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.

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.

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.

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.