Development of environmentally benign microencapsulation with polymer microspheres and liposomes

Microencapsulation means applying a shell-like coating to encapsulate the contents of interest in a particle form with a size range of few micrometers or below. In this work, aqueous liposome systems and polymer based encapsulation of fine particles in supercritical CO2 were studied. Compared to many other microencapsulation methods, these two methodologies feature reduction/prevention of using organic solvents, making them particularly attractive as green technology. For polymer microencapsulation, a novel in situ polymerization based process to encapsulate various types of fine particles, include drugs, fire retardant, inorganic nanoparticles, and carbon nanotubes, was developed. In the process, host particles, monomers and other components are first mixed together followed by polymerization and encapsulation. Thin-film coating was achieved for particle size above 1 μm For nanoparticles, surface functionalization was employed for increasing interfacial interactions and dispersion. Under appropriate conditions, nano-silica particles were found undergoing sol-gel transition to form porous monoliths. Dispersion, debundling, and polymer encapsulation of single-walled carbon nanotubes (SWNTs) were also reported. Despite the great potential posed by bio-mimetic phospholipids in drug delivery, commercial products are quite limited. To address the structure stability of liposome based microencapsulationm in a more fundamental level, we studied the mechanism of spontaneous formation of monodispersed unilamellar vesicles with scattering technique using neutron and light sources. Vesicle phase was studied systematically as a function of lipid concentration, salinity, temperature and time duration, etc. The results contribute to the understanding and selection of appropriate lipid system and process for microencapsulation of drugs

The sequence and de novo assembly of Takifugu bimaculatus genome using PacBio and Hi-C technologies.

Takifugu bimaculatus is a native teleost species of the southeast coast of China where it has been cultivated as an important edible fish in the last decade. Genetic breeding programs, which have been recently initiated for improving the aquaculture performance of T. bimaculatus, urgently require a high-quality reference genome to facilitate genome selection and related genetic studies. To address this need, we produced a chromosome-level reference genome of T. bimaculatus using the PacBio single molecule sequencing technique (SMRT) and High-through chromosome conformation capture (Hi-C) technologies. The genome was assembled into 2,193 contigs with a total length of 404.21 Mb and a contig N50 length of 1.31 Mb. After chromosome-level scaffolding, 22 chromosomes with a total length of 371.68 Mb were constructed. Moreover, a total of 21,117 protein-coding genes and 3,471 ncRNAs were annotated in the reference genome. The highly accurate, chromosome-level reference genome of T. bimaculatus provides an essential genome resource for not only the genome-scale selective breeding of T. bimaculatus but also the exploration of the evolutionary basis of the speciation and local adaptation of the Takifugu genus

Robotic versus laparoscopic right hemicolectomy with complete mesocolic excision: a retrospective multicenter study with propensity score matching

ObjectiveDuring the past decade, the concept of complete mesocolic excision (CME) has been developed in an attempt to minimize recurrence for right-sided colon cancer. This study aims to compare outcomes of robotic versus laparoscopic right hemicolectomy with CME for right-sided colon cancer.MethodsWe performed a retrospective multicenter propensity score matching study. From July 2016 to July 2021, 382 consecutive patients from different Chinese surgical departments were available for inclusion out of an initial cohort of 412, who underwent robotic or laparoscopic right hemicolectomy with CME. Data of all patients were retrospectively collected and reviewed. Of these, 149 cases were performed by a robotic approach, while the other 233 cases were done by laparoscopy. Propensity score matching was applied at a ratio of 1:1 to compare perioperative, pathologic, and oncologic outcomes between the robotic and the laparoscopic groups (n = 142).ResultsBefore propensity score matching, there were no statistical differences regarding the sex, history of abdominal surgery, body mass index (BMI), American Joint Committee on Cancer (AJCC) staging system, tumor location, and center between groups (p > 0.05), while a significant difference was observed regarding age (p = 0.029). After matching, two comparable groups of 142 cases were obtained with equivalent patient characteristics (p > 0.05). Blood loss, time to oral intake, return of bowel function, length of stay, and complications were not different between groups (p > 0.05). The robotic group showed a significantly lower conversion rate (0% vs. 4.2%, p = 0.03), but a longer operative time (200.9 min vs. 182.3 min, p < 0.001) and a higher total hospital cost (85,016 RMB vs. 58,266 RMB, p < 0.001) compared with the laparoscopic group. The number of harvested lymph nodes was comparable (20.4 vs. 20.5, p = 0.861). Incidence of complications, mortality, and pathologic outcomes were similar between groups (p > 0.05). The 2-year disease-free survival rates were 84.9% and 87.1% (p = 0.679), and the overall survival rates between groups were 83.8% and 80.7% (p = 0.943).ConclusionDespite the limitations of a retrospective analysis, the outcomes of robotic right hemicolectomy with CME were comparable to the laparoscopic procedures with fewer conversions to open surgery. More clinical advantages of the robotic surgery system need to be further confirmed by well-conducted randomized clinical trials with large cohorts of patients

Accurate Wheat Lodging Extraction from Multi-Channel UAV Images Using a Lightweight Network Model

The extraction of wheat lodging is of great significance to post-disaster agricultural production management, disaster assessment and insurance subsidies. At present, the recognition of lodging wheat in the actual complex field environment still has low accuracy and poor real-time performance. To overcome this gap, first, four-channel fusion images, including RGB and DSM (digital surface model), as well as RGB and ExG (excess green), were constructed based on the RGB image acquired from unmanned aerial vehicle (UAV). Second, a Mobile U-Net model that combined a lightweight neural network with a depthwise separable convolution and U-Net model was proposed. Finally, three data sets (RGB, RGB + DSM and RGB + ExG) were used to train, verify, test and evaluate the proposed model. The results of the experiment showed that the overall accuracy of lodging recognition based on RGB + DSM reached 88.99%, which is 11.8% higher than that of original RGB and 6.2% higher than that of RGB + ExG. In addition, our proposed model was superior to typical deep learning frameworks in terms of model parameters, processing speed and segmentation accuracy. The optimized Mobile U-Net model reached 9.49 million parameters, which was 27.3% and 33.3% faster than the FCN and U-Net models, respectively. Furthermore, for RGB + DSM wheat lodging extraction, the overall accuracy of Mobile U-Net was improved by 24.3% and 15.3% compared with FCN and U-Net, respectively. Therefore, the Mobile U-Net model using RGB + DSM could extract wheat lodging with higher accuracy, fewer parameters and stronger robustness

Collaborative Wheat Lodging Segmentation Semi-Supervised Learning Model Based on RSE-BiSeNet Using UAV Imagery

Lodging is a common natural disaster during wheat growth. The accurate identification of wheat lodging is of great significance for early warnings and post-disaster assessment. With the widespread use of unmanned aerial vehicles (UAVs), large-scale wheat lodging monitoring has become very convenient. In particular, semantic segmentation is widely used in the recognition of high-resolution field scene images from UAVs, providing a new technical path for the accurate identification of wheat lodging. However, there are still problems, such as insufficient wheat lodging data, blurred image edge information, and the poor accuracy of small target feature extraction, which limit the recognition of wheat lodging. To this end, the collaborative wheat lodging segmentation semi-supervised learning model based on RSE-BiseNet is proposed in this study. Firstly, ResNet-18 was used in the context path of BiSeNet to replace the original backbone network and introduce squeeze-and-excitation (SE) attention, aiming to enhance the expression ability of wheat lodging characteristics. Secondly, the segmentation effects of the collaborative semi-supervised and fully supervised learning model based on RSE-BiSeNet were compared using the self-built wheat lodging dataset. Finally, the test results of the proposed RSE-BiSeNet model were compared with classic network models such as U-Net, BiseNet, and DeepLabv3+. The experimental results showed that the wheat lodging segmentation model based on RSE-BiSeNet collaborative semi-supervised learning has a good performance. The method proposed in this study can also provide references for remote sensing UAVs, other field crop disaster evaluations, and production assistance

Rapid Detection and Counting of Wheat Ears in the Field Using YOLOv4 with Attention Module

The detection and counting of wheat ears are very important for crop field management, yield estimation, and phenotypic analysis. Previous studies have shown that most methods for detecting wheat ears were based on shallow features such as color and texture extracted by machine learning methods, which have obtained good results. However, due to the lack of robustness of these features, it was difficult for the above-mentioned methods to meet the detection and counting of wheat ears in natural scenes. Other studies have shown that convolutional neural network (CNN) methods could be used to achieve wheat ear detection and counting. However, the adhesion and occlusion of wheat ears limit the accuracy of detection. Therefore, to improve the accuracy of wheat ear detection and counting in the field, an improved YOLOv4 (you only look once v4) with CBAM (convolutional block attention module) including spatial and channel attention model was proposed that could enhance the feature extraction capabilities of the network by adding receptive field modules. In addition, to improve the generalization ability of the model, not only local wheat data (WD), but also two public data sets (WEDD and GWHDD) were used to construct the training set, the validation set, and the test set. The results showed that the model could effectively overcome the noise in the field environment and realize accurate detection and counting of wheat ears with different density distributions. The average accuracy of wheat ear detection was 94%, 96.04%, and 93.11%. Moreover, the wheat ears were counted on 60 wheat images. The results showed that R2 = 0.8968 for WD, 0.955 for WEDD, and 0.9884 for GWHDD. In short, the CBAM-YOLOv4 model could meet the actual requirements of wheat ear detection and counting, which provided technical support for other high-throughput parameters of the extraction of crops

Finite element analysis of the Poisson–Boltzmann equation coupled with chemical equilibriums: redistribution and transport of protons in nanophase separated polymeric acid–base proton exchange membranes

<p>The finite element analysis is applied to the study of the redistribution and transport of protons in model nanophase separated polymeric acid–base composite membranes by the Poisson–Boltzmann equation coupled with the acid and base dissociation equilibriums for the first time. Space charge redistribution in terms of proton and hydroxide redistributions is observed at the interfaces of acidic and basic domains. The space charge redistribution causes internal electrostatic potential, and thus, promotes the macroscopic transport of protons in the acid–base composite membranes.</p

Difference analysis of aroma components in tobacco leaves based on GA-SVM

Eleven different aromatic components including megastigmatrienone,beta-Ionone,Ionone oxide and solanone from middle and lower tobacco leaves were determined successfully via high performance liquid chromatography-gas chromatography-mass spectrometry (HPLC-GC-MS) system.By using genetic algorithm(GA),8 aromatic components were selected to build a support vector machine(SVM) classification model for discriminating middle and lower tobacco leaves.The results showed that the accuracies of modeling,leave-one-out,and prediction were 95.45%,89.39% and 81.25%,respectively.The spatial distribution of middle and lower tobacco leaves was investigated by Fisher discriminant vector method,which showed that megastigmatrienone,beta-Ionone,and Ionone oxide were evidently different in the middle and lower tobaccos leaves