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Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

High dam discharge can lead to total dissolved gas (TDG) supersaturation in downstream rivers, causing fish to suffer from bubble trauma and even mortality. Focusing on the Datengxia hydropower station in the Xijiang River basin, we conducted in-situ experiments to explore the tolerance patterns of economic fish species, including Ctenopharyngodon idella, Hypophthalmichthys molitrix, and Cirrhinus molitorella, under the influence of TDG supersaturation at different compensation depths. Moreover, the development and recovery patterns of bubble trauma and the swimming ability of fish exposed to TDG supersaturated water were investigated. In-situ experiments showed that TDG supersaturation ranged from 112.2 % to 125.2 %, averaging 118.3 % at the experiment site. The results revealed that compensation depth is favorable in fish avoidance of TDG supersaturation. The survival rate of the experimental fish at the surface was lower than for those at the 0–3 m water depth. The survival rates of Ctenopharyngodon idella, Hypophthalmichthys molitrix, and Cirrhinus molitorella at the surface were only 30 %, 47.5 %, and 70 %, respectively, while at the 0–3 m water depth, the survival rates were 97.5 %, 87.5 %, and 87.5 %, respectively. Additionally, the survival rate of fish was related to their preferred water depth. The bubble trauma scores of the experimental fish in TDG supersaturated water significantly increased with exposure time and significantly decreased after recovery in freshwater. The relative and absolute critical swimming speed (Ucrit) of Ctenopharyngodon idella ranged from 10.91 to 12.98 BL/s and 83.3–102.9 cm/s respectively, and there were no significant changes in the Ucrit with increasing TDG supersaturation exposure.

In this study, nano-Nb-modified AlSi10Mg composites were fabricated using laser powder bed fusion (LPBF). The effects of Nb addition on the microstructure and mechanical properties of the AlSi10Mg alloy were systematically investigated. Compared with the as-built AlSi10Mg alloy, the microstructure along the building direction of the AlSi10Mg–Nb composites changed from coarse columnar grains to ultrafine equiaxed grains. Moreover, the preferential grain growth orientation was significantly weakened. Grain refinement occurs due to the presence of abundant in-situ Al3Nb precipitates dispersed within the grain, serving as heterogeneous nucleation inoculants. Simultaneously, the precipitation of NbSi2 particles along the eutectic network also contributed to refining the structure of the eutectic network. As a result, the LPBF-processed AlSi10Mg–Nb samples demonstrated a very high ultimate tensile strength (∼560 MPa) and good ductility (∼7%), comparable to those of high-strength aluminum alloys. The underlying mechanisms for this strength enhancement are discussed based on the improved mechanical properties. This study provides a novel perspective for the fabrication of high-performance AlSi10Mg alloys using LPBF.

Summary: Cervical lesions pose a significant threat to women’s health worldwide. Colposcopy is essential for screening and treating cervical lesions, but its effectiveness depends on the doctor’s experience. Artificial intelligence-based solutions via colposcopy images have shown great potential in cervical lesions screening. However, some challenges still need to be addressed, such as low algorithm performance and lack of high-quality multi-modal datasets. Here, we established a multi-modal colposcopy dataset of 2,273 HPV+ patients, comprising original colposcopy images, acetic acid reactions at 60s and 120s, iodine staining, diagnostic reports, and pathological results. Utilizing this dataset, we developed CerviFusionNet, a hybrid architecture that merges convolutional neural networks and vision transformers to learn robust representations. We designed a temporal module to capture dynamic changes in acetic acid sequences, which can boost the model performance without sacrificing inference speed. Compared with several existing methods, CerviFusionNet demonstrated excellent accuracy and efficiency.

Global climate change (GCC), characterized by warming, affects the hydrological conditions at the basin scale and whether harmful algal blooms (HABs) occur at the scale of river ecological systems. Research on HABs mainly focuses on oceans and lakes, and there is still less research on the effects of GCC on river-type HABs that differ from oceans and lakes in hydrodynamic, water temperature, and nutrient conditions. This study constructed a coupled model framework that includes the GCC model, downscaling model, hydrological model, hydrodynamic model, and eutrophication model, analyzing and exploring the effect of changes in the aquatic ecological environment caused by GCC on river-type HABs in the middle and lower reaches of the Hanjiang River (MLHR). Firstly, based on the three GCC models and statistical downscaling model in CMIP6, high-precision meteorological factors such as future precipitation and temperature were obtained. Secondly, a coupled model based on SWAT and MIKE21-ECOLab was used with the digital elevation model (DEM), land use, soil, meteorological, pollution source, and measured terrain data in the MLHR Basin, which was validated by observed data. Thirdly, there has not been a significant increase in Chl-a, and the impact of GCC has not fundamentally changed the temporal and spatial distribution of HABs. Fourthly, this study proposed to use 0.2 m/s (Corresponding discharge 1160 m3/s) as the hydrodynamic condition for preventing and controlling HABs in the Shayang section.

Identifying the compound formulae-related xenobiotics in bio-samples is full of challenges. Conventional strategies always exhibit the insufficiencies in overall coverage, analytical efficiency, and degree of automation, and the results highly rely on the personal knowledge and experience. The goal of this work was to establish a software-aided approach, by integrating ultra-high performance liquid chromatography/ion-mobility quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS) and in-house high-definition MS2 library, to enhance the identification of prototypes and metabolites of the compound formulae in vivo, taking Sishen formula (SSF) as a template. Seven different MS2 acquisition methods were compared, which demonstrated the potency of a hybrid scan approach (namely high-definition data-independent/data-dependent acquisition (HDDIDDA)) in the identification precision, MS1 coverage, and MS2 spectra quality. The HDDIDDA data for 55 reference compounds, four component drugs, and SSF, together with the rat bio-samples (e.g., plasma, urine, feces, liver, and kidney), were acquired. Based on the UNIFI™ platform (Waters), the efficient data processing workflows were established by combining mass defect filtering (MDF)-induced classification, diagnostic product ions (DPIs), and neutral loss filtering (NLF)-dominated structural confirmation. The high-definition MS2 spectral libraries, dubbed in vitro-SSF and in vivo-SSF, were elaborated, enabling the efficient and automatic identification of SSF-associated xenobiotics in diverse rat bio-samples. Consequently, 118 prototypes and 206 metabolites of SSF were identified, with the identification rate reaching 80.51% and 79.61%, respectively. The metabolic pathways mainly involved the oxidation, reduction, hydrolysis, sulfation, methylation, demethylation, acetylation, glucuronidation, and the combined reactions. Conclusively, the proposed strategy can drive the identification of compound formulae-related xenobiotics in vivo in an intelligent manner.

Although the rational use of water resources through damming rivers can improve economic benefits, it inevitably changes the connectivity of rivers and the hydraulic characteristics of water reduced river reaches, posing a threat to fish habitats. Habitat assessment can provide a reference for assessing the ecological impact of hydropower development and the effectiveness of habitat restoration. However, previous evaluation methods only considered the number of habitats, which may mislead the evaluation results. In this article, we proposed a novel fish habitat assessment model based on the landscape pattern index method and circuit theory, which additionally considers the spatial pattern and connectivity of habitat patches. This model is applied to the Anshunchang reach of the Dadu River, and the target fish is Schizothorax prenanti. We obtained a suitable ecological discharge range for this river reach from 163.0 m3/s to 217.4 m3/s. To improve the suitability of fish habitats, we have also attempted multiple restoration measures. Comparing different habitat restoration measures, it was ultimately found that the river dredging scenario had the best ecological improvement effect, with a maximum improvement rate of 9.87 %. The habitat assessment model in this article can help reservoir managers evaluate fish habitats and formulate habitat restoration plans.

With the economic development, social progress and the increase in power demand, the number of water conservancy projects has gradually increased. However, the reservoir dams inevitably lead to changes in the river connectivity and hydraulic characteristics of rivers, causing damage to fish habitats. Regulating the operation of dams to supplement the ecological flow required by fish is usually an effective way to protect river fish habitats. Based on the graph theory algorithm, we proposed an improved three-dimensional habitat model that comprehensively considered the quantity and quality of fish habitat and used it to the effect assessment of water conservancy project construction on fish habitat. The fish habitat quality evaluation indicators in this paper mainly emphasized the pivotal role of core habitat patches and the connectivity between habitat patches, namely, habitat importance index (HII) and habitat connectivity index (HCI). Then, the model was combined with the Instream Flow Incremental Methodology (IFIM) and applied to the habitat assessment of Schizothorax prenanti (S. prenanti) in the Anshunchang reach of the Dadu River. The results showed that the change trend of the habitat index curves of different water depths showed a trend of first increasing and then decreasing, but the inflection points of the curves and the habitat indexes of different water depths were different. Taking into account the bottom living habits of target fish, the most suitable flow range for S. prenanti was from 271.7 m3/s to 489.1 m3/s. Compared with previous studies, the improved three-dimensional habitat model effectively avoids simulation errors caused by ignoring habitat quality and differences in fish habits.

The urbanization process of the Hanjiang Basin is speeding up and facing many challenges. It is necessary to take active actions to realize the balance between urbanization development and water environment protection. This paper aims to establish a comprehensive evaluation model of urbanization and water environment (UAWE) and explore the development of UAWE in the Hanjiang Basin in China from 2010 to 2018. In terms of methodology, these evaluation models are combined with the Coupling coordination degree model (CCDM), Exploratory Spatial Data Analysis (ESDA), and Geographically and Temporally Weighted Regression (GTWR) to measure and analyze the coupling degree (CD) and spatiotemporal heterogeneity of UAWE. Our findings include the following: (1) From 2010 to 2018, in all regions of the Hanjiang Basin, urbanization showed a gradual upward trend. Among Them, the comprehensive urbanization index (CUI) of Xiangyang increased by 25.37%. (2) The comprehensive water environment index (CWEI) presents a fluctuating growth, with the rate of increase in Jingmen's reaching 17.44%. (3) There is heterogeneity in time and space between cities in the Hanjiang Basin, and the positive interaction effect of UAWE is mainly focused on the middle and lower reaches of the Hanjiang River. (4) Coupling coordination degree (CCD) in the basin shows an increasing trend, and the areas with high CCD are mainly concentrated in Xiangyang and Xiantao. (5) ESDA shows that the CCD among cities in the basin has spatial correlation and heterogeneity, and the regions with similar CCD tend to be distributed centrally in space.

Background and Objective: The growing impact of the COVID-19 pandemic has heightened the urgency of identifying individuals most at risk of infection. Per- and poly-fluoroalkyl substances (PFASs) are manufactured fluorinated chemicals widely used in many industrial and household products. The objective of this case-control study was to assess the association between PFASs exposure and COVID-19 susceptibility and to elucidate the metabolic dysregulation associated with PFASs exposure in COVID-19 patients. Methods: Total 160 subjects (80 COVID-19 patients and 80 symptom-free controls) were recruited from Shanxi and Shandong provinces, two regions heavily polluted by PFASs in China. Twelve common PFASs were quantified in both urine and serum. Urine metabolome profiling was performed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Results: In unadjusted models, the risk of COVID-19 infection was positively associated with urinary levels of perfluorooctanesulfonic acid (PFOS) (Odds ratio: 2.29 [95% CI: 1.52–3.22]), perfluorooctanoic acid (PFOA) (2.91, [1.95–4.83], and total PFASs (∑ (12) PFASs) (3.31, [2.05–4.65]). After controlling for age, sex, body mass index (BMI), comorbidities, and urine albumin-to-creatinine ratio (UACR), the associations remained statistically significant (Adjusted odds ratio of 1.94 [95% CI: 1.39–2.96] for PFOS, 2.73 [1.71–4.55] for PFOA, and 2.82 [1.97–3.51] for ∑ (12) PFASs). Urine metabolome-PFASs association analysis revealed that 59% of PFASs-associated urinary endogenous metabolites in COVID-19 patients were identified to be produced or largely regulated by mitochondrial function. In addition, the increase of PFASs exposure was associated with the accumulation of key metabolites in kynurenine metabolism, which are involved in immune responses (Combined β coefficient of 0.60 [95% CI: 0.25–0.95, P = 0.001]). Moreover, alternations in PFASs-associated metabolites in mitochondrial and kynurenine metabolism were also correlated with clinical lab biomarkers for mitochondrial function (serum growth/differentiation factor-15) and immune activity (lymphocyte percentage), respectively. Conclusion: Elevated exposure to PFASs was independently associated with an increased risk of COVID-19 infection. PFASs-associated metabolites were implicated in mitochondrial function and immune activity. Larger studies are needed to confirm our findings and further understand the underlying mechanisms of PFASs exposure in the pathogenesis of SARS-CoV2 infection.

Total dissolved gas (TDG) supersaturation caused by dam operations can cause fish gas bubble disease (GBD) and even fish kill. Few studies have examined the effects on pelagic species. Here, we examined the tolerance and avoidance characteristics of silver carp (Hypophthalmichthys molitrix), a pelagic fish widely distributed in the Yangtze River basin in China, under stress caused by TDG supersaturation. Silver carp had an average mortality rate of 7.5% ± 1.8%, 92.5% ± 1.8%, and 97.5% ± 1.8% under 130%, 140% and 150% TDG supersaturation for 72 h of exposure, respectively. The average median lethal time (LT50) of silver carp was 18.1 h and 8.0 h under 140% and 150% TDG supersaturation, respectively. Bubbles and congestion appeared in the fins, gills and skin of silver carp. Silver carp can detect and avoid high TDG supersaturation. Significant avoidance behaviors were displayed by silver carp and the final avoidance rate was over 80% under 130% or above TDG conditions. The results of this study indicate that 130% TDG supersaturation triggered silver carp avoidance behaviors, and can be considered as the tolerance threshold.

The swimming abilities of Spinibarbus hollandi, Siniperca chuatsi, Siniperca loona, Odontobutis sinensis and Rhinogobius giurinus were tested in this study, including induced swimming speed (Uind), critical swimming speed (Ucrit) and constant acceleration test speed (UCAT). The results showed that the Uind of the experimental fish in the Huishui River ranged from 5 to 30 cm/s. The average Ucrit of fish was 68.2 cm/s and 5 times the average Uind. The average UCAT was 97.6 cm/s and 7 times the Uind. The swimming speed of fish is related to their body size. The results showed that the swimming speed was more correlated with the shape than the body length of fish. The relationships between the three speeds and the shape of the experimental fish wereUind=9.809S0.128Ucrit=37.954S0.185, andUCAT=58.418S0.099. The differences in Uind between the five fishes were not obvious, while there were significant differences in Ucrit and UCAT between the five fish species. The results of this study suggested that Spinibarbus hollandi, Siniperca chuatsi, and Siniperca loona have stronger swimming abilities than Odontobutis sinensis and Rhinogobius giurinus.

Determining how to restore fish migration routes blocked by large dams is the subject of great debate. Fishways are effective engineering measures restoring fish migration routes. However, most fishways in China do not carry out their target functions. Additionally, no studies have investigated key fishway technologies in the plateaus of Western China. In this research, the relationship between fish behavior and hydrodynamics was assessed by conducting quantitative descriptions of flow speed, turbulence, and other hydraulic indicators. First, target fish species of the plateaus of Western China were determined; then, swimming capabilities and migration characteristics of target fish were assessed. Then the preliminary hydraulic conditions for fishway projects in the plateaus of Western China were proposed. Finally, on-site migration experiments in the fishways were conducted to validate the preliminary hydraulic conditions. Keywords: Ecological hydraulics, Ecological restoration, Environmental management, Fish migration, Fish swimming capability, Fishway