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AbstractThe Alashan ground squirrel (Spermophilus alashanicus) is primarily distributed in the regions of Inner Mongolia and Ningxia, China. In this study, we present the first complete mitochondrial genome of S. alashanicus. The genome spans 16,464 base pairs and comprises 13 protein-coding genes, 22 tRNA genes, two rRNA genes, and a single control region with a marked AT bias. The overall GC content is 35.4%. Phylogenetic analyses indicate that S. alashanicus clusters are closely associated with S. dauricus. This comprehensive characterization of the S. alashanicus mitochondrial genome serves as a foundational resource for future studies on mitochondrial evolution, species identification, population genomics, and phylogenetics.

In the operation of medium-voltage distribution cables, the local insulation performance may degrade due to inherent defects, environmental influences, and external forces, leading to consecutive self-recovering latent faults in the cables. If not addressed promptly, these faults may escalate into permanent failures. To address this issue, this paper analyzes the development mechanism and characteristics of latent cable faults. A 10kV low-resistance cable latent fault model based on the Kizilcay arc model is built in the PSCAD/EMTDC platform. Furthermore, the paper analyzes and extracts the time-domain, frequency-domain, and time-frequency domain features of fault current samples. Effective fault feature vectors are constructed using multivariate analysis of variance (MANOVA) and Principal Component Analysis (PCA). Based on the fault feature vectors and Extreme Learning Machine (ELM), an intelligent fault identification model for cable latent faults is developed. The initial parameters of the ELM model are optimized using the Particle Swarm Optimization (PSO) algorithm. Finally, the superiority of the proposed model is validated in terms of classification accuracy, training time, and robustness compared to other machine learning algorithms.

Wildlife activity patterns, which reveal the daily allocation of time and energy, are crucial for understanding survival pressures, adaptive strategies, and behavioral characteristics in different environments. Among ungulates, grouping behavior is a prevalent adaptive trait that reflects the population structure, mating systems, and life history strategies formed over long-term evolutionary processes. This study aimed to elucidate the daily activity patterns and grouping characteristics of the rare goitered gazelle (Gazella subgutturosa) in the Helan Mountains of western China from 2022 to 2023 using camera trap monitoring. With a total of 3869 camera days of effective trapping, we recorded 442 independent detections of goitered gazelles. The results revealed the following: (1) Goitered gazelle is primarily active during the day, showing an activity pattern similar to crepuscular animals, with two activity peaks occurring after dawn and before dusk. (2) Daily activity patterns showed both seasonal and sex differences. In the warm season, morning activity peaks occurred earlier, and afternoon peaks occurred later compared to the cold season. The overlap in daily activity patterns between females and males in the warm season was lower than that in the cold season, and this trend persisted throughout the year. (3) The number of times different types of groups were observed varied significantly, with single males and single females accounting for a larger proportion of all observed groups. There was no significant difference in group size across seasons, with groups typically consisting of 1–2 individuals. Our study provides detailed insights into the temporal ecology and population structure of goitered gazelles in arid and semi-arid ecosystems. This information will guide the identification of future conservation priorities and the development of management plans for the reserve.

With the rapid development of global trade, a large number of goods and resources are imported and exported via seaports. Multiple thermal loads and renewable energy merge into seaports, making the energy supply and demand structure increasingly complex. The traditional seaport becomes an integrated seaport energy system (ISES). Due to the complicated energy interaction of cooling, heating, electricity, and gas subsystems, the ISES urgently require reliable and secure operation. Hence, this paper proposes a new reliability assessment method for the ISES that considers thermal inertia. Firstly, the operational structure of the ISES is established considering multi-energy coupling relationships. Then, a two-stage optimal load curtailment model is constructed with multiple thermal inertias. In addition, the reliability assessment method for the ISES is proposed based on the sequential Monte Carlo simulation method. Simulations are presented to verify the effectiveness of the proposed method.

Progress in development of prognostic and therapeutic options for the rare cholestatic liver diseases, primary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC), is hampered by limited knowledge of their pathogeneses. In particular, the potential role of hepatotoxic and/or metabolism‐altering environmental chemicals in the pathogenesis of these diseases remains relatively unstudied. Moreover, the extent to which metabolic pathways are altered due to ongoing cholestasis and subsequent liver damage or possibly influenced by hepatotoxic chemicals is poorly understood. In this study, we applied a comprehensive exposomics‐metabolomics approach to uncover potential pathogenic contributors to PSC and PBC. We used untargeted high‐resolution mass spectrometry to characterize a wide range of exogenous chemicals and endogenous metabolites in plasma and tested them for association with disease. Exposome‐wide association studies (EWAS) identified environmental chemicals, including pesticides, additives and persistent pollutants, that were associated with PSC and/or PBC, suggesting potential roles for these compounds in disease pathogenesis. Metabolome‐wide association studies (MWAS) found disease‐associated alterations to amino acid, eicosanoid, lipid, co‐factor, nucleotide, mitochondrial and microbial metabolic pathways, many of which were shared between PSC and PBC. Notably, this analysis implicates a potential role of the 5‐lipoxygenase pathway in the pathogenesis of these diseases. Finally, EWAS × MWAS network analysis uncovered linkages between environmental agents and disrupted metabolic pathways that provide insight into potential mechanisms for PSC and PBC. Conclusion: This study establishes combined exposomics‐metabolomics as a generalizable approach to identify potentially pathogenic environmental agents and enumerate metabolic alterations that may impact PSC and PBC, providing a foundation for diagnostic and therapeutic strategies.

Abstract Online monitoring of gases dissolved in transformer oil is widely applied. Improving the performance of dissolved gas analysis (DGA)‐based fault diagnosis methods by exploring new features of time‐series data has become an appealing topic. In this study, a new type of correlation features between characteristic gases was extracted from time‐series data based on the maximal information coefficient (MIC), and a fuzzy inference system was established. After the introduction of the principle of the MIC and a method for calculating the MIC‐based correlation features, the dominant symptom features that can be used to classify fault types were extracted through the receiver operating characteristic curve. Then, fuzzy rules were learnt, and a fuzzy inference system was designed. In addition, to improve the feasibility of the method, the Newton interpolation method was used for adaptation to the existing sampling cycle. The diagnostic results of the test data show that the proposed method has excellent performance and outperforms some prevailing traditional rule‐based methods as well as some artificial intelligent methods. The results also show that by exploring new correlation features from time‐series data based on the MIC, the performance of DGA‐based methods can be improved.

IntroductionArbuscular mycorrhizal fungi (AMF) perform a vital role in terrestrial ecosystems.MethodsTo investigate the diversity of AMF communities on the western slope of Helan Mountain at different altitudes and their influence factors, high-throughput sequencing was used to study the structure and diversity of soil AMF communities under different environments and their interrelationships between AMF and environmental factors.ResultsThe results revealed that there were significant differences (p < 0.05) in the physical and chemical properties of the soil along the different altitudes. A total of 1,145 OTUs were obtained by high-throughput sequencing, belonging to 1 phylum, 4 class, 6 orders, 13 families, 18 genera and 135 species, with the dominant genus being Glomus, which accounted for 75.27% of the relative abundance of the community. Soil AMF community structure was shown to be variable at the generic level according to NMDS analysis. Correlation analysis showed that soil pH, water content (WC), organic matter (OM), available K, available P and N were significantly correlated with AMF community diversity and species abundance (p < 0.05, p < 0.01). Based on redundancy analysis (RDA) and Monte Carlo test results, soil pH, WC and OM had highly significant effects (p < 0.01) on AMF community diversity and species abundance.DiscussionThis study investigates the relationship between AMF community structure and diversity and soil physicochemical properties at different elevations on the western slope of Helan Mountain, which is of great significance to the study of the Helan Mountain ecosystem.

Due to the high penetration of virtual power plants (VPPs), the bi-directional power flow between VPPs and active distribution grids makes the grid operation complex. Without congestion management, the operation schedule only considers the economic benefits, and power flow constraints might be violated. Hence, it is necessary to conduct power interaction within the operation constraints. This paper proposes a coordinated economic dispatch method under voltage security constraints. The linear expressions were derived by simplifying the AC power flow equations to reduce the computation complicity. Then, optimal economic dispatch models with voltage security constraints were established for the active distribution grid and VPPs, respectively. Meanwhile, the transacted power and clearing price were set as the communication variables, and a coordinated strategy was proposed for the overall optimal goal. The modified IEEE 33-node and PG&E-node distribution grids were utilized for the simulations, and the results affirmed the validity of the proposed method.

Humans are exposed to millions of chemicals but mass spectrometry (MS)-based targeted biomonitoring assays are usually limited to a few hundred known hazards. Here, the authors develop a workflow for MS-based untargeted exposome profiling of known and unidentified environmental chemicals.

Both environmental exposure to vanadium pentoxide (V2O5, V+5 for its ionic counterparts) and fibroblast senescence are associated with pulmonary fibrosis, but whether V+5 causes fibroblast senescence remains unknown. We found in a dose-response study that 2–40 μM V+5 caused human lung fibroblasts (HLF) senescence with increased senescence-associated β-galactosidase activity and p16 expression, while cell death occurred at higher concentration (LC50, 82 μM V+5). Notably, measures of reactive oxygen species (ROS) production with fluorescence probes showed no association of ROS with V+5-dependent senescence. Preloading catalase (polyethylene-conjugated), a H2O2 scavenger, did not alleviate the cellular senescence induced by V+5. Analyses of the cellular glutathione (GSH) system showed that V+5 oxidized GSH, increased GSH biosynthesis, stimulated cellular GSH efflux and increased protein S-glutathionylation, and addition of N-acetyl cysteine inhibited V+5-elevated p16 expression, suggesting that thiol oxidation mediates V+5-caused senescence. Moreover, strong correlations between GSSG/GSH redox potential (Eh), protein S-glutathionylation, and cellular senescence (R2 > 0.99, p

Compressed natural gas (CNG) main stations are critical components of the urban energy infrastructure for CNG distribution. Due to its high electrification and significant power consumption, researching the economic operation of the CNG main station in demand response (DR)-based electricity pricing environments is crucial. In this paper, the dehydration process is considered in the CNG main station energy consumption model to enhance its participation in DR. A bilevel economic dispatch model for the CNG main station is proposed, considering critical peak pricing. The upper-level and lower-level models represent the energy cost minimization problems of the pre-system and rear-system, respectively, with safety operation constraints. The bilevel programming model is solved using a genetic algorithm combined with a bilevel programming method, which has better efficiency and convergence. The proposed optimization scheme has better control performance and stability, reduces the daily electricity cost by approximately 21.04%, and decreases the compressor switching frequency by 50.00% without changing the CNG filling demand, thus significantly extending the compressor’s service life. Moreover, the average comprehensive power cost of processing one unit of CNG reduces 20.62%.

Background: Advances in untargeted metabolomic technologies have great potential for insight into adverse metabolic effects underlying exposure to environmental chemicals. However, important challenges need to be addressed, including how biological response corresponds to the environmental chemical burden in different target tissues. Aim: We performed a pilot study using state-of-the-art ultra-high-resolution mass spectrometry (UHRMS) to characterize the burden of lipophilic persistent organic pollutants (POPs) in metabolic tissues and associated alterations in the plasma metabolome. Methods: We studied 11 adolescents with severe obesity at the time of bariatric surgery. We measured 18 POPs that can act as endocrine and metabolic disruptors (i.e. 2 dioxins, 11 organochlorine compounds [OCs] and 5 polybrominated diphenyl ethers [PBDEs]) in visceral and subcutaneous abdominal adipose tissue (vAT and sAT), and liver samples using gas chromatography with UHRMS. Biological pathways were evaluated by measuring the plasma metabolome using high-resolution metabolomics. Network and pathway enrichment analysis assessed correlations between the tissue-specific burden of three frequently detected POPs (i.e. p,p’-dichlorodiphenyldichloroethene [DDE], hexachlorobenzene [HCB] and PBDE-47) and plasma metabolic pathways. Results: Concentrations of 4 OCs and 3 PBDEs were quantifiable in at least one metabolic tissue for > 80% of participants. All POPs had the highest median concentrations in adipose tissue, especially sAT, except for PBDE-154, which had comparable average concentrations across all tissues. Pathway analysis showed high correlations between tissue-specific POPs and metabolic alterations in pathways of amino acid metabolism, lipid and fatty acid metabolism, and carbohydrate metabolism. Conclusions: Most of the measured POPs appear to accumulate preferentially in adipose tissue compared to liver. Findings of plasma metabolic pathways potentially associated with tissue-specific POPs concentrations merit further investigation in larger populations.

In this paper, a model is proposed for the optimal operation of multi-energy microgrids (MEMGs) in the presence of solar photovoltaics (PV), heterogeneous energy storage (HES) and integrated demand response (IDR), considering technical and economic ties among the resources. Uncertainty of solar power as well as the flexibility of electrical, cooling and heat load demand are taken into account. A p-efficient point method is applied to compute PV power at different confidence levels based on historical data. This method converts the uncertain PV energy from stochastic to deterministic to be included in the optimization model. The concept of demand response is extended and mathematically modeled using a linear function based on the quantized flexibility interval of multi-energy load demand. As a result, the overall model is formulated as a mixed-integer linear program, which can be effectively solved by the commercial solvers. The proposed model is implemented on two typical summer and winter days for various cases. Results of case studies show the important benefits for maximum PV utilization, energy efficiency and economic system operation. Moreover, the influence of the different confidence levels of PV power and effectiveness of IDR in the stochastic circumstances are addressed in the optimization-based operation.

Due to its short lifespan, ease of use and age-related pathologies that mirror those observed in humans, the common marmoset (Callithrix jacchus) is poised to become a standard nonhuman primate model of aging. Blood and extracellular fluid possess two major thiol-dependent redox nodes involving cysteine (Cys), cystine (CySS), glutathione (GSH) and glutathione disulfide (GSSG). Alteration in these plasma redox nodes significantly affects cellular physiology, and oxidation of the plasma Cys/CySS redox potential (EhCySS) is associated with aging and disease risk in humans. The purpose of this study was to determine age-related changes in plasma redox metabolites and corresponding redox potentials (Eh) to further validate the marmoset as a nonhuman primate model of aging. We measured plasma thiol redox states in marmosets and used existing human data with multivariate adaptive regression splines (MARS) to model the relationships between age and redox metabolites. A classification accuracy of 70.2% and an AUC of 0.703 were achieved using the MARS model built from the marmoset redox data to classify the human samples as young or old. These results show that common marmosets provide a useful model for thiol redox biology of aging.

SUMMARY Classic galactosemia is a genetic disorder that results from profound loss of galactose-1P-uridylyltransferase (GALT). Affected infants experience a rapid escalation of potentially lethal acute symptoms following exposure to milk. Dietary restriction of galactose prevents or resolves the acute sequelae; however, many patients experience profound long-term complications. Despite decades of research, the mechanisms that underlie pathophysiology in classic galactosemia remain unclear. Recently, we developed a Drosophila melanogaster model of classic galactosemia and demonstrated that, like patients, GALT-null Drosophila succumb in development if exposed to galactose but live if maintained on a galactose-restricted diet. Prior models of experimental galactosemia have implicated a possible association between galactose exposure and oxidative stress. Here we describe application of our fly genetic model of galactosemia to the question of whether oxidative stress contributes to the acute galactose sensitivity of GALT-null animals. Our first approach tested the impact of pro- and antioxidant food supplements on the survival of GALT-null and control larvae. We observed a clear pattern: the oxidants paraquat and DMSO each had a negative impact on the survival of mutant but not control animals exposed to galactose, and the antioxidants vitamin C and α-mangostin each had the opposite effect. Biochemical markers also confirmed that galactose and paraquat synergistically increased oxidative stress on all cohorts tested but, interestingly, the mutant animals showed a decreased response relative to controls. Finally, we tested the expression levels of two transcripts responsive to oxidative stress, GSTD6 and GSTE7, in mutant and control larvae exposed to galactose and found that both genes were induced, one by more than 40-fold. Combined, these results implicate oxidative stress and response as contributing factors in the acute galactose sensitivity of GALT-null Drosophila and, by extension, suggest that reactive oxygen species might also contribute to the acute pathophysiology in classic galactosemia.

High-resolution metabolomics has created opportunity to integrate nutrition and metabolism into genetic studies to improve understanding of the diverse radiation of primate species. At present, however, there is very little information to help guide experimental design for study of wild populations. In a previous non-targeted metabolomics study of common marmosets (Callithrix jacchus), Rhesus macaques, humans, and four non-primate mammalian species, we found that essential amino acids (AA) and other central metabolites had interspecies variation similar to intraspecies variation while non-essential AA, environmental chemicals and catabolic waste products had greater interspecies variation. The present study was designed to test whether 55 plasma metabolites, including both nutritionally essential and non-essential metabolites and catabolic products, differ in concentration in common marmosets and humans. Significant differences were present for more than half of the metabolites analyzed and included AA, vitamins and central lipid metabolites, as well as for catabolic products of AA, nucleotides, energy metabolism and heme. Three environmental chemicals were present at low nanomolar concentrations but did not differ between species. Sex and age differences in marmosets were present for AA and nucleotide metabolism and warrant additional study. Overall, the results suggest that quantitative, targeted metabolomics can provide a useful complement to non-targeted metabolomics for studies of diet and environment interactions in primate evolution.

The quality of original data is crucial to the performance of diagnosis model. To improve the performance of transformer diagnosis model based on Dissolved Gas Analysis (DGA), a new diagnosis scheme suitable for time-series dissolved gas data is proposed in this paper. After the analysis of traditional transformer diagnosis architecture, a fault data extraction step is added to the architecture to improve the quality of original fault data. The fault data extraction step is mainly composed of two parts, invalid data correction and determination of possible initial fault time based on fault early warning. Finally, the numerical results validate that the accuracy and sensitivity of DGA based fault diagnosis for the transformer are improved by extracting fault feature of time-series data. [ABSTRACT FROM AUTHOR]

The exposome is the cumulative measure of environmental influences and associated biological responses throughout the lifespan, including exposures from the environment, diet, behavior, and endogenous processes. A major challenge for exposome research lies in the development of robust and affordable analytic procedures to measure the broad range of exposures and associated biologic impacts occurring over a lifetime. Biomonitoring is an established approach to evaluate internal body burden of environmental exposures, but use of biomonitoring for exposome research is often limited by the high costs associated with quantification of individual chemicals. High-resolution metabolomics (HRM) uses ultra-high resolutionmass spectrometry with minimal sample preparation to support high-throughput relative quantification of thousands of environmental, dietary, and microbial chemicals. HRM also measures metabolites inmost endogenous metabolic pathways, thereby providing simultaneous measurement of biologic responses to environmental exposures. The present research examined quantification strategies to enhance the usefulness of HRM data for cumulative exposome research. The results provide a simple reference standardization protocol in which individual chemical concentrations in unknown samples are estimated by comparison to a concurrently analyzed, pooled reference sample with known chemical concentrations. The approach was tested using blinded analyses of amino acids in human samples and was found to be comparable to independent laboratory results based on surrogate standardization or internal standardization. Quantification was reproducible over a 13-month period and extrapolated to thousands of chemicals. The results show that reference standardization protocol provides an effective strategy that will enhance data collection for cumulative exposome research. In principle, the approach can be extended to other types of mass spectrometry and other analytical methods. [ABSTRACT FROM AUTHOR]

Few data are available on plasma redox responses to sulfur amino acid (SAA) loads. In this study, we had 2 aims: to determine whether the SAA content of a meal affected postprandial plasma cysteine (Cys), cystine (CySS), or redox potential (EhCySS) in humans and whether SAA intake level (adequate or inadequate) in the days preceding the meal challenge affected these postprandial levels. Eight healthy individuals aged 18-36 y were equilibrated for 3 d to adequate SAA, fed chemically defined meals without SAA for 5 d (inadequate SM) and then fed isoenergetic, isonitrogenous meals with adequate SAA for 5 d. On the first and last days with the chemically defined meals, a morning meal containing 60% of the daily food intake was given, and plasma Cys, CySS, and ECySS were determined over an 8-h postprandial time course. Following equilibration to adequate intake, provision of the meal with SAA resulted in increased plasma Cys and CySS concentrations and more reduced plasma EhCySS compared with the postprandial values following the same meal without SM. Equilibration to inadequate SAA intake for the days preceding the meal challenge did not affect this response. The magnitude of the difference in postprandial plasma EhCySS (10 mV) due to meal content of SAA was comparable to those which alter physiologic signaling and/or are associated with disease risk. Consequently, the SAA content of meals could affect physiologic signaling and associated disease mechanisms in the postprandial period by changes in Cys, CySS, or EhCySS. [ABSTRACT FROM AUTHOR]