanol extract and 70% ethanol extract of J. gendarussa leaves significantly inhibited of HIV replication by inhibition of syncytia formation, where the 50% effective concentration (EC50) values of the 70% fractionated- ethanol extract and 70% ethanol extract are 70.5 μg/mL and 228.7 μg/mL, respectively. Both of the extracts were also significantly inhibited HIV replication by decreasing HIV p24 antigen level where the EC 50 values of the 70% fractionatedethanol extract and 70% ethanol extract are 88.8 μg/mL and 540.7 μg/mL, respectively. Moreover, it was found that 70% fractionated- ethanol extract of J. gendarussa leaves has anti-HIV activity since its EC50 values less than 100 μg/mL. It was concluded that J. gendarussa could be potentially developed into a phyto-pharmaceutical product due to its anti-HIV activity.

Introduction
Human Immunodeficiency Virus (HIV) is a retrovirus that infects human immune system, causing decline of human immune system and leading to a condition called Acquired Immune Deficiency Syndrome (AIDS). 1 HIV transmission causes high number of HIV infection and high number of death in the world. Antiretroviral (ARV) is a therapy that used to treat patients with HIV infection. ARV has some disadvantages, namely toxicity associated with inability of patients to withstand adverse effects of drugs, high cost of ARV therapy, limited availability of drugs combination, 2 lack of any curative effect and viral resistance to ARV drugs. [3][4][5] One alternative strategy for prevention of HIV infection is using medicinal plants. Some medical plants have potential sources to discovery of new active agent with anti-HIV activity. Medicinal plants are relatively non-toxic. Hence, medicinal plants more tolerable than chemical drugs. 6 Justicia gendarussa Burm.f. has been known to have anti-HIV activity 7 mainly from the inhibition of reverse transcriptase enzym. At 200 µg/mL concentrations, water extracts of J. gendarussa (aerial part) has HIV-1 RT inhibition ratio (% IR) higher than 90%. 8 Leaves of J. gendarussa plants contain a substituted aromatic amine, 9 flavonoid glycosides including gendarusin A and B, 10 and justidrusamide alkaloids A, B, C, and D. 11 Gendarusin A is a major compound of J. gendarussa leaves that has anti-HIV activity on plasma blood of HIV patients in vitro with IC50 value was 10.24 µg/mL. A previous study found that an alkaloids-free 70% ethanol extract of J. gendarussa leaves could significantly reduce viral load in blood plasma of HIV patients. Viral load reduction of an alkaloids-free 70% ethanol extract of J. gendarussa leaves greater than the viral load reduction of methanol extract. 12 In addition, 70% alkaloids-free ethanol extract of J. gendarussa leaves has inhibitory activity of the reverse transcriptase enzyme. 13 In this study, the effect of 70 % fractionated-ethanol extract (with releasing alkaloids) and 70% ethanol extract (without releasing alkaloids) of J. gendarussa leaves on in vitro HIV-infected of MOLT-4 cell were compared using syncytia formation and p24 antigen assay.

Plant Material
Leaves of Justicia gendarussa used in this study were obtained from a cultivated crop in Trawas, Mojokerto, East Java province, Indonesia. The medicinal plants were identified by Department of Pharmacognosy and Phytochemistry, Faculty of Pharmacy, Universitas Airlangga, Surabaya.
The ethanol extracts Powder of J. gendarussa leaves was divided into two fractions, namely releasing-alkaloid leaf powder and a non releasing-alkaloid leaf powder. Releasingalkaloid leaf powder was prepared by adding an acid solution so that the alkaloids were changed into water-soluble salt. Both of powders were extracted by using 70% ethanol during 3´24 hours in a macerator to obtain filtrate. Then, the filtrate then was evaporated using a rotary evaporator (Buchi). After that, the extracts were dried at 50 °Cuntil70% fractionated-ethanol extract (alkaloid-free; 2.4% w/w) and 70% ethanol extract (10.8% w/w) could be obtained.

Detection of Alkaloids in J. gendarussa Leaves Extracts
Content of alkaloids in extracts of J. gendarussa leaves was analyzed by using Thin Layer Chromatography (TLC) methods. We used silica gel GF254 TLC plate (Merck) as stationary phase and dichloromethane:methanol (9:1) as mobile phase. The Dragendorf reagent was used as spray reagent to qualitatively identify the presence of alkaloids compounds. The presence of alkaloids compunds was marked with orange spot. Extract of Piper nigrum fruits was used as a comparator of the presence of alkaloids in the J. gendarussa extracts.

Detection of gendarusin A in J. gendarussa Leaves Extracts
The content of gendarusin A, the major flavonoid in J. gendarussa leaves, was analyzed by High Performance Liquid Chromatography (HPLC) methods using HPLC Agilent 1100, reverse phase NovaPack® column C-18 3.9´150 mm (Waters) as stationary phase and mixture of water and methanol (water:methanol =30:70) as mobile phase. The analysis was performed at these conditions: flow rate 1 mL/min, stop time 15 minutes, and UV detector at 254 nm wavelength.

Preparation of ethanol extracts stock solutions
A stock solution was made by dissolve 100.0 mg of each extract in a 1000 µL DMSO. Then, the solution was diluted by using RPMI-1640 medium (Gibco) with 10% Fetal Bovine Serum (FBS) (Gibco) (inactivated at 56 °C for 30 min) to obtain various concentrations (7.8; 15.6; 31.2; 62.5; 125.0; 250.0; 500.0; and 1000.0 mg/ml) for each trial extract. Concentration of DMSO used in this research less than 1 %. At this concentration level, DMSO does not affect viability. 14

Preparation of cell and virus
MOLT-4 cells clone#8 (human T lymphocytes cancer cells line) 15 were obtained from Bio-safety Level-3 facility CRC-ERID, Institute of Tropical Disease, Surabaya. MOLT-4 cells were cultured on a RPMI-1640 media, with 10%FBS and were kept in CCF T25 at 37°C in a 5% CO2 incubator (Sanyo).
HIV virus isolate from a seropositive HIV donor were obtained from Bio-safety Level-3 facility CRC-ERID, Institute of Tropical Disease, Surabaya. The HIV virus isolate was labeled that labeled by IDU-18 code. HIV isolate were cultured on MT-4 cell in RPMI-1640 medium, with 10% FBS and kept in CCF T25 at 37 °C in 5% CO2 incubator (Sanyo).

Syncytia Formation Assay
Effect of J. gendarussa leaves extracts on HIV-infected MOLT-4 cell was measured by using syncytia formation. A 100 mL of the extract solution with various concentrations were plated in each well on 96-well micro plate. The treatment group and negative control group (without extract) were tested in triple replication. 50 mL of 2´10 4 cell/mL MT-4/HIV were added to each well and were incubated for 30 minutes at 37°C in a5% CO2 incubator. After 30 minute of incubation, 50 mL of 4´10 5 cell/mL MOLT-4 cells were added into each well. The total volume in each well was 200 µL. The microplate was incubated for 72 hours at 37°C in a 5% CO2 incubator. After 72 hourof post-infection, the syncytia formation (giant cell with ≥4 nucleus per cell) 16 in each well of microplate was observed and calculated under an inverted microscope (Olympus) (10´10). 17 The inhibitory percentage of syncytia formation was determined by the following equation:

Detection of HIV p24 Antigen
Effect of J. gendarussa leaves extracts on HIV replication was assayed by measuring p24 antigen. Level of p24 antigen was used as indicator of HIV infection in HIVinfected MOLT-4 cells. A 200 mL of cell suspension in each well from 96-well micro plate of syncytia assay were transferred into 1.5 mL sterile Eppendorf tube. The cell suspension was inactivated by using 70% ethanol for 1 hour. Then, supernatant of the suspension were collected to determine viral production levels through measurement of HIV p24 antigen level using HIV-1 p24 capture ELISA Kit. The supernatant was plated in each well on a micro titter plate. Then the supernatant was coated with murine anti-HIV-1 p24 monoclonal antibody. HIV-1 p24 antigen from cell culture supernatant was quantified by ELISA (HIV-1 p24 ELISA kit, Xpress-Bio, USA) according to manufacturer's instructions. The lower limit of the assay is 1.7 pg/mL. The amount of captured p24 was determined through measuring the absorbance using micro plate absorbance reader (Bio-Rad) at 450 nm. The p24 content was estimated by Forecast formula in Microsoft Excel 2007 software.

Statistical Analysis
The collected datawas analyzed with regression analysis using Microsoft Excel 2007 software. From the regression analysis, the concentration level of J. gendarussa leaves extracts that can inhibit of 50% virus growth (50% Effective Concentration, EC50) could be determined. The difference activity of the extracts was considered to be significant the probability was P>0.05.

Alkaloids Analysis
The presence of alkaloids compounds in extracts of J. gendarussawas was analyzed using TLC method. It was found that the 70 % fractionated ethanol extract does not contain alkaloids compounds, while the 70% ethanol extract of J. gendarussaleaves contains alkaloids compounds (Rfvalue = 2.7) (Figure 1).

Gendarusin A Analysis
HPLC chromatograms showed that the 70 %-fractionatedethanol extract ( Figure  2b) and the 70% ethanol extract (Figure 2c) contain gendarusin A as a major flavonoid component of J. gendarussa leaves. The 70% fractionated ethanol extract and 70% ethanol extract of J. gendarussa leaves contains 0.53% and 0.95% of gendarusin A, respectively. The results were compared with the alkaloids extracts of Piper nigrum fruits (Figure 2a) using Dragendorf spray reagent.

Effect of J. gendarussa leaves extracts on syncytia assay
Syncytia formation was siginificantly inhibited by various concentration of the 70%-fractionated ethanol extract and 70% ethanol extract of J. gendarussa leaves (P<0.05). This result could be seen in Figure 3. It was found that J. gendarussa leaves extracts were potent drug ability as anti-HIV agent. It was found that EC50 value of the 70%-fractionated ethanol extract of J. gendarussa leaves less than 100 µg/ml. Also, it was found that the 70%fractionated ethanol extract of J. gendarussa leaves was more active as anti-HIV agent than 70% ethanol extract of J. gendarussa leaves in inhibition of syncytia formation (multinucleate giant cells). This result could be seen in Table 1.

Figure 3. The percentage of syncytia formation inhibition on HIV-infected MOLT-4 cells of 70% fractionated-ethanol extract and 70% ethanol extract from J. gendarussa leaves with various concentrations. It was measured after 72 hr incubation under conditions.
Error bars show the standard deviation from average of three data.

Effect of J. gendarussa leaves extracts on HIV p24 antigen expression
By using HIV p24 antigen detection assay, it was found that virus production on MOLT-4 cells exposed by MT-4/HIV was significantly inhibited by various concentration of the 70%-fractionated ethanol extract and 70% ethanol extract of J. gendarussa leaves (P<0.05). This result could be seen in Figure 4. The result showed that the 70%-fractionated ethanol extract of J. gendarussa leaves was the more active as anti-HIV agent than 70% ethanol extract of J. gendarussa leaves by parameter reduction of HIV p24 antigen expression because EC50 value of the 70%-fractionated ethanol extract of J. gendarussa leaves less than 100 µg/mL. It can be seen in Table 1.

Discussion
In this research, MT-4/HIV cell was cultured in the presence and absence of various concentrations of the extracts. Then, the MT-4/HIV cell was exposed to MOLT-4 cell. After 72-hour, syncytia formation was observed and the supernatant were collected. Then, the exposed MOLT-4 cell was subjected to p24 antigen detection assay. Through a series of tests, the corresponding EC50 (Effective Concentration 50%) value was determined.
HIV replication in an in vitro cell culture could be observed through the cytopathic effect such as syncytia formation. Also, HIV replication could be monitored through detection of viral products such as p24 antigen. 18 The p24 antigen is an important structural component of retroviral particle. The retroviral particle corresponds with mature virus at the end of HIV replication process. The p24 antigen is a protein capsid surrounding the viral RNA genome. A p24 antigen is still detected in a patient's serum or blood plasma in which it's RNA undetectable in an early infection. Detection of p24 antigen could be used in monitoring early treatment of acute infection (early infection). A p24 antigen could be detected outside viral particle or in the supernatant cell culture, while RNA is located inside viral particle. 19 It is well-known that the increasing of viral transcription will normally lead to an increasing of HIV replication. HIV replication is followed releasing of viral particles (such as p24 antigen) from mature virus. 19 During viral replication process, increasing of viral transcription also lead to increasing of production of viral surface proteins (gp120) on the surface of host cells. Interaction of virus surfaces proteins (gp120) with CD4 molecule found on the surface of uninfected T-cells causes syncytia formation. With the presence of 70%fractionated ethanol extract of J. gendarussa leaves in a viral replication process, viral transcription could be prevented.. In addition, production of viral surface proteins on the surface of the host cell is not occurred. Thus, interaction between the virus surfaces proteins with the CD4 molecule found on the surface of uninfected T cells will not occur. As a result, syncytia formation could be inhibited.
The level of anti-HIV activity was indicated by the value of 50% Effective Concentration (EC50). EC50 values were determined as concentration of the extracts could inhibit 50% of HIV growth. When EC50 value of an extract less than 100 mg/mL, then the extract has an antivirus activity. 18 Based on the result, we found that the two extracts could inhibit the expression of p24 antigen. This fact was evidenced by decreasing of the amount of p24 antigen in MOLT-4 cell cultures were infected with HIV. The reduction of p24 antigen by the extracts leads the inhibition of HIV replication. This means that the extracts could reduce the growth of new viruses in vitro. On the other hand, amount increasing of p24 antigen in a cell culture showed that the higher expression of p24 antigen the in cell culture, the higher syncytia formation in the infected cell cultures is.
Alkaloids need to be removed from the extract because the existence of alkaloids would cause to the vomiting effect. Neurotransmitters, particularly alkaloid compounds, act directly on receptors located in the brain's 4th ventricle, a cavity near the brainstem. When these receptors are activated, nausea results. Some conditions, such as inflammation of the gut, stimulate the brain to induce nausea directly. 4 Previous research shown that a 70% alkaloids-free ethanol extract of J. gendarussa leaves could inhibit reverse transcriptase enzyme. As a result, the transcription process of viral genetic material to form a new virus was inhibited. 13 The results of this study confirmed the previous results: the presence of a reverse transcriptase enzyme inhibitor will make the transcription process that changes the viral RNA to be provirus DNA. In addition, presence of the enzyme inhibitor will prevent new virus production.
The difference of EC50 value form two extracts might be a consequence of complicity of HIV infection process. 20 It might be due to the fact that each extract has slightly different chemical content. So, the extracts efficacy in preventing the virus replication is also different. The preventing of virus replication activity might be due to the presence of useful compounds in the extracts of J. gendarussa leaves. Without presence of alkaloids, this extract has anti-HIV activity greater than the extract containing alkaloids. The major flavonoids component in J. gendarussa leaves is gendarusin A, which consist of a flavonoid, apigenin glycosides, a flavone group as aglycone, and two arabinose groups, as glycine. 10 Flavonoids of flavonols and flavones groups (such as apigenin) could inhibit the reverse transcriptase enzyme. The general mechanisms in which enzyme activities are affected by inhibitors is well known, including irreversible damage of enzyme active sites or interference with substrate binding. 21 The diversity of compounds was contained in the 70%-fractionated ethanol extract and 70% ethanol extract of J. gendarussa leaves ensures that the extract could work to reduce viral growth by preventing viral replication. The preventing of viral replication might be caused by reverse transcriptase inhibition. In addition, the 70%-fractionated ethanol extract and 70% ethanol extract of J. gendarussa leaves also inhibit the viral fusion process. This result was confirmed by syncytia assay. The extracts might contain some compounds that act as good anti-viruses, but the quantity of the compounds is not enough to inactivate all viral particles. Separation of the active compounds in a multilevel extraction method of the plant might be useful to obtain a new and a more effective antivirus.

Conclusions
It was found that 70%-fractionated ethanol extract (with releasing alkaloids) of J. gendarussahas has an anti-HIV activity on HIV-infected MOLT-4 cells greater than the anti-HIV activity of 70 % ethanol extract, without releasing alkaloids. This result was shown by syncytia formation assay and HIV p24 antigen assay. It can be concluded that the J. gendarussa could be used as a useful resource in developing a phyto-pharmaceutical product that has in vitro anti-HIV activity.