Nanopore sequencing data analysis using Microsoft Azure cloud computing service

Genetic information provides insights into the exome, genome, epigenetics and structural organisation of the organism. Given the enormous amount of genetic information, scientists are able to perform mammoth tasks to improve the standard of health care such as determining genetic influences on outcome of allogeneic transplantation. Cloud based computing has increasingly become a key choice for many scientists, engineers and institutions as it offers on-demand network access and users can conveniently rent rather than buy all required computing resources. With the positive advancements of cloud computing and nanopore sequencing data output, we were motivated to develop an automated and scalable analysis pipeline utilizing cloud infrastructure in Microsoft Azure to accelerate HLA genotyping service and improve the efficiency of the workflow at lower cost. In this study, we describe (i) the selection process for suitable virtual machine sizes for computing resources to balance between the best performance versus cost effectiveness; (ii) the building of Docker containers to include all tools in the cloud computational environment; (iii) the comparison of HLA genotype concordance between the in-house manual method and the automated cloud-based pipeline to assess data accuracy. In conclusion, the Microsoft Azure cloud based data analysis pipeline was shown to meet all the key imperatives for performance, cost, usability, simplicity and accuracy. Importantly, the pipeline allows for the on-going maintenance and testing of version changes before implementation. This pipeline is suitable for the data analysis from MinION sequencing platform and could be adopted for other data analysis application processes.

Toxic effects of polystyrene microplastics on the intestine of Amphioctopus fangsiao (Mollusca: Cephalopoda): From physiological responses to underlying molecular mechanisms

Microplastics are broadly used and among the most studied environmental pollutants due to their potential impacts on organisms and human health. Amphioctopus fangsiao (Cephalopoda: Octopodidae) is an important commercial species in the Pacific Northwest and is very popular among consumers owing to its rich nutritional value and fresh flavor. However, the toxic effects of microplastic exposure on A. fangsiao, including phenotypical effect and underlying molecular mechanism, remain limited. In this study, the octopus A. fangsiao were exposed to microplastics (polystyrene microplastics, Micro-PS) at concentrations of 100 and 1000 μg/L for 21 days, and then the physiological response, histopathological analysis, biomarkers of oxidative stress and glycolipid metabolism, microbiome perturbations and transcriptomic profiles in the intestines were performed. Results demonstrated that Micro-PS exposure had distinct adverse effects on the food intake of A. fangsiao. Histological analysis revealed that Micro-PS exposure has resulted in histopathological damage, thus causing early inflammation of the intestine. Oxidative stresses, metabolic disorders and microbiome perturbations were also detected in the intestine of A. fangsiao based on physiological biomarkers and microbiome analyses. Moreover, transcriptome analysis detected the differentially expressed genes (DEGs) and significantly enriched KEGG pathways in response to oxidative stress, glycolipid metabolism, DNA damage and transmembrane transport of intestinal cells, revealing distinct toxic effects at the molecular level. In summary, Micro-PS exposure has a strong impact on the intestines of A. fangsiao. For the first time, this study uses multiple approaches based on the physiological and biochemical response as well as transcriptional regulation analysis. The first assessment of the toxic impact of this species under Micro-PS exposure is also reported.

Synthesis and Antimycobacterial Activity of Isoniazid Derivatives Tethered with Aliphatic Amines

BACKGROUND

There is an urgent need for new antitubercular compounds. Modification of antimycobacterial isonicotinohydrazide at hydrazide N2 provided antimycobacterial active compounds.

OBJECTIVE

Combining this scaffold with various aliphatic amines that are also frequently present in antitubercular compounds, we have designed, synthesized, and evaluated twenty-three N- (cyclo)alkyl-2-(2-isonicotinoylhydrazineylidene)propanamides and their analogues as potential antimycobacterial compounds. By increasing lipophilicity, we intended to facilitate the penetration of mycobacteria's highly impermeable cell wall.

METHODS

The target amides were prepared via condensation of isoniazid and pyruvic acid, followed by carbodiimide-mediated coupling with yields from 35 to 98 %. The compounds were screened against Mycobacterium tuberculosis H37Rv and two nontuberculous mycobacteria (M. avium, M. kansasii).

RESULTS

All the derivatives exhibited low minimum inhibitory concentrations (MIC) from ≤0.125 and 2 μM against M. tuberculosis and nontuberculous mycobacteria, respectively. The most active molecules were substituted by a longer n-alkyl from C8 to C14. Importantly, the compounds showed comparable or even several-fold lower MIC than parent isonicotinohydrazide. Based on in silico predictions, a vast majority of the derivatives share suitable physicochemical properties and structural features for drug-likeness.

CONCLUSION

Presented amides are promising antimycobacterial agents.

A Combined QSAR and Molecular Docking Approach for Identifying Pyrimidine Derivatives as Penicillin Binding Protein Inhibitors

Background:

Antimicrobial resistance has been rising continuously in the past few years due to the overuse and exploitation of existing antimicrobials. This has motivated the search for a novel scaffold that has the capability of rapid antimicrobial action. The hybridized pyrimidines have attracted us due to their widespread biological activities, such as anti-bacterial and antifungal activities.

Objective:

The present study incorporates a series of pyrimidine-based antimicrobial agents for the 2D quantitative structure-activity relationship analysis (2D QSAR) and docking analysis.

Methods:

The exploration of the chemical structures in combination with the biological activity in CPMLR led to the detection of six descriptors (Constitutional descriptors, Topological descriptors, Modified Burden Eigenvalues and 2D autocorrelations) for modeling the activity. The resulted QSAR model has been validated using combinatorial protocol in multiple linear regression (CP-MLR) and partial least squares (PLS) analysis.

Methods:

The exploration of the chemical structures in combination with the biological activity in CPMLR led to the detection of six descriptors (Constitutional descriptors, Topological descriptors, Modified Burden Eigenvalues and 2D autocorrelations) for modeling the activity. The resulted QSAR model has been validated using a combinatorial protocol in multiple linear regression (CP-MLR) and partial least squares (PLS) analysis.

Results:

The best QSAR model displays the r2 t value of 0.594, Q2 LOO value of 0.779, Q2 L5O value of 0.767. Further docking study was executed using Autodock Vina against Penicillin-binding protein (PBP2a).

Conclusion:

From the results, Compounds 4, 11and 24 were found to possess a good binding affinity towards PBP2a.

Antibiotic prescription for acute upper respiratory tract infections: Understanding patient and physician contributions via patients' migration

Inappropriate antibiotic use is a main driver in microbes' development of antibiotic resistance. This study explored the extent to which patient, provider, and other factors contribute to antibiotic prescriptions for acute upper respiratory tract infection. We exploited exogenous patients' temporary and permanent migration from their residential area to robustly separate patient-related, provider-related, and other factors in terms of their contributions to antibiotic use. We analyzed claims of 914,013 URI patients from the 2002-2019 Korean National Health Insurance Sample Cohort Database. The results showed that both patient- and provider-related factors affect antibiotic use for upper respiratory tract infection treatment, although providers' impact is stronger than that of patients. Further decomposition analysis confirmed that provider-related factors explain about 55% of the total variance in antibiotic use. The demand side contributes to approximately 33-34% of the variance. Providers' local market share and market competitiveness are associated with antibiotic prescription. The findings suggest that regulations to reduce antibiotic consumption in Korea should target both patients and providers with appropriate quantifiable penalties.

The Δ1-pyrroline-5-carboxylate synthetase family performs diverse physiological functions in stress responses in pear (Pyrus betulifolia)

Δ1-Pyrroline-5-carboxylate synthetase (P5CS) acts as the rate-limiting enzyme in the biosynthesis of proline in plants. Although P5CS plays an essential role in plant responses to environmental stresses, its biological functions remain largely unclear in pear (Pyrus betulifolia). In the present study, 11 putative pear P5CSs (PbP5CSs) were identified by comprehensive bioinformatics analysis and classified into five subfamilies. Segmental and tandem duplications contributed to the expansion and evolution of the PbP5CS gene family. Various cis-acting elements associated with plant development, hormone responses, and/or stress responses were identified in the promoters of PbP5CS genes. To investigate the regulatory roles of PbP5CS genes in response to abiotic and biotic stresses, gene expression patterns in publicly available data were explored. The tissue-specific expressional dynamics of PbP5CS genes indicate potentially important roles in pear growth and development. Their spatiotemporal expression patterns suggest key functions in multiple environmental stress responses. Transcriptome and real-time quantitative PCR analyses revealed that most PbP5CS genes exhibited distinct expression patterns in response to drought, waterlogging, salinity-alkalinity, heat, cold, and infection by Alternaria alternate and Gymnosporangium haraeanum. The results provide insight into the versatile functions of the PbP5CS gene family in stress responses. The findings may assist further exploration of the physiological functions of PbP5CS genes for the development and enhancement of stress tolerance in pear and other fruits.

Application of Two-Dimensional Entropy Measures to Detect the Radiographic Signs of Tooth Resorption and Hypercementosis in an Equine Model

Dental disorders are a serious health problem in equine medicine, their early recognition benefits the long-term general health of the horse. Most of the initial signs of Equine Odontoclastic Tooth Resorption and Hypercementosis (EOTRH) syndrome concern the alveolar aspect of the teeth, thus, the need for early recognition radiographic imaging. This study is aimed to evaluate the applicability of entropy measures to quantify the radiological signs of tooth resorption and hypercementosis as well as to enhance radiographic image quality in order to facilitate the identification of the signs of EOTRH syndrome. A detailed examination of the oral cavity was performed in eighty horses. Each evaluated incisor tooth was assigned to one of four grade-related EOTRH groups (0-3). Radiographs of the incisor teeth were taken and digitally processed. For each radiograph, two-dimensional sample (SampEn2D), fuzzy (FuzzEn2D), permutation (PermEn2D), dispersion (DispEn2D), and distribution (DistEn2D) entropies were measured after image filtering was performed using Normalize, Median, and LaplacianSharpening filters. Moreover, the similarities between entropy measures and selected Gray-Level Co-occurrence Matrix (GLCM) texture features were investigated. Among the 15 returned measures, DistEn2D was EOTRH grade-related. Moreover, DistEn2D extracted after Normalize filtering was the most informative. The EOTRH grade-related similarity between DistEn2D and Difference Entropy (GLCM) confirms the higher irregularity and complexity of incisor teeth radiographs in advanced EOTRH syndrome, demonstrating the greatest sensitivity (0.50) and specificity (0.95) of EOTRH 3 group detection. An application of DistEn2D to Normalize filtered incisor teeth radiographs enables the identification of the radiological signs of advanced EOTRH with higher accuracy than the previously used entropy-related GLCM texture features.

Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening

Objective: To evaluate the accuracy and feasibility of the auto-detection of 15 retinal disorders with artificial intelligence (AI)-assisted optical coherence tomography (OCT) in community screening. Methods: A total of 954 eyes of 477 subjects from four local communities were enrolled in this study from September to December 2021. They received OCT scans covering an area of 12 mm × 9 mm at the posterior pole retina involving the macular and optic disc, as well as other ophthalmic examinations performed using their demographic information recorded. The OCT images were analyzed using integrated software with the previously established algorithm based on the deep-learning method and trained to detect 15 kinds of retinal disorders, namely, pigment epithelial detachment (PED), posterior vitreous detachment (PVD), epiretinal membranes (ERMs), sub-retinal fluid (SRF), choroidal neovascularization (CNV), drusen, retinoschisis, cystoid macular edema (CME), exudation, macular hole (MH), retinal detachment (RD), ellipsoid zone disruption, focal choroidal excavation (FCE), choroid atrophy, and retinal hemorrhage. Meanwhile, the diagnosis was also generated from three groups of individual ophthalmologists (group of retina specialists, senior ophthalmologists, and junior ophthalmologists) and compared with those by the AI. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated, and kappa statistics were performed. Results: A total of 878 eyes were finally enrolled, with 76 excluded due to poor image quality. In the detection of 15 retinal disorders, the ROC curve comparison between AI and professors' presented relatively large AUC (0.891-0.997), high sensitivity (87.65-100%), and high specificity (80.12-99.41%). Among the ROC curve comparisons with those by the retina specialists, AI was the closest one to the professors' compared to senior and junior ophthalmologists (p < 0.05). Conclusion: AI-assisted OCT is highly accurate, sensitive, and specific in auto-detection of 15 kinds of retinal disorders, certifying its feasibility and effectiveness in community ophthalmic screening.

M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity

Emotion recognition, or the ability of computers to interpret people's emotional states, is a very active research area with vast applications to improve people's lives. However, most image-based emotion recognition techniques are flawed, as humans can intentionally hide their emotions by changing facial expressions. Consequently, brain signals are being used to detect human emotions with improved accuracy, but most proposed systems demonstrate poor performance as EEG signals are difficult to classify using standard machine learning and deep learning techniques. This paper proposes two convolutional neural network (CNN) models (M1: heavily parameterized CNN model and M2: lightly parameterized CNN model) coupled with elegant feature extraction methods for effective recognition. In this study, the most popular EEG benchmark dataset, the DEAP, is utilized with two of its labels, valence, and arousal, for binary classification. We use Fast Fourier Transformation to extract the frequency domain features, convolutional layers for deep features, and complementary features to represent the dataset. The M1 and M2 CNN models achieve nearly perfect accuracy of 99.89% and 99.22%, respectively, which outperform every previous state-of-the-art model. We empirically demonstrate that the M2 model requires only 2 seconds of EEG signal for 99.22% accuracy, and it can achieve over 96% accuracy with only 125 milliseconds of EEG data for valence classification. Moreover, the proposed M2 model achieves 96.8% accuracy on valence using only 10% of the training dataset, demonstrating our proposed system's effectiveness. Documented implementation codes for every experiment are published for reproducibility.

HELIOS: High-speed sequence alignment in optics

In response to the imperfections of current sequence alignment methods, originated from the inherent serialism within their corresponding electrical systems, a few optical approaches for biological data comparison have been proposed recently. However, due to their low performance, raised from their inefficient coding scheme, this paper presents a novel all-optical high-throughput method for aligning DNA, RNA, and protein sequences, named HELIOS. The HELIOS method employs highly sophisticated operations to locate character matches, single or multiple mutations, and single or multiple indels within various biological sequences. On the other hand, the HELIOS optical architecture exploits high-speed processing and operational parallelism in optics, by adopting wavelength and polarization of optical beams. For evaluation, the functionality and accuracy of the HELIOS method are approved through behavioral and optical simulation studies, while its complexity and performance are estimated through analytical computation. The accuracy evaluations indicate that the HELIOS method achieves a precise pairwise alignment of two sequences, highly similar to those of Smith-Waterman, Needleman-Wunsch, BLAST, MUSCLE, ClustalW, ClustalΩ, T-Coffee, Kalign, and MAFFT. According to our performance evaluations, the HELIOS optical architecture outperforms all alternative electrical and optical algorithms in terms of processing time and memory requirement, relying on its highly sophisticated method and optical architecture. Moreover, the employed compact coding scheme highly escalates the number of input characters, and hence, it offers reduced time and space complexities, compared to the electrical and optical alternatives. It makes the HELIOS method and optical architecture highly applicable for biomedical applications.

Acute serum free thiols: a potentially modifiable biomarker of oxidative stress following traumatic brain injury

Serum concentrations of free thiols (key components of the extracellular antioxidant machinery) reflect the overall redox status of the human body. The objective of this exploratory study was to determine the concentrations of serum free thiols in the acute phase after traumatic brain injury (TBI) and their association with long-term outcome. In this observational cohort study, patients with TBI of various severity were included from a biobank of prospectively enrolled TBI patients. Further eligibility criteria included an available blood sample and head computed tomography data, obtained within 24 h of injury, as well as a functional outcome assessment (Glasgow Outcome Scale Extended (GOSE)) at 6 months post-injury. Serum free thiol concentrations were markedly lower in patients with TBI (n = 77) compared to healthy controls (n = 55) (mean ± standard deviation; 210.3 ± 63.3 vs. 301.8 ± 23.9 μM, P < 0.001) indicating increased oxidative stress. Concentrations of serum free thiols were higher in patients with complete functional recovery (GOSE = 8) than in patients with incomplete recovery (GOSE < 8) (median [interquartile range]; 235.7 [205.1-271.9] vs. 205.2 [173-226.7] μM, P = 0.016), suggesting that patients with good recovery experience less oxidative stress in the acute phase after TBI or have better redox function. Acute TBI is accompanied by a markedly lower concentration of serum free thiols compared to healthy controls indicating that serum free thiols may be a novel biomarker of TBI. Future studies are warranted to validate our findings and explore the clinical applicability and prognostic capability of this candidate-biomarker.

Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean

Using a reliable and accurate method to phenotype disease incidence and severity is essential to unravel the complex genetic architecture of disease resistance in plants, and to develop disease resistant cultivars. Genome-wide association studies (GWAS) involve phenotyping large numbers of accessions, and have been used for a myriad of traits. In field studies, genetic accessions are phenotyped across multiple environments and replications, which takes a significant amount of labor and resources. Deep Learning (DL) techniques can be effective for analyzing image-based tasks; thus DL methods are becoming more routine for phenotyping traits to save time and effort. This research aims to conduct GWAS on sudden death syndrome (SDS) of soybean [Glycine max L. (Merr.)] using disease severity from both visual field ratings and DL-based (using images) severity ratings collected from 473 accessions. Images were processed through a DL framework that identified soybean leaflets with SDS symptoms, and then quantified the disease severity on those leaflets into a few classes with mean Average Precision of 0.34 on unseen test data. Both visual field ratings and image-based ratings identified significant single nucleotide polymorphism (SNP) markers associated with disease resistance. These significant SNP markers are either in the proximity of previously reported candidate genes for SDS or near potentially novel candidate genes. Four previously reported SDS QTL were identified that contained a significant SNPs, from this study, from both a visual field rating and an image-based rating. The results of this study provide an exciting avenue of using DL to capture complex phenotypic traits from images to get comparable or more insightful results compared to subjective visual field phenotyping of traits for disease symptoms.

Offset-FA: A Uniform Method to Handle Both Unbounded and Bounded Repetitions in Regular Expression Matching

With the exponential growth of cyber-physical systems (CPSs), security challenges have emerged; attacks on critical infrastructure could result in catastrophic consequences. Intrusion detection is the foundation for CPS security protection, and deep-packet inspection is the primary method for signature-matched mechanisms. This method usually employs regular expression matching (REM) to detect possible threats in the packet payload. State explosion is the critical challenge for REM applications, which originates primarily from features of large character sets with unbounded (closures) or bounded (counting) repetitions. In this work, we propose Offset-FA to handle these repetitions in a uniform mechanism. Offset-FA eliminates state explosion by extracting the repetitions from the nonexplosive string fragments. Then, these fragments are compiled into a fragment-DFA, while a fragment relation table and a reset table are constructed to preserve their connection and offset relationship. To our knowledge, Offset-FA is the first automaton to handle these two kinds of repetitions together with a uniform mechanism. Experiments demonstrate that Offset-FA outperforms state-of-the-art solutions in both space cost and matching speed on the premise of matching correctness, and achieves a comparable matching speed with that of DFA on practical rule sets.

Visual, Anatomic Outcomes, and Natural History of Retinal Nerve Fiber Layer Schisis in Patients Undergoing Epiretinal Membrane Surgery

PURPOSE

To evaluate the anatomic and visual outcomes of patients with idiopathic epiretinal membranes (ERMs) complicated by schisis of the retinal nerve fiber layer (sRNFL) in routine clinical practice.

DESIGN

Retrospective case-control study.

PARTICIPANTS

Patients undergoing idiopathic ERM surgery at Cole Eye Institute from 2013 to 2021.

METHODS

Patients were grouped by the presence or absence of sRNFL before surgery. Preoperative and postoperative data were collected regarding visual acuity (VA), changes in central subfield thickness (CST) over time, and presence of cystoid macular edema.

MAIN OUTCOME MEASURES

Frequency of sRNFL in patients undergoing idiopathic ERM surgery.

RESULTS

Overall, 48 (53.9%) of 89 patients presented with sRNFL. Schisis of the retinal nerve fiber layer patients presented with significantly decreased VA compared with those without (58.63 ± 12.48 vs. 67.68 ± 7.84 ETDRS letters, P < 0.001, respectively). At the final follow-up after ERM removal, there was no significant difference in final VA in patients with sRNFL compared with those without (71.16 ± 2.93 vs. 74.11 ± 2.76, P = 0.467). At presentation, patients with sRNFL had greater CST than those without (454 ± 10.01 vs. 436 ± 0.23, P = 0.23). This difference persisted at the 90-day follow-up after ERM removal (402 ± 8.08 vs. 375 ± 10.19 μm, P = 0.043). The resolution of sRNFL was reported at postoperative week 1 in 30 (96.7%) of 31 cases.

CONCLUSIONS

Schisis of the retinal nerve fiber layer is a microstructural feature in > 50% of idiopathic ERMs in routine clinical practice and carries visual significance on presentation and anatomic significance postoperatively.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references.

Automated Detection of Epiretinal Membranes in OCT Images Using Deep Learning

INTRODUCTION

Development and validation of a deep learning algorithm to automatically identify and locate epiretinal memberane (ERM) regions in OCT images.

METHODS

OCT images of 468 eyes were retrospectively collected from a total of 404 ERM patients. One expert manually annotated the ERM regions for all images. A total of 422 images (90%) and the remainig 46 images (10%) were used as the training dataset and validation dataset for deep learning algorithm training and validation, respectively. One senior and one junior clinician read the images. The diagnostic results were compared.

RESULTS

The algorithm accurately segmented and located the ERM regions in OCT images. The image-level accuracy was 95.65%, and the ERM region-level accuracy was 90.14%, respectively. In comparison experiments, the accuracies of the junior clinician improved from 85.00% to 61.29% without the assistance of the algorithm to 100.00% and 90.32% with the assistance of the algorithm. The corresponding results of the senior clinician were 96.15%, 95.00% without the assistance of the algorithm, and 96.15%, 97.50% with the assistance of the algorithm.

CONCLUSIONS

The developed deep learning algorithm can accurately segment ERM regions in OCT images. This deep learning approach may help clinicians in clinical diagnosis with better accuracy and efficiency.

Detection of Tip-Burn Stress on Lettuce Grown in an Indoor Environment Using Deep Learning Algorithms

Lettuce grown in indoor farms under fully artificial light is susceptible to a physiological disorder known as tip-burn. A vital factor that controls plant growth in indoor farms is the ability to adjust the growing environment to promote faster crop growth. However, this rapid growth process exacerbates the tip-burn problem, especially for lettuce. This paper presents an automated detection of tip-burn lettuce grown indoors using a deep-learning algorithm based on a one-stage object detector. The tip-burn lettuce images were captured under various light and indoor background conditions (under white, red, and blue LEDs). After augmentation, a total of 2333 images were generated and used for training using three different one-stage detectors, namely, CenterNet, YOLOv4, and YOLOv5. In the training dataset, all the models exhibited a mean average precision (mAP) greater than 80% except for YOLOv4. The most accurate model for detecting tip-burns was YOLOv5, which had the highest mAP of 82.8%. The performance of the trained models was also evaluated on the images taken under different indoor farm light settings, including white, red, and blue LEDs. Again, YOLOv5 was significantly better than CenterNet and YOLOv4. Therefore, detecting tip-burn on lettuce grown in indoor farms under different lighting conditions can be recognized by using deep-learning algorithms with a reliable overall accuracy. Early detection of tip-burn can help growers readjust the lighting and controlled environment parameters to increase the freshness of lettuce grown in plant factories.

An intelligent monitoring system of diseases and pests on rice canopy

Accurate and timely surveys of rice diseases and pests are important to control them and prevent the reduction of rice yields. The current manual survey method of rice diseases and pests is time-consuming, laborious, highly subjective and difficult to trace historical data. To address these issues, we developed an intelligent monitoring system for detecting and identifying the disease and pest lesions on the rice canopy. The system mainly includes a network camera, an intelligent detection model of diseases and pests on rice canopy, a web client and a server. Each camera of the system can collect rice images in about 310 m² of paddy fields. An improved model YOLO-Diseases and Pests Detection (YOLO-DPD) was proposed to detect three lesions of Cnaphalocrocis medinalis, Chilo suppressalis, and Ustilaginoidea virens on rice canopy. The residual feature augmentation method was used to narrow the semantic gap between different scale features of rice disease and pest images. The convolution block attention module was added into the backbone network to enhance the regional disease and pest features for suppressing the background noises. Our experiments demonstrated that the improved model YOLO-DPD could detect three species of disease and pest lesions on rice canopy at different image scales with an average precision of 92.24, 87.35 and 90.74%, respectively, and a mean average precision of 90.11%. Compared to RetinaNet, Faster R-CNN and Yolov4 models, the mean average precision of YOLO-DPD increased by 18.20, 6.98, 6.10%, respectively. The average detection time of each image is 47 ms. Our system has the advantages of unattended operation, high detection precision, objective results, and data traceability.

Motivation and competition in health care

Non-pecuniary sources of motivation are a strong feature of the health care sector and the impact of competitive incentives on behavior may be lower where pecuniary motivation is low. This paper measures the marginal utility of income (MUY) of physicians from a stated-choice experiment, and examines whether this measure influences the association between competition faced by physicians and the prices they charge. We find that physicians are more likely to exploit a lack of competition with higher prices if they have a high MUY.

The matrix protein of Newcastle disease virus inhibits inflammatory response through IRAK4/TRAF6/TAK1/NF-κB signaling pathway

The matrix (M) protein of several cytoplasmic RNA viruses has been reported to be an NF-κB pathway antagonist. However, the function and mechanism of NDV M protein antagonizing NF-κB activation remain largely unknown. In this study, we found that the expression levels of IRAK4, TRAF6, TAK1, and RELA/p65 were obviously reduced late in NDV infection. In addition, the cytoplasmic M protein rather than other viral proteins decreased the expression of these proteins in a dose-dependent manner. Further indepth analysis showed that the N-terminal 180 amino acids of M protein were not only responsible for the reduced expression of these proteins, but also responsible for the inhibition of NF-κB activation and nuclear translocation of RELA/p65, as well as the production of inflammatory cytokines. Moreover, small interference RNA-mediated knockdown of IRAK4 or overexpression of IRAK4 markedly enhanced or reduced NDV replication by decreasing or increasing inflammatory cytokines production through the IRAK4/TRAF6/TAK1/NF-κB signaling pathway. Strangely, there were no interactions detected between NDV M protein and IRAK4, TRAF6, TAK1 or RELA/p65. Our findings described here contribute to a better understanding of the innate immune antagonism function of M protein and the molecular mechanism underlying the replication and pathogenesis of NDV.

Improved Delivery Performance of n-Butylidenephthalide-Polyethylene Glycol-Gold Nanoparticles Efficient for Enhanced Anti-Cancer Activity in Brain Tumor

n-butylidenephthalide (BP) has been verified as having the superior characteristic of cancer cell toxicity. Furthermore, gold (Au) nanoparticles are biocompatible materials, as well as effective carriers for delivering bio-active molecules for cancer therapeutics. In the present research, Au nanoparticles were first conjugated with polyethylene glycol (PEG), and then cross-linked with BP to obtain PEG-Au-BP nanodrugs. The physicochemical properties were characterized through ultraviolet-visible spectroscopy (UV-Vis), Fourier-transform infrared spectroscopy (FTIR), and dynamic light scattering (DLS) to confirm the combination of PEG, Au, and BP. In addition, both the size and structure of Au nanoparticles were observed through scanning electron microscopy (SEM) and transmission electron microscopy (TEM), where the size of Au corresponded to the results of DLS assay. Through in vitro assessments, non-transformed BAEC and DBTRG human glioma cells were treated with PEG-Au-BP drugs to investigate the tumor-cell selective cytotoxicity, cell uptake efficiency, and mechanism of endocytic routes. According to the results of MTT assay, PEG-Au-BP was able to significantly inhibit DBTRG brain cancer cell proliferation. Additionally, cell uptake efficiency and potential cellular transportation in both BAEC and DBTRG cell lines were observed to be significantly higher at 2 and 24 h. Moreover, the mechanisms of endocytosis, clathrin-mediated endocytosis, and cell autophagy were explored and determined to be favorable routes for BAEC and DBTRG cells to absorb PEG-Au-BP nanodrugs. Next, the cell progression and apoptosis of DBTRG cells after PEG-Au-BP treatment was investigated by flow cytometry. The results show that PEG-Au-BP could remarkably regulate the DBTRG cell cycle at the Sub-G1 phase, as well as induce more apoptotic cells. The expression of apoptotic-related proteins in DBTRG cells was determined through Western blotting assay. After treatment with PEG-Au-BP, the apoptotic cascade proteins p21, Bax, and Act-caspase-3 were all significantly expressed in DBTRG brain cancer cells. Through in vivo assessments, the tissue morphology and particle distribution in a mouse model were examined after a retro-orbital sinus injection containing PEG-Au-BP nanodrugs. The results demonstrate tissue integrity in the brain (forebrain, cerebellum, and midbrain), heart, liver, spleen, lung, and kidney, as they did not show significant destruction due to PEG-Au-BP treatment. Simultaneously, the extended retention period for PEG-Au-BP nanodrugs was discovered, particularly in brain tissue. The above findings identify PEG-Au-BP as a potential nanodrug for brain cancer therapies.

Detection of stage of lung changes in COVID-19 disease based on CT images: a radiomics approach

The aim of this study is to classify patients suspected from COVID-19 to five stages as normal, early, progressive, peak, and absorption stages using radiomics approach based on lung computed tomography images. Lung CT scans of 683 people were evaluated. A set of statistical texture features was extracted from each CT image. The people were classified using the random forest algorithm as an ensemble method based on the decision trees outputs to five stages of COVID-19 disease. Proposed method attains the highest result with an accuracy of 93.55% (96.25% in normal, 74.39% in early, 100% in progressive, 82.19% in peak, and 96% in absorption stage) compared to the other three common classifiers. Radiomics method can be used for the classification of the stage of COVID-19 disease with good accuracy to help decide the length of time required to hospitalize patients, determine the type of treatment process required for patients in each category, and reduce the cost of care and treatment for hospitalized individuals.

Deep Learning Models for Segmenting Non-perfusion Area of Color Fundus Photographs in Patients With Branch Retinal Vein Occlusion

Purpose

To develop artificial intelligence (AI)-based deep learning (DL) models for automatically detecting the ischemia type and the non-perfusion area (NPA) from color fundus photographs (CFPs) of patients with branch retinal vein occlusion (BRVO).

Methods

This was a retrospective analysis of 274 CFPs from patients diagnosed with BRVO. All DL models were trained using a deep convolutional neural network (CNN) based on 45 degree CFPs covering the fovea and the optic disk. We first trained a DL algorithm to identify BRVO patients with or without the necessity of retinal photocoagulation from 219 CFPs and validated the algorithm on 55 CFPs. Next, we trained another DL algorithm to segment NPA from 104 CFPs and validated it on 29 CFPs, in which the NPA was manually delineated by 3 experienced ophthalmologists according to fundus fluorescein angiography. Both DL models have been cross-validated 5-fold. The recall, precision, accuracy, and area under the curve (AUC) were used to evaluate the DL models in comparison with three types of independent ophthalmologists of different seniority.

Results

In the first DL model, the recall, precision, accuracy, and area under the curve (AUC) were 0.75 ± 0.08, 0.80 ± 0.07, 0.79 ± 0.02, and 0.82 ± 0.03, respectively, for predicting the necessity of laser photocoagulation for BRVO CFPs. The second DL model was able to segment NPA in CFPs of BRVO with an AUC of 0.96 ± 0.02. The recall, precision, and accuracy for segmenting NPA was 0.74 ± 0.05, 0.87 ± 0.02, and 0.89 ± 0.02, respectively. The performance of the second DL model was nearly comparable with the senior doctors and significantly better than the residents.

Conclusion

These results indicate that the DL models can directly identify and segment retinal NPA from the CFPs of patients with BRVO, which can further guide laser photocoagulation. Further research is needed to identify NPA of the peripheral retina in BRVO, or other diseases, such as diabetic retinopathy.

Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach

The use of optical coherence tomography (OCT) in medical diagnostics is now common. The growing amount of data leads us to propose an automated support system for medical staff. The key part of the system is a classification algorithm developed with modern machine learning techniques. The main contribution is to present a new approach for the classification of eye diseases using the convolutional neural network model. The research concerns the classification of patients on the basis of OCT B-scans into one of four categories: Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), Drusen, and Normal. Those categories are available in a publicly available dataset of above 84,000 images utilized for the research. After several tested architectures, our 5-layer neural network gives us a promising result. We compared them to the other available solutions which proves the high quality of our algorithm. Equally important for the application of the algorithm is the computational time, which is reduced by the limited size of the model. In addition, the article presents a detailed method of image data augmentation and its impact on the classification results. The results of the experiments were also presented for several derived models of convolutional network architectures that were tested during the research. Improving processes in medical treatment is important. The algorithm cannot replace a doctor but, for example, can be a valuable tool for speeding up the process of diagnosis during screening tests.

Optimal Deployment of WSN Nodes for Crop Monitoring Based on Geostatistical Interpolations

This paper proposes an integrated method for the estimation of soil moisture in potato crops that uses a low-cost wireless sensor network (WSN). Soil moisture estimation maps were created by applying the Kriging technique over a WSN composed of 11×11 nodes. Our goal is to estimate the soil moisture of the crop with a small-scale WSN. Using a perfect mesh approach on a potato crop, experimental results demonstrated that 25 WSN nodes were optimal and sufficient for soil moisture characterization, achieving estimations errors <2%. We provide a strategy to select the number of nodes to use in a WSN, to characterize the moisture behavior for spatio-temporal analysis of soil moisture in the crop. Finally, the implementation cost of this strategy is shown, considering the number of nodes and the corresponding margin of error.

A finger in every pie - the versatility of chemokines

In this issue of Biomedical Journal we encounter the chemokine superfamily and its clinical potential. The time course from 56 days zero COVID-19 to a resurgence in cases is presented, as well as a possible solution to overcome rejection in vascularized composite allotransplantation. We are shown the opportunity deep learning (DL) offers in the case of tracking single cells and particles, and also use of DL to bring all hands on deck to counter the current challenge of the COVID-19 pandemic. This issue contains articles about the effect of low energy shock waves in cystitis; the negative effect of high fructose on aortic valve stenosis; a study about the outcome of fecal microbiota transplantation in case of refractory Clostridioides difficile infection; a novel long non-coding RNA that could serve in treating triple-negative breast cancer; the benefits of acupressure in patients with restless leg syndrome; and Filamin A mutations in abnormal neuronal migration development. Finally, a link between jaw surgery and the psychological impact on the patient is explored; a method presented that allows identification of cervical characteristics associated with difficult embryo transfer; and a letter suggesting new parameters to evaluate the use of bone-substitute augmentation in the treatment of osteoporotic intertrochanteric fractures.

2D QSAR, design, docking study and ADMET of some N-aryl derivatives concerning inhibitory activity against Alzheimer disease

Background

Alzheimer disease (AD) is an ailment that disturbs mainly people of old age. The fundamental remedial way to deal with AD depends on the utilization of AChEI. The design of new intense and particular AChEI is critical in drug discovery. In silico technique will be used to solve the above problem. A new method was established to discover novel agents with better biological activity against Alzheimer disease.

Results

A validated model was established in this research to predict the biological activities of some anti-Alzheimer compounds and to design new hypothetical drugs influenced with molecular properties in the derived model; ATS4i, MATS2e, SpMax7_BhS, Energy(HOMO) and Molecular Weight and showed good correlation R2 = 0.936, R2adj = 0.907, Q2cv = 0.88, LOF = 0.0154 and R2ext = 0.881. All the descriptors in the model were in good agreement with the 15 test set predicted values. Five compounds were designed using D35rm as a template with improved activity. The compounds have higher and better binding scores (− 10.1, − 9.4, − 9.3, − 9.1 and − 8.1 all in kcal/mol) than the approved drugs (Donepezil =  − 7.4 kcal/mol).

Conclusion

As the outcome, every one of the selected and the designed compounds is created and improved as potential anti-Alzheimer agents. Despite this, the further test examines and in vivo investigations are recommended to assess the method of the activities and other pharmacological impacts on these compounds.

Effective and efficient multi-crop pest detection based on deep learning object detection models

Traditional machine learning-based pest classification methods are a tedious and time-consuming process A method of multi-class pest detection based on deep learning and convolutional neural networks could be the solution. It automatically extracts the complex features of different pests from the crop pest images. In this paper, various significant deep learning-based object detection models like SSD, EfficientDet, Faster R-CNN, and CenterNet are implemented based on the Tensorflow Object Detection framework. Several significant networks like MobileNet_V2, ResNet101_V1, Inception_ResNet_V2, EfficientNet, and HourGlass104 are employed as backbone networks for these models to extract the different features of the pests. Object detection models are capable of identifying and locating pests in crops. Initially, these models are pre-trained with the COCO dataset and later be fine-tuned to the target pest dataset of 20 different pest classes. After conducting experiments on these models using the pest dataset, we demonstrate that Faster R-CNN_ResNet101_V1 outperformed every other model and achieved mAP of 74.77% . Additionally, it is developed as a lightweight model, whose size is ∼9 MB, and can detect pest objects in 130 milliseconds per image, allowing it to be used on resources-constrained devices commonly used by farmers.

An Overview of Antiviral Peptides and Rational Biodesign Considerations

Viral diseases have contributed significantly to worldwide morbidity and mortality throughout history. Despite the existence of therapeutic treatments for many viral infections, antiviral resistance and the threat posed by novel viruses highlight the need for an increased number of effective therapeutics. In addition to small molecule drugs and biologics, antimicrobial peptides (AMPs) represent an emerging class of potential antiviral therapeutics. While AMPs have traditionally been regarded in the context of their antibacterial activities, many AMPs are now known to be antiviral. These antiviral peptides (AVPs) have been shown to target and perturb viral membrane envelopes and inhibit various stages of the viral life cycle, from preattachment inhibition through viral release from infected host cells. Rational design of AMPs has also proven effective in identifying highly active and specific peptides and can aid in the discovery of lead peptides with high therapeutic selectivity. In this review, we highlight AVPs with strong antiviral activity largely curated from a publicly available AMP database. We then compile the sequences present in our AVP database to generate structural predictions of generic AVP motifs. Finally, we cover the rational design approaches available for AVPs taking into account approaches currently used for the rational design of AMPs.

Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting Fluid

Surface roughness is considered as an important measuring parameter in the machining industry that aids in ensuring the quality of the finished product. In turning operations, the tool and workpiece contact develop friction and cause heat generation, which in turn affects the machined surface. The use of cutting fluid in the machining zone helps to minimize the heat generation. In this paper, minimum quantity lubrication is used in turning of AISI 304 steel for determining the surface roughness. The cutting fluid is enriched with alumina nanoparticles of two different average particle sizes of 30 and 40 nm. Among the input parameters chosen for investigation are cutting speed, depth of cut, feed rate, and nanoparticle concentration. The response surface approach is used in the design of the experiment (RSM). For the purpose of estimating the surface roughness and comparing the experimental value to the predicted values, three machine learning-based models, including linear regression (LR), random forest (RF), and support vector machine (SVM), are utilized in addition. For the purpose of evaluating the accuracy of the predicted values, the coefficient of determination (R2), mean absolute percentage error (MAPE), and mean square error (MSE) were all used. Random forest outperformed the other two models in both the particle sizes of 30 and 40 nm, with R-squared of 0.8176 and 0.7231, respectively. Thus, this study provides a novel approach in predicting the surface roughness by varying the particle size in the cutting fluid using machine learning, which can save time and wastage of material and energy.

Nanotechnology in the development of small and large molecule tyrosine kinase inhibitors and immunotherapy for the treatment of HER2-positive breast cancer

The HER2 receptor tyrosine kinase is a member of the epidermal growth factor receptor family which includes EGFR, HER3 and HER4. They are known to play critical roles in both normal development and cancer. A subset of breast cancers is associated with the HER2 gene, which is amplified and/or overexpressed in 20-25% of invasive breast cancers and is correlated with tumor resistance to chemotherapy, Metastatic Breast Cancer (MBC) and poor patient survival. The advent of receptor tyrosine kinase inhibitors has improved the prognosis of HER2-postive breast cancers; however, HER2+MBC invariably progresses (acquired resistance or de novo resistance). The monoclonal antibody-based drugs (large molecule TKIs) target the extracellular binding domain of HER2; while the small molecule TKIs act intracellularly to inhibit proliferation and survival signals. We reviewed the modes of action of the TKIs with a view to showing which of the TKIs could be combined in nanoparticles to benefit from the power of nanotechnology (reduced toxicity, improved solubility of hydrophobic drugs, long circulation half-lives, circumventing efflux pumps and preventing capture by the reticuloendothelial system (mononuclear phagocyte system). Nanotherapeutics also mediate the synchronization of the pharmacokinetics and biodistribution of multiple drugs incorporated in the nanoparticles. Novel TKIs that are currently under investigation with or without nanoparticle delivery are mentioned, and nano-based strategies to improve their delivery are suggested. Immunotherapies currently in clinical practice, clinical trials or at the preclinical stage are discussed. However, immunotherapy only works well in relatively small subsets of patients. Combining nanomedicine with immunotherapy can boost therapeutic outcomes, by turning "cold" non-immunoresponsive tumors and metastases into "hot" immunoresponsive lesions.

Selection of Filtering and Image Texture Analysis in the Radiographic Images Processing of Horses' Incisor Teeth Affected by the EOTRH Syndrome

Equine odontoclastic tooth resorption and hypercementosis (EOTRH) is one of the horses’ dental diseases, mainly affecting the incisor teeth. An increase in the incidence of aged horses and a painful progressive course of the disease create the need for improved early diagnosis. Besides clinical findings, EOTRH recognition is based on the typical radiographic findings, including levels of dental resorption and hypercementosis. This study aimed to introduce digital processing methods to equine dental radiographic images and identify texture features changing with disease progression. The radiographs of maxillary incisor teeth from 80 horses were obtained. Each incisor was annotated by separate masks and clinically classified as 0, 1, 2, or 3 EOTRH degrees. Images were filtered by Mean, Median, Normalize, Bilateral, Binomial, CurvatureFlow, LaplacianSharpening, DiscreteGaussian, and SmoothingRecursiveGaussian filters independently, and 93 features of image texture were extracted using First Order Statistics (FOS), Gray Level Co-occurrence Matrix (GLCM), Neighbouring Gray Tone Difference Matrix (NGTDM), Gray Level Dependence Matrix (GLDM), Gray Level Run Length Matrix (GLRLM), and Gray Level Size Zone Matrix (GLSZM) approaches. The most informative processing was selected. GLCM and GLRLM return the most favorable features for the quantitative evaluation of radiographic signs of the EOTRH syndrome, which may be supported by filtering by filters improving the edge delimitation.

Multimodality MRI-based radiomics for aggressiveness prediction in papillary thyroid cancer

OBJECTIVE

To investigate the ability of a multimodality MRI-based radiomics model in predicting the aggressiveness of papillary thyroid carcinoma (PTC).

METHODS

This study included consecutive patients who underwent neck magnetic resonance (MR) scans and subsequent thyroidectomy during the study period. The pathological diagnosis of thyroidectomy specimens was the gold standard to determine the aggressiveness. Thyroid nodules were manually segmented on three modal MR images, and then radiomics features were extracted. A machine learning model was established to evaluate the prediction of PTC aggressiveness.

RESULTS

The study cohort included 107 patients with PTC confirmed by pathology (cross-validation cohort: n = 71; test cohort: n = 36). A total of 1584 features were extracted from contrast-enhanced T1-weighted (CE-T1 WI), T2-weighted (T2 WI) and diffusion weighted (DWI) images of each patient. Sparse representation method is used for radiation feature selection and classification model establishment. The accuracy of the independent test set that using only one modality, like CE-T1WI, T2WI or DWI was not particularly satisfactory. In contrast, the result of these three modalities combined achieved 0.917.

CONCLUSION

Our study shows that multimodality MR image based on radiomics model can accurately distinguish aggressiveness in PTC from non-aggressiveness PTC before operation. This method may be helpful to inform the treatment strategy and prognosis of patients with aggressiveness PTC.

Online Surface Roughness Prediction for Assembly Interfaces of Vertical Tail Integrating Tool Wear under Variable Cutting Parameters

Monitoring surface quality during machining has considerable practical significance for the performance of high-value products, particularly for their assembly interfaces. Surface roughness is the most important metric of surface quality. Currently, the research on online surface roughness prediction has several limitations. The effect of tool wear variation on surface roughness is seldom considered in machining. In addition, the deterioration trend of surface roughness and tool wear differs under variable cutting parameters. The prediction models trained under one set of cutting parameters fail when cutting parameters change. Accordingly, to timely monitor the surface quality of assembly interfaces of high-value products, this paper proposes a surface roughness prediction method that considers the tool wear variation under variable cutting parameters. In this method, a stacked autoencoder and long short-term memory network (SAE–LSTM) is designed as the fundamental surface roughness prediction model using tool wear conditions and sensor signals as inputs. The transfer learning strategy is applied to the SAE–LSTM such that the surface roughness online prediction under variable cutting parameters can be realized. Machining experiments for the assembly interface (using Ti6Al4V as material) of an aircraft’s vertical tail are conducted, and monitoring data are used to validate the proposed method. Ablation studies are implemented to evaluate the key modules of the proposed model. The experimental results show that the proposed method outperforms other models and is capable of tracking the true surface roughness with time. Specifically, the minimum values of the root mean square error and mean absolute percentage error of the prediction results after transfer learning are 0.027 μm and 1.56%, respectively.

Identifying the Strength Level of Objects' Tactile Attributes Using a Multi-Scale Convolutional Neural Network

In order to solve the problem in which most currently existing research focuses on the binary tactile attributes of objects and ignores identifying the strength level of tactile attributes, this paper establishes a tactile data set of the strength level of objects' elasticity and hardness attributes to make up for the lack of relevant data, and proposes a multi-scale convolutional neural network to identify the strength level of object attributes. The network recognizes the different attributes and identifies differences in the strength level of the same object attributes by fusing the original features, i.e., the single-channel features and multi-channel features of the data. A variety of evaluation methods were used for comparison with multiple models in terms of strength levels of elasticity and hardness. The results show that our network has a more significant effect in accuracy. In the prediction results of the positive examples in the predicted value, the true value has a higher proportion of positive examples, that is, the precision is better. The prediction effect for the positive examples in the true value is better, that is, the recall is better. Finally, the recognition rate for all classes is higher in terms of f1_score. For the overall sample, the prediction of the multi-scale convolutional neural network has a higher recognition rate and the network's ability to recognize each strength level is more stable.

Serum concentrations of free indoxyl and p-cresyl sulfate are associated with mineral metabolism variables and cardiovascular risk in hemodialysis patients

BACKGROUND

Indoxyl sulfate (IS) and p-cresyl sulfate (PCS) are uremic toxins associated with cardiovascular outcome in CKD patients. The present work is an analysis of the association of serum free, total IS and PCS with cardiovascular events and calcium-phosphate metabolism variables in hemodialysis patients.

METHODS

Serum levels of total and free IS and PCS were measured in 139 hemodialysis patients. Their relationship with calcium-phosphate metabolism variables were tested in an observational cohort study. In addition, their association with cardiovascular events was investigated during a 4-year follow-up.

RESULTS

Patients in the highest tertile (T3) of serum free IS showed lower serum 1,25(OH)₂D compared to patients in the middle (T2) and lowest tertile (T1); in addition to this, T3 patients showed lower serum irisin than T1 patients and lower serum PTH than all the other subjects (T1 + T2) combined. Serum PTH was also measured during the two years after the baseline measurement and was higher in patients in the T1 than in those in the T3 of serum free IS. Cox regression analysis showed that cardiovascular risk was lower in T1 patients than in those in the T3 of serum free PCS, both using a univariate (OR 2.55, 95% CI 1.2-5.43; p = 0.015) or multivariate model (OR 2.48, 95% CI 1.12-5.51; p = 0.003).

CONCLUSIONS

Serum free IS may be associated with PTH and 1,25(OH)₂D secretion, whereas free PCS may predict cardiovascular risk in hemodialysis patients.

Rapid Temperature-Dependent Rheological Measurements of Non-Newtonian Solutions Using a Machine-Learning Aided Microfluidic Rheometer

Biofluids such as synovial fluid, blood plasma, and saliva contain several proteins which impart non-Newtonian properties to the biofluids. The concentration of such protein macromolecules in biofluids is regarded as an important biomarker for the diagnosis of several health conditions, including cardiovascular disorders, joint quality, and Alzheimer's. Existing technologies for the measurements of macromolecules in biofluids are limited; they require a long turnaround time, or require complex protocols, thus calling for alternative, more suitable, methodologies aimed at such measurements. According to the well-established relations for polymer solutions, the concentration of macromolecules in solutions can also be derived via measurement of rheological properties such as shear-viscosity and the longest relaxation time. We here introduce a microfluidic rheometer for rapid simultaneous measurement of shear viscosity and longest relaxation time of non-Newtonian solutions at different temperatures. At variance with previous technologies, our microfluidic rheometer provides a very short turnaround time of around 2 min or less thanks to the implementation of a machine-learning algorithm. We validated our platform on several aqueous solutions of poly(ethylene oxide). We also performed measurements on hyaluronic acid solutions in the clinical range for joint grade assessment. We observed monotonic behavior with the concentration for both rheological properties, thus speculating on their use as potential rheo-markers, i.e., rheological biomarkers, across several disease states.

Selection of Image Texture Analysis and Color Model in the Advanced Image Processing of Thermal Images of Horses following Exercise

Simple Summary

Detecting horse state after exercise is critical for maximizing athletic performance. The horse’s response to fatigue includes exercise termination or exercise continuation at a lower intensity, which significantly limit the results achieved in races and equestrian competition. As conventional methods of detecting and quantifying exercise effort have shown some limitations, infrared thermography was proposed as a method of contactless detection of exercise effect. The promising correlation between body surface temperature and exercise-dependent blood biomarkers has been demonstrated. As the application of conventional thermography is limited by low specificity, advanced thermal image analysis was proposed here to visualize the link between blood biomarkers and texture of thermal images. Twelve horses underwent standardized exercise tests for six consecutive days, and both thermal images and blood samples were collected before and after each test. The images were analyzed using four color models (RGB, red-green-blue; YUV, brightness-UV-components; YIQ, brightness-IQ-components; HSB, hue-saturation-brightness) and eight texture-features approaches, including 88 features in total. In contrast to conventional temperature measures, as many as twelve texture features in two color models (RGB, YIQ) were linked with blood biomarker levels as part of the horse’s response to exercise.

Abstract

As the detection of horse state after exercise is constantly developing, a link between blood biomarkers and infrared thermography (IRT) was investigated using advanced image texture analysis. The aim of the study was to determine which combinations of RGB (red-green-blue), YUI (brightness-UV-components), YIQ (brightness-IQ-components), and HSB (hue-saturation-brightness) color models, components, and texture features are related to the blood biomarkers of exercise effect. Twelve Polish warmblood horses underwent standardized exercise tests for six consecutive days. Both thermal images and blood samples were collected before and after each test. All 144 obtained IRT images were analyzed independently for 12 color components in four color models using eight texture-feature approaches, including 88 features. The similarity between blood biomarker levels and texture features was determined using linear regression models. In the horses’ thoracolumbar region, 12 texture features (nine in RGB, one in YIQ, and two in HSB) were related to blood biomarkers. Variance, sum of squares, and sum of variance in the RGB were highly repeatable between image processing protocols. The combination of two approaches of image texture (histogram statistics and gray-level co-occurrence matrix) and two color models (RGB, YIQ), should be considered in the application of digital image processing of equine IRT.

A Comprehensive Survey of the Recent Studies with UAV for Precision Agriculture in Open Fields and Greenhouses

The increasing world population makes it necessary to fight challenges such as climate change and to realize production efficiently and quickly. However, the minimum cost, maximum income, environmental pollution protection and the ability to save water and energy are all factors that should be taken into account in this process. The use of information and communication technologies (ICTs) in agriculture to meet all of these criteria serves the purpose of precision agriculture. As unmanned aerial vehicles (UAVs) can easily obtain real-time data, they have a great potential to address and optimize solutions to the problems faced by agriculture. Despite some limitations, such as the battery, load, weather conditions, etc., UAVs will be used frequently in agriculture in the future because of the valuable data that they obtain and their efficient applications. According to the known literature, UAVs have been carrying out tasks such as spraying, monitoring, yield estimation, weed detection, etc. In recent years, articles related to agricultural UAVs have been presented in journals with high impact factors. Most precision agriculture applications with UAVs occur in outdoor environments where GPS access is available, which provides more reliable control of the UAV in both manual and autonomous flights. On the other hand, there are almost no UAV-based applications in greenhouses where all-season crop production is available. This paper emphasizes this deficiency and provides a comprehensive review of the use of UAVs for agricultural tasks and highlights the importance of simultaneous localization and mapping (SLAM) for a UAV solution in the greenhouse.

Classification of Fruit Flies by Gender in Images Using Smartphones and the YOLOv4-Tiny Neural Network

The fruit fly Drosophila melanogaster is a classic research object in genetics and systems biology. In the genetic analysis of flies, a routine task is to determine the offspring size and gender ratio in their populations. Currently, these estimates are made manually, which is a very time-consuming process. The counting and gender determination of flies can be automated by using image analysis with deep learning neural networks on mobile devices. We proposed an algorithm based on the YOLOv4-tiny network to identify Drosophila flies and determine their gender based on the protocol of taking pictures of insects on a white sheet of paper with a cell phone camera. Three strategies with different types of augmentation were used to train the network. The best performance (F1 = 0.838) was achieved using synthetic images with mosaic generation. Females gender determination is worse than that one of males. Among the factors that most strongly influencing the accuracy of fly gender recognition, the fly’s position on the paper was the most important. Increased light intensity and higher quality of the device cameras have a positive effect on the recognition accuracy. We implement our method in the FlyCounter Android app for mobile devices, which performs all the image processing steps using the device processors only. The time that the YOLOv4-tiny algorithm takes to process one image is less than 4 s.

Computational Approach to Combat COVID-19 Infection: Emerging Tool for Accelerating Drug Research

BACKGROUND

Drug discovery and development process is an expensive, complex, time-consuming and risky. There are different techniques involved in the drug development process which include random screening, computational approach, molecular manipulation and serendipitous research. Among these methods, the computational approach is considered as an efficient strategy to accelerate and economize the drug discovery process.

OBJECTIVE

This approach is mainly applied in various phases of drug discovery process including target identification, target validation, lead identification and lead optimization. Due to increase in the availability of information regarding various biological targets of different disease states, computational approaches such as molecular docking, de novo design, molecular similarity calculation, virtual screening, pharmacophore-based modeling and pharmacophore mapping have been applied extensively.

METHODS

Various drug molecules can be designed by applying computational tools to explore the drug candidates for treatment of Coronavirus infection. The world health organization has announced the novel corona virus disease as COVID-19 and declared it as pandemic globally on 11 February 2020. So, it is thought of interest to scientific community to apply computational methods to design and optimize the pharmacological properties of various clinically available and FDA approved drugs such as remdesivir, ribavirin, favipiravir, oseltamivir, ritonavir, arbidol, chloroquine, hydroxychloroquine, carfilzomib, baraticinib, prulifloxacin, etc for effective treatment of COVID-19 infection.

RESULTS

Further, various survey reports suggest that the extensive studies are carried out by various research communities to find out the safety and efficacy profile of these drug candidates.

CONCLUSION

This review is focused on the study of various aspects of these drugs related to their target sites on virus, binding interactions, physicochemical properties etc.

The Effect of Rider:Horse Bodyweight Ratio on the Superficial Body Temperature of Horse's Thoracolumbar Region Evaluated by Advanced Thermal Image Processing

Appropriate matching of rider-horse sizes is becoming an increasingly important issue of riding horses' care, as the human population becomes heavier. Recently, infrared thermography (IRT) was considered to be effective in differing the effect of 10.6% and 21.3% of the rider:horse bodyweight ratio, but not 10.1% and 15.3%. As IRT images contain many pixels reflecting the complexity of the body's surface, the pixel relations were assessed by image texture analysis using histogram statistics (HS), gray-level run-length matrix (GLRLM), and gray level co-occurrence matrix (GLCM) approaches. The study aimed to determine differences in texture features of thermal images under the impact of 10-12%, >12 ≤15%, >15 <18% rider:horse bodyweight ratios, respectively. Twelve horses were ridden by each of six riders assigned to light (L), moderate (M), and heavy (H) groups. Thermal images were taken pre- and post-standard exercise and underwent conventional and texture analysis. Texture analysis required image decomposition into red, green, and blue components. Among 372 returned features, 95 HS features, 48 GLRLM features, and 96 GLCH features differed dependent on exercise; whereas 29 HS features, 16 GLRLM features, and 30 GLCH features differed dependent on bodyweight ratio. Contrary to conventional thermal features, the texture heterogeneity measures, InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, expressed consistent measurable differences when the red component was considered.

Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm

Fatty liver disease is considered a critical illness that should be diagnosed and detected at an early stage. In advanced stages, liver cancer or cirrhosis arise, and to identify this disease, radiologists commonly use ultrasound images. However, because of their low quality, radiologists found it challenging to recognize this disease using ultrasonic images. To avoid this problem, a Computer-Aided Diagnosis technique is developed in the current study, using Machine Learning Algorithms and a voting-based classifier to categorize liver tissues as being fatty or normal, based on extracting ultrasound image features and a voting-based classifier. Four main contributions are provided by our developed method: firstly, the classification of liver images is achieved as normal or fatty without a segmentation phase. Secondly, compared to our proposed work, the dataset in previous works was insufficient. A combination of 26 features is the third contribution. Based on the proposed methods, the extracted features are Gray-Level Co-Occurrence Matrix (GLCM) and First-Order Statistics (FOS). The fourth contribution is the voting classifier used to determine the liver tissue type. Several trials have been performed by examining the voting-based classifier and J48 algorithm on a dataset. The obtained TP, TN, FP, and FN were 94.28%, 97.14%, 5.71%, and 2.85%, respectively. The achieved precision, sensitivity, specificity, and F1-score were 94.28%, 97.05%, 94.44%, and 95.64%, respectively. The achieved classification accuracy using a voting-based classifier was 95.71% and in the case of using the J48 algorithm was 93.12%. The proposed work achieved a high performance compared with the research works.

Cordycepin as a Promising Inhibitor of SARS-CoV-2 RNA Dependent RNA Polymerase (RdRp)

BACKGROUND

SARS-CoV-2, which emerged in Wuhan, China, is a new global threat that has killed millions of people and continues to do so. This pandemic has not only threatened human life but has also triggered economic downturns across the world. Researchers have made significant strides in discovering molecular insights into SARSCoV- 2 pathogenesis and developing vaccines, but there is still no successful cure for SARS-CoV-2 infected patients.

OBJECTIVE

The present study has proposed a drug-repositioning pipeline for the design and discovery of an effective fungal-derived bioactive metabolite as a drug candidate against SARS-CoV-2.

METHODS

Fungal derivative "Cordycepin" was selected for this study to investigate the inhibitory properties against RNA-dependent RNA polymerase (RdRp) (PDB ID: 6M71) of SARS-CoV-2. The pharmacological profile, intermolecular interactions, binding energy, and stability of the compound were determined utilizing cheminformatic approaches. Subsequently, molecular dynamic simulation was performed to better understand the binding mechanism of cordycepin to RdRp.

RESULTS

The pharmacological data and retrieved molecular dynamics simulations trajectories suggest excellent drug-likeliness and greater structural stability of cordycepin, while the catalytic residues (Asp760, Asp761), as well as other active site residues (Trp617, Asp618, Tyr619, Trp800, Glu811) of RdRp, showed better stability during the overall simulation span.

CONCLUSION

Promising results of pharmacological investigation along with molecular simulations revealed that cordycepin exhibited strong inhibitory potential against SARSCoV- 2 polymerase enzyme (RdRp). Hence, cordycepin should be highly recommended to test in a laboratory to confirm its inhibitory potential against the SARS-CoV-2 polymerase enzyme (RdRp).

Automatic classification of severity of COVID-19 patients using texture feature and random forest based on computed tomography images

Severity assessment of the novel Coronavirus (COVID-19) using chest computed tomography (CT) scan is crucial for the effective administration of the right therapeutic drugs and also for monitoring the progression of the disease. However, determining the severity of COVID-19 needs a highly expert radiologist by visual assessment, which is time-consuming, boring, and subjective. This article introduces an advanced machine learning tool to determine the severity of COVID-19 to mild, moderate, and severe from the lung CT images. We have used a set of quantitative first- and second-order statistical texture features from each image. The first-order texture features extracted from the image histogram are variance, skewness, and kurtosis. The second-order texture features extraction methods are gray-level co-occurrence matrix, gray-level run length matrix, and gray-level size zone matrix. Finally, using the extracted features, CT images of each person are classified using random forest (RF) as an ensemble method based on majority voting of the decision trees outputs to four classes. We have used a dataset of CT scans labeled as being normal (231), mild (563), moderate (120), and severe (42) determined by expert radiologists. The experimental results indicate the combination of all feature extraction methods, and RF achieves the highest result compared with the other strategies in detecting the four classes of severity of COVID-19 from CT images with an accuracy of 90.95%. This proposed system can work well and can be used as an assistant diagnostic tool for quantification of lung involvement of COVID-19 to monitor the progression of the disease.

AI-based object detection latest trends in remote sensing, multimedia and agriculture applications

Object detection is a vital research direction in machine vision and deep learning. The object detection technique based on deep understanding has achieved tremendous progress in feature extraction, image representation, classification, and recognition in recent years, due to this rapid growth of deep learning theory and technology. Scholars have proposed a series of methods for the object detection algorithm as well as improvements in data processing, network structure, loss function, and so on. In this paper, we introduce the characteristics of standard datasets and critical parameters of performance index evaluation, as well as the network structure and implementation methods of two-stage, single-stage, and other improved algorithms that are compared and analyzed. The latest improvement ideas of typical object detection algorithms based on deep learning are discussed and reached, from data enhancement, a priori box selection, network model construction, prediction box selection, and loss calculation. Finally, combined with the existing challenges, the future research direction of typical object detection algorithms is surveyed.

Advances in Thermal Image Analysis for the Detection of Pregnancy in Horses Using Infrared Thermography

Infrared thermography (IRT) was applied as a potentially useful tool in the detection of pregnancy in equids, especially native or wildlife. IRT measures heat emission from the body surface, which increases with the progression of pregnancy as blood flow and metabolic activity in the uterine and fetal tissues increase. Conventional IRT imaging is promising; however, with specific limitations considered, this study aimed to develop novel digital processing methods for thermal images of pregnant mares to detect pregnancy earlier with higher accuracy. In the current study, 40 mares were divided into non-pregnant and pregnant groups and imaged using IRT. Thermal images were transformed into four color models (RGB, YUV, YIQ, HSB) and 10 color components were separated. From each color component, features of image texture were obtained using Histogram Statistics and Grey-Level Run-Length Matrix algorithms. The most informative color/feature combinations were selected for further investigation, and the accuracy of pregnancy detection was calculated. The image texture features in the RGB and YIQ color models reflecting increased heterogeneity of image texture seem to be applicable as potential indicators of pregnancy. Their application in IRT-based pregnancy detection in mares allows for earlier recognition of pregnant mares with higher accuracy than the conventional IRT imaging technique.