Monitoring and Predicting the Surface Generation and Surface Roughness in Ultraprecision Machining: A Critical Review

The aim of manufacturing can be described as achieving the predefined high quality product in a short delivery time and at a competitive cost. However, it is unfortunately quite challenging and often difficult to ensure that certain quality characteristics of the products are met following the contemporary manufacturing paradigm, such as surface roughness, surface texture, and topographical requirements. Ultraprecision machining (UPM) requirements are quite common and essential for products and components with optical finishing, including larger and highly accurate mirrors, infrared optics, laser devices, varifocal lenses, and other freeform optics that can satisfy the technical specifications of precision optical components and devices without further post-polishing. Ultraprecision machining can provide high precision, complex components and devices with a nanometric level of surface finishing. Nevertheless, the process requires an in-depth and comprehensive understanding of the machining system, such as diamond turning with various input parameters, tool features that are able to alter the machining efficiency, the machine working environment and conditions, and even workpiece and tooling materials. The non-linear and complex nature of the UPM process poses a major challenge for the prediction of surface generation and finishing. Recent advances in Industry 4.0 and machine learning are providing an effective means for the optimization of process parameters, particularly through in-process monitoring and prediction while avoiding the conventional trial-and-error approach. This paper attempts to provide a comprehensive and critical review on state-of-the-art in-surfaces monitoring and prediction in UPM processes, as well as a discussion and exploration on the future research in the field through Artificial Intelligence (AI) and digital solutions for harnessing the practical UPM issues in the process, particularly in real-time. In the paper, the implementation and application perspectives are also presented, particularly focusing on future industrial-scale applications with the aid of advanced in-process monitoring and prediction models, algorithms, and digital-enabling technologies.

Comparative Study of Popular Deep Learning Models for Machining Roughness Classification Using Sound and Force Signals

This study compared popular Deep Learning (DL) architectures to classify machining surface roughness using sound and force data. The DL architectures considered in this study include Multi-Layer Perceptron (MLP), Convolution Neural Network (CNN), Long Short-Term Memory (LSTM), and transformer. The classification was performed on the sound and force data generated during machining aluminum sheets for different levels of spindle speed, feed rate, depth of cut, and end-mill diameter, and it was trained on 30 s machining data (10–40 s) of the machining experiments. Since a raw audio waveform is seldom used in DL models, Mel-Spectrogram and Mel Frequency Cepstral Coefficients (MFCCs) audio feature extraction techniques were used in the DL models. The results of DL models were compared for the training–validation accuracy, training epochs, and training parameters of each model. Although the roughness classification by all the DL models was satisfactory (except for CNN with Mel-Spectrogram), the transformer-based modes had the highest training (>96%) and validation accuracies (≈90%). The CNN model with Mel-Spectrogram exhibited the worst training and inference accuracy, which is influenced by limited training data. Confusion matrices were plotted to observe the classification accuracy visually. The confusion matrices showed that the transformer model trained on Mel-Spectrogram and the transformer model trained on MFCCs correctly predicted 366 (or 91.5%) and 371 (or 92.7%) out of 400 test samples. This study also highlights the suitability and superiority of the transformer model for time series sound and force data and over other DL models.

A Lightweight YOLOv4-Based Forestry Pest Detection Method Using Coordinate Attention and Feature Fusion

Traditional pest detection methods are challenging to use in complex forestry environments due to their low accuracy and speed. To address this issue, this paper proposes the YOLOv4_MF model. The YOLOv4_MF model utilizes MobileNetv2 as the feature extraction block and replaces the traditional convolution with depth-wise separated convolution to reduce the model parameters. In addition, the coordinate attention mechanism was embedded in MobileNetv2 to enhance feature information. A symmetric structure consisting of a three-layer spatial pyramid pool is presented, and an improved feature fusion structure was designed to fuse the target information. For the loss function, focal loss was used instead of cross-entropy loss to enhance the network's learning of small targets. The experimental results showed that the YOLOv4_MF model has 4.24% higher mAP, 4.37% higher precision, and 6.68% higher recall than the YOLOv4 model. The size of the proposed model was reduced to 1/6 of that of YOLOv4. Moreover, the proposed algorithm achieved 38.62% mAP with respect to some state-of-the-art algorithms on the COCO dataset.

Understanding Drug Delivery to the Brain Using Liposome-Based Strategies: Studies that Provide Mechanistic Insights Are Essential

Brain drug delivery may be restricted by the blood-brain barrier (BBB), and enhancement by liposome-based drug delivery strategies has been investigated. As access to the human brain is limited, many studies have been performed in experimental animals. Whereas providing interesting data, such studies have room for improvement to provide mechanistic insight into the rate and extent of specifically BBB transport and intrabrain distribution processes that all together govern CNS target delivery of the free drug. This review shortly summarizes BBB transport and current liposome-based strategies to overcome BBB transport restrictions, with the emphasis on how to determine the individual mechanisms that all together determine the time course of free drug brain concentrations, following their administration as such, and in liposomes. Animal studies using microdialysis providing time course information on unbound drug in plasma and brain are highlighted, as these provide the mechanistic information needed to understand BBB drug transport of the drug, and the impact of a liposomal formulations of that drug on BBB transport. Overall, these studies show that brain distribution of a drug administered as liposomal formulation depends on both drug properties and liposomal formulation characteristics. In general, evidence suggests that active transporters at the BBB, either being influx or efflux transporters, are circumvented by liposomes. It is concluded that liposomal formulations may provide interesting changes in BBB transport. More mechanistic studies are needed to understand relevant mechanisms in liposomal drug delivery to the brain, providing an improved basis for its prediction in human using animal data.

Developing the Smart Sorting Screw System Based on Deep Learning Approaches

The deep learning technique has turned into a mature technique. In addition, many researchers have applied deep learning methods to classify products into defective categories. However, due to the limitations of the devices, the images from factories cannot be trained and inferenced in real-time. As a result, the AI technology could not be widely implemented in actual factory inspections. In this study, the proposed smart sorting screw system combines the internet of things technique and an anomaly network for detecting the defective region of the screw product. The proposed system has three prominent characteristics. First, the spiral screw images are stitched into a panoramic image to comprehensively detect the defective region that appears on the screw surface. Second, the anomaly network comprising of convolutional autoencoder (CAE) and adversarial autoencoder (AAE) networks is utilized to automatically recognize the defective areas in the absence of a defective-free image for model training. Third, the IoT technique is employed to upload the screw image to the cloud platform for model training and inference, in order to determine if the defective screw product is a pass or fail on the production line. The experimental results show that the image stitching method can precisely merge the spiral screw image to the panoramic image. Among these two anomaly models, the AAE network obtained the best maximum IOU of 0.41 and a maximum dice coefficient score of 0.59. The proposed system has the ability to automatically detect a defective screw image, which is helpful in reducing the flow of the defective products in order to enhance product quality.

Integrating object detection and image segmentation for detecting the tool wear area on stitched image

Flank wear is the most common wear that happens in the end milling process. However, the process of detecting the flank wear is cumbersome. To achieve comprehensively automatic detecting the flank wear area of the spiral end milling cutter, this study proposed a novel flank wear detection method of combining the template matching and deep learning techniques to expand the curved surface images into panorama images, which is more available to detect the flank wear areas without choosing a specific position of cutting tool image. You Only Look Once v4 model was employed to automatically detect the range of cutting tips. Then, popular segmentation models, namely, U-Net, Segnet and Autoencoder were used to extract the areas of the tool flank wear. To evaluate the segmenting performance among these models, U-Net model obtained the best maximum dice coefficient score with 0.93. Moreover, the predicting wear areas of the U-Net model is presented in the trend figure, which can determine the times of the tool change depend on the curve of the tool wear. Overall, the experiments have shown that the proposed methods can effectively extract the tool wear regions of the spiral cutting tool. With the developed system, users can obtain detailed information about the cutting tool before being worn severely to change the cutting tools in advance.

Artificial intelligence for improving sickle cell retinopathy diagnosis and management

Sickle cell retinopathy is often initially asymptomatic even in proliferative stages, but can progress to cause vision loss due to vitreous haemorrhages or tractional retinal detachments. Challenges with access and adherence to screening dilated fundus examinations, particularly in medically underserved areas where the burden of sickle cell disease is highest, highlight the need for novel approaches to screening for patients with vision-threatening sickle cell retinopathy. This article reviews the existing literature on and suggests future research directions for coupling artificial intelligence with multimodal retinal imaging to expand access to automated, accurate, imaging-based screening for sickle cell retinopathy. Given the variability in retinal specialist practice patterns with regards to monitoring and treatment of sickle cell retinopathy, we also discuss recent progress toward development of machine learning models that can quantitatively track disease progression over time. These artificial intelligence-based applications have great potential for informing evidence-based and resource-efficient clinical diagnosis and management of sickle cell retinopathy.

An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine Learning

Benign prostatic hyperplasia (BPH) is the main cause of lower urinary tract symptoms (LUTS) in aging males. Transurethral resection of the prostate (TURP) surgery is performed by a cystoscope passing through the urethra and scraping off the prostrate piece by piece through a cutting loop. Although TURP is a minimally invasive procedure, bleeding is still the most common complication. Therefore, the evaluation, monitoring, and prevention of interop bleeding during TURP are very important issues. The main idea of this study is to rank bleeding levels during TURP surgery from videos. Generally, to judge bleeding level by human eyes from surgery videos is a difficult task, which requires sufficient experienced urologists. In this study, machine learning-based ranking algorithms are proposed to efficiently evaluate the ranking of blood levels. Based on the visual clarity of the surgical field, the four ranking of blood levels, including score 0: excellent; score 1: acceptable; score 2: slightly bad; and 3: bad, were identified by urologists who have sufficient experience in TURP surgery. The results of extensive experiments show that the revised accuracy can achieve 90, 89, 90, and 91%, respectively. Particularly, the results reveal that the proposed methods were capable of classifying the ranking of bleeding level accurately and efficiently reducing the burden of urologists.

Evolutionary Optimization of Machining Parameters Based on Surface Roughness in End Milling of Hot Rolled Steel

Surface roughness measurements of machined parts are usually performed off-line after the completion of the machining operation. The objective of this work is to develop a surface roughness prediction method based on the processing of vibration signals during steel end milling operation performed on a vertical CNC machining center. The milling cuts were run under varying conditions (such as the spindle speed, feed rate, and depth of cut). This is a first step in the attempt to develop an online milling process monitoring system. The study presented here involves the analysis of vibration signals using statistical time parameters, frequency spectrum, and time-frequency wavelet decomposition. The analysis resulted in the extraction of 245 features that were used in the evolutionary optimization study to determine optimal cutting conditions based on the measured surface roughness of the milled specimen. Three feature selection methods were used to reduce the extracted feature set to smaller subsets, followed by binarization using two binarization methods. Three evolutionary algorithms—a genetic algorithm, particle swarm optimization and two variants, differential evolution and one of its variants, have been used to identify features that relate to the “best” surface finish measurements. These optimal features can then be related to cutting conditions (cutting speed, feed rate, and axial depth of cut). It is shown that the differential evolution and its variant performed better than the particle swarm optimization and its variants, and both differential evolution and particle swarm optimization perform better than the canonical genetic algorithm. Significant differences are found in the feature selection methods too, but no difference in performance was found between the two binarization methods.

In Silico Evaluation of Iranian Medicinal Plant Phytoconstituents as Inhibitors against Main Protease and the Receptor-Binding Domain of SARS-CoV-2

The novel coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initially appeared in Wuhan, China, in December 2019. Elderly individuals and those with comorbid conditions may be more vulnerable to this disease. Consequently, several research laboratories continue to focus on developing drugs to treat this infection because this disease has developed into a global pandemic with an extremely limited number of specific treatments available. Natural herbal remedies have long been used to treat illnesses in a variety of cultures. Modern medicine has achieved success due to the effectiveness of traditional medicines, which are derived from medicinal plants. The objective of this study was to determine whether components of natural origin from Iranian medicinal plants have an antiviral effect that can prevent humans from this coronavirus infection using the most reliable molecular docking method; in our case, we focused on the main protease (Mpro) and a receptor-binding domain (RBD). The results of molecular docking showed that among 169 molecules of natural origin from common Iranian medicinal plants, 20 molecules (chelidimerine, rutin, fumariline, catechin gallate, adlumidine, astragalin, somniferine, etc.) can be proposed as inhibitors against this coronavirus based on the binding free energy and type of interactions between these molecules and the studied proteins. Moreover, a molecular dynamics simulation study revealed that the chelidimerine-Mpro and somniferine-RBD complexes were stable for up to 50 ns below 0.5 nm. Our results provide valuable insights into this mechanism, which sheds light on future structure-based designs of high-potency inhibitors for SARS-CoV-2.

Metagenomics Reveals That Proper Placement After Long-Distance Transportation Significantly Affects Calf Nasopharyngeal Microbiota and Is Critical for the Prevention of Respiratory Diseases

Transportation is an inevitable phase for the cattle industry, and its effect on the respiratory system of transported cattle remains controversial. To reveal cattle's nasopharyngeal microbiota dynamics, we tracked a batch of beef calves purchased from an auction market, transported to a farm by vehicle within 3 days, and adaptively fed for 7 days. Before and after the transport and after the placement, a total of 18 nasopharyngeal mucosal samples were collected, and microbial profiles were obtained using a metagenomic shotgun sequencing approach. The diversity, composition, structure, and function of the microbiota were collected at each time point, and their difference was analyzed. The results showed that, before the transportation, there were a great abundance of potential bovine respiratory disease (BRD)-related pathogens, and the transportation did not significantly change their abundance. After the transportation, 7 days of placement significantly decreased the risk of BRD by decreasing the abundance of potential BRD-related pathogens even if the diversity was decreased. We also discussed the controversial results of transportation's effect in previous works and the decrease in diversity induced by placement. Our work provided more accurate information about the effect of transportation and the followed placement on the calf nasopharyngeal microbial community, indicated the importance of adaptive placement after long-distance transport, and is helpful to prevent BRD induced by transportation stress.

Serverless computing in omics data analysis and integration

A comprehensive analysis of omics data can require vast computational resources and access to varied data sources that must be integrated into complex, multi-step analysis pipelines. Execution of many such analyses can be accelerated by applying the cloud computing paradigm, which provides scalable resources for storing data of different types and parallelizing data analysis computations. Moreover, these resources can be reused for different multi-omics analysis scenarios. Traditionally, developers are required to manage a cloud platform’s underlying infrastructure, configuration, maintenance and capacity planning. The serverless computing paradigm simplifies these operations by automatically allocating and maintaining both servers and virtual machines, as required for analysis tasks. This paradigm offers highly parallel execution and high scalability without manual management of the underlying infrastructure, freeing developers to focus on operational logic. This paper reviews serverless solutions in bioinformatics and evaluates their usage in omics data analysis and integration. We start by reviewing the application of the cloud computing model to a multi-omics data analysis and exposing some shortcomings of the early approaches. We then introduce the serverless computing paradigm and show its applicability for performing an integrative analysis of multiple omics data sources in the context of the COVID-19 pandemic.

A Novel Machine-Learning-Based Procedure to Determine the Surface Finish Quality of Titanium Alloy Parts Obtained by Heat Assisted Single Point Incremental Forming

Single point incremental forming (SPIF) is a cheap and flexible sheet metal forming process for rapid manufacturing of complex geometries. Additionally, it is important for engineers to measure the surface finish of work pieces to assess their quality and performance. In this paper, a predictive model based on machine learning and computer vision was developed to estimate arithmetic mean surface roughness (Ra) and maximum peak to valley height (Rz) of Ti6Al4V parts obtained by SPIF. An image database was prepared to train different classification algorithms in accordance with a supervised learning approach. A speeded up robust feature (SURF) detector was used to obtain visual vocabulary so that the classifiers are able to group the photographs into classes. The experimental results indicated that the proposed predictive method shows great potential to determine the surface quality, as classifiers based on a support vector machine with a polynomial kernel are suitable for this purpose.

A database resource and online analysis tools for coronaviruses on a historical and global scale

The recent outbreak of COVID-19 caused by a new zoonotic origin coronavirus (SARS-CoV-2 or 2019-nCoV) has sound the alarm for the potential spread of epidemic coronavirus crossing species. With the urgent needs to assist disease control and to provide invaluable scientific information, we developed the coronavirus database (CoVdb), an online genomic, proteomic and evolutionary analysis platform. CoVdb has brought together genomes of more than 5000 coronavirus strains, which were collected from 1941 to 2020, in more than 60 countries and in hosts belonging to more than 30 species, ranging from fish to human. CoVdb presents comprehensive genomic information, such as gene function, subcellular localization, topology and protein structure. To facilitate coronavirus research, CoVdb also provides flexible search approaches and online tools to view and analyze protein structure, to perform multiple alignments, to automatically build phylogenetic trees and to carry on evolutionary analyses. CoVdb can be accessed freely at http://covdb.popgenetics.net. Hopefully, it will accelerate the progress to develop medicines or vaccines to control the pandemic of COVID-19.

Recent Progress in the Drug Development for the Treatment of Alzheimer's Disease Especially on Inhibition of Amyloid-peptide Aggregation

As the world 's population is aging, Alzheimer's disease (AD) has become a big concern since AD has started affecting younger people and the population of AD patients is increasing worldwide. It has been revealed that the neuropathological hallmarks of AD are typically characterized by the presence of neurotoxic extracellular amyloid plaques in the brain, which are surrounded by tangles of neuronal fibers. However, the causes of AD have not been completely understood yet. Currently, there is no drug to effectively prevent AD or to completely reserve the symptoms in the patients. This article reviews the pathological features associated with AD, the recent progress in research on the drug development to treat AD, especially on the discovery of natural product derivatives to inhibit Aβ peptide aggregation as well as the design and synthesis of Aβ peptide aggregation inhibitors to treat AD.

Genetic diversity of Indian garlic core germplasm using agro-biochemical traits and SRAP markers

The characterization of garlic germplasm improves its utility, despite the fact that garlic hasn't been used much in the past. Garlic has an untapped genetic pool of immense economic and medicinal value in India. Hence, using heuristic core collection approach, a core set of 46 accessions were selected from 625 Indian garlic accessions based on 13 quantitative and five qualitative traits. The statistical measures (CV per cent, CR per cent, VR per cent) were used to sort the core set using Shannon-Wiener diversity index and the Nei diversity index. In addition, the variation within the core set was tested for 18 agro-morphological and six biochemical characteristics (allicin, phenol content, pyruvic acid, protein, allyl methyl thiosulfinate (AMTHS), and methyl allyl thiosulfinate (MATHS)). Further study of the core set's molecular diversity was performed using sequence related amplified polymorphism (SRAP) markers, which revealed a wide range of diversity among the core set's accessions, with an average polymorphism efficiency (PE) of 80.59 percent, polymorphism information content (PIC) of 0.29, effective multiplex ratio (EMR) of 3.51, and marker index (MI) of 0.99. The findings of this study will be useful in identifying high-yielding, elite garlic germplasm lines with the trait of interest. Since this core set is indicative of total germplasm, these selected breeding lines will be used for genetic improvement of garlic in the future.

Genomics pipelines to investigate susceptibility in whole genome and exome sequenced data for variant discovery, annotation, prediction and genotyping

Over the last few decades, genomics is leading toward audacious future, and has been changing our views about conducting biomedical research, studying diseases, and understanding diversity in our society across the human species. The whole genome and exome sequencing (WGS/WES) are two of the most popular next-generation sequencing (NGS) methodologies that are currently being used to detect genetic variations of clinical significance. Investigating WGS/WES data for the variant discovery and genotyping is based on the nexus of different data analytic applications. Although several bioinformatics applications have been developed, and many of those are freely available and published. Timely finding and interpreting genetic variants are still challenging tasks among diagnostic laboratories and clinicians. In this study, we are interested in understanding, evaluating, and reporting the current state of solutions available to process the NGS data of variable lengths and types for the identification of variants, alleles, and haplotypes. Residing within the scope, we consulted high quality peer reviewed literature published in last 10 years. We were focused on the standalone and networked bioinformatics applications proposed to efficiently process WGS and WES data, and support downstream analysis for gene-variant discovery, annotation, prediction, and interpretation. We have discussed our findings in this manuscript, which include but not are limited to the set of operations, workflow, data handling, involved tools, technologies and algorithms and limitations of the assessed applications.

Recognition Method of Digital Meter Readings in Substation Based on Connected Domain Analysis Algorithm

Aiming at the problem that the number and decimal point of digital instruments in substations are prone to misdetection and missed detection, a method of digital meter readings in a substation based on connected domain analysis algorithm is proposed. This method uses Faster R-CNN (Faster Region Convolutional Neural Network) as a positioning network to localize the dial area, and after acquiring the partial image, it enhances the useful information of the digital area. YOLOv4 (You Only Look Once) convolutional neural network is used as the detector to detect the digital area. The purpose is to distinguish the numbers and obtain the digital area that may contain a decimal point or no decimal point at the tail. Combined with the connected domain analysis algorithm, the difference between the number of connected domain categories and the area ratio of the digital area is analyzed, and the judgment of the decimal point is realized. The method reduces the problem of mutual interference among categories when detecting YOLOv4. The experimental results show that the method improves the detection accuracy of the algorithm.

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.

A Large-Scale and Serverless Computational Approach for Improving Quality of NGS Data Supporting Big Multi-Omics Data Analyses

Various types of analyses performed over multi-omics data are driven today by next-generation sequencing (NGS) techniques that produce large volumes of DNA/RNA sequences. Although many tools allow for parallel processing of NGS data in a Big Data distributed environment, they do not facilitate the improvement of the quality of NGS data for a large scale in a simple declarative manner. Meanwhile, large sequencing projects and routine DNA/RNA sequencing associated with molecular profiling of diseases for personalized treatment require both good quality data and appropriate infrastructure for efficient storing and processing of the data. To solve the problems, we adapt the concept of Data Lake for storing and processing big NGS data. We also propose a dedicated library that allows cleaning the DNA/RNA sequences obtained with single-read and paired-end sequencing techniques. To accommodate the growth of NGS data, our solution is largely scalable on the Cloud and may rapidly and flexibly adjust to the amount of data that should be processed. Moreover, to simplify the utilization of the data cleaning methods and implementation of other phases of data analysis workflows, our library extends the declarative U-SQL query language providing a set of capabilities for data extraction, processing, and storing. The results of our experiments prove that the whole solution supports requirements for ample storage and highly parallel, scalable processing that accompanies NGS-based multi-omics data analyses.

PIP-SNP: a pipeline for processing SNP data featured as linkage disequilibrium bin mapping, genotype imputing and marker synthesizing

Genome-wide association study data analyses often face two significant challenges: (i) high dimensionality of single-nucleotide polymorphism (SNP) genotypes and (ii) imputation of missing values. SNPs are not independent due to physical linkage and natural selection. The correlation of nearby SNPs is known as linkage disequilibrium (LD), which can be used for LD conceptual SNP bin mapping, missing genotype inferencing and SNP dimension reduction. We used a stochastic process to describe the SNP signals and proposed two types of autocorrelations to measure nearby SNPs' information redundancy. Based on the calculated autocorrelation coefficients, we constructed LD bins. We adopted a k-nearest neighbors algorithm (kNN) to impute the missing genotypes. We proposed several novel methods to find the optimal synthetic marker to represent the SNP bin. We also proposed methods to evaluate the information loss or information conservation between using the original genome-wide markers and using dimension-reduced synthetic markers. Our performance assessments on the real-life SNP data from a rice recombinant inbred line (RIL) population and a rice HapMap project show that the new methods produce satisfactory results. We implemented these functional modules in C/C++ and streamlined them into a web-based pipeline named PIP-SNP (https://bioinfo.noble.org/PIP_SNP/) for processing SNP data.

Texture analysis based on U-Net neural network for intracranial hemorrhage identification predicts early enlargement

BACKGROUND AND OBJECTIVE

Early hemorrhage enlargement in hypertensive cerebral hemorrhage indicates a poor prognosis. This study aims to predict the early enlargement of cerebral hemorrhage through the intelligent texture analysis of cerebral hemorrhage after segmentation.

METHODS

A total of 54 patients with hypertensive intracerebral hemorrhage were selected and divided into enlarged hematoma (enlarged group) and non-enlarged hematoma (negative group). The U-Net Neural network model and contour recognition were used to extract the brain parenchymal region, and Mazda texture analysis software was used to extract regional features. The texture features were reduced by Fisher coefficient (Fisher), classification error probability combined average correlation coefficients (POE + ACC), and mutual information (MI) to select the best feature parameters. B11 module was used to analyze the selected features. The misclassified rate of feature parameters screened by different dimensionality reduction methods was calculated.

RESULTS

The neural network based on U-Net can accurately identify the lesion of cerebral hemorrhage. Among the 54 patients, 18 were in the enlarged group and 36 in the negative group. The parameters of gray level co-occurrence matrix and gray level run length matrix can be used to predict the enlargement of intracerebral hemorrhage. Among the features screened by Fisher, POE + ACC and MI, the texture features of MI showed the lowest misclassified rate, which was 0.

CONCLUSION

The texture analysis based on U-Net neural network is helpful to predict the early expansion of hypertensive cerebral hemorrhage, and the parameters of gray level co-occurrence matrix and gray level run length matrix under MI dimensionality reduction have the most excellent predictive value.

Monitoring and prediction of dust concentration in an open-pit mine using a deep-learning algorithm

PURPOSE

Dust pollution is currently one of the most serious environmental problems faced by open-pit mines. Compared with underground mining, open-pit mining has many dust sources, and a wide area of influence and complicated changes in meteorological conditions can result in great variations in dust concentration. Therefore, the prediction of dust concentrations in open-pit mines requires research and is of great significance for reducing environmental pollution and personal health hazards.

METHODS

This study is based on monitoring of the concentration of total suspended particulate (TSP) in the Anjialing open-pit coal mine in Pingshuo. This paper proposes a hybrid model based on a long short-term memory (LSTM) network and the attention mechanism (LSTM-Attention) and applies it to the prediction of TSP concentration. The LSTM model reflects the historical process of an input time series, and the attention mechanism extracts the inherent characteristics of the input parameters to assign weights based on the importance of the influencing factors. The autoregressive integrated moving average (ARIMA) and LSTM models are also used to predict the TSP concentration. Finally, several statistical measures of error are used to evaluate the accuracy of the model and perform a sensitivity analysis.

RESULTS

It was found that, in general, the TSP concentration was highest in the period 08:00-09:00 and lowest in the period 15:00-16:00. In addition to the influence of meteorological parameters and normal operations, the reason for this trend is the presence of an inversion layer above the open-pit mine. The results show that, compared with the ARIMA and LSTM models, the LSTM-Attention model is more stable and has a prediction accuracy that is 5.6% and 3.0% greater, respectively.

CONCLUSION

This model can be applied to the prediction of dust concentrations in open-pit mines and provide guidance on when to carry out dust-suppression work. It has expansibility and is potentially valuable for application in a wide range of areas.

Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0

Smart Agriculture or Agricultural Internet of things, consists of integrating advanced technologies (e.g., NFV, SDN, 5G/6G, Blockchain, IoT, Fog, Edge, and AI) into existing farm operations to improve the quality and productivity of agricultural products. The convergence of Industry 4.0 and Intelligent Agriculture provides new opportunities for migration from factory agriculture to the future generation, known as Agriculture 4.0. However, since the deployment of thousands of IoT based devices is in an open field, there are many new threats in Agriculture 4.0. Security researchers are involved in this topic to ensure the safety of the system since an adversary can initiate many cyber attacks, such as DDoS attacks to making a service unavailable and then injecting false data to tell us that the agricultural equipment is safe but in reality, it has been theft. In this paper, we propose a deep learning-based intrusion detection system for DDoS attacks based on three models, namely, convolutional neural networks, deep neural networks, and recurrent neural networks. Each model’s performance is studied within two classification types (binary and multiclass) using two new real traffic datasets, namely, CIC-DDoS2019 dataset and TON_IoT dataset, which contain different types of DDoS attacks.

CAT-CAD: A Computer-Aided Diagnosis Tool for Cataplexy

Narcolepsy with cataplexy is a severe lifelong disorder characterized, among others, by sudden loss of bilateral face muscle tone triggered by emotions (cataplexy). A recent approach for the diagnosis of the disease is based on a completely manual analysis of video recordings of patients undergoing emotional stimulation made on-site by medical specialists, looking for specific facial behavior motor phenomena. We present here the CAT-CAD tool for automatic detection of cataplexy symptoms, with the double aim of (1) supporting neurologists in the diagnosis/monitoring of the disease and (2) facilitating the experience of patients, allowing them to conduct video recordings at home. CAT-CAD includes a front-end medical interface (for the playback/inspection of patient recordings and the retrieval of videos relevant to the one currently played) and a back-end AI-based video analyzer (able to automatically detect the presence of disease symptoms in the patient recording). Analysis of patients’ videos for discovering disease symptoms is based on the detection of facial landmarks, and an alternative implementation of the video analyzer, exploiting deep-learning techniques, is introduced. Performance of both approaches is experimentally evaluated using a benchmark of real patients’ recordings, demonstrating the effectiveness of the proposed solutions.

Knowledge-Based System for Crop Pests and Diseases Recognition

With the rapid increase in the world’s population, there is an ever-growing need for a sustainable food supply. Agriculture is one of the pillars for worldwide food provisioning, with fruits and vegetables being essential for a healthy diet. However, in the last few years the worldwide dispersion of virulent plant pests and diseases has caused significant decreases in the yield and quality of crops, in particular fruit, cereal and vegetables. Climate change and the intensification of global trade flows further accentuate the issue. Integrated Pest Management (IPM) is an approach to pest control that aims at maintaining pest insects at tolerable levels, keeping pest populations below an economic injury level. Under these circumstances, the early identification of pests and diseases becomes crucial. In this work, we present the first step towards a fully fledged, semantically enhanced decision support system for IPM. The ultimate goal is to build a complete agricultural knowledge base by gathering data from multiple, heterogeneous sources and to develop a system to assist farmers in decision making concerning the control of pests and diseases. The pest classifier framework has been evaluated in a simulated environment, obtaining an aggregated accuracy of 98.8%.

Risk factor analysis of allergic rhinitis in 6-8 year-old children in Taipei

The incidence of allergic rhinitis (AR) has increased rapidly in Taiwan during the past 30 years; however, potential risk factors of AR have yet to be examined. The purpose of this study is to explore the prevalence, personal and environmental risk factors of rhinitis. A cross-sectional survey was conducted in 26418 first graders (6–8 years old) in Taipei with a response rate of 94.6% (24999/26418). Modified International Study of Asthma and Allergies in Childhood (ISAAC) questionnaires were completed by their parents or main caregivers. Logistic regression was used to examine possible personal and environmental (in early life and current) factors related to rhinitis. The prevalence of rhinitis in the past 12 months was 42.8% in 6–8 years old children. Multivariate logistic regression analysis for both males and females revealed that male gender, antibiotic use in first year of life, bronchiolitis before the age of two years, diagnosed asthma, and diagnosed eczema, having a cat the first year of life were associated with an increased risk of rhinitis. Having older siblings, on the other hand, may reduce the risk of rhinitis. Based on the present study, we may recommend less use of antibiotics the first year of life and not having a cat in the home in the child’s first year of life as preventive measures to reduce the risk of rhinitis. From the subgroup analysis, we can take preventive measures for the different risk factors of rhinitis and the severity of rhinitis in each subgroup.

Semi-Automatic Guidance vs. Manual Guidance in Agriculture: A Comparison of Work Performance in Wheat Sowing

The use of digital systems in precision agriculture is becoming more and more attractive for farmers at every level. A few years ago, the use of these technologies was limited to large farms, due to the considerable income needed to amortize the large investment required. Although this technology has now become more affordable, there is a lack of scientific data directed to demonstrate how these systems are able to determine quantifiable advantages for farmers. Thus, the transition towards precision agriculture is still very slow. This issue is not just negatively affecting the agriculture economy, but it is also slowing down potential environmental benefits that may result from it. The starting point of precision agriculture can be considered as the introduction of satellite tractor guidance. For instance, with semi-automatic and automatic tractor guidance, farmers can profit from more accuracy and higher machine performance during several farm operations such as plowing, harrowing, sowing, and fertilising. The goal of this study is to compare semi-automatic guidance with manual guidance in wheat sowing, evaluating parameters such as machine performance, seed supply and operational costs of both the configurations.

Molecular characteristics of Brucella melitensis isolates from humans in Qinghai Province, China

BACKGROUND

The prevalence of human brucellosis in Qinghai Province of China has been increasing rapidly, with confirmed cases distributed across 31 counties. However, the epidemiology of brucellosis transmission has not been fully elucidated. To characterize the infecting strains isolated from humans, multiple-locus variable-number tandem repeats analysis (MLVA) and whole-genome single-nucleotide polymorphism (SNP)-based approaches were employed.

METHODS

Strains were isolated from two males blood cultures that were confirmed Brucella melitensis positive following biotyping and MLVA. Genomic DNA was extracted from these two strains, and whole-genome sequencing was performed. Next, SNP-based phylogenetic analysis was performed to compare the two strains to 94 B. melitensis strains (complete genome and draft genome) retrieved from online databases.

RESULTS

The two Brucella isolates were identified as B. melitensis biovar 3 (QH2019001 and QH2019005) following conventional biotyping and were found to have differences in their variable number tandem repeats (VNTRs) using MLVA-16. Phylogenetic examination assigned the 96 strains to five genotype groups, with QH2019001 and QH2019005 assigned to the same group, but different subgroups. Moreover, the QH2019005 strain was assigned to a new subgenotype, IIj, within genotype II. These findings were then combined to determine the geographic origin of the two Brucella strains.

CONCLUSIONS

Utilizing a whole-genome SNP-based approach enabled differences between the two B. melitensis strains to be more clearly resolved, and facilitated the elucidation of their different evolutionary histories. This approach also revealed that QH2019005 is a member of a new subgenotype (IIj) with an ancient origin in the eastern Mediterranean Sea.

Epiretinal membrane: A review

The prevalence of epiretinal membrane (ERM) is 7% to 11.8%, with increasing age being the most important risk factor. Although most ERM is idiopathic, common secondary causes include cataract surgery, retinal vascular disease, uveitis and retinal tears. The myofibroblastic pre-retinal cells are thought to transdifferentiate from glial and retinal pigment epithelial cells that reach the retinal surface via defects in the internal limiting membrane (ILM) or from the vitreous cavity. Grading schemes have evolved from clinical signs to ocular coherence tomography (OCT) based classification with associated features such as the cotton ball sign. Features predictive of better prognosis include absence of ectopic inner foveal layers, cystoid macular oedema, acquired vitelliform lesions and ellipsoid and cone outer segment termination defects. OCT-angiography shows reduced size of the foveal avascular zone. Vitrectomy with membrane peeling remains the mainstay of treatment for symptomatic ERMs. Additional ILM peeling reduces recurrence but is associated with anatomical changes including inner retinal dimpling.

Positively Charged Nanoparticle Delivery of n-Butylidenephthalide Enhances Antitumor Effect in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the second and sixth leading cause of cancer death in men and woman in 185 countries statistics, respectively. n-Butylidenephthalide (BP) has shown anti-HCC activity, but it also has an unstable structure that decreases its potential antitumor activity. The aim of this study was to investigate the cell uptake, activity protection, and antitumor mechanism of BP encapsulated in the novel liposome LPPC in HCC cells. BP/LPPC exhibited higher cell uptake and cytotoxicity than BP alone, and combined with clinical drug etoposide (VP-16), BP/LPPC showed a synergistic effect against HCC cells. Additionally, BP/LPPC increased cell cycle regulators (p53, p-p53, and p21) and decreased cell cycle-related proteins (Rb, p-Rb, CDK4, and cyclin D1), leading to cell cycle arrest at the G₀/G₁ phase in HCC cells. BP/LPPC induced cell apoptosis through activation of both the extrinsic (Fas-L and Caspase-8) and intrinsic (Bax and Caspase-9) apoptosis pathways and activated the caspase cascade to trigger HCC cell death. In conclusion, the LPPC complex improved the antitumor activity of BP in terms of cytotoxicity, cell cycle regulation and cell apoptosis, and BP/LPPC synergistically inhibited cell growth during combination treatment with VP-16 in HCC cells. Therefore, BP/LPPC is potentially a good candidate for clinical drug development or for use as an adjuvant for clinical drugs as a combination therapy for hepatocellular carcinoma.

A novel approach to thermographic images analysis of equine thoracolumbar region: the effect of effort and rider's body weight on structural image complexity

BACKGROUND

The horses' backs are particularly exposed to overload and injuries due to direct contact with the saddle and the influence of e.g. the rider's body weight. The maximal load for a horse's back during riding has been suggested not to exceed 20% of the horses' body weight. The common prevalence of back problems in riding horses prompted the popularization of thermography of the thoracolumbar region. However, the analysis methods of thermographic images used so far do not distinguish loaded horses with body weight varying between 10 and 20%.

RESULTS

The superficial body temperature (SBT) of the thoracolumbar region of the horse's back was imaged using a non-contact thermographic camera before and after riding under riders with LBW (low body weight, 10%) and HBW (high body weight, 15%). Images were analyzed using six methods: five recent SBT analyses and the novel approach based on Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM). Temperatures of the horse's thoracolumbar region were higher (p < 0.0001) after then before the training, and did not differ depending on the rider's body weight (p > 0.05), regardless of used SBT analysis method. Effort-dependent differences (p < 0.05) were noted for six features of GLCM and GLRLM analysis. The values of selected GLCM and GLRLM features also differed (p < 0.05) between the LBW and HBW groups.

CONCLUSION

The GLCM and GLRLM analyses allowed the differentiation of horses subjected to a load of 10 and 15% of their body weights while horseback riding in contrast to the previously used SBT analysis methods. Both types of analyzing methods allow to differentiation thermal images obtained before and after riding. The textural analysis, including selected features of GLCM or GLRLM, seems to be promising tools in considering the quantitative assessment of thermographic images of horses' thoracolumbar region.

Nanocarrier-Based Drug Delivery for Melanoma Therapeutics

Melanoma, as a tumor cell derived from melanocyte transformation, has the characteristics of malignant proliferation, high metastasis, rapid recurrence, and a low survival rate. Traditional therapy has many shortcomings, including drug side effects and poor patient compliance, and so on. Therefore, the development of an effective treatment is necessary. Currently, nanotechnologies are a promising oncology treatment strategy because of their ability to effectively deliver drugs and other bioactive molecules to targeted tissues with low toxicity, thereby improving the clinical efficacy of cancer therapy. In this review, the application of nanotechnology in the treatment of melanoma is reviewed and discussed. First, the pathogenesis and molecular targets of melanoma are elucidated, and the current clinical treatment strategies and deficiencies of melanoma are then introduced. Following this, we discuss the main features of developing efficient nanosystems and introduce the latest reports in the literature on nanoparticles for the treatment of melanoma. Subsequently, we review and discuss the application of nanoparticles in chemotherapeutic agents, immunotherapy, mRNA vaccines, and photothermal therapy, as well as the potential of nanotechnology in the early diagnosis of melanoma.

Radiomics based on multiparametric MRI for extrathyroidal extension feature prediction in papillary thyroid cancer

BACKGROUND

To determine the predictive capability of MRI-based radiomics for extrathyroidal extension detection in papillary thyroid cancer (PTC) pre-surgically.

METHODS

The present retrospective trial assessed individuals with thyroid nodules examined by multiparametric MRI and subsequently administered thyroid surgery. Diagnosis and extrathyroidal extension (ETE) feature of PTC were based on pathological assessment. The thyroid tumors underwent manual segmentation, for radiomic feature extraction. Participants were randomized to the training and testing cohorts, at a ratio of 7:3. The mRMR (maximum correlation minimum redundancy) algorithm and the least absolute shrinkage and selection operator were utilized for radiomics feature selection. Then, a radiomics predictive model was generated via a linear combination of the features. The model's performance in distinguishing the ETE feature of PTC was assessed by analyzing the receiver operating characteristic curve.

RESULTS

Totally 132 patients were assessed in this study, including 92 and 40 in the training and test cohorts, respectively). Next, the 16 top-performing features, including 4, 7 and 5 from diffusion weighted (DWI), T2-weighted (T2 WI), and contrast-enhanced T1-weighted (CE-T1WI) images, respectively, were finally retained to construct the radiomics signature. There were 8 RLM, 5 CM, 2 shape, and 1 SZM features. The radiomics prediction model achieved AUCs of 0.96 and 0.87 in the training and testing sets, respectively.

CONCLUSIONS

Our study indicated that MRI radiomics approach had the potential to stratify patients based on ETE in PTCs preoperatively.

An Object-Based Image Analysis Approach Using Bathymetry and Bathymetric Derivatives to Classify the Seafloor

In this paper, object-based image analysis classification methods are developed that do not rely on backscatter in order to classify the seafloor. Instead, these methods make use of bathymetry, bathymetric derivatives, and grab samples for classification. The classification is performed on image object statistics. One of the methods utilizes only texture-based features, that is, features that are related to the spatial arrangement of image characteristics. The second method is similar, but relies on a wider set of image object features. The methods were developed and tested using a dataset from Norwegian waters, specifically the Røstbanken area off the coast of Lofoten. The classification results were compared to backscatter-based classification and to grab sample ground-reference data. The algorithm that performed the best was then also applied to a dataset from the Borkumer Stones area close to the island of Schiermonnikoog in Dutch waters. This allowed testing the applicability of the algorithm for different datasets. Because the algorithms that were developed do not require backscatter, the availability of which is much more scarce than bathymetry, and because of the low computational requirements, they could be applied to any area where high-resolution bathymetry and grab samples are available.

Multiple Alignment of Promoter Sequences from the Arabidopsis thaliana L. Genome

In this study, we developed a new mathematical method for performing multiple alignment of highly divergent sequences (MAHDS), i.e., sequences that have on average more than 2.5 substitutions per position (x). We generated sets of artificial DNA sequences with x ranging from 0 to 4.4 and applied MAHDS as well as currently used multiple sequence alignment algorithms, including ClustalW, MAFFT, T-Coffee, Kalign, and Muscle to these sets. The results indicated that most of the existing methods could produce statistically significant alignments only for the sets with x < 2.5, whereas MAHDS could operate on sequences with x = 4.4. We also used MAHDS to analyze a set of promoter sequences from the Arabidopsis thaliana genome and discovered many conserved regions upstream of the transcription initiation site (from -499 to +1 bp); a part of the downstream region (from +1 to +70 bp) also significantly contributed to the obtained alignments. The possibilities of applying the newly developed method for the identification of promoter sequences in any genome are discussed. A server for multiple alignment of nucleotide sequences has been created.

Enhancement of cytotoxicity and induction of apoptosis by cationic nano-liposome formulation of n-butylidenephthalide in breast cancer cells

Breast cancer is the second most common malignancy in women. Current clinical therapy for breast cancer has many disadvantages, including metastasis, recurrence, and poor quality of life. Furthermore, it is necessary to find a new therapeutic drug for breast cancer patients to meet clinical demand. n-Butylidenephthalide (BP) is a natural and hydrophobic compound that can inhibit several tumors. However, BP is unstable in aqueous or protein-rich environments, which reduces the activity of BP. Therefore, we used an LPPC (Lipo-PEG-PEI complex) that can encapsulate both hydrophobic and hydrophilic compounds to improve the limitation of BP. The purpose of this study is to investigate the anti-tumor mechanisms of BP and BP/LPPC and further test the efficacy of BP encapsulated by LPPC on SK-BR-3 cells. BP inhibited breast cancer cell growth, and LPPC encapsulation (BP/LPPC complex) enhanced the cytotoxicity on breast cancer by stabilizing the BP activity and offering endocytic pathways. Additionally, BP and LPPC-encapsulated BP induced cell cycle arrest at the G₀/G₁ phase and might trigger both extrinsic as well as intrinsic cell apoptosis pathway, resulting in cell death. Moreover, the BP/LPPC complex had a synergistic effect with doxorubicin of enhancing the inhibitory effect on breast cancer cells. Consequently, LPPC-encapsulated BP could improve the anti-cancer effects on breast cancer in vitro. In conclusion, BP exhibited an anti-cancer effect on breast cancer cells, and LPPC encapsulation efficiently improved the cytotoxicity of BP via an acceleration of entrapment efficiency to induce cell cycle block and apoptosis. Furthermore, BP/LPPC exhibited a synergistic effect in combination with doxorubicin.

Deep Learning for SARS COV-2 Genome Sequences

The SARS-CoV-2 virus which originated in Wuhan, China has since spread throughout the world and is affecting millions of people. When there is a novel virus outbreak, it is crucial to quickly determine if the epidemic is a result of the novel virus or a well-known virus. We propose a deep learning algorithm that uses a convolutional neural network (CNN) as well as a bi-directional long short-term memory (Bi-LSTM) neural network, for the classification of the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) amongst Coronaviruses. Besides, we classify whether a genome sequence contains candidate regulatory motifs or otherwise. Regulatory motifs bind to transcription factors. Transcription factors are responsible for the expression of genes. The experimental results show that at peak performance, the proposed convolutional neural network bi-directional long short-term memory (CNN-Bi-LSTM) model achieves a classification accuracy of 99.95%, area under curve receiver operating characteristic (AUC ROC) of 100.00%, a specificity of 99.97%, the sensitivity of 99.97%, Cohen's Kappa equal to 0.9978, Mathews Correlation Coefficient (MCC) equal to 0.9978 for the classification of SARS CoV-2 amongst Coronaviruses. Also, the CNN-Bi-LSTM correctly detects whether a sequence has candidate regulatory motifs or binding-sites with a classification accuracy of 99.76%, AUC ROC of 100.00%, a specificity of 99.76%, a sensitivity of 99.76%, MCC equal to 0.9980, and Cohen's Kappa of 0.9970 at peak performance. These results are encouraging enough to recognise deep learning algorithms as alternative avenues for detecting SARS CoV-2 as well as detecting regulatory motifs in the SARS CoV-2 genes.

Genomic recombination events may reveal the evolution of coronavirus and the origin of SARS-CoV-2

To trace the evolution of coronaviruses and reveal the possible origin of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19), we collected and thoroughly analyzed 29,452 publicly available coronavirus genomes, including 26,312 genomes of SARS-CoV-2 strains. We observed coronavirus recombination events among different hosts including 3 independent recombination events with statistical significance between some isolates from humans, bats and pangolins. Consistent with previous records, we also detected putative recombination between strains similar or related to Bat-CoV-RaTG13 and Pangolin-CoV-2019. The putative recombination region is located inside the receptor-binding domain (RBD) of the spike glycoprotein (S protein), which may represent the origin of SARS-CoV-2. Population genetic analyses provide estimates suggesting that the putative introduced DNA within the RBD is undergoing directional evolution. This may result in the adaptation of the virus to hosts. Unsurprisingly, we found that the putative recombination region in S protein was highly diverse among strains from bats. Bats harbor numerous coronavirus subclades that frequently participate in recombination events with human coronavirus. Therefore, bats may provide a pool of genetic diversity for the origin of SARS-CoV-2.

The evolution of the huntingtin-associated protein 40 (HAP40) in conjunction with huntingtin

BACKGROUND

The huntingtin-associated protein 40 (HAP40) abundantly interacts with huntingtin (HTT), the protein that is altered in Huntington's disease (HD). Therefore, we analysed the evolution of HAP40 and its interaction with HTT.

RESULTS

We found that in amniotes HAP40 is encoded by a single-exon gene, whereas in all other organisms it is expressed from multi-exon genes. HAP40 co-occurs with HTT in unikonts, including filastereans such as Capsaspora owczarzaki and the amoebozoan Dictyostelium discoideum, but both proteins are absent from fungi. Outside unikonts, a few species, such as the free-living amoeboflagellate Naegleria gruberi, contain putative HTT and HAP40 orthologs. Biochemically we show that the interaction between HTT and HAP40 extends to fish, and bioinformatic analyses provide evidence for evolutionary conservation of this interaction. The closest homologue of HAP40 in current protein databases is the family of soluble N-ethylmaleimide-sensitive factor attachment proteins (SNAPs).

CONCLUSION

Our results indicate that the transition from a multi-exon to a single-exon gene appears to have taken place by retroposition during the divergence of amphibians and amniotes, followed by the loss of the parental multi-exon gene. Furthermore, it appears that the two proteins probably originated at the root of eukaryotes. Conservation of the interaction between HAP40 and HTT and their likely coevolution strongly indicate functional importance of this interaction.

The application of computer graphics processing in visual communication design

The purpose of this paper is to improve the existing computer graphics image processing technology, so that designers can produce more inspiration, improve the author’s ability to innovate. Based on the information in the field of graphics visual communication as the research object, through the elaboration of graphical information characteristics, development course, and the visual communication of computer graphical related, such as cognitive psychology, semiology theory research, analyzes the computer graphics into a kind of economic and effective way of conveying information, the significance of interface design for mobile media. Experiments demonstrate the unique advantages of graphics in the process of information transmission. In 2022, the market size of computer graphics and vision will expand to 755.5 million RMB. It can be known that the communication mode integrating information and graphics, as the future development trend, will also be applied to more fields and play a greater role.

Enhanced anticancer activity and endocytic mechanisms by polymeric nanocarriers of n-butylidenephthalide in leukemia cells

PURPOSE

The purpose of this study was to investigate the antitumor mechanisms of n-butylidenephthalide (BP) and to further examine the delivery efficacy of polycationic liposome containing PEI and polyethylene glycol complex (LPPC)-encapsulated BP in leukemia cells.

METHODS

MTS, flow cytometric and TUNEL assays were performed to assess cell viability and apoptosis. BP and BP/LPPC complex delivery efficiency was analyzed by full-wavelength fluorescent scanner and fluorescence microscope. The expressions of cell cycle- and apoptosis-related proteins were conducted by Western blotting.

RESULTS

The results showed that BP inhibited leukemia cell growth by inducing cell cycle arrest and cell apoptosis. LPPC-encapsulated BP rapidly induced endocytic pathway activation, resulting in the internalization of BP into leukemia cells, causing cell apoptosis within 1 h.

CONCLUSIONS

LPPC encapsulation enhanced the cytotoxic activity of BP and did not influence the effects of BP induction that suggested LPPC-encapsulated BP might be developed as anti-leukemia drugs in future.

pmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP

BACKGROUND

Structure comparison can provide useful information to identify functional and evolutionary relationship between proteins. With the dramatic increase of protein structure data in the Protein Data Bank, computation time quickly becomes the bottleneck for large scale structure comparisons. To more efficiently deal with informative multiple structure alignment tasks, we propose pmTM-align, a parallel protein structure alignment approach based on mTM-align/TM-align. pmTM-align contains two stages to handle pairwise structure alignments with Spark and the phylogenetic tree-based multiple structure alignment task on a single computer with OpenMP.

RESULTS

Experiments with the SABmark dataset showed that parallelization along with data structure optimization provided considerable speedup for mTM-align. The Spark-based structure alignments achieved near ideal scalability with large datasets, and the OpenMP-based construction of the phylogenetic tree accelerated the incremental alignment of multiple structures and metrics computation by a factor of about 2-5.

CONCLUSIONS

pmTM-align enables scalable pairwise and multiple structure alignment computing and offers more timely responses for medium to large-sized input data than existing alignment tools such as mTM-align.

Perceived Competition and Process of Care in Rural China

Purpose

Although there is much debate about the effect of hospital competition on healthcare quality, its impact on the process of care remains unclear. This study aimed to determine whether hospital competition improves the process of care in rural China.

Patients and Methods

The county hospital questionnaire survey data and the randomly sampled medical records of bacterial pneumonia patients in 2015 in rural area of Guizhou, China, were used in this study. The processes of care for bacterial pneumonia were measured by the following three measures: 1) oxygenation assessment, 2) antibiotic treatment, and 3) first antibiotic treatment within 6 hours after admission. Hospital competition was measured by asking hospital directors to rate the competition pressure they perceive from other hospitals. Multivariate logistic regression models were employed to determine the relationship between perceived competition and the processes of care for patients with bacterial pneumonia.

Results

A total of 2167 bacterial pneumonia patients from 24 county hospitals in 2015 were included in our study. Our results suggested that the likelihood of receiving antibiotic treatment and first antibiotic treatment within 6 hours after admission was significantly higher in the hospitals perceiving higher competition pressure. However, no significant relationship was found between perceived competition and oxygenation assessment for patients with bacterial pneumonia.

Conclusion

This study revealed the role of perceived competition in improving the process of care under the fee-for-service payment system and provided empirical evidence to support the pro-competition policies in China’s new round of national healthcare reform.

Structure, catalytic mechanism, posttranslational lysine carbamylation, and inhibition of dihydropyrimidinases

Dihydropyrimidinase catalyzes the reversible hydrolytic ring opening of dihydrouracil and dihydrothymine to N-carbamoyl-β-alanine and N-carbamyl-β-aminoisobutyrate, respectively. Dihydropyrimidinase from microorganisms is normally known as hydantoinase because of its role as a biocatalyst in the synthesis of d- and l-amino acids for the industrial production of antibiotic precursors and its broad substrate specificity. Dihydropyrimidinase belongs to the cyclic amidohydrolase family, which also includes imidase, allantoinase, and dihydroorotase. Although these metal-dependent enzymes share low levels of amino acid sequence homology, they possess similar active site architectures and may use a similar mechanism for catalysis. By contrast, the five human dihydropyrimidinase-related proteins possess high amino acid sequence identity and are structurally homologous to dihydropyrimidinase, but they are neuronal proteins with no dihydropyrimidinase activity. In this chapter, we summarize and discuss current knowledge and the recent advances on the structure, catalytic mechanism, and inhibition of dihydropyrimidinase.

Computational investigation on the effects of pharmaceutical polymers on the structure and dynamics of interleukin2 in heat stress

Application of proteinous drugs can be associated with difficulties during both in storage/transportation and in the body when they are used. However, using pharmaceutical carbohydrates that are widely employed in drug delivery systems, besides the drug can be protected, these systems leading to gradually release the drug over time, or deliver it to the target cell. Using a combination of molecular modeling and simulation techniques, in this study the effects of five carbohydrate polymers of Chitosan, Alginate, Cyclodextrin, Hyaluronic acid and Pectin on structure and dynamics of interleukin2 protein at 298 K and 343 K, are investigated. Data achieved using molecular modeling methods showed that when the temperature rises, the protein stability decreases. Among different polymers, Chitosan and Cyclodextrin have shown to be able to protect protein against the negative effects of high temperatures in comparison with other polymers which suggests that the use of Cyclodextrin biopolymer for the preparation of pharmaceutical formulations of interleukin2 can be the best possible choice among other polymers investigated in this research.Communicated by Ramaswamy H. Sarma.

Liposome Consolidated with Cyclodextrin Provides Prolonged Drug Retention Resulting in Increased Drug Bioavailability in Brain

Although butylidenephthalide (BP) is an efficient anticancer drug, its poor bioavailability renders it ineffective for treating drug-resistant brain tumors. However, this problem is overcome through the use of noninvasive delivery systems, including intranasal administration. Herein, the bioavailability, drug stability, and encapsulation efficiency (EE, up to 95%) of BP were improved by using cyclodextrin-encapsulated BP in liposomal formulations (CDD1). The physical properties and EE of the CDD1 system were investigated via dynamic light scattering, transmission electron microscopy, UV–Vis spectroscopy, and nuclear magnetic resonance spectroscopy. The cytotoxicity was examined via MTT assay, and the cellular uptake was observed using fluorescence microscopy. The CDD1 system persisted for over 8 h in tumor cells, which was a considerable improvement in the retention of the BP-containing cyclodextrin or the BP-containing liposomes, thereby indicating a higher BP content in CDD1. Nanoscale CDD1 formulations were administered intranasally to nude mice that had been intracranially implanted with temozolomide-resistant glioblastoma multiforme cells, resulting in increased median survival time. Liquid chromatography–mass spectrometry revealed that drug biodistribution via intranasal delivery increased the accumulation of BP 10-fold compared to oral delivery methods. Therefore, BP/cyclodextrin/liposomal formulations have potential clinical applications for treating drug-resistant brain tumors.