Effects of carbon limitation and carbon fertilization on karst lake-reservoir productivity.

Nitrogen and phosphorus are universally recognized as limiting elements in the eutrophication processes affecting the majority of the world's lakes, reservoirs, and coastal ecosystems. However, despite extensive research spanning several decades, critical questions in eutrophication science remain unanswered. For example, there is still much to understand about the interactions between carbon limitation and ecosystem stability, and the availability of carbon components adds significant complexity to aquatic resource management. Mounting evidence suggests that aqueous CO2 could be a limiting factor, influencing the structure and succession of aquatic plant communities, especially in karstic lake and reservoir ecosystems. Moreover, the fertilization effect of aqueous CO2 has the potential to enhance carbon sequestration and phosphorus removal. Therefore, it is important to address these uncertainties to achieve multiple positive outcomes, including improved water quality and increased carbon sinks in karst lakes and reservoirs.

Effects of Family-Supported Healthcare on Children with Asthma.

Introduction: Healthcare is essential for asthma control, however, whether family-supported healthcare improves therapeutic effects in childhood asthma remains unclear. Methods: The enrolled patients were randomly divided into control and intervention groups. The pulmonary function was evaluated by forced expiratory volume in 1 s as a percentage of forced vital capacity (FEV1/FVC) and fractional exhaled nitric oxide (FeNO). Asthma control and life quality were assessed via a childhood asthma control test and pediatric asthma quality of life questionnaire. Inflammatory cytokines interleukin-6 (IL-6) and interleukin-17 (IL-17) were determined by enzyme-linked immunosorbent assay. Results: No significant differences existed in the basic characteristics of asthma children and their parents among two groups. The increase of FEV1/FVC was higher in the intervention group versus the control group (76.47 ± 10.76% vs 69.76 ± 8.88%, p = 0.001 at the time of post-intervention), and the decrease of FeNO was greater in the intervention group (30.43 ± 6.85 bbp vs 35.64 ± 6.62 bbp, p = 0.003 at the time of post-intervention). Family-supported healthcare highly improved asthma control and quality of life in childhood asthma post-treatment. Meanwhile, the inflammatory cytokines IL-17 (118.14 ± 25.79 pg/mL in intervention group vs 142.86 ± 28.68 pg/mL in control group, p = 0.004 at the time of post-intervention) and IL-6 (103.76 ± 23.11 pg/mL in intervention group vs 119.73 ± 22.68 pg/mL in control group, p = 0.009 at the time of post-intervention) significantly decreased by family-supported healthcare intervention. Importantly, acute exacerbation (80.8% in intervention group vs 95.7% in control group, p = 0.030) and rehospitalization cases (88.5% in intervention group vs 100% in control group, p = 0.028) also decreased by family-supported healthcare intervention. Discussion: Family-supported healthcare improves pulmonary function and quality of life while alleviates inflammation, acute exacerbation, and rehospitalization in childhood asthma post-routine treatment.

Control of carbon dioxide exchange fluxes by rainfall and biological carbon pump in karst river-lake systems.

As an important component of inland water, the primary factors affecting the carbon cycle in karst river-lake systems require further investigation. In particular, the impacts of climatic factors and the biological carbon pump (BCP) on carbon dioxide (CO2) exchange fluxes in karst rivers and lakes deserve considerable attention. Using quarterly sampling, field monitoring, and meteorological data collection, the spatiotemporal characteristics of CO2 exchange fluxes in Erhai Lake (a typical karst lake in Yunnan, SW China) and its inflow rivers were investigated and the primary influencing factors were analyzed. The average river CO2 exchange flux reached 346.80 mg m-2 h-1, compared to -6.93 mg m-2 h-1 for the lake. The carbon cycle in rivers was strongly influenced by land use within the basin; cultivated and construction land were the main contributors to organic carbon (OC) in the river (r = 0.66, p < 0.01) and the mineralization of OC was a major factor in CO2 oversaturation in most rivers (r = 0.76, p < 0.01). In addition, the BCP effect of aquatic plants and the high pH in karst river-lake systems enhance the ability of water body to absorb CO2, resulting in undersaturated CO2 levels in the lake. Notably, under rainfall regulation, riverine OC and dissolved inorganic carbon (DIC) flux inputs controlled the level of CO2 exchange fluxes in the lake (rOC = 0.78, p < 0.05; rDIC = 0.97, p < 0.01). We speculate that under future climate and human activity scenarios, the DIC and OC input from rivers may alleviate the CO2 limitation of BCP effects in karst eutrophication lakes, possibly enabling aquatic plants to convert more CO2 into OC for burial. The results of this research can help advance our understanding of CO2 emissions and absorption mechanisms in karst river-lake systems.

Multidimensional optimization for accelerating light-powered biocatalysis in Rhodopseudomonas palustris.

BACKGROUND: Whole-cell biocatalysis has been exploited to convert a variety of substrates into high-value bulk or chiral fine chemicals. However, the traditional whole-cell biocatalysis typically utilizes the heterotrophic microbes as the biocatalyst, which requires carbohydrates to power the cofactor (ATP, NAD (P)H) regeneration. RESULTS: In this study, we sought to harness purple non-sulfur photosynthetic bacterium (PNSB) as the biocatalyst to achieve light-driven cofactor regeneration for cascade biocatalysis. We substantially improved the performance of Rhodopseudomonas palustris-based biocatalysis using a highly active and conditional expression system, blocking the side-reactions, controlling the feeding strategy, and attenuating the light shading effect. Under light-anaerobic conditions, we found that 50 mM ferulic acid could be completely converted to vanillyl alcohol using the recombinant strain with 100% efficiency, and > 99.9% conversion of 50 mM p-coumaric acid to p-hydroxybenzyl alcohol was similarly achieved. Moreover, we examined the isoprenol utilization pathway for pinene synthesis and 92% conversion of 30 mM isoprenol to pinene was obtained. CONCLUSIONS: Taken together, these results suggested that R. palustris could be a promising host for light-powered biotransformation, which offers an efficient approach for synthesizing value-added chemicals in a green and sustainable manner.

Indolent T-lymphoblastic proliferation with fibrolamellar hepatocellular carcinoma developed after colorectal adenocarcinoma: a case report.

Objective: Indolent T-lymphoblastic proliferation (iT-LBP) is a non-neoplastic disease with an indolent clinical course, manifesting as hyperplasia of immature extrathymic T-lymphoblastic cells. Isolated iT-LBP has been observed, but the majority of iT-LBP cases has been seen in conjunction with other diseases. iT-LBP is easily misdiagnosed as T-lymphoblastic lymphoma/leukemia, and understanding the disease of indolent T-lymphoblastic proliferation may prevent misdiagnosis and missed diagnosis in pathological diagnosis. Case presentation: We report a case morphology, immunophenotypic, and molecular features of iT-LBP combined with fibrolamellar hepatocellular carcinoma developed after colorectal adenocarcinoma and review relevant literature. Conclusion: iT-LBP combined with fibrolamellar hepatocellular carcinoma developed after colorectal adenocarcinoma is relatively rare and should always be considered as a differential diagnosis of T-lymphoblastic lymphoma and scirrhous hepatocellular carcinoma as the two disorders show highly similar clinical features.

A Novel Study on a Generalized Model Based on Self-Supervised Learning and Sparse Filtering for Intelligent Bearing Fault Diagnosis.

Recently, deep learning has become more and more extensive in the field of fault diagnosis. However, most deep learning methods rely on large amounts of labeled data to train the model, which leads to their poor generalized ability in the application of different scenarios. To overcome this deficiency, this paper proposes a novel generalized model based on self-supervised learning and sparse filtering (GSLSF). The proposed method includes two stages. Firstly (1), considering the representation of samples on fault and working condition information, designing self-supervised learning pretext tasks and pseudo-labels, and establishing a pre-trained model based on sparse filtering. Secondly (2), a knowledge transfer mechanism from the pre-training model to the target task is established, the fault features of the deep representation are extracted based on the sparse filtering model, and softmax regression is applied to distinguish the type of failure. This method can observably enhance the model's diagnostic performance and generalization ability with limited training data. The validity of the method is proved by the fault diagnosis results of two bearing datasets.

Mercury migration to surface water from remediated mine waste and impacts of rainfall in a karst area - Evidence from Hg isotopes.

Mine waste (MW) in historical mercury (Hg) mining areas continuously emits Hg into local environment, including aquatic ecosystems. Tracing Hg migration process from MW and determining its relative contribution to Hg pollution is critical for understanding the environmental impact of MW remediation. In this study, we combined data of Hg concentration, speciation, and isotope to address this issue in the Wanshan Hg mining area in southwest China. We found that rainfall can elevate Hg concentrations in river water and control the partitioning and transport of Hg in karst fissure zones through changing the hydrological conditions. A consistently large offset of δ202Hg (1.24‰) was observed between dissolved Hg (DHg) and particulate Hg (PHg) in surface water during the low-flow period (LFP), which may have been related to the relatively stable hydrologic conditions and unique geological background (karst fissure zones) of the karst region (KR). Results from the ternary Hg isotopic mixing model showed that, despite an order of magnitude reduction in Hg concentration and flux in river water after remediation, the remediated MW is still a significant source of Hg pollution to local aquatic ecosystems, accounting for 49.3 ± 11.9% and 37.8 ± 11.8% of river DHg in high flow period (HFP) and LFP, respectively. This study provides new insights into Hg migration and transportation in aquatic ecosystem and pollution source apportionment in Hg polluted area, which can be used for making polices for future remediation actions.

N, P-doped carbon supported ruthenium doped Rhenium phosphide with porous nanostructure for hydrogen evolution reaction using sustainable energies.

Developing efficient and cost-effective catalysts for hydrogen evolution reaction (HER) is vital to hydrogen energy's commercial applications. In this study, N,P-doped carbon supported ruthenium (Ru) doped triruthenium tetraphosphide (Re3P4) (Ru-Re3P4/NPC) with porous nanostructure is prepared using the low-toxic melamine phosphate as the carbon and phosphorous source. The in-situ generated N,P-doped carbon layers play a pivotal role in regulating the electrocatalytic activity by avoiding the aggregation of the nanoparticles and increasing the specific surface area. Moreover, Ru doping contributes to the remarkable electrocatalytic performance of the prepared nanomaterials. Impressively, the as-synthesized Ru-Re3P4/NPC presents remarkable electrocatalytic performances toward HER with small overpotentials of 39 mV, 115 mV, and 88 mV to deliver 10 mA cm-2 in alkaline, neutral, and acidic media. Moreover, the prepared electrocatalyst can drive water-splitting with a small potential of 1.45 V@10 mA cm-2 and use sustainable energies, including solar, wind, and thermal, as electric resources. This work paves a novel and valuable way to enhance the electrocatalytic performances of metal phosphides.

Prenatal exposure to polycyclic aromatic hydrocarbons could increase the risk of low birth weight by affecting the DNA methylation states in a Chinese cohort.

Polycyclic aromatic hydrocarbons (PAHs), as a kind of endocrine disruptors, can enter the fetus body cross the placental barrier from prenatal PAHs exposure to cause adverse birth outcomes. However, it is controversial association between prenatal PAHs exposure and low birth weight (LBW) of their infants. So the present study aimed to estimate the effects of prenatal PAHs exposure during the pregnancy on the risk of LBW in a Chinese cohort through modifying the DNA methylation states. A longitudinal prospective study with 407 pregnant women was established from May to October 2019. The prenatal PAHs exposure during the pregnancy was assessed using the internal dose such as the PAHs metabolites and PAH-DNA adducts in the umbilical cord blood. The methylation levels of genomic DNA and growth-related genes (IGF1 and IGF2) were assessed, while the expressions of these genes were both determined by RT-PCR and Elisa methods. The growth outcomes and relevant Z-scores were recorded at birth. The correlations between the DNA methylation status and concentrations of PAHs, expression levels of growth-related genes and body weight/WAZ were investigated as the measures. According to the PAH-DNA adducts, the subjects were divided into two groups: PAHs-exposed group (PAH-DNA adducts>0, n = 55) and non-exposed group (PAH-DNA adducts = 0, n = 352). Compared with the non-exposed group, it displayed marked decreased birth weight, and increased concentrations of PAHs and DNA methylation levels of the global genomic, IGF1 and IGF2 with their lower expressions in the PAHs-exposed group. These hypermethylation (global genomic, CpG14 and CpG15 of IGF1, and CpG14 of IGF2) were positively correlated with the contents of PAHs in the umbilical cord blood, and negatively correlated with the growth outcomes and their expressions. Totally, prenatal PAHs exposures may contribute to an increased risk of LBW of their infants by modulating the DNA methylation states of genomic DNA and growth-related genes (IGF1 and IGF2) in the umbilical cord blood, which could provide the prenatal prevention of PAHs exposure from possible environmental media except from the occupation and tobacco usage to ensure the health of their infants.

Inhibition of NOD1 Attenuates Neonatal Hypoxia-Ischemia Induced Long-Term Cognitive Impairments in Mice Through Modulation of Autophagy-Related Proteins.

BACKGROUND: Autophagy is implicated in neonatal hypoxia-ischemia (HI) induced cognitive impairment. The nucleotide-oligomerizing domain-1 (NOD1), a protein involved in inflammatory responses, has been shown to activate autophagy to promote progression of other diseases. We aimed to investigate whether and how NOD1 is involved in HI-induced brain injury using an HI mouse model. METHODS: We induced HI in neonatal mice and examined levels of NOD1 and genes associated with autophagy. We then inhibited NOD1 by intracerebroventricular injection of si-NOD1 following HI induction and tested the effects on autophagy, inflammatory responses and long-term behavioral outcomes through Morris water maze and open field tests. RESULTS: We found that HI induction significantly elevated mRNA levels of NOD1 (3.54 folds change) and autophagy-related genes including Atg5 (3.89 folds change) and Beclin-1 (3.34 folds change). NOD1 inhibition following HI induction suppressed autophagy signaling as well as HI induced proinflammatory cytokine production. Importantly, NOD1 inhibition after HI improved long-term cognitive function, without impacting exploratory and locomotor activities. CONCLUSION: We show here that NOD1 is involved in the pathogenesis of HI-induced brain injury through modulation of autophagy-related proteins and inflammatory responses. Our findings suggest that NOD1 may be a potent target for developing therapeutic strategies for treating HI-induced brain injury.

A Novel Domain Adaptation-Based Intelligent Fault Diagnosis Model to Handle Sample Class Imbalanced Problem.

As the key component to transmit power and torque, the fault diagnosis of rotating machinery is crucial to guarantee the reliable operation of mechanical equipment. Regrettably, sample class imbalance is a common phenomenon in industrial applications, which causes large cross-domain distribution discrepancies for domain adaptation (DA) and results in performance degradation for most of the existing mechanical fault diagnosis approaches. To address this issue, a novel DA approach that simultaneously reduces the cross-domain distribution difference and the geometric difference is proposed, which is defined as MRMI. This work contains three parts to improve the sample class imbalance issue: (1) A novel distance metric method (MVD) is proposed and applied to improve the performance of marginal distribution adaptation. (2) Manifold regularization is combined with instance reweighting to simultaneously explore the intrinsic manifold structure and remove irrelevant source-domain samples adaptively. (3) The ℓ2-norm regularization is applied as the data preprocessing tool to improve the model generalization performance. The gear and rolling bearing datasets with class imbalanced samples are applied to validate the reliability of MRMI. According to the fault diagnosis results, MRMI can significantly outperform competitive approaches under the condition of sample class imbalance.

A Novel Frequency Stabilization Approach for Mass Detection in Nonlinear Mechanically Coupled Resonant Sensors.

Frequency stabilization can overcome the dependence of resonance frequency on amplitude in nonlinear microelectromechanical systems, which is potentially useful in nonlinear mass sensor. In this paper, the physical conditions for frequency stabilization are presented theoretically, and the influence of system parameters on frequency stabilization is analyzed. Firstly, a nonlinear mechanically coupled resonant structure is designed with a nonlinear force composed of a pair of bias voltages and an alternating current (AC) harmonic load. We study coupled-mode vibration and derive the expression of resonance frequency in the nonlinear regime by utilizing perturbation and bifurcation analysis. It is found that improving the quality factor of the system is crucial to realize the frequency stabilization. Typically, stochastic dynamic equation is introduced to prove that the coupled resonant structure can overcome the influence of voltage fluctuation on resonance frequency and improve the robustness of the sensor. In addition, a novel parameter identification method is proposed by using frequency stabilization and bifurcation jumping, which effectively avoids resonance frequency shifts caused by driving voltage. Finally, numerical studies are introduced to verify the mass detection method. The results in this paper can be used to guide the design of a nonlinear sensor.

Effect of slip on the contact-line instability of a thin liquid film flowing down a cylinder.

Liquid coating films on solid surfaces exist widely in a plethora of industrial processes. In this study, we focus on the falling of a liquid film on the side surface of a vertical cylinder, where the surface is viewed as slippery, such as a liquid-infused surface. The evolution profiles and flow instability of the advancing contact line are comprehensively analyzed. The governing equation of the thin film flow is derived according to the lubrication model, and the traveling-wave solutions are numerically obtained. The results show that the wave speed increases with the increase of a larger slippery length. A linear stability analysis (LSA) is carried out to verify the traveling solutions and time responses. Although previous studies tell us that the wall slippage always promotes the surface flow instability of the thin film flow, the linear stability analysis, numerical simulations, and nonlinear traveling-wave solutions in the current study present a different conclusion. The analysis show that for a thin film flow with a dynamic contact line the wall slippage in different directions plays much more complex roles. The streamwise slippery effect always impedes the instability of the flow and suppresses the wave height of traveling wave, while the transverse slippery effect has a dual effect on the surface instability. The transverse slippery effect significantly improves the instability while the wave number of the perturbation is small, and simultaneously it reduces the cutoff wave number. The transverse slippery effect will change its role if the wave number of the perturbation exceeds a critical value, which can stabilize the contact line.

Nonlinear Behavior of Electrostatically Actuated Microbeams with Coupled Longitudinal⁻Transversal Vibration.

Microelectromechanical switch has become an essential component in a wide variety of applications, ranging from biomechanics and aerospace engineering to consumer electronics. Electrostatically actuated microbeams and microplates are chief parts of many MEMS instruments. In this study, the nonlinear characteristics of coupled longitudinal-transversal vibration are analyzed, while an electrostatically actuated microbeam is designed considering that the frequency ratio is two to one between the first longitudinal vibration and transversal vibration. The nonlinear governing equations are truncated into a set of coupled ordinary differential equations by the Galerkin method. Then the equations are solved using the multiple-scales method and the nonlinear dynamics of the internal resonance is investigated. The influence of bias voltage, longitudinal excitation and frequency detuning parameters are mainly analyzed. Results show that using the pseudo-arclength continuation method, the nonlinear amplitude-response curves can be plotted continuously. The saturation and jump phenomena are greatly affected by the bias voltage and the detuning frequency. Beyond the critical excitation amplitude, the response energy will transfer from the longitudinal motion to the transversal motion, even the excitation is employed on the longitudinal direction. The large-amplitude jump of the low-order vibration mode can be used to detect the variation of the conditions or parameters, which shows great potential in improving precision of MEMS switches.

[Migration and Transformation of Dissolved Organic Matter in Karst Water Systems and an Analysis of Their Influencing Factors].

Fluorescent substances are used as good tracers in dissolved organic matter (DOM) to identify the source of DOM and its geochemical behavior in a hydrological system. However, there are few studies on the karst aquifer system. Many parameters in karst systems affect the DOM spectral information. A typical karst watershed in Northern China was selected in this research. Excitation-emission matrices (EEMs), parallel factor analysis (PARAFAC), and hydrochemical data were applied to reveal the relationship between the composition and transformation of DOM fluorescent substances in different karst water-bearing spaces. The source of DOM and the effect of water chemistry on DOM transfer were also discussed. The results showed that DOM in exogenous surface water and karst surface water in the Yufu River watershed were mainly composed of tryptophan-like substances, while the DOM in shallow karst water and deep karst water consisted of tryptophan-like and tyrosine-like substances. A comprehensive analysis by fluorescence index (FI), biological index (BIX), and humification index (HIX) displayed that the DOM in shallow and deep karst water resulted from microbial decomposition. In contrast, the DOM in karst surface water and exogenous surface water resulted from land-based input and endogenous microbial decomposition, in which endogenous contributions occupy a large proportion. Due to the chemical parameters of karst water, these three kinds of fluorescent substances extracted by PARAFAC had obviously different characteristics, i.e., ① the tyrosine-like substances had a strong adaptability to Ca2+ and HCO3-, and the proportion of the tyrosine in karst water was relatively large; ② the tryptophan substance followed an opposite trend; and ③ there was a significant positive correlation between fulvic acid and TDS, turbidity, Cl-, and SO42-. Observations of the watershed runoff revealed that the DOM in shallow karst water in the upper reaches came mainly from the soil and microbial degradation. The organic matter underwent a large amount of microbial decomposition and exogenous input when the water was rejuvenated with springs. After infiltration to the deep karst water in the lower reaches, the DOM gradually were converted to low aromatic hydrocarbon organic compounds and decreased macromolecules of DOM. Subsequently, the fluorescence intensity was weakened. The principal component analysis (PCA) extracted three principal components. They were the water mineralization index, soil leaching index, and hydrochemical/biochemical process index. The water mineralization index consists of hydrochemical parameters reflecting the water infiltration, transformation, and flow conditions in the karst system. The soil leaching index contains TOC, NO3-, and protein-like indicators relating to the relationship between protein-like substances and soil and natural leaching. The hydrochemical/biochemical process index is composed of Ca2+, HCO3-, FI, and fulvic acid indicators that illustrate the water chemistry and biochemical processes in the karst water system. In addition, the study also showed that total fluorescence intensity, fulvate-like substances, and protein-like substances can be used as a tracer for rapid seepage, transformation, and aquifer fragility for karst water, respectively. The results of the study are important in understanding the biogeochemical cycle of DOM in the karst water system and also helpful for controlling organic pollution. It also provides a new tool for characterizing the geochemical processes of organic matter in karst system.

Boosting compound-protein interaction prediction by deep learning.

The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets.

Revisiting topological properties and models of protein-protein interaction networks from the perspective of dataset evolution.

Protein-protein interaction (PPI) networks are crucial for organisms. Many research efforts have thus been devoted to the study on the topological properties and models of PPI networks. However, existing studies did not always report consistent results on the topological properties of PPI networks. Although a number of PPI network models have been introduced, yet in the literature there is no convincing conclusion on which model is best for describing PPI networks. This situation is primarily caused by the incompleteness of current PPI datasets. To solve this problem, in this study, the authors propose to revisit the topological properties and models of PPI networks from the perspective of PPI dataset evolution. Concretely, they used 12 PPI datasets of Arabidopsis thaliana and 10 PPI datasets of Saccharomyces cerevisiae from different Biological General Repository for Interaction Datasets (BioGRID) database versions, and compared the topological properties of these datasets and the fitting capabilities of five typical PPI network models over these datasets.

Identifying TF-MiRNA Regulatory Relationships Using Multiple Features.

MicroRNAs are known to play important roles in the transcriptional and post-transcriptional regulation of gene expression. While intensive research has been conducted to identify miRNAs and their target genes in various genomes, there is only limited knowledge about how microRNAs are regulated. In this study, we construct a pipeline that can infer the regulatory relationships between transcription factors and microRNAs from ChIP-Seq data with high confidence. In particular, after identifying candidate peaks from ChIP-Seq data, we formulate the inference as a PU learning (learning from only positive and unlabeled examples) problem. Multiple features including the statistical significance of the peaks, the location of the peaks, the transcription factor binding site motifs, and the evolutionary conservation are derived from peaks for training and prediction. To further improve the accuracy of our inference, we also apply a mean reciprocal rank (MRR)-based method to the candidate peaks. We apply our pipeline to infer TF-miRNA regulatory relationships in mouse embryonic stem cells. The experimental results show that our approach provides very specific findings of TF-miRNA regulatory relationships.