Effects of Different Storage Treatments on Physicochemical Indexes of Green Plums

[Objectives]This study was conducted to discuss the change laws and relationships of the physiological indexes in green plums treated with nisin or 1-MCP in the cold storage process.[Methods]Different storage methods on the physiological and quality indexes of green plums were investigated through two kinds of different treatment methods(treatment 1:1-MCP+cold storage,treatment 2:nisin+cold storage),with rotten fruit rate,weight loss rate,yellow ripeness index,hardness,Vc content,titratable acid,soluble solids and peroxidase(POD)activity as the evaluation indexes.[Results]The storage method of treating green plums with 1-MCP combined with cold storage could delay the quality decline of green plums after harvest,while the effect of nisin treatment was slightly inferior.1-MCP treatment followed by cold storage could more effectively delay the senescence of green plums,delay the decrease of fruit hardness and soluble solid content to the greatest extent,and maintain the titratable acid and Vc contents at relatively good levels.It further reduced weight loss rate and rotten fruit rate,postponed the time of the peak of POD enzyme activity,and reduced the loss of nutrients in the fruit,thereby maintaining good commercial properties of the fruit.[Conclusions]This study plays an important role in the post-harvest storage,preservation and transportation,as well as the development of the green plum industry.

Geometric-Spectral Reconstruction Learning for Multi-Source Open-Set Classification With Hyperspectral and LiDAR Data

Dear editor,This letter presents an open-set classification method of remote sensing images(RSIs)based on geometric-spectral reconstruction learning.More specifically,in order to improve the ability of RSI classification model to adapt to the open-set environment,an openset classification method based on geometric and spectral feature fusion is proposed.

Effect of Sodium Selenite-Chitosan Compound Preservative on Storability of Kumquats

[Objectives]To explore the effect of sodium selenite-chitosan compound preservative on storability of kumquats.[Methods]Under the condition of room temperature,fresh kumquats were coated with different concentrations of sodium selenite-chitosan compound preservative,respectively.[Results]Sodium selenite-chitosan compound preservative reduced the weight loss rate,delayed the decline of vitamin C,soluble solids,titratable acid and GSH contents,slowed down the accumulation of MDA,inhibited the increase of PPO activity,and increased to a certain extent the activity of SOD in fresh kumquats.[Conclusions]Sodium selenite-chitosan compound preservative maintained the quality and prolonged the shelf life of kumquats.The preservation effect of compound preservative composed of 4 mg/L sodium selenite and 8 g/L chitosan was the best.

Cocktail nano-adjuvant enhanced cancer immunotherapy based on NIR-Ⅱ-triggered in-situ tumor vaccination

Second near-infrared(NIR-Ⅱ)light triggered in-situ tumor vaccination(ISTV)represents one of the most promising strategies in boosting the whole-body antitumor immunity.While most of previously developed nano-adjuvants for NIR-Ⅱ-triggered ISTV are“all-in-one”formulations,which may indiscriminately damage both the tumor cells and the immune cells,limiting the overall effect of immune response.To overcome this obstacle,we designed a“cocktail”nano-adjuvant by physically mixing hyaluronidases(HAase)-decorated gold nanostars(HA)for NIR-Ⅱlight triggered in situ production of tumor-associated antigens and CpG functionalized gold nanospheres(CA)for immune cells activation.Compared to“all-in-one”formulation,the“cocktail”nano-adjuvants displayed a significantly stronger immune response on NIR-Ⅱlight induced dendritic cells(DCs)mutation and T cells differentiation,greater effect on tumor-growth inhibition,and higher efficacy in inhibition of pulmonary metastases.What is more,increasing the molar ratio of HA to CA led to an enhanced anticancer immune responses.This study highlight the nano-adjuvant formulation effects on the treatment of tumors with multiple targets.

A High-similarity shellfish recognition method based on convolutional neural network

The high similarity of shellfish images and unbalanced samples are key factors affecting the accuracy of shellfish recognition.This study proposes a new shellfish recognition method FL_Net based on a Convolutional Neural Network(CNN).We first establish the shellfish image(SI)dataset with 68 species and 93574 images,and then propose a filter pruning and repairing model driven by an output entropy and orthogonality measurement for the recognition of shellfish with high similarity features to improve the feature expression ability of valid information.For the shellfish recognition with unbalanced samples,a hybrid loss function,including regularization term and focus loss term,is employed to reduce the weight of easily classified samples by controlling the shared weight of each sample species to the total loss.The experimental results show that the accuracy of shell-fish recognition of the proposed method is 93.95%,13.68%higher than the benchmark network(VGG16),and the accuracy of shellfish recognition is improved by 0.46%,17.41%,17.36%,4.46%,1.67%,and 1.03%respectively compared with AlexNet,GoogLeNet,ResNet50,SN_Net,MutualNet,and ResNeSt,which are used to verify the efficiency of the proposed method.

High performance vegetable classification from images based on AlexNet deep learning model

Deep learning techniques can automatically learn features from a large number of image data set.Automatic vegetable image classification is the base of many applications.This paper proposed a high performance method for vegetable images classification based on deep learning framework.The AlexNet network model in Caffe was used to train the vegetable image data set.The vegetable image data set was obtained from ImageNet and divided into training data set and test data set.The output function of the AlexNet network adopted the Rectified Linear Units(ReLU)instead of the traditional sigmoid function and the tanh function,which can speed up the training of the deep learning network.The dropout technology was used to improve the generalization of the model.The image data extension method was used to reduce overfitting in the learning process.With AlexNet network model used for training different number of vegetable image data set,the experimental results showed that the classification accuracy decreases as the number of data set decreases.The experimental verification indicated that the accuracy rate of the deep learning method in the test data set reached as high as 92.1%,which was greatly improved compared with BP neural network(78%)and SVM classifier(80.5%)methods.

Key immunity characteristics of diverse stages of brucellosis in rural population from Inner Mongolia, China

Background: Brucellosis poses a serious threat to human and animal health,particularly in developing countries such as China.The Inner Mongolia Autonomous Region is one of the most severely brucellosis-endemic provinces in China.Currently,the host immune responses functioning to control Brucella infection and development remain poorly understood.The aim of this study is to further clarify the key immunity characteristics of diverse stages of brucellosis in Inner Mongolia.Methods: We collected a total of 733 blood samples from acute(n=137),chronic(n=316),inapparent(n=35),recovery(n=99),and healthy(n=146)groups from the rural community of Inner Mongolia between 2014 and 2015.The proportions of CD4^(+),CD8^(+),Th1,Th2,and Th17 T cells in peripheral blood and the expression of TLR2 and TLR4 in lymphocytes,monocytes and granulocytes were examined using flow cytometry analysis.The differences among the five groups were compared using one-way ANOVA and the Kruskal–Wallis method,respectively.Results: Our results revealed that the proportions of CD4^(+) and CD8^(+) T cells were significantly different among the acute,chronic,recovery,and healthy control groups(P<0.05),with lower proportions of CD4^(+) T cells and a higher proportion of CD8^(+) T cells in the acute,chronic,and recovery groups.The proportion of Th1 cells in the acute,chronic,and inapparent groups was higher than that in the healthy and recovery groups;however,there was no significant difference between patients and healthy individuals(P>0.05).The proportion of Th2 lymphocytes was significantly higher in the acute and healthy groups than in the inapparent group(P<0.05).The proportion of Th17 cells in the acute group was significantly higher than that in the healthy control,chronic,and inapparent groups(P<0.05).Finally,the highest expression of TLR4 in lymphocytes,monocytes and granulocytes was observed in the recovery group,and this was followed by the acute,chronic,healthy control,and inapparent groups.There was a significant difference between the recovery group and the other groups,except for the acute group(P<0.05).Moreover,a correlation in TLR4 expression was observed in lymphocytes,monocytes and granulocytes among the five groups(r>0.5),except for the inapparent group between lymphocytes and granulocytes(r=0.34).Conclusions: Two key factors(CD8^(+)T cells and TLR4)in human immune profiles may closely correlate with the progression of brucellosis.The detailed function of TLR4 in the context of a greater number of cell types or tissues in human or animal brucellosis and in larger samples should be further explored in the future.

A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features

To increase prediction accuracy of dissolved oxygen(DO)in aquaculture,a hybrid model based on multi-scale features using ensemble empirical mode decomposition(EEMD)is proposed.Firstly,original DO datasets are decomposed by EEMD and we get several components.Secondly,these components are used to reconstruct four terms including high frequency term,intermediate frequency term,low frequency term and trend term.Thirdly,according to the characteristics of high and intermediate frequency terms,which fluctuate violently,the least squares support vector machine(LSSVR)is used to predict the two terms.The fluctuation of low frequency term is gentle and periodic,so it can be modeled by BP neural network with an optimal mind evolutionary computation(MEC-BP).Then,the trend term is predicted using grey model(GM)because it is nearly linear.Finally,the prediction values of DO datasets are calculated by the sum of the forecasting values of all terms.The experimental results demonstrate that our hybrid model outperforms EEMD-ELM(extreme learning machine based on EEMD),EEMD-BP and MEC-BP models based on the mean absolute error(MAE),mean absolute percentage error(MAPE),mean square error(MSE)and root mean square error(RMSE).Our hybrid model is proven to be an effective approach to predict aquaculture DO.

Multi-kernel dictionary learning for classifying maize varieties

The automatic classification and identification of maize varieties is one of the important research contents in agriculture.A multi-kernel maize varieties classification approach was proposed in this paper in order to improve the recognition rate of maize varieties.In this approach,four kinds of maize varieties were selected,in each variety 200 grains were selected randomly as the samples,and in each sample 160 grains were taken as the training samples randomly;the characteristics of maize grain were extracted as the typical characteristics to distinguish maize varieties,by which the dictionary required by K-SVD was constructed;for the test samples,the feature-matrixes were extracted by dimension reduction method which were mapped to the high-dimension space by muti-kernel function mapping.The high-dimension characteristic matrixes were trained by K-SVD method and the corresponding feature dictionary was obtained respectively.Finally,the test samples representing were trained and classified by l2,1 minimization sparse coefficient.The experiment results showed that recognition rate was improved obviously through this approach,and the poor-effect to maize variety identification from partial occlusion can be eliminated effectively.

Delivery of therapeutic oligonucleotides in nanoscale

Therapeutic oligonucleotides(TOs)represent one of the most promising drug candidates in the targeted cancer treatment due to their high specificity and capability of modulating cellular pathways that are not readily druggable.However,efficiently delivering of TOs to cancer cellular targets is still the biggest challenge in promoting their clinical translations.Emerging as a significant drug delivery vector,nanoparticles(NPs)can not only protect TOs from nuclease degradation and enhance their tumor accumulation,but also can improve the cell uptake efficiency of TOs as well as the following endosomal escape to increase the therapeutic index.Furthermore,targeted and on-demand drug release of TOs can also be approached to minimize the risk of toxicity towards normal tissues using stimuli-responsive NPs.In the past decades,remarkable progresses have been made on the TOs delivery based on various NPs with specific purposes.In this review,we will first give a brief introduction on the basis of TOs as well as the action mechanisms of several typical TOs,and then describe the obstacles that prevent the clinical translation of TOs,followed by a comprehensive overview of the recent progresses on TOs delivery based on several various types of nanocarriers containing lipid-based nanoparticles,polymeric nanoparticles,gold nanoparticles,porous nanoparticles,DNA/RNA nanoassembly,extracellular vesicles,and imaging-guided drug delivery nanoparticles.

Recognition of abnormal body surface characteristics of oplegnathus punctatus

To identify the abnormal characteristics of the oplegnathus punctatus is great importance to the detection of iridovirus disease in the breeding environment.In this paper,an advanced neural network model to identify the characteristics of the oplegnathus puncta-tus and predict its different periods of suffering from iridovirus disease is proposed based on the establishment of a data set.First of all,a standard format data set of oplegnathus punctatus and an abnormal format date set are established in order to verify the effective-ness of the method in this paper.And then,the feature extraction fusion method is used for preprocessing in terms of the abnormal format data set,which combines the edge fea-tures extracted by the improved multi-template Sobel operator and the color features extracted by the HSV model.Finally,an improved VGG-GoogleNet network recognition model comes into being through the fusion and improvement of the VGG and GoogleNet neural network structure.The experiments results show that the prediction accuracy rate for oplegnathus punctatus suffering from iridovirus disease in the the abnormal format data set and the standard format data set are improved,which reach 98.55%and 69.18%.

A multi-scale features-based method to detect Oplegnathus

It is of great significance to use underwater video and image processing technology to detect and analyze fish behaviors.In this paper,an Oplegnathus image dataset for fish behaviors study by deep learning algorithm is constructed,and the data is captured from two cameras(one above water and another below water);and then an improved Neural Network model based on multi-scale features is proposed for fish behaviors learning auto-matically.To overcome the occlusion and blur problems of the images,the lightweight neu-ral network MobileNet-SSD is improved by adding a dilate convolution,and SE blocks are added to the feature maps at different scales to establish a self-attention mechanism;the Focal Loss function is used to calculate the classification loss and to balance the propor-tion of background and target samples.The results of the experiments show that the aver-age behaviors detection accuracy of our method reach 90.94%and 88.36%in both overwater and underwater datasets.

Application of acid protease in the industrial production of corn ethanol

Bioethanol is a renewable,clean energy and a very important basic chemical raw material,improving production efciency and reducing production cost are the goals pursued by ethanol enterprises.Adding acid protease in the process of producing ethanol from grain will have many efects on fermentation,one of the most concerned efects is to increase liquor yield.Acid protease can hydrolyze the protein in cereals to produce free amino acids that can be used directly by yeast,thus reducing the amount of sugar consumed for amino acid metabolism,and allowing more sugar to be converted to ethanol.The application method and efect of acid protease in the industrial production of ethanol from corn were studied in this study,the results showed that the addition of acid protease at the dosage of 10 U/g(raw material)during yeast seeding could replace urea,promote yeast proliferation,reduce the infection rate,shorten the fermentation period by 7.5 h and increase the alcohol yield of raw materials by 0.33 percentage point.When acid protease was used in ethanol production line with an annual output of 180,000 tons,the net proft increased by at least US$4900/day compared with the control process.It is hoped that this study can lay a foundation for the application of acid protease in ethanol industry.

Double regularization control based on level set evolution for tablet packaging image segmentation

This paper proposes a novel double regular- ization control （DRC） method which is used for tablet packaging image segmentation. Since the intensities of tablet packaging images are inhomogenous, it is difficult to make image segmentation. Compared to methods based on level set, the proposed DRC method has some advantages for tablet packaging image segmentation. The local re- gional control term and the rectangle initialization contour are first employed in this method to quickly segment un- even grayscale images and accelerate the curve evolution rate. Gaussian filter operator and the convolution calcula- tion are then adopted to remove the effects of texture noises in image segmentation. The developed penalty energy function, as regularization term, increases the constrained conditions based on the gradient flow conditions. Since the potential function is embedded into the level set of evo- lution equations and the image contour evolutions are bi- laterally extended, the proposed method further improves the accuracy of image contours. Experimental studies show that the DRC method greatly improves the computational efficiency and numerical accuracy, and achieves better results for image contour segmentation compared to other level set methods.