Characterization of human skin under tension using terahertz time domain spectroscopy.

This paper introduces a spectral analysis method for monitoring the human skin in vivo based on a combination of terahertz time-domain spectroscopy (THz-TDS) and optical coherence tomography (OCT). The method can quantitatively measure the refractive index, thickness and transmission coefficient of epidermis, and the refractive index of dermis in natural, as well as the tension condition of the skin. An optically reflective model for the multilayer structure of the skin is first established. The initial thickness of the epidermis is then measured by OCT as a known quantity for the established model. By fitting the established model to the experimentally obtained THz-TDS signals, the above parameters of the skin can be calibrated. Furthermore, the dependence of these skin parameters on the tension status are investigated. This study provides a means for terahertz technology to measure the skin in vivo.

WSHNN: A Weakly Supervised Hybrid Neural Network for the Identification of DNA-Protein Binding Sites.

INTRODUCTION: Transcription factors are vital biological components that control gene expression, and their primary biological function is to recognize DNA sequences. As related research continues, it was found that the specificity of DNA-protein binding has a significant role in gene expression, regulation, and especially gene therapy. Convolutional Neural Networks (CNNs) have become increasingly popular for predicting DNa-protein-specific binding sites, but their accuracy in prediction needs to be improved. METHODS: We proposed a framework for combining multi-Instance Learning (MIL) and a hybrid neural network named WSHNN. First, we utilized sliding windows to split the DNA sequences into multiple overlapping instances, each instance containing multiple bags. Then, the instances were encoded using a K-mer encoding. Afterward, the scores of all instances in the same bag were calculated separately by a hybrid neural network. RESULTS: Finally, a fully connected network was utilized as the final prediction for that bag. The framework could achieve the performances of 90.73% in Pre, 82.77% in Recall, 87.17% in Acc, 0.8657 in F1-score, and 0.7462 in MCC, respectively. In addition, we discussed the performance of K-mer encoding. Compared with other art-of-the-state efforts, the model has better performance with sequence information. CONCLUSION: From the experimental results, it can be concluded that Bi-directional Long-ShortTerm Memory (Bi-LSTM) can better capture the long-sequence relationships between DNA sequences (the code and data can be visited at https://github.com/baowz12345/Weak_ Super_Network).

Golgi_DF: Golgi proteins classification with deep forest.

Introduction: Golgi is one of the components of the inner membrane system in eukaryotic cells. Its main function is to send the proteins involved in the synthesis of endoplasmic reticulum to specific parts of cells or secrete them outside cells. It can be seen that Golgi is an important organelle for eukaryotic cells to synthesize proteins. Golgi disorders can cause various neurodegenerative and genetic diseases, and the accurate classification of Golgi proteins is helpful to develop corresponding therapeutic drugs. Methods: This paper proposed a novel Golgi proteins classification method, which is Golgi_DF with the deep forest algorithm. Firstly, the classified proteins method can be converted the vector features containing various information. Secondly, the synthetic minority oversampling technique (SMOTE) is utilized to deal with the classified samples. Next, the Light GBM method is utilized to feature reduction. Meanwhile, the features can be utilized in the penultimate dense layer. Therefore, the reconstructed features can be classified with the deep forest algorithm. Results: In Golgi_DF, this method can be utilized to select the important features and identify Golgi proteins. Experiments show that the well-performance than the other art-of-the state methods. Golgi_DF as a standalone tools, all its source codes publicly available at https://github.com/baowz12345/golgiDF. Discussion: Golgi_DF employed reconstructed feature to classify the Golgi proteins. Such method may achieve more available features among the UniRep features.

Oral_voting_transfer: classification of oral microorganisms' function proteins with voting transfer model.

Introduction: The oral microbial group typically represents the human body's highly complex microbial group ecosystem. Oral microorganisms take part in human diseases, including Oral cavity inflammation, mucosal disease, periodontal disease, tooth decay, and oral cancer. On the other hand, oral microbes can also cause endocrine disorders, digestive function, and nerve function disorders, such as diabetes, digestive system diseases, and Alzheimer's disease. It was noted that the proteins of oral microbes play significant roles in these serious diseases. Having a good knowledge of oral microbes can be helpful in analyzing the procession of related diseases. Moreover, the high-dimensional features and imbalanced data lead to the complexity of oral microbial issues, which can hardly be solved with traditional experimental methods. Methods: To deal with these challenges, we proposed a novel method, which is oral_voting_transfer, to deal with such classification issues in the field of oral microorganisms. Such a method employed three features to classify the five oral microorganisms, including Streptococcus mutans, Staphylococcus aureus, abiotrophy adjacent, bifidobacterial, and Capnocytophaga. Firstly, we utilized the highly effective model, which successfully classifies the organelle's proteins and transfers to deal with the oral microorganisms. And then, some classification methods can be treated as the local classifiers in this work. Finally, the results are voting from the transfer classifiers and the voting ones. Results and discussion: The proposed method achieved the well performances in the five oral microorganisms. The oral_voting_transfer is a standalone tool, and all its source codes are publicly available at https://github.com/baowz12345/voting_transfer.

Disease-related compound identification based on deeping learning method.

Acute lung injury (ALI) is a serious respiratory disease, which can lead to acute respiratory failure or death. It is closely related to the pathogenesis of New Coronavirus pneumonia (COVID-19). Many researches showed that traditional Chinese medicine (TCM) had a good effect on its intervention, and network pharmacology could play a very important role. In order to construct "disease-gene-target-drug" interaction network more accurately, deep learning algorithm is utilized in this paper. Two ALI-related target genes (REAL and SATA3) are considered, and the active and inactive compounds of the two corresponding target genes are collected as training data, respectively. Molecular descriptors and molecular fingerprints are utilized to characterize each compound. Forest graph embedded deep feed forward network (forgeNet) is proposed to train. The experimental results show that forgeNet performs better than support vector machines (SVM), random forest (RF), logical regression (LR), Naive Bayes (NB), XGBoost, LightGBM and gcForest. forgeNet could identify 19 compounds in Erhuang decoction (EhD) and Dexamethasone (DXMS) more accurately.

The study on the morphological changes of oropharynx in patients with complete unilateral cleft lip and palate after palatopharyngeal closure.

Cleft lip and palate can be treated as one of the most common craniofacial congenital malformations in humans. Such disease influences tens of millions of patients all over the world. Cleft lip and palate deformity affects many important physiological functions, including breathing, swallowing, speech, chewing, and aesthetics. This work focuses on investigating the morphology and airway volume of oropharynx patients with unilateral complete cleft lip and palate after palatopharyngeal closure. In addition, this work evaluated the similarities and differences between patients with cleft lip and palate and those without such an issue. The employed data, selected from the Department of Stomatology of Xuzhou First People's Hospital, are based on the conical beam CT images. The study sample was divided into two groups: the selected experimental group, who confronted the cleft lip, cleft palate, and velopharyngeal closure surgery, and the selected control group, who are healthy children at the corresponding age. The parameters, including the airway volume, the airway volume of velopharyngeal and oropharyngeal segments, the minimum cross-sectional area of the pharynx, the horizontal plane airway area of the hard palate and soft one, the horizontal airway area of the hyoid bone, and the vertical distance between the hard palate and soft palate, can be measured by Dolphin. These parameters were analyzed with a statistical approach. The analysis of the above-mentioned parameters reveals that the airway volume, the minimum cross-sectional area of the pharynx, the horizontal cross-sectional area of the hyoid, and the distance between the hard palate and soft palate tip in patients with complete unilateral cleft lip and palate show significant differences between the experimental group and the control group. Meanwhile, other parameters, including the horizontal cross-sectional area of the airway in the horizontal plane of the hard palate and the horizontal plane of the soft palate, did not show noticeable differences in the two groups. The patients, who confronted the unilateral complete cleft lip and palate, can improve with the velopharyngeal closure surgery. Furthermore, the length and vertical distance of the soft palate and the volume of each segment of the airway exhibit differences between the experimental group and the control group.

PGRNIG: novel parallel gene regulatory network identification algorithm based on GPU.

Molecular biology has revealed that complex life phenomena can be treated as the result of many gene interactions. Investigating these interactions and understanding the intrinsic mechanisms of biological systems using gene expression data have attracted a lot of attention. As a typical gene regulatory network (GRN) inference method, the S-system has been utilized to deal with small-scale network identification. However, it is extremely difficult to optimize it to infer medium-to-large networks. This paper proposes a novel parallel swarm intelligent algorithm, PGRNIG, to optimize the parameters of the S-system. We employed the clone selection strategy to improve the whale optimization algorithm (CWOA). To enhance the time efficiency of CWOA optimization, we utilized a parallel CWOA (PCWOA) based on the compute unified device architecture (CUDA) platform. Decomposition strategy and L1 regularization were utilized to reduce the search space and complexity of GRN inference. We applied the PGRNIG algorithm on three synthetic datasets and two real time-series expression datasets of the species of Escherichia coli and Saccharomyces cerevisiae. Experimental results show that PGRNIG could infer the gene regulatory network more accurately than other state-of-the-art methods with a convincing computational speed-up. Our findings show that CWOA and PCWOA have faster convergence performances than WOA.

Single_cell_GRN: gene regulatory network identification based on supervised learning method and Single-cell RNA-seq data.

Single-cell RNA-seq overcomes the shortcomings of conventional transcriptome sequencing technology and could provide a powerful tool for distinguishing the transcriptome characteristics of various cell types in biological tissues, and comprehensively revealing the heterogeneity of gene expression between cells. Many Intelligent Computing methods have been presented to infer gene regulatory network (GRN) with single-cell RNA-seq data. In this paper, we investigate the performances of seven classifiers including support vector machine (SVM), random forest (RF), Naive Bayesian (NB), GBDT, logical regression (LR), decision tree (DT) and K-Nearest Neighbor (KNN) for solving the binary classification problems of GRN inference with single-cell RNA-seq data (Single_cell_GRN). In SVM, three different kernel functions (linear, polynomial and radial basis function) are utilized, respectively. Three real single-cell RNA-seq datasets from mouse and human are utilized. The experiment results prove that in most cases supervised learning methods (SVM, RF, NB, GBDT, LR, DT and KNN) perform better than unsupervised learning method (GENIE3) in terms of AUC. SVM, RF and KNN have the better performances than other four classifiers. In SVM, linear and polynomial kernels are more fit to model single-cell RNA-seq data.

Gene Regulatory Identification Based on the Novel Hybrid Time-Delayed Method.

Gene regulatory network (GRN) inference with biology data is a difficult and serious issue in the field of system biology. In order to detect the direct associations of GRN more accurately, a novel two-step GRN inference technique based on the time-delayed correlation coefficient (TDCC) and time-delayed complex-valued S-system model (TDCVSS) is proposed. First, a TDCC algorithm is utilized to construct an initial network. Second, a TDCVSS model is utilized to prune the network topology in order to delete false-positive regulatory relationships for each target gene. The complex-valued restricted additive tree and complex-valued differential evolution are proposed to approximate the optimal TDCVSS model. Finally, the overall network could be inferred by integrating the regulations of all target genes. Two real gene expression datasets from E. coli and S. cerevisiae gene networks are utilized to evaluate the performances of our proposed two-step GRN inference algorithm. The results demonstrated that the proposed algorithm could infer GRN more correct than classical methods and time-delayed methods.

A survey on protein-DNA-binding sites in computational biology.

Transcription factors are important cellular components of the process of gene expression control. Transcription factor binding sites are locations where transcription factors specifically recognize DNA sequences, targeting gene-specific regions and recruiting transcription factors or chromatin regulators to fine-tune spatiotemporal gene regulation. As the common proteins, transcription factors play a meaningful role in life-related activities. In the face of the increase in the protein sequence, it is urgent how to predict the structure and function of the protein effectively. At present, protein-DNA-binding site prediction methods are based on traditional machine learning algorithms and deep learning algorithms. In the early stage, we usually used the development method based on traditional machine learning algorithm to predict protein-DNA-binding sites. In recent years, methods based on deep learning to predict protein-DNA-binding sites from sequence data have achieved remarkable success. Various statistical and machine learning methods used to predict the function of DNA-binding proteins have been proposed and continuously improved. Existing deep learning methods for predicting protein-DNA-binding sites can be roughly divided into three categories: convolutional neural network (CNN), recursive neural network (RNN) and hybrid neural network based on CNN-RNN. The purpose of this review is to provide an overview of the computational and experimental methods applied in the field of protein-DNA-binding site prediction today. This paper introduces the methods of traditional machine learning and deep learning in protein-DNA-binding site prediction from the aspects of data processing characteristics of existing learning frameworks and differences between basic learning model frameworks. Our existing methods are relatively simple compared with natural language processing, computational vision, computer graphics and other fields. Therefore, the summary of existing protein-DNA-binding site prediction methods will help researchers better understand this field.

Disease-Ligand Identification Based on Flexible Neural Tree.

In order to screen the disease-related compounds of a traditional Chinese medicine prescription in network pharmacology research accurately, a new virtual screening method based on flexible neural tree (FNT) model, hybrid evolutionary method and negative sample selection algorithm is proposed. A novel hybrid evolutionary algorithm based on the Grammar-guided genetic programming and salp swarm algorithm is proposed to infer the optimal FNT. According to hypertension, diabetes, and Corona Virus Disease 2019, disease-related compounds are collected from the up-to-date literatures. The unrelated compounds are chosen by negative sample selection algorithm. ECFP6, MACCS, Macrocycle, and RDKit are utilized to numerically characterize the chemical structure of each compound collected, respectively. The experiment results show that our proposed method performs better than classical classifiers [Support Vector Machine (SVM), random forest (RF), AdaBoost, decision tree (DT), Gradient Boosting Decision Tree (GBDT), KNN, logic regression (LR), and Naive Bayes (NB)], up-to-date classifier (gcForest), and deep learning method (forgeNet) in terms of AUC, ROC, TPR, FPR, Precision, Specificity, and F1. MACCS method is suitable for the maximum number of classifiers. All methods perform poorly with ECFP6 molecular descriptor.

Phage_UniR_LGBM: Phage Virion Proteins Classification with UniRep Features and LightGBM Model.

Phage, the most prevalent creature on the planet, serves a variety of critical roles. Phage's primary role is to facilitate gene-to-gene communication. The phage proteins can be defined as the virion proteins and the nonvirion ones. Nowadays, experimental identification is a difficult process that necessitates a significant amount of laboratory time and expense. Considering such situation, it is critical to design practical calculating techniques and develop well-performance tools. In this work, the Phage_UniR_LGBM has been proposed to classify the virion proteins. In detailed, such model utilizes the UniRep as the feature and the LightGBM algorithm as the classification model. And then, the training data train the model, and the testing data test the model with the cross-validation. The Phage_UniR_LGBM was compared with the several state-of-the-art features and classification algorithms. The performances of the Phage_UniR_LGBM are 88.51% in Sp,89.89% in Sn, 89.18% in Acc, 0.7873 in MCC, and 0.8925 in F1 score.

Determining the association between hypertension and bone metabolism markers in osteoporotic patients.

ABSTRACT: The aim of the case study is to examine the association between hypertension and the level of bone metabolism markers in newly diagnosed osteoporotic patients.A cross-sectional study of 518 subjects was done to see the association between hypertension and the level of osteocalcin (OC), bone-specific alkaline phosphatase (B-ALP), Tartrate-resistant acid phosphatase (TRAP.5B), and 25-hydroxy vitamin D (25-OHD). There were 243 (46.9%) osteoporosis patients with hypertension. Both univariate and multivariate analysis have suggested that lower OC and 25-OHD levels were associated with hypertension. The potential confounders-adjusted OC level was significantly lower in hypertensive female group than that in the female without hypertension group [ß = -0.20, 95% confidence interval (95% CI) = -0.37 to -0.03, P = .02 in final adjust model]. The potential confounders-adjusted 25-OHD level was significantly lower in hypertensive male group than that in male without hypertension group (ß = -0.34, 95% CI = -0.58 to -0.10, P = .01 in final adjust model). The B-ALP and TRACP.5B levels were positively associated with hypertension in all patients or subgroup analysis. However, all the correlations had no statistical significance for the B-ALP and TRACP.5B.In conclusion, the hypertension was associated with low level of OC and 25-OHD. Hypertension probably led to low bone turnover, which may be one of the mechanisms of hypertension-related osteoporosis.

Designing and Testing of a System for Aerosolization and Recovery of Viable Porcine Reproductive and Respiratory Syndrome Virus (PRRSV): Theoretical and Engineering Considerations.

Porcine reproductive and respiratory syndrome virus (PRRSV) infections cause significant economic losses to swine producers every year. Aerosols containing infectious PRRSV are an important route of transmission, and proper treatment of air could mitigate the airborne spread of the virus within and between barns. Previous bioaerosol studies focused on the microbiology of PRRSV aerosols; thus, the current study addressed the engineering aspects of virus aerosolization and collection. Specific objectives were to (1) build and test a virus aerosolization system, (2) achieve a uniform and repeatable aerosol generation and collection throughout all replicates, (3) identify and minimize sources of variation, and (4) verify that the collection system (impingers) performed similarly. The system for virus aerosolization was built and tested (Obj. 1). The uniform airflow distribution was confirmed using a physical tracer (<12% relative standard deviation) for all treatments and sound engineering control of flow rates (Obj. 2). Theoretical uncertainty analyses and mass balance calculations showed <3% loss of air mass flow rate between the inlet and outlet (Obj. 3). A comparison of TCID50 values among impinger fluids showed no statistical difference between any two of the three trials (p-value = 0.148, 0.357, 0.846) (Obj. 4). These results showed that the readiness of the system for research on virus aerosolization and treatment (e.g., by ultraviolet light), as well as its potential use for research on other types of airborne pathogens and their mitigation on a laboratory scale.

Evaluation of TiO2 Based Photocatalytic Treatment of Odor and Gaseous Emissions from Swine Manure with UV-A and UV-C.

It is essential to mitigate gaseous emissions that result from poultry and livestock production to increase industry sustainability. Odorous volatile organic compounds (VOCs), ammonia (NH3), hydrogen sulfide (H2S), and greenhouse gases (GHGs) have detrimental effects on the quality of life in rural communities, the environment, and climate. This study's objective was to evaluate the photocatalytic UV treatment of gaseous emissions of odor, odorous VOCs, NH3, and other gases (GHGs, O3-sometimes considered as by-products of UV treatment) from stored swine manure on a pilot-scale. The manure emissions were treated in fast-moving air using a mobile lab equipped with UV-A and UV-C lights and TiO2-based photocatalyst. Treated gas airflow (0.25-0.76 m3∙s-1) simulates output from a small ventilation fan in a barn. Through controlling the light intensity and airflow, UV dose was tested for techno-economic analyses. The treatment effectiveness depended on the UV dose and wavelength. Under UV-A (367 nm) photocatalysis, the percent reduction of targeted gases was up to (i) 63% of odor, (ii) 51%, 51%, 53%, 67%, and 32% of acetic acid, propanoic acid, butanoic acid, p-cresol, and indole, respectively, (iii) 14% of nitrous oxide (N2O), (iv) 100% of O3, and 26% generation of CO2. Under UV-C (185 + 254 nm) photocatalysis, the percent reductions of target gases were up to (i) 54% and 47% for p-cresol and indole, respectively, (ii) 25% of N2O, (iii) 71% of CH4, and 46% and 139% generation of CO2 and O3, respectively. The results proved that the UV technology was sufficiently effective in treating odorous gases, and the mobile lab was ready for farm-scale trials. The UV technology can be considered for the scaled-up treatment of emissions and air quality improvement inside livestock barns. Results from this study are needed to inform the experimental design for future on-farm research with UV-A and UV-C.

Analyzing knowledge entities about COVID-19 using entitymetrics.

COVID-19 cases have surpassed the 109 + million markers, with deaths tallying up to 2.4 million. Tens of thousands of papers regarding COVID-19 have been published along with countless bibliometric analyses done on COVID-19 literature. Despite this, none of the analyses have focused on domain entities occurring in scientific publications. However, analysis of these bio-entities and the relations among them, a strategy called entity metrics, could offer more insights into knowledge usage and diffusion in specific cases. Thus, this paper presents an entitymetric analysis on COVID-19 literature. We construct an entity-entity co-occurrence network and employ network indicators to analyze the extracted entities. We find that ACE-2 and C-reactive protein are two very important genes and that lopinavir and ritonavir are two very important chemicals, regardless of the results from either ranking.

Design and Testing of Mobile Laboratory for Mitigation of Gaseous Emissions from Livestock Agriculture with Photocatalysis.

Livestock production systems generate nuisance odor and gaseous emissions affecting local communities and regional air quality. There are also concerns about the occupational health and safety of farmworkers. Proven mitigation technologies that are consistent with the socio-economic challenges of animal farming are needed. We have been scaling up the photocatalytic treatment of emissions from lab-scale, aiming at farm-scale readiness. In this paper, we present the design, testing, and commissioning of a mobile laboratory for on-farm research and demonstration of performance in simulated farm conditions before testing to the farm. The mobile lab is capable of treating up to 1.2 m3/s of air with titanium dioxide, TiO2-based photocatalysis, and adjustable UV-A dose based on LED lamps. We summarize the main technical requirements, constraints, approach, and performance metrics for a mobile laboratory, such as the effectiveness (measured as the percent reduction) and cost of photocatalytic treatment of air. The commissioning of all systems with standard gases resulted in ~9% and 34% reduction of ammonia (NH3) and butan-1-ol, respectively. We demonstrated the percent reduction of standard gases increased with increased light intensity and treatment time. These results show that the mobile laboratory was ready for on-farm deployment and evaluating the effectiveness of UV treatment.

Pilot-Scale Testing of UV-A Light Treatment for Mitigation of NH3, H2S, GHGs, VOCs, Odor, and O3 Inside the Poultry Barn.

Poultry farmers are producing eggs, meat, and feathers with increased efficiency and lower carbon footprint. Technologies to address concerns about the indoor air quality inside barns and the gaseous emissions from farms to the atmosphere continue to be among industry priorities. We have been developing and scaling up a UV air treatment that has the potential to reduce odor and other gases on the farm scale. In our recent laboratory-scale study, the use of UV-A (a less toxic ultraviolet light, a.k.a. "black light") and a special TiO2-based photocatalyst reduced concentrations of several important air pollutants (NH3, CO2, N2O, O3) without impact on H2S and CH4. Therefore, the objectives of this research were to (1) scale up the UV treatment to pilot scale, (2) evaluate the mitigation of odor and odorous volatile organic compounds (VOCs), and (3) complete preliminary economic analyses. A pilot-scale experiment was conducted under commercial poultry barn conditions to evaluate photocatalyst coatings on surfaces subjected to UV light under field conditions. In this study, the reactor was constructed to support interchangeable wall panels and installed on a poultry farm. The effects of a photocatalyst's presence (photocatalysis and photolysis), UV intensity (LED and fluorescent), and treatment time were studied in the pilot-scale experiments inside a poultry barn. The results of the pilot-scale experiments were consistent with the laboratory-scale one: the percent reduction under photocatalysis was generally higher than photolysis. In addition, the percent reduction of target gases at a high light intensity and long treatment time was higher. The percent reduction of NH3 was 5-9%. There was no impact on H2S, CH4, and CO2 under any experimental conditions. N2O and O3 concentrations were reduced at 6-12% and 87-100% by both photolysis and photocatalysis. In addition, concentrations of several VOCs responsible for livestock odor were reduced from 26 to 62% and increased with treatment time and light intensity. The odor was reduced by 18%. Photolysis treatment reduced concentrations of N2O, VOCs, and O3, only. The initial economic analysis has shown that LEDs are more efficient than fluorescent lights. Further scale-up and research at farm scale are warranted.