Evaluation of left ventricular systolic function in patients with iron deficiency anemia based on non-invasive left ventricular pressure-strain loops.

BACKGROUND: Iron deficiency anemia (IDA) is a common health problem worldwide. The objective of this study was to noninvasively and quantitatively evaluate early changes in left ventricular systolic function in patients with IDA using the left ventricular press-strain loop (LV-PSL). METHODS: Sixty-two patients with IDA were selected and divided into two groups based on hemoglobin (Hb) concentration: Group B with Hb > 9 g/dL and group C with 6 g/dL < Hb < 9 g/dL. Thirty-three healthy individuals were used as the control (Group A). The global longitudinal strain (GLS), global work index (GWI), global constructive work (GCW), global waste work (GWW), global work efficiency (GWE) were derived using LV-PSL analysis. Receiver operating characteristic (ROC) curves were constructed for MW parameters to detect abnormal left ventricular systolic function in IDA patients. RESULTS: Compared to group A, GWI and GCW were reduced in group B (both P < 0.01). Compared with groups B and A, GLS, GWI, GCW and GWE, and E/A were all diminished, and GWW, LVEDV, LVESV, and E/mean e' were all increased in group C (all P < 0.01). GLS was positively correlated with GWI, GCW, and GWE (r = 0.679, 0.681, and 0.447, all P < 0.01), and negatively associated with GWW (r = - 0.411, all P < 0.01). For GWI, area under the ROC curve (AUROC) was 0.783. The optimal GWI threshold for detecting abnormal LV systolic function in IDA was1763 mmHg%, with sensitivity of 0.71 and specificity of 0.78. CONCLUSIONS: LV-PSL allows noninvasive quantitative assessment of early impaired LV systolic function in IDA patients with preserved LV ejection fraction, and GWI has high sensitivity and specificity compared with other parameters.

HerbMet: Enhancing metabolomics data analysis for accurate identification of Chinese herbal medicines using deep learning.

INTRODUCTION: Chinese herbal medicines have been utilized for thousands of years to prevent and treat diseases. Accurate identification is crucial since their medicinal effects vary between species and varieties. Metabolomics is a promising approach to distinguish herbs. However, current metabolomics data analysis and modeling in Chinese herbal medicines are limited by small sample sizes, high dimensionality, and overfitting. OBJECTIVES: This study aims to use metabolomics data to develop HerbMet, a high-performance artificial intelligence system for accurately identifying Chinese herbal medicines, particularly those from different species of the same genus. METHODS: We propose HerbMet, an AI-based system for accurately identifying Chinese herbal medicines. HerbMet employs a 1D-ResNet architecture to extract discriminative features from input samples and uses a multilayer perceptron for classification. Additionally, we design the double dropout regularization module to alleviate overfitting and improve model's performance. RESULTS: Compared to 10 commonly used machine learning and deep learning methods, HerbMet achieves superior accuracy and robustness, with an accuracy of 0.9571 and an F1-score of 0.9542 for distinguishing seven similar Panax ginseng species. After feature selection by 25 different feature ranking techniques in combination with prior knowledge, we obtained 100% accuracy and an F1-score for discriminating P. ginseng species. Furthermore, HerbMet exhibits acceptable inference speed and computational costs compared to existing approaches on both CPU and GPU. CONCLUSIONS: HerbMet surpasses existing solutions for identifying Chinese herbal medicines species. It is simple to use in real-world scenarios, eliminating the need for feature ranking and selection in classical machine learning-based methods.

Pancreatic lipase immobilization on cellulose filter paper for inhibitors screening and network pharmacology study of anti-obesity mechanism.

The discovery of pancreatic lipase (PL) inhibitors is an essential route to develop new anti-obesity drugs. In this experiment, chitosan was used to add amino groups to cellulose filter paper (CFP) and then glutaraldehyde was used to covalently combine PL with amino-modified CFP through the Schiff base reaction. Under optimal immobilization conditions, CFP immobilized PL has a wide range of pH and temperature tolerance, as well as excellent reproducibility, reusability and storage stability. Subsequently, 26 natural products (NPs) were screened by immobilized PL with black tea extract having the highest inhibition rate. Three compounds with binding effects on PL (epigallocatechin gallate, theaflavin-3-gallate and theaflavin-3,3'-digallate) were captured. Molecular docking proved that these three compounds have a strong binding affinity for PL. Fluorescence spectra further revealed that theaflavin-3,3'-digallate could statically quench the intrinsic fluorescence of pancreatic lipase. The molecular docking and thermodynamic parameters indicated that electrostatic interaction was considered as the main interaction force between PL and theaflavin-3,3'-digallate. Finally, the potential anti-obesity targets and pathways of the three compounds were discussed through network pharmacology. This study not only proposes a simple and efficient method for screening PL inhibitors, but also sheds light on the anti-obesity mechanism of active compounds in black tea.

Regional brain structural network topology mediates the associations between white matter damage and disease severity in first-episode, Treatment-naïve pubertal children with major depressive disorder.

Puberty is a vulnerable period for the onset of major depressive disorder (MDD) due to considerable neurodevelopmental changes. Prior diffusion tensor imaging (DTI) studies in depressed youth have had heterogeneous participants, making assessment of early pathology challenging due to illness chronicity and medication confounds. This study leveraged whole-brain DTI and graph theory approaches to probe white matter (WM) abnormalities and disturbances in structural network topology related to first-episode, treatment-naïve pediatric MDD. Participants included 36 first-episode, unmedicated adolescents with MDD (mean age 15.8 years) and 29 age- and sex-matched healthy controls (mean age 15.2 years). Compared to controls, the MDD group showed reduced fractional anisotropy in the internal and external capsules, unveiling novel regions of WM disruption in early-onset depression. The right thalamus and superior temporal gyrus were identified as network hubs where betweenness centrality changes mediated links between WM anomalies and depression severity. A diagnostic model incorporating demographics, DTI, and network metrics achieved an AUROC of 0.88 and a F1 score of 0.80 using a neural network algorithm. By examining first-episode, treatment-naïve patients, this work identified novel WM abnormalities and a potential causal pathway linking WM damage to symptom severity via regional structural network alterations in brain hubs.

Memristive True Random Number Generator for Security Applications.

This study explores memristor-based true random number generators (TRNGs) through their evolution and optimization, stemming from the concept of memristors first introduced by Leon Chua in 1971 and realized in 2008. We will consider memristor TRNGs coming from various entropy sources for producing high-quality random numbers. However, we must take into account both their strengths and weaknesses. The comparison with CMOS-based TRNGs will serve as an illustration that memristor TRNGs stand out due to their simpler circuits and lower power consumption- thus leading us into a case study involving electroless YMnO3 (YMO) memristors as TRNG entropy sources that demonstrate good security properties by being able to produce unpredictable random numbers effectively. The end of our analysis sees us pinpointing challenges: post-processing algorithm optimization coupled with ensuring reliability over time for memristor-based TRNGs aimed at next-generation security applications.

OD-MVSNet: Omni-dimensional dynamic multi-view stereo network.

Multi-view stereo based on learning is a critical task in three-dimensional reconstruction, enabling the effective inference of depth maps and the reconstruction of fine-grained scene geometry. However, the results obtained by current popular 3D reconstruction methods are not precise, and achieving high-accuracy scene reconstruction remains challenging due to the pervasive impact of feature extraction and the poor correlation between cost and volume. In addressing these issues, we propose a cascade deep residual inference network to enhance the efficiency and accuracy of multi-view stereo depth estimation. This approach builds a cost-volume pyramid from coarse to fine, generating a lightweight, compact network to improve reconstruction results. Specifically, we introduce the omni-dimensional dynamic atrous spatial pyramid pooling (OSPP), a multiscale feature extraction module capable of generating dense feature maps with multiscale contextual information. The feature maps encoded by the OSPP module can generate dense point clouds without consuming significant memory. Furthermore, to alleviate the issue of feature mismatch in cost volume regularization, we propose a normalization-based 3D attention module. The 3D attention module aggregates crucial information within the cost volume across the dimensions of channel, spatial, and depth. Through extensive experiments on benchmark datasets, notably DTU, we found that the OD-MVSNet model outperforms the baseline model by approximately 1.4% in accuracy loss, 0.9% in completeness loss, and 1.2% in overall loss, demonstrating the effectiveness of our module.

1D p-type molecular-based coordination polymer semiconductor with ultrahigh mobility.

One-dimensional (1D) semiconductor nanostructures exhibit exceptional performance in mitigating short-channel effects and ensuring low power consumption. However, the scarcity of high-mobility p-type 1D materials impedes further advancement. Molecular-based materials offer high designability in structure and properties, making them a promising candidate for 1D p-type semiconductor materials. A molecular-based 1D p-type material was developed under the guidance of coordination chemistry. Cu-HT (HT is the abbreviation of p-hydroxy thiophenol) combines the merits of highly orbital overlap between Cu and S, fully covered surface modification with phenol functional groups, and unique cuprophilic (Cu-Cu) interactions. As such, Cu-HT has a remarkable hole mobility of 27.2 cm2 V-1 s-1, which is one of the highest reported values for 1D molecular-based materials to date and even surpass those of commonly used amorphous silicon as well as the majority of 1D inorganic materials. This achievement underscores the significant potential of coordination polymers in optimizing carrier transport and represents a major advancement in the synthesis of high-performance, 1D p-type semiconductor materials.

Analyzing urinary hippuric acid as a metabolic health biomarker through a supramolecular architecture.

The prevalence of metabolic disorders has been found to increase concomitantly with alternations in habitual diet and lifestyle, indicating the importance of metabolic health monitoring for early warning of high-risk status and suggesting effective intervention strategies. Hippuric acid (HA), as one of the most abundant metabolites from the gut microbiota, holds potential as a regulator of metabolic health. Accordingly, it is imperative to establish an efficient, sensitive, and affordable method for large-scale population monitoring, revealing the association between HA level and metabolic disorders. Upon systematic screening of macrocycle•dye reporter pair, a supramolecular architecture (guanidinomethyl-modified calix[5]arene, GMC5A) was employed to sense urinary HA by employing fluorescein (Fl), whose complexation behavior was demonstrated by theoretical calculations, accomplishing quantification of HA in urine from 249 volunteers in the range of 0.10 mM and 10.93 mM. Excitedly, by restricted cubic spline, urinary HA concentration was found to have a significantly negative correlation with the risk of metabolic disorders when it exceeded 0.76 mM, suggesting the importance of dietary habits, especially the consumption of fruits, coffee, and tea, which was unveiled from a simple questionnaire survey. In this study, we accomplished a high throughput and sensitive detection of urinary HA based on supramolecular sensing with the GMC5A•Fl reporter pair, which sheds light on the rapid quantification of urinary HA as an indicator of metabolic health status and early intervention by balancing the daily diet.

Identification of T2W hypointense ring as a novel noninvasive indicator for glioma grade and IDH genotype.

BACKGROUND: This study aimed to evaluate the T2W hypointense ring and T2-FLAIR mismatch signs in gliomas and use these signs to construct prediction models for glioma grading and isocitrate dehydrogenase (IDH) mutation status. METHODS: Two independent radiologists retrospectively evaluated 207 glioma patients to assess the presence of T2W hypointense ring and T2-FLAIR mismatch signs. The inter-rater reliability was calculated using the Cohen's kappa statistic. Two logistic regression models were constructed to differentiate glioma grade and predict IDH genotype noninvasively, respectively. Receiver operating characteristic (ROC) analysis was used to evaluate the developed models. RESULTS: Of the 207 patients enrolled (119 males and 88 females, mean age 51.6 ± 14.8 years), 45 cases were low-grade gliomas (LGGs), 162 were high-grade gliomas (HGGs), 55 patients had IDH mutations, and 116 were IDH wild-type. The number of T2W hypointense ring signs was higher in HGGs compared to LGGs (p < 0.001) and higher in the IDH wild-type group than in the IDH mutant group (p < 0.001). There were also significant differences in T2-FLAIR mismatch signs between HGGs and LGGs, as well as between IDH mutant and wild-type groups (p < 0.001). Two predictive models incorporating T2W hypointense ring, absence of T2-FLAIR mismatch, and age were constructed. The area under the ROC curve (AUROC) was 0.940 for predicting HGGs (95% CI = 0.907-0.972) and 0.830 for differentiating IDH wild-type (95% CI = 0.757-0.904). CONCLUSIONS: The combination of T2W hypointense ring, absence of T2-FLAIR mismatch, and age demonstrate good predictive capability for HGGs and IDH wild-type. These findings suggest that MRI can be used noninvasively to predict glioma grading and IDH mutation status, which may have important implications for patient management and treatment planning.

Enhanced multi view 3D reconstruction with improved MVSNet.

Although 3D reconstruction has been widely used in many fields as a key component of environment perception, existing technologies still have the potential for further improvement in 3D scene reconstruction. We propose an improved reconstruction algorithm based on the MVSNet network architecture. To glean richer pixel details from images, we suggest deploying a DE module integrated with a residual framework, which supplants the prevailing feature extraction mechanism. The DE module uses ECA-Net and dilated convolution to expand the receptive field range, performing feature splicing and fusion through the residual structure to retain the global information of the original image. Moreover, harnessing attention mechanisms refines the 3D cost volume's regularization process, bolstering the integration of information across multi-scale feature volumes, consequently enhancing depth estimation precision. When assessed our model using the DTU dataset, findings highlight the network's 3D reconstruction scoring a completeness (comp) of 0.411 mm and an overall quality of 0.418 mm. This performance is higher than that of traditional methods and other deep learning-based methods. Additionally, the visual representation of the point cloud model exhibits marked advancements. Trials on the Blended MVS dataset signify that our network exhibits commendable generalization prowess.

DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases.

Combination therapy is an important direction of continuous exploration in the field of medicine, with the core goals of improving treatment efficacy, reducing adverse reactions, and optimizing clinical outcomes. Machine learning technology holds great promise in improving the prediction of drug synergy combinations. However, most studies focus on single disease-oriented collaborative predictive models or involve excessive feature categories, making it challenging to predict the majority of new drugs. To address these challenges, the DrugSK comprehensive model was developed, which utilizes SMILES-BERT to extract structural information from 3492 drugs and trains on reactions from 48,756 drug combinations. DrugSK is an integrated learning model capable of predicting interactions among various drug categories. First, the primary learner is trained from the initial data set. Random forest, support vector machine, and XGboost model are selected as primary learners and logistic regression as secondary learners. A new data set is then "generated" to train level 2 learners, which can be thought of as a prediction for each model. Finally, the results are filtered using logistic regression. Furthermore, the combination of the new antibacterial drug Drafloxacin with other antibacterial agents was tested. The synergistic effect of Drafloxacin and Isavuconazonium in the fight against Candida albicans has been confirmed, providing enlightenment for the clinical treatment of skin infection. DrugSK's prediction is accurate in practical application and can also predict the probability of the outcome. In addition, the tendency of Drafloxacin and antifungal drugs to be synergistic was found. The development of DrugSK will provide a new blueprint for predicting drug combination synergies.

Tolerance threshold of a pelagic species in China to total dissolved gas supersaturation: from the perspective of survival characteristics and swimming ability.

Total dissolved gas (TDG) supersaturation downstream of dams can occur in the Yangtze River basin and is known to cause stress and even death in fish. Consequently, it is important to establish tolerance thresholds of endemic fish to protect local aquatic resources. We conducted experiments to assess survival characteristics and swimming ability of bighead carp, an important commercial fish dwelling in the Yangtze River, to evaluate its tolerance threshold to TDG supersaturation. The typical external symptoms of gas bubble trauma (GBT) were observed and the time when the fish lost equilibrium and died were recorded. The results showed that the mortality occurred when TDG level exceeded 125%, with obvious symptoms such as exophthalmos and bubbles on the head. The interval between loss of equilibrium and mortality decreased with an increase in TDG level. Neither exposure time nor TDG level significantly affected the critical swimming speed (Ucrit) of fish exposed to non-lethal exposure (110%, 120% and 125% TDG) over a 7 day period. Significant reductions in Ucrit were found under 130% and 135% TDG conditions when the exposure lasted 52.0 h and 42.9 h, respectively. The Ucrit also significantly decreased after exposure of 1.6 h under 140% TDG condition. Moreover, after exposure to 140% TDG for 39.2 h, 135% TDG for 56.5 h and 130% TDG for 95.9 h, bighead carp were transferred into air saturated water to recover for 24 h or 48 h; however, swimming performance remained impaired. The results of this study indicate that 125% TDG was the highest TDG level where limited mortality was observed and the swimming ability was not impaired, showing that 125% TDG can be set as the tolerance threshold of this species to guide the operation of dams in the Yangtze River Basin.

TDG prediction model improvement by analysis and validation of experiments on a dam model.

The combination of aerated flows and a high-pressure environment in a stilling basin can result in the supersaturation of total dissolved gas (TDG) downstream of hydraulic projects, posing an ecological risk to aquatic populations by inducing gas bubble disease (GBD) or other negative effects. There is limited literature reporting TDG mass transfer experiments on a complete physical dam model; most existing research is based on measurements in prototype tailwaters. In this study, TDG mass transfer experiments were conducted on a physical model of an under-constructed dam, with TDG-supersaturated water as the inflow, and TDG concentrations were meticulously monitored within the stilling basin. The measurements indicate that the TDG saturation at the outlet of the stilling basin decreased by 13.7% and 10.6% compared to the inlet for the two cases, respectively. Subsequently, an improved TDG prediction model was developed by incorporating a sub-grid air entrainment model and a phase-constrained scalar model. The numerical simulation results were compared with experimental data, indicating a maximum error in TDG saturation at all measured points of less than ± 3%. Moreover, the TDG saturation showed an error of only ± 0.3% at the outlet of the stilling basin. This model has broad applicability to various flow types for obtaining TDG mass transfer results and evaluating mitigation measures of TDG supersaturation to reduce the harmful effects on aquatic ecosystems.

Metabolic network analysis of pre-ASD newborns and 5-year-old children with autism spectrum disorder.

Classical metabolomic and new metabolic network methods were used to study the developmental features of autism spectrum disorder (ASD) in newborns (n = 205) and 5-year-old children (n = 53). Eighty percent of the metabolic impact in ASD was caused by 14 shared biochemical pathways that led to decreased anti-inflammatory and antioxidant defenses, and to increased physiologic stress molecules like lactate, glycerol, cholesterol, and ceramides. CIRCOS plots and a new metabolic network parameter, V ° net, revealed differences in both the kind and degree of network connectivity. Of 50 biochemical pathways and 450 polar and lipid metabolites examined, the developmental regulation of the purine network was most changed. Purine network hub analysis revealed a 17-fold reversal in typically developing children. This purine network reversal did not occur in ASD. These results revealed previously unknown metabolic phenotypes, identified new developmental states of the metabolic correlation network, and underscored the role of mitochondrial functional changes, purine metabolism, and purinergic signaling in autism spectrum disorder.

A design of a nature-like fishway to solve the fractured river connectivity caused by small hydropower based on hydrodynamics and fish behaviors.

Achieving a harmonious alignment between the biological characteristics of fish and hydrodynamics patterns is crucial for ensuring the efficacy of fish passage facilities. In this study, based on the hydrodynamic characteristics of the river and the biological characteristics of fish, we evaluated the internal flow field in the nature-like fishway of Congen II hydropower station located along the Chabao river and explored methods to improve the operation efficiency. Based on comprehensive considerations of the flow field, turbulent kinetic energy, and the migration pathways of fish, it is found that the implementation of a continuous oblique bottom slope represents a more cost-effective and operationally convenient solution. The influence of different permutation of bulkheads in the nature-like fishway on operational efficiency was further examined. Our investigation revealed that the nature-like fishway with the continuous slope of 2% and the arrangement of three bulkheads in each row (model 3) exhibited a relatively simple velocity distribution and linear flow line, which poses challenges for fish in locating resting areas. In addition, the distribution of low turbulence kinetic energy area in the mainstream made it less favorable for fish to transition from the mainstream to the rest area within the fishway. The nature-like fishway with the continuous slope of 2% and the arrangement of two or three bulkheads in staggered rows (model 4) demonstrated better performance. Several potential fish migration routes for both model 3 and model 4 were proposed based on the numerical simulation results. In model 3, fish exhibited a continuous sprint through the concentrated high-speed area, which was less favorable for fish to rest and forage. In contrast, model 4 exhibited a diversified flow velocity distribution, enabling fish to make timely changes in their direction during migration. This feather proved to be advantageous in enhancing fish migration within the passage. The design of nature-like fishway in this study provides an important reference and technical support for the construction and optimization of the nature-like fishway for low dams, and is of great significance for restoring river connectivity destroyed by small hydropower construction and improving fish migration.

Estimating the water quality index based on interpretable machine learning models.

The water quality index (WQI) is an important tool for evaluating the water quality status of lakes. In this study, we used the WQI to evaluate the spatial water quality characteristics of Dianchi Lake. However, the WQI calculation is time-consuming, and machine learning models exhibit significant advantages in terms of timeliness and nonlinear data fitting. We used a machine learning model with optimized parameters to predict the WQI, and the light gradient boosting machine achieved good predictive performance. The machine learning model trained based on the entire Dianchi Lake water quality data achieved coefficient of determination (R2), mean square error, and mean absolute error values of 0.989, 0.228, and 0.298, respectively. In addition, we used the Shapley additive explanations (SHAP) method to interpret and analyse the machine learning model and identified the main water quality parameter that affects the WQI of Dianchi Lake as NH4+-N. Within the entire range of Dianchi Lake, the SHAP values of NH4+-N varied from -9 to 3. Thus, in future water environmental governance, it is necessary to focus on NH4+-N changes. These results can provide a reference for the treatment of lake water environments.

MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks.

Clinical metabolomics is growing as an essential tool for precision medicine. However, classical machine learning algorithms struggle to comprehensively encode and analyze the metabolomics data due to their high dimensionality and complex intercorrelations. This article introduces a new method called MetDIT, designed to analyze intricate metabolomics data effectively using deep convolutional neural networks (CNN). MetDIT comprises two components: TransOmics and NetOmics. Since CNN models have difficulty in processing one-dimensional (1D) sequence data efficiently, we developed TransOmics, a framework that transforms sequence data into two-dimensional (2D) images while maintaining a one-to-one correspondence between the sequences and images. NetOmics, the second component, leverages a CNN architecture to extract more discriminative representations from the transformed samples. To overcome the overfitting due to the small sample size and class imbalance, we introduced a feature augmentation module (FAM) and a loss function to improve the model performance. Furthermore, we systematically optimized the model backbone and image resolution to balance the model parameters and computational costs. To demonstrate the performance of the proposed MetDIT, we conducted extensive experiments using three different clinical metabolomics data sets and achieved better classification performance than classical machine learning methods used in metabolomics, including Random Forest, SVM, XGBoost, and LightGBM. The source code is available at the GitHub repository at https://github.com/Li-OmicsLab/MetDIT, and the WebApp can be found at http://metdit.bioinformatics.vip/.

Lightweight Vehicle Detection Based on Improved YOLOv5s.

A vehicle detection algorithm is an indispensable component of intelligent traffic management and control systems, influencing the efficiency and functionality of the system. In this paper, we propose a lightweight improvement method for the YOLOv5 algorithm based on integrated perceptual attention, with few parameters and high detection accuracy. First, we propose a lightweight module IPA with a Transformer encoder based on integrated perceptual attention, which leads to a reduction in the number of parameters while capturing global dependencies for richer contextual information. Second, we propose a lightweight and efficient multiscale spatial channel reconstruction (MSCCR) module that does not increase parameter and computational complexity and facilitates representative feature learning. Finally, we incorporate the IPA module and the MSCCR module into the YOLOv5s backbone network to reduce model parameters and improve accuracy. The test results show that, compared with the original model, the model parameters decrease by about 9%, the average accuracy (mAP@50) increases by 3.1%, and the FLOPS does not increase.

Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases.

The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.

TDG generation in a ski-jump spillway with a fully or partial-flip bucket.

Ski-jump spillways are frequently used as discharge structures for high dams during floods with high energy heads. The selection of bucket types at the end of spillways has a pronounced effect on the hydraulics of jet characteristics, such as trajectories and entrained air features. However, there is no literature reporting how changes in the bucket types influence TDG generation. This study compares the hydraulic characteristics and TDG mass transfer properties of a hydraulic project under construction using both the traditional fully-flip bucket and the partial-flip bucket configurations. The results indicate that, the use of the partial-flip bucket at the end of the spillway significantly disperses the water flow and yields better energy dissipation effects. At low flow rates (lower than 400 m3/s for the dam in this study), there is little difference in the downstream TDG saturation between the traditional fully-flip bucket and the partial-flip bucket, the average difference is 1.6 % in three cases with a low flow rate. However, at high flow rates (higher than 400 m3/s), the partial-flip bucket generates more TDG compared to the traditional fully-flip bucket, reaching up to 6.2 % at the maximum flow rate. This phenomenon stems from significant changes in the hydrodynamics of the stilling basin at high flow rates due to variations in the flip bucket type. When strict control of TDG generation is necessary downstream of dams, the use of the partial-flip bucket should be carefully considered. This is because, at high flow rates, the partial-flip bucket might result in higher TDG saturation than the fully-flip bucket.