Investigation of multidirectional toxicity induced by high-dose molybdenum exposure with Allium test.

In this study, the multifaceted toxicity induced by high doses of the essential trace element molybdenum in Allium cepa L. was investigated. Germination, root elongation, weight gain, mitotic index (MI), micronucleus (MN), chromosomal abnormalities (CAs), Comet assay, malondialdehyde (MDA), proline, superoxide dismutase (SOD), catalase (CAT) and anatomical parameters were used as biomarkers of toxicity. In addition, detailed correlation and PCA analyzes were performed for all parameters discussed. On the other hand, this study focused on the development of a two hidden layer deep neural network (DNN) using Matlab. Four experimental groups were designed: control group bulbs were germinated in tap water and application group bulbs were germinated with 1000, 2000 and 4000 mg/L doses of molybdenum for 72 h. After germination, root tips were collected and prepared for analysis. As a result, molybdenum exposure caused a dose-dependent decrease (p < 0.05) in the investigated physiological parameter values, and an increase (p < 0.05) in the cytogenetic (except MI) and biochemical parameter values. Molybdenum exposure induced different types of CAs and various anatomical damages in root meristem cells. Comet assay results showed that the severity of DNA damage increased depending on the increasing molybdenum dose. Detailed correlation and PCA analysis results determined significant positive and negative interactions between the investigated parameters and confirmed the relationships of these parameters with molybdenum doses. It has been found that the DNN model is in close agreement with the actual data showing the accuracy of the predictions. MAE, MAPE, RMSE and R2 were used to evaluate the effectiveness of the DNN model. Collective analysis of these metrics showed that the DNN model performed well. As a result, it has been determined once again that high doses of molybdenum cause multiple toxicity in A. cepa and the Allium test is a reliable universal test for determining this toxicity. Therefore, periodic measurement of molybdenum levels in agricultural soils should be the first priority in preventing molybdenum toxicity.

Specific connectivity optimizes learning in thalamocortical loops.

Thalamocortical loops have a central role in cognition and motor control, but precisely how they contribute to these processes is unclear. Recent studies showing evidence of plasticity in thalamocortical synapses indicate a role for the thalamus in shaping cortical dynamics through learning. Since signals undergo a compression from the cortex to the thalamus, we hypothesized that the computational role of the thalamus depends critically on the structure of corticothalamic connectivity. To test this, we identified the optimal corticothalamic structure that promotes biologically plausible learning in thalamocortical synapses. We found that corticothalamic projections specialized to communicate an efference copy of the cortical output benefit motor control, while communicating the modes of highest variance is optimal for working memory tasks. We analyzed neural recordings from mice performing grasping and delayed discrimination tasks and found corticothalamic communication consistent with these predictions. These results suggest that the thalamus orchestrates cortical dynamics in a functionally precise manner through structured connectivity.

Develop prediction model to help forecast advanced prostate cancer patients' prognosis after surgery using neural network.

Background: The effect of surgery on advanced prostate cancer (PC) is unclear and predictive model for postoperative survival is lacking yet. Methods: We investigate the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database, to collect clinical features of advanced PC patients. According to clinical experience, age, race, grade, pathology, T, N, M, stage, size, regional nodes positive, regional nodes examined, surgery, radiotherapy, chemotherapy, history of malignancy, clinical Gleason score (composed of needle core biopsy or transurethral resection of the prostate specimens), pathological Gleason score (composed of prostatectomy specimens) and prostate-specific antigen (PSA) are the potential predictive variables. All samples are divided into train cohort (70% of total, for model training) and test cohort (30% of total, for model validation) by random sampling. We then develop neural network to predict advanced PC patients' overall. Area under receiver operating characteristic curve (AUC) is used to evaluate model's performance. Results: 6380 patients, diagnosed with advanced (stage III-IV) prostate cancer and receiving surgery, have been included. The model using all collected clinical features as predictors and based on neural network algorithm performs best, which scores 0.7058 AUC (95% CIs, 0.7021-0.7068) in train cohort and 0.6925 AUC (95% CIs, 0.6906-0.6956) in test cohort. We then package it into a Windows 64-bit software. Conclusion: Patients with advanced prostate cancer may benefit from surgery. In order to forecast their overall survival, we first build a clinical features-based prognostic model. This model is accuracy and may offer some reference on clinical decision making.

PresRecST: a novel herbal prescription recommendation algorithm for real-world patients with integration of syndrome differentiation and treatment planning.

OBJECTIVES: Herbal prescription recommendation (HPR) is a hot topic and challenging issue in field of clinical decision support of traditional Chinese medicine (TCM). However, almost all previous HPR methods have not adhered to the clinical principles of syndrome differentiation and treatment planning of TCM, which has resulted in suboptimal performance and difficulties in application to real-world clinical scenarios. MATERIALS AND METHODS: We emphasize the synergy among diagnosis and treatment procedure in real-world TCM clinical settings to propose the PresRecST model, which effectively combines the key components of symptom collection, syndrome differentiation, treatment method determination, and herb recommendation. This model integrates a self-curated TCM knowledge graph to learn the high-quality representations of TCM biomedical entities and performs 3 stages of clinical predictions to meet the principle of systematic sequential procedure of TCM decision making. RESULTS: To address the limitations of previous datasets, we constructed the TCM-Lung dataset, which is suitable for the simultaneous training of the syndrome differentiation, treatment method determination, and herb recommendation. Overall experimental results on 2 datasets demonstrate that the proposed PresRecST outperforms the state-of-the-art algorithm by significant improvements (eg, improvements of P@5 by 4.70%, P@10 by 5.37%, P@20 by 3.08% compared with the best baseline). DISCUSSION: The workflow of PresRecST effectively integrates the embedding vectors of the knowledge graph for progressive recommendation tasks, and it closely aligns with the actual diagnostic and treatment procedures followed by TCM doctors. A series of ablation experiments and case study show the availability and interpretability of PresRecST, indicating the proposed PresRecST can be beneficial for assisting the diagnosis and treatment in real-world TCM clinical settings. CONCLUSION: Our technology can be applied in a progressive recommendation scenario, providing recommendations for related items in a progressive manner, which can assist in providing more reliable diagnoses and herbal therapies for TCM clinical task.

Evaluating the Robustness of a Deep Learning Bone Age Algorithm to Clinical Image Variation Using Computational Stress Testing.

Purpose To evaluate the robustness of an award-winning bone age deep learning (DL) model to extensive variations in image appearance. Materials and Methods In December 2021, the DL bone age model that won the 2017 RSNA Pediatric Bone Age Challenge was retrospectively evaluated using the RSNA validation set (1425 pediatric hand radiographs; internal test set in this study) and the Digital Hand Atlas (DHA) (1202 pediatric hand radiographs; external test set). Each test image underwent seven types of transformations (rotations, flips, brightness, contrast, inversion, laterality marker, and resolution) to represent a range of image appearances, many of which simulate real-world variations. Computational "stress tests" were performed by comparing the model's predictions on baseline and transformed images. Mean absolute differences (MADs) of predicted bone ages compared with radiologist-determined ground truth on baseline versus transformed images were compared using Wilcoxon signed rank tests. The proportion of clinically significant errors (CSEs) was compared using McNemar tests. Results There was no evidence of a difference in MAD of the model on the two baseline test sets (RSNA = 6.8 months, DHA = 6.9 months; P = .05), indicating good model generalization to external data. Except for the RSNA dataset images with an appended radiologic laterality marker (P = .86), there were significant differences in MAD for both the DHA and RSNA datasets among other transformation groups (rotations, flips, brightness, contrast, inversion, and resolution). There were significant differences in proportion of CSEs for 57% of the image transformations (19 of 33) performed on the DHA dataset. Conclusion Although an award-winning pediatric bone age DL model generalized well to curated external images, it had inconsistent predictions on images that had undergone simple transformations reflective of several real-world variations in image appearance. Keywords: Pediatrics, Hand, Convolutional Neural Network, Radiography Supplemental material is available for this article. © RSNA, 2024 See also commentary by Faghani and Erickson in this issue.

Optimization of ultrasonic-assisted supramolecular solvent microextraction of coumarins from Cortex fraxini using response surface methodology combined with artificial neural network-genetic algorithm.

A simple, fast, and efficient ultrasonic-assisted supramolecular solvent microextraction combined with high performance liquid chromatography method was developed for the determination of coumarins in Cortex fraxini, including esculin, esculetin and fraxetin. In this study, a novel supramolecular solvent was prepared with 1-octanol, tetrahydrofuran and water for the first time, and its composition, viscosity, density, structure, and micromorphology were characterized. The prepared supramolecular solvent exhibited vesicular structures and had the characteristics of low viscosity. Through single-factor experiments, response surface methodology and artificial neural network-genetic algorithm, the optimal extraction conditions were obtained as follows: NaCl concentration of 1 mol mL-1, pH value of 10, solid-liquid ratio of 10:1, vortex time of 30 s, ultrasonic power of 100 W, ultrasonic temperature of 60 °C, ultrasonic time of 15 min, centrifugation speed of 5000 rpm, and centrifugation time of 1 min. The results demonstrated that the artificial neural network model exhibited maximum R-values of 0.98703, 0.97440, 0.99836, and 0.95447 for training, testing, validation, and all dataset, respectively. The minimum mean square errors were 0.75, 10.15, 1.99, and 2.63, respectively. This indicated that the predicted values were almost consistent with the actual values. Under the optimal conditions, the total extraction yields of target analytes reached 2.80 %. The calibration curves for each analyte exhibited excellent linearity within the linear range (r > 0.9993). The limits of detection and quantification ranged from 4.87 to 6.55 ng mL-1 and 16.24 to 21.84 ng mL-1, respectively. The recoveries ranged from 98.71 % to 111.01 % with relative standard deviations of less than 3.6 %. The present method had the advantages of short extraction time (15 min) and less solvent consumption (0.5 mL). The prepared supramolecular solvent was proved to have great potential in extracting coumarins from medicinal plants.

Intelligence detection of oil absorption in French fries by surface profiles.

Surface profiles are important evaluation indices for oil absorption behavior of fried foods. This research established two intelligent models of partial least-squares regression (PLSR) and back propagation artificial neural network (BP-ANN) for monitoring the oil absorption behavior of French fries based on the surface characteristics. Surface morphology and texture of French fries by rapeseed oil (RO) and high-oleic peanut oil (HOPO) at different temperatures were investigated. Results showed that oil content of samples increased with frying temperature, accounting for 37.7% and 41.4% of samples fried by RO and HOPO respectively. The increase of crust ratio, roughness and texture parameters (Fm, Nwr, fwr, Wc) and the decrease of uniformity were observed with the frying temperature. Coefficients of prediction set of PLSR and BP-ANN models were more than 0.93, which indicated that surface features combined with chemometrics were rapid and precise methods for determining the oil content of French fries.

TIRPnet: Risk prediction of traditional Chinese medicine ingredients based on a deep neural network.

ETHNOPHARMACOLOGICAL RELEVANCE: Traditional Chinese medicine (TCM) has a history of over 3000 years of medical practice. Due to the complex ingredients and unclear pharmacological mechanism of TCM, it is very difficult to predict its risks. With the increase in the number and severity of spontaneous reports of adverse drug reactions (ADRs) of TCM, its safety has received widespread attention. AIM OF THE STUDY: In this study, we proposed a framework based on deep learning to predict the probability of adverse reactions caused by TCM ingredients and validated the model using real-world data. MATERIALS AND METHODS: The spontaneous reporting data from Jiangsu Province of China was selected as the research data, which included 72,561 ADR reports of TCMs. All the ingredients of these TCMs were collected from the medical website and correlated with the corresponding ADRs. Then, a risk prediction model was constructed based on a deep neural network (DNN), named TIRPnet. Based on one-hot encoded data, our model achieved the optimal performance by fine-tuning some hyperparameters. The ten most commonly used TCM ingredients and their ADRs were collected as the test set to evaluate their performance as objective criteria. RESULTS: TIRPnet was constructed as a 7-layer DNN. The experimental results showed that TIRPnet performs excellently in all indicators, with a sensitivity of 0.950, specificity of 0.995, accuracy of 0.994, precision of 0.708, and F1 of 0.811. CONCLUSIONS: The proposed TIRPnet owns the ability to predict the ADRs of a single TCM ingredient by learning a large number of TCM-related spontaneous reports, which can help doctors design safe prescriptions and provide technical support for the pharmacovigilance of TCM.

Classification method of traditional Chinese medicine compound decoction duration based on multi-dimensional feature weighted fusion.

This paper extends a text classification method utilizing natural language processing (NLP) into the field of traditional Chinese medicine (TCM) compound decoction to effectively and scientifically extend the TCM compound decoction duration. Specifically, a TCM compound decoction duration classification named TCM-TextCNN is proposed to fuse multi-dimensional herb features and improve TextCNN. Indeed, first, we utilize word vector technology to construct feature vectors of herb names and medicinal parts, aiming to describe the herb characteristics comprehensively. Second, considering the impact of different herb features on the decoction duration, we use an improved Term Frequency-Inverse Word Frequency (TF-IWF) algorithm to weigh the feature vectors of herb names and medicinal parts. These weighted feature vectors are then concatenated to obtain a multi-dimensional herb feature vector, allowing for a more comprehensive representation. Finally, the feature vector is input into the improved TextCNN, which uses k-max pooling to reduce information loss rather than max pooling. Three fully connected layers are added to generate higher-level feature representations, followed by softmax to obtain the final results. Experimental results on a dataset of TCM compound decoction duration demonstrate that TCM-TextCNN improves accuracy, recall, and F1 score by 5.31%, 5.63%, and 5.22%, respectively, compared to methods solely rely on herb name features, thereby confirming our method's effectiveness in classifying TCM compound decoction duration.

Quantitative analysis of the quality constituents of Lonicera japonica Thunberg based on Raman spectroscopy.

Quantitative analysis of the quality constituents of Lonicera japonica (Jinyinhua [JYH]) using a feasible method provides important information on its evaluation and applications. Limitations of sample pretreatment, experimental site, and analysis time should be considered when identifying new methods. In response to these considerations, Raman spectroscopy combined with deep learning was used to establish a quantitative analysis model to determine the quality of JYH. Chlorogenic acid and total flavonoids were identified as analysis targets via network pharmacology. High performance liquid chromatograph and ultraviolet spectroscopy were used to construct standard curves for quantitative analysis. Raman spectra of JYH extracts (1200) were collected. Subsequently, models were built using partial least squares regression, Support Vector Machine, Back Propagation Neural Network, and One-dimensional Convolutional Neural Network (1D-CNN). Among these, the 1D-CNN model showed superior prediction capability and had higher accuracy (R2 = 0.971), and lower root mean square error, indicating its suitability for rapid quantitative analysis.

Hand function after neonatal stroke: A graph model based on basal ganglia and thalami structure.

INTRODUCTION: Neonatal arterial ischemic stroke (NAIS) is a common model to study the impact of a unilateral early brain insult on developmental brain plasticity and the appearance of long-term outcomes. Motor difficulties that may arise are typically related to poor function of the affected (contra-lesioned) hand, but surprisingly also of the ipsilesional hand. Although many longitudinal studies after NAIS have shown that predicting the occurrence of gross motor difficulties is easier, accurately predicting hand motor function (for both hands) from morphometric MRI remains complicated. The hypothesis of an association between the structural organization of the basal ganglia (BG) and thalamus with hand motor function seems intuitive given their key role in sensorimotor function. Neuroimaging studies have frequently investigated these structures to evaluate the correlation between their volumes and motor function following early brain injury. However, the results have been controversial. We hypothesize the involvement of other structural parameters. METHOD: The study involves 35 children (mean age 7.3 years, SD 0.4) with middle cerebral artery NAIS who underwent a structural T1-weighted 3D MRI and clinical examination to assess manual dexterity using the Box and Blocks Test (BBT). Graphs are used to represent high-level structural information of the BG and thalami (volumes, elongations, distances) measured from the MRI. A graph neural network (GNN) is proposed to predict children's hand motor function through a graph regression. To reduce the impact of external factors on motor function (such as behavior and cognition), we calculate a BBT score ratio for each child and hand. RESULTS: The results indicate a significant correlation between the score ratios predicted by our method and the actual score ratios of both hands (p < 0.05), together with a relatively high accuracy of prediction (mean L1 distance < 0.03). The structural information seems to have a different influence on each hand's motor function. The affected hand's motor function is more correlated with the volume, while the 'unaffected' hand function is more correlated with the elongation of the structures. Experiments emphasize the importance of considering the whole macrostructural organization of the basal ganglia and thalami networks, rather than the volume alone, to predict hand motor function. CONCLUSION: There is a significant correlation between the structural characteristics of the basal ganglia/thalami and motor function in both hands. These results support the use of MRI macrostructural features of the basal ganglia and thalamus as an early biomarker for predicting motor function in both hands after early brain injury.

Multiclass classification of motor imagery tasks based on multi-branch convolutional neural network and temporal convolutional network model.

Motor imagery (MI) is a cognitive process wherein an individual mentally rehearses a specific movement without physically executing it. Recently, MI-based brain-computer interface (BCI) has attracted widespread attention. However, accurate decoding of MI and understanding of neural mechanisms still face huge challenges. These seriously hinder the clinical application and development of BCI systems based on MI. Thus, it is very necessary to develop new methods to decode MI tasks. In this work, we propose a multi-branch convolutional neural network (MBCNN) with a temporal convolutional network (TCN), an end-to-end deep learning framework to decode multi-class MI tasks. We first used MBCNN to capture the MI electroencephalography signals information on temporal and spectral domains through different convolutional kernels. Then, we introduce TCN to extract more discriminative features. The within-subject cross-session strategy is used to validate the classification performance on the dataset of BCI Competition IV-2a. The results showed that we achieved 75.08% average accuracy for 4-class MI task classification, outperforming several state-of-the-art approaches. The proposed MBCNN-TCN-Net framework successfully captures discriminative features and decodes MI tasks effectively, improving the performance of MI-BCIs. Our findings could provide significant potential for improving the clinical application and development of MI-based BCI systems.

Bidirectional fermentation of Monascus and Mulberry leaves enhances GABA and pigment contents: establishment of strategy, studies of bioactivity and mechanistic.

Bidirectional fermentation is a technology that utilizes fungi to ferment medicinal edible substrates, with synergistic and complementary advantages. In this work, a fermentation strategy was established to produce a high yield of γ-aminobutyric acid (GABA) and Monascus pigments (MPs) using Monascus and mulberry leaves (MLs). Firstly, the basic fermentation parameters were determined using single-factor experiments, followed by Plackett-Burman (PB) experimental design to identify MLs, glucose, peptone, and temperature as significant influencing factors. The fermentation parameters were optimized using an artificial neural network (ANN). Finally, the effects of bidirectional fermentation of MLs and Monascus were investigated by bioactivity analysis, microstructure observation, and RT-qPCR. The outcomes showed that the bidirectional fermentation significantly increased the bioactive content and promoted the secondary metabolism of Monascus. The established fermentation conditions were 44.2 g/L of MLs, 57 g/L of glucose, 15 g/L of peptone, 1 g/L of MgSO4, 2 g/L of KH2PO4, 8% (v/v) of inoculum, 180 rpm, initial pH 6, 32 °C and 8 days. The content of GABA reached 13.95 g/L and the color value of MPs reached 408.07 U/mL. This study demonstrated the feasibility of bidirectional fermentation of MLs and Monascus, providing a new idea for the application of MLs and Monascus.

Classification of oolong tea varieties based on computer vision and convolutional neural networks.

BACKGROUND: In the contemporary food industry, accurate and rapid differentiation of oolong tea varieties holds paramount importance for traceability and quality control. However, achieving this remains a formidable challenge. This study addresses this lacuna by employing machine learning algorithms - namely support vector machines (SVMs) and convolutional neural networks (CNNs) - alongside computer vision techniques for the automated classification of oolong tea leaves based on visual attributes. RESULTS: An array of 13 distinct characteristics, encompassing color and texture, were identified from five unique oolong tea varieties. To fortify the robustness of the predictive models, data augmentation and image cropping methods were employed. A comparative analysis of SVM- and CNN-based models revealed that the ResNet50 model achieved a high Top-1 accuracy rate exceeding 93%. This robust performance substantiates the efficacy of the implemented methodology for rapid and precise oolong tea classification. CONCLUSION: The study elucidates that the integration of computer vision with machine learning algorithms constitutes a promising, non-invasive approach for the quick and accurate categorization of oolong tea varieties. The findings have significant ramifications for process monitoring, quality assurance, authenticity validation and adulteration detection within the tea industry. © 2023 Society of Chemical Industry.

Cortical activation and BCI performance during brief tactile imagery: A comparative study with motor imagery.

Brain-computer interfaces (BCIs) rely heavily on motor imagery (MI) for operation, yet tactile imagery (TI) presents a novel approach that may be advantageous in situations where visual feedback is impractical. The current study aimed to compare the cortical activity and digit classification performance induced by TI and MI to assess the viability of TI for use in BCIs. Twelve right-handed participants engaged in trials of TI and MI, focusing on their left and right index digits. Event-related desynchronization (ERD) in the mu and beta bands was analyzed, and classification accuracy was determined through an artificial neural network (ANN). Comparable ERD patterns were observed in both TI and MI, with significant decreases in ERD during imagery tasks. The ANN demonstrated high classification accuracy, with TI achieving a mean±SD of 79.30 ± 3.91 % and MI achieving 81.10 ± 2.96 %, with no significant difference between the two (p = 0.11). The study found that TI induces substantial ERD comparable to MI and maintains high classification accuracy, supporting its potential as an effective mental strategy for BCIs. This suggests that TI could be a valuable alternative in BCI applications, particularly for individuals unable to rely on visual cues.

Rapid screening, isolation, and activity evaluation of potential xanthine oxidase inhibitors in Polyporus umbellatus and mechanism of action in the treatment of gout.

INTRODUCTION: Studies show that Polyporus umbellatus has some pharmacological effects in enhancing immunity and against gout. OBJECTIVES: We aimed to establish new techniques for extraction, biological activity screening, and preparation of xanthine oxidase inhibitors (XODIs) from P. umbellatus. METHODS: First, the extraction of P. umbellatus was investigated using the back propagation (BP) neural network genetic algorithm mathematical regression model, and the extraction variables were optimised to maximise P. umbellatus yield. Second, XODIs were rapidly screened using ultrafiltration, and the change of XOD activity was tested by enzymatic reaction kinetics experiment to reflect the inhibitory effect of active compounds on XOD. Meanwhile, the potential anti-gout effects of the obtained active substances were verified using molecular docking, molecular dynamics simulations, and network pharmacology analysis. Finally, with activity screening as guide, a high-speed countercurrent chromatography (HSCCC) method combined with consecutive injection and two-phase solvent system preparation using the UNIFAC mathematical model was successfully developed for separation and purification of XODIs, and the XODIs were identified using MS and NMR. RESULTS: The results verified that polyporusterone A, polyporusterone B, ergosta-4,6,8(14),22-tetraen-3-one, and ergosta-7,22-dien-3-one of P. umbellatus exhibited high biological affinity towards XOD. Their structures have been further identified by NMR, indicating that the method is effective and applicable for rapid screening and identification of XODIs. CONCLUSION: This study provides new ideas for the search for natural XODIs active ingredients, and the study provide valuable support for the further development of functional foods with potential therapeutic benefits.

Combination of in silico prediction and convolutional neural network framework for targeted screening of metabolites from LC-HRMS fingerprints: A case study of "Pericarpium Citri Reticulatae - FructusAurantii".

In this study, a novel approach is introduced, merging in silico prediction with a Convolutional Neural Network (CNN) framework for the targeted screening of in vivo metabolites in Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS) fingerprints. Initially, three predictive tools, supplemented by literature, identify potential metabolites for target prototypes derived from Traditional Chinese Medicines (TCMs) or functional foods. Subsequently, a CNN is developed to minimize false positives from CWT-based peak detection. The Extracted Ion Chromatogram (EIC) peaks are then annotated using MS-FINDER across three levels of confidence. This methodology focuses on analyzing the metabolic fingerprints of rats administered with "Pericarpium Citri Reticulatae - Fructus Aurantii" (PCR-FA). Consequently, 384 peaks in positive mode and 282 in negative mode were identified as true peaks of probable metabolites. By contrasting these with "blank serum" data, EIC peaks of adequate intensity were chosen for MS/MS fragment analysis. Ultimately, 14 prototypes (including flavonoids and lactones) and 40 metabolites were precisely linked to their corresponding EIC peaks, thereby providing deeper insight into the pharmacological mechanism. This innovative strategy markedly enhances the chemical coverage in the targeted screening of LC-HRMS metabolic fingerprints.

Neuro-fuzzy modelling of a continuous stirred tank bioreactor with ceramic membrane technology for treating petroleum refinery effluent: a case study from Assam, India.

A continuous stirred tank bioreactor (CSTB) with cell recycling combined with ceramic membrane technology and inoculated with Rhodococcus opacus PD630 was employed to treat petroleum refinery wastewater for simultaneous chemical oxygen demand (COD) removal and lipid production from the retentate obtained during wastewater treatment. In the present study, the COD removal efficiency (CODRE) (%) and lipid concentration (g/L) were predicted using two artificial intelligence models, i.e., an artificial neural network (ANN) and a neuro-fuzzy neural network (NF-NN) with a network topology of 6-25-2 being the best for NF-NN. The results revealed the superiority of NF-NN over ANN in terms of determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). Three learning algorithms were tested with NF-NN; among them, the Bayesian regularization backpropagation (BR-BP) outperformed others. The sensitivity analysis revealed that, if solid retention time and biomass concentrations were maintained between 35 and 75 h and 3.0 g/L and 3.5 g/L, respectively, high CODRE (93%) and lipid concentration (2.8 g/L) could be obtained consistently.

Utilization of multiple-dilution fluorescence fingerprint facilitates prediction of chemical attributes in spice extracts.

Fluorescence Fingerprint (FF) is a powerful tool for rapid quality assessment of various foods and plant-derived products. However, the conventional utilization of FFs measured at a single dilution level (DL) to substitute chemical analyses is extremely challenging, especially for multicomponent materials like spice extracts because fluorescence intensity and concentration widely differ between components, with complex phenomena like inner filter effects. Here, we proposed a new strategy to use the meta-data comprised of FFs measured at multiple DLs with machine learning to estimate common chemical attributes including total polyphenol and flavonoid contents, and antioxidant abilities. This strategy achieved more consistently satisfactory performance in estimation of all chemical attributes of spice extracts compared to using a single DL. Hence, the workflow employed in this study is expected to serve as an alternative method to quickly evaluate the chemical quality of spice extracts, as well as other plant products and food materials.

Influencing factors of selenium transformation in a soil-rice system and prediction of selenium content in rice seeds: a case study in Ninghua County, Fujian Province.

Selenium (Se) is an essential element for human and animal health and has antioxidant, anticancer, and antiviral effects. However, more than 100 million people in China do not have enough Se in their diets, resulting in a state of low Se in the human body. Since the absorption of Se by crop seeds depends not only on the Se content in soil, there are many omissions and misjudgments in the division of Se-rich producing areas. Soil pH, total iron oxide content (TFe2O3), soil organic matter (SOM), and P and S contents were the main factors affecting Se migration and transformation in the soil-rice system. In this study, we compared the performance of the back propagation neural network (BP network) and multiple linear regression (MLR) using 177 pairs of soil-rice samples. Our results showed that the BP network had higher accuracy than MLR. The accuracy and precision of the prediction data met the requirements, and the prediction data were reliable. Based on the Se data of surface paddy fields, 26,900 ha of Se-rich rice planting area was planned using this model, accounting for 77% of the paddy field area. In the planned Se-rich area for rice, the proportion of soil Se content greater than 0.4 mg·kg-1 was only 5.29%. Our research is of great significance for the development of Se-rich lands.